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Published on: June 8, 2018
Structural conserved moiety splitting of a stoichiometric matrix
Susan Ghaderi1, Hulda S Haraldsdóttir1, Masoud Ahookhosh2
1Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, Belvaux L-4362, Luxembourg.
Biochemical network structure can be mathematically defined using reaction stoichiometry. This study reveals that stoichiometric matrices can be decomposed into moiety transition matrices, revealing network subnetworks.
Area of Science:
- Biochemistry
- Systems Biology
- Computational Biology
Background:
- Understanding biochemical reaction networks is crucial for inferring functional consequences.
- Network structure can be represented by reaction stoichiometry or molecular transformations.
- Stoichiometric matrices (N∈Zm×n) capture molecule-reaction relationships.
Purpose of the Study:
- To mathematically characterize biochemical reaction network structure.
- To explore the relationship between network structure and functional consequences.
- To develop new mathematical approaches for analyzing biochemical networks.
Main Methods:
- Representing biochemical networks using stoichiometric matrices.
- Decomposing stoichiometric matrices into moiety transition matrices.
- Analyzing subnetworks accessible to structurally identifiable conserved moieties.
Main Results:
- Demonstrated that a stoichiometric matrix can be decomposed into m-rank(N) moiety transition matrices.
- Showed that this moiety matrix splitting is a unique property of stoichiometric matrices.
- Identified subnetworks corresponding to structurally identifiable conserved moieties.
Conclusions:
- Mathematical characterization of biochemical networks enables functional inference.
- Stoichiometric matrix decomposition reveals fundamental network properties.
- The moiety matrix splitting property distinguishes biochemical network matrices from general matrices.
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