On Functional Module Detection in Metabolic Networks
AbstractFunctional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models. View Full-Text
Share & Cite This Article
Koch, I.; Ackermann, J. On Functional Module Detection in Metabolic Networks. Metabolites 2013, 3, 673-700.
Koch I, Ackermann J. On Functional Module Detection in Metabolic Networks. Metabolites. 2013; 3(3):673-700.Chicago/Turabian Style
Koch, Ina; Ackermann, Jörg. 2013. "On Functional Module Detection in Metabolic Networks." Metabolites 3, no. 3: 673-700.