Optimization of Microbial Cell Factories with Systems Biology
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UNIVERSITY OF CALIFORNIA, SAN DIEGO Optimization of microbial cell factories with systems biology A Dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Bioengineering by Zachary A. King Committee in charge: Professor Bernhard Ø. Palsson, Chair Professor Jeff Hasty Professor Andrew D. McCulloch Professor Christian M. Metallo Professor Glenn Tesler 2016 Copyright Zachary A. King, 2016 All rights reserved. The Dissertation of Zachary A. King is approved, and it is acceptable in quality and form for publication on microfilm and electronically: Chair University of California, San Diego 2016 iii DEDICATION For Elise, what a delight to see the world through your eyes iv EPIGRAPH When I’m working on a problem, I never think about beauty. I think only how to solve the problem. But when I have finished, if the solution is not beautiful, I know it is wrong. —R. Buckminster Fuller Hofstadter’s Law: It always takes longer than you expect, even when you take into account Hofstadter’s Law. —Douglas Hofstadter, Gödel, Escher, Bach: An Eternal Golden Braid Coffee? —@coffee_dad v TABLE OF CONTENTS Signature Page................................... iii Dedication...................................... iv Epigraph......................................v Table of Contents.................................. vi List of Figures................................... ix List of Tables.................................... xi Acknowledgements................................. xii Vita......................................... xvi Abstract of the Dissertation............................ xviii Chapter 1 Introduction.............................1 1.1 Systems biology........................2 1.2 Microbial cell factories....................4 1.3 Introducing the Thesis....................9 Chapter 2 BiGG Models: A platform for integrating, standardizing, and sharing genome-scale models.................... 11 2.1 Introduction.......................... 11 2.2 Knowledge base content................... 15 2.3 Getting started with BiGG Models............. 17 2.3.1 BiGG website..................... 17 2.3.2 Using BiGG Models for COBRA modeling..... 19 2.3.3 Using BiGG Models for building GEMs....... 20 2.3.4 Accessing the API................... 21 2.4 Implementation of standards................. 23 2.4.1 Loading genomes and GEMs............. 23 2.4.2 BiGG identifiers.................... 24 2.4.3 ModelPolisher..................... 26 2.4.4 Design and implementation............. 27 2.5 Conclusion........................... 27 2.6 Availability and requirements................ 28 vi Chapter 3 Escher: A web application for building, sharing, and embedding data-rich visualizations of biological pathways.......... 30 3.1 Introduction.......................... 30 3.2 Results............................. 34 3.2.1 Building pathway maps................ 34 3.2.2 Visualizing data.................... 37 3.3 Design and Implementation................. 40 3.4 Availability and Future Directions.............. 46 3.5 Availability and requirements................ 47 Chapter 4 Optimizing Cofactor Specificity of Oxidoreductase Enzymes for the Generation of Microbial Production Strains—OptSwap... 49 4.1 Introduction.......................... 49 4.2 Methods............................ 54 4.2.1 Modeling and computational tools.......... 54 4.2.2 Model reduction and selection of reaction set for knockouts....................... 55 4.2.3 Selection of reaction set for cofactor specificity swaps 56 4.2.4 MILP formulation................... 57 4.3 Results............................. 63 4.4 Discussion........................... 71 Chapter 5 Optimal cofactor swapping can increase the theoretical yield for chemical production in Escherichia coli and Saccharomyces cere- visiae ................................. 75 5.1 Introduction.......................... 75 5.2 Methods............................ 79 5.2.1 Models and parameters................ 79 5.2.2 Non-native pathways................. 81 5.2.3 Selection of the reaction sets for cofactor-specificity swaps......................... 81 5.2.4 MILP formulation................... 85 5.2.5 Non-unique solutions................. 87 5.2.6 Sensitivity analysis.................. 90 5.2.7 Determining cofactor usage.............. 91 5.3 Results............................. 91 5.3.1 Native Pathways................... 91 5.3.2 Non-native pathways in E. coli ........... 95 5.3.3 NADPH yield and parameter sensitivity...... 95 5.4 Discussion........................... 99 5.4.1 Cofactor swaps for certain enzymes have a global impact on theoretical yields............. 99 5.4.2 Simulated theoretical yield improvement matches ex- perimental observations for a GAPD swap..... 101 vii 5.4.3 Optimal cofactor swaps increase ATP availability. 