Donghyuk Kim
Posters-Accepted Abstracts: Biol Syst Open Access
A computational genome-scale systems biology approach to experimental design was applied to elucidate the transcriptional regulation of nitrogen metabolism in Escherichia coli by two major transcription factors, NtrC and Nac. Two alternative nitrogen sources, cytosine and cytidine were predicted by genome-scale models to maximally activate the NtrC and Nac regulons and thus optimally elucidate their function. Genome-wide ChIP-exo and RNA-seq measurements were performed and 19, 249, 153 and 2171 binding sites for NtrC, Nac, RpoN and RpoD respectively were identified resulting in 262 new binding sites for NtrC and Nac., In addition to guiding experimental design, a genome-scale model of E. coli metabolism was used to gain a detailed quantitative understanding of how the entire metabolic network responds to different nitrogen sources in order to carry out its integrated function. While NtrC primarily responds to nitrogen limitation by striving to increase nitrogen availability, Nac rebalances metabolic fluxes through carbon metabolism to accommodate a nitrogen source change. The study shows how computational models based on system biology serve to both optimally design experiments to elucidate regulons and reveal the full physiological roles of transcription factors.
