For over a century, antivenoms have relied on serum extraction from animals — a process that’s costly, inconsistent, and limited to specific snake species. Today, advances in synthetic biology and antibody engineering are pointing toward a different future: a universal antivenom capable of neutralizing toxins across the world’s deadliest snakes. This session dives into the science and story behind this breakthrough — from the man who endured more than 200 bites to generate a unique immune response, to the researchers using those antibodies to design broad-spectrum, recombinant therapies. Together, they’re charting the path from survival experiment to programmable immunity.

AlphaFold cracked the code of protein structure. The next major frontier is decoding the dynamic behavior of the living cell itself. A fundamental challenge in modern biology is that of precise, engineered cellular control. Cells possess their own language for communicating with each other -- cell signaling -- which directs core biological processes like development and is frequently dysregulated in disease. Remarkably, biology achieves this complexity using a surprisingly concise vocabulary: only around 20 fundamental molecular signaling pathways have been identified to date. It is the combinations and orders in which they are used that underlies how such a small number of pathways can give rise to the staggering diversity of human cell types and states. In principle, because these pathways are readily manipulated by small molecules, they provide a potent mechanism through which we could control cellular decision-making. However, despite decades of effort, we have not yet deciphered the grammar of this language. Today, the effects of a given signal are largely determined through an empirical, trial-and-error process -- due to two compounding challenges: combinatorial complexity and context dependence. This talk outlines Cellular Intelligence's solution: the construction of the first Universal Virtual Cell-Signaling Model, a platform intended to compute how any cell state will change in response to external signals. By combining the paradigm of developmental biology -- nature's own proving ground -- with our proprietary multiplexing platform, we transform cell signaling from an empirical art into an engineering discipline built for therapeutic design. We aim to unlock high-impact applications: from guided cell therapies that replace lost tissues, to context-specific drug response prediction, to new ways of modeling disease as signaling network failures. Our goal is to understand, predict, and ultimately control cellular behavior.

Synthetic biology has changed, but the way DNA is made largely hasn’t. From mRNA therapeutics and gene editing to protein engineering and advanced vaccines, today’s most important applications demand DNA that is more accurate, more complex, and easier to use, without bacterial constraints. Yet much of the industry still depends on cloning workflows that introduce delays, contamination risk, and unnecessary limits on what can be built and how quickly teams can move. In this talk, we’ll explore why DNA remains one of the field’s most persistent bottlenecks, and why the future of DNA manufacturing will need to move beyond cloning. We’ll look at how cell-free approaches are removing bacterial constraints, and how the field is opening up through both cell-free DNA as a service and more accessible, kit-based assembly models that bring DNA manufacturing closer to the user. Together, these models have the potential to make advanced DNA easier to access, easier to integrate, and better aligned with the pace of modern biological development

Biology will be the center of the next industrial revolution, representing a $4 trillion economic opportunity. Yet, this value remains overwhelmingly unrealised for one fundamental reason: nature never intended to power industrial manufacturing. Biology was optimized for survival, not for the high-efficiency processes required to transform the global economy. For too long, the industry has relied on incremental improvements, essentially duct-taping enzymes and calling them industrial. At Biomatter, we believe that complete freedom to design any enzyme is the only way to realize the full potential of biomanufacturing. By combining Generative AI with rigorous physics engines, our Intelligent Architecture™ platform allows us to step outside the bounds of natural selection and build enzymes from the bottom up. We are turning the "previously impossible" into routine. From liberating enzymes of their cofactor dependencies for mRNA raw materials to designing lactases that reject the trade-off between lactose removal and high GOS fiber formation, we are proving that biology’s limits are negotiable. Join us to see how we are building the enzymes nature couldn't.

As artificial intelligence dramatically accelerates the design of proteins, manufacturing has become a critical bottleneck for medical breakthroughs reaching patients. The infrastructure for producing biologics has remained largely unchanged for over fifty years, and is capital-intensive, slow to scale, and dependent on fragile supply chains. Neion Bio is replacing nearly every component of this infrastructure, from bioreactors to cell culture media, with nature’s most prolific protein factory: the chicken egg. Enabled by breakthroughs in genome engineering and stem cell technology, Neion creates genetically engineered chickens that lay eggs filled with medicines. As a result, they produce life-saving therapeutics at the cost of egg production, eliminate scale-up risk with simple breeding programs, and solve the problem of resiliency by running on grain and water. A small Neion farm can produce the global supply of essential medicines, and can be set up nearly anywhere in the world. 

