In this study, we used the ORACLE (Optimization and Risk Analysis of Complex Living Entities)[1] framework to study the impact of extracellular acetic acid on the S. cerevisiae metabolism with the aim to improve ethanol production in the presence of this inhibitor found in significant concentrations in lignocellulosic hydrolysates. First, we derived a consistently reduced core model (279 metabolites and 382 reactions) of S. cerevisiae from the iMM904 genome scale reconstruction. We integrated thermodynamic and experimentally measured information about the metabolite concentrations and reaction fluxes, to identify thermodynamically feasible operational configurations of the network under different experimental conditions using the novel Flux Directionality Profile Analysis (FDPA) technique[2,3]. We then computed a population of stoichiometrically, thermodynamically, kinetically and physiologically consistent log-linear kinetics models. These models were used to (i) explore the flexibility and robustness of the operational states; (ii) identify the differences of the flux profiles for different doses of acetate during ethanol production; and (iii) derive optimal strategies for improvement of the ethanol production under these physiological conditions.