[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127611-en":3,"doc-seo-127611-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127611,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","The Development of Metabolic Models and Machine Learning Methods for Biofuel-Producing Bacteria","Focuses on modeling bacterial metabolism to advance second-generation biofuel production, using non-model biofuel-producing bacteria as the research target. Develops a concerted systems biology analysis of phenol metabolism in Rhodococcus opacus PD630, combining experimental labeling, LC-MS metabolomics, flux calculations, RNA-seq, and pathway interpretation. Proposes a high-quality genome-scale metabolic model and integrates machine-learning methods to improve predictive capability and support insights into fluxes, cofactor balance, energy metabolism, and metabolic network adaptation.","Washington University in St. Louis  \nWashington University Open Scholarship  \n\n| McKelvey School of Engineering Theses & Dissertations | McKelvey School of Engineering |\n| --- | --- |\n| Spring 5-15-2023\u003Cbr>The Development of Metabolic Models and Machine Learning Methods for Biofuel-Producing Bacteria\u003Cbr>Garrett Roell\u003Cbr>Washington University in St. Louis\u003Cbr>Follow this and additional works at: [https://openscholarship.wustl.edu/eng_etds](https://openscholarship.wustl.edu/eng_etds) |  |\n\nRecommended Citation  \nRoell, Garrett, \"The Development of Metabolic Models and Machine Learning Methods for BiofuelProducing Bacteria\" (2023) . McKelvey School of Engineering Theses & Dissertations. 905.  \n[https://openscholarship.wustl.edu/eng_etds/905](https://openscholarship.wustl.edu/eng_etds/905)  \nThis Dissertation is brought to you for free and open access by the McKelvey School of Engineering at Washington University Open Scholarship. It has been accepted for inclusion in McKelvey School of Engineering Theses & Dissertations by an authorized administrator of Washington University Open Scholarship. For more information, please contact [digital@wumail.wustl.edu](digital@wumail.wustl.edu).  \nWASHINGTON UNIVERSITY IN ST. LOUIS  \nMcKelvey School of Engineering  \nDepartment of Energy, Environmental & Chemical Engineering  \nDissertation Examination Committee:  \nYinjie Tang, Chair  \nDouglas Allen  \nGautam Dantas  \nMarcus Foston  \nJoshua Yuan  \nThe Development of Metabolic Models and Machine Learning Methods for Biofuel-Producing  \nBacteria  \nby  \nGarrett William Roell  \nA dissertation presented to  \nthe McKelvey School of Engineering  \nof Washington University in  \npartial fulfillment of the  \nrequirements for the degree  \nof Doctor of Philosophy  \nMay 2023  \nSt. Louis, Missouri  \n© 2023, Garrett William Roell  \nTable of Contents  \nList of Figures ................................................................................................................................. v  \nList of Tables ................................................................................................................................ vii  \nAcknowledgments........................................................................................................................ viii  \nAbstract ........................................................................................................................................... x  \nChapter 1: Motivations for Studying Non-Model Bacteria for Biofuel Production ....................... 1  \n1.1 Motivation for second-generation biofuel production ................................................ 1  \n1.2 Lignocellulose and syngas as renewable feedstocks .................................................. 1  \n1.3 Systems biology methods ........................................................................................... 3  \n1.4 Mechanistic modeling of bacterial metabolism .......................................................... 3  \n1.5 Data-driven modeling of bacterial metabolism........................................................... 4  \n1.6 References ................................................................................................................... 5  \nChapter 2: A Concerted Systems Biology Analysis of Phenol Metabolism in Rhodococcus opacus PD630 ................................................................................................................................. 7  \n2.1 Abstract ........................................................................................................................... 7  \n2.2 Introduction ..................................................................................................................... 8  \n2.3 Materials and methods .................................................................................................. 10  \n2.3.1 Chemicals, strains, and growth conditions .............................................................","cbCaig5edO016ScA","https://ap.wps.com/l/cbCaig5edO016ScA","pdf",9359810,1,180,"English","en",105,"# List of Figures\n# List of Tables\n# Acknowledgments\n# Abstract\n# Chapter 1: Motivations for Studying Non-Model Bacteria for Biofuel Production\n## 1.1 Motivation for second-generation biofuel production\n## 1.2 Lignocellulose and syngas as renewable feedstocks\n## 1.3 Systems biology methods\n## 1.4 Mechanistic modeling of bacterial metabolism\n## 1.5 Data-driven modeling of bacterial metabolism\n## 1.6 References\n# Chapter 2: A Concerted Systems Biology Analysis of Phenol Metabolism in Rhodococcus opacus PD630\n## 2.1 Abstract\n## 2.2 Introduction\n## 2.3 Materials and methods\n## 2.4 Results and discussion\n## 2.5 Conclusions and perspectives\n## 2.6 References\n# Chapter 3: A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism","[{\"question\":\"What is the main research goal of this dissertation?\",\"answer\":\"The dissertation aims to develop metabolic models and machine-learning methods to better understand and predict metabolism in biofuel-producing bacteria for second-generation biofuel production.\"},{\"question\":\"How is phenol metabolism studied in Rhodococcus opacus PD630?\",\"answer\":\"Phenol metabolism is analyzed through a concerted systems biology workflow, including pulse-tracing labeling, LC-MS metabolite measurements, flux calculations, and RNA-seq data to connect pathway activity to network fluxes.\"},{\"question\":\"What kind of modeling is developed for Rhodococcus opacus metabolism?\",\"answer\":\"A high-quality genome-scale metabolic model is constructed for Rhodococcus opacus, supporting interpretation of flux maps and related aspects such as cofactor balance and energy metabolism.\"}]","The Development of Metabolic Models and Machine Learning Methods for Biofuel-Producing Bacteria | 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is the main research goal of this dissertation?","Question",{"text":76,"@type":77},"The dissertation aims to develop metabolic models and machine-learning methods to better understand and predict metabolism in biofuel-producing bacteria for second-generation biofuel production.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is phenol metabolism studied in Rhodococcus opacus PD630?",{"text":81,"@type":77},"Phenol metabolism is analyzed through a concerted systems biology workflow, including pulse-tracing labeling, LC-MS metabolite measurements, flux calculations, and RNA-seq data to connect pathway activity to network fluxes.",{"name":83,"@type":74,"acceptedAnswer":84},"What kind of modeling is developed for Rhodococcus opacus metabolism?",{"text":85,"@type":77},"A high-quality genome-scale metabolic model is constructed for Rhodococcus opacus, supporting interpretation of flux maps and related aspects such as cofactor balance and energy 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