人类可解释语法对多细胞系统生物学模型进行编码，以使虚拟细胞实验室实现普及。

Human interpretable grammar encodes multicellular systems biology models to democratize virtual cell laboratories.

作者信息

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

机构信息

Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA; Marlene & Stuart Greenbaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA.

Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Department of Pharmacology, Physiology, and Drug Development, University of Maryland School of Medicine, Baltimore, MD, USA; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA; Marlene & Stuart Greenbaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA; University of Maryland Institute of Health Computing, University of Maryland School of Medicine, Baltimore, MD, USA.

出版信息

Cell. 2025 Aug 21;188(17):4711-4733.e37. doi: 10.1016/j.cell.2025.06.048. Epub 2025 Jul 26.

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.

摘要

细胞作为动态演化的生态系统相互作用。虽然最近的单细胞和空间多组学技术能够量化单个细胞的特征，但预测它们的演化需要数学建模。我们提出了一个概念框架——细胞行为假设语法，它使用自然语言陈述（细胞规则）来创建数学模型。这使得生物知识和多组学数据能够系统整合，从而生成计算机模拟模型，实现虚拟的“思想实验”，用以测试和拓展我们对多细胞系统的理解，并生成新的可测试假设。本文阐述并介绍了该语法，提供了一个参考实现，并展示了其在开发全新机制模型以及多组学数据驱动模型方面的应用。我们通过癌症方面的例子展示了其潜力，以及它在模拟大脑发育方面更广泛的适用性。这种方法为大规模数学建模搭建了生物学、临床和系统生物学研究之间的桥梁，使科学界能够预测多细胞行为的出现。