Speaking Mathematical Models into Existence

Area of Science:

• Multicellular systems biology
• Computational modeling
• Cancer research

Background:

• Mathematical and computational models are crucial for in silico testing and experimental design.
• Complex model development requires specialized technical and software skills, limiting accessibility and collaboration.
• This inaccessibility hinders broader adoption, reproducibility, and the pace of scientific discovery.

Purpose of the Study:

• To introduce a human-interpretable grammar for encoding multicellular systems biology models.
• To bridge the gap between biological reasoning and mathematical formalism.
• To enable model composition, modification, and reproduction without programming expertise.

Main Methods:

• Development of a human-interpretable grammar encoding models as human-readable statements.
• Translation of plain-text biological hypotheses into executable agent-based models.
• Application of the grammar to cancer-relevant examples.

Main Results:

• Demonstration of a framework that translates biological hypotheses into executable models.
• Successful application to cancer-relevant scenarios, showcasing the grammar's utility.
• Facilitation of model sharing and reproduction across disciplines.

Conclusions:

• The introduced grammar significantly lowers the barrier for constructing and applying computational models.
• Democratization of modeling can accelerate discovery and broaden participation in computational oncology.
• This approach facilitates the translation of modeling insights into experimental and clinical research.