Title

Genome-scale metabolic analysis as a tool for host-pathogen studies and microbial fuel cell optimization

Date of Completion

January 2007

Keywords

Engineering, Chemical

Degree

Ph.D.

Abstract

Systems biology is the combination of experimental and computational techniques for understanding the complexities of the biological world. Genome-scale metabolic modeling, a tool based on the principles of systems biology, was further developed and applied to – (1) understand the metabolic effects of viral infection on the host, (2) to metabolically analyze Escherichia coli used in a microbial fuel cell. ^ The first part of the study was to develop a model RNA virus system to understand the changes that occur in the host metabolism as a result of a viral infection. Bacteriophage MS2 that infects F+ Escherichia coil cells, was identified as one such model system. The initial step was to define the viral dynamics of MS2 phage to differentiate between the characteristics of infected and uninfected cells. Model discrimination technique was used to compare five mathematical models, including one created by our group, that describe the intercellular dynamics of the phage and host populations. ^ The uninfected genome-scale metabolic model was based on an existing model, iJR904 GSM/GPR. This model was further enhanced by comparing five different objective functions using a Bayesian discrimination technique to experimental data of Escherichia coli growing on succinate. Contrary to the widely used objective function of maximizing growth rate, minimization of redox potential production was determined to be the most probable objective function. ^ Finally, the first genome-scale metabolic model of MS2 phage infected host cell was created. Methods of incorporating regulated viral gene expression, viral genome replication and a suitable objective function to an infected cell model were shown. The outcome of the study linked the viral infection to the up-regulation of biosynthesis of amino acids and the pentose phosphate cycle and the down-regulation of the TCA cycle. ^ In another thrust, the genome-scale metabolic modeling approach was used to optimize a microbial fuel cell by identifying pathways that could increase the electron production capacity of Escherichia coli. A novel objective function of maximizing the electric potential production was formulated. As a result, up-regulation of the enzyme, dihydroxyacetone phosphotransferase, led to additional pyruvate generation for oxidation ensuing in more than 2-fold increase in electron production. ^

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