Date of Award:
Doctor of Philosophy (PhD)
Bart C. Weimer
In the post-genomic era, there is a dire need for tools to perform metabolic analyses that include the structural, functional, and regulatory analysis of metabolic networks. This need arose because of the lag between the two phases of metabolic engineering, namely, synthesis and analysis. Molecular biological tools for synthesis like recombinant DNA technology and genetic engineering have advanced a lot farther than tools for systemic analysis. Consequently, bioinformatics is poised to play an important role in bridging the gap between the two phases of metabolic engineering, thereby accelerating the improvement of organisms by using predictive simulations that can be done in minutes rather than mutant constructions that require weeks to months.
In addition, metabolism occurs at a rapid speed compared to other cellular activities and has two states, dynamic state and steady state. Dynamic state analysis sheds more light on the mechanisms and regulation of metabolism than its steady state counterpart. Currently, several in silico tools exist for steady-state analysis of metabolism, but tools for dynamic analysis are lacking. This research focused on simulating the dynamic state of metabolism for predictive analysis of the metabolic changes in an organism during metabolic engineering.
The goals of this research were accomplished by developing two software tools. Metabolome Searcher, a web-based high throughput tool, facilitates putative compound identification and metabolic pathway mapping of mass spectrometry data by applying genome-restriction. The second tool, DynaFlux, uses these compound identifications along with time course data obtained from a mass spectrometer in conjunction with the pathways of interest to simulate and estimate dynamic-state metabolic flux, as well as to analyze the network properties. The features available in DynaFlux are: 1) derivation of the metabolic reconstructions from Pathway Tools software for the simulation; 2) automated building of the mathematical model of the metabolic network; 3) estimation of the kinetic parameters, KR, v, Vmaxf, Vmaxr, and Kdy, using hybrid-mutation random-restart hill climbing search; 4) perturbation studies of enzyme activities; 5) enumeration of feasible routes between two metabolites; 6) determination of the minimal enzyme set and dispensable enzyme set; 7) imputation of missing metabolite data; and 8) visualization of the network.
Dhanasekaran, Arockia R., "A Dynamic State Metabolic Journey: From Mass Spectrometry to Network Analysis via Estimation of Kinetic Parameters" (2011). All Graduate Theses and Dissertations. 1097.
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