Data-Driven Discovery of Novel Therapeutic Targets Against Community-Associated Methicillin-Resistant Staphylococcus Aureus

We recently uncovered that cellular metabolism is geared towards growth maximization but constrained by an upper rate of cellular Gibbs energy dissipation. Exploiting this principle in flux balance analysis generated excellent predictions of exchange and intracellular fluxes across a wide range of conditions and carbon sources, and even including some metabolite concentrations. This method requires a thermodynamically consistent formulation of cellular processes including Gibbs energies of reaction for every metabolic process, and a Gibbs energy balance, which states that the Gibbs energy dissipated by all cellular processes is equal to the Gibbs energy exchanged with the environment. We here present a detailed workflow, exemplified for a genome-scale metabolic model of Escherichia coli, how to implement and apply this method starting from any stoichiometric metabolic reconstruction. This workflow encompasses the formulation of such a model, the parameterization using experimental data together with regression analysis and the application in flux balance analysis. Furthermore, we present strategies how to solve the required nonlinear and nonconvex optimizations. Given the limited amount of required input data, and the precision and extent of the model predictions, we consider this method a valuable addition to current flux balance analysis approaches.