Staphylococcus aureus is a human commensal bacteria, living in up to 30% of the population harm-free. However, in a number of cases, it can lead to opportunistic pathogenic infection causing serious harm.

The main goal of this work is to investigate the difference in metabolic pathway usage by S. aureus under different media conditions, simulating growth during infection as well as in minimal media. The purpose of this is to find novel drug targets against this antibiotic resistant pathogen.

We have built an enzyme constrained genome scale metabolic model (ECMM) of the livestock-associated MRSA strain ST398, in accordance with our aforementioned main goal. We have used novel analysis techniques to both predict, and offer explanations for, differential proteomic abundances under different defined media conditions. By identifying the most important pathways for growth, we also investigate potential novel drug targets against this antibiotic resistant strain.

In building an ECMM of S. aureus, we can increase the accuracy in phenotype predictions over the existing non-enzyme constrained genome scale metabolic models. We are able predict lethal phenotypes resulting from full or partial enzyme inhibition. These enzymes are potential drug targets.

This works highlights a promising way in which we can incorporate omics data into computational modelling for an interdisciplinary approach to fighting antimicrobial resistance.