A systematic investigation into the quantitative effect of pH changes on the upper glycolytic enzymes of Escherichia coli and Saccharomyces cerevisiae

[摘要] ENGLISH ABSTRACT: Kinetic modelling of biological phenomena in an attempt to understand the underlyingdynamics and complexity of life is becoming an indispensable tool to systemsbiology. A new paradigm of mathematical and computational integration of experimentaldata has shifted the focus in biological sciences from mere characterisationand cataloguing of the components of life, to a more holistic view. The functioning ofthese components in dynamic interactions in non-linear biochemical networks is nowa major field of interest for many biologists. Classically, enzyme kinetic assays areoptimised for yielding the maximal activity of the enzyme of interest. This raises thequestion of how applicable the obtained kinetic parameters are for systems biology,especially when considering how the intracellular reality (in terms of pH and ionicstrength and composition) affects the catalytic activity of enzymes in vivo. Anotherconcern is how accurate and predictive the kinetic models, constructed from such obtaineddata, can be. Much effort has been directed towards the standardisation ofenzyme kinetics for systems biology and in vivo-like assay media have been developedfor the determination of enzyme kinetic parameters in both Escherichia coli and Saccharomycescerevisiae. However, the effect of pH changes on kinetic parameters ofenzymes, has been somewhat neglected in systems biology studies. With this in mindwe investigated the quantitative effects elicited by pH changes on the upper glycolyticenzymes in Escherichia coli and Saccharomyces cerevisiae using NMR spectroscopy.This is especially important as recent studies have shown that intracellular pH, whileremaining a tightly homeostatically controlled parameter, is not as constant as oncethought and has been shown to vary in response to environmental perturbation. Theinvestigation focused on parameter estimation and the unique identifiability of theestimated parameters. The main aim of this project is the development of a robust,reliable technique for parameter identification from experimental data using mathematicaland computational approaches.

[发布机构] Stellenbosch University