title: Stability of Ensemble Models Predicts Productivity of Enzymatic Systems.
abstract: Stability in a metabolic system may not be obtained if incorrect amounts of enzymes are used. Without stability, some metabolites may accumulate or deplete leading to the irreversible loss of the desired operating point. Even if initial enzyme amounts achieve a stable steady state, changes in enzyme amount due to stochastic variations or environmental changes may move the system to the unstable region and lose the steady-state or quasi-steady-state flux. This situation is distinct from the phenomenon characterized by typical sensitivity analysis, which focuses on the smooth change before loss of stability. Here we show that metabolic networks differ significantly in their intrinsic ability to attain stability due to the network structure and kinetic forms, and that after achieving stability, some enzymes are prone to cause instability upon changes in enzyme amounts. We use Ensemble Modelling for Robustness Analysis (EMRA) to analyze stability in four cell-free enzymatic systems when enzyme amounts are changed. Loss of stability in continuous systems can lead to lower production even when the system is tested experimentally in batch experiments. The predictions of instability by EMRA are supported by the lower productivity in batch experimental tests. The EMRA method incorporates properties of network structure, including stoichiometry and kinetic form, but does not require specific parameter values of the enzymes.
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, United States of America.|Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Matthew K
authors.initials: MK
authors.lastname: Theisen
authors.affiliation: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Jimmy G
authors.initials: JG
authors.lastname: Lafontaine Rivera
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, United States of America.|Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, California, United States of America.|UCLA-DOE Institute, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: James C
authors.initials: JC
authors.lastname: Liao
mesh_terms: D000818:Animals; D003198:Computer Simulation; D004795:Enzyme Stability / Q000502:physiology*; D006801:Humans; D055442:Metabolome / Q000502:physiology; D008954:Models, Biological; D015233:Models, Statistical; D009097:Multienzyme Complexes / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D009097:Multienzyme Complexes; D020543:Proteome