UC San Diego UC San Diego Electronic Theses and Dissertations Title Genome-scale Models of Metabolism and Gene Expression : : Construction and Use for Growth Phenotype Prediction Permalink https://escholarship.org/uc/item/8zq4p227 Author Lerman, Joshua Adam Publication Date 2014 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, SAN DIEGO Genome-scale Models of Metabolism and Gene Expression: Construction and Use for Growth Phenotype Prediction A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Bioinformatics & Systems Biology by Joshua Adam Lerman Committee in charge: Professor Bernhard Ø. Palsson, Chair Professor Milton H. Saier, Jr., Co-Chair Professor Philip E. Bourne Professor Terence Hwa Professor Victor Nizet 2014 Copyright Joshua Adam Lerman, 2014 All rights reserved. The dissertation of Joshua Adam Lerman is approved, and it is acceptable in quality and form for publication on microfilm and electronically: Co-Chair Chair University of California, San Diego 2014 iii DEDICATION To my mother and father, for your love, guidance, and all the sacrifices you made for Justin, Rachel, and I. To Lauren, for your love and all those times I told you, \One sec." To the loving memory of Bubby. iv EPIGRAPH Tony Stark was able to build this in a cave! WITH A BOX OF SCRAPS!! |Obadiah Stane, Iron Man v TABLE OF CONTENTS Signature Page................................. iii Dedication.................................... iv Epigraph....................................v Table of Contents................................ vi List of Figures................................. xi List of Tables.................................. xiii Acknowledgements............................... xiv Vita....................................... xviii Abstract of the Dissertation.......................... xx Chapter 1 Introduction..........................1 1.1 Life, constraints, and being good enough........1 1.2 Systems microbiology and its promise..........2 1.3 Systems microbiology as a four-step procedure: Then and Now............................3 1.3.1 Then: M-Models (c. 2008)............4 1.3.2 Now: ME-Models (c. 2013)............5 1.4 There's more than one way to model a cell, so where do ME-Models fit in?.....................7 Chapter 2 The genome organization of Thermotoga maritima reflects its lifestyle............................. 10 2.1 Abstract.......................... 10 2.2 Author Summary..................... 11 2.3 Introduction........................ 11 2.4 Results........................... 14 2.4.1 An integrative, multi-omic approach for the an- notation of the genome organization....... 14 2.4.2 Identification of promoters and RBSs followed by quantitative intra- and interspecies analysis of bind- ing free energies.................. 18 2.4.3 T. maritima promoter-containing intergenic re- gions reveal a unique distribution of 50UTRs and spatial limitations on regulation......... 25 vi 2.4.4 T. maritima has an actively transcribed genome that is tightly correlated to protein abundances 28 2.5 Discussion......................... 30 2.6 Materials and Methods.................. 33 2.6.1 Culture conditions and physiology........ 33 2.6.2 Genome resequencing and annotation updates. 33 2.6.3 Transcription start site determination...... 34 2.6.4 Transcriptome characterization and gene expres- sion......................... 34 2.6.5 Proteomics, peptide mapping, and protein abun- dance quantitation................ 35 2.6.6 Promoter element motif analysis and position weight matrix (PWM) generation............ 36 2.6.7 Information content calculations......... 37 2.6.8 Ribosome binding site energy calculations... 38 2.6.9 Rho-independent terminator site determination 39 2.6.10 Prediction of small RNAs............ 39 2.6.11 Transcription unit assembly........... 39 2.6.12 Transcription factor binding site mapping.... 40 2.6.13 Data deposition.................. 40 2.7 Acknowledgments..................... 40 Chapter 3 In silico method for modelling metabolism and gene product expression at genome scale.................. 42 3.1 Abstract.......................... 42 3.2 Introduction........................ 43 3.3 Results........................... 46 3.3.1 Genome-scale modelling of metabolism and ex- pression...................... 46 3.3.2 Molecularly efficient simulation of cellular physi- ology........................ 49 3.3.3 Gene product production and turnover alters path- way activity.................... 52 3.3.4 Simulation of systems-level molecular phenotypes 55 3.3.5 In silico gene expression profiling drives discovery 58 3.4 Discussion......................... 63 3.5 Methods.......................... 66 3.5.1 Network reconstruction procedure........ 