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Metabolic Network Reconstruction and Modeling of Microbial Communities Metabolic Network Reconstruction and Modeling of Microbial Communities by Yu Chen A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Chemical Engineering) in the University of Michigan 2012 Doctoral Committee: Assistant Professor Xiaoxia Nina Lin, Chair Professor Erdogan Gulari Professor Lutgarde M. Raskin Peter J. Woolf, Foodwiki LLC c Yu Chen 2012 All Rights Reserved ACKNOWLEDGEMENTS My first acknowledgment goes to Dr. Xiaoxia Lin as my doctoral advisor. She patiently provided the vision, encouragement and advice for me to proceed through the doctorial program and my dissertation. Her wide knowledge and critical thinking have been of great value for me and will sitll guide me during my lifetime studies. Special thanks to my committee members, Dr. Erdogan Gulari, Dr. Lutgarde M. Raskin and Dr. Peter J. Woolf, for their support, guidance and helpful suggestions. Their guidance has served me well and I owe them my heartfelt appreciation. Thanks should be given to all the members in the group of Dr. Lin. Dr. Jihyang Park, Dr. Marjan Varedi, Dr. Fengming Lin, Jeremy Minty, Alissa Kerner, Mike Nelson, Steven Wang, and Mathieu Rossion have given me useful suggestions on my studies, as well as preparing this dissertation. Marc E. Singer also helped me in organizing TransportDB datasets. I wish to thank all the faculty members and staff in the Chemical Engineering Department. Without their help, I would never finish my PhD study. I owe my thanks to many graduate students in the department, who have given me kind help for both my class and GSI work. I thank Dr. Manhong Dai and Dr. Fan Meng at the University of Michigan for providing technical support on a high-performance computer cluster. Dr. James Cavalcoli and Mr. Zachary Wright at the UM Center for Computational Medicine and Bioinformatics helped develop the web interface of PEER. I also need to thank Dr. Jill Banfield and Daniela Goltsman for providing some of the datasets and valuable ii suggestions along the work. I also thank Dr. Gregory Dick for helpful discussions about the Acid Mine Drainage(AMD) microbial community. Dr. Ian T. Paulsen at Macquarie University helped me in predicting transporters and transmembrane proteins from AMD metagenomic sequences. This project was partly supported by a University of Michigan Rackham Faculty Research Grant. iii TABLE OF CONTENTS ACKNOWLEDGEMENTS .......................... ii LIST OF FIGURES ............................... vii LIST OF TABLES ................................ xii LIST OF APPENDICES ............................ xiv ABSTRACT ................................... xv CHAPTER I. Introduction .............................. 1 1.1 Genome-scale Metabolic Network Reconstruction . 1 1.1.1 In silico Reconstructed Metabolic Networks and TheirApplications. 1 1.1.2 Metabolic Network Reconstruction Methods . 8 1.2 Metabolic Network Modeling of Microbial Communities . 13 1.2.1 Meta-genomics on Microbial Communities . 13 1.2.2 Metabolic Modeling of Microbial Communities . 15 1.3 DissertationOverview . 18 II. Bioinformatic Pipeline for Automated Genome-scale Metabolic Network Reconstruction ................ 22 2.1 IntroductionandBackground . 22 2.2 Pipeline for Metabolic Network Reconstruction (PEER) . 25 2.2.1 PreliminaryNetworkGeneration . 27 2.2.2 Mixed Integer Linear Programming (MILP) Based NetworkCuration ................... 30 2.2.3 Probability of Metabolic Network Prediction . 32 2.2.4 Online Tool for Automated Metabolic Network Reconstruction. 34 iv 2.3 Automated Metabolic Network Reconstruction for Model Organism Escherichia coli .................... 34 2.3.1 Automated Metabolic Network Reconstructions of Escherichia coli fromThreeDatasets . 35 2.3.2 Comparison of Automated Metabolic Network Reconstruction With Reference Metabolic Networks 37 2.4 Automated Metabolic Network Reconstruction for Twelve Strains of Prochlorococcus marinus ............... 40 2.4.1 Pan and Core Metabolic Networks of P. marinus .. 43 2.5 DiscussionandConclusions . 43 III. Metabolic Network Reconstruction of Acid Mine Drainage Biofilms ................................. 50 3.1 Introduction ........................... 50 3.2 Metabolic Network Reconstruction and Modeling Methods . 53 3.2.1 Metabolic Network Reconstruction for Individual OrganismsinAMDBiofilm. 53 3.2.2 PredictionofPathwayProbability . 54 3.2.3 Multiple-organism Metabolic Model of Microbial Community ...................... 55 3.3 Reconstructed Metabolic Networks of Individual Organisms in AMDBiofilm .......................... 56 3.3.1 Metabolic Network of the Five Organisms . 56 3.3.2 Network Gap Filling in the Reconstructed Networks 58 3.3.3 CarbonFlowsintheAMDBiofilm. 62 3.3.4 Pathway Distribution in the AMD Biofilm . 