Using network inference to discover molecular pathways underlying cytokine synergism and age-related neurodegeneration
Name
1078698840-MIT.pdf
Description
Full printable version
Size
11.35 MB
Format
Adobe PDF
Checksum (MD5)
380e3f5681efc2bc9e090f4d2fa84b54
Author(s)
Hwang, Bryce
Advisor(s)
Ernest Fraenkel.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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