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SBML to bond graphs: From conversion to composition.

Niloofar Shahidi1, Michael Pan2, Kenneth Tran1

  • 1Auckland Bioengineering Institute, University of Auckland, Auckland, 1010, New Zealand.

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|September 12, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a framework to convert Systems Biology Markup Language (SBML) models into bond graphs, ensuring physical plausibility and thermodynamic consistency for complex biological models.

Keywords:
Automatic conversionBioModelsBond graphsGlycolysisPentose phosphate pathwaySBML

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Area of Science:

  • Systems biology
  • Computational biology
  • Biophysics

Background:

  • Systems Biology Markup Language (SBML) is a standard for computational models in biology.
  • The BioModels Database hosts numerous SBML models, but lacks physical plausibility checks.
  • Current tools for SBML model reuse do not guarantee thermodynamic consistency, leading to unrealistic models.

Purpose of the Study:

  • To develop a framework for automatic conversion of SBML models to bond graphs.
  • To ensure physical plausibility and thermodynamic consistency in biological models.
  • To facilitate the creation of realistic, complex, and coupled biological models.

Main Methods:

  • Developed a framework for automated conversion of SBML models to bond graphs.
  • Implemented energy conservation laws within the bond graph representation.
  • Demonstrated model coupling by merging a pyruvate distribution model with a pentose phosphate pathway model.

Main Results:

  • Successfully converted SBML models into physically plausible bond graphs.
  • The bond graph framework enforces energy conservation laws.
  • Achieved mergeable, thermodynamically consistent coupled models.

Conclusions:

  • The proposed framework enhances the reliability of biological models by ensuring physical consistency.
  • Automated conversion to bond graphs enables the development of more realistic and complex systems biology models.
  • This approach supports the integration and reuse of biological models with guaranteed physical validity.