Biotechnology is enabling a new generation of ingredients that elevate both product performance and sustainability. In this fireside chat, leaders from Procter & Gamble will discuss how biotech is being applied across key material classes to enhance consumer products, highlighting innovations behind their latest Native launch and emerging bio-derived ingredients for hair and beauty. The conversation will also explore how biotech platforms are helping shape the future of high-performing, sustainable consumer goods.

While Twist Bioscience may look like an overnight success, its rise reflects a decade of persistence, innovation, and platform building. In this main stage keynote, CEO and co-founder Emily Leproust shares the journey from startup vision to global leader in DNA synthesis and programmable biology, highlighting lessons learned scaling deep technology, navigating industry cycles, and building trusted infrastructure for biotech and pharma. Looking ahead, Twist is positioning itself at the forefront of the convergence between AI and biology, using DNA as an information layer to accelerate drug discovery and advance human health. This keynote explores how long-term thinking and bold ambition are shaping the next era of AI-driven therapeutics.

While the industry has seen massive AI breakthroughs lately, predicting actual biologics affinity and immunogenicity remains the industry's greatest challenge. DeepSeq.AI is driving a paradigm shift from "Structure-First" to "Function-First" by training protein language models on billions to trillions of experimental protein interactions in a single experiment. This enables high-fidelity mapping of biologics against a broad spectrum of critical antigens, including viruses, human immune receptors, and the entire human proteome. Such scale is critical for designing broad-spectrum biologics that remain safe and effective against evolving variants. Validated by Genentech and funded by DARPA and the NSF, our platform further scales to human proteome profiling for pharmacokinetics optimization. In this presentation, we will share this novel platform that decodes the "protein-protein interaction grammar" to advance candidates into the clinic with unprecedented accuracy.

Drug discovery often measures biology at the cell level, while therapies must ultimately work across tissues, organs, and whole patients. This scale mismatch means that even highly accurate cellular predictions can fail to translate in the clinic. This session explores strategies to bridge that gap. How do we connect single-cell dynamics to organ-level physiology and patient outcomes? How do we preserve biological context while scaling models? And how do we ensure that virtual biology does not stop at simulation, but informs real therapeutic decisions? Speakers will discuss multiscale modeling that links molecular and cellular systems to higher-order biology; spatial and high-dimensional phenotypic data that retain context; and integrated computational–experimental loops that translate cellular signals into clinically meaningful biomarkers. Together, we ask: how do we ensure virtual biology reflects not just what cells do in isolation, but how biology behaves in the full complexity of patients?

For 20+ years, the synthetic biology community has generated breakthrough targets, but too many continue to stall at the same choke points: freedom-to-operate, productivity, process robustness, CMC readiness, and the leap from “works in the lab” to “works at scale.” In this lightning talk, Ingenza will share how we’ve repeatedly helped teams cross that valley of death, turning innovative discoveries into manufacturable realities across industrial biotech and therapeutics. We’ll spotlight our inGenius® platform: a proven panel of high-performing microbial and mammalian production hosts paired with AI/ML-driven enzyme discovery and gene design optimisation (codABLE®), scalable upstream and downstream platform process workflows, and a comprehensive suite of high-end analytical tools that accelerate and de-risk the path from early discovery to market readiness. Powered by 20+ years of successful delivery, expect rapid, real, case study driven lessons from the front lines: what fails most often, what fixes it fastest, and how to design with manufacturability from day one without slowing innovation. If you’re engineering biology to improve human health or the planet, this talk is your shortcut to faster timelines and better outcomes that help SynBio move at the speed it promises.

Any device designed for human operators is ultimately limited by human cognitive bandwidth. Modern biological manufacturing still relies heavily on human operators to monitor and adjust living fermentation processes in real time, a bottleneck that limits scale and consistency at both R&D and commercial scale. Pow.Bio co-founder and CTO Ouwei Wang will reveal how building an AI-driven control platform - one that layers predictive simulation, self-optimization, and autonomous decision-making in a system managing these processes without human intervention - is turning biomanufacturing from an art into an autonomous science.

