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A model reduction method for biochemical reaction networks
Shodhan Rao, Arjan van der Schaft, Karen van Eunen
1Systems Biology Center for Energy Metabolism and Ageing, University of Groningen, ERIBA, Antonius Deusinglaan 1 9713 AV Groningen, Netherlands. b.jayawardhana@rug.nl.
BMC Systems Biology
|June 3, 2014
Summary
This study introduces an automated method to simplify complex biochemical reaction networks. The reduced models accurately represent key metabolite behaviors, aiding in parameter fitting and integration into larger biological systems.
Area of Science:
- Biochemistry
- Systems Biology
- Computational Biology
Background:
- Biochemical reaction networks are fundamental to understanding cellular processes.
- Existing models can be computationally intensive due to their complexity.
- Model reduction is crucial for efficient analysis and simulation of these networks.
Purpose of the Study:
- To develop an automated model reduction method for biochemical reaction networks.
- To preserve the dynamic behavior of significant metabolites in reduced models.
- To retain the structural and kinetic properties of the original network.
Main Methods:
- Stepwise reduction of complexes (reaction sides) in the network.
- Application of Kron reduction to the weighted Laplacian matrix.
- Automated procedure not requiring prior knowledge of network dynamics.
Main Results:
- Successfully reduced yeast glycolysis model (12 to 7 variables) with 8% average metabolite concentration difference.
- Reduced rat liver fatty acid beta-oxidation model (42 to 29 variables) with 7.5% average difference.
- Reduced networks retained structure and kinetics of original models.
Conclusions:
- The reduction method enhances understanding of network dynamics.
- Deleted metabolites did not necessarily have the shortest convergence times, differing from time-scale separation methods.
- The method facilitates parameter fitting and embedding detailed models into coarser environments.
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