Navigating and Managing the Complexity of Genome Scale Metabolic Networks for Studies in Cellular Physiology and Industrial Biotechnology

Metabolism is a network of biochemical reactions that converts carbon sources to cellu-lar fuels and building blocks. Metabolism of many organisms has been studied for many decades. These studies define metabolism as a very large and complex network. Stoichi-ometric models have been used extensively to study these complex networks. With the introduction of Genome Scale Models (GEMs), the studies on metabolism entered a new era, and gained a more systems approach. GEMs have been used in a broad range of are-as, from cancer research to industrial applications. By using GEMs, it is possible to study metabolism with an input/output manner, and decompose it to individual fluxes by de-tailed stoichiometric definition. However, due to the large degrees of freedom and the underdetermined nature of GEMs, it is crucial to develop methods to further constrain these complex networks to reveal the actual state of the metabolism. Integrating ther-modynamics constraints to metabolic networks is a popular and powerful approach to address this need. Moreover, these methods allow the incorporation of steady-state me-tabolite concentrations into GEMs, which cannot be achieved by other methods such as Flux Balance Analysis (FBA). In this thesis, we firstly discussed different methods to in-corporate this constraint into metabolic models and used the most comprehensive one, Thermodynamics-based Flux Analysis (TFA) for studies in the next chapters. Firstly, we used TFA to study the overall behavior of E. coli in terms of bioenergetics efficiency, P:O ratio and Gibbs free energy dissipation and revealed their connection. Following this analysis, we focused on the complexity that emerges from the size of the GEMs. GEMs are composed of large metabolic modules, called subsystems or pathways. In this thesis, we aimed to re-define the pathway definition by generating subnetworks for the synthe-sis of biomass building blocks using lumpGEM, a tool that extracts parts of metabolism for certain tasks, such as synthesis of an amino acid. lumpGEM identified additional re-actions from different parts of metabolism along with textbook pathways for synthesis of many biomass building blocks. lumpGEM also builds lumped reactions for the gener-ated subnetworks, which represent the subnetwork with 1 overall reaction, thus reduc-ing complexity. redGEM uses this property to build reduced models, moreover it re-defines the central carbon metabolism definition, and build core models consistent with their GEMs. These reduced models are valuable platforms for many studies, such as ki-netic modeling, FBA/TFA studies and comparison of central carbon networks among different organisms. We used a reduced model of E. coli to study further the characteris-tics of the core models, and their still big solution space by enumerating all possible Flux Directionality Profiles (FDP). We identified the effect of directionality of reactions on overall network behavior, such as specific growth rate. We finally focused on the meta-bolic capabilities of E. coli and identified possible biotransformation that E. coli can per-form by using We built a super network for E. coli and studied its characteris-tics for biomass production and metabolic gaps. As a conclusion, in the last chapter we discuss the potential applications of the methods and tools that we developed in this thesis.


  • Thesis submitted - Forthcoming publication

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