Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric,thermodynamic and physiological constraints

Mathematical modeling is an essential tool for the comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information on enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works considered only mass action kinetics for reactions in metabolic networks. Herein, we applied the optimization and risk analysis of complex living entities (ORACLE) framework and constructed a large-scale mechanistic kinetic model of optimally grown Escherichia coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of metabolic fluxes and metabolite concentrations. Our results further suggest that enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of cellular metabolism.     

Isoenzyme expression changes in response to high temperature determine the metabolic regulation of increased glycolytic flux in yeast

Qualitative phenotypic changes are the integrated result of quantitative changes at multiple regulatory levels. To explain the temperature-induced increase of glycolytic flux in fermenting cultures of Saccharomyces cerevisiae, we quantified the contributions of changes in activity at many regulatory levels. We previously showed that a similar temperature increase in glucose-limited cultivations lead to a qualitative change from respiratory to fermentative metabolism, and this change was mainly regulated at the metabolic level. In contrast, in fermenting cells, a combination of different modes of regulation was observed. Regulation by changes in expression and the effect of temperature on enzyme activities contributed much to the increase in flux. Mass spectrometric quantification of glycolytic enzymes revealed that increased enzyme activity did not correlate with increased protein abundance, suggesting a large contribution of post-translational regulation to activity. Interestingly, the differences in the direct effect of temperature on enzyme kinetics can be explained by changes in the expression of the isoenzymes. Therefore, both the interaction of enzyme with its metabolic environment and the temperature dependence of activity are in turn regulated at the hierarchical level.     

Metabolomics‐driven quantitative analysis of ammonia assimilation in E. coli

Despite extensive study of individual enzymes and their organization into pathways, the means by which enzyme networks control metabolite concentrations and fluxes in cells remains incompletely understood. Here, we examine the integrated regulation of central nitrogen metabolism in Escherichia coli through metabolomics and ordinary‐differential‐equation‐based modeling. Metabolome changes triggered by modulating extracellular ammonium centered around two key intermediates in nitrogen assimilation, α‐ketoglutarate and glutamine. Many other compounds retained concentration homeostasis, indicating isolation of concentration changes within a subset of the metabolome closely linked to the nutrient perturbation. In contrast to the view that saturated enzymes are insensitive to substrate concentration, competition for the active sites of saturated enzymes was found to be a key determinant of enzyme fluxes. Combined with covalent modification reactions controlling glutamine synthetase activity, such active‐site competition was sufficient to explain and predict the complex dynamic response patterns of central nitrogen metabolites.     

Homeostatic regulation upon changes of enzyme activities in the Calvin cycle as an example for general mechanisms of flux control. What can we expect from transgenic plants?

In order to explain the mechanisms of Calvin-cycle regulation, the general properties of metabolic systems under homeostatic flux control are analyzed. It is shown that the main characteristic point for an enzyme in such a system can be the value of a sharp transition from some constant homeostatic flux to a limitation by this enzyme. A special method for the quantitative treatment of the experimental dependence of a metabolic flux such as photosynthesis on enzyme content is developed. It is pointed out that reactions close to a thermodynamic equilibrium under normal conditions can considerably limit the homeostatic fluxes with a decrease of the enzyme content. Calvin-cycle enzymes are classified as non-limiting, near-limiting and limiting. The deduced rules for the regulation of a homeostatic metabolic pathway are used to explain the data obtained for transgenic plants with reduced activities of Calvin-cycle enzymes. The role of compensating mechanisms that maintain the photosynthesis rate constant upon the changes of enzyme contents is analyzed for the Calvin cycle. The developed analysis explains the sharp transitions between limiting and non-limiting conditions that can be seen in transgenic plants with reduced content of some Calvin-cycle enzymes, and the limiting role of such reversible enzymes as aldolase, transketolase and others. The attempt is made to predict the properties of plants with increased enzyme contents in the Calvin cycle.     

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

Accurate determination of physiological states of cellular metabolism requires detailed information about metabolic fluxes, metabolite concentrations and distribution of enzyme states. Integration of fluxomics and metabolomics data, and thermodynamics-based metabolic flux analysis contribute to improved understanding of steady-state properties of metabolism. However, knowledge about kinetics and enzyme activities though essential for quantitative understanding of metabolic dynamics remains scarce and involves uncertainty. Here, we present a computational methodology that allow us to determine and quantify the kinetic parameters that correspond to a certain physiology as it is described by a given metabolic flux profile and a given metabolite concentration vector. Though we initially determine kinetic parameters that involve a high degree of uncertainty, through the use of kinetic modeling and machine learning principles we are able to obtain more accurate ranges of kinetic parameters, and hence we are able to reduce the uncertainty in the model analysis. We computed the distribution of kinetic parameters for glucose-fed E. coli producing 1,4-butanediol and we discovered that the observed physiological state corresponds to a narrow range of kinetic parameters of only a few enzymes, whereas the kinetic parameters of other enzymes can vary widely. Furthermore, this analysis suggests which are the enzymes that should be manipulated in order to engineer the reference state of the cell in a desired way. The proposed approach also sets up the foundations of a novel type of approaches for efficient, non-asymptotic, uniform sampling of solution spaces.     

Metabolic control at the cytosol–mitochondria interface in different growth phases of CHO cells

Metabolism at the cytosol–mitochondria interface and its regulation is of major importance particularly for efficient production of biopharmaceuticals in Chinese hamster ovary (CHO) cells but also in many diseases. We used a novel systems-oriented approach combining dynamic metabolic flux analysis and determination of compartmental enzyme activities to obtain systems level information with functional, spatial and temporal resolution. Integrating these multiple levels of information, we were able to investigate the interaction of glycolysis and TCA cycle and its metabolic control. We characterized metabolic phases in CHO batch cultivation and assessed metabolic efficiency extending the concept of metabolic ratios. Comparing in situ enzyme activities including their compartmental localization with in vivo metabolic fluxes, we were able to identify limiting steps in glycolysis and TCA cycle. Our data point to a significant contribution of substrate channeling to glycolytic regulation. We show how glycolytic channeling heavily affects the availability of pyruvate for the mitochondria. Finally, we show that the activities of transaminases and anaplerotic enzymes are tailored to permit a balanced supply of pyruvate and oxaloacetate to the TCA cycle in the respective metabolic states. We demonstrate that knowledge about metabolic control can be gained by correlating in vivo metabolic flux dynamics with time and space resolved in situ enzyme activities.     

Mapping photoautotrophic metabolism with isotopically nonstationary (13)C flux analysis

Understanding in vivo regulation of photoautotrophic metabolism is important for identifying strategies to improve photosynthetic efficiency or re-route carbon fluxes to desirable end products. We have developed an approach to reconstruct comprehensive flux maps of photoautotrophic metabolism by computational analysis of dynamic isotope labeling measurements and have applied it to determine metabolic pathway fluxes in the cyanobacterium Synechocystis sp. PCC6803. Comparison to a theoretically predicted flux map revealed inefficiencies in photosynthesis due to oxidative pentose phosphate pathway and malic enzyme activity, despite negligible photorespiration. This approach has potential to fill important gaps in our understanding of how carbon and energy flows are systemically regulated in cyanobacteria, plants, and algae.