Good morning. Good morning, everybody. We are just about ready to get started, so everyone, gather round. Gather round. Come on down to the main stage area. Come on down to the main stage area. We have got a great show for you today. We have got a great ferment. Come on down. Everybody, really, really get in here. It's going to be a full house. We're expecting a full house. Okay, so if we were doing a ferment, I want this to look like one of those yeast beers where all the yeast really pack in at the bottom. All right? That's how you know it's a good one. So let's do me a favor. If you have an open seat next to you, raise your hand for me. Oh my God, look at all these open seats. So many open seats. People on the sides, hold on. Keep them up. Keep them up. People on the sides, those seats are for you. Come on in. A few more seconds. Squeeze them in. All right. Thanks, everybody. Thanks, everybody. We got a good one. We got a good one for you today. This is going to be a ferment for the ages, a sparkling vintage, one for the collectors. It's time to kick it off. Will you please join me in giving a very warm, a very bubbly, a very sparkling ferment welcome to the CEO of Ginkgo Bioworks, Jason Kelly? Welcome to Ginkgo Ferment. I want to start by thanking Quinn Berkman and the Ferment team for putting together this gorgeous venue we all get to experience today. Let's do a round of applause. Okay, so I will start by saying the thing that gets me the most excited about Ferment is that all of you are here. Okay, this is a very self-selected group of people. You're interested in how to apply AI to biotechnology, synthetic biology, automation, biosecurity, and you have an unbelievable range of different backgrounds. So you should really all meet each other. And to support that, we have over here a really cool button station where you can pick out the biotech button that best describes your vibe. Okay? And ask people about that. Get to know them. We do Ferment to get you all here in the same room. Also today, we're going to have 17 of our customers up here for lightning talks. We heard from you last year. This was what people liked the best. You're getting more of it. And then tonight at the party, I want to thank Aqua, Ayana, Motif, and Verb because we're going to get to eat and drink some of the products of Ginkgo's customers. And I really encourage you to go upstairs and you can visit some GMO plants from Light Bio and Living Carbon. All right? So again, but meet each other today. All right. This is my favorite slide, I'll be honest. One of the earliest hypotheses about Ginkgo was that you could have a common engineering platform for any industry in biotech. And I think we've pretty much accomplished it. If you look at our customers in agriculture, three of the largest ag biotech companies, many of the large chemistry companies, and many of the large pharma companies. Many of you are in the room today, as well as a lot of startups that want to be these companies in the future. But I have to say, the missing column that we pretty much filled in in 2023 was in biopharma. This is something, an area we've wanted to get Ginkgo into for a long time. Deals with Pfizer, Boehringer Ingelheim, Merck, and successfully completed our deal with Biogen. We grew the number of programs we did, 70%, revenue 40%. Biopharma is becoming a thing on top of Ginkgo's platform. That's why I was so excited yesterday to announce our expansion of our partnership with Novo Nordisk. We're lucky to have Marcus Schindler, the CSO of Novo, here with us today. Unless you've been living under a rock, Novo is changing almost the way people think about what medicine can do for people. I'm so excited to hear what Marcus is going to share being interviewed by Jen Wipf, our head of commercial, Chief Commercial Officer here at Ginkgo. Marcus had a quote in our press release yesterday that I just want to read. "We're eager to explore more flexible models for external partnership, and this agreement allows Novo to start more projects with Ginkgo in a faster and more agile manner." Faster and more agile. I'm going to be announcing a new service offering at Ginkgo that I hope you're all excited about, that is going to make it a hell of a lot easier to access our platform agilely today. So get ready for that. Our cell engineering services and pharma, for those of you in the room from that industry, we've done deals now in most of the major modalities: RNA therapeutics, cell therapy, gene therapy, small molecule, biologics, microbiome. And when we look in these areas, what we're trying to solve is a common problem for that modality. And this is also true, by the way, in ag and industrial, too. You all are developing products, but regardless of what disease you're trying to treat with an RNA therapeutic, for example, you'd like RNA to last longer in cells, to be more efficacious. That's a common problem for every RNA therapeutic. Well, to give you a sense of scale, we ended last year with nearly $1 billion in the bank. I don't spend any of that money on clinical trials. I don't spend it on ag field trials. I don't have products. I spend it on things like that robotics infrastructure over there that you really need to go see, that we build in-house at Ginkgo. I spend it on trying to solve these common problems in these areas that'll be relevant to your industry. So come tell us what is the thing you'd like to solve. What are those pre-competitive common problems? We want to work on them. And you're going to be lucky to hear from three of our technology leaders today, Emily, Uri, and Shadi, talking about some of those challenges in enzymes, RNA, and cell therapy. It's not just biopharma that grew last year. We love all of our customers in these other industries as well. We grew 78 new programs last year. So that's more than one new R&D partnership a week with you all. That's 30% growth. You'll see many of our lightning talks are going to be across an incredible range of industries. I think you're really going to enjoy hearing what people are doing with it. Okay. Our cell engineering solutions, I'm really happy with it. It's growing well. I love the pharma thing. I have to admit, I'm a student of the tech industry, and I'm a little jealous of how I see they've organized themselves. You've probably heard about a company called Nvidia. It just hit $2 trillion in market cap. All right? They sell GPUs. They sell these semiconductor processors. Amazing thing about Nvidia, they don't have a semiconductor fab. They don't manufacture their own chips. $2 trillion company. They use a company called Taiwan Semiconductor. It's part of the reason the U.S. and China are concerned about Taiwan. Taiwan Semiconductor is not outsourcing manufacturing of something easy. This is basically the only place Nvidia can get their chips made because it's so advanced. So there's a total dependency. Look at OpenAI and Salesforce and Netflix. These are built on top of the cloud providers at Google and Microsoft. There's a dependency. How did OpenAI scale to 200 million customers in six months? Well, they were using Microsoft's server infrastructure to do that. Well, what does it look like for us in biotech? No dependencies. Everybody's building all their own stack, doing their technology in-house. And I looked at Ginkgo, and I said, "Well, are we helping this problem?" And the answer today is only a little bit. Right? If you look, I've got a little Ginkgo lab code up there. That's a Ginkgo scientist on top of Ginkgo's Foundry and code base. That's who gets to use my infrastructure today. Someone like Shadi on that last slide. She orders, actually, almost virtually. She's basically on a computer ordering services from our infrastructure. And I looked at that, and I said, "Well, why couldn't that be a scientist at your company instead ordering from our robotics, accessing our data?" And so I went and I talked to a bunch of you about this. And I said, "Hey, if Ginkgo opened up our platform to you, would you care?" And two things came back to me loud and clear. First, those of you who are working on the application of AI, and I'm going to talk about this, need lots of data, and you're interested in getting access to our robotics. Secondly, many of you are engaging with traditional CROs in the market, and they're coming up short in some areas, and you'd like to close that gap. All right? So let me talk about AI first. All right. The tech company so this is Jensen Huang, CEO of Nvidia. You might recognize him with the black leather jacket. I'm thinking about starting wearing a black leather jacket. Yeah, no, I'm not. Okay. I'm just going to read this quote from Jensen. "Where do I think the next amazing revolution is going to come? And this is going to be flat out one of the biggest ones ever. There's no question that digital biology we would call it synthetic biology is going to be it. Biology has the opportunity to be engineering, not science. When something becomes engineering, not science, it becomes exponentially improving. It can compound on the benefits of previous years. Okay? This is what we have been preaching in synthetic biology for 20 years. It's awesome to be hearing this from the CEO of a major tech company about synthetic biology, about digital biology. Even Endpoints, the sort of pharma magazine, tech website, had an article about Nvidia's launch of BioNeMo, which is their services for AI in biotech specifically. They don't have labs, but they do have these services. Great. We should bet on Nvidia. We should welcome them into the club. And I like that they're a big tech company saying AI is going to matter in biotech. That's a signal. Where there's another signal? At the White House. This is an executive order that came out last year on AI. It's great. You should read it. I'm going to highlight one part. They basically say, "Hey, if you're building an AI model and you use 10 to the 26 flops, it's like I got amount of computing, you better call the White House. You better let us know." All right? Because we're worried it might be something dangerous. However, they added, "Or if you're using primarily biological sequence data, in other words, you're training a model on DNA, 10 to the 23rd." So actually, a lower limit if you're training a model on DNA. Two things about that. One, in addition to text and images and video, the only other type of AI model mentioned was DNA-based models. Okay? These are a real thing that's coming. All right? Number two, they're more worried about them than they are the language models. So I would encourage all of you. I was asked to chair a National Security Commission down in D.C. It's been eye-opening for me over the last year to engage more with the government. I encourage those of us in the industry. I see some of us are from our government here today. But if you don't engage, please do engage. Both on academia and industry, it will help. And the last people who are betting a lot on AI and bio is Ginkgo Bioworks. We signed a $300 million deal with Google Cloud. We acquired Patch Bio and Reverie Labs, two leading AI biotech companies, to bring that talent and compute infrastructure in-house. All right. But I know I got a bunch of scientists in the room, and everybody's always a skeptic. Hey, is AI a bunch of hype? And so I want to give you a little bit of background on how AI actually works so you can think about how it applies to bio. So if you'll bear with me, I'm going to just nerd out for five minutes about how AI works. All right? So ChatGPT, how did OpenAI make it? Well, they trained a large neural net on billions of human sentences. Let me explain what that sentence means. First, what is a neural net? All right? And this is an old-school neural net. Okay? It's not exactly what they did, but it's close. Okay. So a neural net and apologies to the neuroscientists is supposed to model a human brain. Okay? It's not a human. Not at all. But think of it like those two circles at the tops are like neurons. All right? So you have these circles. They're nodes. They're connected by lines to another circle below. That's like neurons being connected. Fires, fires. So these nodes can fire, and they send a signal down the line to the node below. And on each line is a number, 0.5, 0.25, let's say. Those are called weights. So when you hear about weights, that's what they're talking about. And you send a signal down, and the node below says, "Oh, 0.5 and 0.25. Let me add them. 0.75." And it has a rule. If you're above the threshold of 0.5, I fire. So he fires, and he sends a signal to the nodes below him. And that's it. You just have these signals propagating through based on this firing. And the magic of OpenAI is they built a model like this with 175 billion lines, weights. So just unimaginably big. All right? So that's the neural net. Now they train it. They trained it on these human sentences. They took, and this is in number of tokens, which you can think of like fragments of a word. So 3 billion words from Wikipedia, 60 billion from books, and 410 billion from something called Common Crawl. This is a scrape of the internet. So you saw New York Times is suing OpenAI? That's why. That's why. Okay? Common Crawl. So they took all these sentences, and let's say I have a sentence with 10 words in it. I leave out the fourth word, and I give the other 9 to the neural net. And it fires. And then down it goes, and out the bottom, it tries to predict that missing fourth word. I know what it was. I had the original sentence. If it's right, I say, "Good job, neural net," and I leave it alone. If it's wrong, I update the weights. And then I leave out the third word and the sixth word and the ninth word. And I do this billions of times across billions of human sentences, and I swear to God that group of circles and lines learns English grammar. It learns to write poems. It learns English. And if I give it Chinese sentences, it learns Chinese. All right? It was not designed to know English. I just showed it enough stuff. Does that make sense? And they weren't done after they did that. That wasn't ChatGPT. There was a second step. They did something called reinforcement learning with human feedback, RLHF. Sounds very serious. Basically, it works like this. "Hey, ChatGPT, write me a poem." Comes out with five options, and I, as a human, say, "That one's good. That one's terrible. That one's in the middle." And I rank the answer. It's expensive to do that. I have to pay people to, but that type of learning made this neural net more conversational and made ChatGPT. So those are the two things together. Unsupervised learning plus this more expensive reinforcement learning led to the biggest computer science breakthrough in the last 20 years on top of that neural net. So what does this mean for biology? Do we have any sentences that are written in letters and read end to end in biology? Yes, we do. We have genes. Okay? A gene is read end to end. The letters are all A's, T's, C's, and G's, but you could leave out parts of that sentence and ask the neural net to predict what's missing. And this is exactly what was done with things like AlphaFold, ESM, and so on. And so where are our sentences? They come from nature. Who has the most of them? I actually think Ginkgo might have the most of them. We have been collecting, we just announced the acquisition of AgBiome today, which may add another 500 million to the stack. We have to deduplicate. But we acquired Warp Drive Bio, Radiant, Lodo, Zymergen. We have been piling up metagenomic and microbial gene collections. The purple bars are what's in UniProt. Okay? About 250 million genes, more than 2.5 billion in our database. This has only been available to Ginkgo scientists and AI engineers until today. So one of the things I want to do in the interest of people building on top of each other in the industry is we're opening up this database. And Ankit Gupta, who came in through our acquisition of Reverie, said, "I can't emphasize enough. We have an outrageous amount of compute power. We should advertise that more forwardly." So for those of you that are AI folks in the crowd, we have hundreds, up to thousands, of reserved GPUs and TPUs that we can put on top of this data plus your data to train models. Okay? And so I want to make this available to you today. Second, that second step OpenAI did, where they did reinforcement learning, what does that look like in biology? Remember, what's a good poem? Well, these models are going to generate DNA designs. And you're going to want to say, "Hey, design me a protein that binds well." And you're going to want to measure in the lab, "Does this design bind well?" You're going to say, "Make it more soluble. Make it less viscous." Whatever. You're going to measure those characteristics, and that's the equivalent of giving feedback to the model about whether it's doing a good job. That is expensive because it's lab work. And so the other thing I'm excited to announce today is lab data as a service. And this means accessing Ginkgo, where you own the data that's generated, where there's no royalties from Ginkgo, where you just pay. And again, go check out those robotics. We build them in-house. Go visit us tomorrow. We generate these huge data sets for you to have that reinforcement learning on your model. And we're talking to a bunch of you in the room already about developability, immunogenicity, target ID, generating these huge data sets for you. And I think they're just as applicable outside of pharma too. Okay. We have an awesome panel to talk AI. Karen and Molly and Iya and Debbie and Anna Marie are going to be up here right after me. It's going to be amazing. And they're going to tell you what's happening practically in the market. I love this. This is a Wall Street Journal article that basically says that Common Crawl, like all the data on the internet, was not enough for the next generation of neural nets that OpenAI is building. And so if that's not enough for them, let me tell you, we are short on it for bio. So you're going to hear from Renee. Sorry. Move forward to the slide. Renee and Jon. So Renee is the head of ARPA-H. Jon is the CSO of CRISPR. And they're going to talk about, "Are these data sets the right thing?" And Michael Specter is going to be moderating that. Okay. I'm just going to draw your attention one more time to that model thing. You could roughly interpret this like the government saying that the DNA-based models are 1,000 times more dangerous than the language models, that they're setting 1,000 times lower limits. I think it's worth us recognizing that biology is powerful. Right? COVID-19 was a national security threat. Infectious disease is a national security threat. I'm excited you're going to get to hear from Matt McKnight, who heads up our biosecurity business, about the global sort of bioradar network we've been building. We just announced a partnership with the Gates Foundation for measles tracking yesterday. Okay. You're going to hear from a great group of leaders, Nikki and Wilmot and Cindy and Allison, about what it's like at the front lines deploying this biosecurity technology today. All right. That's it on AI. The other thing I was hearing from all of you was when you're engaging with traditional CROs right now as part of, say, preclinical research, you're not getting access to the latest technology. It's hard to integrate in other people's technology. It doesn't get cheaper over time. And the data you get back doesn't play well with your data science team. And so that's another reason we've decided to open up lab data as a service is I looked at this and I said, "All right. Well, what does it look like to be a Ginkgo scientist ordering from our infrastructure?" Well, you get transparent pricing and turnaround time. You know what it costs when we run the robotics. You know how long our processes take. You get data back in a beautiful format. You get access to our technology network. I'm going to talk about it in a minute, but all these technology-advanced companies that we're bringing to integrate with the platform. You get all these things already. And so the question is, could I open that up to all of you? And we're going to be and by the way, be patient with us. This is the first step on a long journey. But we are opening up our first lab data as a service where you can order it just off the menu. So antibody developability, expression, target binding, thermostability. We do a ton of enzyme performance assays, stability, salt tolerance, activity, and so on. You'll be able to order these things. You'll know what you're paying for. You'll know how long it takes. Again, you own the IP. There's no royalties. This is just lab data as a service. And boy, I hope I can make it cheaper for you next year. All right? And the reason I believe this is you can look at Ginkgo. And this is our last earnings call. We increased the number of programs on our platform 36% last year. Alongside that, our cell engineering CNA fell 6%, and our R&D fell 18%. So we're getting cheaper on a unit basis as we scale. Right? And we have a thing we call campaigns internally. It's basically generating large end-to-end data assets. That fell by 50%. And so later today, you're going to hear from one of our tech leaders, Will Serber, about what we've been doing. And again, I encourage you go check out the automation that we are building, and Will's team is building in Emeryville. That automation plus operations—we have an operations team that's driving operational efficiency as well as bioprocess improvements—dropped our cost 50% last year. Well, if we can keep that scaling up, that means that I should be able to drop costs for these services over time to you as well. I want to spend a minute on the technology network. At lunch, if you go upstairs, you can meet a bunch of these companies. Many of them are in the room. We added 30. This just got announced two months ago. 30 inaugural members of our tech network. There's sort of two ways to engage with Ginkgo as a tech network partner. You could be adjacent to us. So Ginkgo does a cell engineering project, and then you need, I don't know, fermentation optimization from Culture or downstream manufacturing from Resilience or something like that. Or you could build directly on those lab data as a service offerings I just mentioned above. You could be an AI company where instead of trying to do the lab work yourself, you push it through to Ginkgo. And I'm super excited that we have our first instance of that. I want to invite Stef, the CEO of Cradle, as our first tech network partner building on top of the platform. Come on on the stage, Stef. Thanks, everyone. It's really exciting to be here. My name is Stef, and I'm one of the co-founders and CEO of Cradle. And we're one of the first partners of the Ginkgo technology network that will be building directly onto the Foundry. And I'm super excited to talk to you a little bit about what that's going to entail today. So Cradle is a generative AI platform for engineering proteins. We've been focusing mostly on building software to make these types of techniques super accessible to any one of you. A lot of biotech companies out there don't want to hire 20, 30 ML engineers to go build models for them. So we provide best-in-class machine learning software that you can directly use on the internet, share with your CRO, and work with your team. Now, why would you be wanting to be using this type of generative AI to improve and engineer your proteins? This is, I think, a really great example of a customer that we've worked with that we're trying to improve activity, thermostability, and a lot of the cool assays that we just talked about for a P450, which is typically a pretty finicky enzyme to be working with. Our customer spent about 10 rounds of optimization on this protein. They told us we're really about to shut it down because we can't get to target product profile to make this a commercially viable molecule. So we came in with the Cradle platform, gave the software to their science teams, and within three rounds, they were able to optimize this protein to target product profile, significantly increasing the speed by which they were able to get to market. Obviously, what I would love to happen for every one of you here is that you can get a lot more products into the market a lot faster. Were it not for Cradle in this instance, this molecule would have never made it to the market. So what are we doing with Ginkgo? It's basically what Jason has been saying to you just before. One of the first partners that are going to be integrating directly with the Foundry here. So if you're a team of scientists and you really want to start working on your molecule, but you don't have the bandwidth to build a workflow, you don't want to build and train custom models, you can come to Cradle and build directly onto the Ginkgo platform. Now, why do we like Ginkgo? There are a couple of reasons. First of all, they have a really high-throughput, high-quality assay setup here that's really tried and proven. So that's awesome. Turnaround time is actually quite short. I think for a lot of our customers, when they build in-house workflows, 10-12 weeks is a lot more common. What you're seeing here on the slide, 6 weeks turnaround time. That means you could double your time to market. And it's very scalable, very predictable. So how would that work? This is what Cradle looks like today. You would go onto it, sign into it, work with your science team to really select the right set of sequences that you want to then go and build. And then typically, what would happen today is there's a little "Download my sequences" button there. People would go to Twist, order their libraries, get everything to be built, sent to them, and then they would run the experiments themselves and bring the data back. With Ginkgo, you will now have the "Built with Ginkgo" button that will get here where you can directly order some of these amazing assays on the platform and get tested straight away. And within six weeks, you'll get an email into your inbox that all of your experimental data is ready for analysis to maybe go another round. So we're announcing today our first launching customer, sorry, Syensqo, formerly known as Solvay for some of you, to really deliver that impactful result to them, yeah, in a short amount of time. Really excited to be here today. We'll be upstairs during lunch and we'll be roaming around. You can see the little Cradle logo here. So find me if you're interested in using the Surface as well. And excited to be here again. Thank you. Okay. I have to admit that 20 years ago, Ph.D. Jason Kelly miserably pipetting at the bench is very jealous of genetic engineering from your browser. Okay? This is why we're doing this. I'm really excited to see it. Okay. So again, those are the two things we're trying to enable for you. We wanted to give you data, and we want to close this gap with what you're seeing from your CROs in terms of lab data as a service. Oh, and just to flag it, cell engineering solutions isn't going anywhere. Okay? And I mentioned Shadi, that's our lab scientist, the Ginkgo scientist with the logo. She still has access to every service at Ginkgo, all of the data, all of the intellectual property. So for a lot of these big projects we're doing with many of you, you really need to access the whole suite of services at Ginkgo. That's still going to happen. I think most of our conversations will stay the same. But I want you to take a look and watch that website, and we'll send you announcements as we add more to lab data as a service where over time, it should ultimately get to the point where you have access to the same set of infrastructure that Shadi does. All right? And that's the history of things like Amazon Web Services. Right? It started with amazon.com engineers using it, and they had access to doing tons of stuff. And the first thing AWS launched was just a way to store files on the internet. And then they kept adding, and they kept adding. Today, it doesn't matter where you work, you have basically access to the same set of tools that the people at Amazon do if you use AWS. That's where we want to get with this service eventually. If that tickles you, you should come to one of our developer workshops. We're going to have scientist-to-scientist meetings. There's a link in that QR code. Sign up, and you can learn more about our lab data as a service. Come to our open house tomorrow from 8:00 A.M. to 12:00 P.M. and go see what you can have at your fingertips at Ginkgo. I'm going to end on this. Ginkgo launches a magazine every year alongside of Ginkgo Ferment called Grow Magazine. Our edition this year was on scale. There's a great article from Claire Evans. I encourage you to read it called Against Scale. And she said, "In synthetic biology, the way forward may not be a matter of producing at scale, but rather inquiring at scale, changing the volume at which we converse with the living world." Right? What I'm hoping happens with these AI tools and with this scale of data that you can get back is it's almost like a translator from you into the language of biology. Right? That's what ChatGPT can translate for me into other human languages. Why can't these neural nets also ultimately translate to let us communicate and inquire at scale with biology? And I know that sounds crazy, but I just want to show you two photos that mean a lot to me that show that this has happened before. So this is a photo of Tom Knight right here in the front row, my co-founder here at MIT in the early 1970s, standing in front of his master's thesis, which was a refrigerator-sized mini computer. All right? If you wanted to program that thing back then, you better have been Tom. You better have been an expert computer scientist surrounded by expert computer scientists who knows how to use an oscilloscope. Okay? Right? This picture up here on the right, this is my daughter Quinn last weekend using ChatGPT, my GPT function, to update the weights of a 1 trillion parameter neural net from the comfort of her living room, 50 years apart. What's it look like if we make these investments in the next 50 years of bioengineering? I'm excited. We're going to end today with a panel where you're going to hear from Alexis and Kristina