Background: Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints\r\nto predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have\r\nbeen integrated into constraint-based modeling, kinetic rate laws have not been extensively used.\r\nResults: In this study, an in vivo kinetic parameter estimation problem was formulated and solved using multi-omic\r\ndata sets for Escherichia coli. To narrow the confidence intervals for kinetic parameters, a series of kinetic model\r\nsimplifications were made, resulting in fewer kinetic parameters than the full kinetic model. These new parameter\r\nvalues are able to account for flux and concentration data from 20 different experimental conditions used in our\r\ntraining dataset. Concentration estimates from the simplified kinetic model were within one standard deviation for\r\n92.7% of the 790 experimental measurements in the training set. Gibbs free energy changes of reaction were\r\ncalculated to identify reactions that were often operating close to or far from equilibrium. In addition, enzymes\r\nwhose activities were positively or negatively influenced by metabolite concentrations were also identified. The\r\nkinetic model was then used to calculate the maximum and minimum possible flux values for individual reactions\r\nfrom independent metabolite and enzyme concentration data that were not used to estimate parameter values.\r\nIncorporating these kinetically-derived flux limits into the constraint-based metabolic model improved predictions\r\nfor uptake and secretion rates and intracellular fluxes in constraint-based models of central metabolism.\r\nConclusions: This study has produced a method for in vivo kinetic parameter estimation and identified strategies\r\nand outcomes of kinetic model simplification. We also have illustrated how kinetic constraints can be used to\r\nimprove constraint-based model predictions for intracellular fluxes and biomass yield and identify potential\r\nmetabolic limitations through the integrated analysis of multi-omics datasets.
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