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Extending BioMASS to construct mathematical models from external knowledge.

Kiwamu Arakane1, Hiroaki Imoto1, Fabian Ormersbach2

  • 1Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.

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|April 12, 2024
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Summary
This summary is machine-generated.

BioMASS framework with Text2Model automates mechanistic model construction from natural language, overcoming manual curation bottlenecks in systems biology. This accelerates the integration of prior knowledge for scalable, data-driven mathematical modeling.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Mathematical Modeling

Background:

  • Mechanistic modeling using ordinary differential equations is crucial in systems biology.
  • Manual knowledge curation for model construction presents a significant bottleneck.
  • Growing knowledge accumulation necessitates scalable methods for executable model generation.

Purpose of the Study:

  • To introduce and demonstrate the capabilities of the Text2Model feature within the BioMASS framework.
  • To facilitate the construction of large-scale mechanistic models of signaling networks.
  • To enable a more data-driven approach to mathematical model development.

Main Methods:

  • Utilized the BioMASS framework, an open-source Python tool for mechanistic model construction, simulation, and analysis.
  • Employed the Text2Model feature to define models using a natural language-like format.
  • Generated Text2Model files from pathway databases and large language models for simulation.

Main Results:

  • Demonstrated Text2Model's efficacy in integrating external knowledge into mathematical models.
  • Successfully generated and simulated mechanistic models using Text2Model inputs.
  • Showcased the framework's ability to encourage exploration of prior knowledge.

Conclusions:

  • Text2Model within BioMASS effectively addresses the bottleneck of manual knowledge curation.
  • The framework supports the scalable construction of executable mechanistic models.
  • This approach paves the way for fully data-driven mathematical modeling in systems biology.