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Speaking Mathematical Models into Existence.

Ernesto A B F Lima1,2, David A Hormuth1,3, Thomas E Yankeelov1,3,4,5,6

  • 1Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas.

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Summary
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A new human-interpretable grammar allows researchers to create multicellular systems biology models using plain-text sentences. This approach democratizes computational modeling, accelerating cancer research and discovery by lowering technical barriers.

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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.