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University of California, San Diego UNIVERSITY OF CALIFORNIA, SAN DIEGO Identification of growth-coupled and kinetically robust production in strain designs using genome-scale models A thesis submitted in partial satisfaction of the requirements for the degree Master of Science in Bioengineering by Hoang Viet Dinh Committee in charge: Professor Bernhard O. Palsson, Chair Professor Adam M. Feist Professor Nathan E. Lewis Professor Christian M. Metallo 2017 Copyright Hoang Viet Dinh, 2017 All rights reserved. The Thesis of Hoang Viet Dinh is approved and it is acceptable in quality and form for publication on microfilm and electronically: _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Chair University of California, San Diego 2017 iii DEDICATION I dedicate this work to my parents who wholefully support their children education iv TABLES OF CONTENTS Signature Page...........................................................................................................iii Dedication...................................................................................................................iv Table of Contents.........................................................................................................v List of Figures.............................................................................................................vi List of Tables..............................................................................................................vii Acknowledgements...................................................................................................viii Abstract of the Thesis..................................................................................................x Chapter 1 Introduction...........................................................................................1 Chapter 2 Genome-scale models: System biology calculation platform for rational metabolic engineering strain designs...................................................4 2.1. Introduction..................................................................................4 2.2. Model and design modifications...................................................5 Chapter 3 Growth-coupled production evaluation.................................................7 3.1. Cell growth simulation..................................................................7 3.2. Growth-coupled production........................................................10 3.3. Removal of redundant knockouts..............................................13 3.4. Robustness evaluation of growth-coupled production with kinetic parameter sampling in Metabolic-Expression model..................16 3.5. Term definitions.........................................................................20 Chapter 4 Results...............................................................................................22 4.1. Integrated strain designs search and evaluation workflow.........22 4.2. Growth-coupled production diversity in Eschericihia coli............27 4.3. Knockout prediction...................................................................31 4.4. Robust designs after kinetic parameters sampling test in Metabolic-Expression model......................................................33 Chapter 5 Discussion..........................................................................................36 Bibliography...............................................................................................................39 v LIST OF FIGURES Figure 3.1 Iterative workflow to remove redundant knockouts.............................14 Figure 3.2 Visualization of growth-coupled production slope...............................15 Figure 3.3 Sampled magnitude space for target and competitive pathways’ kinetic parameters.........................................................................................17 Figure 3.4 Status of kinetic parameters sampling with discretization...................18 Figure 3.5 High resolution discretization with binary search................................19 Figure 4.1 Integrated model-driven strain design search and evaluation workflow using Metabolic and Metabolic-Expression models............................25 Figure 4.2 Filtration workflow results...................................................................26 Figure 4.3 Growth-coupled production pathway map..........................................29 Figure 4.4 Growth-coupled production of target molecules in specific conditions 30 Figure 4.5 Knockout strategies suggested by strain design search algorithm for growth-coupled production.................................................................32 Figure 4.6 Suggestion of robust 1,4-butanediol production pathway by Metabolic- Expression model...............................................................................35 vi LIST OF TABLES Table 2.1 Model and design modifications...........................................................6 Table 3.1 Nonphysiological pathway shutdowns..................................................8 Table 3.2 Molecules growth-coupled production status......................................11 Table 3.3 Table of terms and their definitions used in this work..........................21 Table 4.1 Properties of strain designs................................................................28 vii ACKNOWLEDGEMENTS First and foremost, I want to thank Dr. Adam Feist for the initial idea of the project that eventually turns into this thesis. His insight on rational metabolic engineering study using system biology at large-scale was present not only in his guidance for this project but also from his previous published works that stimulated my thought on how to tackle the problem at visionary level. I want to thank my advisor Professor Bernhard Palsson for the given resources and opportunities. This project could not be realized and actualized without the level of foundation he has established in his lab and the system biology and metabolic engineering community as a whole. He has organized his lab in multiple disciplinaries and enhance collaboration from which this project is one of its fruit. I want to thank Dr. Zachary King for his close supervision which help maintain the project on the right track. He has provided much of the technical help and vision without which the project would stop at general workflow level without an elegant execution. He is also a great mentor which help establish my simulation expertise, critical thinking, and idea presentation skills. On the modeling side, I want to thank Colton Lloyd and Dr. Laurence Yang. Their expertise from countless questions that I have asked shaped the core on the simulation side of this project. I want to thank COBRApy community for their contribution on an excellent and convenient toolbox for system biology researchers. I want to thank everyone in System Biology Research Group for all the stimulating discussion and presentation that I have experienced during my stay which has shaped my thinking. viii Last but not least, I want to thank my parents for the infinite amount of interests and support for their children education. My primitive seeds of knowledge and academia appreciation were planted by my dad without which I could have not been where I am today. The thesis, including chapters 1-5, is adapted from an in preparation manuscript: Dinh, H. V., King Z. A., Palsson B. Ø., Feist A. M. Identification of growth- coupled and kinetic parameters robust production in strain designs using genome- scale models. The thesis author was the primary author of the manuscript and was responsible for the research. ix ABSTRACT OF THE THESIS Identification of growth-coupled and kinetically robust production in strain designs using genome-scale models by Hoang Viet Dinh Master of Science in Bioengineering University of California, San Diego, 2017 Professor Bernhard O. Palsson, Chair Conversion of renewable biomass to useful molecules in microbial cell factories can be approached in a rational and systematic manner using constraint- based reconstruction and analysis. Filtering for high confidence in silico designs is critical as in vivo construction and testing is expensive and time consuming. As such, a workflow was devised to analyze the robustness of growth-coupled production against variability in enzyme kinetic parameters through applying a genome-scale model of metabolism and macromolecules expression (ME-model). A collection of 2632 knockout designs in E. coli was evaluated by a workflow that removed 40 x redundant knockouts and returned pools of 634 growth-coupled designs and 41 ME- model robust designs. The resulting knockout strategies revealed how enzyme efficiency and pathway tradeoffs can affect growth-coupled production phenotypes. xi Chapter 1 Introduction The chemical industry has relied on petroleum as raw material for the last century (Sittig and Weil 1954). As of recent, shifting to renewable biomass has gained interest in both industry and academia (United Nations Industrial Development Organization; Lee and Kim 2015). Microbial cell factories can be used in efficient bioprocesses to convert biomass to a wide array of useful products (Lee and Kim 2015). Microbial cell factories needs to be designed to optimize for production rate, yield, and titer as
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