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Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
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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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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.