Systematic Drug Targeting of Microbial Pathogens Using Metabolic Networks

Abstract

Development for methods identifying effective drug targets and antibacterial is urgently needed because microbial pathogens are constantly developing resistance to currently available antibiotics. Such trend has been threatening mankind, especially those with compromised immune system. We employed genome-scale metabolic modeling as it has proved to be very useful in capturing the physiological characteristics of various organisms. In this metabolic modeling, two systematic approaches, namely constraints-based flux analysis and chokepoint analysis were applied to genome-scale metabolic networks of Escherichia coli, Helicobacter pylori, Mycobacterium tuberculosis and Staphylococcus aureus; the former is an optimization- based simulation technique that calculates metabolic fluxes, while the latter is networktopology-based method that selects enzymes or metabolites as a target that has a single ingoing and/or outgoing reaction. We combined these methods to generate novel drug targets in the aforementioned emerging drug-resistant pathogens. This study demonstrates that drug targeting using in silico approaches enables a rational design of experiments applicable to biomedical science.