and Bruno, moderated by oh, could you go back one? Moderated by Kristina, who are the two directors and the producer for Vesper. It's an indie movie. It came out about a year ago. It is my pitch, the best biotech movie since Jurassic Park. Okay? That is high praise for me. All right? It is a bit dystopian, I'm not going to lie, but it envisions a beautiful world where there is a young female bioengineer named Vesper. What they view in terms of what you can design with biology and how she does it is worth seeing. We're going to show some clips from the movie. Also, if you want a little taste of that future today, back at the booth stand, you can smell a fragrance from Arcaea. It is the scent of an extinct flower. It's for sale at Nordstrom's. It's like Jurassic Park in a bottle. And up in the back corner, I encourage you to go up and see 50 glowing petunias that we have in a display up there from Light Bio that have been engineered to glow in the dark. All right? And this is a little taste of what it's like when you start to expand the accessibility of this technology to more people. And I want to end with that. Ginkgo's mission has been making biology easier to engineer since the day we started the company 15 years ago. It's always been a little bit like making biology easier to engineer for people that work at Ginkgo Bioworks. When we are opening up our data and opening up our Foundry, I think we are getting to be true to what we really wanted to do when we started this company, Tom, which is to make biology easier to engineer for all of you and someday for all of our kids. I think that's going to be just a beautiful world. Thank you for coming on that journey with us, and welcome to Ginkgo Ferment. I want to invite to the stage Anna Marie, our head of AI and corporate development, to introduce the AI panel. All right. What? Yeah. Thanks. Welcome, everybody, to my favorite holiday of the year, Ginkgo Ferment. I am so excited for this absolutely, am I allowed to say, badass panel of women talking about AI. I promised the panel that I am not going to ask or talk about anything that you can find on Google. We want to take this a little bit deeper than panels like this normally go. All of these women have had incredible careers, sometimes second or third careers, in AI, have been focused on this space in decades. We're really excited to have a conversation here. I wanted to start with the opportunity in AI. You all have decided to spend your lives focusing on AI. It's a buzzy topic, but you get up every day excited to work on this problem. I want to know, what is that thing that you are most excited about transforming in the next few years, the next decade of your careers? Maybe Karen, I'll start with you. Yeah. Great. It's great to be here. So one of the things that I think we have an opportunity to address right now is the fact that we know so little about human biology. In all of history, we have essentially tested and developed and launched about 1,300 drugs when there are about 10,000 diseases. So we have a long way to go. I think the other thing is when we think about chemistry, we've essentially, as an industry, only tested enough compounds to fit in a bucket of water compared to the entire oceans on the Earth. So that tells you that there's so much headroom that we haven't yet explored that helps us, I think, as a society, go ahead and try and solve for human disease. Molly? Awesome. Well, thank you for having me. I'm really excited to be here. So I feel like we've gotten to a place in the last 5, 6 years where this vision of being able to first represent biology in AI and then engineer it is becoming reality. And that wasn't true 5, 10 years ago to the degree it is today. And so the questions that I'm thinking about now of where we go and where we take that are really about how do you do science, and how do you think about science as a scientific endeavor, and ask questions, test hypotheses, and really start to expand knowledge and add to the world that we don't, to your point, we know actually very little about how to expand knowledge. The types of technologies that are being developed and the types of things Jason talked about today are the keys to being able to do that. There's more to do. We know today that LLMs, for example, are very bad at using numbers. Science is a quantitative endeavor. How do you start to integrate? How do you generate the right numbers to give us the right answers and generate hypotheses and test hypotheses and update models? Those are the types of things that I'm thinking about that aren't necessarily super near term. I'm not saying this is easy. I think it will be hard. That's what keeps me going. Is that working? The speaker. Oh, yeah. Okay. Oh, perfect. All right. Thanks for having me here. Great conference. I really enjoyed the keynote, right, talking about automation and scale. So I want to just kind of work backwards. I really feel like we're living at a time where a lot of what was either incurable or difficult to treat from a disease modification perspective is actually now we're at the cusp of being able to do that. Right? We can kind of see the hints of it coming from looking just at even oncology, new immunological drugs that if you give it early enough, in enough patients with combinations, you can actually cure or even change the course of the disease. And we're starting to take that thinking and apply it to other areas like immunology. Just take, for example, IBD, Irritable Bowel Syndrome. It's a disease that at first, for patients, feels like an annoyance, but eventually, it becomes super debilitating. And for me, it's what can we start to learn about the mechanisms of disease so that we can treat patients in a just much better way and find lifesaving medicines much faster? And the opportunity that AI now affords today, after it's shown that it could actually do something really remarkable, right, so we can AlphaFold, that announcement by the DeepMind Google team showed that if you put enough of the right data into an AI system and leverage architectures, whether it's transformer architectures, reinforcement learning, and combine it with genetics and physics-based methods, that you could actually predict every single structure for every single protein in the human genome. AI did this in 2021. Right? So now when I think about how we're going to get AI to get to that next transformation, for me, it's that level of being able to now measure what we need to measure in biology so that we can truly learn biology. And so while AI is getting better at learning, we are getting better at measuring. Soon it will be possible to take any slice of tissue and measure every single transcript for every single cell type and every single organ in the human body. It's amazing. We finally have the tools and the technologies to measure biology at the right level of resolution: your genes, your proteins, your metabolites, every single cell and at scale. All right. How are you going to put that to use? How are you going to put it to use to understand biology and accelerate our ability to get to better medicines? That's what I'm super excited about. Debbie, you want to close us off? So I'm going to go very zoom out. And I'm pretty sure you could find this on Google. Apologies. But I wake up in the morning, and what I want to do is build and develop models that make biology a quantitative, predictive science. And I think that the generative models that we've started to talk about now have really opened the world for that. I don't think it's the end. It's the beginning. And one of the things, again, zoom out, I like to say is that just as math enabled mathematics, as it's called here, physics, AI will enable a quantitative biology, biology for the 21st century as we expect it. Now, my vision, basically, after listening to Jason, you've already done it, and I'm not going to repeat that, that's partly a joke. But I do think we're moving extremely fast in what we can do. The vision here is making biology quantitative and predictive, and the different models and different data we'll need for that will affect disease prevention, the early diagnosis, as well as treatment and monitoring and sustainability efforts. I know we want to focus here today on disease and the prospects for that, but it's important to remember that it really is going to open up all those other fields, which will then impact treatment and democratize it. When we were chatting earlier this week about this panel, we talked about what are the components, what are the ingredients that are needed to actually achieve this vision. Largely, our industry talks about three ingredients. You have compute, which our friends at NVIDIA and Google and Amazon are working on. You have technology, which is getting rapidly, rapidly democratized, and we're all benefiting from that. Then there's data. I think we all agree that one of the biggest limiters, and Iya, you were talking about this earlier, historically, has been access to data. One of the things we're all, I think, really excited about are new technologies, whether it's the kind of scale automation that Ginkgo is building or new machines that are allowing us to get greater resolution on biology. I'd love to go to a few of you and talk about where you see the greatest needs for data that are going to unlock the next real answers in biology and help us there. Maybe, Iya, because you kicked off that topic, I'll start with you. Oh, sure. So I think, for me, the greatest need for data is going to so let me just give a landscape of where the data landscape really is right now, and then we can get into what we need. Okay. So right now, if I wanted access to whole genome sequences in large populations of patients, that exists today. It does. There's lots of consortia out there. One of them is one we just joined. Merck joined. It's called National Biosystems. It will have, very soon, amassed 250,000 whole genome sequencings along with clinical records and over time. That's amazing. There are companies and clinical centers that will give you access to a tumor tissue bank, and you can get RNA and genetics and longitudinal records. And believe me, we're going to make hay of this data. We're going to find insights from that data combined with AI and machine learning to really understand disease better. But where we need to go now is that next level of resolution. Right? Because when I take this data and I ask it the question, are there variants that can cause disease biology, I'm going to find them. But what do those variants mean? What gene does it map to? What does that gene actually do? What is its function? How is it expressed? Which cell types is it expressed in? So we've gotten to sort of level one, all these great consortia and databases. We need now level two. We need to actually resolve what's happening in every cell, every tissue, and over time and over the course of disease. And new technologies are coming on board that are going to help us do that. Technologies like we know about RNA, but now with spatial. Right? We can do spatial transcriptomics and measure what's happening. And what I'm hoping folks get inspired by is, I think in about a year, we'll be able to do this whole genome, which is pretty amazing. Right? Every transcript in every cell, every human tissue, we're going to be able to do it. But then I need scale. I need to be able to do it under lots of conditions, lots of perturbations. And so if the theme of Ferment is scale, then what we need to do is take some of these really cool advances in biology and measurement and scale them now so that we can see enough of what's happening in cells, bodies, tissues over time in different diseases so the AI can truly, fundamentally learn biology. Maybe, Karen, because you sit at the intersection of biology and physics, chemistry and physics, I'd be really curious for your take on this question as well and where you also see the opportunities for cross-learning between these different disciplines. Yeah. Sure. I mean, I think you opened the panel with a question about what is the potential impact and how have things changed. If you look back in history, computers weren't fast enough. 21 years ago, maybe 22 years ago, we didn't really have the human genome. We now have those things. But are we making progress towards what I think we can define as really the atomistic level of what's going on inside our bodies? Right? Chemistry is really an explanation for biology. Biology is really about chemistry, and chemistry is really about physics. I think context is everything here because when we talk about the genome, when we talk about the proteome, when we talk about proteins, what we're really talking about is how atoms interact with each other at the end of the day, whether they're healthy in the context of a healthy protein or a diseased protein. I think what's exciting is that we're now at the point where we can actually start to interact with that physics. As you know, it was really hard to simulate physics in the setting of a biological system. I think we're now approaching the time because of the speed of compute, our ability to predict these atomistic interactions, we're now at a time where we can actually reduce to practice the question of what's happening at the level of physics. That's really exciting. In fact, I think some of you may have come across this term that physics is actually the new data. Right? We are now at a point where the sort of macro experiments we used to do, we can actually now go ahead and, at least when you're designing a drug, really focus on what's going on at the physics level. And that, I think, is super exciting. It's important to note, though, that when we talk about training sets, those training sets really, I think, are most powerful when they're most sort of accurately representing the system that we want to model. And again, I think that's where we are now at a point where those training sets can actually be developed based on the physics at enormous scale. Right? We used to screen like 1 million compound library, and we were super excited about that when I first started in the pharma industry. Now we're screening like 1 billion compounds in a few days. So having access to the physics and being able to scale that physics, I think, and use it as a training set, I think, is super exciting. Well, one of the things we were talking about was the ability to use these models to also inform where to generate data that is most useful, to help identify the context, help identify the questions so that you're generating data that really answers the question you want to answer. And I know, Debbie, you've got strong feelings about this and our ability to take advantage of the data that we already have and to ask it better questions and organize it in better ways. And you're a leading expert in driving absolutely remarkable results with quite scrappy resources. Thank you. Scrappy. Yes. And so I'm curious for where do you see the biggest bang for our buck in new data sets that are available? So I'm asked quite a lot about what data we would want, ideally, and as I'm sure other panelists have expressed. My question back is still, for what? I think, to a large extent, it's okay still to be quite task-oriented because there are, despite the fact we're going to be able to take every human tissue at every stage and every cell type, there are infinite measurements we could make or develop measurements to do. The trick is going to be to be able to build those models that can learn what kind of multimodal data, what is the minimum to do the rigorous work. I mean minimum like get away with it. I mean minimum comfortably. What modalities do we need for what task? It turns out you can learn a heck of a lot, for instance, about thermostability from natural sequences. We've all seen that, and we've talked about it even earlier this morning. I think we've even done some studies to show you've got a couple of data points, and you've got natural sequences, and you can pretty much predict, to some extent, the thermostability of many other proteins. Now you take another example where you want to engineer a protein, which it hasn't quite done naturally, and it's got to exist in a particular cell type and report because it's a biosensor as well on a particular state. And that will need different kinds and different extent of the data collection. So for me, it's about building those models that can be multimodal and then those models also learning how to learn what data to generate. So it's circular, but. Appreciate that. So there's data, and then there's an organizational question that I think we talked about. And I think that organizational aspect comes into play within companies and the sequential nature of the research process today, especially in the large pharma drug development cycle. But also, I think there's an interesting question about the role of the academic and open science community versus the role of private industry. And so maybe, Molly, I'll go to you because you're in a really interesting spot in the ecosystem where you're incubating new ideas, and you're really sort of sitting in the middle of this. And I'm curious, organizationally, how do you think we most quickly accelerate our learning and our ability to bring patients new medicines and bring new materials to the world on these domains? Yeah. Definitely. So when I hear this question about how do we organizationally transform how we bring medicines to patients, the first thing that comes to mind is the people, the people and the culture and what we're actually building. I think one of the things that is really true about this industry and about what we're doing today is that people have to be bought into this future of a connection between technology and biology. And you've seen this throughout history that some people just don't buy in. So how do you build a culture where people are bought in and they see how their world is changing? And I mean, I was drawn to the image that Jason put up this morning of interacting with the computer 50 years ago to how we interact with it today and how our kids are going to interact with it in the future. I see that transition of technology happening even more rapidly in the next 20 years than it has in the last 50 years. And so we're all going to have to rethink, how do we build organizations? How do we interact as a community of people, either in industry or in academia? And how do we connect those communities in the right ways? And a lot of it's about how do we find the right research questions, and then how do we apply those to the most important problems that we face today and bringing those together in a way that they're synergistic, not competitive? And that's a lot of what I spend my time doing is thinking about how do we identify the unique insights that come from many people? It's not usually one unique insight. It's not one person. At least in my experience, it doesn't feel like a genius in the center. It has felt like a community of ideas coming together to have a greater whole. And so I think that's how I spend my time thinking about this. I interact a lot with academia as a part of this, bring in many ideas to create something bigger together. And there's a role for everybody in that. And it's about the culture, the people, the community that is going to transform science in the future. Maybe I'd love to get your take on this. As the head of AI at a large pharmaceutical company, how are you seeing the industry react to the emergence of AI, the importance of AI? Everyone now is trying to adopt it, but I think in different ways and at different paces. Where do you see the roadblocks, and how do you think the industry needs to evolve that R&D process? Yeah. So first of all, I think every pharma company is going to have its own strategy around this. And a big part of it is, as many of you know, to go from an early discovery to drug-to-market, it's not, "What can AI do?" It's, "What can AI do for target discovery? What can AI do for small molecule discovery? What can AI do for biologic discovery? What can AI do for predicting safety outcomes? And what can it do for identifying the right patient for the right drug at the right time?" So it's many different levels of AI. And different pharma companies have organized differently to go from what I described end to end. But I'll tell you how we're thinking about it. Right? So the key for us is you have to embed. That's it. So your small molecule department, medicinal chemistry department, has at the table folks running the large assay screens. It has a physicist maybe even thinking about physics-based models and how to integrate that with transformer-based models. And it's got your AI architect engineer. And it's all one function, one unit advancing our ability to develop small molecules faster. So you've got to embed those folks at every single function end to end because your goal isn't just to find a great target, a great drug, a safer drug. You've got to do all of that end to end. And so the big thing is, how do you attract the right talent? How do you get folks engaged and wanting to do this and wanting to do this not just as a kind of little tiny pet project, but excited by the scale of what's possible in pharma and as well as even how pharma interacts with the biotech system? We take that very seriously, and we've set up initiatives that are both around getting the right talent inside of pharma and inside our departments, but as well as once they're in, that they're part of communities of practice, communities of experts, and they can really move the needle within each of the functions that they serve. Maybe, Debbie, to wrap up this topic, how can industry better partner with academia? Well, I think it's a really interesting question. When I first started my lab, which was about nine years ago, you were saying to me, "What are you encouraging your students to do? Where are they going to go and do their postdocs? And what faculty position? They better go to this conference." And I said, "Well, hang on a minute. Some of the" and they said, "Don't let them go to industry. That's the bad ones." They were still saying that. And we all know that that's fictitious. And we know that it might have even if it ever was true, it's certainly not been true in the last 10 years. And what's more, I think we've seen now I'm going to put down academia. And we've really seen huge developments in AI methods in industry that have then come back into academia. I think I really want to emphasize this point about the fluidity that we need. I think there is fluidity, people going back and forth between industry and academia. I think we'll see it a lot more. At the moment, it's very serendipity. It will be really good to have mechanisms in place where that's organized and encouraged, and students are empowered to do that. We can train together. I think that's going to be mechanisms for training together is going to be a big step that we need to take. I like it. I know that we have a diverse audience. We have folks in industry. We have many students here. I'm sort of heartbroken. We're just about out of time. I'd love to talk to you all day. But I am hopeful that, if nothing else, this panel raises the bar a little bit for the industry and that none of us are ever again the only woman on a panel, the only woman at a dinner on AI. I'd be curious just for the folks that are in the audience, if you have one piece of advice to folks that want to build a career in this field who may not have a lot of role models, what would you say? Karen, I'll start with you, and we'll go down. So I'm going to borrow something from linguistics. Right? I think being bilingual or trilingual is going to be really important. Actually, we're spending a lot of time. Do you mean that actual language, or do you mean like natural content? What I mean by that is, it comes back to what I said. Our teams are really interesting. We've got physicists, machine learning experts, medicinal chemists, synthetic chemists as CMO, right, all sitting together thinking about, "How do we solve problems?" What we're seeing coming up through college, through even high school now, are people who don't train in silos. They actually are bilingual. They know something about data. They know how to handle data. But they also have a sense of medicine. Right? So these people who are bi or even trilingual, medicinal chemists who know how to write command code, this is, I think, a super exciting opportunity for the next generation. And so I'd say, "Don't train in a silo. Be bilingual, trilingual. Molly? So I think one of the big things to keep in mind also, in addition to I totally agree with the bilingual nature, is to really find your superpower and believe in yourself. I think there's a lot of trying to fit into what exists. And I think the more of us that are identifying the ways to create new opportunities and believe in yourself and believe in your vision as a part of that and seek out community and allies, men and women, all backgrounds, in doing that, just believe in yourself. Create. Awesome. That's all of it. Great advice. So I can only add to what's been said. And so 20 years ago, when I started in this field, I was always back to what you had said, the only woman, whether it's at the panel, the room, the this, the that, the that, the that. I think it's changed a lot. I hired my head of AIML last year. She's a woman. Now, I wasn't looking for a woman. It just so happened that's the resume that stood out. But I think it's because, given that we're in this new field, right, and when something's new, you have a real opportunity to kind of join it and shape it. And so my advice is, it's a new era. Don't go by what has been. Really dig into where we are today and realize you have the opportunity to be a part of it, to shape it as part of the ecosystem of both men and women. And then lastly, I just one word of advice. If this is about targeting all the women out there, just be fearless. You have nothing to fear. You can do this. All right. You. Debbie, take us home. Right. So all of the above, and especially the last comment, I was asked this about 5 years ago. I was on a panel for the Chan Zuckerberg Initiative. And I said, "What's your advice to everybody?" I said, "Go big. Go bold. Learn statistics." And now I would add one more thing to that, actually, is go big, go bold, communicate, talk to people, and learn statistics. All right. Learn statistics. Thank you all. Enjoy Ferment. Next up, we are going to be hearing from some of our customers. At Ginkgo, we don't make our own products. We are a platform. People make use of that platform to build their products. So if our mission is to make biology easier to engineer, these are the people who we're doing that for. I think about if we are a platform company, then these are our platform developers. I think about developers all the time, literally all the time. I'm the developers guy. It's my thing. I heard a woo. Thank you. Thank you for that. I think about it for two reasons. So first of all, it's kind of obvious. These are our customers. And without them, we don't exist. But the second reason is that I really think that the interface between Foundry and developer is the most interesting space in tech today. We've seen lots of examples in tech of the power of that relationship between a technology user and a technology platform, so websites and web services, operating systems and applications. The question that's on my mind all the time is, how do we take the power, the flexibility, and the scale of the Ginkgo Foundry and make it as available as possible to the people who are going to use that to build products? So these are the questions that fascinate me. And it's why I love hearing about the developers who are building on our platform. Here's how this is going to work. We've got four lightning talks. I'm going to introduce them as a block. And then I'll go ahead and get off the stage to not interrupt the flow. We've got Arie Abbo, CTO of Imagindairy. Do I see you there, Ari? Hi? We've got Limber Acosta, Director of Innovation at Centient Pharmaceuticals. We've got Keith Wood, CEO of Light Bio, and Todd Beckman, CEO of Verb Biotics. Take it away. Thank you. Thank you. Did you know that amoxicillin is the most commonly prescribed antibiotic in the world? It is. The reason for that is because amoxicillin has a broad spectrum activity. It is used to treat many different types of infections, like bronchitis, like pneumonia. It's also used to enable advanced medical procedures, like cancer treatment, like organ transplants. Amoxicillin is a critically important antimicrobial for human medicine. Good morning. My name is Limber Acosta. I'm super excited to be here on behalf of Centient Pharmaceuticals. Centient Pharmaceuticals is the global business-to-business leader in the manufacturing of sustainable enzymatic antibiotics, next-generation statins, and antifungals. We have a global footprint with manufacturing sites in Mexico, China, India, and Europe. We understand that with such global presence, we have a huge responsibility to supply the world with life-saving medicines because these are the medicines that form the basis of any healthcare system in any place of the world. These medicines are absolutely essential because they make surgeries possible. They save the lives of our children. They protect us from deadly diseases. We take this responsibility very seriously. Last year, we produced active pharmaceutical ingredients for 1.5 billion patient treatments. We have been awarded with a gold sustainability rating by EcoVadis. I like this slide a lot. Our purpose is to improve lives through innovation and through sustainable manufacturing of medicines. We have embedded this purpose in our production processes. Our fermentation and enzymatic technologies have replaced the traditional chemical process for manufacturing these antibiotics. I can say today that these are the world's purest antibiotics in the world. We are investing in our technologies. That's why, together with Ginkgo, we have improved our enzymes. We have already implemented these enzymes in five manufacturing sites. But this is not the only area where Centient Pharmaceuticals is investing. We are also investing