Figure 2

Data-driven framework for predictive metabolic flux analysis. (A) Schematic representation of a metabolic network with an unknown or ill-defined portion corresponding to the synthesis of a complex recombinant product. These poorly defined pathways are substituted by a statistical sub-model bridging the known well-defined stoichiometry with the target product formation rate. (B) Given a set of measured fluxes (V _m - usually exchange fluxes of metabolic consumption and production), metabolic flux analysis is used to estimate the entire flux distribution (V _e ) in a predefined metabolic network. Then, PLS is performed to find a linear regression model between the estimated fluxome and the vector of a measured target such as productivity, V _t . As a result, a list of regression coefficients representing how strongly each flux correlates with the target is obtained (B), making it possible to predict the productivity of independent cultures after metabolic manipulation.