title: Ensemble Modeling for Robustness Analysis in engineering non-native metabolic pathways.
abstract: Metabolic pathways in cells must be sufficiently robust to tolerate fluctuations in expression levels and changes in environmental conditions. Perturbations in expression levels may lead to system failure due to the disappearance of a stable steady state. Increasing evidence has suggested that biological networks have evolved such that they are intrinsically robust in their network structure. In this article, we presented Ensemble Modeling for Robustness Analysis (EMRA), which combines a continuation method with the Ensemble Modeling approach, for investigating the robustness issue of non-native pathways. EMRA investigates a large ensemble of reference models with different parameters, and determines the effects of parameter drifting until a bifurcation point, beyond which a stable steady state disappears and system failure occurs. A pathway is considered to have high bifurcational robustness if the probability of system failure is low in the ensemble. To demonstrate the utility of EMRA, we investigate the bifurcational robustness of two synthetic central metabolic pathways that achieve carbon conservation: non-oxidative glycolysis and reverse glyoxylate cycle. With EMRA, we determined the probability of system failure of each design and demonstrated that alternative designs of these pathways indeed display varying degrees of bifurcational robustness. Furthermore, we demonstrated that target selection for flux improvement should consider the trade-offs between robustness and performance.
authors: Department of Chemical and Biomolecular Engineering, University of California, 5531 Boelter Hall, Los Angeles, CA 90095, USA.
authors: Yun
authors: Y
authors: Lee
authors: Department of Chemical and Biomolecular Engineering, University of California, 5531 Boelter Hall, Los Angeles, CA 90095, USA.
authors: Jimmy G
authors: JG
authors: Lafontaine Rivera
authors: Department of Chemical and Biomolecular Engineering, University of California, 5531 Boelter Hall, Los Angeles, CA 90095, USA; UCLA-DOE Institute for Genomics and Proteomics, University of California, 611 Young Drive East, Los Angeles, CA 90095, USA. Electronic address: liaoj@ucla.edu.
authors: James C
authors: JC
authors: Liao
mesh_terms: D003198:Computer Simulation; D004926:Escherichia coli / Q000502:physiology*; D029968:Escherichia coli Proteins / Q000502:physiology*; D007700:Kinetics; D008657:Metabolic Clearance Rate; D064688:Metabolic Flux Analysis / Q000379:methods*; D055442:Metabolome / Q000502:physiology*; D008954:Models, Biological; D015398:Signal Transduction / Q000235:genetics*
chemical_list: D029968:Escherichia coli Proteins
keywords: Ensemble Modeling; Metabolic engineering; Robustness; Synthetic biology