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The Pennsylvania State University The Graduate School College of Engineering RECONSTRUCTION AND ANALYSIS OF GENOME-SCALE METABOLIC MODELS OF PHOTOSYNTHETIC ORGANISMS A Dissertation in Chemical Engineering by Rajib Saha © 2014 Rajib Saha Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2014 The dissertation of Rajib Saha was reviewed and approved* by the following: Costas D. Maranas Donald B. Broughton Professor of Chemical Engineering Dissertation Advisor Chair of Committee Reka Albert Professor of Physics and Biology Howard M. Salis Assistant Professor in Chemical Engineering and Agricultural and Biological Engineering Andrew Zydney Walter L. Robb Chair and Professor of Chemical Engineering Head of the department of Chemical Engineering *Signatures are on file in the Graduate School iii ABSTRACT The scope and breadth of genome-scale metabolic reconstructions has continued to expand over the last decade. However, only a limited number of efforts exist on photosynthetic metabolism reconstruction. Cyanobacteria are an important group of photoautotrophic organisms that can synthesize valuable bio-products by harnessing solar energy. They are endowed with high photosynthetic efficiencies and diverse metabolic capabilities that confer the ability to convert solar energy into a variety of biofuels and their precursors. However, less well studied are the similarities and differences in metabolism of different species of cyanobacteria as they pertain to their suitability as microbial production chassis. Here we assemble, update and compare genome-scale models (iCyt773 and iSyn731) for two phylogenetically related cyanobacterial species, namely Cyanothece sp. ATCC 51142 and Synechocystis sp. PCC 6803. Comparisons of model predictions against gene essentiality data reveal a specificity of 0.94 (94/100) and a sensitivity of 1 (19/19) for the Synechocystis iSyn731 model. The diurnal rhythm of Cyanothece 51142 metabolism is modeled by constructing separate (light/dark) biomass equations and introducing regulatory restrictions over light and dark phases. Specific metabolic pathway differences between the two cyanobacteria alluding to different bio- production potentials are reflected in both models. In addition to these cyanobacterial species we also develop a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). The metabolic model Zea mays i1563 contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct evidence for the participation of the reaction in maize was found. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). Recently, we develop a second-generation genome-scale metabolic model for the maize leaf to capture C4 carbon fixation by modeling the interactions between the bundle sheath and mesophyll cells. Condition-specific biomass descriptions are introduced that account for amino acids, fatty acids, soluble sugars, proteins, chlorophyll, lingo-cellulose, and nucleic acids as experimentally measured biomass constituents. Compartmentalization of the model is based on proteomic/transcriptomic data and literature evidence. With the incorporation of the information from MetaCrop and MaizeCyc databases, this updated model spans 5824 genes, 8484 reactions, and 8918 metabolites, an increase of approximately five times the size of the earlier iRS1563 model. Transcriptomic and proteomic data is also used to introduce regulatory constraints in the model to simulate the limited nitrogen condition and glutamine synthetase gln1-3 and gln1-4 mutants. In silico results have achieved over 62% accuracy in predicting the direction of change in the metabolite pool under each of the mutant conditions compared to the wild-type condition with 82% accuracy determined in the limited nitrogen condition. The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for any plant tissue-type. iv TABLE OF CONTENTS LIST OF FIGURES .......................................................................................................... x LIST OF TABLES .......................................................................................................... xii ACKNOWLEDGEMENTS .......................................................................................... xiii Chapter 1 RECENT ADVANCES IN THE RECONSTRUCTION OF METABOLIC MODELS AND INTEGRATION OF OMICS DATA ........................ 1 1.1 Introduction ................................................................................................ 1 1.2 Metabolic model reconstruction approaches .............................................. 3 1.3 Integration of omics data in metabolic models .......................................... 6 1.4 Concluding remarks ................................................................................. 10 Chapter 2 RECONSTRUCTION AND COMPARISON OF THE METABOLIC POTENTIAL OF CYANOBACTERIA CYANOTHECE SP. ATCC 51142 AND SYNECHOCYSTIS SP. PCC 6803 ................................................................................. 11 2.1 Introduction .............................................................................................. 11 2.2 Materials and methods ............................................................................. 14 2.2.1 Measurement of biomass precursors .................................................. 14 2.2.1.1 Growth conditions ....................................................................... 14 2.2.1.2 Pigments ...................................................................................... 14 2.2.1.3 Amino Acids ................................................................................ 15 2.2.1.4 Other cellular components ........................................................... 15 2.2.2 Model simulations .............................................................................. 16 v 2.3 Results and Discussion ............................................................................. 19 2.3.1 Model components ............................................................................. 19 2.3.1.1 Biomass composition and diurnal cycle ...................................... 19 2.3.1.2 Identification and correction of network gaps ............................. 20 2.3.2 GPR associations and elemental and charge balancing ..................... 23 2.3.3 Comparison of iSyn731 model predicted flux ranges against experimental measurements .............................................................. 23 2.3.4 iSyn731 model testing using in vivo gene essentiality data ............... 28 2.3.5 Model comparisons ............................................................................ 32 2.3.5.1 Synechocystis 6803 model comparisons ..................................... 32 2.3.5.2 Cyanothece 51142 model comparisons ....................................... 35 2.3.5.3 iSyn731 and iCyt773 models comparison .................................. 37 2.3.6 Using iSyn731 and iCyt773 to estimate production yields ................ 42 2.4 Conclusion ............................................................................................... 43 Chapter 3 SYNTHETIC BIOLOGY OF CYANOBACTERIA: UNIQUE CHALLENGES AND OPPORTUNITIES ................................................................... 46 3.1 Introduction .............................................................................................. 46 3.2 Genetic modification of cyanobacteria .................................................... 47 3.3 Genetic modification in cis: chromosome editing ................................... 49 3.4 Genetic modification in trans: foreign plasmids ...................................... 52 3.5 Unique challenges of the cyanobacterial lifestyle .................................... 54 3.5.1 Life in a diurnal environment ............................................................ 54 vi 3.5.2 Redirecting Carbon Flux by decoupling growth from production ..... 56 3.5.3 RNA-based regulation ....................................................................... 56 3.6 Parts for Cyanobacterial Synthetic Biology ............................................. 57 3.6.1 Inducible Promoter ............................................................................. 58 3.6.2 Reporters ............................................................................................ 66 3.6.3 Cultivation systems ............................................................................ 68 3.7 Genome-scale modeling and fluxomics of cyanobacteria ....................... 68 3.7.1 Challenges .......................................................................................... 70 3.7.1.1 Incorporating photoautotrophy into metabolic models ................ 70 3.7.1.2 Incompleteness of genome annotation ......................................... 71 3.7.1.3 Fewer mutant resources to test model accuracy .......................... 72 3.7.2 Recent advances ................................................................................. 73 3.7.2.1 Detailed genome-scale models .................................................... 73 3.7.2.2 13C MFA analysis .......................................................................