in our pipeline. We have a long history of biomanufacturing. We are bringing 80 years, more or less, of commercialization of sustainable antibiotics. If you want to be part of that purpose to supply the world with life-saving medicines, and you have a new product or a new idea, please don't hesitate. Let's connect. Thank you. Well, good morning, everybody. My name's Keith. I'm the CEO of Light Bio. And I'm here today to tell you the story behind our bioluminescent petunias, which we have on display here at the event today. So this is our firefly petunia. In the daytime, it's just a nice white petunia. It just grows like any other petunia. But at nighttime, it creates this light similar to moonlight. This is living light coming from the energy of the plant. You're literally able to see into the inner vitality of these plants. Now, there are no bioluminescent plants in nature. But using gene transfer to make glowing plants is nothing new. This is an image that I made 40 years ago of the very first bioluminescent plant. So why did it take us so long to make a commercial product? Well, the problem is we only had one way to do it. We had these bioluminescent genes from glowing bacteria. They just don't work very well in plants. They're dim and really not impressive. My colleagues, about 10 years ago, discovered a new way. They discovered a new set of genes from bioluminescent mushrooms. That has made all the difference. You see, these genes encode for a catalytic cycle that uses metabolites that are naturally present in plants. In fact, the only thing that's novel to the plants are these two at the bottom, these green highlighted molecules, which are the substrate and products from the actual light-emitting reaction. Everything else you see comes from the plants themselves. Importantly, it's not just anywhere in the plant, but it lies in the most active part of plant metabolism. The energy of photosynthesis is used primarily to make cell walls. Cell walls are made of two components, mostly, cellulose and lignin. We've inserted the bioluminescence into that lignin pathway. The energy needed to make light comes from the most energetic part of the plants. But even though we are using the natural metabolism of the plants to make light, the genes themselves and the enzymes, they encode, come from a very different organism. They come from fungi. That's an entirely different living kingdom. These enzymes have not evolved to work well in plants. That's why we are partnering with Ginkgo to make better enzymes. We're making enzymes that will work well inside the plants. Our objective is to make plants that are as much as tenfold brighter than the plants we have here today. Now, this is not just a pretty flower. This has captured the public's imagination. You can see this by the amount of wonderful press that we've been receiving. In fact, just this week, we were featured on several programs on NPR. This fascination with bioluminescent plants is opening up new markets. In fact, most of our customers have never even bought a petunia before. What about all this fear of GMOs by the public that we keep hearing about? Well, we're not seeing it. It seems that when you can produce a tangible benefit for the end user, they are perfectly willing to accept the products of synthetic biology. Now, we're not going to stop with petunias because the bioluminescence is inserted into the essential metabolism of the plants. We can do this for any plant. So we're going to create these brighter plants using this new gene. From that, we're going to create more varieties of potted plants. We're going to be moving more towards the kinds of house plants that we have routinely in our homes. Then we're going to move into cut flowers. Ultimately, we're going to be moving into landscaping. I mean, imagine someday having trees that glow in the dark. Each of these markets is about $50 billion globally. These are major markets. But for today, we have the Firefly Petunia. I want to thank the team at Ginkgo for putting together a wonderful display today of these plants. Please, if you have time, come up and see these plants. Now, bioluminescence is a nighttime phenomenon. So coming in from the daytime, it'll take you a minute or two to adjust to the darker environment. But take a little time. It's a wonderful meditative space. I think you're going to enjoy it. And for those of you who want to take these petunias home with you, we have a special with the event today. Just use the code FERMENTGLOWUP on our website. And you'll get 10% off. Thank you very much. Good morning, everybody. It's a pleasure of being here. It's very exciting to be in this meeting. Thanks, Ginkgo, for inviting us to tell you our story. I'm Ari Abbo. I'm a co-founder and Chief Scientific Officer of Imagindairy, a company based in Israel that is focused on producing the milk protein to create dairy product that is animal-free. No matter how you slice it, we need a better solution. Everybody here in this room and is extending the market right now in different countries, like in Asia, love dairy product, the cheese, the yogurt, the ice cream. However, we have a problem because this is not sustainable. As you know, the dairy industry contribution for global contamination of the environment is about 3.4%, which is about twice the contribution for environmental impact that global aviation industry. Clearly, we need a better solution. The current solution that is now in the market is a plant-based product that tries to mimic dairy product. It's not cutting it right. Most of the plant-based products do not provide the nutritional values and the functionality that you can get from dairy products. We are on a mission to create a dairy product without a single cow. Several years ago, this notion that if you generate or produce milk proteins, you could reconstitute dairy products sounds like science fiction. Several companies in that space were able to show it. However, the challenge here is how do you get into production scale. You can buy purified dairy proteins from Sigma for a few micrograms for thousands of dollars and can do the experiments and can reconstitute the dairy product by having the dairy protein supplemented with fat and minerals. You can create cheese and yogurt and other dairy products. However, to produce this protein, there is a challenge. We, from the beginning, created a strategy that essentially incorporated AI, functional genomics, and synthetic biology to create a strain that could produce a large amount of the milk protein. The strain is designed to secrete the target milk protein into the media during the fermentation. When you scale up the process, you can harvest the protein from the media and purify it and use it to generate dairy product. As I said, the challenge here is how do you actually create an efficient process to create this scale so you can produce a lot of proteins to really disrupt the market? Please play the video. So we were able to generate, with a recombinant protein that we made in scale, all of this product that you see here. Essentially, it's a single protein from the whey family that you could add it and to create yogurt, you create cheese, you create milk that is very similar and almost identical to a product that you can get from cow's milk. This is a real product that we produced in the lab. And this is the real thing. It tastes the same. It has the mouthfeel and the functionality and nutritional value and the right price, where the additional benefits, it's lactose-free, no cholesterol, and hormone-free, and antibiotic-free, and cow-free. That's the most important things. And this was done only in the last 4 years. We started our journey 4 years ago, where we invested in the specific strain development. We're using an AI-directed technology that is partnered from Tel Aviv University and developed it for the last 15 years, all the way from developing a strain to scale up to produce a powder that contains pure milk proteins. This powder is used to create all the product that we showed you. Most recently, we achieved three milestones, which is we got an FDA approval to launch a product in the U.S. We were able to acquire a production facility to scale up the production of the proteins. We were able to get a grant to develop the additional non-whey protein with Ginkgo through a BIRD Foundation grant, the Binational Foundation grant that's supporting this project. Thank you very much. Good morning, everybody. I hope you're having a great ferment. This is such an incredible event. My name is Todd Beckman. I am the CEO of Verb Biotics. I'd like to tell you a story today about Verb Biotics and our mission. I'm also excited to talk to you today about our first ingredient launch called Keystone Postbiotics. At Verb Biotics, we're on a mission to improve health through gut microbiome innovation. We are a B2B ingredient company. We sell microbial ingredients to the consumer products industry. We are also a spin-out of Ginkgo Bioworks. Yes. Let's talk microbiome. You might have heard this said before, that our gut is our second brain. Well, at Verb Biotics, we think it might be the main guy. It kind of is competing. Which guy is number one, this guy or this guy? Either way, we know that the vitality of our microbiome is directly linked to our overall health and wellness. So at Verb Biotics, we embrace the gut microbiome as a vital organ and a key indicator of our overall health and wellness. So our microbiome is an ecosystem. Just like a forest, an ocean, when an ecosystem is in balance, life thrives. When our gut microbiome is in balance, things like digestion, immune health, and other vital functions work as intended. But sometimes life gets in the way: stress, antibiotic use, poor diet, fancy conferences where we're doing too much stuff. Because sometimes throw our microbiome out of balance, and we need help. We recently conducted a consumer survey of over 2,000 consumers. We found that 7 in 10 had some type of gut issues: stress or, I'm sorry, gas, bloating, just general issues. But more alarming, 40% reported that this impacted their life every single day. Let that sink in. 40% have this issue every single day of their life. At Verb Biotics, our north star is Feel the Effect ingredients. We aim to make a meaningful difference in everyday consumers' lives. So Ginkgo and our friends at Ferment, well, the other Ferment, Jason Kelly and team, identified an opportunity in the probiotic industry, the probiotic ingredient industry. Now, this is a $2 billion ingredient industry, lacked innovation. So we decided to spin out a company. So in September of 2021, we spun out Verb Biotics. And we quickly got to work assembling a team, I mean, an incredible team of microbiome innovators. And we got to work. So at Verb Biotics, we develop microbial ingredients for gut microbiome health that address both foundational and complex health indications. On the foundational side, our postbiotic, we call Keystone Postbiotic, that addresses the foundational microbiome health, which is gut and immune health. We've also launched a probiotic that produces GABA, which is a neurotransmitter. So that really fits in nicely to a gut-brain health positioning. Gut-brain thinks sleep, stress, and mood. We're working on other platforms. So as an example, we're working on technologies in healthy aging. In healthy aging, think cognitive, think joint health, think muscle maintenance. So how do we do this? How do we innovate in this $2 billion category? Well, we take a function-first approach. We start with the end in mind. We start with a health indication. From there, we try to understand what's the mechanism of action to implicate that health condition. Then we develop our biotic solutions. Then we partner with Ginkgo to help accelerate our microbial development as well as accelerate our speed to market. Our first loop through this process is something we call Keystone Postbiotic. So Keystone Postbiotic is intended for foundational gut microbiome health. It has three different mechanisms of action. One is for immune support. One is for overall gut health. And the third is for intestinal barrier function. What makes our postbiotic so unique is it's shelf-stable. Traditionally, live probiotics, they don't survive in things like beverages or gummies. They just can't do it. Our postbiotic is shelf-stable. It's an inert product. So it can go into any format. So today, you should try it. It's an oat milk creamer. We put the ingredient in there. It's delicious. You have to give it a shot. But anyways, that's our story. Appreciate your time. Thank you very much. Enjoy Ferment. All right. If you want to continue the conversation, we're going to try a little experiment. Here on the Ginkgo Events team, we are optimizers. We are going to optimize your networking experience. Please direct your attention to the button-making station. It is over here to your left. You can go there. You can get a button that will indicate your interests. They are very funny. They are very well designed. I got one that says, "Hi, Taq." I got one that says, "I give a flux about enzymes." I get one. All right. It's good stuff. All right. So get a button relating to your interests. Gather over at the button zone. Meet other people who share your interests. If your interests are robotics and automation, meet up over by the rack automation demo. But who am I kidding? You are going to go there anyway. Those robots are awesome. That is break. We will return at 11:15. This is your 5-minute warning. The show will resume in 5 minutes. Repeat. This is your 5-minute warning. Hello, everyone. Your attention, please. It is time for us to ferment once again. Please take your seats in the main stage area. Hello. Hello, everybody. Welcome back. We are back. We are back from break. Welcome back. Come on back to the main stage area. We got a good one. We got a good one. So I am the emcee. I am emceeing this event. And I have perhaps one of the hardest jobs in the industry, which is pulling people away from their very good and very important conversations and trying to direct their attention back to the main stage area. It is very hard. It is very hard to do this. I'm going to try. I'm going to try a little provocation. Let me hit you with a little provocation and see if I can bring in everybody's attention. Is biology scary? Is biology scary? Are we scared of biology? Does biology make us feel fear? Is it a scary thing? I don't know. I don't know. I know biology is awesome. Biology is awesome. It's both in the fun sense, in the hip, young sense of awesome. But biology is also awesome in the old-school sense. It's big. It's powerful. It's bigger than we are. So it's appropriate, I think, looking at the living world to feel awe. And I also think sometimes it's appropriate to feel fear. And we take that seriously at Ginkgo. We take biosecurity seriously. And we integrate it into our mission. So for the next session, we are going to hear more about that mission. And so please join me in welcoming to the stage the general manager of biosecurity at Ginkgo Bioworks, Matt McKnight. Good morning, everybody. It's great to be here. You've already heard some of the amazing things going on on the cell engineering platform at Ginkgo. We're going to hear tons more today. But I'm also excited to have the opportunity to share a bit about what we're doing, like Jake mentioned, around building the capabilities and technologies to ensure we also have a safe and secure future with biology. It's something we care very deeply about here at Ginkgo. I'm going to talk today, actually, about the products that we are partnering with governments to bring to the world in that capacity. But I wanted to start because though I got some army guys in the front, probably pay more attention to this. Not everybody thinks about this topic every day. So I wanted to give you the two big framing things for how we think about what we're building. First, we live in an age of pandemics. And that's not just a tagline, right? Actually, and I'd encourage you to read this report led by Nita Madhav, our head of epidemiological analytics at Ginkgo. It really is a numbers game. And the basic kind of takeaway from the report, not to simplify it too much, is we have a lot of airports. We have a lot of people. We travel a lot. And we are encroaching upon natural lands more and more. There's likely to be, within the next 25 years, I think about the careers that we have building this company, another event on the order of SARS-CoV-2. We should be really thoughtful about that. It's really about how we live as humans. But I think the one that we talk less about because it's scary, that we have to be clear-eyed about, is what Jason alluded to, is that there are also human-made threats. Every time in human history when we have taken a base science, whether that be chemistry or physics, and turned it into an engineerable discipline, humans have chosen to use those technologies in new and creative ways inhuman to stop that. This is a massively scaled program. We have a great pipeline of other countries that are interested in deploying Ginkgo Canopy radar for biology detection at their airports. This is a little bit of an image of what that means. What is the product? It turns the environment into a high-fidelity data asset. This is a picture from Rwanda, a device that's a proprietary device where we collect wastewater off of the airplanes, move them directly onto sequencers, and turn that into a dataset that can be used for early warning and other detection capabilities. What's exciting is if you get enough countries involved in building Ginkgo Canopy in their own airports, you start to be able to build a new dataset of how infectious disease in real time flows around the world. And so this is an example of the last year and a half of COVID variants from our different programs tracking again, remember back to natural pandemics, the airports tracking over human activity, how do pathogens move in correspondence with humans. But Ginkgo Canopy doesn't just have to be airports. If you think about radar configurations, we're working with our most innovative customers in different countries to say, "How can you adapt and mold this for other areas of high biorisk? Hospitals, borders, right? Animal production farms. These are areas where threats can emerge that if you want to stop something, you need to find them immediately and understand what it is." Our second primary product that we're making available is essentially the so what product, the decision product. This is called Ginkgo Horizon. It seeks to answer the actual questions. Once you've done detect, okay, what is this threat? And how bad is it? So some of you might remember back last fall, there was this mystery pneumonia that was putting kids in hospitals in northeast China, right? It was all headline news. And kind of nobody knew exactly what was going on. Is this a new pandemic? Is this going to impact kids? Or is this just because China just has opened and then there's a need to recreate immunity? Nobody really knew. Those are the kind of questions that we need to be able to answer quickly for national security decision makers, for public health decision makers. The other piece, for example, for our national security customers, we're able to layer in things like our program called Endar, which is a bioinformatics tool that allows us to do genetic engineering detection in samples. Is this new sample something that has been engineered? Or is it natural? That's the kind of idea of get to insight, get to so what so you can make better decisions. What Ginkgo Horizon rests on are two primary data assets. One, when appropriate and authorized by the countries we partner with, we can make the Ginkgo Canopy data feeds available. That's a really powerful tool of molecular detection from collection at airports and other locations that we can make available on this platform. The other side of it is all the other important information, essentially digital monitoring. What is happening on the Ministry of Health's website in X country? Did they just post an alert? Should we be tuning our analysis to a new location? Today, for our customers, this is available in a set of data feeds focused on those questions. But also, we're launching and building this into a set of interactive platforms to increase the pace of decision making. You want it to do detection and then rapid decision making if you're meant to close the gap on biothreat reduction. Lastly, if you build this with a mindset like you build cyber, you're constantly improving your capabilities. You can keep deploying new modules into these platforms. So we're very excited in the last few weeks to have announced collaborations, as Jason mentioned, with the Bill and Melinda Gates Foundation, with CDC's Center for Forecasting Analytics, really thinking about next-level, next-era prediction. So we can go from reaction, "Oh, there's somebody in a hospital. We must do something about it," to what's going to put people in a hospital and how do we mitigate it. We've got collaborators across the country, across the world. Northeastern is one of our major collaborators. I know folks are in the room today. And we really appreciate doing this in collaboration with others. The last thing I'll just close on is the last piece of this with some of our kind of deepest partnerships globally. We're thinking about how do you take Canopy radar for biology and put it with the decision support in one unified facility that does detection through support for mitigation. And that's what the Center for Unified Biosecurity Excellence is that we announced a couple of months ago called CUBE. One plus one, in that sense, is meant to be greater than two. We've launched our first design and build process in Doha called CUBE-D. This is a unique place to do this. We're building it in the Qatar Free Zone, right next to Hamad International Airport, which touches about 60% of the world's population within an eight-hour flight. It's a really powerful place to put detection for global biothreat alert. What is it actually? It's a large-scale sequencing, biobanking, bioinformatics, and analytics center, right? It both serves our major programs with Qatar. But what's really unique about it is it gives access to data generation to partners at nodes in other countries around that can't necessarily always be versioning the most cutting-edge bioinformatics analysis, the most cutting-edge data interpretation environment to data type mindset. So I guess I would say, in closing for us, if you are going to take those first two threats, natural and manmade, and reduce the risk that they impact our next 25 or 50 years, you have to think about doing detection, characterization, and response in hours or days, not months and years. That has to be the aspiration for what we're doing. And it feels like a big task. But also, creating a world free of catastrophic biological incidents is a big task. And so Ginkgo is going to do everything we can to push this forward. But honestly, biotech is a big, big tent, right? It's going to take everybody in this room and beyond this room to do it really effectively. So thanks for the opportunity to give you that. We'll keep giving updates on kind of what we're building and trying to put in the world. But my last comment would be you don't actually have to just take it from me. We have an amazing, amazing panel today. Alison Snyder, who is the Managing Editor of Axios, is going to moderate. Allison has, if you go through her writing on biology, biosecurity, biorisk over the last couple of years, has been one of the really thoughtful writers on this topic. Then we'll go quickly down through. We'll take a quick pause to put chairs up on the table. But Dr. Cindy Friedman, really the leader at CDC that put together the Traveler Genomic Surveillance Program, the innovative idea to monitor airports, that's been an amazing program that we've been proud to partner with. Dr. Wilmot James is one of these larger-than-life human beings who has done so much in his career. In this context, I'll let him share some of his other background. He is one of the global thought leaders on health surveillance and how to do that, in particular, in low and middle-income countries. Dr. Nikki Romanik, who is the deputy director of the Office of Pandemic Preparedness and Response Policy at the White House, working closely with General Friedrichs, figuring out how we protect the United States and the world going forward. Thank you for your time. I will turn it over to the chairpeople and then to Allison and the panel. You found the water already? Excellent. Hi, everybody. Thank you so much for being here. I'm really excited to talk to this group. You've all been sort of very extensive experience sort of putting science and engineering into practice around this issue. And I'm really excited for everyone to hear about what you've seen and are seeing and are building. So I think I want to start in the end with Nikki. As part of your, I'm sure, many tasks, you are meant to create a report for Congress about the lessons learned in COVID-19. Can you give us a sneak peek of what might be in there? Hi. First, I should tell you a little bit about the brand new office because it started in August. So we're brand new. I am the deputy director and chief of staff of a new office in the White House called the Office of Pandemic Preparedness and Response Policy. We are our own component. We were created by Congress. I would say we are the lesson learned from the past. We were created because, in the past, the White House has always had a coordinating structure, a team that came in to coordinate any kind of outbreak or response. Now, this office is permanent. We'll always be at the White House no matter what and who the administration is so that we can continue to do preparedness efforts and coordinate federal responses both in peacetime and during actual responses. So as Allison mentioned, we are doing a report to Congress as mandated. It'll be coming out in the end of 2024. Important to highlight, this report is not just what the federal government thinks about itself. We could do that. But I don't know how useful that would be, to be honest. This report is going to include after-action reports from all of our stakeholders, partners, anybody who wants to contribute to it, and also, of course, the agencies and the federal government. The report will also highlight the wins that we had during COVID, the incredible programs like Cindy's program, and also those programs that perhaps are going to go away because there's no longer funding for them. It'll also capture the gaps that we identified during COVID, like the data sharing challenges that we'll talk about here today. It'll, more importantly, give recommendations for how to fund things in the future in the federal government. That's what I can tell you now. Wilmot, can you give us your perspective about what you've seen working with Africa CDC and across the continent? Well, let me start off by saying that I do most of my work on biosecurity governance, basically looking after how one creates a community of people who take responsibility, safe custodians for looking after issues of biosecurity. I do most of my work in Africa. I'm a former member of Parliament from South Africa. So I go home often. And 3 years ago, 4 years ago, I would go home and speak to ministers in government and raise the question about biosecurity issues. And they would say, "Well, that's a Western issue. That's not our issue. This is the West worrying about security globally." They don't say that anymore, okay? COVID-19 has actually illustrated what the risk is. We built on the African continent over 40 laboratories, some biosafety level 3, biosafety level 2, and a few additional biosafety level 4 labs being constructed. We did it quickly because we had a public health emergency. Certain things had to be retrofitted to make sure that those labs are safe and secure. To say that on the African continent, people have recognized that this is a big issue. The community that's recognized this is the national security community. That's where the signal's coming from. Let me tell you, when the national security community takes notice, there is greater political will, greater energy, more budgets available to take care of this issue. It's a massive opportunity. Let me just say that after four years of talking to my colleagues in various governments, the penny has dropped. There's a window to do something about this. The window's actually quite small. So now, we have to go about the business of building platforms to make sure that digital information that can cause harm, like now powered by synthetic biology and artificial intelligence when it comes to diagnostics, surveillance systems, and vaccine R&D and manufacturing, has to be kept in safe hands, in a safe pair of hands. That requires government action. It also requires public education so that people understand why it is that the science community is focused on this, what the public purpose is. So to say that that's the lessons that come out of this experience. We have lots of work to do. So glad to speak to you today about what that work actually is. We're going to come back to several of those points, hopefully. Thank you. Cindy, Nikki just flagged your program as a highlight of something that worked. Can you talk a little bit about the Wastewater Surveillance Program, where it stands, what you've learned after four years? So thank you. It's great to be here. I think before I talk about the Wastewater Surveillance Program, I just want to level set and make sure that everybody knows why travelers are so important for sentinel surveillance. We figured this out pretty quickly in the pandemic. I think everybody sees that travelers get and spread infectious diseases all over the globe really quickly. If nothing else, it was evident in COVID. So they can act as sentinels, a valuable tool for early detection. I think Matt talked about that half of the equation. We might argue about different public health strategies or mitigation strategies or what should be done. But I think we can all agree that buying critical time with early detection is good for everyone. I think no one would disagree