title: System-level insights into yeast metabolism by thermodynamic analysis of elementary flux modes.
abstract: One of the most obvious phenotypes of a cell is its metabolic activity, which is defined by the fluxes in the metabolic network. Although experimental methods to determine intracellular fluxes are well established, only a limited number of fluxes can be resolved. Especially in eukaryotes such as yeast, compartmentalization and the existence of many parallel routes render exact flux analysis impossible using current methods. To gain more insight into the metabolic operation of S. cerevisiae we developed a new computational approach where we characterize the flux solution space by determining elementary flux modes (EFMs) that are subsequently classified as thermodynamically feasible or infeasible on the basis of experimental metabolome data. This allows us to provably rule out the contribution of certain EFMs to the in vivo flux distribution. From the 71 million EFMs in a medium size metabolic network of S. cerevisiae, we classified 54% as thermodynamically feasible. By comparing the thermodynamically feasible and infeasible EFMs, we could identify reaction combinations that span the cytosol and mitochondrion and, as a system, cannot operate under the investigated glucose batch conditions. Besides conclusions on single reactions, we found that thermodynamic constraints prevent the import of redox cofactor equivalents into the mitochondrion due to limits on compartmental cofactor concentrations. Our novel approach of incorporating quantitative metabolite concentrations into the analysis of the space of all stoichiometrically feasible flux distributions allows generating new insights into the system-level operation of the intracellular fluxes without making assumptions on metabolic objectives of the cell.
authors.affiliation: Life Science Zurich PhD Program on Systems Biology of Complex Diseases, ETH Zurich, Switzerland.
authors.forename: Stefan J
authors.initials: SJ
authors.lastname: Jol
authors.forename: Anne
authors.initials: A
authors.lastname: Kümmel
authors.forename: Marco
authors.initials: M
authors.lastname: Terzer
authors.forename: Jörg
authors.initials: J
authors.lastname: Stelling
authors.forename: Matthias
authors.initials: M
authors.lastname: Heinemann
mesh_terms: D003198:Computer Simulation; D004734:Energy Metabolism / Q000502:physiology*; D008954:Models, Biological; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D013816:Thermodynamics
chemical_list: D029701:Saccharomyces cerevisiae Proteins