title: Regularizing capacity of metabolic networks.
abstract: Despite their topological complexity almost all functional properties of metabolic networks can be derived from steady-state dynamics. Indeed, many theoretical investigations (like flux-balance analysis) rely on extracting function from steady states. This leads to the interesting question as to how metabolic networks avoid complex dynamics and maintain a steady-state behavior. Here, we expose metabolic network topologies to binary dynamics generated by simple local rules. We find that the networks' response is highly specific: Complex dynamics are systematically reduced on metabolic networks compared to randomized networks with identical degree sequences. Already small topological modifications substantially enhance the capacity of a network to host complex dynamic behavior and thus reduce its regularizing potential. This exceptionally pronounced regularization of dynamics encoded in the topology may explain why steady-state behavior is ubiquitous in metabolism.
authors.affiliation: Bioinformatics Group, Department of Biology, Darmstadt University of Technology, D-64287 Darmstadt, Germany. marr@bio.tu-darmstadt.de
authors.forename: Carsten
authors.initials: C
authors.lastname: Marr
authors.forename: Mark
authors.initials: M
authors.lastname: Müller-Linow
authors.forename: Marc-Thorsten
authors.initials: MT
authors.lastname: Hütt
mesh_terms: D000465:Algorithms; D001703:Biophysics / Q000379:methods*; D020134:Catalytic Domain; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D019277:Entropy; D053858:Metabolic Networks and Pathways; D008660:Metabolism; D008954:Models, Biological; D008959:Models, Neurological; D015233:Models, Statistical; D009991:Oscillometry; D049490:Systems Biology; D013816:Thermodynamics