Modern agriculture runs on synthetic nitrogen fertilizer—a system that feeds the world, but depends on energy-intensive production and fragile global supply chains. Every few years, a crisis reminds us how vulnerable it is. Biology has long promised an alternative, but making nitrogen-fixing microbes work reliably in the field has remained one of the hardest challenges in synthetic biology. At Switch Bioworks, we’re taking a new approach: engineering microbial systems that dynamically control nitrogen release directly on plant roots. This talk will cover what has held the field back, what’s changing now, and how biology could fundamentally reshape one of the largest industries on Earth. It's time to reinvent fertilizer. Its time to Switch.

Current bioprocess monitoring is limited to basic environmental proxies like pH and dissolved oxygen. Schmidt Sciences is changing this paradigm by adapting advanced physics for biology. This talk introduces three cutting-edge sensing platforms currently in development: fluorescent nanodiamonds, single-cell Raman spectroscopy, and non-invasive optical frequency combs. Join us to learn how these high-dimensional data streams are being integrated with machine learning to predict campaign outcomes and revolutionize how we monitor cell health at scale.

AI is reshaping drug discovery, but its real impact comes when companies collaborate, since no one company has enough data to encompass every chemical space.  In this talk, you will learn about Lilly TuneLab, a collaborative web-based platform that hosts AI/ML models trained on decades of proprietary Lilly data, where members contribute data via federated learning to help make the models more generalizable.  Biotechs and start ups can immediately accelerate their own programs - predicting the drug-like properties of small molecules or assessing antibody developability properties, without having to build their own models or find training data. 

Jenny Rooke, PhD, of Genoa Ventures and Richard Kenny of Hawkwood Biotech unveil the BioAtlas, the first open-source map of industrial biotechnology. They explore what it reveals about the companies, products, and markets shaping bio-based manufacturing, how underwriting approaches to industrial biotech investments are evolving, and why this kind of visibility matters for investors, operators, and the future of the bioeconomy.

​Once a year we put the investors in a room by themselves. No startups. No pitches. No slides. ​Just the people actually moving capital across biotech and synthetic biology, talking honestly about what they're seeing: what's working, what might be overhyped, and where the real opportunities are right now. It's the conversation you can't have when founders are listening. ​It's invitation-only and space is limited

​This invitation-only luncheon convenes CEOs and founders from across the synthetic biology and biotechnology ecosystem for a candid, peer-level conversation. Designed as an intimate gathering, the session offers a chance to step away from the main stage and connect with fellow leaders navigating the opportunities and challenges of building and scaling companies in this rapidly evolving field. The program will also feature a fireside chat with Leroy “Lee” Hood, offering unique insights from one of the pioneers shaping modern biotechnology. Join us for thoughtful discussion, shared insights, and meaningful connections with other executives shaping the future of the bioeconomy. Please note: Conference registration is required to attend this event. If you haven't registered yet, secure your spot at https://www.syntheticbiologysummit.com/

Join David Kong (MIT Media Lab), Michael Chen (Nuclera), and Christina Agapakis (American Wetware) for a live edition of the global synthetic biology course “How to Grow (Almost) Anything” (HTGAA)—born at the MIT Media Lab and taught to thousands of students worldwide. Michael Chen will share the latest developments in digital microfluidics, cell-free protein synthesis, and autonomous labs, and Christina Agapakis will discuss the frontiers of design and engineering biology, including introducing her new initiative, “American Wetware,” a design studio for building bioproducts. David Kong will host, moderate, and invite both the SynBioBeta and HTGAA global communities to participate in a live “biopixel artwork” experiment, bridging digital artwork with cloud lab-enabled synthetic biology. This special session of HTGAA will be broadcast live, from SynBioBeta, to students at MIT and around the world!

This talk will introduce the development of artificial Agents to model biological phenomena in molecular biology, biotechnology, and synthetic biology incorporating reinforcement learning, differential equation modeling of molecular dynamics, and agentic bio-causal reasoning. Agent to agent interaction with the A2A and PoR protocols, and MCP and API interfaces to Machine Learning (Neural Network) Models including causal reasoning models and bio-specific models will be discussed. Synthetic biology deals with huge possibility spaces in terms of the combinatorics of nuceotide and proteomic sequences in proposed novel genes and proteins and how to constrain possibility spaces into computable functional novel genes, genetic circuits, gene regulatory networks and novel functional proteins will be discussed. Hence the sheer complexity of biological phenomena requires advanced Agentic AI and machine learning models to efficiently process, find patterns in, and reason about these complex systems with hundreds of thousands of variables, millions of connections, and potentially trillions of parameters. The current state of Agentic Bio research will be covered and where the research needs to go will be elucidated. Finally an application of Agentic Inter and Intra-cellular Signaling will be presented in detail to see the nuts and bolts of how Agentic AI can model a biological phenomenon with molecular biological, medical, and synthetic biological applications. The presenter’s background includes advanced degrees in computer science and computational molecular biology with experience in bio-computational modeling including a computational neuroscience project at Stanford where the neurogenetic and synaptic development of the C.elegans’ brain was modeled. Synthetic Biology: the possibility spaces are endless!

