title: An entropy-like index of bifurcational robustness for metabolic systems.
abstract: Natural and synthetic metabolic pathways need to retain stability when faced against random changes in gene expression levels and kinetic parameters. In the presence of large parameter changes, a robust system should specifically avoid moving to an unstable region, an event that would dramatically change system behavior. Here we present an entropy-like index, denoted as S, for quantifying the bifurcational robustness of metabolic systems against loss of stability. We show that S enables the optimization of a metabolic model with respect to both bifurcational robustness and experimental data. We then demonstrate how the coupling of ensemble modeling and S enables us to discriminate alternative designs of a synthetic pathway according to bifurcational robustness. Finally, we show that S enables the identification of a key enzyme contributing to the bifurcational robustness of yeast glycolysis. The different applications of S demonstrated illustrate the versatile role it can play in constructing better metabolic models and designing functional non-native pathways.
authors: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, 5531 Boelter Hall, Los Angeles, California 90095, USA. liaoj@g.ucla.edu.
authors: Jimmy G
authors: JG
authors: Lafontaine Rivera
authors: Yun
authors: Y
authors: Lee
authors: James C
authors: JC
authors: Liao
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004734:Energy Metabolism / Q000502:physiology*; D019277:Entropy; D053858:Metabolic Networks and Pathways / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome