Robustness of MetaNet graph models: predicting control of urea production in humans.

Published

Journal Article

Urea production in human liver was described by a MetaNet graph, a flowchart-like representation of metabolic pathways that includes parameters for the kinetic constants of the constituent enzymes. Formal operations on the graph facilitate the identification of ligand-binding equilibria that participate in feedback regulation in the network of biochemical reactions. The state of the biochemical network is specified by the concentrations of the intermediates. At any particular time, the influence of an identified locus of regulation is proportional to the respective fractional saturation of the corresponding binding site. Enzymes that make or consume the feedback chemicals share in the control of the strength of the feedback signal in proportion to their fractional saturation. This model predicts control of urea production by the processes that deliver amino groups to the urea cycle enzymes more than by the cycle enzymes themselves. Mitochondrial membrane transport processes are important for transmission of information through the network, but irreversible enzymes and processes far from equilibrium control the strength of the feedback signal. Systematic variation of the parameter values by amounts comparable to the expected variability of their measured values indicated a high probability of invariance in the identities of the predicted control points. The properties of the model are consistent with those of error-tolerant scale-free networks. These results demonstrate the robustness of a MetaNet model's predictions with respect to uncertainties in the values of its parameters.

Full Text

Duke Authors

Cited Authors

  • Kohn, MC; Tohmaz, AS; Giroux, KJ; Blumenthal, GM; Feezor, MD; Millington, DS

Published Date

  • February 2002

Published In

Volume / Issue

  • 65 / 1

Start / End Page

  • 61 - 78

PubMed ID

  • 11888664

Pubmed Central ID

  • 11888664

International Standard Serial Number (ISSN)

  • 0303-2647

Digital Object Identifier (DOI)

  • 10.1016/s0303-2647(02)00002-3

Language

  • eng

Conference Location

  • Ireland