| Summary: | Thesis: M. Eng. in Computer Science and Molecular Biology, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. === This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. === Cataloged from student-submitted PDF version of thesis. === Includes bibliographical references (pages 83-90). === New high-throughput "omic" methods can help shed light on molecular pathways underpinning diseases ranging from cancers to neurodegenerative disorders. However, effectively integrating information across these diverse data types is challenging. Network modeling approaches can help bridge this gap. In particular, the Prize- Collecting Steiner Forest approach (PCSF) is a network modeling method that provides high-confidence subnetworks of physically interacting molecules by integrating diverse "omics" data with prior knowledge from protein-protein interaction networks (PPIs). However, PCSF is sensitive to initial parameterization and generating biological hypotheses from the resulting subnetworks can often be difficult. This study increases the interpretability of subnetwork solutions generated PCSF by studying the effect of varying PCSF free parameters and adding annotations for subcellular localization. The PCSF approach is then used to elucidate pathways underlying synergy between cytokines, pro-inflammatory molecules that mediate diverse biological phenomena ranging from anti-viral immunity to autoimmune disorders like inflammatory bowel disease (IBD). In addition, PCSF approach is applied in a cross-species context to integrate information from Drosophila models for neurodegeneration and human Alzheimer's Disease (AD) patients to investigate proximal conserved mechanisms of age-related neurodegeneration. === by Bryce Hwang. === M. Eng. in Computer Science and Molecular Biology
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