Accelerated Construction of Kinetic Models for Cell Metabolism

Abstract

The use of mathematical models is enriching biological research, as it allows biologists to learn how the different components within a biological system interact, leading to a holistic approach to research. This is a result of technology improvements that enable the generation of high-throughput data, and of increased collaboration with mathematicians, physicists and computer scientists. There are various methodologies to model a biological system. A dynamic model allows users to represent quantitative information and follow the temporal changes of the system, which are very important for understanding complex systems. The construction of kinetic models is often impeded by incomplete information on kinetic data, including the kinetic parameters and rate laws. Additionally, data are frequently collected under different conditions. Previously, the software GRaPe was introduced to address these issues by automatically generating generic kinetic rate equations and estimating kinetic parameters by searching a local solution in the parameter space using only steady state data. This thesis introduces an upgraded version of the software that uses convenience kinetics (allowing for inclusion of regulatory effects to models) and a global solution of equations for parameter estimation, using a genetic algorithm with both time-series and steady state data. As a proof of concept for the software, the glycolytic network of Saccharomyces cerevisiae was modelled using the software, and produced favourable results. Following this, trehalose metabolism of Saccharomyces cerevisiae was studied using a model generated with the new tool. It confirmed that the increase of flux during heat stress is caused by the positive feedback on pyruvate kinase. Additionally, the model was able to determine the best enzymes to overexpress in order to increase the yield of trehalose, a commercially valuable product. This thesis introduces an intuitive software that will serve as a gateway tool for building kinetic models of cell metabolism, aimed at non-expert users that wish to study complex biological systems or to generate rapid prototype of models.

Date of Award	1 Aug 2019
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Simon Hubbard (Supervisor) & Jean-Marc Schwartz (Supervisor)