Metabolic Flux Analysis (MFA) is a powerful tool of systems biology in describing the functioning of an entire cell. MFA by applying mass balances as well as kinetic relations results unfortunately in huge undetermined equation systems. Thermodynamics might help in reducing solution space by eliminating solutions that fulfill mass balances but violate second law of thermodynamics. For this reason an algorithm called Thermodynamic Feasibility Analysis (TFA) has already been tested on well-known glycolysis pathway.[1] Unfortunately, even glycolysis pathway has been estimated thermodynamically unfeasible. Neglecting activity coefficients, poor thermodynamic basic data as well as neglecting the influence of special conditions in the cytosol of the cell (e.g. macromolecular crowding, ionic strength etc.) have been detected as possible reasons of the unexpected outcome. [2] Within the algorithm of TFA, ΔgR is calculated for each single reaction of a certain metabolic pathway as well as for combinations of consecutive reactions using the measured concentration range of the metabolites. Reactions that will reveal a positive value of ΔgR will be considered as thermodynamically unfeasible and designated as localized bottlenecks. Combinations of consecutive reactions with a positive value of ΔgR for the total reaction result in so called distributed bottlenecks. Both, the occurrence of localized and distributed bottlenecks, will make a given metabolic pathway unfeasible. In the current work, the applicability of TFA on glycolysis will be explored using state of the art thermodynamic data and models. Three areas will be researched. First, physical and thermochemical basic data of selected pure metabolites will be determined. Second, the reaction equilibria of these metabolites under real cytoplasmic conditions (e.g. ionic strength, molecular crowing) are determined and modelled with e-PC-SAFT. Finally, the pure component data as well as the reaction data are applied to a dynamic metabolic network. This project will help to clarify the potential role of thermodynamics to enlighten complex intra-cellular metabolic networks.