{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113271"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113271","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Characterizing the higher order metabolism of oral streptococci","abstract":"The metabolism of microbial species is very diverse, displaying a wide range of capabilities. This diversity is often explainable by differences in the metabolic networks that underly microbial metabolism. However, closely related microbial species that have structurally conserved metabolic networks with relatively few differences still display diverse growth profiles under the same growth conditions. This diversity cannot be explained by metabolic capabilities given the similarity in metabolism across these closely related species. In this dissertation, I explore this phenomenon by comparing the growth fitness of a group of closely related oral streptococci using high throughput combinatorial growth experiments. I compare these experimental results to predictions from genome-scale metabolic models that I constructed for each species under study. These models capture the entire metabolism of a species and predict its metabolic capabilities. Disagreements between experimental results and model predictions point to differences in utilization of metabolism that can help explain diversity in growth phenotypes despite similarity in metabolic networks. I develop an algorithm that can analyze these differences and suggest gene suppressions that reconcile model predictions with experimental results, suggesting points of genetic or enzymatic regulation.","abstract_html":"The metabolism of microbial species is very diverse, displaying a wide range of capabilities. This diversity is often explainable by differences in the metabolic networks that underly microbial metabolism. However, closely related microbial species that have structurally conserved metabolic networks with relatively few differences still display diverse growth profiles under the same growth conditions. This diversity cannot be explained by metabolic capabilities given the similarity in metabolism across these closely related species. In this dissertation, I explore this phenomenon by comparing the growth fitness of a group of closely related oral streptococci using high throughput combinatorial growth experiments. I compare these experimental results to predictions from genome-scale metabolic models that I constructed for each species under study. These models capture the entire metabolism of a species and predict its metabolic capabilities. Disagreements between experimental results and model predictions point to differences in utilization of metabolism that can help explain diversity in growth phenotypes despite similarity in metabolic networks. I develop an algorithm that can analyze these differences and suggest gene suppressions that reconcile model predictions with experimental results, suggesting points of genetic or enzymatic regulation.","abstract_has_math":false,"creators":["Jijakli, Kenan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Bioengineering","degree_department":null,"school":null,"contributors":["Jensen, Paul A","Maslov, Sergei","Sirk, Shannon","Vanderpool, Cari"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T22:54:09Z","date_published":"2022-01-12T22:54:09Z","updated_at":"2026-07-22T22:24:53Z","subjects":["oral streptococci, genpme-scale metabolic models, gene regulation, prediction"],"languages":["en"],"rights":["Copyright 2021 Kenan Jijakli"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113271","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jensen, Paul A","Maslov, Sergei","Sirk, Shannon","Vanderpool, Cari"]},{"key":"dc:creator","label":"Author","values":["Jijakli, Kenan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T22:54:09Z","2024-01-12T22:56:20Z","2021-07-12","2021-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Bioengineering"]},{"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":["oral streptococci, genpme-scale metabolic models, gene regulation, prediction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Kenan Jijakli"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113271"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The metabolism of microbial species is very diverse, displaying a wide range of capabilities. This diversity is often explainable by differences in the metabolic networks that underly microbial metabolism. However, closely related microbial species that have structurally conserved metabolic networks with relatively few differences still display diverse growth profiles under the same growth conditions. This diversity cannot be explained by metabolic capabilities given the similarity in metabolism across these closely related species. In this dissertation, I explore this phenomenon by comparing the growth fitness of a group of closely related oral streptococci using high throughput combinatorial growth experiments. I compare these experimental results to predictions from genome-scale metabolic models that I constructed for each species under study. These models capture the entire metabolism of a species and predict its metabolic capabilities. Disagreements between experimental results and model predictions point to differences in utilization of metabolism that can help explain diversity in growth phenotypes despite similarity in metabolic networks. I develop an algorithm that can analyze these differences and suggest gene suppressions that reconcile model predictions with experimental results, suggesting points of genetic or enzymatic regulation.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-08-01","The student, Kenan Jijakli, accepted the attached license on 2021-07-05 at 16:46.","The student, Kenan Jijakli, submitted this Dissertation for approval on 2021-07-05 at 16:55.","This Dissertation was approved for publication on 2021-07-12 at 16:31.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16757 on 2022-01-12 at 13:03:53","Made available in DSpace on 2022-01-12T22:54:09Z (GMT). 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This diversity is often explainable by differences in the metabolic networks that underly microbial metabolism. However, closely related microbial species that have structurally conserved metabolic networks with relatively few differences still display diverse growth profiles under the same growth conditions. This diversity cannot be explained by metabolic capabilities given the similarity in metabolism across these closely related species. In this dissertation, I explore this phenomenon by comparing the growth fitness of a group of closely related oral streptococci using high throughput combinatorial growth experiments. I compare these experimental results to predictions from genome-scale metabolic models that I constructed for each species under study. These models capture the entire metabolism of a species and predict its metabolic capabilities. Disagreements between experimental results and model predictions point to differences in utilization of metabolism that can help explain diversity in growth phenotypes despite similarity in metabolic networks. I develop an algorithm that can analyze these differences and suggest gene suppressions that reconcile model predictions with experimental results, suggesting points of genetic or enzymatic regulation.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-08-01","The student, Kenan Jijakli, accepted the attached license on 2021-07-05 at 16:46.","The student, Kenan Jijakli, submitted this Dissertation for approval on 2021-07-05 at 16:55.","This Dissertation was approved for publication on 2021-07-12 at 16:31.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16757 on 2022-01-12 at 13:03:53","Made available in DSpace on 2022-01-12T22:54:09Z (GMT). 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