{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/109626"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/109626","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Regulation and switching in bacterial gene expression networks in response to nutrients","abstract":"Bacteria must respond to various types of fluctuations in surrounding environment, such as frequent alterations in temperature, salinity, osmolarity, pH, and nutrient availability. What strategies do these tiny microorganisms employ to optimize their chances of growth and survival in an ever-changing environment? It is of general interest to understand cell decision making in response to environmental cues. One well-known strategy is phenotypical heterogeneity where different individuals inside a population utilize different niches and resources within the same environment. Such strategy has the potential to increase the overall fitness of the species. The underlying genetic network plays a key role in the mechanisms that govern phenotypical variation. Many notable examples have been identified and principles and molecular mechanisms governing cell decision making have been extensively investigated. In this work, we investigated regulation and switching of two widely studied bacterial gene expression networks: sugar utilization networks in Escherichia coli and flagellar assembly networks in Salmonella enterica serovar Typhimurium in response to nutrients. Firstly, chapters 3-5 focus on sugar utilization pathways in E. coli. Bacteria rely on utilizing various carbon sources for growth and survival. When multiple carbon sources are available, bacteria make decision on which carbon source to utilize. Usually, bacteria preferentially utilize the carbon source that ensures faster growth and easier accessibility. Catabolite repression refers to the process where the metabolism of one carbon source represses the genes involved in metabolizing another carbon source. The most classic example is glucose repression, which is investigated by many studies, but much less is known about non-glucose repression. Many other sugars are also known to cause catabolite repression, but less is known about the mechanism for catabolite repression by these non-glucose sugars. In Chapter 3, we investigate the mechanism of catabolite repression in the bacterium E. coli during growth on lactose, L-arabinose, and D-xylose. The metabolism of these sugars is regulated in a hierarchical manner, where lactose is the preferred sugar, followed by arabinose, and then xylose. Previously, the preferential utilization of arabinose over xylose was found to result from transcriptional crosstalk. However, others have proposed that cAMP also plays a role in the hierarchical regulation of other non-glucose sugars. In this work, we investigate whether lactose-induced repression of arabinose and xylose gene expression is due to transcriptional crosstalk or cAMP. Our results demonstrate that reciprocal regulation by lactose is due to cAMP and not transcriptional crosstalk using fluorescent reporters to quantify the sugar metabolic gene expression. The previous chapter (Chapter 3) only focuses on the bulk expression in E.coli, in Chapter 4 and 5, we investigate the mechanism of single-cell response of several sugar utilization pathways, since single-cell response provides insights and more clues on the principles and mechanisms governed by regulatory networks in terms of expression and regulation patterns. Understanding repression microscopically helps us to understand repression macroscopically. Chapter 4 focuses on regulation and bistability for single sugar utilization and the effects of positive and negative feedback due to transporters and catabolic enzymes. To explain experimental observed single-cell response data, we have developed a Markov model that describes the intracellular sugar concentration that can be used to explain switching between graded and bistable responses and the role played by transporters and catabolic enzymes. This mathematical framework also provides insights on how the sugar utilization pathway is induced over time and how hysteresis occurs. In Chapter 5, we extend the study to single-cell response when grown on mixture of multiple sugars, in which reciprocal repression may occur. We showed how the global regulator cAMP contributes to reciprocal repression at single-cell resolution for various substrates. Intracellular cAMP concentration tunes the fraction of induced cells by changing the external sugar concentration needed to induce its utilization pathways. We also showed how the global regulation from cAMP-CRP also favors cell decision making to maximize the overall growth rate by allocating subpopulations to utilize none, one or two sugars. Collectively, the results further our understanding of metabolism, regulation and cell decision making during growth on both single sugar and multiple sugars. Chapter 6 focus on bimodality of class 3 flagellar gene expression in S. enterica. Many bacterial use flagella to swim in liquids and swarm over surface. In S. enterica, over fifty genes are required to assemble flagella. The expression of these genes is tightly regulated. Flagellar gene expression is bimodal in S. enterica. Under certain growth conditions, some cells express the flagellar genes whereas others do not. This results in mixed populations of motile and non-motile cells. In the present study, we found that two independent mechanisms control bimodal expression of the flagellar genes. One was previously found to result from a double negative-feedback loop involving the