Anne Kümmel

Systematic assignment of thermodynamic constraints in metabolic network models

BackgroundThe availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that particularly assist in identifying and compiling the organism-specific lists of metabolic reactions. In contrast, the last step of the model reconstruction process, which is the definition of the thermodynamic constraints in terms of reaction directionalities, still needs to be done manually. No computational method exists that allows for an automated and systematic assignment of reaction directions in genome-scale models.ResultsWe present an algorithm that – based on thermodynamics, network topology and heuristic rules – automatically assigns reaction directions in metabolic models such that the reaction network is thermodynamically feasible with respect to the production of energy equivalents. It first exploits all available experimentally derived Gibbs energies of formation to identify irreversible reactions. As these thermodynamic data are not available for all metabolites, in a next step, further reaction directions are assigned on the basis of network topology considerations and thermodynamics-based heuristic rules. Briefly, the algorithm identifies reaction subsets from the metabolic network that are able to convert low-energy co-substrates into their high-energy counterparts and thus net produce energy. Our algorithm aims at disabling such thermodynamically infeasible cyclic operation of reaction subnetworks by assigning reaction directions based on a set of thermodynamics-derived heuristic rules. We demonstrate our algorithm on a genome-scale metabolic model of E. coli. The introduced systematic direction assignment yielded 130 irreversible reactions (out of 920 total reactions), which corresponds to about 70% of all irreversible reactions that are required to disable thermodynamically infeasible energy production.ConclusionAlthough not being fully comprehensive, our algorithm for systematic reaction direction assignment could define a significant number of irreversible reactions automatically with low computational effort. We envision that the presented algorithm is a valuable part of a computational framework that assists the automated reconstruction of genome-scale metabolic models.

System-Level Insights into Yeast Metabolism by Thermodynamic Analysis of Elementary Flux Modes

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.

Temporal system-level organization of the switch from glycolytic to gluconeogenic operation in yeast.

The diauxic shift in Saccharomyces cerevisiae is an ideal model to study how eukaryotic cells readjust their metabolism from glycolytic to gluconeogenic operation. In this work, we generated time‐resolved physiological data, quantitative metabolome (69 intracellular metabolites) and proteome (72 enzymes) profiles. We found that the diauxic shift is accomplished by three key events that are temporally organized: (i) a reduction in the glycolytic flux and the production of storage compounds before glucose depletion, mediated by downregulation of phosphofructokinase and pyruvate kinase reactions; (ii) upon glucose exhaustion, the reversion of carbon flow through glycolysis and onset of the glyoxylate cycle operation triggered by an increased expression of the enzymes that catalyze the malate synthase and cytosolic citrate synthase reactions; and (iii) in the later stages of the adaptation, the shutting down of the pentose phosphate pathway with a change in NADPH regeneration. Moreover, we identified the transcription factors associated with the observed changes in protein abundances. Taken together, our results represent an important contribution toward a systems‐level understanding of how this adaptation is realized.

Differential glucose repression in common yeast strains in response to HXK2 deletion

Under aerobic, high glucose conditions, Saccharomyces cerevisiae exhibits glucose repression and thus a predominantly fermentative metabolism. Here, we show that two commonly used prototrophic representatives of the CEN.PK and S288C strain families respond differently to deletion of the hexokinase 2 (HXK2) - a key player in glucose repression: In CEN.PK, growth rate collapses and derepression occurs on the physiological level, while the S288C descendant FY4 Deltahxk2 still grows like the parent strain and shows a fully repressed metabolism. A CEN.PK Deltahxk2 strain with a repaired adenylate cyclase gene CYR1 maintains repression but not growth rate. A comparison of the parent strains physiology, metabolome, and proteome revealed higher metabolic rates, identical biomass, and byproduct yields, suggesting a lower Snf1 activity and a higher protein kinase A (PKA) activity in CEN.PK. This study highlights the importance of the genetic background in the processes of glucose signaling and regulation, contributes novel evidence on the overlap between the classical glucose repression pathway and the cAMP/PKA signaling pathway, and might have the potential to resolve some of the conflicting findings existing in the field.

Thermodynamic Calculations for Biochemical Transport and Reaction Processes in Metabolic Networks

Thermodynamic analysis of metabolic networks has recently generated increasing interest for its ability to add constraints on metabolic network operation, and to combine metabolic fluxes and metabolite measurements in a mechanistic manner. Concepts for the calculation of the change in Gibbs energy of biochemical reactions have long been established. However, a concept for incorporation of cross-membrane transport in these calculations is still missing, although the theory for calculating thermodynamic properties of transport processes is long known. Here, we have developed two equivalent equations to calculate the change in Gibbs energy of combined transport and reaction processes based on two different ways of treating biochemical thermodynamics. We illustrate the need for these equations by showing that in some cases there is a significant difference between the proposed correct calculation and using an approximative method. With the developed equations, thermodynamic analysis of metabolic networks spanning over multiple physical compartments can now be correctly described.

Liquid films on shake flask walls explain increasing maximum oxygen transfer capacities with elevating viscosity

In biotechnological screening and production, oxygen supply is a crucial parameter. Even though oxygen transfer is well documented for viscous cultivations in stirred tanks, little is known about the gas/liquid oxygen transfer in shake flask cultures that become increasingly viscous during cultivation. Especially the oxygen transfer into the liquid film, adhering on the shake flask wall, has not yet been described for such cultivations. In this study, the oxygen transfer of chemical and microbial model experiments was measured and the suitability of the widely applied film theory of Higbie was studied. With numerical simulations of Ficks law of diffusion, it was demonstrated that Higbies film theory does not apply for cultivations which occur at viscosities up to 10 mPa s. For the first time, it was experimentally shown that the maximum oxygen transfer capacity OTRmax increases in shake flasks when viscosity is increased from 1 to 10 mPa s, leading to an improved oxygen supply for microorganisms. Additionally, the OTRmax does not significantly undermatch the OTRmax at waterlike viscosities, even at elevated viscosities of up to 80 mPa s. In this range, a shake flask is a somehow self‐regulating system with respect to oxygen supply. This is in contrary to stirred tanks, where the oxygen supply is steadily reduced to only 5% at 80 mPa s. Since, the liquid film formation at shake flask walls inherently promotes the oxygen supply at moderate and at elevated viscosities, these results have significant implications for scale‐up. Biotechnol. Bioeng. 2014;111: 295–308.