102 5.4.4 Cofactor swapping in yeast has a greater effect with D-xylose as a substrate................ 103 5.4.5 Theoretical yield of non-native products increases with swaps....................... 105 5.4.6 Theoretical yields are sensitive to knowledge of co- factor preference and enzyme promiscuity...... 106 5.5 Conclusion........................... 107 Chapter 6 Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion.............. 108 6.1 Introduction.......................... 108 6.2 Results............................. 111 6.2.1 Literature mining provides a diverse set of strains and phenotypes..................... 111 6.2.2 Genome-scale models do not differentiate between isozymes......................... 115 6.2.3 Larger models solve false predictions of cell death.. 118 6.2.4 Simulations suggest that some strains have room to evolve.......................... 119 6.2.5 Next-generation ME-models improve predictions but require parameterization................ 120 6.3 Discussion........................... 122 6.4 Methods............................ 124 6.4.1 Literature mining.................... 124 6.4.2 Simulations....................... 125 6.4.3 Parameter sampling.................. 127 6.4.4 Failure model categorization.............. 127 Chapter 7 Conclusions and Outlook...................... 128 7.1 Next-generation models and predictions........... 128 7.1.1 New cellular networks................. 130 7.1.2 Modularity....................... 132 7.2 Conclusion........................... 134 Bibliography.................................... 135 viii LIST OF FIGURES Figure 1.1: Three types of COBRA predictions have been successfully imple- mented for systems metabolic engineering..............9 Figure 2.1: BiGG Models content......................... 16 Figure 2.2: The BiGG Models homepage..................... 18 Figure 2.3: Accessing BiGG............................ 21 Figure 2.4: Standardizing GEMs......................... 25 Figure 3.1: The Escher interface.......................... 35 Figure 3.2: Data visualization........................... 38 Figure 3.3: The organization of the Escher project................ 41 Figure 3.4: The import and export types in Escher and the EscherConverter. 44 Figure 4.1: The OptSwap formulation for optimizing cofactor specificity of major metabolic enzymes (NAD(H) vs. NADP(H)) coupled to reaction knockouts........................... 59 Figure 4.2: Calculated production envelopes for OptSwap designs predicted to have significantly higher optimal production rate or substrate- specific productivity than designs with just reaction knockouts.. 66 Figure 4.3: Network diagrams showing the shift in reduced cofactor usage between wild-type and the L-alanine production design...... 67 Figure 4.4: The predicted effect of combinatorial interventions on the design for anaerobic production of L-alanine on glucose minimal media.. 68 Figure 5.1: Cofactor specificity modifications of oxidoreductase reactions can improve the maximum theoretical yield of metabolic byproducts. 87 Figure 5.2: Results from optimizing the cofactor specificity of oxidoreductases in the E. coli iJO1366 model to increase the maximum theoretical yield of native metabolic compounds................. 88 Figure 5.3: Results from optimizing the cofactor specificity of oxidoreductases in the S. cerevisiae iMM904 model to increase the maximum theo- retical yield of native metabolic compounds............. 89 Figure 5.4: The effect of swapping cofactor specificity of oxidoreductases for the production of non-native compounds in E. coli......... 90 Figure 5.5: A sensitivity analysis on the impact of cofactor swapping when varying modeling parameters..................... 97 Figure 6.1: The bibliomic database........................ 112 Figure 6.2: The engineered fermentation pathways in E. coli.......... 113 Figure 6.3: Simulations of the bibliomic dataset in E. coli GEMs........ 115 Figure 6.4: Comparing simulations with experiments.............. 117 ix Figure 7.1: COBRA tools have advanced through (a) increased scope of cellular systems which can be modeled, and (b) highly modular simulation strategies, built upon genome-scale models of metabolism..... 130 x LIST OF TABLES Table 1.1: A comparison of modelling and analysis techniques for high-throughput data...................................5 Table 2.1: Comparing BiGG (2010) to BiGG Models (2015).......... 14 Table 2.2: BiGG Identifiers............................ 26 Table 4.1: Oxidoreductase reactions targeted for analysis with OptSwap... 58 Table 4.2: Calculated maximum optimal production rate for designs selected by OptSwap and designs selected by RobustKnock......... 69 Table 5.1: Non-native pathways reconstructed in