with that. For a long time, we looked at travelers at Travelers' Health Clinics. When they came back, we were able to detect SARS-1 early in a travel clinic. We were able to find malaria in the Caribbean where we didn't think it existed before. It takes time. We have to make sure that the traveler goes to a clinic, that they get to see a doctor who orders the right test, who then sends it to the lab. Then we need genomic sequencing. That takes more time. With the COVID pandemic, we were always behind and out of time. We didn't have the luxury of time. We set up this program. I think the key to the program was really the public-private partnership between CDC and our two partners, which is Ginkgo Bioworks and XpresCheck. There was a need for the industry side. XpresCheck was a spa company in an airport who was looking to do testing of travelers. Ginkgo was doing K through 12 genomic testing and could do a really fast throughput of sequencing data. We, in public health, were getting information late. So we set up this program where we could test travelers. We didn't even know if it would work because we asked travelers to volunteer and give us a nasal swab sort of on their way running for a cab in the airport. We could get that swab. It was anonymous and voluntary, get it to the lab, get it sequenced, all within 10 days, and get that information out into the public domain to detect new variants. We were able to do it. We get about 9,000 people volunteering a week to do that. We've enrolled 500,000 travelers since 2021. And. But. Oh, go ahead. Sorry. Go ahead. I was just going to say, but now, these sort of contracts are drying up for these programs. So I'm curious, how are you now starting to think about wastewater surveillance at the CDC? Right. We also show that wastewater surveillance is another modality. So you don't even have to engage the traveler. It doesn't disrupt the transportation, the ground handlers, or the travel sector. And you can get that information. And you can use it as a proxy for what's going on in the community for early detection. But you're right. We're at a point where funding, it's an interpandemic period. And there's this cycle of complacency and crisis that we live through over and over again. And I can't explain why. But I think collaboration with global partners, there are some models showing that if we do airplane wastewater surveillance, the group at Northeastern that Matt mentioned, Alessandro Vespignani's lab, has shown that we can buy three months of early detection capability if we have about 10-20 nodes of countries that collaborate with wastewater surveillance. We can go from a cycle of crisis and complacency or complacency and crisis to collaboration and stability if we work together and combine resources. Can I ask, and Matt mentioned it. And I think I've heard about it a lot, data and information sharing being sort of a linchpin in all of this. How do you think about what is the mechanism right now? You each represent very different parts of this process. What is the mechanism right now for putting the right data in front of the right decision makers with the right amount of time so they can make the decision? How do you think about that? Nikki, maybe you want to. I mean, I think I heard your question. It's hard to hear you. It is hard, yeah. Sorry. You just asked about data sharing, correct? Yeah. Okay. Yes. So for us, our goal, we are a coordinating entity across the entire federal government. So it is very important for all of the different programs that share data, including the VA and DOD, every program within the federal government, to be sharing the data themselves. It is something that we are working on to make sure that everybody's coordinated. But it was a gap that was identified during COVID. So it's something we're working on hard to try and get those agreements. But then, on top of that, it's important to get data sharing agreements from every single jurisdiction, which is something CDC's working very hard to accomplish. But as the coordinating entity, we just are trying to have all the different data sets. Imagine if we could actually have the data from all of the different areas that the DOD is in in the entire United States and the world. That would be a great data set. So we're trying to think a little bit outside the box to try and coordinate everything within the federal government. And then how do you think about the handoff to something like the Africa CDC, right? I mean, going even further beyond, what are some of the concerns about how data is distributed, shared, accessed? I mean, it's really important when you're fighting a pandemic that you have a common set of norms in terms of which data you collect. And as you know, data's collected on a national basis. And so the kind of fancy word is an interoperable data collection system has to be built so that when you share data, you're sharing the same data, not different data about this or that. So you need some norms to guide that. So what's required is, in fact, to find those norms, define the systems, get the categories down as globally applicable categories. And then you need to try and harmonize that with individual countries. And clearly, you can't do it for the entire globe. So you need to build on that system on a regional basis. So the work we're doing in Africa is to build it according to sub-region. So Southern Africa needs to develop a common system, say, associated with the Southern African Development Community, which has 15 countries, and so on and so forth. So that's the first thing. The second is we must be able to have an agreement on what we share and in respect of the issues related to the constitutional rights. People have to have privacy on the one hand and, therefore, to deal with proprietary information. And you know that is a controversial issue. That has to be negotiated. We suggest negotiating it on a regional basis. Trying to do it on a global basis is very difficult. So that's what I would say. But it's really important. You can't fight a pandemic if you have different systems and you're measuring different things. You just can't do it. The fact is, the globe is made up of countries. Countries are the actors. Therefore, we need to work with individual countries along the way. Right. Obviously, we're focused on COVID and the frameworks that are coming out of COVID. But how do you create or how do you think about setting up a system that isn't biased to the system that precipitated it being set up? So when you think about future threats, future pathogens, how do you think about that, taking what you're learning and applying it more broadly? I would probably say the one thing that comes to mind is we can't do it alone. I think any kind of system that is created has to be a fantastic collaboration and partnership between multiple stakeholders across multiple different areas. That would be probably the most important thing if we really want to have a true data sharing entity that shares both sides, of course, shares within the federal government back and forth, but then also shares outside. The most important thing is stakeholders and partnership. All right. Go ahead. Yeah. So I mean, I just think that, imagine in 2020, if we had a global network that was set up for early detection, what a different place we could have been in. And I think we need to think about that. And we need a supranational body, really, to coordinate. There are a lot of global efforts now. But some of the data sharing, I think, needs to be coordinated. So there's a lot of effort right now in Europe to set up a global wastewater consortium that harmonizes methods and has data that's interoperable and that also shares that data. And I hope that that grows and comes to fruition. But we definitely need global cooperation and supranational leadership because, really, it's in all our best interests because the early detection piece and whatever mitigation and public health actions come from that for whatever pathogen really can help avoid the costly border closures. They can help avoid travel and trade disruptions, which is in everybody's best interest, not just a public health perspective but really a global perspective. Yeah. Oh, go ahead. I was just going to say that, I mean, to get very practical about the issue, in low-resource countries in the developing world, I think we should invest our efforts and our money in upscaling early warning systems. And early warning systems are best advanced if you get early detection right. So with early detection, you know what the pathogen looks like. You know what it is you're looking for. And we have the technology and the systems and the people to, in fact, make early warning systems work properly. So I would say that's where the effort is. If you look at event-based surveillance and you're talking about genomic surveillance, that becomes expensive when it comes to looking at developing early warning systems as opposed to early detection systems. That's great. That comes later. But focus on making early detection systems operate. And there are norms for that. We are saying you should be able to detect something in 7 days. If you're not, put systems in place to make that possible, report it then in 1 day, and then respond within 7 days because warning is useless if you can't respond to it when you're done with the systems. We were talking about this before. Early detection systems, how much of it needs to be new tech? What's the role of existing technology? What can be done with what you have in terms of where you put those efforts? Well, what you require would be diagnostic capabilities that can be easily mobilized depending on the pathogen you're looking for and its variants. And therefore, you can imagine how AI, for example, can help with that process and accelerate it. So diagnostic technologies for fast detection and characterization. And you require innovations in that space. And you already see it happening. Yeah. I would add to that. I totally agree that we shouldn't leave lower and middle-income countries behind. And wastewater surveillance is a valuable tool. But we just need to be cognizant of using the right tech for the right public health need. And there are some really good use cases for airplane wastewater surveillance, community wastewater surveillance, and all these innovations that have come out of the pandemic. But we should never be doing it just to do it because it's a shiny new object. We really need to know that it has public health value. And I think mpox has been another example where this technology has been valuable. There are others like dengue and other concerning threats where this technology can be used. And we're starting to the traveler genomic system. It has been used looking at other pathogens besides SARS-CoV-2. I know you asked that before. But we were looking at flu and RSV and other pathogens in both airplane wastewater as a proxy for early detection and from the travelers themselves. Is there an element of infrastructure that you're not working in that you think would be interesting? Sewer water, airports, is there somewhere? Well, I think it depends on the individual use case. If there's a need, I think that's where the innovation and the private partnerships and I was saying this earlier, that earlier in my career, I just never would think that I need to work with air traffic, air ground handlers, or airline executives, or sewer workers, or wastewater treatment plant workers. But it really shows you that pandemic preparedness is a whole society approach. For successful pandemic preparedness, we really have to integrate the community into our approach. Oh, go ahead. Oh, I had something on the mpox component. Before I joined this wonderful team, I helped lead the mpox response at the White House. I vividly remember when we sent that email to CDC. The email was just to ask to see if it would be useful to have wastewater surveillance. Remember, we didn't know a ton about wastewater surveillance before COVID. We just floated that thought over just to see if we could actually use it, in this case, data for action. CDC was more than willing and jumped on it. We did wastewater surveillance. I will tell you that wastewater surveillance ended up being not only data for action. We identified clusters before they happened because of the wastewater. We were able to have public health interventions immediately on the ground. It was incredible. Journalists love to talk about AI. I will ask, how do AI, gene editing, and other sort of, I don't know, emerging technologies change the calculus in terms of when you think about the threat and the response from your perspectives? Do you want to start? Oh, go ahead. You can start. The benefits of AI applications when it comes to biotechnology are extraordinary. We can speak about the potential for how it would improve clinical medicine, how it would improve public health, and how it would improve biotechnology innovation. The benefits are, I think, extraordinary. The risks are also great. We don't understand a lot of the risks. You can imagine how AI can power the production of pathogens that are enhancing their transmissibility and enhancing their pathogenicity as well. It's really important for us to develop biosecurity systems as we go along the way. For me, I would imagine, not being a scientific expert in the area of artificial intelligence, to say that the potential for risks, for benefits, are extraordinary. I won't put a number on it. It's certainly more than 50%. I would imagine if you're sitting with 90% benefit and 10% risk, that's a good balance to have. What we need to make sure is that we have systems in place, guardrails in place to look after the risks. We have just like a minute left. So I'm wondering if either of you want to add to that from your perspective? No. I would just say that I think that AI, if we have limited resources to set up a global network and get airplane wastewater and know where the pathogen or the risk is coming from, we can utilize, with the data that we have, we can utilize AI, machine learning, to really put the data together into models and predict where an outbreak is occurring because it's hard to get all of the nodes set up. And so I think AI could be a tool that would be beneficial. Vicky. I mean, when I think of AI, not only do I think of what it can do in pathogen detection but also medical countermeasures. I think the options are endless. And my personal passion is just diagnostics. I think AI is probably a resource that will take us further in our preparedness efforts than anything else has before. I'm excited. But as Wilmot said, it's really important to have guardrails to protect against bad actors. Thank you all so much. I really appreciate it. It was great to hear from you about very, very different things. So thank you. Thanks, Bruno. Thank you. Thanks. All right. And that brings us to our next session of lightning talks. These are our customers, our partners, our developers. These are the people doing the most interesting things in biotech today. Let me tell you. Let me tell you, folks. Can we talk? Can we talk for a minute? Working at Ginkgo is great. But sometimes it hurts. It hurts a little. That's real. That's real. That's true. And here's why. Because working at Ginkgo, we don't make our own products. The customers make the products. And so every day, I get to see people building the coolest stuff on our platform. And I love it. And I love it. And I want to build it all. But I can't. I'm just one guy. I'm just one guy. And I can't build it all. But working at Ginkgo, I can support them, the developers, in building it all. So just like last session, we've got four talks. I'm going to introduce everyone as a group. And then I'll get out of the way. We're featuring Benoit Hartmann, Head of Biologics R&D at Bayer Crop Science, John Wells, CEO of Factorial Bio, Trent Munro, Senior VP of Therapeutics at Microba, and Julien Sylvestre, CEO of OneOne Biosciences. Hello. Good morning, everyone. I'm Benoit Hartmann, head of biologics R&D at Bayer Crop Science. Really, really excited to be here today with you in Boston to share our vision for the future of biologicals in agriculture and also the role of synthetic biology in that. Last Sunday, I was actually flying from Europe to the U.S. and was reading the New York Times in the plane. So first page and then one full page inside, the title of the first page, "Angry Farmers Are Reshaping Europe," nothing less than that, due to the EU requirements to cut the use of pesticides and fertilizers, New York Times. Pesticides are providing clear benefits for the growers. They want to protect their crops, their yield. They want to run a sustainable and profitable business. But they have currently less and less options. So that's where biologicals will play really an important role in the future in providing alternative options to growers. Sorry. I missed the first slide. But you got it all. Oops. So we really need to accelerate now. Biologicals do not have a huge share of the market right now. It's really limited. Why is that? I think the first thing is that not a lot of science has been invested in biologicals. The second thing is that it's complex. We are talking about a lot of different microorganisms, about plant extracts, natural products, very diverse, very complex. There's not one company that can invest in all these technology platforms. It's too complicated. Acceleration will only happen through collaboration. That's why two years ago at Bayer, we have decided to stop internal discovery and to fully partner with innovators. So we are partnering with innovators that are able, that are willing to push the boundary of science. So what does it have to do with synthetic biology? The next generation of biologicals will be designed, will be produced for specific properties, properties like providing a very good level of protection against pests and disease, doing things that chemicals cannot do, fix nitrogen, sequence of carbon, so really exciting things, a lot of opportunities to come. And that's where synthetic biology comes into play with its ability to engineer biological organisms coupled with all the automation, high-throughput testing that we are seeing here, coupled with the understanding of very complex biological systems. That's where Ginkgo and Bayer are joining forces here. As an example, we have one project that has the objective to fix nitrogen for cereal crops. They don't do that. Soybean do that. But cereal crops, corn, rice, wheat does not do that. The objective is to design, to produce a microorganism that is just applied on the seed, able to fix nitrogen from the air, transform it into usable form for the crop. So really exciting. We are really excited about the progress that the Ginkgo team is making. So in summary, we all want, we all need to have happy farmers, not angry farmers. For that, we need to accelerate the impact of biologicals in agriculture. This will only happen through collaboration and through pushing the boundaries of innovation. Thank you. Hi. I'm excited to be here with all of you and tell you about what we're building at Factorial Biotechnologies. I'm John Wells, the founder and CEO. We're enabling full-resolution NGS at scale. The solution that we're developing, over the last 15 or 20 years, there's been a lot of value created for patients and researchers performing what we call bulk NGS library prep, where you take a population of cells, you lyse them, and you purify nucleic acids and sequence. You get sort of an average of all the cells in the population. It turns out that in order to really understand biology, you need to be able to characterize cells at single-cell resolution to understand the differences between these cells. What we're really excited to power with our early access customers are applications like understanding tumor heterogeneity and subclonal expansion under selective pressure and also be able to characterize off-target edits at genome scale in CRISPR systems. How we're doing this is we're unifying single-cell NGS content and throughput. The last 8 years have seen tremendous progress in single-cell studies. But there has been a trade-off that's had to be made. You had to either pick high content with a very low number of cells or high throughput and more basic applications like counting applications and ATAC-seq. What we're coming into the market is to enable extremely high-throughput, high-content assays to be able to perform whole genome sequencing, full-length transcriptome, comprehensive genome profiling at the scale of tens of thousands of cells. How we're able to do this is we've developed chemistries to perform massively parallel reactions inside of intact cells. We can prepare complete NGS libraries inside of cells, massively parallel on all of the cells in a suspension simultaneously. What this allows us to do is perform complex stacked reactions and at extreme scale, so not on one cell in a well but on tens of thousands of cells simultaneously. You can imagine it takes some special work with enzymes to prepare libraries inside of cells. We're happy to be working with Ginkgo Enzyme Services to really realize the full potential of our platform. It really is a very simple workflow to go from cell suspension to single-cell whole genome libraries in a single day. You perform our in-cell library prep. Then you load this microfluidic chip that we have that our instrument can process 4 of them in parallel. Each chip can process up to 40,000 cells. You load the chip in a single stream and put it on the thermocycler. You have a library in a single day. When I'm back up here, hopefully in a couple of years, what we'll see is that we've generated a significant amount of high-content single-cell genomics data, applied advanced analytics and model training, and have new biomarkers and drug targets to show for it. Thank you very much. All right. It's great to be back here at Ferment. Today, we want to share a little bit of our journey of the project we've been doing with Ginkgo, which has really been designed to look at detecting the immunomodulatory activity of a large panel of gut microbiome-derived bacterial strains that we've identified from our proprietary databank. Now, when we think about microbiome therapeutics, there's really two aspects that I want to highlight. One, I think we'll all agree that the science is unequivocal on the ability of the gut microbiome to essentially be a sensor, a modulator, and a translator of key biological signals. However, our ability to identify the source of these signals and then harness them as precision therapeutics has really lagged. At Microba, we think it's lagged due to three primary problems: one, deficient analysis and fragmented data sets; two, if you look at the bioinformatics tools, they're riddled with errors; and three, generally underpowered data sets to try and make the correlations that we need to turn these things into products. The way that we've addressed this is to think about a very large-scale combination of host data and microbiome metagenomics with deep neural network analysis to really identify the biological dark matter, the things that other people can't see, and then turn that into the products of tomorrow. What's been super exciting in our program with Ginkgo is that we've been able to probe our proprietary data set and extensive microbial biobank to drive initial lead identification of around 200 strains. Importantly, that was downselected from around 60,000 genomes. We used a bioinformatic rationalization to identify these. We've then worked with Ginkgo to design both a high-throughput growth system and then a deep bioanalytical cascade to probe the biology that sits underneath that. We're about two years into our program. And we're now super excited to finally narrow down on those lead organisms that we're going to take forward. I also wanted to share with you what does this actually look like, how far across the microbiome we're actually going. So if we look at our full 200 strains, you can see that it spans the complete phylogenetic space of the microbiome. If we narrow down to around the 30 or 40 that we've selected now, we're still seeing a huge amount of diversity. And typically, these are things that no one else has seen before. They've never been cultured before. These are really novel bacteria. I guess I just want to finish with showing what have we actually seen in the data? What's been the snapshot? Well, we've seen an incredible dynamic range of the ability to modulate the immune system. We're talking about inflammasome activity. We're talking about TH1-driven activity. Then we've been able to actually do that downselection looking at a raft of measures, human metadata and disease association, assay potency and biological activity, and finally, in silico, safety and manufacturability. With that, I'll wrap up and just say, in summary, this approach has been super exciting for us to deploy a large-scale drug discovery program for autoimmune disease on an unparalleled number of unique microbiome-derived strains. Thanks very much. Hello. Pleasure to be here. Pretty big event. There is another big event in a couple of months in Europe, the Olympics. So the question I'd like to address now is a bit similar to this one. So imagine you qualify for the Olympics. Would you rather train, prepare for what's to be done there, or go to bed and wake up on the starting line? So I think here we are kind of all trying to transition the world from chemistry towards biology. And if we think about how the world really works, to mention a great book, well, synthetic nitrogen fertilizers are really on top of what's needed for modern civilization alongside energy. I mean, they feed half the world. By the same token, they generate severe negative environmental externalities. So there's an opportunity with this nitrogen crisis, again, with the microbiome. Yeah, we can use bacteria to promote plant growth, but also bring other economic benefits, including a reduction of their addiction to fertilizers. I'm sure in this room, you're all familiar with the way these bacteria are made. What we do is we change a couple of things. We alleviate the need for large-scale fermentation. We don't do long-term stabilization. What we add is a step of amplification on-site of the bacteria, what we call fermentation. If you've been to a hotel room recently, you know these coffee machines where you put a capsule sauce? Very similar conceptually to what we use, a simple device with single-use consumable for aseptic transfer. A device can treat about 400 acres. We can replicate it in an array. This way, we can deliver fresh bacteria. Here is the exciting part. I'll talk about three examples. We have a new process. So we unlock novel biology. So the first thing, and I think, yeah, I appreciated the M&A announcement from this morning, is we can now use bacteria, requalify bacteria that were previously discarded in any screening because they were great. But they had poor manufacturability. They were difficult to stabilize. We can include them in our process and productize them. The other is, if you think about the bacterial growth curve, we have bacteria that are actively dividing, not in lag phase. So when the next challenge for them is to enter the plants through stomata, they have a limited window of opportunity. So we feel we prepare them the best for what they need to do. Third one is on the media. So there's data showing that growing the same bacteria in two different media, you can get different metabolism in a subsequent media, both for phosphate solubilization in an in vitro assay. But even agronomic effect can be different. In one case, the same bacteria will lead to heavier roots or heavier shoots. So obviously, we are generating fine-grained data with the great teams and the great tech at Ginkgo. But I think already, the ability to access this subset of parameters that's not accessed now should enable us to make the best-performing bacteria and win. Obviously, this is amenable to data-led personalization. My name is Gillian. The company is called One One Bio. I'll stop here. I think human metabolism is quite important. And it's time for lunch. Thank you. If you want to continue the conversation, here's what you're going to do. You're going to head over to the Button-Up Zone. You are going to get some buttons that are thematic. They're going to indicate your interests. They're going to help you find other people who share those interests. You all remember the movie Office Space? Office Space, from Jennifer Aniston's character? She works at that restaurant where you have to wear flair. But you all remember that from the 1990s? I'm a millennial. That's a fun movie. It's like that. It's like that. It's fun. Don't forget to check out if you want to connect with our Technet partners; they are upstairs behind you and to your left. You can hang out with our Technet partners. The Light Bio exhibition is also up there. So check that out. That is lunch. We will return at 1:45 P.M. [Music] This is your 10-minute warning. Our show will resume in 10 minutes. This is your 5-minute warning. The show will resume in 5 minutes. Repeat: This is your 5-minute warning. Hello, everyone. Your attention, please. It is time for us to ferment once again. Please take your seats in the main stage area. And we're back. And we're back. Hello, everybody. Hello, everybody. I hope you enjoyed the lunch break. Please make your way back to the main stage area. We got a good one for you. We got a good one. Let me set the scene. Let me set the scene, all right? It's a Thursday afternoon here in Boston. Outside, it's starting to gently rain. But in here, we are very cozy. We are very thoughtful. We are ready to gather round and enjoy a nice fireside chat. Fireside chat. So gather round. Gather round. Come one. Come all. Come ye to the fireside for this fireside chat. Get out your marshmallows. What else do you do at a fireside? Your campfire coffee. Bring it on over. Bring it on over. This is going to be a good one. Come and join us for this interactive fireside chat. You guys. You guys. You guys. Okay. I know it's my job to be excited for these. I know it's my job. Okay. This is Novo. This is Novo. This is my Taylor