{"647340":{"#nid":"647340","#data":{"type":"event","title":"PhD Defense by Justin Lee","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EBioE Ph.D. Thesis Defense\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EJustin Lee\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMonday, May 24, 2021, 10:00 AM\u0026nbsp;EST\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003ELink:\u0026nbsp;\u003Ca href=\u0022https:\/\/bluejeans.com\/8461156006\/\u0022\u003Ehttps:\/\/bluejeans.com\/8461156006\/\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAdvisor:\u003C\/strong\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMark P. Styczynski, Ph.D. \u0026nbsp;(ChBE, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee Members:\u0026nbsp;\u003C\/strong\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EFani Boukouvala, Ph.D. (ChBE, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMelissa Kemp, Ph.D. (BME, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAndrew Medford, Ph.D. (ChBE, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EEberhard Voit, Ph.D. \u0026nbsp;(BME, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EComputational modeling of metabolic pathways toward predicting dynamic phenotypes\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMetabolic systems are important to a wide variety of applications, including therapeutic development, agricultural crop production, and manufacturing of industrial chemicals. Developing metabolic models is one of the best approaches to study metabolism, as computational experiments are generally cheaper and faster to perform than experiments in a laboratory. While there are computational frameworks that can model large metabolic systems at steady state or the metabolite dynamics of a small number of key metabolic pathways, it is substantially more difficult to model the dynamics of metabolism at the genome scale. In this thesis dissertation, I present three computational platforms that address several of the challenges in developing dynamic genome-scale metabolic models. First, I devised a stepwise machine learning strategy for identifying the regulatory topology within metabolic systems, which can be used to construct more accurate metabolic models. I then developed a framework for inferring absolute concentrations from relative abundances in metabolomics data, which will allow metabolomics (the systems-scale study of metabolites) to be more easily used with metabolic modeling tools. Finally, I implemented new constraints within a linear programming dynamic modeling framework that increase its ability to model a wider variety of metabolic systems. Together, these three platforms create a cohesive workflow for modeling the dynamics of metabolism at any scale.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Computational modeling of metabolic pathways toward predicting dynamic phenotypes"}],"uid":"27707","created_gmt":"2021-05-11 13:27:49","changed_gmt":"2021-05-11 13:27:49","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2021-05-24T11:00:00-04:00","event_time_end":"2021-05-24T13:00:00-04:00","event_time_end_last":"2021-05-24T13:00:00-04:00","gmt_time_start":"2021-05-24 15:00:00","gmt_time_end":"2021-05-24 17:00:00","gmt_time_end_last":"2021-05-24 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}