flagellar regulators RflP and FliZ. This feedback loop governs bimodal expression of class 2 genes. In this work, a second mechanism was found to govern bimodal expression of class 3 genes. In particular, class 3 gene expression is still bimodal even when class 2 gene expression is not. Using a combination of experimental and modeling approaches, we found that class 3 bimodalilty results from the σ28-FlgM developmental checkpoint. Collectively, these results further our understanding of how flagellar gene expression is regulated in S. enterica.","abstract_html":"Bacteria must respond to various types of fluctuations in surrounding environment, such as frequent alterations in temperature, salinity, osmolarity, pH, and nutrient availability. What strategies do these tiny microorganisms employ to optimize their chances of growth and survival in an ever-changing environment? It is of general interest to understand cell decision making in response to environmental cues. One well-known strategy is phenotypical heterogeneity where different individuals inside a population utilize different niches and resources within the same environment. Such strategy has the potential to increase the overall fitness of the species. The underlying genetic network plays a key role in the mechanisms that govern phenotypical variation. Many notable examples have been identified and principles and molecular mechanisms governing cell decision making have been extensively investigated. In this work, we investigated regulation and switching of two widely studied bacterial gene expression networks: sugar utilization networks in Escherichia coli and flagellar assembly networks in Salmonella enterica serovar Typhimurium in response to nutrients. Firstly, chapters 3-5 focus on sugar utilization pathways in E. coli. Bacteria rely on utilizing various carbon sources for growth and survival. When multiple carbon sources are available, bacteria make decision on which carbon source to utilize. Usually, bacteria preferentially utilize the carbon source that ensures faster growth and easier accessibility. Catabolite repression refers to the process where the metabolism of one carbon source represses the genes involved in metabolizing another carbon source. The most classic example is glucose repression, which is investigated by many studies, but much less is known about non-glucose repression. Many other sugars are also known to cause catabolite repression, but less is known about the mechanism for catabolite repression by these non-glucose sugars. In Chapter 3, we investigate the mechanism of catabolite repression in the bacterium E. coli during growth on lactose, L-arabinose, and D-xylose. The metabolism of these sugars is regulated in a hierarchical manner, where lactose is the preferred sugar, followed by arabinose, and then xylose. Previously, the preferential utilization of arabinose over xylose was found to result from transcriptional crosstalk. However, others have proposed that cAMP also plays a role in the hierarchical regulation of other non-glucose sugars. In this work, we investigate whether lactose-induced repression of arabinose and xylose gene expression is due to transcriptional crosstalk or cAMP. Our results demonstrate that reciprocal regulation by lactose is due to cAMP and not transcriptional crosstalk using fluorescent reporters to quantify the sugar metabolic gene expression. The previous chapter (Chapter 3) only focuses on the bulk expression in E.coli, in Chapter 4 and 5, we investigate the mechanism of single-cell response of several sugar utilization pathways, since single-cell response provides insights and more clues on the principles and mechanisms governed by regulatory networks in terms of expression and regulation patterns. Understanding repression microscopically helps us to understand repression macroscopically. Chapter 4 focuses on regulation and bistability for single sugar utilization and the effects of positive and negative feedback due to transporters and catabolic enzymes. To explain experimental observed single-cell response data, we have developed a Markov model that describes the intracellular sugar concentration that can be used to explain switching between graded and bistable responses and the role played by transporters and catabolic enzymes. This mathematical framework also provides insights on how the sugar utilization pathway is induced over time and how hysteresis occurs. In Chapter 5, we extend the study to single-cell response when grown on mixture of multiple sugars, in which reciprocal repression may occur. We showed how the global regulator cAMP contributes to reciprocal repression at single-cell resolution for various substrates. Intracellular cAMP concentration tunes the fraction of induced cells by changing the external sugar concentration needed to induce its utilization pathways. We also showed how the global regulation from cAMP-CRP also favors cell decision making to maximize the overall growth rate by allocating subpopulations to utilize none, one or two sugars. Collectively, the results further our understanding of metabolism, regulation and cell decision making during growth on both single sugar and multiple sugars. Chapter 6 focus on bimodality of class 3 flagellar gene expression in S. enterica. Many bacterial use flagella to swim in liquids and swarm over surface. In S. enterica, over fifty genes are required to assemble flagella. The expression of these genes is tightly regulated. Flagellar gene expression is bimodal in S. enterica. Under certain growth conditions, some cells express the flagellar genes whereas