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.

Identification of Metabolic Engineering Targets through Analysis of Optimal and Sub-Optimal Routes

“…To compare reversibility scores to thermodynamics-based analysis, we used a network thermodynamics approach [32] [33] to restrict the reaction reversibilities and subsequently remove infeasible pathways. Supplement S7 presents the methods and discusses the results.…”

Quantification of Microbial Phenotypes

“…The first attempt of thermodynamic analysis of EFMs was realized to an E. coli metabolic network [48]. Later, a full NET analysis of EFMs was performed for a yeast model [49]. The latest approach is tEFMA, a software that, while enumerates the EFMs is checking for thermodynamics feasibility in order to reduce the time in the EFMs generation [50,51].…”

The Problem of Futile Cycles in Metabolic Flux Modeling: Flux Space Characterization and Practical Approaches to Its Solution

“…The computation of EMs and ExPAs are restricted to metabolic networks of moderate size and connectivity, because the number of modes and the computation time rise exponentially with increasing network complexity [56]. For example, 71 million elementary flux modes (EFMs) were found in a medium size metabolic network of Saccharomyces cerevisiae (230 reactions and 218 metabolites) [57]. Computation of EMs for a central metabolism network of E. coli (106 reactions and 89 metabolites) results in about 26 millions [56].…”

“…This method shares some ideas (further detailed in the next subsection) with [57], in which the solution space is described in terms of three key characteristics: linealities which are the reversible (bidirectional) infinite reactions, rays which are the irreversible infinite reactions, and vertices which are the corner points of the shape formed by interception of the polyhedral cone representing the convex constraint space with the objective plane. However, there could be millions of vertices, from which one cannot identify biological significance.…”

“…Therefore, FATMIN intends to remove only the rays and linealities, which matched irreversible and reversible cycles. The rays and linealities are independent of the growth medium [57]. Since FATMIN is based on FVA, the method inherits the function of FVA in elucidating the phenomena of equivalent optima in the network.…”