Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

• Type of Material: Article
• Authors: Jeanette AI Johnson, Johns Hopkins University; Genevieve L Stein-O’Brien, Johns Hopkins University; Max Booth, Johns Hopkins University; Randy Heiland, Indiana University; Furkan Kurtoglu, Indiana University; Daniel R Bergman, Johns Hopkins University; Elmar Bucher, Indiana University; Atul Deshpande, Johns Hopkins University; André Forjaz, Johns Hopkins University; Michael Getz, Indiana University; Ines Godet, Memorial Sloan Kettering Cancer Center; Melissa Lyman, Johns Hopkins University; John Metzcar, Indiana University; Jacob Mitchell, Johns Hopkins University; Andrew Raddatz, Emory University; Heber Rocha, Indiana University; Jacobo Solorzano, Centre de Recherches en Cancerologie de Toulouse; Aneequa Sundus, Indiana University; Yafei Wang, Indiana University; Danielle Gilkes, Johns Hopkins University; Luciane T Kagohara, Johns Hopkins University; Ashley L Kiemen, Johns Hopkins University; Elizabeth D Thompson, Johns Hopkins University; Denis Wirtz, Johns Hopkins University; Pei-Hsun Wu, Johns Hopkins University; Neeha Zaidi, Johns Hopkins University; Lei Zheng, Johns Hopkins University; Jacquelyn W Zimmerman, Johns Hopkins University; Elizabeth M Jaffee, Johns Hopkins University; Young Hwan Chang, Oregon Health & Science University; Lisa M Coussens, Oregon Health & Science University; Joe W Gray, Oregon Health & Science University; Laura M Heiser, Oregon Health & Science University; Elana J Fertig, Johns Hopkins University; Paul Macklin, Indiana University
• Language: English
• Date: 2023-11-05
• Publisher: National Institutes of Health
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): https://doi.org/10.1101/2023.09.17.557982
• Title of Journal or Parent Work: bioRxiv
• Start Page: 2023.09.17.557982
• Grant/Funding Information: Funding was provided by P01CA247886 (to EMJ, NZ, LTK, JZ, EJF), K08CA248624 (to NZ), the Lustgarten Foundation ‘A Translational Convergence Program of Personalized Immunotherapy for Pancreatic Cancer Patients at Johns Hopkins’ (to EMJ, NZ, LTK, JZ, EJF), a Lustgarten Foundation-AACR Career Development Award for Pancreatic Cancer Research, in honor of Ruth Bader Ginsburg (ALK), GI SPORE P50CA062924 (EMJ, EJF), U01CA253403 (EJF, AD, EMJ), U54CA274371 (EJF, AK, DW), U01CA212007 (EJF, LTK), U54CA268083 (DW, AK, PH, EMJ, EJF), R00NS122085 (GSO), T32GM148383 (JM), T32CA153952 (DB), NSF 1720625 (RH, YW, PM), NSF 2303695 (RH, HR, MG, PM), the National Foundation for Cancer Research (EJF, LC), the Jayne Koskinas Ted Giovanis Foundation for Health and Policy (RH, MG, IG, JM, DG, HR, PM), U01CA232137 (RH, FK, PM), NSF 1818187 (RH, AS, PM), Leidos Biomedical Research contract 75N91019D00024 (RH, HR, PM), Maryland Cancer Moonshot Research Grant to the Johns Hopkins Medical Institutions (FY24) (AD, EJF), a Luddy Faculty Fellowship (HR, PM), R01CA169702 (LZ), R01CA197296 (LZ), and P30CA006973 (LZ).
• Keywords: tumor biology; cell behavior hypothesis; immunotherapy; mathematical modeling; systems biology approach; cell rules
• Research Categories: Biology, Cell; Health Sciences, Oncology

Abstract

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.