A Synthetic Biology Application in Metabolic Engineering by Nikolaos
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A synthetic biology application in metabolic engineering by Nikolaos Anesiadis A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Chemical Engineering and Applied Chemistry University of Toronto Copyright © 2014 by Nikolaos Anesiadis Abstract A synthetic biology application in metabolic engineering Nikolaos Anesiadis Doctor of Philosophy Graduate Department of Chemical Engineering and Applied Chemistry University of Toronto 2014 Since the 1970s, bioprocess engineering has focussed on the optimization of the production of chemicals via biological transformations. In particular, much emphasis has been placed on estimating the optimal process variables to maximize the production of the desired chem- ical. Notably, engineers were limited to using macroscopic process variables, such as the feed rate of the bioreactor. Optimization involves the trade-off between productivity and yield. High values of both metrics are required for a viable plant; however, the two metrics are in competition. Recently, the emergence of synthetic biology has enabled bioengineers to extend the optimization of bioprocesses from the macroscopic level to the genetic level. With this in mind, we propose a novel synthetic biology approach for bioprocess optimiza- tion. Our case study involves a lactic acid-producing Escherichia coli strain with the adh (alcohol dehydrogenase) and pta (phosphotransacetylase) genes deleted. Deletion of these genes increases the yield of lactic acid; but, at the same time, growth rate and productivity decrease drastically. Initially, we introduce the model-based design of an integrated genetic circuit that links a density sensory mechanism to a dynamic genetic controller, and subse- quently to bacterial metabolism. In this way, the genetic circuit dynamically controls genes that contribute to growth and productivity. Then, we conduct a mathematical analysis of the model to help us in the initial design and further optimization of the integrated circuit. The analysis can minimize the time required to design and troubleshoot the genetic circuit. Also, the analysis showed that the induction time is the most important process variable we ii can optimize. Finally, we carried out experimental results in an attempt to utilize the ge- netic toggle switch as a controller to manipulate genes adh and pta in an ON-OFF fashion. While we expected to observe some growth restoration and productivity improvement, it is common for synthetic biology constructs to behave differently in different environments or strains. Indeed, the experimental results show that our assumption that the genetic toggle switch will restore wild-type levels of adh-pta expression may not be true. In summary, this work introduces a novel synthetic biology approach for the optimization of bioprocesses and attempts a proof of concept implementation of the strategy. Although, the initial im- plementation was not successful, we have done some troubleshooting with respect to the problems involved and suggestions are given for future experiments. iii Acknowledgements I am fortunate to have many people to acknowledge for the help, support and contributions that made this work possible. First, my supervisors Professor W. R. Cluett and Professor R. Mahadevan for their continuous support and advice. They have both been an inspira- tion to me and they have stimulated my intellectual curiosity. To my advisory committee, Professor E. Edwards, Professor E. Master and Professor A. Yakunin thank you for your advice and support. Your suggestions have improved this thesis in all respects; your en- couragement is greatly appreciated. Biozone made graduate school a great experience. I thank all the professors, students, and staff for creating a positive and pleasant environment. In particular, Laurence Yang, Victor Balderas, Pratish Gawand and Naveen Venayak have been very knowledgeable lab- partners and friends. Susie Susilawati, Christina Heidorn and Weijun Gao have also been a continuous source of support. Collaborators outside the University of Toronto have been tremendously helpful. Pro- fessor V. Martin and Dr. A. Ekins from Concordia University, Dr. H. Kobayashi from the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and Professor S. Fong from the Virginia Commonwealth University have shared their experience with our group. Finally, I want to thank my family: my parents Anastasia and Michael, my brother Stelio, and my wife Azi for their constant trust, love and support. iv Contents List of Tables xi List of Figures xiii List of Abbreviations and Symbols xxii 1 Introduction 1 1.1 Motivation . .1 1.2 Challenges and objectives . .3 1.3 Contributions . .4 1.3.1 Model-based design of dynamic metabolic engineering . .5 1.3.2 Analysis of dynamic strategy . .6 1.3.3 Experimental implementation of the dynamic strategy . .7 1.3.4 Additional contributions . .8 2 Literature review 9 2.1 Experimental and computational approaches to metabolic engineering . 