SynBioBeta Speaker
Micha Breakstone
Cellular Intelligence
Co-founder & CEO
Micha Breakstone is a serial entrepreneur known for his transformative impact on the AI and tech startup ecosystem, particularly within Israel. As Co-Founder and President of Chorus.ai, Micha led the company to pioneer the field of conversation intelligence, ultimately resulting in its acquisition by ZoomInfo for $575 million. Building on this success, he founded Somite.ai, securing a $47M Series A round led by Khosla Ventures (May 2025) to develop AI-driven foundation models for human stem cells, enabling novel cell therapies at scale (Forbes).Somite.ai takes a full-stack approach—generating data at 1,000x lower cost, training large-scale AI models with unmatched predictive power, and leveraging these models to discover, refine, and optimize groundbreaking cell therapy applications. The founding team combines Micha’s entrepreneurial expertise with world-renowned scientists, including the Head of the Fundamental AI Group at MIT, who pioneered foundational research on scaling laws, and three Harvard Medical School professors and National Academy of Sciences and Medicine members, notably the Chair of the Genetics Department at HMS. Based in Boston and officially launching operations in January 2024, Somite.ai has raised ~$60M to date.Before these achievements, Micha made his mark by selling his NLP (Natural Language Processing) business unit to Intel in 2014, showcasing his expertise in AI and machine learning early in his career. Further cementing his role as a cornerstone of the Israeli tech community, he founded the Founders Club Tel Aviv, the largest network of its kind in Israel, bringing together over 500 venture-backed founders.Micha's commitment to nurturing the next generation of tech innovation is evident in his mentoring of over 130 Israeli startups and investments in 32 startups, playing a pivotal role in building several AI Israeli unicorns "from the outside." His broad experience in designing Machine Learning systems is underpinned by a strong academic foundation, holding a Master's in Mathematics and a Ph.D. in Cognitive Science.Through his entrepreneurial ventures, mentorship, and investments, Micha Breakstone has not only advanced the field of AI but has also significantly contributed to the growth and success of the Israeli tech ecosystem and beyond, with his latest venture promising revolutionary advances in healthcare.
SynBioBeta 2026 Tickets are Live
Confirmed Speakers
Sessions Featuring
Micha
This Year
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AIxBIO
Engineering Cell Fate: A Foundation Model to Transform Biology into an Engineering Discipline
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.
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AIxBIO
Engineering Cell Fate: A Foundation Model to Transform Biology into an Engineering Discipline
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.
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AIxBIO
Where Does Biology Compute? From Molecular Signals to Clinical Reality
As we move toward the “virtual cell” and ultimately the “virtual organism,” the AIxBIO ecosystem faces a fundamental challenge: where does biology actually compute? While our ability to measure molecular events has advanced dramatically, predicting how those signals translate into emergent, system-level outcomes remains a core bottleneck in programmable biology. This session brings together leaders across AI, synthetic biology, and medicine to explore the computational bottleneck, mapping where predictive power breaks down from molecules to cells to organisms. It will examine how to measure emergence at scale by generating causal, time-resolved, perturbation-rich datasets across diverse biological contexts, and how to close the reality gap with in vivo feedback, using next-generation sensors and real-world data to continuously calibrate and validate models in living systems.
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AIxBIO
Where Does Biology Compute? From Molecular Signals to Clinical Reality
As we move toward the “virtual cell” and ultimately the “virtual organism,” the AIxBIO ecosystem faces a fundamental challenge: where does biology actually compute? While our ability to measure molecular events has advanced dramatically, predicting how those signals translate into emergent, system-level outcomes remains a core bottleneck in programmable biology. This session brings together leaders across AI, synthetic biology, and medicine to explore the computational bottleneck, mapping where predictive power breaks down from molecules to cells to organisms. It will examine how to measure emergence at scale by generating causal, time-resolved, perturbation-rich datasets across diverse biological contexts, and how to close the reality gap with in vivo feedback, using next-generation sensors and real-world data to continuously calibrate and validate models in living systems.
Session lineup still growing
Featuring
Speaker Coming Soon
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Human Health
From Cells to Patients: Solving the Scale Mismatch in Virtual Biology
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?
Featuring
Speaker Coming Soon
