A low-complexity metabolic network model for the respiratory and fermentative metabolism of Escherichia coli.

ABSTRACT: Over the last decades, predictive microbiology has made significant advances in the mathematical description of microbial spoiler and pathogen dynamics in or on food products. Recently, the focus of predictive microbiology has shifted from a (semi-)empirical population-level approach towards mechanistic models including information about the intracellular metabolism in order to increase model accuracy and genericness. However, incorporation of this subpopulation-level information increases model complexity and, consequently, the required run time to simulate microbial cell and population dynamics. In this paper, results of metabolic flux balance analyses (FBA) with a genome-scale model are used to calibrate a low-complexity linear model describing the microbial growth and metabolite secret

SUBMITTER: Tack ILMM 

PROVIDER: S-EPMC6114798 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature