{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129840"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129840","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Using population dynamics to decipher phage infection","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Yuncong Geng, accepted the attached license on 2025-07-05 at 16:23.","The student, Yuncong Geng, submitted this Dissertation for approval on 2025-07-05 at 17:42.","This Dissertation was approved for publication on 2025-07-10 at 15:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22416 on 2025-10-20 at 16:57:27","Bacteriophage infection outcomes are shaped by dynamic interactions between virus, host, and environment. Understanding how these factors govern the phage life cycle requires integrated experimental and theoretical approaches. This dissertation presents a framework for decoding phage developmental strategies through high-throughput measurements and mechanistic modeling of the population dynamics of phage-infected bacteria. In Chapter 2, I present a high-throughput assay that uses optical density (OD) measurements in a microplate reader to monitor the growth dynamics of infected bacterial cultures. This method enables quantification of phage titer, lysogeny frequency, and the lytic growth rate of phages. Applying this method to E. coli and phage lambda reveals that as bacterial growth slows, the lytic growth rate decreases and the propensity for lysogeny increases, demonstrating the influence of host physiology on phage decision-making. In Chapter 3, I introduce the preliminary efforts to extend this framework to additional systems. I apply the OD-based assay to study how phage phi3T uses its own quorum-sensing peptide to regulate lysogeny in B. subtilis. I also examine how B. subtilis defends against phi3T infection and use a mathematical model to suggest that resistance emerges in a cell density-dependent manner. Finally, I use optimality theory to interpret lambda’s lysogeny strategy. Together, this work provides a unified experimental and theoretical toolkit for probing phage-host-environment interactions."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Using population dynamics to decipher phage infection"]}]}],"canonical_facts":{"dc:contributor":["Golding, Ido","Maslov, Sergei","Kim, Sangjin","Whitaker, Rachel J."],"dc:creator":["Geng, Yuncong"],"dc:date":["2025-07-10","2025-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Yuncong Geng, accepted the attached license on 2025-07-05 at 16:23.","The student, Yuncong Geng, submitted this Dissertation for approval on 2025-07-05 at 17:42.","This Dissertation was approved for publication on 2025-07-10 at 15:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22416 on 2025-10-20 at 16:57:27","Bacteriophage infection outcomes are shaped by dynamic interactions between virus, host, and environment. Understanding how these factors govern the phage life cycle requires integrated experimental and theoretical approaches. This dissertation presents a framework for decoding phage developmental strategies through high-throughput measurements and mechanistic modeling of the population dynamics of phage-infected bacteria. In Chapter 2, I present a high-throughput assay that uses optical density (OD) measurements in a microplate reader to monitor the growth dynamics of infected bacterial cultures. This method enables quantification of phage titer, lysogeny frequency, and the lytic growth rate of phages. Applying this method to E. coli and phage lambda reveals that as bacterial growth slows, the lytic growth rate decreases and the propensity for lysogeny increases, demonstrating the influence of host physiology on phage decision-making. In Chapter 3, I introduce the preliminary efforts to extend this framework to additional systems. I apply the OD-based assay to study how phage phi3T uses its own quorum-sensing peptide to regulate lysogeny in B. subtilis. I also examine how B. subtilis defends against phi3T infection and use a mathematical model to suggest that resistance emerges in a cell density-dependent manner. Finally, I use optimality theory to interpret lambda’s lysogeny strategy. Together, this work provides a unified experimental and theoretical toolkit for probing phage-host-environment interactions."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129840"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Yuncong Geng"],"dc:subject":["Phage","Population Dynamics"],"dc:title":["Using population dynamics to decipher phage infection"],"dc:type":["text"],"thesis:degree_discipline":["Biophysics & Quant Biology"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}