Dynamic FBA with Time-Course Transcriptomics

Genome-scale metabolic models provide a framework for the computational prediction of a wide range of phenotypes, such as growth in different nutrient environments, and effects of genetic manipulations, as well as aiding in the interpretation of ’omics data. Dynamic flux balance analysis (dFBA) is a method where the dynamics of growth is incorporated into the framework of steady-state flux balance analysis (FBA) by updating environmental conditions at regular time-intervals. Here, we present tt-dFBA by extending the dFBA methodology through incorporating time-course transcriptome data to account for changes in gene expression levels during batch fermentation.

We apply our framework to the case of batch fermentation of Streptomyces coelicolor, using the recent reconstruction iKS1317, to explain the observed metabolic switching and onset of secondary metabolite production when the organism faces phosphate depletion. Our results provide insights into the metabolic flux rerouting causing the observed metabolic changes.