Swift. All right? Get excited. Get excited. Yeah. Yeah. Yeah. That's right. All right. Let's introduce the speakers. So for this interactive fireside chat, we are going to be joined by Jen Wipf. She is Ginkgo Bioworks Chief Commercial Officer. And Marcus Schindler, Executive VP and Chief Scientific Officer at Novo Nordisk. Come on out. Thanks, Jake. I'm glad we couldn't help you into your Novo Nordisk era. Before we begin, we mentioned we're going to have some audience participation. So we're really excited to field some of your questions, and I'll be able to choose them here on my screen. Jake, can you give everyone the details on what they should do? Yes. Yes. That's right. All right. So this is going to be an interactive session. So go ahead and get out your smartphones, your smart devices. We are going to put up a QR code. There it is here on the big screen. So you can scan that, and it will take you to an app that is going to allow you to submit questions. You can submit your questions. Yeah. Yeah. I love it. I'm loving all the smartphones. Is it working? Is it going to get some? Yeah? It's working? All right. Very cool. So you can put your questions in there. They're going to be beamed through the internet to Jennifer's tablet, and she is going to choose her favorites, and she is going to ask them live here on stage, bringing you into this fireside chat. Great. Thanks, Jake. We'll let folks get their smartphones out. Thanks, Marcus, for joining me today. It's great to talk to you. Thanks for having me. Yeah. We're going to talk a little bit about R&D productivity and the goal of sort of achieving growth and innovation. So maybe I'll start with a provocative question, if you will. Late last year, Wall Street Journal published an article about a trillion-dollar pharma company. Certainly, Novo Nordisk and Lilly are on the watch list to be maybe the first. What's your take on that? Why are there no trillion-dollar pharma companies, and is that something we should be pursuing? Yeah. It's a great question. Thank you. And I don't have a complete list of trillion-dollar companies out there. But if I recall, it's largely around computers, telephones, and online shopping, and oil, I think, as well, and not health, which I think is an interesting reflection to start with. And by the way, we did some headline news in Europe or in Denmark when we actually overtook a luxury consumer goods brand as Europe's most valuable company, which was a great day because health was more important than handbags, which I always think maybe is something for us to think about, where actually capital goes. But the point is also, market cap is obviously how the company does financially and how they perform, but also expectations. And I think that's an interesting one. The real question to me, usually as a scientist, or the one that I enjoy, is how many millions of patients are we actually serving with our products, with our therapies? Currently, that number is somewhere around, for Novo Nordisk, 45 million, which is amazing. Yeah? But that leaves, if we just talk about obesity alone, 950 million, roughly, people without any treatment. All right? Then you can, of course, start extrapolating that. That is a tremendous opportunity also. We've, I think, taken an unmet medical need to a treatable condition to a market. Then everybody can have a fantasy how big that market one day might be. So if growth, for you, is not sort of market cap, it's more about addressing health and providing new medicines, why then is the perception of pharma at an all-time low if health and medicine is so important, so critical? What's your view on society's perception of pharma, and what needs to change? Yeah. And I think we see the polls. We see the results of what the general public usually thinks about pharma. And if I'm honest, I would have thought that post the pandemic, we would fare better. And I think we did for a time. But again, I mean, that has disappeared. And that is interesting because, at the end of the day, we would like to be part of a solution, i.e., living a better life, living a healthier life. But clearly, now we're seen as part of the problem. And it probably doesn't help that politicians also use it for various purposes and declare a victory against pharma on some things. I don't think that is particularly helpful because, fundamentally and this is why I do this job and why I'm here, I go to work because we change people's lives. I would argue that probably some of us wouldn't be here today if it wasn't for the pharmaceutical company, the biotech company, and, of course, the academic work that has been ongoing. In the pandemic, we managed to get from the discovery of a target to a vaccine in less than a year. And then it was rolled out in billions of doses, actually in quite interesting ways also, how those manufacturers then contracted with governments without middlemen, without people who also made money out of this process. And I find that really intriguing also to think about what could we truly learn from this as a pharmaceutical organization to go forward. In trying to treat more patients and provide novel medicines, you're continually thinking about what kinds of R&D challenges you have. I'm curious to hear a little bit more about how you foster innovation, what kinds of steps you're taking to build that within your own teams, but maybe leverage external partnerships as well and external innovation. Yeah. I think, first of all, we are in a complex and difficult business. I think sometimes people come to me and say, "Oh, this is also complex. We're working on. Can we not simplify it?" I think there's a confusion between complexity and complication. There's inherent complexity in the systems we're working with. I actually think that is a good thing because it needs educated people, really important technologies to solve some of those problems. It was also great to see Jason mentioned Jensen Huang from Nvidia, that now the tech companies actually would like to deploy what they can do to our field of research rather than doing and no disrespect to gaming or self-driving cars, but maybe understanding biological systems and better medicines are also a good use of CPUs. Yeah? So I think that is really good. Where I think we are different in a way, and I know it's not a very common thing in the U.S. in particular, is that we are owned by a foundation. Actually, our founders set up this foundation because they felt the proceeds of the money that we make should actually go, to a large degree, back to society, which I think is actually a really, really interesting setup. What it gives us is also a long-term view on things. We're trying to work on big chronic diseases in particular that, frankly, also in obesity, many others have not worked on for the last two decades. And now everybody is waking up to this opportunity. And it brings with it, I think, persistence, a level of focus, but also really the understanding that solving those complex problems takes time. All right? There's an iterative process. Maybe we'll talk about this. But this is not something you can fix overnight. I'm a bit provocative. I don't think the investment community, in particular in environments like here, has, over the last years, maybe understood that and bought into that value proposition, that it is worthwhile also investing in some of those big diseases. Yeah? The ability to have that patience to pursue something that's very long-term, I think, is quite unique. How does that change sort of the decisions that you make in the R&D process? How does that change the teams that you build and sort of the solutions that you're trying to drive among your team? Yeah. I think it is actually fundamentally important. There's obviously a dark side to long-term outlook and foundation ownership. Sometimes you might run the risk that there isn't so much of a sense of urgency. All right? Because, well, there's always tomorrow, and we can solve it later. All right? It's finding that right balance and also instilling that our patients have no time to waste out there if they don't get the medicines. In particular, now where we talk about the challenges of scalability of some of the medicines that we're making, that is very imminent. This is not in a decade or so from now. I think it's finding that balance. But it does allow us to do things, I would say, properly and not find quick fixes and also get to the bottom of understanding the diseases we're working on, but also putting ourselves out some big, hairy goals that we would like to achieve. What if we could treat hundreds of millions of patients out there? What if we could get to curative approaches? All right? And that is a privilege to be in such a position that we can think about these problems. Yeah. I might be remiss to not mention our collaboration that we announced the expansion of yesterday. How do you think about leveraging external innovation, the kind of scalability that companies like Ginkgo and others that you work with provide in leveraging that kind of growth? Again, we are a company, actually, that comes from Denmark and had traditionally the vast majority of their workforce innovation internally, so through organic growth, which, by the way, also helps now, with hindsight, to define productivity because we own every invention that we've made, which means it's all great, even if we were, maybe, on the face of it, the most unproductive company ever, which we probably aren't. But maybe we're not as productive. Right? And this is where you guys come in, is that we cannot assume this will continue forever. Right? So first of all, are we going into spaces that are really unknown, where no one knows? Is that the right biology we're going after? Are those diseases we should be tackling? The world is bigger, and we have a room full of smart people with, I mean, amazing ideas here, what I've just seen earlier today. So I mean, for me, there's a bit of a no-brainer that we should get together on those things. And then there are some near-term challenges, right, where the complexities and the opportunities are so big that it just calls for collaboration. And what I've been inspired by when I first met you is this real industrial scaling applied to biological or chemical problems. All right? And I think we saw that quote also earlier on this engineering mindset in science. And that actually is intriguing. And I think you're putting this into practice. And that's, I feel, where we're quite energized by, and also to see how can we get to some of those solutions for big problems. Pinging on that thread, a little continuation, I think we have a collection of people here today from wide-ranging industries. Right? We heard from folks in agriculture and all across the board. Are there challenges that you see that are exclusive to the pharmaceutical industry? Or what shared challenges do you think exist across those industries and any solutions that we should think about globally? So obviously, coming from R&D, I would think there is a fundamental proposition here that when we go out to discover or to do research, we venture into the unknown. We go to places where nobody has been. And that's the fun part. But certainly, for some of my colleagues working in finance, that is also the scary part. Yeah? So I hope anyone who works in any form of R&D setting shares that passion to find novel things, but not just because they're novel, but because they could help. They could make a difference. And that's why I work in pharma, not in academia, because it's science with a purpose. Right? And we can think of a product. We can think how some of the solutions, even we saw today, might actually change the world. So I think that is common between all of us. But it's also really difficult, then, sometimes to articulate what is the project and when is it coming to the market. Right? And it's challenging to extrapolate this. So the finance model beats through an external community, but also internally, is challenging. So how can we actually build trust? How can we build stepwise approaches of de-risking our business, right, where we show we're moving forward and give confidence? And that is one thing and I don't know whether it's specific to pharma, but it's, I think, very deep that our cycle times are incredibly long. So the learnings that we can get are years out. And sometimes it's way too late to react to them. So I'm actually really intrigued also by maybe hearing from some of you how we should think about prototyping and truly agile ways of working where we have minimal viable product. Start to think about this in medicine is maybe not simple. But how could we do this to actually get to fast learning cycles and moving on much, much quicker? The other piece, if I may, is shared platforms. Right? So as a German, I like cars, high-quality cars. But they're sharing some platforms. Right? There are some fundamental principles that everybody's using, and then everybody puts their brand sticker on it. We're not doing that in pharma. All right? We're starting with right from scratch, and then we need to own it. Defining pre-competitive space is still very, very hard for us. I don't think that is very sustainable. Yeah? We spoke about data today. Right? I mean, who on earth can we think that we will own data, really? Yeah? But we might own the analytics or the prompting, whatever have you. It's very interesting to think about sort of the tools and technologies that are coming to bear with AI. We were chatting about this earlier, this sort of model in pharma that's unique around the ownership of data. How do you see that changing over time? And should it? Yeah. I'm really intrigued by this thought. Now with machine learning moving into our space, the machine will make molecules. Those molecules might one day be the best molecules well, probably not even we can think of because we clearly didn't think of those molecules. The machine did. So what is then the inventive step? What is then actually IP? Right? And how will this be defined? And if you really take that further, then you can think we have a rather unique model in pharma. And maybe also that comes back a little bit to the perception that we have a protectionist area here where we have decades of patent protection. Nobody can work in this space. What if we were to think about products, product concepts, and improvement of our products, a.k.a. medicines? Yeah? We have medicine version 1, 2, 3, 4, and so forth, rather than clinging on to this patent protection to fund research that, with the best intent, is not very efficient right now. Yeah? I have no solution for this, but I find this a really interesting thing to think about. Yeah. It's very interesting that other industries have had those common platforms and yet been able to create successful businesses around those. Right? There's space for Uber and Lyft and many other rideshares that are basically the same platform, a very different model than pharma today. I don't think we've really spent time to think about, as a pharma industry, how true disruption to our business could look like. I think that's a dangerous position, by the way, because if we don't think about it, somebody else might. Yeah? Before you know it, you have an executive order, and things happen that you clearly aren't prepared for. Yeah? Let's see what has come up from the audience. Let me see if I check my technology here. Let's see. So this is an interesting one. Someone is a maybe ESG follower here. Novo Nordisk has been working on striving for zero environmental impact by 2045. How does that goal impact your decision-making? Does it spark fear or sort of smaller thinking or creativity or a mix? How does that work? Yeah. The first question, we should always ask, why 2045? Right? And why not earlier? So again, we said 2045 and interim 2030 and so forth. The important thing here and this is, actually, I think, is really important that this is not just Novo Nordisk as it is today, but actually our entire ecosystem. So you might feel that as well. Right? Or any supplier who works with us actually needs to be in this because I think this is really important in everything that we do, be it on carbon emissions, be it on the usage of animals. It doesn't stop with us and our walls. It actually is everybody who works with us. And it has impact on our decisions, actually, on a daily basis. So environmental responsibility and societal impact are criteria that we're actually using even at the start of a drug discovery project. All right? We obviously also look into the commercial opportunity. We look into whether we can develop it. What is the technology? But can we make it? Can we make it in a sustainable way? Can we actually bring it to the patients that need it? They are core decisions we're taking at that juncture, very, very early on. And I actually think that that is important, that it brings us to a sort of full payment model. Right? Often, we try to make molecules fast and also cheap. But the cheap is short-lived, often, because we might use chemicals that aren't particularly healthy. We might use processes that we can't scale. So to put this at the front and think, are we making the molecule in the right way so we don't have to fix problems later on? All right? I mean, if the molecule is only in the body for a day, then yeah, you have to give an injection or swallow a pill every day. That's not very sustainable. What if we find a molecule that can last a month or a year? Yeah? Wouldn't that be cool? We don't need plastic pens for that. Someone says, "We love Novo Nordisk." That's a great question. I like this one. Marcus, what has been your biggest failure? What did you learn from it? Sorry. Can you repeat? What has been your biggest failure? And what did you learn from it? I usually tell my organization, "We don't fail. We learn." Yeah? But seriously, it is an important mindset. Of course, I've made decisions where, at some point in time, other data were coming out, and I felt, OK, if I'd known this at this point in time, I would have done something different. Or I didn't recognize it. I backed a few programs, and I let go of some other programs where I felt, yeah, maybe, with hindsight, that wasn't so smart. But honestly, that is a gift of hindsight. Right? And it's so easy to live your life with hindsight. Right? For me, it's always, do you make a decision at this juncture? Do you have the data at hand? And do you give good attention to the data? Do you allow a diversity of thought, opinion? Right? So I don't decide well. I eventually decide many things, but through wise counsel with many people. And being able to listen and take also extreme positions, I think, is really important. Now, a failure would be, for me, if I don't listen. Right? If I close my mind and I say, whatever, I have this preconceived notion, and this is what it's going to be. And of course, I've made some personal failures along the way. But I have to admit, in a work context, in drug discovery, I actually don't look back with regret. It's the past. What have I learned from it that actually makes me better going forward? Thank you. There are a number of questions that are sort of all around the same theme, really around sort of Novo Nordisk's focus to date on diabetes and obesity. The questions are, are there other therapeutic areas that you all are particularly interested in? And/or are there new modalities that you're particularly excited about? Yeah. So for the last couple of years, we've also recognized that diabetes in isolation, yes, it is a disease, and it requires important medicine. But linked to diabetes are obviously a lot of comorbidities: renal disease, cardiovascular disease. Linked to obesity are dyslipidemia, osteoarthritis, joint problems. Right? So we've actually opened up also the science to say we're a bit more disease agnostic, and we actually work on systems because our body doesn't really care about the departments that we put up to say, I work in diabetes research, and I work in heart research. Yeah, guys, they're kind of interlinked. Yeah? They don't work in isolation. And that was a bit of a culture change, I have to say. So we are much, much broader in this cardiometabolic space. At the end of the day, if you look at the results of our diabetes and obesity medicines, we're running cardiovascular outcome trials. So at the end of the day, what matters is, do we live longer? And I would also argue better with those medicines. But we also do because and maybe these days, this is a little bit ignored. We actually have a sizable group working on rare diseases. Novo Nordisk, for a number of decades, has worked on hemophilia, in particular, and factor VIII. We're working with 2seventy here in town on a potential gene editing approach. We're working on sickle cell diseases. And why do we do this? Because actually, we like to entertain two different business models. One is general practitioner, big diseases, many patients, but also specialty care, close to the patient, shorter timelines from research to development to the market. That's actually a really interesting yeah, sometimes tension even, but I think a good model we're running. Any new modalities that you're really excited about? Yeah. That's a problem with me. I'm easily excited. So give me something new, and I go, oh, go, cool. Yeah, wow. OK, why are we not doing this? But at the end of the day, modalities are this is our tools. Right? The modalities in themselves, they are useless if you don't know how to imply them. It's a little bit like AI. We can do a lot of number crunching. If you don't ask the question, right, then you won't understand the answer. Right? But yes, we are obviously known for peptide and protein therapeutics. We made an acquisition here in town on Dicerna, on siRNA, because this modality has some interesting features: A, high precision to the gene of interest, and then usually a long-term knockdown of the gene. And I actually think that actually brings us much, much closer to infrequent dosing and reducing the burden of treatment on patients and improving compliance because if you take a medicine once every half year or every year, you simply don't have to worry about it. Yeah? But we also have an interesting group working on cell therapies, which gives us an opportunity to replace what is no longer there in the human body, beta cells, or, for example, dopaminergic neurons, where we have a trial running in Parkinson's disease, a trial running in heart failure. And I think those are all really, really interesting modalities to inquire on and learn. They're not mature yet, I would say, but worth exploring. Yeah. And I've probably forgotten 1,000 modalities that I'm interested in, but. Shall we list them all? Yeah, yeah. There's several questions here that I'll try to consolidate. We've spoken a lot about AI and machine learning. So what is your overall outlook? Is this hype, or do you have hope? Phew. I like that. That's probably not for me to say. But I would say, generically, when you see movements like this, yes, there will be some hype, and there will be some overpromise. I think we are doing a lot of due diligences also on potential partners. And we're also finding out that, yeah, there's real stuff, and we can see that, and there also isn't. Yeah? And there's a lot of imagination that is not really anchored in reality. But what we already see, we have the first use cases. We worked, actually, in a collaboration with Microsoft. Real impact on productivity and some processes. But honestly, I think that is the boring part, if I may say so. This will be done, and we don't even think about it anymore at some point in time. And the real interesting things are the ones that go beyond human cognition. Yeah? I think some of our biological systems that we're dealing with are just too complex for us to understand what really happens in real time in a cell, let alone in an organ or in a body. If we put computing power to this, I think this could potentially be amazing. Then, of course, all the data we'll generate through our wearable friends. What if we could get into disease prediction? What if we could identify, hey, you're on a difficult course here. Your lifestyle, your postcode, your upbringing, your genetics together put you on a really, really bad trajectory. There's something for you to do. We actually put what we call a transformational prevention unit at work. Maybe you think it's crazy because we're not even treating many people with obesity, but we're already thinking now about how we could prevent it. Maybe, as a last question, let's wrap up with your sort of closing thoughts. In the broad field of engineering biology, what do you think the next 10, 20 years will bring? Yeah, wow. What are your predictions? Yeah. These are the difficult questions. And coming back maybe a little bit to what I also said at the beginning, I mean, with all the advances we've made in medicines, I think we're just really scratching the surface for some of the therapies that we're using. When we simply talk about obesity, people ask me, so how much more efficacy do we want to see in obesity? And my answer is usually to the point where people can live a healthy life. And whatever you define as a normal weight, that doesn't matter, but where you live a reasonably healthy life, how can we actually roll it out to hundreds of millions of people who might benefit from this? So there's a piece around moving on that. I was really, as a scientist, inspired about COVID vaccination. And in the U.K., the National Health Service actually thinks about a one-shot strategy. So what if we had this thought? You go once a year to your physician and administer a compound of drugs against cardiovascular disease, against diabetes, and so forth. What are the features of the molecules? What are the technologies we should be using to do this? And then really, I mean, I would say turn it into true health care because it's sick care right now. Right? We're treating when it's late. What if we could actually move the needle to go in early and bring us into a better place? I know these are very big-ticket items, but hopefully, we can make an impact there. We may have some folks who want to talk about some of those big ideas coming up soon. Thank you so much, Marcus, for joining us today. Thank you, Jen. Special thanks to the audience for all of your questions. They were very, very good. Thank you. Thank you. Whew. All right. That was great. All right. So up next, we have our first afternoon session of customer lightning talks. Do we have any biologists in the house today? Any biologists? A few? I'm seeing a few hands. Yeah? Some students of biology? Students of biology? I am a student of biology. If there's one thing that I know about biology, it's that the mitochondria is the powerhouse of the cell. Yeah. That's not the point. That's not the point. That's not the point. If I know two things about biology, it's that DNA is the code of life. The living world is written in DNA. So all that crazy diversity out there is somehow written, is somehow programmed in the unity of one big, long molecule. The science of biology, in some sense, is a quest to make those connections between the diversity of life and the unity of that molecule. And Ginkgo's mission to make biology easier to engineer is part of that quest. So we're a platform company. We don't make products. Our customers make the products. We work, in a sense, on the unity side. We handle the DNA stuff, the DNA programming stuff, so that our customers can work on the diversity side, building stuff with biology that people want. So we've got three talks in this session. First up, it's a double feature: Nicole Richards, Chief Executive Officer of Allonnia; and Stephen Lallo, Director of Corporate Development at GreenLab. Then we've got Prateek Mahalwar, CEO of Bioweg; and Aaron Schacht, CEO of Biome Edit. Take it away. Hi. I'm Nicole Richards, the CEO of Allonnia. I'm Stephen Lallo, VP of Corporate Development at GreenLab. We are here today to shine a light on how important collaboration is for our biotech industry. Nicole, why don't you expand? Sure. At Allonnia, we are a bio-ingenuity company dedicated to extracting value from waste. We believe that waste is a failure of the imagination. We are developing elegant solutions for the world's toughest environmental challenges, combining some of the smallest organisms with groundbreaking engineered systems. That's why it is such a pleasure to be on the stage with Stephen and GreenLab, who are building the factory of the future using nature and a cornfield. Thank you, Nicole. That's right. At GreenLab, we envision a future where the cornfield emerges at the forefront of enzyme production. With corn-grown enzymes already in production, we are revolutionizing the environmental and sweetener industries through the power of plant biotechnology. Our process is synergistic, scalable, sustainable, and most of all, successful. In December, we announced a partnership with Ginkgo Bioworks in Allonnia that matches each of our core skills in a unique and highly effective value chain with the goal of developing a novel enzyme that bioremediates PFAS. Nicole, why don't you tell us about PFAS? Absolutely. I'll start by saying there's a class of chemicals called forever chemicals. They have that name because they do not break down easily in the environment. PFAS is the poster child of these toxic chemicals. It is in 70 million Americans' drinking water. They cause significant issues to the environment and to human health. Two days ago, if you're in the environmental world, it was like our Olympics or the Grammys in that the EPA announced some regulations that limit the amount of these chemicals that can be in our drinking water down to single-digit parts per trillion, which is like one drop in 20 Olympic swimming pools. Big regulations that came down. We've been working for the past three years with landfills and airports and liability holders to remediate and remove these PFAS chemicals from the water. What we've learned is it takes in-depth application knowledge in order for biological solutions to be successful. That is really the powerful goal of this collaboration. Stephen? Sure. Think of Ginkgo, GreenLab, and Allonnia as three synergistic components in a powerful go-to-market formula: discover, deliver, and deploy. Ginkgo is leading a discovery campaign leveraging its vast database to identify the PFAS-degrading enzymes. Then, using ultra-high-throughput screening, it will identify unique enzymes with the desired activity and transfer them back to GreenLab to be inserted in the corn plant. Ginkgo is providing these services under its success-based pricing model created to help companies like GreenLab de-risk their research and development efforts. At GreenLab, our technology allows us to grow enzymes and other proteins inside a corn kernel. By producing enzymes in a cultivated crop, we can readily scale up production across acres of cornfields with little additional upfront capital and infrastructure. The PFAS-remediating enzyme is then extracted from the kernel with minimal waste, and the corn byproduct will then be used for food, feed, or fuel. Allonnia will then commercialize the innovation. Nicole? Absolutely. So Allonnia has become experts at deploying our own biology, starting first with 1,4-D-Stroy, which is an organism that degrades 1,4-dioxane, another chemical considered a forever chemical. We're soon to be releasing some solutions for biosolubilization and biocementation for the mining industry. We've already treated over 200 million gallons of contaminated water using our systems, working with liability holders. Now we're looking for the best biological degradation to pair with that so we can offer the industry a completely closed-loop solution. So contaminated water comes in, and the only thing going out is clean water. So Stephen, how's the work with Ginkgo going? We are getting some good initial results. We'll know much more in a couple of months. Nicole, how do you see this partnership working overall? Three years into this journey, what is the difference between success or not, is collaboration. We've heard that a couple of times today already. The challenges we face today in the environment can be solved with biotech and biotech solutions. But it is too much for one company alone to be able to solve those problems. The promise of biology will be fulfilled when like-minded companies come together in a collaborative way and solve these problems using each of our own individual talents and skills. I remain very optimistic that the path forward is bright. Stephen? Totally agree. For GreenLab, this partnership allows us to scale our plant biotech platform and focus on what we do best, developing the factory of the future. It assures that we are discovering the most optimal PFAS-remediating enzymes, as evidenced by GreenLab producing manganese peroxidase at a commercial scale with the confidence that Allonnia can deploy the solution. If you'd like to know more about Allonnia or GreenLab, please scan the QR code. Thank you. So hi, everyone. My name is Prateek. I'm CEO and co-founder of Bioweg. We are based out of Germany. I would like to start by asking that how many of you know that we are eating 5 grams, 5-8 grams of microplastic every week, equivalent to a credit card? And this microplastic actually carries some toxins on top of them via our food chain and get released in our stomach or our intestine, leading to different kinds of hormonal disbalances or some unknown allergies or sometimes even cancer. And a good portion of this microplastic comes from primary microplastic, which means as humans, we are making microplastics outside and then adding to different products. And then they are lost in the system. These microplastics, roughly 23% of them, come from FMCG or your consumer care products or home care products. For example, micron-sized powder in your skincare and cosmetics goes inside the pore and gives you a nice and smooth skin. It has the functionality of absorbing oil, water, and other kinds of stuff. On the other hand side, the gel of your shampoo is a hardcore acrylic polymer coming from petroleum. The most eye-opening is agriculture. 