{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/106446"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/106446","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Behavioral analysis and protein folding in zebrafish larvae","abstract":"The zebrafish, Danio rerio, is a very important vertebrate model organism with growing popularity in biology and neuroscience. My thesis work covered two topics centered on zebrafish. Chapter 1 and Chapter 2 focused on the swimming behavior of the animal. This topic is of great interest because animal behavior is the direct reflection of neural activity. In particular, because of the episodic nature of the swimming behavior of larval zebrafish, the quantitative analysis of its behavior presents potential in bridging behavior and underlying neural control. In Chapter 1, I looked into the escape response behavior in 2D. Combining an objective behavior analysis approach with a simple neurokinematic model, I was able to explain the difference between two types of escape response behavior by the difference in their corresponding neural circuits. The results from my non-invasive analysis agreed with previous findings in invasive electrophysiological studies, in contradiction with some unconstrained swimming studies. In Chapter 2, I extended the swimming behavior analysis into three dimensions. I developed a physical model-based tracking algorithm that captured both fish position and shape in 3D during swim bouts. I analyzed the swimming behavior in a 3D environment and compared with the behavior in 2D. My results showed a distinction between the behavior in 2D and 3D environments and this distinction revealed that past studies in 2D may not faithfully capture the behavioral repertoire of larval zebrafish in the natural environment. In Chapter 3, still using zebrafish as the model organism, I studied protein folding dynamics in differentiated tissues in a living organism. With CPLC postdoc Caitlin Davis, I developed a customized pipeline that integrates meganuclease-mediated mosaic transformation with fluorescence-detected temperature-jump microscopy to probe dynamics and stability of endogenously expressed proteins in different tissues of living zebrafish.","abstract_html":"The zebrafish, Danio rerio, is a very important vertebrate model organism with growing popularity in biology and neuroscience. My thesis work covered two topics centered on zebrafish. Chapter 1 and Chapter 2 focused on the swimming behavior of the animal. This topic is of great interest because animal behavior is the direct reflection of neural activity. In particular, because of the episodic nature of the swimming behavior of larval zebrafish, the quantitative analysis of its behavior presents potential in bridging behavior and underlying neural control. In Chapter 1, I looked into the escape response behavior in 2D. Combining an objective behavior analysis approach with a simple neurokinematic model, I was able to explain the difference between two types of escape response behavior by the difference in their corresponding neural circuits. The results from my non-invasive analysis agreed with previous findings in invasive electrophysiological studies, in contradiction with some unconstrained swimming studies. In Chapter 2, I extended the swimming behavior analysis into three dimensions. I developed a physical model-based tracking algorithm that captured both fish position and shape in 3D during swim bouts. I analyzed the swimming behavior in a 3D environment and compared with the behavior in 2D. My results showed a distinction between the behavior in 2D and 3D environments and this distinction revealed that past studies in 2D may not faithfully capture the behavioral repertoire of larval zebrafish in the natural environment. In Chapter 3, still using zebrafish as the model organism, I studied protein folding dynamics in differentiated tissues in a living organism. With CPLC postdoc Caitlin Davis, I developed a customized pipeline that integrates meganuclease-mediated mosaic transformation with fluorescence-detected temperature-jump microscopy to probe dynamics and stability of endogenously expressed proteins in different tissues of living zebrafish.","abstract_has_math":false,"creators":["Feng, Ruopei"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Chemistry","degree_department":null,"school":null,"contributors":["Gruebele, Martin","Chemla, Yann R","Nelson, Mark E","Lu, Yi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-03-02T22:38:43Z","date_published":"2020-03-02T22:38:43Z","updated_at":"2026-07-22T22:24:47Z","subjects":["zebrafish","danio rerio","behavior","protein folding"],"languages":["en"],"rights":["Copyright 2019 Ruopei Feng"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/106446","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gruebele, Martin","Chemla, Yann R","Nelson, Mark E","Lu, Yi"]},{"key":"dc:creator","label":"Author","values":["Feng, Ruopei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-03-02T22:38:43Z","2022-03-03T10:15:19Z","2019-11-18","2019-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Chemistry"]},{"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":["zebrafish","danio rerio","behavior","protein folding"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Ruopei Feng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/106446"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The zebrafish, Danio rerio, is a very important vertebrate model organism with growing popularity in biology and neuroscience. 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I developed a physical model-based tracking algorithm that captured both fish position and shape in 3D during swim bouts. I analyzed the swimming behavior in a 3D environment and compared with the behavior in 2D. My results showed a distinction between the behavior in 2D and 3D environments and this distinction revealed that past studies in 2D may not faithfully capture the behavioral repertoire of larval zebrafish in the natural environment. In Chapter 3, still using zebrafish as the model organism, I studied protein folding dynamics in differentiated tissues in a living organism. With CPLC postdoc Caitlin Davis, I developed a customized pipeline that integrates meganuclease-mediated mosaic transformation with fluorescence-detected temperature-jump microscopy to probe dynamics and stability of endogenously expressed proteins in different tissues of living zebrafish.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-12-01","The student, Ruopei Feng, accepted the attached license on 2019-11-14 at 17:31.","The student, Ruopei Feng, submitted this Dissertation for approval on 2019-11-14 at 17:56.","This Dissertation was approved for publication on 2019-11-18 at 14:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14555 on 2020-02-28 at 17:36:15","Made available in DSpace on 2020-03-02T22:38:43Z (GMT). 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