The advent of high-throughput technologies has resulted in an explosion of molecular data. A major challenge is found in interpreting and understanding these different types of data sets at a phenotypic level. Systems biology has capitalized on these technologies by consolidating various types of biological information into structured networks for their analysis and computation. The bottom-up systems biology approach, in particular, has been crucial in providing mechanistic foundations for systems-level modeling in microorganisms, and its extension to eukaryotic metabolism has made it possible to elucidate complex phenotypes in a systematic manner. The work presented in this dissertation describes the integrative use of high-throughput data and genome-scale network reconstructions to characterize complex phenotypes of eukaryotic metabolism. First, the genome-scale reconstructions of yeast and human metabolism are discussed, which provide the contextual basis in which "omics" data is analyzed. Previously developed constraint- based modeling approaches were refined to analyze "omics" data sets, in particular for transcriptomic and metabolomic data. Finally, example applications are presented in the evaluation of physiological and perturbed metabolic states of yeast and human cellular systems. The studies discussed herein are: (1) analyzing drug response phenotypes of human metabolism; (2) evaluating genetic and environmentally perturbed processes in yeast ammonium assimilation; and (3) characterizing the pluripotent phenotype of embryonic stem cell metabolism. The work described in these studies represents advancement towards integrating bottom-up and data-driven approaches to understanding broader "omics"-to-phenotype relationships

Drug side effects cause a significant clinical and economic burden. However, mechanisms of drug action underlying side effect pathogenesis remain largely unknown. Here, we integrate pharmacogenomic and clinical data with a human metabolic network and find that non-pharmacokinetic metabolic pathways dysregulated by drugs are linked to the development of side effects. We show such dysregulated metabolic pathways contain genes with sequence variants affecting side effect incidence, play established roles in pathophysiology, have significantly altered activity in corresponding diseases, are susceptible to metabolic inhibitors and are effective targets for therapeutic nutrient supplementation. Our results indicate that metabolic dysregulation represents a common mechanism underlying side effect pathogenesis that is distinct from the role of metabolism in drug clearance. We suggest that elucidating the relationships between the cellular response to drugs, genetic variation of patients and cell metabolism may help managing side effects by personalizing drug prescriptions and nutritional intervention strategies.

Abstract Background Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem. Results Here, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches. Conclusion Taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.