Drug discovery often measures biology at the cell level while interventions work at the tissue, organ, or whole-patient scale. This mismatch can make accurate cell-level predictions irrelevant in the clinic. This session dives into strategies to bridge that gap: multiscale modeling that nests single-cell dynamics within organ-level simulations, spatial transcriptomics that preserve context, and surrogate models that translate cell-level outputs into clinical biomarkers. Speakers will ask: how do we ensure virtual biology reflects not just what cells do in isolation, but how biology behaves in the real complexity of patients?

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?

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.

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.

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?

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.

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.

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.

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.

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!

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.