others do not. This results in mixed populations of motile and non-motile cells. In the present study, we found that two independent mechanisms control bimodal expression of the flagellar genes. One was previously found to result from a double negative-feedback loop involving the flagellar regulators RflP and FliZ. This feedback loop governs bimodal expression of class 2 genes. In this work, a second mechanism was found to govern bimodal expression of class 3 genes. In particular, class 3 gene expression is still bimodal even when class 2 gene expression is not. Using a combination of experimental and modeling approaches, we found that class 3 bimodalilty results from the σ28-FlgM developmental checkpoint. Collectively, these results further our understanding of how flagellar gene expression is regulated in S. enterica.","abstract_has_math":false,"creators":["Wang, Xiaoyi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Chemical Engineering","degree_department":null,"school":null,"contributors":["Rao, Christopher V","Shukla, Diwakar","Kraft, Mary L","Jin, Yong-Su"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-03-05T21:47:29Z","date_published":"2021-03-05T21:47:29Z","updated_at":"2026-07-22T22:24:50Z","subjects":["bacteria","E. coli","bistability","regulation","sugar metabolism","S. enterica","flagellar assembly"],"languages":["en"],"rights":["Copyright 2020 Xiaoyi Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/109626","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rao, Christopher V","Shukla, Diwakar","Kraft, Mary L","Jin, Yong-Su"]},{"key":"dc:creator","label":"Author","values":["Wang, Xiaoyi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-03-05T21:47:29Z","2023-03-05T21:47:41Z","2020-12-03","2020-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Chemical Engineering"]},{"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":["bacteria","E. coli","bistability","regulation","sugar metabolism","S. enterica","flagellar assembly"]}]},{"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 Xiaoyi Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/109626"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Bacteria must respond to various types of fluctuations in surrounding environment, such as frequent alterations in temperature, salinity, osmolarity, pH, and nutrient availability. What strategies do these tiny microorganisms employ to optimize their chances of growth and survival in an ever-changing environment? It is of general interest to understand cell decision making in response to environmental cues. One well-known strategy is phenotypical heterogeneity where different individuals inside a population utilize different niches and resources within the same environment. Such strategy has the potential to increase the overall fitness of the species. The underlying genetic network plays a key role in the mechanisms that govern phenotypical variation. Many notable examples have been identified and principles and molecular mechanisms governing cell decision making have been extensively investigated. In this work, we investigated regulation and switching of two widely studied bacterial gene expression networks: sugar utilization networks in Escherichia coli and flagellar assembly networks in Salmonella enterica serovar Typhimurium in response to nutrients. Firstly, chapters 3-5 focus on sugar utilization pathways in E. coli. Bacteria rely on utilizing various carbon sources for growth and survival. When multiple carbon sources are available, bacteria make decision on which carbon source to utilize. Usually, bacteria preferentially utilize the carbon source that ensures faster growth and easier accessibility. Catabolite repression refers to the process where the metabolism of one carbon source represses the genes involved in metabolizing another carbon source. The most classic example is glucose repression, which is investigated by many studies, but much less is known about non-glucose repression. Many other sugars are also known to cause catabolite repression, but less is known about the mechanism for catabolite repression by these non-glucose sugars. In Chapter 3, we investigate the mechanism of catabolite repression in the bacterium E. coli during growth on lactose, L-arabinose, and D-xylose. The metabolism of these sugars is regulated in a hierarchical manner, where lactose is the preferred sugar, followed by arabinose, and then xylose. Previously, the preferential utilization of arabinose over xylose was found to result from transcriptional crosstalk. However, others have proposed that cAMP also plays a role in the hierarchical regulation of other non-glucose sugars. In this work, we investigate whether lactose-induced repression of arabinose and xylose gene expression is due to transcriptional crosstalk or cAMP. Our results demonstrate that reciprocal regulation by lactose is due to cAMP and not transcriptional crosstalk using fluorescent reporters to quantify the sugar metabolic gene expression. The previous chapter (Chapter 3) only focuses on the bulk expression in E.coli, in Chapter 4 and 5, we investigate the mechanism of single-cell response of several sugar utilization pathways, since single-cell response provides insights and more clues on the principles and mechanisms governed by regulatory networks in terms of expression and regulation patterns. Understanding repression microscopically helps us to understand repression macroscopically. Chapter 4 focuses on regulation and bistability for single sugar utilization and the effects of positive and negative feedback due to transporters and catabolic