Computational Systems Biology Lab

Systems biology adopts an integrated approach to study and understand the function of biological systems, particularly, the response of such systems to perturbations, such as the inhibition of a reaction in a pathway, or the administration of a drug. Mycobacterium tuberculosis (Mtb), the principal aetiological agent of tuberculosis in humans, is estimated to cause two million deaths every year. The existing drugs, although of immense value in controlling the disease to some extent, have several shortcomings, the most important of them being the emergence of drug resistance rendering even the front‐line drugs inactive. Comprehensive in silico analyses of Mtb are essential to obtain a global view of the organism, which can be used for rational identification of therapeutic intervention strategies. This thesis describes systems–level modelling and simulation of mycobacterial metabolism, protein–protein influence networks and host–pathogen interactions. An obvious application of insights gained from these studies is in drug discovery, particularly in the discovery of new drug targets and understanding the emergence of drug resistance, both forming important parts of the thesis.

A comprehensive model of the pathway important to mycobacteria, leading to mycolic acid biosynthesis, has been built, to which flux balance analysis has been applied and critical points in the pathway have been identified, delineating potential drug targets. A pathway level phylogenetic profiling was then performed to identify the determinants of the pathway. Protein–protein interaction networks were constructed next, which captured influences arising from metabolites in addition to structural interactions between protein pairs. This analysis has provided insights into the highly concerted nature of the metabolic network.

Drug resistance is a major problem in combating TB. The protein–protein interactome constructed here has enabled a novel formulation of the problem of drug resistance and forms a first step towards countering drug resistance at the drug discovery stage itself. Shortest paths from drug targets to the resistance machinery in this network have been identified and scored by integrating available microarray expression data. Top pathways that lead to the emergence of drug resistance are delineated. Different targets appear to have different propensities for four drug resistance mechanisms. Based on these results, a new concept of ‘co‐targets’ is proposed to combat resistance.

By integration of several systems–level concepts, including a comprehensive analysis of the reactome, interactome, proteome and ‘pocketome’, a novel target identification pipeline, targetTB has been developed. Factors such as drug resistance and non‐similarity to gut flora proteins and human anti‐targets have also been considered in the analysis. 451 high‐confidence targets in Mtb have been identified through this. Targets specific to Mtb in active state and in persistence have been identified, along with broad‐spectrum pathogenic targets. A comprehensive assessment of existing targets has also been carried out.

Finally, a detailed model of the human–Mtb interactome has been re‐constructed and simulated using Boolean networks, also integrating some quantitative data. In silico knock‐out studies performed identify key components of the human immune response and reiterate the importance of persistence in TB infection. Although this thesis has focussed on Mtb, the analyses carried out are quite generic and can be applied in many drug discovery programmes.