|Ph.D Student||Tepper Naama|
|Subject||Computational Methods for Metabolic Network Analysis of|
Metabolite Levels and Flux
|Department||Department of Computer Science||Supervisor||Professor Tomer Shlomi|
|Full Thesis text|
Cellular metabolism represents fundamental biochemical activities that enable cells to break down food nutrients, generate energy, and produce molecular building blocks required for cell replication. Computational modeling of metabolism is facilitated by metabolic networks in which nodes represent small molecules called metabolites, and edges represent biochemical reactions that transform substrate metabolites to products. The analyses of these networks, and specifically inferring the rate through their reactions, represent a major challenge in Systems Biology and Bioinformatics.
Throughout my research, I focused on metabolic network modeling, using metabolic models to simulate metabolic phenotypes, and the effects of network pertubations on phenotypes. Specifically, my first two studies involved developing computational methods for metabolic engineering. For this purpose, I wrote two algorithms, RobustKnock and auxotrophic-biosensors, where I targeted modifications to the metabolic network to enable enhanced phenotypic behavior in E. coli. In my next study, I developed metabolic tug-of-war (mTOW), where I hypothesized that observed variation in metabolite concentrations within cells can be explained in terms of a compromise between minimizing metabolite pool sizes and maining adequate thermodynamic driving force in metabolic reactions for effective use of enzymes. Later, in my final two studies, I focused on developing efficient algorithms that allow the utilization of of labeled metabolites (metabolites in which some of the atoms are heavier) measured by experimental MS/MS techniques for improved quantification of intracellular metabolic flux.