Integrate protein-DNA interaction network with genomic data to predict the impact of mutations in clinical strains of M. tuberculosis.

In spite of significant medical advances, tuberculosis (TB) is a major killer worldwide. In India alone, more than 12 lakh people are diagnosed with tuberculosis every year, with 2.7 lakh resulting in death. 40% of these cases are children. The major problem with conventional tuberculosis therapy has been the emergence of multi-drug resistance (MDR) and extensively drug resistance (XRD) Mycobacterium tuberculosis strains. Whole genome sequencing of many clinical strains show a number of mutations present in these clinical strains. In this project, we will represent the regulatory network of this bacteria in a Boolean framework. To determine the Boolean function at each node, gene expression data will be utilized along with in-house developed algorithms. The model will be simulated to predict the impact of various mutations that are seen in clinical strains of drug-resistant M. tuberculosis.

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