53% of intentionally added microplastic comes from agriculture. Imagine 90% of seeds and fertilizers, which we are using commercially, are coated with microplastic today, leading to a huge amount of microplastic in our soil all around the globe. There is good news. Many regulatory agencies have looked into this and announced a ban. EU is at the front of this. They have, last year, announced a law that all the companies have to stop using microplastic in their products. The substitute should be fully biodegradable in 60 days. That brings a challenge that you need something which is fully biodegradable in 60 days, but doing the job to be done, it should be fully functional. We have solved that particular challenge. We have developed MicBead, a micron-sized powder, three times better in functionality than PMMA, Nylon 6, Nylon 12, but fully biodegradable in 60 days. A rheology modifier for a gel to change the gel of your shampoo, a bio-based one, and coatings on seeds and fertilizers, basically in order to change the coatings bio-based to have no microplastic in agriculture. We do this with the help of fermentation, waste stream, and bacteria, and using green chemistry to functionalize the bio-based products to fit into different use cases. So one of the major challenges, while having better functionality, is yield and cost. That's exactly why we have started the program with Ginkgo, that we brought our bacteria to Ginkgo and worked with them in order to generate higher yields and reduce the cost drastically. We already have strains which are 3-4x in terms of yield and reducing the cost on a drastic level. These are not only going to bring this product on a global level with a very cost-effective nature, but we are able to reduce the carbon emissions up to 60% as compared to the ones which are used today. These are some of our clients with which we are trying to change your shampoo and my shampoo and the shampoo of everyone and other cosmetic products and agriculture. But this is just the start. This is basically a revolution to make sure that our day-to-day ingredients should be fully bio-based, functional with the help of biology. We have a cosmetic line which we have rolled out with a couple of different clients. It's not only biodegradable but better in functionality in skincare, lipstick, mascara, having a better matte effect. If any one of you would like to try it, please reach out to me. Thank you. Good afternoon. Thank you. Thank you, Prateek. My name's Aaron Schacht. I'm the CEO of a company called BiomEdit. BiomEdit is an animal health-focused biotech company that uses synthetic biology to develop novel products to address key challenges in animal production. We were born as a carve-out from a major animal health company called Elanco. We really got established as a company when we met the folks, Jason Kakoyiannis, Jason Kakoyiannis at Ferment, and colleagues at Anterra Capital, who are an ag tech investor, and dreamed up the idea of taking assets from a major corporation and creating a novel startup focused on unique applications of microbiome understanding, synthetic biology, AI, and machine learning in animal health. We've got great partners in Ginkgo in terms of a technology partner, Nutreco, who's a commercial partner for our early products. We've been proud recipients of a Gates Foundation grant for $4.5 million that's funding our efforts to progress our technology into methane emission reduction. We're focused initially primarily on livestock. We want to solve problems in livestock that go beyond just making the animals produce better but also address the attended problems that come with animal agriculture, such as burdens on the environment in terms of methane emissions or antimicrobial resistance in terms of infectious disease, but also food safety in terms of pathogens that cause diseases in humans. We're very excited about our future. Our technology is really cycling now. We're on the verge of completing our first products. Let me talk a little bit about the platform that we've built. We started life inside Elanco, building large microbial reference libraries that were sampling microbiota from animals in various healthy, diseased, and treated states, and then using that information and multi-omics capabilities to understand which microbes played key roles in maintaining a healthy state in an animal or addressing a diseased state in an animal, and then coming up with ways to productize those observations, either as probiotic consortia, bioactives that were being secreted by some of these microbes that play an important role in the natural process of growth and health, but then also, and most importantly for our future, identifying key microbes that could serve as vehicles to deliver biomolecules that played specific roles in addressing disease process. We've done all of this with a technology stack that includes multi-omics, cultur omics, machine learning, AI, synthetic biology, and strain engineering. This is a highlight of our lead product of the engineered bacterial variety. We've taken a naturally occurring pair of Lactobacillus strains that colonizes very well in the gut of a chicken. We've engineered these strains to express antibodies that neutralize the toxins from a deadly pathogen. Instead of asking the host animal to produce those antibodies, we install an in situ biofactory, a probiotic, to express antibodies that then neutralize these toxins. What we see is a consistent level of protection that we can manifest in a chicken with a single administration on the day the bird is hatched. We simply spray this aqueous solution of these engineered microbes on the back of these baby chicks. They peck and preen each other. They ingest the microbe. The microbe colonizes. The antibodies begin expressing immediately and protect that chicken through the growth cycle. And we've been able to demonstrate this technology to the point where we have good support by the USDA for a unique approval pathway for this product. We intend to bring this product to market in 2026. And then finally, building on the success of this approach, we've imagined how we might tackle something like methane emission reduction. Shown up here on the right is what you can find in the marketplace for products that either address methane emission reduction or cattle or dairy productivity, but you don't find products that can do both. And what we believe is that this is a problem in the rumen where, when the animal is fed, the fermentation that happens in that rumen produces hydrogen. There are organisms in that rumen called archaea who they specialize in one thing. And that's taking hydrogen and converting it to methane. And so the system is set. And actually, the equilibrium is in favor of these archaea getting the hydrogen. What we want to do is reshape the rumen microbiome by engineering a microbial composition that competes effectively for that hydrogen and then also engineer a bug within that consortium that delivers biomolecules that inhibit those archaea, those methanogens directly. And we believe we'll be able to live in that opportunity zone of both increasing the feed conversion and decreasing the methane output in the case of beef, cattle, and dairy production. So very excited. Thanks for everyone's attention. Great conference today. Thank you. All right. So as we set up the fireside here, I'm going to kill some time with a little story. I love synthetic biology. I love synthetic biology. And I am very online. And because I love synthetic biology and I am very online, I get a lot of questions. I get a lot of questions. Hands down, the two most common questions that people ask me are, "Are you making dinosaurs? And are you making Pokémon?" Hands down. Hands down. And I say, "No. You're crazy. You're crazy. You don't understand this company at all. That's impossible. We're a platform company. We don't make products. The developers make the products." You're asking the wrong question. The question is, "Are we making it easier to engineer dinosaurs and Pokémon?" Okay. Are we set up? And the answer is yes. Obviously, it's obviously yes. All right. So next up, a fireside chat with a brilliant scientific storyteller and two brilliant scientific leaders. Please welcome to the stage Michael Specter, staff writer for The New Yorker and visiting scholar at MIT. We've got Renee Wegrzyn, Director of ARPA-H, and Jon Terrett, Head of Research at CRISPR Therapeutics. Hi, everyone. We're going to talk about big data. We have 20 minutes for it. I'm sure that we'll be able to solve that problem in less than 20. Renee, you probably know. She runs what I think is the most advanced experimental and foresighting arm of the U.S. government. I actually mean that. That's a rare thing to say, but it's true. Jon's company is changing the world as we speak. They already have a revolutionary product out to edit your way out of sickle cell anemia. You've probably heard of their other work. He'll talk about it. What we're here really to talk about is how we can obtain, share, and understand the data that exists so that it makes sense and it can be leveraged for these wonderful developments. 25 years ago, I wrote a story for The New Yorker about this small company in Mountain View that was trying to figure out how to do a search engine. I was talking to Larry Page. I said, "What is your goal here?" He said, "It's simple. I just want everyone on Earth to have access to all the information and all the data that exists right in front of you easily and cheaply." I looked at him like he was insane, which I thought he was. Then he said, "Of course, the problem's going to be once we are able to do that, you're going to have to figure out a way to share it, aggregate it, use it." I think that's where we are today. We have endless amounts of data sets. Some are owned by private industries. Some are owned by government. Some we don't even know. Some are from different countries. It's very important that we figure out a way to share that data if you want to solve some of the big, terrible health problems that we actually are, for the first time, capable of solving. I'm going to ask the first mean question to Renee. How do we do this? How do we start making it possible to access the data that you would need to get your job done? Well, I think at ARPA-H, we think a lot about first principles. And so one of the realities is, with the federal government, there's a lot of requirements to share data. But what a lot of people don't know is we don't collect a lot of that data because it's very inefficient to do that in an automated way, in an intuitive way, and to do that at scale. And so some of our first investments as an agency has been to make those tools. So our BioFabric is a great example. It's the least sexy thing that we do at ARPA-H. But it's one of those first things that we need to do. We think once we can bring together and aggregate that data that we know is of high quality and is curated and is tagged in a way that can be used for AI, that's going to be very critical to then bring our tools into the real world. We were talking before the panel today. For example, if we could incentivize pre-competitive data sharing across companies, we may be able to have enough data to train models where, before you even start an experiment or start a clinical trial, you will know if it will be successful and be approved by the FDA. If you had that power with the tool, everybody would use it. But the problem is that we don't have enough data out there to be sharing. And so this is something that we're really trying to work on, incentivizing that data sharing, whether it's looking at genomic sequencing after using a CRISPR tool or doing a drug trial on a small molecule where most of the data is actually negative data that you never share. So that's one of the things that we're really trying to drive. Yeah. Maybe I can add to that. It's all very well having data, but you have to be able to use it. And like you were saying, formatting for Google back in the day would have been a huge thing. There's loads of data out there right now. And people don't know how to use it. And I like to say there was an experiment on humans done in Cambridge, not far from here. Three companies started with the same technology, CRISPR Therapeutics, Editas and Intellia. And if you follow them, they ended up in three completely different places using the same data that was available to them about what drugs can we make that might actually work. And we all did different things. And we all ended up in very different places. And then going back to, should we share? Absolutely. We were discussing earlier. Companies, institutions in general, don't like to share everything for competitive reasons. If I think about how many drugs I've tried to make over the past 20 years, 90% fail. Why was I protecting data around those failures? So fail information is as important as positive information. Well, actually, since you mentioned failure, which is one of my favorite topics, there's endless amounts of people do studies all the time. And when they're good, they get published. And then everyone sees the data. But tons of studies don't get published. They're not good enough. The results aren't what we'd hoped. And you have just an incomprehensible amount of really good data out there that basically gets tossed. Is there some way that you think that that could be used, marshaled? Or is that just hopeless? No. No. Absolutely. If failures aren't published and it's a fairly obvious experiment, lots of other people are going to do the same experiment and fail. So it's absolutely vital that those things are published. Now we get into human nature and scientific journals and all sorts of weird stuff like that. I know there's open-use journals now. We absolutely need more of that information out there. But so when Jonas Salk developed the vaccine for polio, he was interviewed famously by Edward R. Murrow, who said, "Are you going to patent this?" And Salk said, "Would you patent the sun?" Now, today, you bet they've patented the sun. But the point is, there are some things that I think we probably would agree ought to be available to us. And maybe they're not. How do we deal with that in the public arena? Yeah. I mean, this is a little bit philosophical point. I think that right now, data is so valuable that there's a tendency to want to own the data. And I think we're at this issue. We have companies come to ARPA-H all the time. They don't have enough data to move forward in their product or whatever it might be. So I think we need to frame shift, patent your proprietary process, your hardware, your tools, but more data sharing so that I think. At The end of the day, it feels really uncomfortable, but that's actually going to be what's needed to accelerate and advance the state of the art. What is the mechanism by which that can happen? Do we need a new, yet another regulatory body? Is the FDA capable of? We should have 10 more regulatory bodies. I'm just kidding. Oh, my God! So I think what we need is incentives, right? And we need proofs of concept that what I just described actually makes sense. And so one of our big bets in this area is an effort called MATRIX, which is about a $50 million project that we take all 3,000 FDA-approved drugs, and we look at 9,000 diseases where there's been market failures. Most of these are rare diseases. And mine and see, can we find instances where there's a druggable target that maybe is also useful for a rare disease? We have a proof of concept of one where the founder of this company actually cured his own rare disease, Castleman's disease, by mining the literature, bringing together people to say, "Let me just try to take this drug as a last resort." And it worked, and he cured himself. So he's on a mission to do this for all rare diseases. What he needs now is access to that proprietary data. That's one way we want to get to proof of concept. We want to do that for. We've set the market for 25 diseases. Because what that does is that number is big enough that we can go to the FDA, we can go to policymakers, and say, "You have to have a path for this." Not as a one-off for a disease, but you have to start thinking about a process that we can reuse over and over to simplify that path forward. We don't make regulation, but we can create data that creates the pressure that policymakers and regulators can't say no to. So it seems to me that the sort of ideal model of data available to the public is the Protein Data Bank, where all published proteins are deposited in a place. It allowed DeepMind to basically develop AlphaFold, which also, by the way, is publicly available. Is there something else we should, I mean, is there another dataset like the PDB that we really need to pay attention to? I'll take us on a step forward in that answer. So having been through a number of novel modalities with the FDA and other regulators, they build knowledge as you're doing it. And by the time you analyze data, enough data, the regulators have caught up, and they're now okay with these modalities being used in humans, or not if it turns out not to be safe. So when you and I were talking about it beforehand, what's around the corner? And how do we start generating those data now? And I start thinking about gene correction. At the moment, we do CRISPR knockouts. That's creating enough difficulties with different regulators in vivo because we're editing a human. The approved drug has Casgevy's ex vivo and then goes in. They're okay with that. In vivo is tougher. In less than five years, we're going to be inserting whole genes and correcting genetic disease. We don't want to learn just with the regulators. We want to learn ahead of time. That's really where we can start getting conglomerate data together about the safety of doing it. Yeah. Well, I just want to kind of add on to that. By looking around the corner, at ARPA-H right now, we have several imaging programs that are launching. And what we're realizing as we launch these programs is FDA doesn't have a high-quality, highly curated dataset that they could use for not only our performers and awardees, but anybody in the industry to use to get their approvals. And so we have a solicitation out right now, a request for information for datasets that FDA can qualify and use for us to bring our products forward. So I really challenge the audience here. What is that for this room? What is it for gene editing? What is it for synthetic biology that we can start to put together as a dataset that we can all use to sort of accelerate to those approvals? I didn't realize until I was in this role what a huge gap that was in the regulatory domain. How do you? I've spent my whole life writing about these things in which we discover an exciting thing, and then it takes. The science moves fast and humans not quite as fast. That's understandable. I think it's human nature. But now, I hate to use the term that everyone's using, but in the world of AI, things have the potential to really accelerate. And in a good way and possibly in a bad way. I can imagine that AI would be a godsend to aggregating data. But how do you do that in such a way that it's universally accessible? We're also dealing with other countries. Do we want to be competitive with other countries? Do we want to share with them? How does that work? Well, we heard a little bit today about something like biosurveillance, right? I think that's a great case study for the public good where we have to figure out a way to share data country to country so that we're not in the same situations we were at the start of the last pandemic, where we kind of were on lockdown. Even countries that were our friends weren't sure how to share data with us. And I think it's a great use case. If we can figure that out, we can figure out other diseases. But we absolutely need that because we talk about competitiveness. We don't have enough data to build our models. Italy doesn't. Japan doesn't. And so we need to really start working together, figure out bilateral agreements so we can come together and share that data and empower those models. But that doesn't exist today. I think there's pieces CDC and others are starting to bridge that gap. We need to do that in a much bigger way. Maybe you could create a system where the data is shared, and then those that want to pay for it could use it. And then those that don't want to pay for it because they think they're going to go the other way don't. Because this all comes down to human nature at the end of the day, how much we can share. I think we can try and build the systems. If we could get the regulatory authorities around the world to share, they share at those moments those good things or those bad things. But generally, sharing, you'll see companies go on clinical hold. And you don't know why. And it would be really useful to know why so that you don't do that same thing. Okay. So this is my idea. Every time I bring it up, people say I'm insane. But I'm good with that, by the way. But eminent domain is a thing where you're allowed to seize land if you're the government, if you need to. And maybe we should have that for data. Shouldn't there be some higher calling where the government can say, "I don't care, Novo Nordisk, sorry, guys. You're great. I need that data, and we're going to have it"? Is that not possible? Is that not logical? I think it happens. Really? Does it? The government just takes data? Just takes data? I would never imagine. I'm blaming you. You're the government in this scenario. I don't know. I guess I feel like the incentives really are to hoard. When we look at other countries, China's doing some really cool stuff, and they're beating our ass in some areas of this research. It would be nice to share with them, but the words that come out of politicians' mouths are not encouraging when it comes to the idea of sharing data with what they consider to be an adversary. We're talking about sickle cell. We're talking about cancer. We're talking about all sorts of diseases. This shouldn't be an adversarial process. I think a way forward, the human race probably isn't ready for it now, is to mandate the. Is what? To mandate the data be shared. Any drug that's going forward, you'll know that some conferences you go to, they say, "Well, you have to show us the structure of the compound. It can't just be compound A." But I think that's just something we're not ready for yet. Yeah. Also, it really doesn't fit into a presidential campaign bumper sticker. There's a book called Bad Pharma. Has anyone ever read the book called Bad Pharma? We have. It's supposed to expose. It's a book listing exposures of drug companies that did things wrong, that ended up in court. Even in court, there's a letter from, I think, Pfizer. And it's all blacked out apart from the words "and" and "the." So I don't know we can get there yet. Well, I think you've almost flipped the script a little bit, Michael, from nation-states to individuals. I do think that there are areas like rare disease where patients and their families are such incredible advocates. They understand the value of their data, of their sequences. I think that's another really interesting place to start. Because in some cases, there's 25 people across the globe that have a given disease, and we're not coming together. It costs so much to develop a drug. That's right. Your argument is that the data can really bring that down and make it specific. Well, first of all, just the speed to diagnosis is 5-7 years now. How can we bring that to 5-7 weeks if we start sharing data, right? It can be a complete paradigm shift, but we need to figure out how to empower those patients to share data, but then get those agreements with countries. Well, one of the shared data issues that I don't know how you solve is so we developed the mRNA vaccines in a year, and that was kind of miraculous. Actually, I think there's a company in Cambridge who had the vaccine in three days. But you needed time to test it, and there's a certain way that you have to carry these tests out. Would greater access to data and surrogate markers make it possible to move that kind of thing much more rapidly? Because I don't think we even though a year is a super fast time, I don't think we want a year anymore. I'll take the first stab, but I think so. Part of the data that we need to be sharing is not just around the right sequences for efficacy, but around manufacturability, solubility, all the things that we didn't have time to think about with the last vaccine, and have those really understood before the next pandemic. Our program, APEX, is designed to do that. It actually separates design of antigen for all the other vaccines that we don't have vaccines for—I'm sorry, all the other viral families that we don't have vaccines for—and separate that from manufacture. Maybe it's better as an mRNA or a DNA or whatever that might be. I think we need to collect that data now so that we have that library to look at and start from. So instead of 100 days, maybe it's 20 days next time. Let me go back to optimism then. Instead of like, "Oh, this is tricky." The data exists out there now for many, many things where we could make better decisions. AI can help that, but we've got to format the data in the right way. Manufacturability would be one of them. Potency of mRNA would be another. People aren't pulling all of that together and saying, "If I want to do this, whether it's a vaccine or a genetic therapy or whatever it is," people are not putting all that together and just asking simple questions. Data are out there. We actually only have a couple of minutes left. I'll ask each of you this. It's an unfair question, which is my specialty. 