The bacterium Clostridium difficile causes thousands of deaths every year in the United Kingdom through healthcare-associated infections. The impact upon the healthcare system is enormous and set to worsen due to the increasing prevalence of hypervirulent and antibiotic-resistant strains. An urgent need to develop novel therapies to tackle C. difficile infection thus exists. C. difficile infection is controlled by a complex network of genes that detects optimal conditions for a successful infection, regulating the production of toxins and the initiation of related mechanisms accordingly. The complexity of such nonlinear networks defies their understanding by a single discipline alone. This fellowship provides microbiology training for crucial experimental work to be undertaken in tandem with computational models of these networks. Mathematical models can capture conventional and novel therapies (the latter targeting toxin production directly), enabling hypotheses to be generated upon the relative effectiveness of these therapies and estimates made upon required dosages.