Berkeley Lab is transforming and creating large-scale, multi-modal data to train AI models for usable predictions. The Lab is creating data lakehouses that allow programmatic access for querying across data types. This effort includes generating integrated, multi-omics and high-quality datasets that allow modeling of dynamic biological systems; this requires accurate annotation, curation, and accompanying metadata. 

This press conference at SynBioBeta 2026 brings together members of the media with key voices from across the synthetic biology and biotechnology ecosystem. The session will highlight major announcements, emerging innovations, and important themes shaping the future of the industry, while offering press the opportunity to hear directly from leaders and innovators participating in the conference.

Drug discovery has long meant screening vast libraries and optimizing whatever survives. Semiconductors and aircraft broke from this pattern, designed computationally before fabrication. Biologics may now be crossing the same threshold. Generative AI enables antibodies to be designed from first principles, rather than found. Latent-X2 generates drug-like candidates with developability and low ex vivo immunogenicity from the first generation, properties that previously required years of optimization, matching or exceeding approved therapeutics in head-to-head comparisons. Latent-Y extends this into fully autonomous campaigns: from text prompt through target analysis, candidate design, and computational validation, to lab-ready sequences. Across nine targets, it produced confirmed binders against six, a 67% success rate at single-digit nanomolar affinities, completed 56 times faster than expert estimates. In this keynote, Simon Kohl (CEO, Latent Labs) will share what this shift looks like in practice: what works, what remains hard, and what it means when the starting point is no longer a library, but a prompt.

The PURE system, invented 25 years ago, established a fully reconstituted approach to cell-free protein synthesis. What began as a system to better understand translation has evolved into a versatile platform for engineering biology. This talk highlights how PURE-derived platforms such as PUREfrex® enable rapid prototyping, high-throughput screening, and AI/ML-driven optimization, accelerating synthetic biology and next-generation biologics development.

Meat is one of the world’s most complex biomanufacturing systems—and also one of its least optimized. For 12,000 years, we’ve cycled crops through animals to make meat. Drawing from his new book Meat, Bruce Friedrich contends that advances across science and engineering now make it possible to produce meat far more efficiently, which will reduce meat’s contribution to hunger, climate change, deforestation, antibiotic resistance, and pandemic risk. Most importantly for the success of alternative meats, these new technologies will also improve food security and add to GDP for the nations that lean in. It’s been exactly ten years since the first plant-based burgers were introduced and also exactly ten years since the first cultivated meat companies were incorporated. Bruce will reflect on how far we’ve come, how far we have to go, and what it's going to take to get there. Welcome to the next agricultural revolution—courtesy of science.

The primary barrier to a general-purpose biological simulator, (a model that can actually predict the phenotypic level response of the human body to any intervention),  is not a lack of compute, but the absence of causal, human-relevant datasets. In this talk, Matt Osman outlines emerging approaches to address this gap through iPSC-derived tissues that function as both therapeutic platforms and scalable engines for data generation. Polyphron explores why high-fidelity human tissue is uniquely capable of capturing emergence, where molecular interactions translate into functional outcomes, and why generating this data across diverse genotypes is essential to building true ground-truth datasets. By closing the loop between lab-grown tissue and clinical outcomes, this approach points toward a shift from sparse, mechanism-limited data to a more predictive and programmable framework for human health.

With AI design algorithms and increasingly ambitious goals across synthetic biology, the need for long and complex synthetic DNA is surging. Hear from CEO Jason T. Gammack about how Ansa Biotechnologies is innovating to meet this demand. He’ll present real-world evidence to demonstrate how the company’s novel take on enzymatic DNA synthesis is addressing challenges in manufacturing complex sequences and in the build vs. buy dilemma for long constructs that have historically required a substantial investment in assembly and cloning.

