In silico models of M. tuberculosis infection provide a route to new therapies.

Tuberculosis (TB) is a global health problem responsible for ~2 million deaths per year. Current antibiotic treatments are lengthy and fraught with compliance and resistance issues. There is a crucial need for additional approaches to provide a cost-effective means of exploring the design space for potential therapies. We discuss the use of mathematical and computational models in virtual experiments and virtual clinical trials both to develop new hypotheses regarding the disease and to provide a cost-effective means of discovering new treatment strategies.

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