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.

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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.

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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.

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?

Our industrial economy was built on energy-intensive, centralized manufacturing. The future economy will be grown with biology—making what we need, where we need it, when we need it—with superior unit economics and seamless integration into nature. The bottleneck: biotech has largely been confined to fermenters. Switch Bioworks breaks that constraint by engineering microbes that operate directly in nature—starting with agriculture. Today, 4 billion people depend on expensive synthetic fertilizer for food security—a $200B market. Switch is replacing it with engineered microbes that colonize plant roots and “switch” on fertilizer production precisely when needed. This time-controlled switching architecture solves the tradeoffs that have limited prior biofertilizers and delivers unbeatable unit economics for farmers. Founded by Tim Schnabel, who invented the core technology at Stanford, Switch is led by a world-class team spanning microbial engineering, ag commercialization, IP, and regulatory leadership, with advisors from Corteva, BASF, Syngenta, Nutrien, and leading universities. Switch is preparing to raise a Series A to reach commercialization and lead the Biology on Demand movement. Fertilizer has opened the door, the platform is ready for more.

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.

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.

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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.

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.

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.

Food is no longer just sustenance—it’s becoming a programmable interface with human biology. Advances in synthetic biology and foodtech are enabling the design of bioactive molecules that target specific health outcomes: regulating glucose and lipid metabolism, strengthening cardiovascular resilience, and even enhancing cognitive performance. From engineered microbes that secrete beneficial metabolites to programmable synbiotics tuned to the gut, this session will explore how programmable biology is transforming food into a therapeutic platform. Panelists will ask: what if the next breakthroughs in managing obesity, dementia, and heart disease don’t come from pharmaceuticals, but from intelligently designed foods and functional ingredients?

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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.

AI-enabled, self-driving labs are still emerging, but their foundations are already transforming how teams design, run, and interpret experiments. This session offers a practical guide for scientists and R&D leaders who want to understand what can be done today — from tightening design–test–learn loops to reducing manual error and capturing early benefits of autonomous experimentation. Rather than presenting an unrealized future, speakers will focus on practical, real-world steps that give organizations a competitive edge as SDL capabilities evolve and mature. Speakers will explore what’s working, what’s not, and how autonomous lab systems are reshaping protein engineering, pathway optimization, and therapeutic design.

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.

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.

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.

ARPA-H Program Managers are charting some of the most ambitious trajectories in biomedical innovation — from tissue regeneration and whole-body replacement strategies to ultra-scalable manufacturing, programmable immunity, cognitive resilience, and next-generation diagnostics. In this high-velocity block of lightning talks and roundtable, PMs will unveil the problems they’re trying to solve, the technical leaps they believe are now possible, and the kinds of audacious proposals they want from the community. It’s a rare, fast-paced look into the “high-risk, high-reward” experiments that could shift the boundaries of what medicine can do.

What if brain damage—from strokes, neurodegeneration, or even aging itself—could be reversed? ARPA-H is launching an ambitious effort to make this vision real, catalyzing technologies that repair neural circuits, restore lost tissue, and recover cognition. In this interactive workshop, we’ll ask: What could be the role of programmable biology in healing the brain? Could engineered cells rebuild damaged regions? Could synthetic gene circuits guide regeneration? Could AI-designed therapies restore function after injury or decline? Participants will join ARPA-H leaders to explore these questions, identify high-risk, high-reward opportunities, and help shape the future of neurorepair powered by programmable biology.

Venture capital alone is no longer enough to power the next wave of biotechnology innovation. Across health, biosecurity, climate, and advanced biomanufacturing, government agencies are emerging as catalytic partners, deploying billions in non-dilutive funding to accelerate high-risk, high-impact breakthroughs. But accessing this capital requires more than strong science. Founders must understand how agencies like ARPA-H, DARPA, BARDA, and others evaluate risk, define mission impact, and structure partnerships that bridge research and real-world deployment. This session brings together agency leaders, founders, and experienced operators to demystify how government funding actually works in today’s market. The panelists will explore how startups can position themselves for success, avoid common pitfalls in proposal development and contracting, and strategically leverage non-dilutive funding to extend runway, de-risk technology, and unlock new commercial pathways.

As AI, automation, and scalable biotechnologies accelerate the design and deployment of biology, the line between innovation and risk is increasingly blurred. This session explores how advances in programmable biology are reshaping biosecurity and biodefense, from dual-use risks and supply-chain vulnerabilities to new models for detection, governance, and defense. Leaders from industry, government, and research will discuss how to responsibly accelerate biology while protecting public health and national security.

