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University of Cincinnati UNIVERSITY OF CINCINNATI Date: 22-Jan-2010 I, Amruta Desai , hereby submit this original work as part of the requirements for the degree of: Master of Science in Computer Engineering It is entitled: Design support for biomolecular systems Student Signature: Amruta Desai This work and its defense approved by: Committee Chair: Carla Purdy, C, PhD Carla Purdy, C, PhD Wen-Ben Jone, PhD Wen-Ben Jone, PhD George Purdy, PhD George Purdy, PhD 2/2/2010 389 Design Support for Biomolecular Systems A thesis submitted to the Division of Graduate Research and Advanced Studies of The University of Cincinnati In partial fulfillment of the Requirements for the degree of Master of Science in the Department of Electrical and Computer Engineering of the College of Engineering By Amruta Desai BE in Electrical Engineering, Rajiv Gandhi Technical University, 2005 January, 2010 Thesis Advisor and Committee Chair: Dr. Carla Purdy Abstract Systems biology is an emerging field which connects system level understanding to molecular level understanding. Biomolecular systems provide a comprehensive view of a biological phenomenon, in the form of a network of inter-related reactions or processes. The work described in this thesis focuses on developing the support for virtual experiments in systems biology. This will help biologists to make choices about which wet lab experiments are likely to be the most informative, thereby saving both time and material resources. Our goal is to support synthetic biology by providing tools which can be employed by biologists, engineers, and computational scientists. Our approach makes use of well-developed techniques from the field of VLSI design. Modeling the biochemical reactions helps in studying and analyzing a biological pathway. This provides an affordable and convenient virtual experimental platform. There are several challenges as it is an emerging field. Our lab introduced a new conversion tool to support mathematical modeling of the biological systems, called as Bio Model Development Language (BMDL). It uses the concept of weighted “gate”. The work in this thesis focuses on metabolic pathways. We extend BMDL to account for the presence of inhibitors, activators and enzymes of biological pathways, components similar to feedback loops in electrical circuits. As a use case we study the pyrimidine pathway. iii iv Acknowledgements I am heartily thankful to my advisor and committee chair Dr. Carla Purdy for her excellent guidance throughout my thesis work. This would not be possible without her help, encouragement, and support from the initial to the final level. I would also like to thank the committee members, Dr. George Purdy and Dr. Wen Ben Jone, for taking the time and effort to review my research and serve on my thesis defense committee. I would like to express my heartfelt gratitude to my parents for their love, encouragement and belief in me. I would like to thank my sister Ankita for her love, encouragement and belief in me. She was always there to help me through the difficult phases in my life and motivated me to complete my thesis. I am thankful to my all cousins specially Krupa, Hitendra and Viraj for their faith and support throughout my Masters. I would like to thank all my friends who have helped me in many ways and made my stay at Cincinnati wonderful. Thanks to University of Cincinnati for giving me an opportunity to study here. v To my family vi Contents 1 Introduction 1 1.1 Motivation 2 1.2 Goals 3 1.2.1 Long term goals 3 1.2.2 Short term goals 3 1.3 Outline 4 2 Biological Pathways 5 2.1 Introduction 5 2.2 Introduction to biological pathways 5 2.2.1 Gene regulatory network 7 2.2.2 Cell signaling pathway 7 2.2.3 Metabolic pathway 8 2.3 Modeling and Control of Biological Pathways 8 2.3.1 Deterministic modeling 9 2.3.2 Stochastic modeling 9 2.3.3 Agent based modeling 9 2.4 Challenges in computational biomodeling 10 2.5 Box algorithm 10 2.6 Bio model development language (BMDL)[2] 14 2.7 Limitations of BMDL 15 vii 2.8 Chapter summary 16 3 Enhancement of BMDL Weighted Gates 17 3.1 Need for enhancement 17 3.1.1 Enzymes and their classifications 17 3.1.2 Activators 18 3.1.3 Inhibitors 18 3.2 BMDL expression to ODE model conversion 19 3.2.1 Gates added to BMDL 23 3.2.1.1 Two input /two output bi-directional reaction 23 3.2.1.2 Two input/ two output reaction with inhibitors and activator 24 3.2.1.3 Three input/ four output reaction with inhibitor and activator 26 3.2.1.4 Four input/ four output reaction with one inhibitor 27 3.3 Generalization of BMDL expressions 29 3.3.1 Bidirectional biochemical reactions 30 3.3.2 Unidirectional biochemical reactions 31 3.3.2.1 Simple biochemical reactions 31 3.3.2.2 Complex biochemical reactions 33 3.4 Classification of different