{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/45486"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/45486","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Transcriptional regulation of metabolism and behavior: insights from reconstruction and modeling of complex biochemical networks","abstract":"The genotype and the environment significantly influence the behavior and phenotype of an organism. Yet the mechanism by which a simple genetic change or environmental perturbation alters the state of an organism at the molecular level, and subsequently its phenotype, is still not completely clear. Large-scale omics experiments are now generating a wealth of data on these various processes. As a result, there is a critical need for methods that rapidly transform these high-throughput data into predictive models for medicine and bioengineering. These predictive models need to seamlessly integrate molecular components of the cell into networks and subsequently predict macroscopic phenotypic changes that emerge from these interacting networks. I have developed new tools and algorithms that address this key challenge of multi-scale network integration in order to assemble a holistic view of the cell. My dissertation involves three main themes: 1. The development of new tools (PROM, GEMINI and ASTRIX) for reconstruction and modeling of biochemical networks 2. Understanding transcriptional regulation of metabolism in various model organisms 3. Applying systems approaches to social behavior to dissect the role of transcriptional regulation.","abstract_html":"The genotype and the environment significantly influence the behavior and phenotype of an organism. Yet the mechanism by which a simple genetic change or environmental perturbation alters the state of an organism at the molecular level, and subsequently its phenotype, is still not completely clear. Large-scale omics experiments are now generating a wealth of data on these various processes. As a result, there is a critical need for methods that rapidly transform these high-throughput data into predictive models for medicine and bioengineering. These predictive models need to seamlessly integrate molecular components of the cell into networks and subsequently predict macroscopic phenotypic changes that emerge from these interacting networks. I have developed new tools and algorithms that address this key challenge of multi-scale network integration in order to assemble a holistic view of the cell. My dissertation involves three main themes: 1. The development of new tools (PROM, GEMINI and ASTRIX) for reconstruction and modeling of biochemical networks 2. Understanding transcriptional regulation of metabolism in various model organisms 3. Applying systems approaches to social behavior to dissect the role of transcriptional regulation.","abstract_has_math":false,"creators":["Chandrasekaran, Sriram"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Biophysics & Computnl Biology","degree_department":null,"school":null,"contributors":["Price, Nathan D.","Robinson, Gene E.","Luthey-Schulten, Zaida A.","Zhong, Sheng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-08-22T16:41:40Z","date_published":"2013-08-22T16:41:40Z","updated_at":"2026-07-22T22:25:36Z","subjects":["Systems Biology","Metabolism","Gene Regulation","Social Behavior","Neuroscience","Machine learning","Genomics","Data Mining"],"languages":["en"],"rights":["Copyright 2013 Sriram Chandrasekaran"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/45486","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Price, Nathan D.","Robinson, Gene E.","Luthey-Schulten, Zaida A.","Zhong, Sheng"]},{"key":"dc:creator","label":"Author","values":["Chandrasekaran, Sriram"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-08-22T16:41:40Z","2013-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biophysics & Computnl Biology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Systems Biology","Metabolism","Gene Regulation","Social Behavior","Neuroscience","Machine learning","Genomics","Data Mining"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Sriram Chandrasekaran"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/45486"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The genotype and the environment significantly influence the behavior and phenotype of an organism. 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