11 2.1.1 Experimental approaches . 11 2.1.2 Computational approaches . 14 2.1.3 The trade-off between yield and productivity of a process . 19 2.2 Dynamic control of gene expression for bioprocesses optimization . 21 2.2.1 Dynamic metabolic optimization . 22 v 2.2.2 Comparison of the dynamic strategy to existing methods . 25 2.3 Quorum sensing in bacteria . 26 2.3.1 The mechanism of the Lux system . 26 2.3.2 Potential for quorum sensing applications . 28 2.4 Genetic controllers . 29 2.4.1 The first synthetic biology construct: toggle switch . 29 2.4.2 Model-based design of the toggle switch . 29 2.4.3 Construction of the toggle switch . 30 2.4.4 Logic gates . 32 2.5 Density-dependent genetic networks . 37 2.5.1 Density-dependent applications . 38 2.5.2 Challenges . 39 2.6 Lactate production in microbial hosts . 41 2.6.1 Natural producers and yeast strains . 41 2.6.2 Lactate from E. coli strains . 42 2.6.3 Anaerobic production in E. coli ..................... 42 2.6.4 Dual-phase production in E. coli .................... 44 2.6.5 Summary . 47 2.7 Summary and synthesis . 49 2.7.1 Summary of the literature review . 49 2.7.2 Synthesis and outline . 52 3 Model-based design for dynamic metabolic engineering 53 3.1 Methods . 55 3.1.1 Quorum sensing modelling . 55 3.1.2 Toggle switch modelling . 57 3.1.3 Dynamics of the genetic circuit . 59 3.1.4 Strain design for serine production . 61 vi 3.1.5 Coupling the genetic circuit to the serine-producing strain . 63 3.2 Results . 66 3.2.1 Static strategy for serine production . 66 3.2.2 Dynamic strategy for serine production . 66 3.2.3 Comparison of static and dynamic strategy . 67 3.3 Conclusions . 69 4 Mathematical analysis of the dynamic strategy 71 4.1 Introduction . 71 4.2 Methods . 73 4.2.1 Global sensitivity analysis . 73 4.2.2 Analysis of the most sensitive parameters . 76 4.3 Results . 78 4.3.1 Ideal dynamic strategy . 78 4.3.2 Global sensitivity analysis . 80 4.3.3 Effect of αC and γC ........................... 82 4.3.4 Effect of αC and LuxR . 84 4.3.5 Effect of γC and LuxR . 86 4.3.6 Summary on the effects of changing two parameters at a time . 87 4.3.7 Effect of all three parameters . 88 4.3.8 Preliminary design considerations . 89 4.4 Conclusions . 91 5 Experimental implementation of the dynamic strategy 93 5.1 Materials and methods . 94 5.1.1 Strains and plasmids . 94 5.1.2 Media and growth conditions for strain SUC-AE . 97 5.1.3 Media and growth conditions for strains SUC-AN and LAC-AN . 98 vii 5.1.4 Genetic methods . 99 5.1.5 Analytical techniques . 100 5.2 Aerobic succinate production . 100 5.3 Anaerobic lactate production: protocol development . 106 5.3.1 Preliminary characterization of the lactate-producing strain . 106 5.3.2 Expression of pTOG(pta) in minerals medium . 106 5.3.3 Use of Luria broth as a supplement . 108 5.3.4 Minimizing the use of Luria-Bertani supplement . 109 5.3.5 Using inexpensive supplements . 111 5.3.6 Different induction times . 112 5.3.7 Bioreactor experiment . 114 5.3.8 Use of pH buffer in the inoculum preparation . 115 5.3.9 Protocol . 116 5.4 Anaerobic lactate production: characterization . 118 5.4.1 Characterization of wild-type and mutant in 100 mM of glucose . 118 5.4.2 Characterization of wild-type and mutant in 50 mM of glucose . 119 5.4.3 Characterization of the toggle switch in 50 mM of glucose . 121 5.4.4 Synopsis of the batch characterizations . 124 5.4.5 Conclusions . 126 5.5 Troubleshooting . 127 5.5.1 Individual signal testing . 127 5.5.2 Sequencing . 128 5.5.3 Flow cytometry . 130 5.5.4 Conclusions . 132 6 Conclusions and recommendations for future work 134 6.1 Conclusions . 134 6.2 Recommendations for future work . 136 viii Bibliography 140 Appendix 153 A Strain design for serine production 153 B Matlab code 154 B.1 M-files for Chapter 3 . 154 B.1.1 Dynamics of the genetic circuit (section 3.1.3) . 154 B.1.2 Production envelope of strain designs predicted by EMILiO (section 3.1.4) . 157 B.1.3 Static strategy for serine production (section 3.2.1) . 160 B.1.4 Dynamic strategy for serine production (section 3.2.2) . 165 B.2 M-files for Chapter 4 . 170 B.2.1 Ideal dynamic strategy (section 4.3.1) . 170 B.2.2 Global sensitivity analysis (section 4.3.2) . 175 B.2.3 Effect of αC and γC (section 4.3.3) . 182 B.2.4 Effect of αC and LuxR (section 4.3.4) . 188 B.2.5 Effect of γC and LuxR (section 4.3.5) .