Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

作者：Jeanette Johnson, Genevieve Stein-O’Brien, M. Booth, Randy Heiland, Furkan Kurtoglu, Daniel Bergman, Elmar Bucher, Atul Deshpande, André Forjaz, Michael Getz, Inês Godet, Melissa R. Lyman, John Metzcar, Jacob T. Mitchell, Andrew D. Raddatz, Heber L. Rocha, Jacobo Solórzano, Aneequa Sundus, Yafei Wang, Danielle Gilkes, Luciane T. Kagohara, Ashley Kiemen, Elizabeth D. Thompson, Denis Wirtz, Pei‐Hsun Wu, Neeha Zaidi, Lei Zheng, Jacquelyn W. Zimmerman, Elizabeth M. Jaffee, Young Hwan Chang, Lisa M. Coussens, Joe W. Gray, Laura M. Heiser, Elana J. Fertig, Paul Macklin · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2023 · DOI：10.1101/2023.09.17.557982 · 被引用次数：14 · 研究领域：Single-cell and spatial transcriptomics、Cell Image Analysis Techniques、Bioinformatics and Genomic Networks

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.