Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Area of Science:

• Systems Biology
• Computational Biology
• Cellular Dynamics

Background:

• Cells are dynamic systems, and understanding their evolution requires mathematical modeling beyond current spatial multi-omics.
• Integrating biological knowledge with multi-omics data is crucial for predicting cellular behavior.

Purpose of the Study:

• To introduce a cell behavior hypothesis grammar for creating computable mathematical models from natural language statements.
• To enable systematic integration of biological knowledge and multi-omics data for predictive modeling.
• To facilitate virtual 'thought experiments' for hypothesis generation in multicellular systems.

Main Methods:

• Development of a conceptual framework: a cell behavior hypothesis grammar.
• Utilizing natural language statements (cell rules) to construct mathematical models.
• Application of the grammar to tumor biology and immunotherapy examples.

Main Results:

• Demonstration of a reference implementation for the cell behavior hypothesis grammar.
• Successful application in generating testable hypotheses through virtual experiments.
• Bridging the gap between single-cell characterization and emergent multicellular behavior.

Conclusions:

• The cell behavior hypothesis grammar provides a scalable approach for mathematical modeling in systems biology.
• It facilitates collaboration between biological, clinical, and systems biology researchers.
• Enables extrapolation from single-cell data to complex multicellular system dynamics and hypothesis generation.