DNA fingerprinting of Mycobacterium tuberculosis strain has been used in combination with conventional epidemiologic investigation, which has improved the understanding of tuberculosis transmission. Restriction Fragment Length Polymorphism (RFLP) based on IS6110 probe has become a standard method of fingerprinting of M tuberculosis. Since the technique is labour intensive and the discriminatory power of IS611 0 fingerprinting method for strains habouring only one to five copies is poor, other typing methods for typing M tuberculosis should be evaluated. In this regard, Amplified Fragment Length Polymorphism (AFLP) has the potential to overcome many of the RFLP problems. The first objective was to determine the suitability of the RFLP and AFLP techniques and to study the extent of transmission of tuberculosis in a referral hospital in South Africa. A total of 47 M tuberculosis isolates were differentiated using RFLP technique. The same samples were typed using the PCR- based AFLP technique and results were compared. The second objective was to determine the prevalence of isoniazid (INH) resistance and estimate the incidence of recent transmission of the disease in the Eastern-Cape (EC) and North-West province (NW) by using the best suited technique. RFLP grouped the 47 typed M. tuberculosis isolates into five families and four clusters. AFLP grouped the analyzed isolates (previously typed by RFLP) into two groups based on the banding patterns observed. As a result of the low degree of genotypic variation among the AFLP band pattern of M tuberculosis isolates, AFLP seemed less promising for individual strain differentiation of M tuberculosis. This technique can be used in future for differentiation of Mycobacterial species and The prevalence of INH resistance was found to be 6.7% in the EC and 8.4% in the NW province. The magnitude of recent transmission in the Eastern Cape studied by RFLP method, was found to be at 22% among the positive tuberculosis isolates identified. Transmission of TB in NW province was associated with reactivation rather than recent transmission due to lack of clustering of strains in that region. / Dissertation (MSc(Microbiology))--University of Pretoria, 2006. / Microbiology and Plant Pathology / unrestricted

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.