{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120101"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120101","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Transcriptional regulatory genomics: from mechanistic modeling to causal inference","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_has_math":false,"creators":["Dibaeinia, Payam"],"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","Zhai, ChengXiang","El-Kebir, Mohammed","Dresch, Jacqueline M."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:56Z","subjects":["Gene Regulation","Gene Regulatory Networks","Machine Learning","Interpretable Ai For Biology","Causal Inference","Counterfactual Inference"],"languages":["en","eng"],"rights":["Copyright 2023 Payam Dibaeinia"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120101","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sinha, Saurabh","Zhai, ChengXiang","El-Kebir, Mohammed","Dresch, Jacqueline M."]},{"key":"dc:creator","label":"Author","values":["Dibaeinia, Payam"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-04-27"]},{"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":["Gene Regulation","Gene Regulatory Networks","Machine Learning","Interpretable Ai For Biology","Causal Inference","Counterfactual Inference"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Payam Dibaeinia"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120101"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Payam Dibaeinia, accepted the attached license on 2023-04-25 at 12:55.","The student, Payam Dibaeinia, submitted this Dissertation for approval on 2023-04-25 at 13:01.","This Dissertation was approved for publication on 2023-04-27 at 13:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19162 on 2023-09-01 at 16:55:28","Gene transcription refers to the process in which coding regions on the genome are copied into mRNA molecules through complex cellular mechanisms. This process is regulated through mechanisms that are encoded in the genome and are activated by cellular signals, enzymes, and proteins. Transcriptional regulation often involves a class of proteins called transcription factors regulating other genes. Although we have a limited understanding of regulatory mechanisms and associations in human and other species, discerning such characteristics of transcriptional regulation is of paramount importance in systems biology. The recent advancements in experimental techniques for high throughput measurements of cellular processes and their molecular signatures have led to increasingly growing biological databases including various ``omics\" datasets. These resources give rise to the emergence of novel computational models in systems biology that aim at understanding the genome of human and other species from data. These efforts include the development of data-driven methods for modeling transcriptional regulation using omics datasets. The general goal of such studies is to understand regulatory mechanisms and molecular interactions that drive transcriptional regulation. In practice, both the predictive accuracy and interpretability of these quantitative models are crucial to improve their efficacy. Especially, interpretability of the model is a key factor in various applications, from learning mechanistic regulatory insights to inferring causal regulatory relationships. This thesis is focused on the applications of interpretable computational models in learning and simulating transcriptional regulatory systems. In this Ph.D. thesis, I develop novel interpretable machine learning models for studying transcriptional regulations from two aspects: (1) learning biophysically-consistent regulatory mechanisms, (2) inference of causal regulatory associations. The first aspect of the study was pursued through quantitative and machine learning models that either explicitly encode regulatory mechanisms using biophysically-inspired functions or learn them in meaningful higher-order representations. The second aspect was achieved through an interpretation of non-linear machine learning models based on causal inference principles. Additionally, I leverage an existing mechanistic model for stochastic expression of genes to develop a novel framework for simulating gene expressions under causal regulatory networks at the cell-level resolution. This tool is useful for assessing the strength and weaknesses of causal regulatory inference algorithms."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Transcriptional regulatory genomics: from mechanistic modeling to causal inference"]}]}],"canonical_facts":{"dc:contributor":["Sinha, Saurabh","Zhai, ChengXiang","El-Kebir, Mohammed","Dresch, Jacqueline M."],"dc:creator":["Dibaeinia, Payam"],"dc:date":["2023-05","2023-04-27"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Payam Dibaeinia, accepted the attached license on 2023-04-25 at 12:55.","The student, Payam Dibaeinia, submitted this Dissertation for approval on 2023-04-25 at 13:01.","This Dissertation was approved for publication on 2023-04-27 at 13:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19162 on 2023-09-01 at 16:55:28","Gene transcription refers to the process in which coding regions on the genome are copied into mRNA molecules through complex cellular mechanisms. This process is regulated through mechanisms that are encoded in the genome and are activated by cellular signals, enzymes, and proteins. Transcriptional regulation often involves a class of proteins called transcription factors regulating other genes. Although we have a limited understanding of regulatory mechanisms and associations in human and other species, discerning such characteristics of transcriptional regulation is of paramount importance in systems biology. The recent advancements in experimental techniques for high throughput measurements of cellular processes and their molecular signatures have led to increasingly growing biological databases including various ``omics\" datasets. These resources give rise to the emergence of novel computational models in systems biology that aim at understanding the genome of human and other species from data. These efforts include the development of data-driven methods for modeling transcriptional regulation using omics datasets. The general goal of such studies is to understand regulatory mechanisms and molecular interactions that drive transcriptional regulation. In practice, both the predictive accuracy and interpretability of these quantitative models are crucial to improve their efficacy. Especially, interpretability of the model is a key factor in various applications, from learning mechanistic regulatory insights to inferring causal regulatory relationships. This thesis is focused on the applications of interpretable computational models in learning and simulating transcriptional regulatory systems. In this Ph.D. thesis, I develop novel interpretable machine learning models for studying transcriptional regulations from two aspects: (1) learning biophysically-consistent regulatory mechanisms, (2) inference of causal regulatory associations. The first aspect of the study was pursued through quantitative and machine learning models that either explicitly encode regulatory mechanisms using biophysically-inspired functions or learn them in meaningful higher-order representations. The second aspect was achieved through an interpretation of non-linear machine learning models based on causal inference principles. Additionally, I leverage an existing mechanistic model for stochastic expression of genes to develop a novel framework for simulating gene expressions under causal regulatory networks at the cell-level resolution. This tool is useful for assessing the strength and weaknesses of causal regulatory inference algorithms."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120101"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Payam Dibaeinia"],"dc:subject":["Gene Regulation","Gene Regulatory Networks","Machine Learning","Interpretable Ai For Biology","Causal Inference","Counterfactual Inference"],"dc:title":["Transcriptional regulatory genomics: from mechanistic modeling to causal inference"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}