Join Stanford’s Le Cong for an immersive, hands-on masterclass exploring how CRISPR, genome engineering, and next-generation biological foundation models are converging to redefine programmable biology. This live workshop will walk attendees through CRISPR/GPT — an emerging class of AI-assisted editing frameworks that pair large biological language models with precise genome engineering tools to accelerate design, improve specificity, and unlock new editing modalities. Participants will step inside real CRISPR/GPT workflows to see how multimodal models interpret genomic context, predict repair outcomes, suggest optimized guide designs, and generate editing strategies for complex loci. Le Cong will demonstrate how AI-driven reasoning is beginning to streamline experimental planning, reduce screening burden, and push forward new frontiers in base editing, prime editing, and programmable gene modulation. This session is designed for scientists, engineers, founders, and R&D leaders who want to understand how AI-powered CRISPR design actually works in practice — and how these tools can accelerate therapeutic development, functional genomics, and next-generation editing technologies.

Join Boltz co-founder Gabriele Corso for an interactive, hands-on masterclass exploring how next-generation AI models are transforming molecular design, protein engineering, and therapeutic development. In this live workshop, attendees will step inside the Boltz platform to learn how structure-based generative modeling pipelines can be applied to real-world design challenges. Participants will see how AI-driven predictions are reshaping the drug discovery workflow — from identifying high-value molecular hits to optimizing binders, and therapeutic candidates. This session is designed for scientists, founders, and R&D leaders looking to understand how to actually use cutting-edge AI to accelerate biological innovation.

Emerging infectious diseases remain a growing global threat, yet most outbreaks are still detected only after widespread transmission. In this visionary talk, Pardis Sabeti introduces Sentinel, a locally led, globally connected pandemic prevention network designed to shift biosurveillance from reactive response to real-time planetary intelligence. Sentinel integrates rapid molecular diagnostics, metagenomics, and environmental surveillance with scalable cloud infrastructure and AI-driven analytics. Real-time phylogenetics, transmission reconstruction, and sequence-based modeling enable early detection of unusual biological signals, rapid pathogen characterization, and accelerated countermeasure development. These continuous data streams feed interoperable dashboards and shared analytic platforms that connect laboratories, clinicians, and public health agencies across regions. By embedding advanced technologies within regional centers and pairing them with sustained local capacity, Sentinel operates as a distributed, continuously learning system that transforms biology into actionable intelligence, enabling earlier intervention and a more equitable, proactive model for global outbreak prevention.

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Brain Computer Interfaces (BCIs) hold immense promise to help restore critical functions now for individuals with neurological conditions, severe speech impairments, and paralysis. Over the last thirty-five years, major advancements in artificial intelligence, brain mapping, and material sciences are laying the foundation for a future where BCI-enabled augmented experience is as common as accessing the internet or using a mobile phone. Join Paradromics CEO Matt Angle, PhD to discuss the latest on neurotechnology today, as well as expansive future BCI applications.

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For the first time, AI is enabling us to imagine medicines that “think” - turning on only inside diseased cells or under specific physiological conditions. Neural networks trained on RNA, protein, and cellular data are unlocking a new generation of programmable therapies with unprecedented precision, from cancer drugs that remain inert until encountering tumor signals to RNA medicines capable of adapting to dynamic biological environments. But designing intelligent molecules is only part of the challenge. As AI expands the space of possible therapeutics, the field must also confront a critical question: how do we reliably build, test, and manufacture increasingly complex biological designs? This session explores the emerging continuum from AI-designed molecules to manufacturable programmable therapeutics, examining how advances in sequence design, synthesis, delivery, and validation are translating computational insight into real-world medicines. The future of medicine isn’t static molecules - it’s intelligent, adaptive therapeutics engineered across the full stack, from algorithm to clinic.

For more than a century, everyday products - from detergents and shampoos to textiles and packaging - have relied on petrochemicals and harsh industrial processes. Today, AI-driven protein design is opening a radically different path: creating custom enzymes and biomolecules that outperform traditional chemistry while reducing environmental impact. This session explores how advances in computational protein design and machine learning enable the rational creation of enzymes tailored for home care, personal care, and next-generation materials—moving beyond incremental discovery to purpose-built performance under real industrial conditions. Critically, this highlights how AI-driven design is being translated into commercially deployed products at scale with partners and customers.