10 years from now, is this going to be kind of worked out, or are we going to be up here again talking about, "What the hell? The private companies have this data, and China has that data. And how are we going to resolve this? Well, I can't wait to 10 years from now. Because even backstage, we were talking about I think you said it was George Church that got a PhD for 3 years to sequence half a gene, right? Where are we going to be 10 years from now? And I think I'm comfortable with not being able to predict that at this phase. But it will be important that we create options. And you know I'm always optimistic on this part. 10 years from now, there will be dinosaurs. There'll be what? There will be dinosaurs. That is not an impossible response. Yeah. Good. I love to have dinosaurs. Speaking of George, do you think we're going to be able to share? I really do worry about the sort of divisive world that we live in, in America, but not just America. I mean, the idea of sharing data with China would be really nice. I don't know how that's going to happen. Well, we can't forget about the defensive part, not only for kind of biodefense, but we're really thinking a lot about healthcare. If you watch the news, you've seen the inability to even file insurance claims because of ransomware attacks. And so we've made investments in the DIGIHEALS program. And probably one of the more fun things, we have a competition at DEFCON this summer and next summer in collaboration with DARPA to look at infrastructure and what happens when somebody tries to take down a biotech facility or tries to take down a hospital. How can we create the tools to patch those vulnerabilities? We don't have them right now. So national security, I see people in the military in the audience. Tip of the spear, cyber. If you came to HHS and understand where we were in healthcare, we are decades behind. How do we catch up? That's going to mean something to everybody in this room when it comes to that data around sequencing, whether it's humans or microbes. Cool. I'm being told that I'm done, and I should get the hell off the stage. These guys are going to solve the future if anyone will. So thank you. Thank you. Thank you. Thanks, Michael. Thanks, Chris. All right. If you want to continue the conversation about whether there will be dinosaurs in 10 years or something else, we want to make it easier for you to do that. So by popular demand, we are iterating on our button-based networking model. It was too popular last time. Got too crowded over in the button-up zone. All right. So we're going to have a more focused button-based networking. Here are the marching orders. If you love gene editing, small molecules, biologics, RNA, proteins, or probiotics, meet over in the button-up zone. You will find your people there. And that is break. We return at 3:30 P.M. [Music] This is your 10-minute warning. Our show will resume in 10 minutes. This is your 5-minute warning. The show will resume in 5 minutes. Repeat: This is your 5-minute warning. Hello, everyone. Your attention, please. It is time for us to ferment once again. Please take your seats in the main stage area. All right. We're back. We are back, everybody. Come on. Circle back around to this main stage area. We are back. Are you ready? Are you all ready for this? This one's going to be a doozy. Are you ready? You think you are. You think you are. Come on back. You're not going to want to miss this one. I almost hesitate to warn you about the chaos that is about to ensue here on this stage, right here on this stage. I want you to be able to experience it fresh. Here's what happened. Here's what happened, all right? We knew we wanted to show off the new Foundry technologies for this year. We knew we had to do it. Turns out, turns out, kind of a lot. There's kind of a lot. We have a lot to talk about. How are we going to make the time? How are we going to make the time? We can't cut the customer talks. Those are the customers, all right? We need to be showcasing the customers. Jason's not given us a minute from his talk, all right? So we had to really, really pack it in. And we know you want to hear it direct from the scientists, all right? So we got a lot of it's going to be a lot of handoffs, a lot of transitions, all right? A lot of very important new technologies coming at you really quickly. And maybe it goes off the rails. Maybe it goes off the rails. I don't know. I don't know. We'll see. We'll see. And so to kick things off, please welcome the CTO of Ginkgo Bioworks, Barry Canton. Good afternoon, everybody. I am very lucky in this afternoon session. As Jake says, I'm going to get to tell you a little bit about the technology we use at Ginkgo and also the scale and the multimodality of the data that we're able to generate. To share that with you all, I'm going to be joined by four incredible technical leaders from across Ginkgo. One of the themes that we're going to touch on today is how we combine scale with flexibility. For our partners, scale means that we can work at high throughput, and the flexibility means that we can customize our workflows to suit the specific needs of the project. For Ginkgo, combining scale with flexibility means that we can run many programs across many market verticals and drive our scaling flywheel. But before we get into all of that, I want to spend a minute talking about why data is such a big bottleneck in biology. My guess is I don't need to convince that many of you about this, but I am going to give three examples that I think are interesting and impactful. The first example is that there are just many fundamental things that we don't know about biology still today. So researchers - this is on the left here, my left - researchers at JCVI took a very simple microorganism, and they made it even simpler, as simple as possible. They eliminated all of the genes that were inessential for the cell to live. What they were left with was a simple microorganism with approximately 500 genes that are all essential for life and that are highly conserved. They occur everywhere. You would think that we would know what all of those genes do. In reality, there's about 20% of those genes that have unclear function. As you move up the layers of the chain of complexity of life, the situation doesn't get better. There's still huge fundamental things we don't know. That's kind of the underlying cell. My second example, let's look at what happens when you go to engineer a specific system. You'll hear more about this system a little bit later. Chimeric Antigen Receptors, or CARs, are sort of the foundation of cell therapy. There's a huge combinatorial design space here, as there is in almost every domain of biology. A CAR is composed of a number of domains. We'd like to be able to optimize each of those domains individually, and we'd also like to be able to find the optimal combination of domains. To do so, you need to look at hundreds of thousands of different combinatorial designs. The real kicker is that you'd like to be able to do that in a context that's relevant, a disease model that's relevant to understand a challenging tumor environment. Third example, everybody is taking as much data as they can find, and they're using it to train AI models, which we hope will solve big problems. We should absolutely do that, and we all are doing it. But a finding that is common across technology and biotechnology is that while we have really impressive AI model technology and we have incredible compute thanks to partners of ours like Google, models tend to be undertrained because there is a shortage of the right data in order to be able to train them effectively. AI has really thrown a focus on this data shortage, but at Ginkgo, we were worried about data before it was cool to be worried about data. This is something that we have been thinking about for a long time, as long as the company has been around. One more slide. What we've been building at Ginkgo, our Foundry, the many of you who've seen or have heard about, is really you can think about it as being a data engine. It's a collection of capabilities for generating high-quality, scalable data that is deep and that is multimodal in nature. Throughout Ginkgo's development, we've made choices to invest in measurement and testing technologies that give us data that is rich and really allows us to see what's happening inside of the cells. We take this collection of data capabilities, some of which are shown up here, and we use them in a variety of ways for our partners. For example, with lab-in-the-loop workflows, where you're going from model to data and back to model again, or where we're collecting large multimodal datasets for pre-training of models. Now we're going to be using these capabilities for lab data as a service, as Jason announced this morning. We're able to do that because of an underlying set of capabilities and technologies, including automation, including our biological code base, AI and ML modeling abilities, and also operational excellence. All of those taken together, they give us a scale and flexibility that allow us to help our partners with data, with product discovery, manufacturing, and making manufacturing cell lines. Cell lines. Today, we're going to highlight one of these technologies, and it's an automation technology that kind of rethinks how lab automation could work. I'm very lucky to have the best person to be able to tell you about that, which is Ginkgo's head of automation. This is a technology that was developed at Zymergen and is now being further developed and deployed at Ginkgo. So please, Will Serber, if you want to join me and take it away. All right. Thank you, Barry. So I'm here to tell you a little bit about our proprietary automation technology, an example of which you can see over on the side of the room. And first, I want to motivate why we decided to innovate in this area in the first place, because that might not be obvious. So I would state that we need high throughput, high quality, and very flexible automation in order to engineer the diverse organisms that we work with and to create the volume and richness of data required to build foundational models. So the challenge really is that you cannot buy this off the shelf. To tell you a tiny bit more about that, it is very common next slide. It is very common to have a paradigm where you have basically one work cell corresponding to one process or one workflow. One of the fundamental reasons for that is there's generally, in the automation continuum, a trade-off between flexibility and scale. If you want maximum flexibility, you'd be over on the manual side of things. If you want maximum scale, you'd be on the integrated work cell side of things with sort of walk-up instrumentation in the middle there. Now, we found this to be an unpleasant sort of trade-off to make, and so we set out to make something that was flexibility at scale, which is a fourth level of automation. If we could go to the next slide, which you see here. I think we succeeded at this. The solution that we came up with is a technology, a modular automation technology next slide, please, that we call racks or reconfigurable automation carts. The traditional work cell is really a big central arm with a bunch of instruments arrayed around it. You can see that this architecture is very different. In this case, we have an enclosure with an instrument inside, and it's got a small six-axis robotic arm that is feeding just that instrument. You have all the things, the sensors, the electronics, the data in, the data out, all that sort of enclosed within that enclosure. It becomes this little self-contained Lego building block, if you will. If you put together a bunch of racks, you get a rack system. You can imagine we spent a lot of time thinking about how to make a system that was easy to reconfigure and easy to build. So the system we have over here arrived in crates from California the other day, and we spent maybe about 3, 3.5 hours getting it to the point where it was actually fully running, which I can assure you is very fast relative to state-of-the-art in the industry. Next slide. So one of the really nice things about having this Lego-like approach is that you can scale very well. So you can make very small systems with, say, 5 racks. You can make really big ones. So here we have ones that we designed. There's 185 connected racks for high throughput phenotyping. There is no other technology that can make a system anything like this. Next slide. So I'm focusing mostly on the hardware, but we spent honestly probably more effort on the software than the hardware. It's just a little less fun to talk about, but I'll tell you a bit about it. So here we have a display of the work cell, the rack system level view. And what we have is at that Gantt on the top, every instrument has its own row. The red line moving along is time. And then every colored brick is an action occurring on an instrument. One of the things that might not be obvious so if you had a really big rack system, it would be very painful if you could only run one workflow at a time. So we put a lot of effort into our scheduler and enabling running of multiple workflows at the same time on the same system. So the different colors there, the blue, the purple, the green, are totally different workflows running on the same system. Next slide. So it is not just cool technology. It is also meaningful in terms of the value delivered to the organization. So as we've gone from level three automation, which is to say traditional work cells, up to racks, we have commonly found that we can reduce the labor time by as much as 80%-90%. Sometimes we are able to have a big impact on cycle times as well. So for example, if we can automate across a team handoff or overnight, we've gotten cycle time reductions of as much as 60% going from level three to level four automation. One of the less clear things, the last point on this slide I'll read out loud. So heavy parameterization entirely changes protocol development and use, is especially non-obvious. So I want to dive into that a little bit. This is an example of the onboarding of a QPCR workflow onto racks. This is the same process that we would take if we were onboarding onto traditional automation. You don't have to worry too much about the details. The main takeaway is that it involves a lot of teams, scientific teams, operational teams, and high-tech teams, to not just write the protocol but also to sort of set up the entire stack upstream and downstream of that so that the operators are getting the right instructions, we're getting the data off, and it all just works seamlessly. This is a very painful process. Now, we onboarded QPCR, the first couple QPCR protocols, and this worked just fine. But our own automation control software allowed us to take the next step, which you couldn't do on traditional automation, which was we made a generalized, highly parameterized version of the QPCR protocol that let us cut out the vast majority of these teams. So instead of those sort of five teams, now it's a program team that is just passing parameters to an operational team, and they can run those workflows, and all the data and all the instructions just sort of comes along for the ride. So this is a transformative change. This leads me to my conclusion, which is that flexible automation drives better scientific outcomes and produces richer data to fuel AI and closed-loop systems. The traditional lab automation view is that automation is useful for capturing lots of data once you have locked down processes. We would like to change that and instead suggest that the best automation accommodates variation and experimentation to produce far richer and more relevant datasets. To leave you with an analogy, traditional sort of manual lab work is a lot like monks transcribing books by hand. It's very error-prone and very slow. The traditional lab automation, the traditional work cells, is a lot like a printing press where you are doing all the typesetting. So it works to produce many copies. It's very high throughput, but it is very painful to change. Whereas what we've made is more like a digital laser printer. So you can produce not just high throughput but also many different works. And if you imagine ChatGPT, it doesn't need 1 million copies of Moby Dick. It needs 1 million different books to learn from. So as we build foundational models in biology, what we need is that ability to have highly parameterized, highly variable workflows. And that's what we've provided. Thank you. Okay. Thank you, Will. So that's great. And please take a moment to check out the automation over here if you have not already. So next, we're going to hear three short vignettes from three technical leaders at Ginkgo from different parts of the company working in different domains and using different kinds of data and generating it in different ways, with robots, with pooled approaches, modeling with AI, and even doing in vivo discovery. First up is going to be Emily Wrenbeck, Ph.D., who leads protein engineering at Ginkgo. And she's going to tell us how we integrate experimental data with AI modeling to make better enzymes for our partners. Emily. Thanks, Barry. Yes. So one of the long-standing challenges in protein design is that protein sequence space is enormous, right? It's something on the order of 10^35 possibilities, right? So the key question is, how do you navigate such an enormous search space, right? And in a more real-life, practical scenario, when you have a specific protein sequence in hand along with a list of, let's say, hundreds of known beneficial mutations to that sequence, how do you smartly recombine those mutations in a way that enables you to take bigger jumps in protein diversification and thus performance improvements? Another challenge is that the sequence-to-function relationship is something that has to be learned. And especially for enzymes, the learnings from one problem space to the next don't necessarily translate. And further, context is extremely important, right? Things like temperature, pH, expression host, etc., for determining what the shape of those sequence-to-function relationships looks like. At Ginkgo, we've built a platform that enables us to use experimental data generated in the Foundry to iteratively improve enzymes with a machine learning-guided design process. We typically start by first screening a library of natural orthologs, which we mine from both public and proprietary sequence databases. In this process, we always typically discover much better starting enzymes as points for engineering further. We'll then take these lead enzymes, and we'll design a first-round protein variant library using all the latest and greatest protein design tools, including some of our own. This library is then screened in the Foundry, and we'll get back a dataset that has that sort of sequence-to-function information. We can then feed this dataset into supervised model training, and we can leverage what those models have learned to then help us design the next round of sequences. This process can be iteratively repeated until we hit some target metric. All right. In practice, what we observe is that this approach is incredibly effective across both diverse engineering objectives as well as enzyme classes. In these plots, what's being shown on the x-axis here is the round of engineering. On the y-axis is the activity fold improvement shown in teal. On the secondary y-axis is the library size at each round shown in gray. The general trend is an uptick in performance round over round, right? In this particular highlight case study, we observed a remarkable 80-fold improvement in three rounds of iterative machine learning-guided design. Another trend that we often see is that in later rounds of engineering, when sufficient data has accumulated and the model performance prediction gets really good, we can often take big jumps in performance in very small library sizes. So in that particular case study, we were able to make a leap from 6- to 10-fold improvement in a library size of only 10 members. And the final point here is that what the machine learning really helps us do is make that smart recombination, right, both within the space but also to new spaces. And so these purple bar charts here show an average mutational depth at each round of engineering. And again, it's just really to help us take those bigger leaps in protein diversification and thus performance improvement. So I'm super excited about the consistency of the effectiveness of this approach across both those problem spaces and enzyme classes. I just want to finish and close by underscoring that it's really all fueled and enabled by the data that's generated in the Foundry, both by those super cool robots, go check them out, but also our exceptional experimentalists. Thanks. Thanks, Emily. Next up is Uri Laserson. I'm very excited about this. Uri just joined Ginkgo. Until very recently, he was the CTO and co-founder of Patch. He's going to tell us a little bit about the work that they did at Patch, and you'll see how naturally it fits into the Ginkgo platform, I think. Uri, please take it away. Thank you. Thanks, Barry. So I'm going to tell you about an approach that we have been taking for discovering new RNA elements that can increase the stability of an mRNA molecule and therefore also increase the protein output of the mRNA molecule. Now, as Emily said before, the space of all possible sequences is astronomically large, and most of it is nonfunctional. And so what people typically do when they're looking for functional sequences is they'll limit themselves to the natural genome, and they'll screen natural sequences to find something that basically works. The problem is that evolution is not optimizing the same set of traits that a biotech company cares about when they're trying to design an RNA therapeutic. And so really, what you want to do is to understand what is the underlying regulatory grammar for how the mRNA is being stabilized. The way that we're doing that is by combining very high throughput pooled assays with the latest advances in AI. So the way that this works is you start with, say, 10,000 candidate sequences that you think might be interesting. In a single pool, in a single tube, you go and you synthesize them, and you clone them, and you transcribe them into mRNA, and you deliver them into cells. Then you take time points of those cells over several days. Then you use next-gen sequencing to quantify the abundance of all of the individual mRNA molecules all at the same time. What you end up with is a dataset like you see on the right, where every single one of these curves is a unique mRNA that we designed. Over time, on the x-axis, what you can see is that they're all degrading, as you would expect. But crucially, they're all degrading at different rates. And so what you traditionally might do is you would pick a dozen of these sequences, the best ones, and then carry them forward in your development pipeline. In contrast, what we actually do is take the entire dataset and train an AI model that learns how to map from any given input sequence what is the predicted stability of this sequence. Now, this is the model that's actually going to kind of understand something about the underlying regulatory grammar. And most excitingly, is that now what we can do is take tens or hundreds of millions of design sequences and crank them through the model and figure out what is their predicted activity going to be. Now, from that huge set of sequences, you can now take another 10,000 set of sequences and then go synthesize them and assay them and generate a new dataset and then use that dataset to update the same AI model. And then you go and you do it again. And the idea is that you've turned the crank a few times. And for any given set of therapeutic design constraints, you can find synthetic sequences that are going to be better than natural sequences. The coolest thing is that this actually works when you translate this into a mouse model. And so what you're looking at here is we've taken a commonly known benchmark UTR sequence and one of our AI design sequences, and we've plopped them into an mRNA that is driving luciferase. Then we package it up in an LNP, and we inject it systemically into a mouse. Then we take time points over two weeks of that mouse to see how much protein is actually being produced. What you can see in the curve in the middle on the bottom is that the difference between these two curves is strikingly different. Actually, we can see that the AI design sequence is driving expression at higher levels and much longer. We even see full changes that are up to 100x stronger than the commonly used benchmark. That's it. Thank you, Uri. Thank you. Okay. We're excited to have the Patch technology deployed across the Ginkgo portfolio of programs. Okay. Last but not least, I'm excited to introduce Shadi Esfahani, who was one of the first mammalian cell engineers to join Ginkgo a number of years ago and build out our platform and now leads a lot of programs in cell therapy. And she's going to pick up the chimeric antigen receptor story that I introduced at the start. Shadi, please take it away. Thanks, Barry. It's great to be back up here to share with you a little bit about how we think about generating large datasets across our biopharma programs, and in particular, the relevance of the model systems in which we're using to generate the data. If you think about the ChatGPT example, again, it wasn't trained on random collections of words and sentences. It was trained on relevant words and sentences from different styles of writing, like Jane Austen or The New York Times. And when we're asking biology to report on what's happening in a complex human disease, we need to get data that's as relevant as possible. And so one example of how we're thinking about this is from our CAR-T story that Barry started out with. We've gotten really good at screening these large libraries in vitro in lots of different tumor cell lines. But we wanted to even push that a little bit more and ask, what is a more relevant model we can use? Can we do this in an actual tumor-bearing mouse? And together with our partners at the University of Wisconsin, we did just that experiment. This is a clinical group that is studying neuroblastoma, which is a devastating pediatric cancer. And they're targeting GD2. And so we built a library of 10,000 chimeric antigen receptors that also targets this antigen. And to be honest, I was really skeptical about this experiment. It's really hard to work with large libraries, even in simple controlled settings like cell lines. I really didn't think we were going to get this kind of positive preliminary data shown here. So on the slide on the left, right, it's tracking the barcodes which report out on each individual CAR design. Our theoretical maximum is 10,000, and we maintain most of that diversity all the way up until the time that we inject the cells into the mouse. 