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Detecting structural invariants in biological reaction networks.
Jörn Behre1, Luís Filipe de Figueiredo, Stefan Schuster
1Theoretical Systems Biology, Institute of Food Research, Norwich Research Park, Colney Lane, Norwich, UK.
Methods in Molecular Biology (Clifton, N.J.)
|December 7, 2011
Summary
This review explores structural invariants in cellular reaction networks, focusing on conserved moieties and elementary flux modes (EFMs). New methods are presented to analyze EFMs in large-scale metabolic networks for better pathway understanding.
Area of Science:
- Systems Biology
- Metabolic Engineering
- Computational Biology
Background:
- Understanding cellular metabolism relies on analyzing structural invariants in reaction networks.
- Petri nets offer a framework for representing these network structures and their invariants.
Purpose of the Study:
- To review different types of structural invariants in reaction networks, including conserved moieties and elementary flux modes (EFMs).
- To discuss the application of EFMs in understanding metabolic pathways, strain improvement, and network robustness.
- To present novel approaches for analyzing EFMs in large-scale metabolic networks.
Main Methods:
- Review of Petri net-based representations for structural invariants.
- Analysis of conserved moieties (P-invariants) and elementary flux modes (EFMs) derived from the stoichiometric matrix.
- Discussion of algorithms for enumerating EFMs in genome-scale networks and elementary flux pattern analysis for subsystems.
Main Results:
- Conserved moieties aid in detecting stoichiometric inconsistencies.
- EFMs provide a rigorous definition of biochemical pathways, enabling strain improvement and robustness analysis.
- New methods address the scalability limitations of EFM analysis in genome-scale networks.
Conclusions:
- Structural invariants, particularly EFMs, are crucial for a comprehensive understanding of metabolism.
- Advanced computational approaches are necessary to analyze complex, large-scale metabolic networks.
- Elementary flux patterns offer a more accurate reflection of metabolic subsystem capabilities and integration.
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