65 3.4 Multiple-Organism Metabolic Modeling of AMD Biofilm . 67 3.4.1 Two-species Model for Major Organisms in the AMD Biofilm......................... 67 3.4.2 Five-species Model for All the Organisms in the AMD Biofilm......................... 73 3.4.3 Incorporation of Proteome Data in Metabolic NetworkReconstruction. 75 3.5 DiscussionandConclusions . 78 IV. Metabolic Network Modeling of Gastrointestinal Microbial Communities .............................. 84 4.1 Introduction ........................... 84 4.2 Metabolic Network Modeling of a Two-species Model Microbial Community............................ 88 4.2.1 Background ...................... 88 v 4.2.2 Three-step Metabolic Network Reconstruction and Modeling Method for Two-Species Model . 93 4.2.3 Results......................... 99 4.3 Metabolic Network Modeling of a Ten-species Model Microbial Community............................ 108 4.3.1 Background ...................... 108 4.3.2 Three-step Metabolic Network Reconstruction and ModelingMethod . 110 4.3.3 Results......................... 114 4.4 DiscussionandConclusions . 123 V. Metabolic and Regulatory Network Design for Biochemical Production ............................... 128 5.1 Introduction ........................... 128 5.1.1 Metabolic and Regulatory Network Design . 128 5.1.2 Fatty Acids As Potential Biofuel Precursors . 131 5.2 Bi-level Optimization Framework for Metabolic and RegulatoryNetworkDesign . 133 5.2.1 AssumptionsandSimplifications . 134 5.2.2 Bi-level optimization Framework . 137 5.3 Strain Network Design for Fatty Acids Derived Hydrocarbons 141 5.3.1 Metabolic and Regulatory Networks of Escherichia coli 141 5.3.2 Experimental Verification of Designed Strain for FattyAcidProduction . 148 5.4 DiscussionandConclusions . 153 VI. Concluding Remarks and Future Directions ........... 156 6.1 ConcludingRemarks. 156 6.2 FutureDirections. 160 6.2.1 Future Directions for Metabolic Network ReconstructionandModeling. 162 6.2.2 Metabolic Network Modeling of Real Human Gut Microbiota ....................... 164 APPENDICES .................................. 170 BIBLIOGRAPHY ................................ 184 vi LIST OF FIGURES Figure 1.1 Overview of the genome-scale metabolic network of Escherichia coli. Adapted from Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Nodes represent metabolites and edges represent metabolic reactions. Cofactors and some small molecules such as water are omittedforsimplicity.. 3 1.2 Growthof Escherichia coli K-12 on malate (Ibarra et al., 2002). a. The line of optimality (LO, in red) predicted by FBA for Escherichia coli under malate-oxygen condition. Open circles are data points collected in separate experiments. b. Three-dimensional representation of growth rates. The x and y axes represent the same variables as in a. The z axis represents the cellular growth rate (h−1). OUR, oxygen uptake rate; MUR, malate uptake rate. 4 1.3 The procedure to iteratively reconstruct metabolic networks as described in the protocol suggested by Thiele and Palsson(2010). The iterative steps should continue until the model predictions are close to the experimental phenotypic characteristics of theorganism. 9 2.1 FlowchartforPipeline for Metabolic Network Reconstruction (PEER) 26 2.2 Two examples of problematic metabolic reactions that commonly happen during the metabolic reconstruction process. .... 29 2.3 Demonstration of the MILP model for metabolic network gap filling. Red: binary variables, blue: continuous variables, black: parameters. Complete model descriptions can be found in Appendix B. 31 2.4 Phenotype phase plane (PPP)(a) and lines of optimality (LO)(b) for Escherichia coli K12 MG1655 predicted by automated metabolic networkreconstruction.. 48 2.5 a) Metabolic reactions and b) pathway information for twelve strains of P. marinus predicted by automated metabolic network reconstruction............................... 49 3.1 The ecological roles of Acid Mine Drainage (AMD) biofilm in AMD formation. ................................ 51 vii 3.2 Metabolic network of the species in a AMD microbial community. a) Darkness of each bar represents the probability of existence. Both the reaction (column) and the species (row) were grouped by dendrogram. b) Metabolic network of Leptospirillum Gp II with 283 intracellular reactions and 288 metabolites, drawn by Pajek (Bategeli and Mrvar, 2003). .................................. 59 3.3 The carbon flow of the two dominant species in the AMD microbial community. The seven potential metabolic reactions directly related to carbon fixation in Leptospirillum groups are highlighted. 63 3.4 The carbon flow of Ferroplasma acidarmanus I and II (a) and Thermoplasmatales archaeon Gp1 (b). The five potential metabolic reactions directly related to carbon fixation in
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