For decades, pharmaceutical supply chains were optimized for cost and scale, stretching across continents to source critical active ingredients. But fragility has made resilience a strategic imperative. Synthetic biology offers a new model: onshoring the production of essential APIs by programming cells to manufacture small molecules, peptides, and novel amino acids with precision and scalability. Instead of relying on distant chemical supply networks, biology becomes the factory—flexible, distributed, and programmable. This session explores how engineered microbes and directed evolution platforms are rebuilding pharma supply chains from the molecular level up, enabling secure, responsive, and locally anchored production of the medicines the world depends on.

As biology becomes an engineering discipline, the way companies are funded is evolving just as quickly. From AI-native biotech startups to capital-intensive biomanufacturing platforms, investors are rethinking how to underwrite risk, structure deals, and support companies from inception to scale. This panel brings together leading investors across venture, crossover, and strategic capital to explore how funding models are adapting to the realities of programmable biology. What makes a compelling company today? How are timelines, capital efficiency, and technical milestones being reframed? And where are the biggest opportunities emerging across human health, climate, and industrial biotech?

Biotech is no longer behind the scenes—it’s on our shelves, in our homes, and part of our daily routines. From sustainable haircare to household cleaning, and high-performance materials, bio-based innovations are redefining everyday consumer experiences. This session explores what drives adoption, how brands communicate the value of biology, and why trust, transparency, and performance are key to building loyalty. Join us to hear from the companies making biology irresistible, accessible, and seamlessly integrated into daily life—and learn what it takes to create bio-products consumers truly love.

The next frontier of biology isn’t in predicting a single static protein structure, but in capturing how proteins move, fold, and interact across time and environments. This session explores how AI can illuminate protein conformations and dynamics, and extend those insights into virtual multi-cellular or tissue models. Experts will discuss the challenge of integrating heterogeneous datasets and instruments, and how breakthroughs in dynamic modeling could reshape drug design, disease understanding, and biomanufacturing. Can we build models that reflect the living, breathing complexity of biology—not just snapshots, but motion?

Scaling bio-based products requires integrated technical collaboration across strain engineering, fermentation, downstream processing, and analytics. Full-stack approaches—where startups, CDMOs, and platform technology providers align early on—can optimize yield, reduce variability, and lower cost of goods (COGS) at commercial scale. This session explores case studies of cross-company collaboration, from co-development of microbial strains and bioreactor designs to shared process analytics and predictive modeling. Hear how teams are breaking down technical silos to accelerate scale-up, improve reproducibility, and create competitive, sustainable manufacturing solutions that bring synthetic biology products from the lab to the market efficiently.

Significant advances have been made in the regulation and oversight of synthetic nucleic acid order screening since the U.S. Department of Health and Humans Services issued its initial guidance. Panelists from industry, policy, and the technical community will discuss major these advances as well as remaining challenges. They will explore what “winning” looks like for the field considering aspects such as international cooperation, balancing innovation and growth with security, and the impact of current initiatives. The conversation will highlight lessons from synthetic nucleic acid governance that can inform oversight of other emerging technologies.

Surfactants (the molecules that power cleaning, foaming, emulsifying, and conditioning in everyday products) are entering a new era of innovation. Advances in bio-engineering are enabling the design of next-generation surfactants with enhanced performance and reduced environmental impact. By leveraging biologically derived and bio-inspired approaches, researchers and product developers are rethinking how these essential ingredients are produced and formulated. New pathways in fermentation, enzyme engineering, and green chemistry are opening opportunities to create surfactants that are not only more effective, but also better aligned with circularity and nature-positive design principles. This session will explore how bio-engineered surfactants are reshaping formulation science: from new production platforms to novel functional properties, and what it will take to scale these innovations into the next generation of sustainable consumer products.

For decades, DNA synthesis has been the limiting reagent in synthetic biology — reliable for short sequences, but increasingly error-prone and costly as designs scale. That ceiling is now cracking. New enzymatic synthesis platforms, error-correction chemistries, and assembly pipelines are extending what’s possible, opening the door to rapid construction of full pathways, microbial genomes, and even mammalian chromosomes. This session will explore how innovators are breaking past barriers, what technical and economic breakthroughs are needed next, and how longer, cheaper, and faster synthesis could fundamentally change how we design biology at scale.