DRAFT: Biologics and engineered proteins have traditionally evolved through cycles of intuition, screening, and incremental optimization. Today, AI is transforming proteins into programmable systems; governed by learnable patterns across activity, stability, expression, specificity, manufacturability, and environmental performance. This shift is unlocking a new generation of biomolecules, from next-generation therapeutics to sustainable enzymes and functional biological systems, that would have been impossible to design by hand. In this session, leaders from biopharma, machine learning, and protein engineering will explore how multiparameter optimization, generative modeling, and closed-loop experimental validation are reshaping biomolecular design across domains. From clinical biologics to planetary-scale applications, we examine the shift from trial-and-error to predictive, constraint-driven design, and what it means for R&D timelines, scalability, and real-world impact.

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!

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Once viewed as reckless experimentation, germline gene editing is re-emerging as a serious scientific frontier. With base and prime editing now able to correct single-letter mutations with remarkable precision, researchers are beginning to demonstrate embryo edits that could one day eliminate devastating inherited diseases. The stakes, however, are profound: these are permanent, heritable changes passed to every future generation. This session examines the cutting edge of germline engineering—how far the science has advanced since CRISPR’s clumsy early days, what challenges remain around mosaicism and long-term safety, and where the ethical boundaries must be drawn. Should we consider germline editing only for rare, fatal conditions when no other reproductive options exist? Or is there a pathway to broader medical use under strict safeguards? Join leading scientists, ethicists, and policymakers as we debate whether rewriting inheritance is an act of compassion—or a step too far.

Instrumentation remains one of the greatest bottlenecks in bioinnovation. For decades, meaningful progress required billion-dollar facilities and industrial-scale reactors. Today, that paradigm is shifting. Emerging tools — from smart shake flasks and modular bioreactors to microfluidic systems, desktop DNA printers, and next-generation sequencing devices — are flipping the economics of scale.

As AI reshapes what's possible in biology, biosecurity needs to keep up. Nucleic acid synthesis screening, which checks what's being ordered and by whom, is one of the field's most important lines of defense. But as AI capabilities advance, the screening infrastructure needs to evolve with them. This panel brings together leaders from the Sequence Biosecurity Risk Consortium, Fourth Eon Bio, SecureDNA, and BioTrust to discuss how sequence and customer screening are adapting to a new threat landscape.

From tissues morphing in development to microbes competing in a bioreactor, biology is inherently emergent. Multi-agent simulations — from platforms like BioDynaMo, CompuCell3D, and BIO-LGCA — are now powerful enough to model billions of interacting agents, capturing diffusion, metabolism, migration, and signaling with physical fidelity. Synthetic biologists are using these frameworks to probe design limits before moving to the lab, asking questions like: How far can diffusion alone carry a signaling molecule? What metabolic bottlenecks emerge in crowded cells? And how do engineered traits play out at population scale? This session will spotlight how agent-based models are becoming essential design environments for synthetic biology, helping teams test hypotheses virtually, anticipate failure modes, and translate biology into an engineering discipline rooted in predictive, quantitative simulation.

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.

T cells are evolving from targeted killers into fully programmable cellular systems. Advances in synthetic biology, AI-driven receptor design, and genome-scale datasets are enabling immune cells that not only recognize disease, but sense context, compute signals, adapt over time, and execute coordinated responses inside the body. This session brings together leaders across academia and industry to explore how next-generation CAR and TCR design, structural modeling, and large biological foundation models are reshaping immune engineering. Beyond receptor optimization, we will examine logic circuits, combinatorial sensing systems, control layers, and in vivo reprogramming strategies that transform T cells into dynamic therapeutic platforms. As immune cell engineering moves toward off-the-shelf products and in vivo editing approaches, we will address the deeper architectural questions: How do we design cells that avoid exhaustion, function within hostile tumor microenvironments, and maintain safety over time? What does it mean to treat T cells as living software systems? And how do we build programmable immune therapies that are scalable, durable, and globally accessible?

AI is transforming biology into a fully integrated computational discipline, where discovery depends on the seamless interaction between models, compute infrastructure, and experimental systems. As foundation models for proteins, genomes, and cellular systems mature, the challenge is no longer prediction alone. It is building a unified stack that connects generative design, large-scale computation, and rapid experimental feedback into continuous learning loops. This session explores how the next generation of computational biology platforms is emerging at the intersection of cloud computing, GPU-accelerated modeling, advanced simulation, and high-throughput experimental infrastructure. Leaders across AI, biotech, and technology will discuss how tightly integrated design-build-test-learn cycles are reshaping therapeutic discovery, enabling adaptive model refinement, and accelerating the transition from in silico hypotheses to real-world biological outcomes.

