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Alex Rives
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A World Model of Life: Biohub’s Alex Rives on Virtual Cells, Measurement, & the $1.8B Data Push
Biohub’s $1.8B initiative joins the Department of Energy, NIH, Google DeepMind, Isomorphic Labs, and Meta to map molecular readouts and imaging. Rives calls observability a fundamental bottleneck; a laser phase plate adds phase contrast in cryo-electron tomography, but virtual cells still cannot meaningfully simulate cells. Atlas drafts target one or two years, with a comprehensive map within five.
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- Biohub just announced a $1.8B expansion of its Virtual Biology Initiative, coordinating the Department of Energy, NIH, Google DeepMind, Isomorphic Labs, and Meta on data generation for a general model of cellular biology. The effort aims to build contextual maps — spatial biology linking molecular readouts to imaging “at the scale of hundreds of billions or more cells” — with an open call for other funders and the worldwide scientific community to join.
- Rives sees observability as a particularly fundamental bottleneck for virtual cell models, but not the only one. “There’s so much about the cell that we just can’t measure”; progress requires models, data generation at scale, and observation tools. He says today’s models “just don’t meet the definition of a model that could actually simulate the cell in a meaningful way and serve as an oracle for experimental biology.”
- The attainable five-year target is an “information-theoretic model of the cell” — a probabilistic transition function from cell state plus inputs to a distribution of next states — building toward “a world model of life.” In “the limit of infinite data and compute,” architectures probably end up the same; cellular biology is far from that limit, so inductive bias still matters.
- Measurement hardware is where Biohub’s thesis gets expressed: the laser phase plate — “the most powerful standing-wave laser in the world” — brings phase contrast to cryo-electron tomography, helping scientists make out individual molecules and how they interact in the living cell. Biohub also uses whole-organism zebrafish imaging to track every cell from embryo to fully developed organism.
- The LLM playbook transfers to biology: pretraining on statistical patterns, elicitation, then “RLVR for biology” with lab verification — “empirical AI” that asks a question, performs the experiment, learns, and proposes a better one in a recursive loop. Unlike math and coding, verification must happen in the laboratory, not through fast computation.
- Timeline and payoff: first drafts of health-and-disease cell atlases within one or two years, a “pretty comprehensive map” within five — then the question becomes “How do I intervene to reverse a state that’s pathologic?” Rives says he has thought less about EHR data; he worries that without a causal basis, models could learn “a bunch of correlations,” while acknowledging that health records could help get existing drugs and care to the right patients.
- The man building toward curing all disease tracks little about his own health: “I have a watch that tracks my sleep. That’s about as far as it gets.” On fusing language and biology latent spaces, he is unsure; the biological facts we most want to know may not be represented in text and can require experiments to discover.