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Sensitivity analysis of biochemical systems using bond graphs.

Peter J Gawthrop1, Michael Pan2

  • 1Department of Biomedical Engineering, Faculty of Engineering & Information Technology, University of Melbourne, Melbourne, Victoria 3010, Australia.

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|July 19, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using bond graphs to analyze parameter sensitivity in biological systems, ensuring thermodynamic consistency. This approach identifies key parameters for physiological function and aids in model refinement.

Keywords:
bond graphenergy-based modellingsensitivitysystems biology

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Area of Science:

  • Systems Biology
  • Biochemical Engineering
  • Computational Biology

Background:

  • Systems biology models are crucial for understanding physiological function.
  • Parameter variations in kinetic models often lead to thermodynamic inconsistency.
  • Identifying sensitive parameters is vital for model accuracy and experimental design.

Purpose of the Study:

  • To develop a method for analyzing parameter sensitivity in biological reaction networks while maintaining thermodynamic consistency.
  • To represent parameter sensitivities as bond graph components for mechanistic insights.
  • To derive a linear system for local sensitivity analysis and identification of sloppy parameters.

Main Methods:

  • Analysis of biological reaction networks using bond graph representations.
  • Derivation of a 'sensitivity system' to re-express parameter variations as system inputs.
  • Linearization of the sensitivity system to obtain a linear system for sensitivity analysis.
  • Verification using a model of the Pentose Phosphate Pathway.

Main Results:

  • Parameter sensitivities can be represented as bond graph components, offering mechanistic interpretations.
  • A derived linear system accurately provides local parameter sensitivity in terms of gain and time constants.
  • The method successfully identifies essential reactions and metabolites in the Pentose Phosphate Pathway model.
  • The approach addresses the challenge of thermodynamic inconsistency in perturbed kinetic models.

Conclusions:

  • The bond graph-based sensitivity analysis provides a robust framework for understanding parameter importance in biological systems.
  • This method enhances the reliability of systems biology models by ensuring thermodynamic consistency.
  • The derived linear system is a valuable tool for identifying critical parameters and simplifying complex biological models.