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Inferring models and structure from biological data: networks and pathways
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Putnins, Matthew. Inferring models and structure from biological data: networks and pathways. Retrieved from https://doi.org/doi:10.7282/t3-rnnm-8y04

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TitleInferring models and structure from biological data: networks and pathways
NamePutnins, Matthew (author); Androulakis, Ioannis (chair); Cai, Li (internal member); Shinbrot, Troy (internal member); Axelrod, David (outside member); Rutgers University; School of Graduate Studies
Date Created2020
Other Date2020-10 (degree)
SubjectEvolution, Biomedical Engineering
Extent1 online resource (xi, 103 pages) : illustrations
DescriptionPhysiological functions are driven by the emergent behaviors of many individual components, whether they are gene, protein, or metabolic interactions. These interactions form biochemical pathways and interaction networks which then lead to more complex cellular or organismal level behaviors that are not knowable from the characteristics of an individual component of that system. Knowing whether a gene is being expressed or knowing the structure of a protein does not necessarily imply the physiological function of either, but within the context of a meaningful biological system, we can infer more complex behaviors. In the enclosed dissertation we present multiple approaches to contextualize biological components into more complex systems. These methods include utilizing Boolean networks to model interactions in a qualitative manner, as well as analyzing expression data in the context of biochemical pathways. We use two distinct approaches for understanding biological systems: We utilize evolutionary algorithms to understand the origin and development of complex systems. This evolutionary framework enables a better understanding of complex network structures as well as evolutionary strategies used in the development of complex biological systems.

We additionally propose a data-driven approach for interrogating gene expression within the context of biochemical pathways. We utilize a novel method for detecting circadian genes and map these genes onto physiologically functional pathways. We utilize this data to validate methods for constructing a Boolean network to infer the causal relationships which exist within gene pathways. This analysis will improve the applications of high throughput data analysis for the purpose of identifying critical components of complex biological systems.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/t3-rnnm-8y04
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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