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A Web Tool for Generating High Quality Machine-readable Biological Pathways
Published on: February 8, 2017
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Human interpretable grammar encodes multicellular systems biology models to democratize virtual cell laboratories
Jeanette A I Johnson1, Daniel R Bergman2, Heber L Rocha3
1Department 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.
Cell
|July 27, 2025
Summary
We introduce a cell behavior hypothesis grammar to build mathematical models from natural language rules. This framework integrates biological knowledge and multi-omics data for predicting cell ecosystem evolution.
Area of Science:
- Systems Biology
- Computational Biology
- Bioinformatics
Background:
- Cells function as dynamic, evolving ecosystems.
- Single-cell and spatial multi-omics technologies provide detailed cellular data.
- Predicting cellular evolution necessitates robust mathematical modeling approaches.
Purpose of the Study:
- To present a novel conceptual framework, the cell behavior hypothesis grammar, for creating mathematical models of cellular systems.
- To enable the systematic integration of biological knowledge and multi-omics data for in silico modeling.
- To facilitate virtual 'thought experiments' for understanding multicellular dynamics and generating hypotheses.
Main Methods:
- Development of a cell behavior hypothesis grammar using natural language statements (cell rules).
- Creation of a reference implementation for the grammar.
- Application of the grammar to develop de novo mechanistic models and multi-omics data-informed models.
Main Results:
- Demonstration of the grammar's utility in developing mechanistic models.
- Successful application in simulating cancer-related biological processes.
- Exemplification of broader applicability in modeling brain development.
Conclusions:
- The cell behavior hypothesis grammar bridges biological, clinical, and systems biology research.
- Enables scalable mathematical modeling for predicting emergent multicellular behavior.
- Facilitates the generation of new, testable hypotheses in multicellular systems research.
Keywords:
agent-based modelingcancer biologycell behavior hypothesis grammarcell behaviorscell interactionsimmunologyimmunotherapymathematical biologymathematical modelingmodeling languagemulti-omicsmulticellular systemsmulticellular systems biologyphysics of multicellular biologysimulationspatial transcriptomicstissue dynamics
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