Biology is entering an AI-driven era, but most experimental infrastructure still produces data designed for individual experiments, not for learning at scale. As a result, much of today’s data is useful in the moment but poorly suited for training robust, long-lived models. This session will explore what biological data matters most today, what data needs to be generated now to support future models, and how leading teams are closing that gap. Panelists will discuss how automation, metadata discipline, and standardized testing pipelines can turn artisanal lab workflows into continuous experiment-to-learning systems. The focus will be on infrastructure and experimental design, highlighting practical bottlenecks, emerging best practices, and what becomes possible when biology produces abundant, high-quality, model-ready data by default.

Enzymes are becoming the precision tools behind cleaner, more efficient, and more sustainable production across both home-care and food manufacturing. In cleaning products, next-generation enzymes replace harsh chemicals with biodegradable, high-performance biocatalysts that work at lower temperatures and deliver superior stain, odor, and grease removal. In food processing, engineered proteases, lipases, amylases, and fiber-modifying enzymes are unlocking new textures, cleaner labels, better stability, and reduced energy use—reshaping how everything from dairy and bakery to beverages and plant proteins are made.

Plant-based food sales may be slowing, but that doesn’t mean innovation on the plate is stalling. Instead, momentum is shifting toward breakthrough technologies and smarter ingredient combinations. Cultured meat and precision fermentation are driving the next wave of sustainable ingredients, from proteins to cultured fats that bring authentic flavor and texture. This session highlights advances in cell culture, fermentation platforms, and scale-up strategies, along with the partnerships moving products from R&D to dining tables. Hear how food innovators are combining biology and culinary creativity to build a resilient, delicious, and sustainable future for global diets.

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.

Despite major advances in the biology of aging, there are still no interventions that clearly slow or reverse aging in humans. In contrast, modern medicine already depends on replacement to restore lost function, from artificial joints and cardiac devices to organ transplants and stem cell therapies. This session examines how a similar framework could be applied to aging: rather than repairing deteriorated cells and tissues, scientists and companies are exploring ways to replace them with newly generated, biologically young equivalents. The discussion will highlight emerging capabilities in engineered cell sources, scalable tissue fabrication, and programmable biology (instead of "integration") strategies that are redefining what can be rebuilt and replaced. New approaches are beginning to address long-standing challenges such as age-related signaling environments, vascularization, and even circuit compatibility in parts of the brain. Together, these advances point toward a future where rejuvenation is achieved through deliberate biological reconstruction. The session asks: How far can replacement take us, and could rebuilding youthful parts become a central path to extending healthy lifespan?

Industrial biology has moved through clear phases. First-generation leaders like Novozymes and ADM built durable businesses on fermentation expertise, process control, and long-term customer contracts. The second wave, including Amyris, Zymergen, and Twist Bioscience, emphasized platform modularity and scientific acceleration, but often scaled ahead of sustained product pull. As capital markets tightened, many of those models were stress-tested. This session explores what a third generation looks like: demand-led, capital disciplined, and grounded in real manufacturing capability. Rather than building brands or speculative platforms, Gen 3 companies start with urgent problems inside large enterprises such as Mars, Nestlé, and Unilever, develop minimum testable products with modest initial investment, and scale only with validated offtake and operational readiness. We will discuss portfolio-style R&D, customer co-development, global manufacturing realities, and the operator mindset required to build resilient industrial bio companies in today’s environment.

AI is rapidly transforming how therapeutic enzymes and protein drug candidates are discovered, engineered, and validated. Generative models can now propose millions of novel variants optimized for specificity, stability, and target engagement. But the true bottleneck is no longer design, it is screening at scale. As model-generated libraries expand exponentially, the need for faster, more predictive experimental systems has become critical to translate computational insights into clinically relevant performance. This session explores the emerging generation of integrated platforms that combine AI-guided design, high-throughput functional screening, automation, and advanced analytics to accelerate therapeutic protein discovery. From self-driving labs and multiplexed cellular assays to adaptive screening strategies that prioritize pharmacologically meaningful readouts over simple activity metrics, speakers will examine how next-gen infrastructure is reshaping enzyme optimization for drug development.