The Design–Build–Test–Learn (DBTL) cycle remains the core engine of biological engineering, yet its iteration speed still lags far behind software development. As AI systems begin to design, plan, and execute experiments, a new paradigm is emerging: DBTL as an autonomous, continuously optimizing system. Next-generation platforms combine AI-assisted rational design, high-throughput construction and perturbation, real-time data acquisition, and active learning to close the loop at unprecedented scale. Agent-powered lab-in-a-loop workflows, lab-on-a-chip systems, and advances at the silicon-to-carbon interface are enabling tighter integration between computation and biology, from semiconductor-enabled sensing to real-time feedback and decision-making. This session explores how autonomous DBTL stacks could unlock software-like iteration velocity in biology, redefine experimentation, and reshape the future of programmable discovery.

Biology has long relied on a limited molecular vocabulary shaped by natural evolution. Today, that alphabet is expanding. Advances in expanded genetic codes, non-canonical amino acids, macrocycles, de novo design, and AI-guided protein engineering are enabling scientists to create biomolecules with properties and functions that nature never evolved. This session explores the rise of programmable biomolecules at the intersection of biology, chemistry, and computation. Rather than simply optimizing existing proteins, researchers are building entirely new classes of functional molecules with novel architectures, chemistries, and therapeutic potential. From next-generation biologics to hybrid molecular scaffolds, the discussion will examine how the field is moving beyond nature’s defaults and toward a future where biomolecules can be designed with increasing precision, flexibility, and intent.

Biology is entering an era where AI agents don’t just analyze data — they co-design, plan, and execute experiments. Multi-agent systems like CRISPR-GPT demonstrate how AI can act as a true lab co-pilot: decomposing complex genome editing projects into stepwise workflows, selecting tools, troubleshooting, and even drafting protocols that allow junior researchers to perform sophisticated edits on their first attempt . Beyond CRISPR, new systems like BioMARS integrate reasoning agents with robotics, while biotech companies are testing “AI lab assistants” that monitor and adjust experiments in real time. This session explores how multi-agent copilots are making biology more reproducible, democratizing complex workflows, and pushing the boundaries of lab autonomy. The central question: when AI can plan, troubleshoot, and validate experiments end-to-end, how should scientists and institutions govern this new power?

Mining has long relied on brute force and chemistry, but biology is opening a new frontier. Biomining uses engineered microbes to extract metals and minerals with precision, efficiency, and far less environmental impact than traditional methods. From rare earth elements essential to clean energy to critical metals powering electronics, synthetic biology is reshaping how we source the building blocks of modern life. This session spotlights innovators designing bio-based recovery systems, scaling sustainable solutions, and reimagining resource extraction.

Why do so many in silico models fail when moved to the lab or clinic? Too often, they’re trained on incomplete, non-human, or non-representative datasets. This session tackles the “data gap” head-on: from interoperability bottlenecks and the black box problem to the limits of current virtual cell simulations (~50 million perturbations vs. the billions biology demands). Panelists will explore how to create “human-first” datasets that reflect real biology, unlock mechanistic interoperability, and close the discovery–development divide. The goal: build AI tools that can directly identify viable drug candidates instead of stalling in silico.

Scaling bio-based products to commercial production requires balancing technical readiness with market and financial realities. This session examines the capital investments, regulatory planning, and supply chain strategies necessary to move beyond the pilot stage. Experts will share lessons on aligning production capacity with demand forecasts, managing operational risk, and structuring partnerships that unlock funding and market access. Attendees will gain practical insights into navigating investor expectations, scaling efficiently without compromising quality, and making strategic decisions that ensure products can succeed commercially while meeting evolving market needs and sustainability goals.

Standard bioreactors often lack the instrumentation required to rapidly monitor cell physiology, leaving critical gaps in our understanding of scale-up dynamics. This session presents active projects from the Schmidt Sciences’ Sensors for Biomanufacturing Program designed to address this challenge through novel sensing modalities. Spanning from near real-time intracellular measurements to non-invasive off-gas fingerprinting, the panel brings together technology developers and industrial bioprocess experts to discuss the translation of these tools from the lab to the plant floor. Together, we will critically evaluate the utility of high-dimensional metabolic data and explore the engineering requirements for integrating physics-based sensors and machine learning into existing biomanufacturing workflows.

