TY - JOUR
T1 - Large-scale metabolic models: From reconstruction to differential equations
AU - Smallbone, Kieran
AU - Mendes, Pedro
N1 - The authors would like to acknowledge the financial support of the EU FP7 (KBBE) grant 289434 âBioPreDyn: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications.â Pedro Mendes also thanks the BBSRC (BB/J019259/1) and NIH (R01 GM080219) for financial support.
PY - 2013/8/1
Y1 - 2013/8/1
N2 - Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level. © Copyright 2013, Mary Ann Liebert, Inc.
AB - Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level. © Copyright 2013, Mary Ann Liebert, Inc.
U2 - 10.1089/ind.2013.0003
DO - 10.1089/ind.2013.0003
M3 - Article
SN - 1550-9087
VL - 9
SP - 179
EP - 184
JO - Industrial Biotechnology
JF - Industrial Biotechnology
IS - 4
ER -