<br/>The yeast cell is an open system coupled with its environment; it can freely exchange molecules with its surroundings and it can also transfer molecules between its different compartments, for example its cytoplasm and mitochondria. Yeast cells’, (<i>Saccharomyces cerevisiae</i>), metabolic pathways and processes react and adjust according to the availability of glucose in their environment. Once they consume the glucose available to them they undergo a diauxic shift from glycolysis to gluconeogenesis; the cells begin using non carbohydrate sources such as glycerol to generate glucose. Concentrations of metabolites were computed by integrating non linear, first order differential kinetic equations which describe the metabolic processes. The kinetic scheme, involving 214 metabolites, uses rate constants chosen to ensure that the simulated metabolic data matches the experimental measurements. Surprisal analysis characterizes the diauxic shift and demonstrates it to be, from a thermodynamics standpoint, particularly noteworthy. This technique first determines the metabolites that have the highest weights in regulating the primary metabolic pathways which govern the glycolytic shift: glycolysis, the citric acid cycle and pentose phosphate pathway. Then, surprisal analysis enables us to determine the balanced state and confirm its stability by demonstrating that it is the state of minimal free energy of the system. This analysis also identifies the main constraint, which primarily characterizes the diauxic, glycolytic shift, and shows that only two additional constraints are needed together with the first displacing constraint to accurately describe the time evolving deviation from the balanced state. Three constraints therefore suffice to compact the data. Furthermore, it is possible to determine how closely we can relate the assessment of the system through this evaluation with the results of clustering based analysis of the metabolic data.

Surprisal analysis based characterization of the temporal change of metabolic networks in yeast

256th National Meeting (2018)

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