An organism's metabolism can be described via a genome-scale network reconstruction (GENRE), a structured collection of biochemical transformations and their associated genes. GENREs serve as platforms for the development of genome-scale metabolic models (GEMs), mathematical models which enable an organism's phenotype to be evaluated computationally via constraint-based methods (CBMs). Constraint-based modeling integrate optimization with physiochemical constraints to define and idenfity feasible cellular behaviors. This dissertation describes computational methods which advance the field of constraint-based modeling in three areas: network comparisons, thermodynamic constraints, and community CBMs.;Advances in genome sequencing and software development have enabled the rapid construction of GEMs, but methods for comparing GEMs remain in their infancy. We have developed an approach to identify functional differences between GEMs by comparing GENREs aligned at the gene level. Our approach (CONGA) seeks genes whose deletion disproportionately changes flux through a selected reaction (e.g., growth) in one model over another. Through a number of examples, we demonstrate this this approach can be used to identify differences in GENRE content which enable unique metabolic capabilities.;The predictive accuracy of CBMs depends on the degree to which constraints eliminate infeasible behaviors. Using thermodynamics-based metabolic flux analysis (TMFA), we implemented thermodynamic constraints on an Escherichia coli GENRE. We examined the effect of these constraints on the flux space, and assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data. We propose that TMFA is a useful tool for validating phenotypes, and that additional types of data and constraints can improve predictions of metabolite concentrations.;In anaerobic syntrophic communities, electrons are transferred between species via reactions which are tightly constrained by thermodynamics. We developed and analyzed a thermodynamic coculture model of the syntrophic association between Syntrophobacter fumaroxidans and Methanosprillum hungatei. Our analysis revealed that thermodynamic constraints alone are insufficient to correctly predict the mechanism of H2 production by S. fumaroxidans. Our model also describes the contributions of different H2 production modes to electron transfer in the community, and predicts that S. fumaroxidans may alter its metabolic behavior in the presence of a high relative abundance of M. hungatei..
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