A kinetic platform for in silico modeling of the metabolic dynamics in Escherichia coli

kinetic platform for in silico modeling of the metabolic dynamics in Escherichia coli Original Research (5310) Total Article Views Authors: Aditya Barve, Anvita Gupta, Suresh M Solapure, et al Published Date December 2010 Volume 2010:3 Pages 97 - 110 DOI: http://dx.doi.org/10.2147/AABC.S14368 Aditya Barve1, Anvita Gupta2, Suresh M Solapure2, Ansu Kumar1, Vasanthi Ramachandran2, Kothandaraman Seshadri2, Shireen Vali1, Santanu Datta2 1Cellworks Research India Pvt. Ltd, Bangalore, India; 2AstraZeneca, Bangalore, India Background: A prerequisite for a successful design and discovery of an antibacterial drug is the identification of essential targets as well as potent inhibitors that adversely affect the survival of bacteria. In order to understand how intracellular perturbations occur due to inhibition of essential metabolic pathways, we have built, through the use of ordinary differential equations, a mathematical model of 8 major Escherichia coli pathways. Results: Individual in vitro enzyme kinetic parameters published in the literature were used to build the network of pathways in such a way that the flux distribution matched that reported from whole cells. Gene regulation at the transcription level as well as feedback regulation of enzyme activity was incorporated as reported in the literature. The unknown kinetic parameters were estimated by trial and error through simulations by observing network stability. Metabolites, whose biosynthetic pathways were not represented in this platform, were provided at a fixed concentration. Unutilized products were maintained at a fixed concentration by removing excess quantities from the platform. This approach enabled us to achieve steady state levels of all the metabolites in the cell. The output of various simulations correlated well with those previously published. Conclusion: Such a virtual platform can be exploited for target identification through assessment of their vulnerability, desirable mode of target enzyme inhibition, and metabolite profiling to ascribe mechanism of action following a specific target inhibition. Vulnerability of targets in the biosynthetic pathway of coenzyme A was evaluated using this platform. In addition, we also report the utility of this platform in understanding the impact of a physiologically relevant carbon source, glucose versus acetate, on metabolite profiles of bacterial pathogens.