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Title: Stability of Ensemble Models Predicts Productivity of Enzymatic Systems

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. Finally, the EMRA method incorporates properties of network structure, includingmore » stoichiometry and kinetic form, but does not require specific parameter values of the enzymes.« less
Authors:
 [1] ;  [2] ;  [3]
  1. Univ. of California, Los Angeles, Los Angeles, CA. (United States).Dept. of Bioengineering; Dept. of Chemical and Biomolecular Engineering
  2. Univ. of California, Los Angeles, Los Angeles, CA. (United States).Dept. of Chemical and Biomolecular Engineering
  3. Univ. of California, Los Angeles, Los Angeles, CA. (United States). UCLA-DOE Inst.; Dept. of Bioengineering; Dept. of Chemical and Biomolecular Engineering
Publication Date:
OSTI Identifier:
1264421
Grant/Contract Number:
SC0012384; SC0001060; SC0008744
Type:
Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 12; Journal Issue: 3; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Research Org:
Univ. of California, Los Angeles, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); National Science Foundation; UCLA-DOE Institute for Genomics and Proteomics
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY phosphofructokinase; thermodynamics; inhibition; hexokinase; glycolysis; metabolism; networks; pathways