enzymes. To explain experimental observed single-cell response data, we have developed a Markov model that describes the intracellular sugar concentration that can be used to explain switching between graded and bistable responses and the role played by transporters and catabolic enzymes. This mathematical framework also provides insights on how the sugar utilization pathway is induced over time and how hysteresis occurs. In Chapter 5, we extend the study to single-cell response when grown on mixture of multiple sugars, in which reciprocal repression may occur. We showed how the global regulator cAMP contributes to reciprocal repression at single-cell resolution for various substrates. Intracellular cAMP concentration tunes the fraction of induced cells by changing the external sugar concentration needed to induce its utilization pathways. We also showed how the global regulation from cAMP-CRP also favors cell decision making to maximize the overall growth rate by allocating subpopulations to utilize none, one or two sugars. Collectively, the results further our understanding of metabolism, regulation and cell decision making during growth on both single sugar and multiple sugars. Chapter 6 focus on bimodality of class 3 flagellar gene expression in S. enterica. Many bacterial use flagella to swim in liquids and swarm over surface. In S. enterica, over fifty genes are required to assemble flagella. The expression of these genes is tightly regulated. Flagellar gene expression is bimodal in S. enterica. Under certain growth conditions, some cells express the flagellar genes whereas others do not. This results in mixed populations of motile and non-motile cells. In the present study, we found that two independent mechanisms control bimodal expression of the flagellar genes. One was previously found to result from a double negative-feedback loop involving the flagellar regulators RflP and FliZ. This feedback loop governs bimodal expression of class 2 genes. In this work, a second mechanism was found to govern bimodal expression of class 3 genes. In particular, class 3 gene expression is still bimodal even when class 2 gene expression is not. Using a combination of experimental and modeling approaches, we found that class 3 bimodalilty results from the σ28-FlgM developmental checkpoint. Collectively, these results further our understanding of how flagellar gene expression is regulated in S. enterica.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-12-01","The student, Xiaoyi Wang, accepted the attached license on 2020-12-02 at 19:25.","The student, Xiaoyi Wang, submitted this Dissertation for approval on 2020-12-03 at 02:25.","This Dissertation was approved for publication on 2020-12-03 at 16:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16046 on 2021-03-04 at 16:33:24","Made available in DSpace on 2021-03-05T21:47:29Z (GMT). No. of bitstreams: 5 WANG-DISSERTATION-2020.pdf: 3951269 bytes, checksum: 7c27ef48d1c6efc9e76ebc062a48506d (MD5) LICENSE.txt: 4208 bytes, checksum: fe79e4d02b7e7e2557599e08af2a3092 (MD5) PROQUEST_LICENSE.txt: 4554 bytes, checksum: a58eef6351ab7169c23deec8b4e3fd47 (MD5) Permissions requests _ Nature Research.pdf: 89316 bytes, checksum: c89ff315b3bd34c53d9cdc061cd29ced (MD5) Statement of Author Rights _ ASM Journals.pdf: 95833 bytes, checksum: 47e133fd66bc36bfa68d5c272ac016ac (MD5) Previous issue date: 2020-12-03","Embargo set by: Seth Robbins for item 117332 Lift date: 2023-03-05T21:47:41Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Regulation and switching in bacterial gene expression networks in response to nutrients"]}]}],"canonical_facts":{"dc:contributor":["Rao, Christopher V","Shukla, Diwakar","Kraft, Mary L","Jin, Yong-Su"],"dc:creator":["Wang, Xiaoyi"],"dc:date":["2021-03-05T21:47:29Z","2023-03-05T21:47:41Z","2020-12-03","2020-12"],"dc:description":["Bacteria must respond to various types of fluctuations in surrounding environment, such as frequent alterations in temperature, salinity, osmolarity, pH, and nutrient availability. What strategies do these tiny microorganisms employ to optimize their chances of growth and survival in an ever-changing environment? It is of general interest to understand cell decision making in response to environmental cues. One well-known strategy is phenotypical heterogeneity where different individuals inside a population utilize different niches and resources within the same environment. Such strategy has the potential to increase the overall fitness of the species. The underlying genetic network plays a key role in the mechanisms that govern phenotypical variation. Many notable examples have been identified and principles and molecular mechanisms governing cell decision making have been extensively investigated. In this work, we investigated regulation and switching of two widely studied bacterial gene expression networks: sugar utilization networks in Escherichia coli and flagellar assembly networks in Salmonella enterica serovar Typhimurium in response to nutrients. Firstly, chapters 3-5 focus on sugar utilization pathways in E. coli. Bacteria rely on utilizing various carbon sources for growth and survival. When multiple carbon sources are available, bacteria make decision on which carbon source to utilize. Usually, bacteria preferentially utilize the carbon source that ensures faster growth and easier accessibility. Catabolite repression refers to the process where the metabolism of one carbon source represses the genes involved in metabolizing another carbon source. The most classic example is glucose repression, which is investigated by many studies, but