66 3.5.2 Protein complexes................. 67 3.5.3 Genetic code determination........... 67 3.5.4 TU architecture determination.......... 67 3.5.5 In silico molecular biology............ 68 3.5.6 In vivo methods.................. 68 vii 3.5.7 RNA modifications................ 69 3.5.8 Sensitivity analysis................ 70 3.5.9 File formats.................... 70 3.5.10 Accession codes.................. 70 3.6 Acknowledgements.................... 70 Chapter 4 Genome-scale models of metabolism and gene expression ex- tend and refine growth phenotype prediction........ 72 4.1 Abstract.......................... 72 4.2 Introduction........................ 73 4.3 Results........................... 75 4.3.1 Integration of genome-scale reaction networks of protein synthesis and metabolism........ 75 4.3.2 Growth demands and general constraints on molec- ular catalysis................... 76 4.3.3 Derivation of constraints on molecular catalytic rates........................ 79 4.3.4 Growth regions under varying nutrient availabil- ity......................... 80 4.3.5 Effect of proteome limitation on secretion pheno- types........................ 85 4.3.6 Central carbon fluxes reflect growth optimization subject to catalytic constraints.......... 86 4.3.7 In silico gene expression profiling from nutrient-limited to batch growth conditions... 89 4.4 Discussion......................... 94 4.5 Materials and methods.................. 98 4.5.1 Network reconstruction.............. 98 4.5.2 Coupling constraint formulation and imposition 98 4.5.3 Optimization procedure.............. 99 4.5.4 Hierarchical clustering.............. 99 4.5.5 File formats and accessibility........... 99 4.6 Acknowledgements.................... 100 Chapter 5 Reconciling a Salmonella enterica metabolic model with ex- perimental data confirms that overexpression of the glyoxylate shunt can rescue a lethal ppc deletion mutant........ 101 5.1 Abstract.......................... 101 5.2 Introduction........................ 102 5.3 Materials and methods.................. 104 5.3.1 Bacterial strains.................. 104 5.3.2 Growth media................... 106 5.3.3 Construction of the ∆ppc mutant........ 106 viii 5.3.4 Growth rate and glucose uptake rate measure- ments....................... 106 5.3.5 In silico modeling................. 107 5.3.6 Construction of pASK1988............ 108 5.3.7 Construction of pS7, pS8, pS10......... 108 5.3.8 Induction and protein overexpression...... 109 5.4 Results........................... 109 5.4.1 In contrast to model simulations, a Salmonella Typhimurium ∆ppc mutant is nonviable in glu- cose M9 medium................. 109 5.4.2 Comparing efficient flux states enables a hypothesis- driven approach to reconcile metabolic models with experimental data................. 110 5.4.3 Deleting iclR from the ∆ppc mutant restores via- bility........................ 113 5.4.4 Simultaneous expression of aceBA and aceK from two separate plasmids can rescue growth in the ∆ppc mutant, but overexpression of aceBA, aceK, or aceBAK individually from a single plasmid can- not......................... 115 5.5 Discussion......................... 117 5.6 Acknowledgements.................... 118 Chapter 6 ME-Models as a conduit for integration of systems and syn- thetic biology.......................... 119 6.1 Introduction........................ 119 6.2 pUC19 cloning vector................... 122 6.3 Production of spider silk proteins............ 125 6.4 Introduction of a 2-step heterologous pathway to produce indole-3-acetaldehyde................... 130 Chapter 7 Conclusions and Outlook................... 133 7.1 Conclusions........................ 133 7.2 Outlook.......................... 139 7.2.1 The most promising basic uses of the E. coli ME- Model....................... 139 7.2.2 The most promising applied uses of the E. coli ME-Model..................... 148 7.2.3 Automating the construction of a ME-Model for a bacteria of your choice............. 149 7.2.4 ME-Models for Yeast and Humans....... 150 7.2.5 Roadmap to a steady-state whole-cell E. coli model................... 150 ix Bibliography.................................. 152 x LIST OF FIGURES Figure 1.1: Systems microbiology in a nutshell...............2 Figure 1.2: Types of omics data and their uses for constructing and applying ME-Models............................6 Figure 1.3: The microbial genotype-phenotype relationship........8 Figure 2.1: Generation of multiple genome-scale datasets integrated with bioinformatics predictions reveals the genome organization.. 15 Figure 2.2: Identification and quantitative comparison of genetic elements for transcription and translation initiation........... 19 Figure 2.3: Arrangement of genomic