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A combined model reduction algorithm for controlled biochemical systems.

Thomas J Snowden1,2, Piet H van der Graaf2,3, Marcus J Tindall4,5

  • 1Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AX, UK.

BMC Systems Biology
|February 15, 2017
PubMed
Summary
This summary is machine-generated.

This study presents a novel model reduction algorithm combining proper lumping and empirical balanced truncation to simplify complex biochemical networks. The method significantly speeds up simulations while maintaining high accuracy, making Systems Biology models more accessible.

Keywords:
Controlled systemsEmpirical balanced truncationLumpingModel reduction

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biochemical Engineering

Background:

  • Growing complexity of Systems Biology models poses challenges for parameter estimation, agent-based modeling, and real-time simulation.
  • Model reduction is crucial for managing complexity by eliminating less influential pathway components.
  • Identifying and removing non-critical parts of biochemical networks is essential for efficient analysis.

Purpose of the Study:

  • To present a novel, automatable model reduction algorithm for complex biochemical networks.
  • To combine proper lumping and empirical balanced truncation for enhanced model simplification.
  • To develop criteria for state-variable elimination using conservation analysis and averaged lumping.

Main Methods:

  • Developed a combined model reduction algorithm integrating proper lumping and empirical balanced truncation.
  • Utilized conservation analysis for state-variable elimination criteria.
  • Employed an 'averaged' lumping inverse for improved reduction accuracy.
  • Applied the algorithm to bacterial chemotaxis and ERK activation models.

Main Results:

  • Reduced an 11-state bacterial chemotaxis model to 2 states with 2.8% maximal error and a 26-fold speedup.
  • Reduced a 99-state ERK activation model to 7 states with 4.8% maximal error and a 10-fold speedup.
  • Demonstrated the algorithm's effectiveness in simplifying complex biological networks while preserving predictive accuracy.

Conclusions:

  • Combined model reduction methods offer significant simplification of Systems Biology models with high predictive accuracy.
  • The integration of proper lumping and empirical balanced truncation yields more accurate reductions than individual methods.
  • The developed algorithm is highly automatable and applicable to controlled biochemical networks, enhancing simulation efficiency.