TY - JOUR
T1 - Building kinetic models for metabolic engineering
AU - Foster, Charles J.
AU - Wang, Lin
AU - Dinh, Hoang V.
AU - Suthers, Patrick F.
AU - Maranas, Costas D.
N1 - Funding Information:
Funding provided by the Center for Bioenergy Innovation , a U.S. Department of Energy Bioenergy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science. This work was partially funded by the DOE Center for Advanced Bioenergy and Bioproducts Science (U.S. Department of Energy, Office of Science, Office of biological and Environmental Research under Award Number DE-SC0018420). Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Energy. Funding also provided by the DOE Office of Science , Office of Biological and Environmental Research (Award Number DE-SC0018260) and NSF Award Number MCB-1615646.
PY - 2021/2
Y1 - 2021/2
N2 - Kinetic formalisms of metabolism link metabolic fluxes to enzyme levels, metabolite concentrations and their allosteric regulatory interactions. Though they require the identification of physiologically relevant values for numerous parameters, kinetic formalisms uniquely establish a mechanistic link across heterogeneous omics datasets and provide an overarching vantage point to effectively inform metabolic engineering strategies. Advances in computational power, gene annotation coverage, and formalism standardization have led to significant progress over the past few years. However, careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges. In this review we highlight fundamental considerations which influence model quality and prediction, advances in methodologies, and success stories of deploying kinetic models to guide metabolic engineering.
AB - Kinetic formalisms of metabolism link metabolic fluxes to enzyme levels, metabolite concentrations and their allosteric regulatory interactions. Though they require the identification of physiologically relevant values for numerous parameters, kinetic formalisms uniquely establish a mechanistic link across heterogeneous omics datasets and provide an overarching vantage point to effectively inform metabolic engineering strategies. Advances in computational power, gene annotation coverage, and formalism standardization have led to significant progress over the past few years. However, careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges. In this review we highlight fundamental considerations which influence model quality and prediction, advances in methodologies, and success stories of deploying kinetic models to guide metabolic engineering.
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U2 - 10.1016/j.copbio.2020.11.010
DO - 10.1016/j.copbio.2020.11.010
M3 - Review article
C2 - 33360621
AN - SCOPUS:85098456951
VL - 67
SP - 35
EP - 41
JO - Current Opinion in Biotechnology
JF - Current Opinion in Biotechnology
SN - 0958-1669
ER -