BMDL functions 36 3.5 Chapter summary 37 4 Use Case: Pyrimidine Pathway 38 4.1 Introduction 38 viii 4.2 Nucleotides 38 4.3 Introduction to the Pyrimidine pathway 39 4.3.1 Biochemical reactions 42 4.3.2 BMDL model of Pyrimidine Pathway 43 4.3.3 ODE model of Pyrimidine pathway 48 4.4 Bio-control database and modifications for metabolic pathway 48 4.4.1 Bio-control database for Pyrimidine pathway 52 4.5 Simulation results of Carbomyl Phosphate (CPSase) 60 4.6 Application of the Box algorithm to Pyrimidine pathway 62 4.7 Chapter summary 62 5 Conclusions and Future Work 63 5.1 Introduction 63 5.2 Conclusions 63 5.3 Suggestions for future work 64 5.4 List of publications and conference proceedings 64 Bibliography 66 Appendix A 71 Appendix B 75 Appendix C 79 Appendix D 80 Appendix E 82 ix Appendix F 82 Appendix G 87 x List of Figures 2.1 Biological pathways studied in our lab or included in database of [7] 7 2.2 Box algorithm with the integrated BMDL and bio-control database [2] 12 2.3 Bio-Model Development Language (BMDL) 15 3.1 Mechanism for a single substrate enzyme catalyzed reaction 17 3.2 Role of competitive inhibitor 18 3.3 Weighted gate symbol used in BMDL expression 20 3.4 Weighted gate representing two input /two output bi-directional reaction 23 3.5 Weighted gate representing two input /two output unidirectional reactions with inhibitors and activators 25 3.6 Weighted gate representing three inputs /four output unidirectional reaction with inhibitors and activators 26 3.7 Weighted gate representing four input /four output unidirectional reaction with inhibitors 28 3.8 Classification of biochemical reactions studied in our lab 29 3.9 Weighted gate representing n input /m output bi-directional reaction 30 3.10 Weighted gate representing n input /m output unidirectional simple reaction 32 3.11 Weighted gate representing n input /m output unidirectional complex reaction 34 4.1 Purine and Pyrimidine rings 39 4.2 Pyrimidine pathway 41 4.3 Effect of UMP on CPSase 60 4.4 Effect of activator 61 xi List of Tables 3.1 Classification of BMDL gates 36 4.1 Bio-control database entry for the rate of transcription for the protein Cro of phage lambda 50 4.2 Bio-control database entry for Carbomyl phosphate synthase of pyrimidine pathway 51 4.3: Bio-control database entry for Carbomyl phosphate synthase of pyrimidine pathway 52 4.4: Bio-control database entry for AspartateCarbomyl transferase of pyrimidine pathway 53 4.5: Bio-control database entry for Dihydro orotate oxidase of pyrimidine pathway 54 4.6: Bio-control database for entry Orotate phosphoribosyl transferase of pyrimidine pathway 55 4.7: Bio-control database entry for Orodtidine- 5'- Phosphate Decarboxylaseas of pyrimidine pathway 56 4.8: Bio-control database entry for Uridine diphosphate kinase of pyrimidine pathway 58 4.9: Bio-control database entry for Cytidine 5'- triphosphate synthetase of pyrimidine pathway 59 xii Chapter 1 Introduction In this chapter we provide an introduction to our research objectives. A detailed description of our thesis goals is also explained. An outline for all the chapters is also provided. The work emphasizes developing support for virtual experiments in systems biology with the help of engineering techniques. Systems biology is an emerging opportunity to connect system level understanding to molecular level understanding. Broadly, systems level understanding can be classified into four different phases. The first phase is structural identification of the system. This lets us know the relationships between various components of the system. The second phase is the study of system dynamics. Various experiments, computational model development and theoretical analysis are required during this phase. The third phase is the control procedure. Here a proper methodology is identified to control the system externally. This helps in various applications such as drug development and environmental remediation. Finally, the fourth phase consists of construction of the modified biological system with desired features.[1] Experimental studies of biological pathways are expensive and time consuming; hence we are trying to develop virtual support to study these biological networks, thereby saving both time and material resources. [2, 3] Previously our lab developed certain algorithms to support this approach. Here we are trying to contribute to this program by extending the work in [2]. 1.1 Motivation Systems biology is an emerging field which connects system level understanding to molecular level understanding. It improves the capability to understand natural phenomena 1 quantitatively. It also nurtures an engineering discipline, known as synthetic biology, for regulating complex cell behaviors in a predictable and reliable fashion [4, 5].
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