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Systems Biology of Tuberculosis: Insights for Drug Discovery

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Understanding the Dynamics of Biological Systems

Abstract

Tuberculosis has been a global health concern for decades and the emergence of resistant strains and co-infection with HIV warrant newer approaches to identify anti-tubercular drugs and targets. The availability of many “omics”-scale data sets, together with the advances in computation and modelling have enabled the application of several systems-level modelling techniques in drug discovery. In this chapter, we focus on how systems-level modelling of Mtb can provide us insights on various aspects of the pathogen, from metabolic pathways to protein-protein interaction networks, and how such models lend themselves to the identification of new and potentially improved drug targets.We present a brief overview of the modelling of mycobacterial metabolism, transcriptome and host-pathogen interactions, as well as how various models can be exploited for a rational identification of potential drug targets. Systems-level modelling and simulation of pathogenic organisms has an immense potential to impact most drug discovery programmes.

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Notes

  1. 1.

    http://www.geneontology.org/.

  2. 2.

    http://www.ebi.ac.uk/interpro/.

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  1. Bioinformatics Centre, Indian Institute of Science, Bangalore, 560 012, India

    Karthik Raman

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  2. Nagasuma Chandra
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  1. Nano Systems Biology Research Group, Biomedical Sciences Research Institute, University of Ulster, Cromore Road, Coleraine, BT52 1SA, United Kingdom

    Werner Dubitzky

  2. Jack Birch Unit of Molecular Carcinogene, Department of Biology, University of York, York, United Kingdom

    Jennifer Southgate

  3. Biomedical Sciences Research Institute, Biomedical Sciences Research Institute, University of Ulster, Cromore Rd., Coleraine, BT52 1SA, United Kingdom

    Hendrik Fuß

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Raman, K., Chandra, N. (2011). Systems Biology of Tuberculosis: Insights for Drug Discovery. In: Dubitzky, W., Southgate, J., Fuß, H. (eds) Understanding the Dynamics of Biological Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7964-3_5

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