{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108019"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108019","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Understanding the functional consequences of genetic variation on gene regulation","abstract":"Understanding the relationship between the non-coding sequence of the genome and the gene expression is one of fundamental goals of regulatory genomics. Perturbing certain locations in the non-coding DNA causes a disturbance to the precise spatial and temporal expression of the genes. Gene regulatory mechanisms determine the amount of change in the gene expression from variations in the sequence. Mathematical modeling of gene expression has been proven to be successful to establish a sequence-to-function relationship in a context aware manner and provide mechanistic explanation of the gene regulatory processes. In this thesis, we aspire to provide tools for understanding sequence-level encoding of gene regulation by applying thermodynamics-based models. More specifically, we provide a probabilistic framework to develop deeper insights about current knowledge of a gene’s regulatory mechanisms, objectively characterizing what a new experiment adds to such knowledge and quantifying how ‘informative’ that experiment is. In order to elucidate mechanisms of transcriptional regulation we use single nucleotide polymorphism data to further investigate different mechanistic hypotheses and provide knowledge of systems-level processes. We construct a probabilistic model to leverage our knowledge of transcriptional regulatory networks and identify variations that lead to a significant change. Through this work we not only advance the field of regulatory genomics, but potentially provide a path for identifying variations in the DNA that significantly effect phenotype and lead to a disease.","abstract_html":"Understanding the relationship between the non-coding sequence of the genome and the gene expression is one of fundamental goals of regulatory genomics. Perturbing certain locations in the non-coding DNA causes a disturbance to the precise spatial and temporal expression of the genes. Gene regulatory mechanisms determine the amount of change in the gene expression from variations in the sequence. Mathematical modeling of gene expression has been proven to be successful to establish a sequence-to-function relationship in a context aware manner and provide mechanistic explanation of the gene regulatory processes. In this thesis, we aspire to provide tools for understanding sequence-level encoding of gene regulation by applying thermodynamics-based models. More specifically, we provide a probabilistic framework to develop deeper insights about current knowledge of a gene’s regulatory mechanisms, objectively characterizing what a new experiment adds to such knowledge and quantifying how ‘informative’ that experiment is. In order to elucidate mechanisms of transcriptional regulation we use single nucleotide polymorphism data to further investigate different mechanistic hypotheses and provide knowledge of systems-level processes. We construct a probabilistic model to leverage our knowledge of transcriptional regulatory networks and identify variations that lead to a significant change. Through this work we not only advance the field of regulatory genomics, but potentially provide a path for identifying variations in the DNA that significantly effect phenotype and lead to a disease.","abstract_has_math":false,"creators":["Khajouei, Farzaneh"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Sinha, Saurabh","Arnosti, David","Campbell, Roy","Dar, Roy","Peng, Jian"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T21:57:58Z","date_published":"2020-08-26T21:57:58Z","updated_at":"2026-07-22T22:24:47Z","subjects":["sequence-to-expression, genomics, gene regulation, modeling"],"languages":["en"],"rights":["Copyright 2020 Farzaneh Khajouei"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108019","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sinha, Saurabh","Arnosti, David","Campbell, Roy","Dar, Roy","Peng, Jian"]},{"key":"dc:creator","label":"Author","values":["Khajouei, Farzaneh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T21:57:58Z","2020-05-08","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"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":["sequence-to-expression, genomics, gene regulation, modeling"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Farzaneh Khajouei"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108019"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Understanding the relationship between the non-coding sequence of the genome and the gene expression is one of fundamental goals of regulatory genomics. Perturbing certain locations in the non-coding DNA causes a disturbance to the precise spatial and temporal expression of the genes. Gene regulatory mechanisms determine the amount of change in the gene expression from variations in the sequence. Mathematical modeling of gene expression has been proven to be successful to establish a sequence-to-function relationship in a context aware manner and provide mechanistic explanation of the gene regulatory processes. In this thesis, we aspire to provide tools for understanding sequence-level encoding of gene regulation by applying thermodynamics-based models. More specifically, we provide a probabilistic framework to develop deeper insights about current knowledge of a gene’s regulatory mechanisms, objectively characterizing what a new experiment adds to such knowledge and quantifying how ‘informative’ that experiment is. In order to elucidate mechanisms of transcriptional regulation we use single nucleotide polymorphism data to further investigate different mechanistic hypotheses and provide knowledge of systems-level processes. We construct a probabilistic model to leverage our knowledge of transcriptional regulatory networks and identify variations that lead to a significant change. Through this work we not only advance the field of regulatory genomics, but potentially provide a path for identifying variations in the DNA that significantly effect phenotype and lead to a disease.","Submission original under an indefinite embargo labeled 'Open Access'. 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Gene regulatory mechanisms determine the amount of change in the gene expression from variations in the sequence. Mathematical modeling of gene expression has been proven to be successful to establish a sequence-to-function relationship in a context aware manner and provide mechanistic explanation of the gene regulatory processes. In this thesis, we aspire to provide tools for understanding sequence-level encoding of gene regulation by applying thermodynamics-based models. More specifically, we provide a probabilistic framework to develop deeper insights about current knowledge of a gene’s regulatory mechanisms, objectively characterizing what a new experiment adds to such knowledge and quantifying how ‘informative’ that experiment is. In order to elucidate mechanisms of transcriptional regulation we use single nucleotide polymorphism data to further investigate different mechanistic hypotheses and provide knowledge of systems-level processes. We construct a probabilistic model to leverage our knowledge of transcriptional regulatory networks and identify variations that lead to a significant change. Through this work we not only advance the field of regulatory genomics, but potentially provide a path for identifying variations in the DNA that significantly effect phenotype and lead to a disease.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Farzaneh Khajouei, accepted the attached license on 2020-05-08 at 12:55.","The student, Farzaneh Khajouei, submitted this Dissertation for approval on 2020-05-08 at 12:59.","This Dissertation was approved for publication on 2020-05-08 at 15:29.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15295 on 2020-08-25 at 17:13:32","Made available in DSpace on 2020-08-26T21:57:58Z (GMT). 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