23 days later, after interacting in the tumor environment, we're recovering 1,000 barcodes, suggesting that there's 1,000 different CARs that have persisted in this complex environment. Now, I've done enough animal studies to never trust one mouse. We wanted to look at how many designs are shared across replicate mice. That's what's shown in this graph here. Remarkably, there's a number of designs that are conserved across three, four, even five mice, suggesting that whatever selection is happening there in the mouse is real and not random. We're now following up on these candidates and evaluating them as potential therapeutic candidates for our partners at Wisconsin. We're doing this kind of pooled in vivo work across our biopharma programs in gene therapy and RNA and gene editing as well. There's some scenarios for which even an animal model isn't good enough to generate large datasets. To take one example from genomic medicines like CRISPR therapies, you need the human genome there to really assess the safety and efficacy of any kind of genomic medicine. A mouse is never going to do that. In another example, mouse tissues are very small. There's a small number of cells. You can't accurately screen a large library. These are scenarios where human stem cell models that we can use in vitro can recapitulate some complex disease states. What's shown here is a diagram of a human choroid plexus organoid. This is a brain tissue where the cerebrospinal fluid is generated, and it's an important target for gene therapy. And so we've generated these organoids in our labs at Ginkgo, and we're going to be scaling this and using it for screening promoter libraries for gene therapy applications. So as much as everyone loves data, I would challenge you to think about the relevance of the data. And while we do use simple model systems to build our methods and scale them, we are always thinking about how to push them to more complex models. And that's it. Thank you, Shadi. Hi. I hope you'd agree that those were some amazing examples of what we can do with the Ginkgo platform. It's here. We're able to do amazing things in enzymes, in RNA, in cell therapy, and beyond. We look forward to helping you with your next program to generate data and to solve some real problems. So thank you for today, and please enjoy the rest of Ferment, and we'll see you at the break. All right. Yeah. All right. Well, actually, that actually went really smooth. That actually went really smooth. I always knew they were going to nail it. It's the kind of consummate professionals that we have in this country. Great job. Great job, guys. I loved it. Let's keep that energy going for our last round of customer lightning talks of the day. And so like before, I'll introduce all the speakers as a group, and then I'll get out of the way. But I just wanted to. Can we get the can we get this? Do we have their names? Okay. I swear we didn't plan this. I swear we didn't plan. I just wanted to there are three Stefans. We have triple Stefans in this panel. We didn't plan it. In some cultures, that's good luck. All right. So, I am very proud to welcome. We have Stefan Baier, CSO of Aqua Cultured Foods. We have Stéphane Corgie, CEO of Zymtronics. Stephan van Sint Fiet, CEO of Vivici. Satoshi Okamoto, Chief Research Coordinator of Sumitomo Chemical. And Weslee Glenn, VP of Innovation at Ayana Bio. Take it away. Hi, everyone. Thank you for joining me as we take you on an exciting journey to Aqua Cultured Foods. I'm Stefan Baier, the first of the Stefans, and I'm the CSO of Aqua. Let's start with the problem. Overfishing is a critical issue. Nearly 80% of the world's fisheries are already completely exploited, depleted, or in the state of collapse. Our oceans, as we know of today, could run out of seafood by 2048. Aqua is here to offer a lifeline for our oceans and our sushi addictions. We have developed a new, low-cost, and scalable biomass fermentation to produce food products. Aqua products can be grown with little to no stainless steel anywhere in the world, providing an impressive sustainability case already at pilot scale. Our patented technology starts with a proprietary microbial consortium. The consortium is grown in a fermentation tank, and then we take the seed to grow fiber-containing scaffolds in our media in vertically stacked trays until we get the perfect whole cup filet. Once the whole cup filet reached the preferred level of thickness, we harvest them and combine the cuts with plant-derived flavors and colors to replicate the exact buttery texture and umami tang of fresh-caught fish. At pilot scale, our grow room is only 500 sq ft, but we can produce up to 5,000 lbs of product. Aqua has recently partnered with Ginkgo to optimize our technology and elevate the quality and consistency of our products. By leveraging Ginkgo's advanced services, we can continue to perfect our consortium and scale up our manufacturing process to take a greater step towards healing our oceans. The first application of our technology is seafood. Our hero products are tuna and scallop, which we will be launching in fine dining establishments across the US, starting in Chicago this summer. Aqua is a guilt-free and ocean-safe alternative to seafood. Aqua is packed with fiber, vegan, and allergen-free, with none of the baggage of traditional seafood, meaning no heavy metals like mercury or pesticides and antibiotics. Plus, it has an impressive six-week shelf life. Enjoy Aqua without the consequences of fish farming and untraceable supply chain and questionable microplastics. Today, during the cocktail hour, we'll be trying two of our products as Aqua Spicy Tuna and Aqua Scallop Crudo. I'll leave you with a video showcasing the dicing of our tuna scallop. Good afternoon, everybody. My name is Stéphane Corgie, the second one, the second Stefan this afternoon. I'm the CEO of Zymtronics. I'm very stoked to be among all the biotech pioneers this afternoon. Zymtronics. Sorry. The industry has been innovating on cell-based technology and breakthroughs in fermentation for decades. But today, we can take it a step further by unlocking the power of biology outside of the cell. Think about it. In a cell, you have about 1,000 enzymes working together at any given moment for the cell to grow, to live, to reproduce, and ultimately produce some ingredients that have been engineered for the fermentation. However, there is only a handful of enzymes that are truly used for the production of these targets. And all of these enzymes need to work together under the cell constraints. Why not remove the constraints of the cellular biology and focus the power on enzymes themselves? We're doing this through the immobilization of multiple enzymes. We've been working with Ginkgo. We have designed new rules for the design of those enzymes that we can assemble for efficient biomanufacturing. We're really thinking precision by design by integrating downstream and upstream processing together at the onset of the ideas. We are building us in bio because those enzymes need to be produced by fermentation. We think, in terms of cores, getting those enzymes together as a set of systems for production. Let me introduce you today with our first core. We call it the Lacto-Core. The Lacto-Core is a set of processes, routes, and enzymes for the conversion of plant sugars into lactose. It bypasses the entire dairy chain where lactose is issued from. It's a highly decarbonized, hypoallergenic version of lactose as an ingredient. We are focusing on the early nutrition market. But by building on this Lacto-Core, we have access to all the ingredients that are present in human milk. Those are carbohydrates, known as human milk oligosaccharides, that have a lot of health benefits. They actually maintain the biological balance between the baby and its environment. If this balance is broken, that can have dire consequences. There's a disease called necrotizing enterocolitis, which is an acute inflammation of the guts with high mortality rate and also long-term consequences for the infant that can be prevented with some specific HMOs called DSLNT, which is so rare right now that it's not even being produced. Nobody has enough of it for production. So we have developed a new set of enzymes for the production of DSLNT in its pure form and in its pure form. So animal-free lactose and DSLNT are two examples of what our cores and our product pipeline can do. Cells can do a lot, but sometimes cell-based processes can struggle. With cell-free biomanufacturing, you can be a process engineer first and a bioengineer second. And that really allows us to unlock new molecules from simple precursors, optimize cost of production for new commercial opportunities, and enhance the power of biology for rapid and sustainable impact. I'm looking forward for more discussion today. And also, if you have any questions, reach out to the Zymtronics team in the room today or reach out online to learn more about cell-free biomanufacturing. Thank you so much. Hi, everybody. My name is Stefan. I'm the CEO of Vivici. I just agreed with the other two Stefans to start a boy band. Stay tuned. Vivici is a Dutch startup making dairy proteins by fermentation. We are a B2B ingredient player, and we're part of the growing community of companies making animal protein in more sustainable and in more ethical ways. But I'm not here to pitch my company today. I thought I'd use this lightning talk for something different and share a thought about the precision fermentation industry with you. I believe that companies making animal protein by fermentation should be chasing customers, not costs. B2B precision fermentation companies need to master a deep value chain. But it is when customers and ingredients come together that value is created. It's there where we develop the premium value propositions around our ingredients. It is there where we work with the brand to build the great consumer stories that ultimately lead to the willingness to pay for those brands. But in our industry, there's a bit of an obsession with the supply chain and with cost, down to the point that there's this narrative that animal proteins from fermentation need to be at price parity with their animal-derived counterparts. But that doesn't make any sense. And here's why. The market for animal protein is so vast that even with the most bullish fermentation capacity scenarios for the next coming years, we will only be able to solve a very small portion of that market. And so we should be focusing on those market segments where premium value propositions can be created for our ingredients, creating value and not chasing price parity. By the way, that little tip there, that little white tip, is enough to build multiple billion-dollar ingredient companies. So put differently, chasing price parity leads to premature commoditization of fermentation-derived ingredients and deprives the industry from the profitability that we need to take these ingredients mainstream. It doesn't make any sense. So based on that, three recommendations for precision fermentation companies. One, don't be a biotech company. Be an ingredient company instead. Two, obsess over your customers, not your supply chain. Invest into a strong food science team so that you really understand your ingredient and how to help your customer get the most out of that ingredient. And three, do chase cost parity, but do it to build the profitability that will help you take these ingredients mainstream. Thank you very much. Hi. Good evening. My name is Satoshi Okamoto. I'm Chief Research Coordinator of Sumitomo Chemical. So I'm so excited to be here again. So do you remember? Yeah. Last year, so I was here. And I introduced several projects, collaboration projects with Ginkgo. Yeah. So the petrochemical replacement, agricultural chemicals, and functional materials. So as you know, commercialization target, so it's not so easy for us. But anyway, so we have started, challenged the collaboration with Ginkgo. So yeah. Last year, I already explained. So when we started the project, so the Daruma has one eye. And if, fortunately, we could succeed in the project, so Daruma has two eyes. So today, I'll be here again. So I will inform you three things. So one is I want to inform you that our project progress. So yeah. See, Daruma now has two eyes. That means, finally, we could have succeeded in the first project. So now, we have so much confidence, so how to collaborate with Ginkgo and so that we can continue the project. And second things, yeah. So now, I have confidence. So that's why I want to deliver the strong message to other companies so that please consider the collaboration with Ginkgo like us. So especially the Japanese company always looks like very hesitant to collaborate. So I want to recommend collaboration with Ginkgo because my dear friend, the Pete of Ginkgo, he can support. And he has already a strong relationship with Japanese bioeconomy. Pete, where are you now? Maybe he's hesitating like Japanese. So yeah. Anyway, okay. So yeah. So if someone wants to collaborate with Ginkgo, so the Japanese company, so please contact with Pete. And third one, yeah. Briefly, I want to show you next year's expo. In Japan, we have a big event, exposition in Osaka from April 13th. Sumitomo has a big pavilion. In that pavilion, we will show you the bioproduct. That's why. Please welcome to see my pavilion. Thank you. Plants are masters of their environment. They can't move, but they can make molecules to protect themselves. For example, they make a class of molecules called carotenoids that function as sunscreens. We humans can use these molecules to protect our eyes. There are countless other examples from the plant kingdom that we humans can also use, such as flavan-3ols for heart and metabolic health. Plants don't always make these compounds. They certainly don't make them for us. As those famous musical philosophers Outkast once said, "You can plant a pretty peony, but you can't predict the weather." What if you could predict how plants responded to stress and, in turn, get them to make the molecules that we humans are most interested in? I'm going to tell you how we use our collaboration with Ginkgo Bioworks to do just that. We start off very simply. We start off with plant parts. We take a part of the plant. We put it onto a media that allows the cells to reenter the cell cycle. And that forms what's called a callus. We then put that callus into a suspension culture. We can then break the suspension culture up into what we call a parameter matrix and test it against a variety of different conditions. The way that we test that is using primary screens internally. But we can also walk our samples right upstairs to Ginkgo Bioworks, who will help us by running transcriptomics experiments to help us understand which genes are being expressed in response to stress and run metabolomics experiments that tell us which molecules are being made in response to stress. And we take this information, and we can revise the cell. We can revise the media. We can revise the process to improve the productivity. We do a quick check of our productivity, and we end up with a cell line, a process, and a media group that we can use for months and years to come. In short, Ginkgo really helps us understand what's going on inside the cell from epigenetics all the way down to phenomics. We don't stop there. We use that information that we get from Ginkgo Bioworks to develop innovative ingredients, innovative ingredients that we can put into consumer packaged goods to make them more nutrient-dense. Imagine you have a delicious bowl of mac and cheese. What if we could put carotenoids in there to increase the nutrient density and protect our eye health? What if we could do that without changing the taste and the flavor profile? Well, I want to invite you all to join us at the bar to taste a couple of our prototypes. We have a Mandarin Spice Immune Support made with our Echinacea Plant Cell Advantage. We also have a blue what does it say? Blue Patch Antioxidant Boost with Dog Rose Plant Cell Advantage. And please come find us. We'd love to talk to you more about our technology and about our products. My name is Weslee Glenn. I'm with Ayana Bio. Thank you for your time. Well, I'm excited. I'm excited we're going to get to try all those at the afterparty. So stick around. So I get to introduce our last panel. I mentioned this earlier. Vesper is, again, in my opinion, the best biotech movie since Jurassic Park. And I want to highlight that I think art and movies like this are what inspire us, right? Myself, a lot of other bioengineers of my generation are doing this because we saw Jurassic Park when we were 13 years old. And I do want to emphasize, Vesper is absolutely a dystopian hellscape, okay? But it is one where the bioengineering is so easy to do and so beautiful that I would go there, okay? It is just wonderful, wonderful what they built. So I want to bring up to the stage Kristina and Bruno, the directors of Vesper, Alexis, the producer, Christina Agapakis from Ginkgo, who's going to moderate. I want to say that they asked me to mention that today is the day they're announcing that they're going to be funding and launching production for Vesper 2. So congratulations and welcome to the stage. All right. So today, you've heard a lot from all of our amazing speakers about how people are building technology, how people are building data, how people are building partnerships and organizations. At Ginkgo, we also believe that building culture is a really important part of making technology and making the future. Science fiction is, of course, a part of the culture of technology. It's about how we grapple with some of the really interesting questions of our time and about thinking about the future of technology. The science fiction author, Ursula Le Guin, she said, "The future is a safe, sterile laboratory for trying out ideas in, a means of thinking about reality." So Vesper certainly is a vision of the future where biology is technology and where technology is biology. And it's definitely not sterile. So I'm going to first hand it over to the team to show you to tell you a little bit more about the film and show you some clips and scenes. And then we'll have some conversation. Thank you, Kristina. So a few words about Vesper. So it's a work, some collaboration between Lithuania—from Kristina is from France, Bruno and I from France, but also Belgium with some British actors. So you'll see it was a big team coming together to make this sci-fi, something we really care about in Europe. It's how do we renew sci-fi? I mean, we all grew up with Jules Verne comic books. And a movie like Vesper for us was how do we reconnect with the audience, inspiring also a younger generation and show a vision of the future where there is hope. I mean, hopefully, we won't spoil too much the movie today for you guys who didn't see the film. But let's dive into the world of Vesper with this trailer. We hope you have popcorn with you. Enjoy the trailer. Let's watch a trailer. You are the leaf I found. What is this? Seeds. Yeah. We are so alike, you and I. Vesper? Silence. Because we won't let this world crush us. But don't think that you can change the order of things. I have skills. I taught myself. The Citadel likes control. If it opened its doors to everyone, there wouldn't be enough resources. But they left us nothing. I need to find a key to unlock the seeds. Make them fertile so we never starve again. I'm Camellia. Vesper. She's not our responsibility. She's from the Citadel. She's taking us there. Let's go. Pilgrims, drifters, bandits? You have to be really careful who you trust. The Citadel will be ruthless. They can't stop me. Vesper. You can change everything. Thank you. For us, it was really very interesting and challenging and inspiring to create the world of Vesper where technology became biological ones. For dramatic reasons, we imagined it in a post-apocalyptic world. We call it the New Dark Ages or the future Middle Ages. When there is a dark time, it always comes the light ones. When we were imagining the Vesper character, for us, it was really important to create a character who is an artist and a scientist who reminds a figure of the Renaissance. Yeah. Like Leonardo da Vinci, for example, who was at the same time a scientist as an artist. So in the world of Vesper, there was, as we say, it's a dystopia. So there was an opposition with the Citadel where lives the elite and the outskirts where lives the people try to survive. And we made a big work on research for the world building about this world when the technology became biological. So we can see, for example, some example of the research we made for the material, the research where actually exists, like bioplastic, mycelium, luminescent bacteria. So we were making all this research. And we put in the world of Vesper to make it consistent and credible. We could see some examples, some picture of the use. Here, for example, there was a bioplaster who can heal. So some example like that. Let's say, in the world of Vesper, it's about 200 years in the future, let's say. There was a new biotechnology from the Citadel. There was a more ancient technology. We are more biomechanical. We can see some example, for example, here about the father of Vesper, for example, is paralyzed and uses a respiratory system inside a real lung, lung-like. He can communicate to outside with this drone, flying drone, in the same time mechanical, but inside is totally biological. All these neurons and systems are totally biological inside. Yeah. And for us, it was also very important to create the technology is very advanced. And it is so advanced, but it becomes like magic. So the creation process is almost like an alchemy. And we gave Vesper a kind of laboratory that helps her to create, to work, to experiment. And we imagined it that it's like a large organism whose organs are all connected. And even in the movie, we give some sound of a cat purr that when she works, the audience also doesn't hear it very well. But subconsciously, it affects them very calm. So yeah, you can see different examples. So yeah, this is Vesper laboratory. As Kristina says, it's like a big organism. But she has an interface. So she works on synthetic biology with an interface. And we wanted to think about a new kind of interface, a new kind of UI. And we imagine an organic, a living interface. So the display, it's a living display. And we get inspiration from the chromatophore, from the squid, for example, and the pixel. The pixel are literally chromatophore on this screen. And it's an organism. It's a soft interface with, in the same time, a display but also a system of analysis. And you can see, for example, she put a drop with DNA. And the screen, the display will analyze itself. So try to have some kind of perfect interface. I mean, all her experiments she's doing in her lab. After, she's having a garden that she's trying to grow, the things that she's creating. For us, it was important to create a character who manages to see the beauty and the potential in all the surroundings. So yes, light flowers that you were able to see them in the second floor today. Yeah, a bit of. Concept art. Yes. Vesper is a very curious mind who tries to take the poisonous, dangerous plants from her surroundings and to change them into beauty ones. Here, you can see by different generations. She's like a gardener who works like now. Who is able to see the beauty and to reveal the beauty. By synthetic biology, you reveal the beauty of this world. So this is some example also of some of her creation. Like, for example, here, we have a hermaphrodite plant who can reproduce themselves with luminescent. One of the beauties of Vesper creation. So it shows also a bit of creative process. Then, in fact, we seek, and we go to take in the nature from living organisms, from different plants, animals. And we blend together to create that. So our creative process, it's very literally similar to the creative process of Vesper. We can see a little bit also the ecosystem where lives Vesper because this ecosystem was totally transformed by genetics. So it's a totally new ecosystem. So some is friendly. Some is hostile also for human. So we were trying to imagine, yes, what could be this new ecosystem. So we see a glimpse in Vesper 1. But the ideas in Vesper 2 will discover really more of this ecosystem. And we will see that it's really more than we imagine. This is an example of our inspiration from different artists, from different sources. And as you can see, it's a very dangerous environment where Vesper lives. So as she's smart, she designed these biotraps. It's kind of a security system, almost like pet slug that she created to defend herself. So that was also part of her world, something that she built on her own to defend herself. And in this world, in the adventure of the movie, you will see she will also discover bigger, better technologies, the technology from the Citadel. And she will face them throughout the story. It will kind of inspire her to go on another adventure. You have the Jugs, which is through the character of Camellia in the movie. Maybe you can talk a bit more about how Camellia with the Jug are designed. Yeah. So the Jug are literally genetically engineered humanoid. And they have, for example, this lid on the back. We are like a bioport when you can put some data inside and organic data. So as we see, it was inspired also from this carnivorous plant. The Citadel uses also they manage to control the slime mold. They use a property of the slime mold which is really perfect to map a territory. So they use to seek and analyze an area. And yeah, example of I think everybody knows slime mold. You're taking inspiration from nature. From nature, yeah. Another example of Citadel Technologies, they have a carrier drone, drone to carry merchandise. We get inspiration from mosquitoes, literally. They are really flying animals, this drone. And yes? This discovery of the Citadel technology will really, for us, open up a new world. I mean, after making this movie, the way it was received all over the world, I mean, quite positively, it also not frustrated part of the audience. But they wanted to see more. So these past two years since we released the movie, we have been developing, I mean, a sequel. We are not yet in the script phase. We are getting there. But we are very excited to announce here, for the first time, we are officially going there because we are at a stage of development. We are really quite happy where we are heading. And the goal is to really explore the world, go to the Citadel, follow Vesper. So hopefully, you see the first one. And you have the same desire as us, as following her in more adventure. We used to say that if Vesper 1 was the seed, Vesper 2 will be the flower. So bigger scale. Awesome. I love it so much. It's so cool. You spoke about this sort of transition in sci-fi from cyberpunk to biopunk. I think, yeah, for my whole life, certainly, the visions of the future are always shiny. There's lots of screens. It is that sort of more cyberpunk aesthetic. What happened in the world of Vesper in these 200 years? How do you imagine that future state of technology sort of transitioned from computing to biology? Tell us about that sort of transition in that world. We imagine. We think we all agree here. In fact, the next big thing in the technology is biotechnology. More will go to advance. More will go to the technology will become organic. It was this quote from Arthur C. Clarke's; they say, "Any advanced technology is understandable from magic." We'll say, "Any advanced technology is understandable from biology. No. But yeah, we believe that more and more, like the computer, for example, as we use neural networks. So why not use real neural networks, in fact, to calculate and to compute things? So we imagine that, yes, little by little, the technology became more organic, more integrated like that. So of course, as it's a movie, we need to go to some dramaturgy. So we was to the dystopic for dramaturgic things. But as Christina says, the world of Vesper is a tipping point to a new renaissance. So we are always, by contrast, to show the darkest, to after go to the lighter things. And Vesper, she is the ferment of this new renaissance. So yeah, the world evolved little by little to this biotechnology, so. So we heard that in 10 years, there's going to be dinosaurs. And you're saying that AI will soon be brains in vats? Yeah. Yeah, yeah. Okay. That's it. You're hearing it here first, the future predictions. So I loved some of my favorite scenes from the film are in Vesper's lab, where she is interacting with that screen and working with biology. And Jason ended his keynote this morning with that quote about inquiry at scale and communing with nature and conversing with nature. I thought the vision of her lab was the most compelling sort of vision I've ever seen of what it feels like to work with biology. I wonder if you could tell us a little bit more about what it looked like to think about that practice of science and the artistry of doing science. As I said, what we wanted to, I mean, to come, the technology is so advanced that it is like child's play and that it is a co-creation. Vesper is doing co-creation with nature, doing co-creation, always exchanging and playing and trying to see different ways, different outcomes. We like that approach really a lot. Yeah, we try to bring it to the movie. This is magic. We really are about co-creation. I mean, in our process, we co-create with other artists. Now, with generative AI, for example, there was a big debate about that. But we like to think. And we are co-creating also with generative AI. And we think also in the process, Vesper is co-creating also with the nature. It's really a co-creation. It's always not a control. But it's always a co-creation and exchange like that and a collaboration, a symbiosis, so. I love that. That's really beautiful. You were actually saying something earlier. You were saying something about technology acting on us versus technology adapting. What was it you were saying about that sort of like, yeah, when technology is alive, there will be more of that sort of co-creation or sort of symbiotic potential? Yeah. In a way, since tens of years, electronic technology, cyber technology, it's like if human tried to adapt, we were trying to adapt to this interface. We were trying to adapt to this way of working, in fact, this technology. Now it's times that the technology adapts to human. We think that the biotechnology, it's more this, in fact. It's more the technology adapts to human more than human has to adapt to technology. That's the way we want to explore creatively. So you said, yeah, and it's clear when you see that it's a dystopia. Actually, many of our slides and folks like Ginkgo, when we sort of close our presentations, often we have a slide that says, "Grow the world you want to see." And so maybe this last question, I'd like to ask you all to tell us about what is the world that you hope to grow? Oh, Alexis, what is the world we want to grow? No, I mean, for us, again, it's a message of hope. I mean, at the end of the day, the world of Vesper is a dark, post-apocalyptic world. But at the end, we show characters that's willing to fight with her creativity, with her artistry, and how to work with nature hand in hand. And I mean, with the sequel, it's where we want to go next to keep exploring this world, showing to the people, inspire them, but show them that just a young character can change everything. And. Yes. It's in Vesper 2. And we will see the world we want to grow. That's what you want to grow? Amazing. Do you want to say anything? Your. Just, I mean to say that, I mean, until there are people who manage to see the beauty and who try to go to it and to make it, so we are all good about growing, about developing, and about reaching whatever goals we want to reach. Yeah. We are part of the living world. And so the ideas and technology is always, in fact, we always consider technology not like something outside of the world, like artificial, but natural because technology, it's an emanation of human. And human is part of the world and the ecosystem. So we are very optimistic for the future. That's beautiful. Thank you so much for being here. Thank you, everybody. Let's go grow something amazing. Thank you. Thank you. Well, that was inspiring. Maybe another quick round of applause for Kristina, Bruno, and Alexis for bringing just a magical world to us. They said that cooperation with nature and sort of bringing artistry and science together is what they were trying to achieve in that future world. I think that is what has a lot of us here in this room. That is the vision for designing biology in the future. I will say, all of you being here, getting on stage, sharing what you're doing, it inspires me. It inspires all of us to build the technology we're building here at Ginkgo. So I want to start by thanking all of the folks that came on stage today, our customers who are sharing their products with us at the party today, all the lightning talks, and the folks in the panels. Thank you for your inspiration. I also want to thank Quinn and the Ferment team, Christina and the creative team, Alex and Nita, Danielle and Sierra, our sort of creative masterminds that put all this together. Enormous round of applause. The team here that executed on it at SoWa. And last but not least, I want to say hi to all our viewers on the live stream. We had many, many folks tuning in today. Finally, I want to give a special thanks to our emcee, Jake, for keeping us all on time and making it happen today. So again, today was really about Ginkgo opening up our platform, our data, and our Foundry to all of you, and eventually taking a first step on a journey to a world where bioengineering is democratized. And again, thank you so much for coming on that journey with us. I want to have you all join us at a party right now. Enjoy this food and drink you just saw on the screen. Thanks again for attending Ginkgo Ferment.