As biology becomes programmable and AI accelerates discovery, startups are generating breakthrough innovations at unprecedented speed. Yet translating these advances into real-world therapies still depends on effective collaboration with global pharmaceutical organizations. This session explores how the innovation ecosystem connects early-stage breakthroughs to scalable development, bringing together leaders from startup incubation, external innovation, and pharma strategy. Speakers will examine how AI-native biotech companies engage with pharma today: how startups become “pharma-ready,” how external innovation teams evaluate and structure partnerships, and what collaboration models are emerging as biology and computation converge. From early ecosystem support and venture building to strategic alliances and co-development pathways, the discussion will provide a practical look at how ideas move from discovery to patient impact in the AI era.

What happens when AI systems stop being tools and begin acting like collaborators in scientific thought? Multi-agent architectures such as SciAgents and Google’s “AI Co-Scientist” are pioneering hypothesis generation by dividing scientific reasoning into specialized sub-agents: literature retrievers, causal mappers, and graph-based reasoners. Unlike single models, these teams of agents mimic the structure of scientific collaboration itself — brainstorming, critiquing, and refining ideas. In synthetic biology, such systems could propose new gene circuits, uncover hidden regulatory logic, or suggest underexplored protein folds. This session asks: how far should we trust AI-generated hypotheses, and how do we validate them responsibly? With machine-driven insight now on the horizon, the very architecture of discovery may shift — from lone researchers and teams of humans to networks of humans and machines co-creating the future of biology.

Industrial biotech faces repeated “valleys of death” between laboratory success and commercial manufacturing, driven by a combination of technological uncertainty, scale-dependent constraints, and (mis)alignment between engineering reality and investment expectations. Promising technologies often fail not because the science is wrong, but because scale-up trajectories are built on insufficient data, optimistic assumptions, and decision-making based on the 1st product specifications from the lab that do not translate to industrial conditions. This panel returns to fundamentals, drawing on real-world experience from piloting, process engineering, and early industrialization to examine where and why scale-up breaks down. Experts will discuss how important the scale-up journey is to align technology performance with investor expectations, support sound business cases, and turn the industrial biotech toolbox into a more robust, scalable, and profitable manufacturing platform.

Drug discovery is not limited by models. It is limited by data. While AI is accelerating molecular design and target discovery, the real bottleneck remains the generation, integration, and interpretation of biological datasets that are complex, heterogeneous, and often not yet predictive. Pharma-scale discovery requires more than algorithms. It requires new approaches to building and operationalizing data itself. This session explores how next-generation DNA synthesis, high-throughput experimentation, and integrated data infrastructures are enabling a new biology data flywheel. From experimental datasets that inform translational decisions to emerging standards for capturing real-world and preclinical signals, leaders will discuss how data generation strategies are reshaping discovery workflows. Speakers from pharma, AI-native biotech, and platform providers will examine how biology is becoming a programmable data layer, enabling faster biologics development, more informed portfolio decisions, and new collaborative models that connect experimental systems, computational tools, and pharma-scale discovery pipelines.

Reading, writing, and editing DNA were just the prelude. The next frontier is composition, designing complex genetic systems and large DNA architectures from first principles using AI-driven models and scalable synthesis technologies. As datasets grow and design tools mature, biology is shifting from incremental editing toward intentional genome-scale engineering. This new paradigm treats DNA not simply as a sequence to modify but as a programmable substrate where genes, regulatory elements, and entire genomic regions can be composed, tested, and iterated like engineered systems. Advances in generative design, large-scale DNA assembly, and precision integration technologies are enabling researchers to construct increasingly complex genetic structures with higher predictability and functional intent. From next-generation recombinases and genome restructuring platforms to AI-guided design workflows that bridge computation and physical DNA construction, the emerging toolkit is redefining how biological complexity is created. The session explores how compositional genome engineering could unlock new capabilities across therapeutics, industrial biology, and synthetic life design.

After a full day of cutting-edge biology, it’s time to laugh about it! Join viral comedian Austin Nasso for a special stand-up set crafted for the SynBioBeta crowd. Known for his sharp impressions and tech-adjacent humor, Austin brings a fast-paced show that pokes fun at startup culture, venture capital, AI hype, and, for the first time, the quirks of the biotech world. Expect an evening of high-energy comedy, insider bio-nerd jokes, and a chance to unwind with fellow founders, scientists, and investors. A perfect late-night break from programmable biology, because even the future of life sciences deserves a good laugh.