The Development of Metabolic Models and Machine Learning Methods for Biofuel-Producing Bacteria

Aug 5, 2026

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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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages180

Reading Time7 h 34 min

AccessFree to download

List of Figures

List of Tables

Acknowledgments

Abstract

Chapter 1: Motivations for Studying Non-Model Bacteria for Biofuel Production

1.1 Motivation for second-generation biofuel production

1.2 Lignocellulose and syngas as renewable feedstocks

1.3 Systems biology methods

1.4 Mechanistic modeling of bacterial metabolism

1.5 Data-driven modeling of bacterial metabolism

1.6 References

Chapter 2: A Concerted Systems Biology Analysis of Phenol Metabolism in Rhodococcus opacus PD630

2.1 Abstract

2.2 Introduction

2.3 Materials and methods

2.4 Results and discussion

2.5 Conclusions and perspectives

2.6 References

Chapter 3: A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism

Washington University in St. Louis Washington University Open Scholarship | McKelvey School of Engineering Theses & Dissertations | McKelvey School of Engineering | | --- | --- | | Spring 5-15-2023
The Development of Metabolic Models and Machine Learning Methods for Biofuel-Producing Bacteria
Garrett Roell
Washington University in St. Louis
Follow this and additional works at: [https://openscholarship.wustl.edu/eng_etds](https://openscholarship.wustl.edu/eng_etds) | | Recommended Citation Roell, Garrett, "The Development of Metabolic Models and Machine Learning Methods for BiofuelProducing Bacteria" (2023) . McKelvey School of Engineering Theses & Dissertations. 905. [https://openscholarship.wustl.edu/eng_etds/905](https://openscholarship.wustl.edu/eng_etds/905) This 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). WASHINGTON UNIVERSITY IN ST. LOUIS McKelvey School of Engineering Department of Energy, Environmental & Chemical Engineering Dissertation Examination Committee: Yinjie Tang, Chair Douglas Allen Gautam Dantas Marcus Foston Joshua Yuan The Development of Metabolic Models and Machine Learning Methods for Biofuel-Producing Bacteria by Garrett William Roell A dissertation presented to the McKelvey School of Engineering of Washington University in partial fulfillment of the requirements for the degree of Doctor of Philosophy May 2023 St. Louis, Missouri © 2023, Garrett William Roell Table of Contents List of Figures ................................................................................................................................. v List of Tables ................................................................................................................................ vii Acknowledgments........................................................................................................................ viii Abstract ........................................................................................................................................... x Chapter 1: Motivations for Studying Non-Model Bacteria for Biofuel Production ....................... 1 1.1 Motivation for second-generation biofuel production ................................................ 1 1.2 Lignocellulose and syngas as renewable feedstocks .................................................. 1 1.3 Systems biology methods ........................................................................................... 3 1.4 Mechanistic modeling of bacterial metabolism .......................................................... 3 1.5 Data-driven modeling of bacterial metabolism........................................................... 4 1.6 References ................................................................................................................... 5 Chapter 2: A Concerted Systems Biology Analysis of Phenol Metabolism in Rhodococcus opacus PD630 ................................................................................................................................. 7 2.1 Abstract ........................................................................................................................... 7 2.2 Introduction ..................................................................................................................... 8 2.3 Materials and methods .................................................................................................. 10 2.3.1 Chemicals, strains, and growth conditions .............................................................

FAQs

What is the main research goal of this dissertation?

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.

How is phenol metabolism studied in Rhodococcus opacus PD630?

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.

What kind of modeling is developed for Rhodococcus opacus metabolism?

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.

Aug 5, 2026

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