This paper introduces a novel computational framework that utilizes a human-interpretable grammar to encode complex multicellular systems biology models, aiming to democratize virtual cell laboratories using the open-source platform PhysiCell. This cell behavior hypothesis grammar translates plain language statements about cell interactions into mathematical models, facilitating the integration of biological knowledge and multi-omics data for in silico hypothesis testing. The authors demonstrate the framework's broad applicability by simulating complex biological processes across different fields, including cancer (tumor microenvironment dynamics, immune evasion, and virtual clinical trials) and neurodevelopment (cortical layer formation). This approach allows researchers to predict emergent multicellular behavior and prioritize new hypotheses for experimental validation, bridging the gap between biological and computational research.

References:

• Johnson J A I, Bergman D R, Rocha H L, et al. Human interpretable grammar encodes multicellular systems biology models to democratize virtual cell laboratories[J]. Cell, 2025, 188(17): 4711-4733. e37.

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