Doctor of Philosophy, The Ohio State University, 2021, Biomedical Sciences

Metabolism is a process that touches all aspects of life, from homeostasis to disease, such that the study of metabolites yields valuable insights into the inner workings of biological systems. Translating the findings of metabolomic and lipidomic experiments into biological insight, biomarkers, or actionable targets associated with disease requires functional interpretation of the data, which is challenging. One common strategy for interpreting metabolomic data is pathway enrichment analysis. Pathway analysis is useful because pathway-level perturbation can be more reproducible across samples than individual metabolite shifts, which are hindered by inconsistent experimental coverage of metabolites and functional redundancy of metabolites. However, pathway analysis of metabolites faces many barriers for success. Issues with metabolite pathway analysis include lack of metabolite pathway annotations, highly overlapping pathway definitions, and (again) lack of reproducibility in metabolite detection between experiments. Here, I present two complementary software resources, RaMP and MetaboSPAN, which I helped to develop in order to address these issues. RaMP is a metabolite annotations database that consolidates pathway, reaction, chemical structure, and other information from multiple publicly available data sources. RaMP's associated R package allows users to query information on metabolites of interest as well as perform pathway enrichment analysis using the Fisher's exact test. MetaboSPAN is an advanced pathway enrichment analysis strategy that infers activity in undetected portions of the metabolome using the vast extent of knowledge in RaMP to expand pathway-level findings and improve reproducibility between experiments. I demonstrate the utility of these tools on a metabolite data set generated in patient-derived cell lines of dedifferentiated liposarcoma with varying amplification of the MDM2 oncogene.

Committee: Ewy Mathe PhD (Advisor); Kevin Coombes PhD (Advisor); Lang Li PhD (Committee Member); Rachel Kopec PhD (Committee Member) Subjects: Bioinformatics; Biomedical Research

Master of Sciences, Case Western Reserve University, 2009, EECS - Computer and Information Sciences

The emerging area of Metabolomics provides new paradigms for diagnosis of diseases by rapidly and non-invasive providing specific bio-markers, useful for the diagnosis of early stages of diseases, and indicators of action sites of drugs. Emerging technologies are continuously increasing the number of metabolites that can be detected. Now there exists a need for computational tools to help clinical researchers to derive meaningful interpretations of metabolomics data. In this thesis we propose a metabolomic analysis workbench called PathCaseMAW which provides (1) A web accessible metabolic pathway database which supports online browsing and querying, and is novel in that it includes location information for pathways and also models transport processes. (2) Tissue-aware visualization support for viewing process, pathways or groups of pathways from our database. (3) An online tool which allows users to upload their own observed/measured metabolite level changes and computationally identifies the mechanisms that produce those metabolite changes.

Committee: Gultekin Ozsoyoglu PhD (Committee Chair); Meral Ozsoyoglu PhD (Committee Member); Mehmet Koyuturk PhD (Committee Member) Subjects: Biochemistry; Computer Science