Figure 2. Schematic depiction of the main steps we used to generate predictions of metabolic fluxes for yeast single and double deletion mutants.

(A) As in any flux balance model, we implement a steady state approximation, yielding for each metabolite in the network a linear constraint on reaction rates (fluxes). (B) We then generate the best possible flux state for the wild type, using flux and growth rate data from the literature [30] as additional constraints, and (C) minimizing the sum of the absolute values of all fluxes. This last step prevents the generation of arbitrarily large loops of fluxes associated with alternative optima. (D) Lastly, to generate flux predictions for the deletion mutants, we impose that the fluxes associated with the deleted genes be set to zero, and identify the flux state for the mutant that is as close as possible to the wild type state, identified in step C. Note that this approach does not employ growth rate maximization, as often done in flux balance analysis. Instead, using the concept of minimization of metabolic adjustment [29], it searches for mutant fluxes that undergo minimal rerouting relative to the (experimentally tuned) wild type flux solution. This approach was proven to be the most accurate way of predicting fluxes in yeast knockout strains.