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. The complexity and large scale of biological systems make modelling and simulation an essential and critical part of systems-level studies. Systems-level modelling of pathogenic organisms has the potential to significantly enhance drug discovery programmes.
In this thesis, we show how systems--level models can positively impact anti-tubercular drug target identification. *Mycobacterium tuberculosis*,
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. As drug discovery efforts are increasingly becoming rational, focussing at a molecular level, the identification of appropriate targets becomes a fundamental pre-requisite.
We have constructed many system-level models, to identify drug targets for tuberculosis. We construct a constraint-based stoichiometric model of mycolic acid biosynthesis, and simulate it using flux balance analysis, to identify critical points in mycobacterial metabolism for targeting drugs. We then analyse protein--protein functional linkage networks to identify influential hubs, which can be targeted to disrupt bacterial metabolism. An important aspect of tuberculosis is the emergence of drug resistance. A network analysis of potential information pathways in the cell helps to
identify important proteins as co-targets, targeting which could counter the emergence of resistance. We integrate analyses of metabolism,
protein--protein interactions and protein structures to develop a generic drug target identification pipeline, for identifying most suitable drug targets. Finally, we model the interplay between the pathogen and the human
immune system, using Boolean networks, to elucidate critical factors influencing the outcome of infection. The strategies described can be applied to understand various pathogens and can impact many drug discovery programmes.
| Identifer | oai:union.ndltd.org:IISc/oai:etd.ncsi.iisc.ernet.in:2005/685 |
| Date | 10 1900 |
| Creators | Raman, Karthik |
| Contributors | Chandra, Nagasuma |
| Source Sets | India Institute of Science |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Relation | G22885 |
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