Faculty, Staff and Student Publications

Publication Date

8-21-2025

Journal

Cell

DOI

10.1016/j.cell.2025.06.048

PMID

40713951

Abstract

Cells interact as dynamically evolving ecosystems. While recent single-cell and spatial multi-omics technologies quantify individual cell characteristics, predicting their evolution requires mathematical modeling. We propose 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 test and expand our understanding of multicellular systems and generate new testable hypotheses. This paper motivates 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 potential through examples in cancer and its broader applicability in simulating brain development. This approach bridges biological, clinical, and systems biology research for mathematical modeling at scale, allowing the community to predict emergent multicellular behavior.

Keywords

Systems Biology, Humans, Models, Biological, Computer Simulation, Neoplasms, agent-based modeling, cancer biology, cell behavior hypothesis grammar, cell behaviors, cell interactions, immunology, immunotherapy, mathematical biology, mathematical modeling, modeling language, multi-omics, multicellular systems, multicellular systems biology, physics of multicellular biology, simulation, spatial transcriptomics, tissue dynamics

Published Open-Access

yes

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