Glycolysis: thermodynamics and pathway predictions

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The application of thermodynamics with published models still does not allow satisfactory predictions of the feasibility of biochemical pathways. This is due to the fact that basic data are either not published in the literature so far or available data is very inaccurate (group contribution methods). These basic data include thermodynamic data for pure metabolites as well as data on reaction equilibria. The latter depend on a number of parameters (e.g., pH, temperature, composition of the reaction medium in the cytoplasm). These data are included in rigorous models that depict metabolic pathways mathematically. To make these models more predictive, accurate thermodynamic data on pure metabolites as well as on reaction equilibria must be available. Further, effects like molecular crowding must be considered to make metabolic networks better predictable. If such data were available, they would be applicable in mathematical models for the description of metabolic networks, similar to it what has already been reached for systems of comparable complexity (e.g., petroleum refineries).This project aims to use new thermodynamic tools for the quantitative determination of properties of pure metabolites and reaction equilibria in order to predict the thermodynamics of the metabolic pathways. Glycolysis is chosen for that purpose as especially thermodynamics of glycolysis is still not understood. In this proposal, in vitro studies are suggested that address complex interactions that are typical for living systems (presence of salts and molecular crowders). This will allow investigating their influence on the thermodynamics of metabolic reactions, a question that is strongly discussed in the community.In a first step, new basic data for selected metabolites and individual reactions within glycolysis will be determined. The influence of real conditions in the cytoplasm on these data will be investigated e.g., pH or composition of the cytoplasm. In a second step, the effects of bio-typical phenomena (species diversity, crowding) on thermodynamics and mass-flow calculations will be examined. This will finally show how consistent thermodynamic data can improve the predictive power of metabolic network calculations.To achieve the goal, thermochemistry, quantum chemistry and thermodynamics have to be combined with material flow analysis:- Verevkin: Determination and prediction of basic data of the pure metabolites- Held: Determination and prediction of the reaction equilibria of the individual reaction steps and under the influence of real cell conditions- Maskow: application of these data in simplified and real metabolic networks under non-equilibrium conditionsThe project results obtained will be applied in biotechnology (bioengineering, systems biology).