Summary


Recent advances in AI inspire visions of universal models of biology. Yet living systems are evolved, emergent processes whose behaviors cannot be inferred from their parts alone. We propose grounding AI in canonical biological processes, constructing data-driven world models with explicit mechanistic links across molecules, cells, and their dynamics in space and time....


 


Effective datasets for AI on biological processes should reflect the structure of the processes they seek to model. Such datasets must characterize multiple biological scales at an appropriate resolution. Components should ideally be captured within their native context, rather than in isolated experimental settings. For example, genomic regulatory elements are best studied within their epigenomic context and cells within their native tissues. Because canonical processes unfold in space and


Modeling processes by linking multi-scale mechanisms for causal inference


Canonical processes not only structure biological data, but they also invite a synthesis between AI and mechanistic reasoning. Combining AI with causal, perturbation-informed frameworks could allow the construction of process-based “world models”13 of biology. Solving such models will require new computational frameworks14 that integrate modules capable of super-human deduction with those that formalize a “systems biology” approach to biological function and dynamics.15 The goal is not to


Multi-scale computational biology world models: Suggested principles


Biological world models are an open challenge for the coming years. Early insights from current approaches suggest some guidelines for the transformation of building blocks working at one scale into multi-scale frameworks via distillation, standardization, and reconnection. Distillation in the biological domain may be used to reduce redundant and correlative model features and enhance their causal robustness. For example, current genomic transformers that perform well in tasks of predicting


Teaching AI to speak biology


In conclusion, we argue here that scaling in biology will require data and learning tasks anchored in principles and mechanisms that reach beyond current AI success stories. AlphaFold represents only the simplest building block in a hierarchy of models that are needed for approaching the deeper problem of biological computation. Higher-order biological functions should be computed within and around captured canonical processes...


Yonatan Stelzer


Amos Tanay


Cite


https://doi.org/10.1016/j.cell.2026.07.003


https://www.sciencedirect.com/science/article/abs/pii/S0092867426008019


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