PhD Thesis
Mass Action Stoichiometric. Simulation for Cell Factory Design
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark1
Research Groups, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark2
Quantitative Modeling of Cell Metabolism, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark3
iLoop, Translational Management, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark4
For a long time microorganisms have been used to produce beer and bread, and in the last century also molecules such as penicillin and insulin. These same microorganisms can potentially be used to produce a diverse range of other molecules and contribute to a more sustainable future by reducing our dependency on oil.
Producing a given molecule at a yield high enough to be commercially viable, however, usually requires cell metabolism to be modified extensively. Traditionally, these modifications have been introduced through random mutagenesis and selection, which has been gradually complemented by more targeted genetic engineering approaches that rely more and more also on computational models of cell metabolism for target selection.
Two main types of models can be used here, stoichiometric models or kinetic models. The former are easily built at genome-scale and assume the cell to be in a steady-state, giving information only about the reactions’ fluxes, while the latter take into account enzyme dynamics which makes it possible to model substrate-level enzyme regulation and get information about metabolite concentrations and reaction fluxes over time, although at the cost of introducing more parameters.
Kinetic models have been plagued by the lack of kinetic data. The focus of this thesis are kinetic models of cell metabolism. In this work we start by developing a software package to create a model ensemble for individual enzymes in metabolism, where we decompose each reaction into elementary steps, using mass action kinetics to model each step.
The resulting rate constants are then fitted to kinetic data (kcat, Km, Ki, etc.). We then use the package as the basis to build a system-level kinetic model. To do so, we take two different approaches, and in both we drop the assumption that χfree ≈ χtot , i.e. that the total concentration of metabolite in the cell is approximately the same as the free concentration.
In both approaches preliminary results show that the fraction of bound metabolite in the cell is not negligible, with some metabolites having an enzyme-bound concentration up to 40%. Next, we address the issue of kinetic data scarcity by using molecular dynamics simulations to estimate the difference in binding energies, ΔΔG, between substrate(s) and a given enzyme and product(s) and the same enzyme for a chosen reaction.
Here, we show that these in silico determined ΔΔG significantly reduce the amount of rate constants combinations allowed in each model ensemble. Finally, we combine a kinetic model of glycolysis in Saccharomyces cerevisiae with time-resolved NMR experiments to study the cellular response to a glucose pulse, and show the model simulations to be in agreement with the experimental results.
Language: | English |
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Publisher: | Technical University of Denmark |
Year: | 2018 |
Types: | PhD Thesis |
ORCIDs: | Matos, Marta R. A. |