{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120576"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120576","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Improving constraint-based metabolic models with deep learning","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_has_math":false,"creators":["Brasch, Brendan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Bioengineering","degree_department":null,"school":null,"contributors":["Jensen, Paul A"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Constraint-based Metabolic Modeling","Genome-scale Models","Neural Networks","Streptococcus Mutans","Cobranet","Deep Learning","Metabolic Modeling","Cobra Modeling"],"languages":["en","eng"],"rights":["Copyright 2023 Brendan Brasch"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120576","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jensen, Paul A"]},{"key":"dc:creator","label":"Author","values":["Brasch, Brendan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-05-02"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Bioengineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Constraint-based Metabolic Modeling","Genome-scale Models","Neural Networks","Streptococcus Mutans","Cobranet","Deep Learning","Metabolic Modeling","Cobra Modeling"]}]},{"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 Brendan Brasch"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120576"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Brendan Brasch, accepted the attached license on 2023-04-28 at 15:19.","The student, Brendan Brasch, submitted this Thesis for approval on 2023-04-28 at 15:30.","This Thesis was approved for publication on 2023-05-02 at 10:39.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19255 on 2023-09-01 at 17:22:11","Constraint-based metabolic modeling is a powerful tool that allows researchers to map out biological systems and run simulations in order to generate hypotheses related to metabolism. However, these systems are limited by their computational complexity and assumptions made during model creation that hinder predictive accuracy. Neural networks represent a promising approach that can be leveraged in combination with constraint-based models to elucidate further predictions from experimental data. However, researchers often have difficulty extracting features from neural network predictions, leading to difficulties generating and supporting research hypotheses. Furthermore, neural networks are limited by the availability and quality of relevant experimental data. Here, we describe a novel framework, CobraNet, which exploits the strengths of constraint-based models and neural networks in order to generate predictions. We demonstrate how this framework can be leveraged to generate predictions of enzyme activity and biological fitness on an understudied bacterium, Streptococcus mutans. This CobraNet model was built using a constraint-based model for S. mutans and simple metabolic experimental data, demonstrating the broad applicability of this framework. The CobraNet framework can be used throughout the field of biological modeling and would greatly enhance the accessibility of genome-scale models within the bioinformatics community."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Improving constraint-based metabolic models with deep learning"]}]}],"canonical_facts":{"dc:contributor":["Jensen, Paul A"],"dc:creator":["Brasch, Brendan"],"dc:date":["2023-05","2023-05-02"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Brendan Brasch, accepted the attached license on 2023-04-28 at 15:19.","The student, Brendan Brasch, submitted this Thesis for approval on 2023-04-28 at 15:30.","This Thesis was approved for publication on 2023-05-02 at 10:39.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19255 on 2023-09-01 at 17:22:11","Constraint-based metabolic modeling is a powerful tool that allows researchers to map out biological systems and run simulations in order to generate hypotheses related to metabolism. However, these systems are limited by their computational complexity and assumptions made during model creation that hinder predictive accuracy. Neural networks represent a promising approach that can be leveraged in combination with constraint-based models to elucidate further predictions from experimental data. However, researchers often have difficulty extracting features from neural network predictions, leading to difficulties generating and supporting research hypotheses. Furthermore, neural networks are limited by the availability and quality of relevant experimental data. Here, we describe a novel framework, CobraNet, which exploits the strengths of constraint-based models and neural networks in order to generate predictions. We demonstrate how this framework can be leveraged to generate predictions of enzyme activity and biological fitness on an understudied bacterium, Streptococcus mutans. This CobraNet model was built using a constraint-based model for S. mutans and simple metabolic experimental data, demonstrating the broad applicability of this framework. The CobraNet framework can be used throughout the field of biological modeling and would greatly enhance the accessibility of genome-scale models within the bioinformatics community."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120576"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Brendan Brasch"],"dc:subject":["Constraint-based Metabolic Modeling","Genome-scale Models","Neural Networks","Streptococcus Mutans","Cobranet","Deep Learning","Metabolic Modeling","Cobra Modeling"],"dc:title":["Improving constraint-based metabolic models with deep learning"],"dc:type":["text"],"thesis:degree_discipline":["Bioengineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}