Human interpretable grammar encodes multicellular systems biology models to democratize virtual cell laboratories
Jeanette Johnson, Daniel Bergman, Heber L. Rocha, David Zhou, Eric Cramer, Ian C. McLean, Yoseph W. Dance, M. Booth, Zachary Nicholas, Tamara Y. Lopez-Vidal, Atul Deshpande, Randy Heiland, Elmar Bucher, Fatemeh Shojaeian, Matthew Dunworth, André Forjaz, Michael Getz, Inês Godet, Furkan Kurtoglu, Melissa R. Lyman, John Metzcar, Jacob T. Mitchell, Andrew D. Raddatz, Jacobo Solórzano, Aneequa Sundus, Yafei Wang, David G. DeNardo, Andrew J. Ewald, Daniele M. Gilkes, Luciane T. Kagohara, Ashley Kiemen, Elizabeth D. Thompson, Denis Wirtz, Laura D. Wood, Pei-Hsun Wu, Neeha Zaidi, Lei Zheng, Jacquelyn W. Zimmerman, Jude M. Phillip, Elizabeth M. Jaffee, Joe W. Gray, Lisa M. Coussens, Young Hwan Chang, Laura M. Heiser, Genevieve Stein-O’Brien, Elana J. Fertig, Paul Macklin
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.