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80 reviewsSUMMARYCells interact as dynamically evolving ecosystems. While recent single-cell and spatial multi-omics technologies quantify individual cell characteristics, predicting their evolution requires mathematical modeling. Wepropose a conceptual framework—a cell behavior hypothesis grammar—that uses natural language statements (cell rules) to create mathematical models. This enables systematic integration of biological knowledge and multi-omics data to generate in silico models, enabling virtual ‘‘thought experiments’’ that testand expand our understanding of multicellular systems and generate new testable hypotheses. This papermotivates and describes the grammar, offers a reference implementation, and demonstrates its use in developing both de novo mechanistic models and those informed by multi-omics data. We show its potentialthrough examples in cancer and its broader applicability in simulating brain development. This approachbridges biological, clinical, and systems biology research for mathematical modeling at scale, allowing thecommunity to predict emergent multicellular behavior.
