| Abstract: |
| Tuberculosis (TB) is a mainly pulmonary disease caused by infection of Mycobacterium Tuberculosis. Treatment of TB can become difficult, as the bacteria can form granuloma as a defense against antibiotic treatment. As a result, treatment of TB can require a combination therapy of three to four antibiotics for up to six months. Because there are many antibiotics to choose from, the treatment space of potential therapies grows rapidly. In this work, we describe a pipeline for estimating the effect of a combination therapy. We simulate the therapy`s impact on Mycobacterium Tuberculosis metabolic networks based on gene differential expression data from single treatment microarray data. The simulations are built on a novel approach - Linear in Flux Expressions (LIFE) methodology describing a system of ordinary differential equations. This system of equations encodes a metabolic network - describing the mass flow through the system. The computation pipeline we describe provides a framework for estimating the efficacy of a combination therapy based on the perturbation of metabolites. This framework may be used to narrow the search for new combination therapies. |
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