title: Optimization-based framework for inferring and testing hypothesized metabolic objective functions.
abstract: An optimization-based framework is introduced for testing whether experimental flux data are consistent with different hypothesized objective functions. Specifically, we examine whether the maximization of a weighted combination of fluxes can explain a set of observed experimental data. Coefficients of importance (CoIs) are identified that quantify the fraction of the additive contribution of a given flux to a fitness (objective) function with an optimization that can explain the experimental flux data. A high CoI value implies that the experimental flux data are consistent with the hypothesis that the corresponding flux is maximized by the network, whereas a low value implies the converse. This framework (i.e., ObjFind) is applied to both an aerobic and anaerobic set of Escherichia coli flux data derived from isotopomer analysis. Results reveal that the CoIs for both growth conditions are strikingly similar, even though the flux distributions for the two cases are quite different, which is consistent with the presence of a single metabolic objective driving the flux distributions in both cases. Interestingly, the CoI associated with a biomass production flux, complete with energy and reducing power requirements, assumes a value 9 and 15 times higher than the next largest coefficient for the aerobic and anaerobic cases, respectively.
authors.affiliation: Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, USA.
authors.forename: Anthony P
authors.initials: AP
authors.lastname: Burgard
authors.forename: Costas D
authors.initials: CD
authors.lastname: Maranas
mesh_terms: D000332:Aerobiosis / Q000502:physiology; D000465:Algorithms; D000693:Anaerobiosis; D001426:Bacterial Proteins / Q000378:metabolism*; D003198:Computer Simulation; D004926:Escherichia coli / Q000254:growth & development* / Q000378:metabolism*; D008954:Models, Biological; D015202:Protein Engineering / Q000379:methods*; D011786:Quality Control
chemical_list: D001426:Bacterial Proteins