much less is known about non-glucose repression. Many other sugars are also known to cause catabolite repression, but less is known about the mechanism for catabolite repression by these non-glucose sugars. In Chapter 3, we investigate the mechanism of catabolite repression in the bacterium E. coli during growth on lactose, L-arabinose, and D-xylose. The metabolism of these sugars is regulated in a hierarchical manner, where lactose is the preferred sugar, followed by arabinose, and then xylose. Previously, the preferential utilization of arabinose over xylose was found to result from transcriptional crosstalk. However, others have proposed that cAMP also plays a role in the hierarchical regulation of other non-glucose sugars. In this work, we investigate whether lactose-induced repression of arabinose and xylose gene expression is due to transcriptional crosstalk or cAMP. Our results demonstrate that reciprocal regulation by lactose is due to cAMP and not transcriptional crosstalk using fluorescent reporters to quantify the sugar metabolic gene expression. The previous chapter (Chapter 3) only focuses on the bulk expression in E.coli, in Chapter 4 and 5, we investigate the mechanism of single-cell response of several sugar utilization pathways, since single-cell response provides insights and more clues on the principles and mechanisms governed by regulatory networks in terms of expression and regulation patterns. Understanding repression microscopically helps us to understand repression macroscopically. Chapter 4 focuses on regulation and bistability for single sugar utilization and the effects of positive and negative feedback due to transporters and catabolic enzymes. To explain experimental observed single-cell response data, we have developed a Markov model that describes the intracellular sugar concentration that can be used to explain switching between graded and bistable responses and the role played by transporters and catabolic enzymes. This mathematical framework also provides insights on how the sugar utilization pathway is induced over time and how hysteresis occurs. In Chapter 5, we extend the study to single-cell response when grown on mixture of multiple sugars, in which reciprocal repression may occur. We showed how the global regulator cAMP contributes to reciprocal repression at single-cell resolution for various substrates. Intracellular cAMP concentration tunes the fraction of induced cells by changing the external sugar concentration needed to induce its utilization pathways. We also showed how the global regulation from cAMP-CRP also favors cell decision making to maximize the overall growth rate by allocating subpopulations to utilize none, one or two sugars. Collectively, the results further our understanding of metabolism, regulation and cell decision making during growth on both single sugar and multiple sugars. Chapter 6 focus on bimodality of class 3 flagellar gene expression in S. enterica. Many bacterial use flagella to swim in liquids and swarm over surface. In S. enterica, over fifty genes are required to assemble flagella. The expression of these genes is tightly regulated. Flagellar gene expression is bimodal in S. enterica. Under certain growth conditions, some cells express the flagellar genes whereas others do not. This results in mixed populations of motile and non-motile cells. In the present study, we found that two independent mechanisms control bimodal expression of the flagellar genes. One was previously found to result from a double negative-feedback loop involving the flagellar regulators RflP and FliZ. This feedback loop governs bimodal expression of class 2 genes. In this work, a second mechanism was found to govern bimodal expression of class 3 genes. In particular, class 3 gene expression is still bimodal even when class 2 gene expression is not. Using a combination of experimental and modeling approaches, we found that class 3 bimodalilty results from the σ28-FlgM developmental checkpoint. Collectively, these results further our understanding of how flagellar gene expression is regulated in S. enterica.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-12-01","The student, Xiaoyi Wang, accepted the attached license on 2020-12-02 at 19:25.","The student, Xiaoyi Wang, submitted this Dissertation for approval on 2020-12-03 at 02:25.","This Dissertation was approved for publication on 2020-12-03 at 16:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16046 on 2021-03-04 at 16:33:24","Made available in DSpace on 2021-03-05T21:47:29Z (GMT). No. of bitstreams: 5 WANG-DISSERTATION-2020.pdf: 3951269 bytes, checksum: 7c27ef48d1c6efc9e76ebc062a48506d (MD5) LICENSE.txt: 4208 bytes, checksum: fe79e4d02b7e7e2557599e08af2a3092 (MD5) PROQUEST_LICENSE.txt: 4554 bytes, checksum: a58eef6351ab7169c23deec8b4e3fd47 (MD5) Permissions requests _ Nature Research.pdf: 89316 bytes, checksum: c89ff315b3bd34c53d9cdc061cd29ced (MD5) Statement of Author Rights _ ASM Journals.pdf: 95833 bytes, checksum: 47e133fd66bc36bfa68d5c272ac016ac (MD5) Previous issue date: 2020-12-03","Embargo set by: Seth Robbins for item 117332 Lift date: 2023-03-05T21:47:41Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/109626"],"dc:language":["en"],"dc:rights":["Copyright 2020 Xiaoyi Wang"],"dc:subject":["bacteria","E. coli","bistability","regulation","sugar metabolism","S. enterica","flagellar assembly"],"dc:title":["Regulation and switching in bacterial gene expression networks in response to nutrients"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Chemical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:50Z"}