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Dynamical Properties of Gene Regulatory Networks Involved in Long-Term Potentiation

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Dynamical Properties of Gene Regulatory Networks Involved in Long-Term Potentiation 7 Dynamical properties of gene regulatory networks involved in long-term potentiation a thesis submitted for the degree of Doctor of Philosophy Gonzalo Sánchez Nido Dynamical properties of gene regulatory networks involved in long-term potentiation Copyright c 2015 Gonzalo Sánchez Nido DEPARTMENT OF COMPUTER SCIENCE,UNIVERSITY OF OTAGO The research work presented on this thesis has been partially published and portions of these publi- cations are reused. Textual portions and figures of these publications are reused with permission. • Nido, G., Williams, J., and Benuskova, L. (2012). Bistable properties of a memory-related gene regulatory network. In Neural Networks (IJCNN), The 2012 International Joint Confer- ence on, 1602–1607. IEEE. “The IEEE does not require individuals working on a thesis to obtain a formal reuse license” • Nido, G., Ryan, M., Benuskova, L., and Williams, J. (2015). Dynamical properties of gene regulatory networks involved in long-term potentiation. Frontiers in Molecular Neuroscience, 8(42). “Under the Frontiers Terms and Conditions, authors retain the copyright to their work. All Frontiers articles are Open Access and distributed under the terms of the Creative Commons Attribution License, (CC-BY 3,0), which permits the use, distribution and reproduction of material from published articles, provided the original authors and source are credited, and subject to any copyright notices concerning any third-party content” The template for this publication (including the front cover), The Legrand Orange Book, was originally compiled by Mathias Legrand ([email protected]) and has subsequently been modified by Vel ([email protected]) and Gonzalo Sánchez Nido (gonzalo.s. [email protected]), and is licensed under the Creative Commons Attribution-NonCommercial 3.0 Unported License (the “License”). You may not use this file except in compliance with the License. You may obtain a copy of the License at http://creativecommons.org/licenses/by-nc/3.0. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. First submitted, March 2015 Hardbound copy deposited, July 2015 Abstract The significant increase in the availability of postgenomic data has stimulated the growth of hypothesis-generating strategies to unravel the molecular basis of nature. The application of systems theory to biological problems emerged in the early 1970s, and yet the computational methods developed to model biological networks and analyse their functionality have been seldom used for understanding the neurogenetic basis of cognition. The main interests of this thesis are the application of computational models to microarray expression data for the identification and analysis of biological networks related with long-term potentiation (LTP), the cellular correlate of learning and memory in mammals. The models include the analysis of co-expression and studies of dynamical stability. The thesis starts with the application of established methods on gene expression analysis on the available expression data from LTP in order to identify networks of closely correlated genes in their patterns of expression to ultimately pinpoint putative key regulators not identified previously by classical differential expression analysis. The thesis continues with the analysis of previously identified gene networks regulated 20 min, 5 h, and 24 h post-LTP induction. A dynamical stability analysis using weight matrices suggests that the early network has a significant sensitivity to perturbations compared with randomly generated networks of similar characteristics. In addition, using random Boolean networks, we study the differential sensitivity to perturbations of these networks and we find that our results are consistent with a model of LTP as a complex cellular switch. In such a scenario, earlier networks are dynamically more unstable than later regulatory networks, which are proposed to be responsible for the new homeostatic state reached by the stimulated neurons. Key genes responsible for the dynamic properties observed are identified and discussed. In particular, we found that Egr2, a member of the Egr family of transcription factors was crucial to the bistability observed in the early-response network. Other genes previously associated with LTP have a more modest contribution. A functional analysis of these networks is presented and integrated with previous knowledge on the molecular basis of LTP. A Miki y a Javi Acknowledgements This thesis would not have been possible without emotional support. I left family and friends behind when I started my PhD at the University of Otago. I have to thank each member of the P.B. group of WhatsApp, made up of my Biology classmates from Uni, now scattered all over the world – Jero, Djordje, Pin, Carol, Pablulu, Palo, Ainhoa, Rincho, Mery, Manolo, Isa, Paula and Paulita. I would also like to extend my thanks to the members of the P.H. group on Telegram, set up during a quick encounter in Berlin with Molly, Pako and David Lee. They helped me to keep up with music and politics, and at the same time share desperation and joy. I would like to express my very great appreciation to the members of the Bioinformatics Unit of the Centre for Molecular Biology Severo Ochoa, where I worked as a research assistant before starting my PhD – Ugo, Alberto, David, Klett, Antonio, Ruben, Fons and Amigo. “Survival” in Dunedin has been possible thank to local friends, of course. Throughout these years, people have come and gone, but some of them have been there for quite a long time. I am deeply grateful to Sam and Andrew. You have made me feel at home up in the summits and back home in Dunedin and I feel a great deal of admiration for you. Also, thank you Dave for being always there when I needed a place to stay or someone to talk to. Pretty much all my friends in New Zealand are amazing climbers – Sarah, Kate, Dani, Rocio, Martin, Paul and Michelle. It has been delightful to climb mountains, ice and rock with you during these years (and the years to come). I would also like to thank Lech for the conversations during coffee breaks and lunches. Your enthusiasm and company have definitely made a difference to my life in the Department. Central to this thesis are my main and co-supervisors, Lubica and Joanna. I would like to express my deep gratitude for the opportunity to embark this journey. Thank you for your help, knowledge and encouragement throughout these years. I would also like to thank Margaret for her advice and assistance with the microarray data. My family, of course, despite the distance, has always been there for me. Mum, dad, Miguel and Javi – thank you so much. I am particularly grateful to Maud, who has been there pretty much every day dur- ing my PhD, dealing with my unbearable personality, my ups and downs, and my contradictions. Contents 1 Introduction .................................................. 15 1.1 Neuroscience and the study of the brain 15 1.2 Learning and memory 16 1.3 Motivation 17 1.4 Thesis goals 18 1.5 Thesis outline 18 2 Overview of genetics and microarrays ......................... 19 2.1 Introduction 19 2.2 Biology of gene expression 20 2.3 Regulation of transcription 22 2.4 Cell homeostasis 23 2.5 Gene expression profiling 24 2.6 Oligonucleotide microarrays 24 2.7 Analysis of microarray data 25 2.7.1 Low-level analysis................................................ 25 2.7.2 High-level analysis............................................... 27 2.8 Microarrays in neuroscience 27 3 Biology of long-term potentiation .............................. 29 3.1 The neuron 29 3.1.1 The resting potential............................................. 29 3.1.2 The action potential............................................. 30 3.1.3 The chemical synapse............................................ 32 3.1.4 The excitatory synaptic transmission................................. 33 3.2 Long-term potentiation 33 3.2.1 The discovery of long-term potentiation.............................. 33 3.2.2 Definition and properties.......................................... 34 3.2.3 Types of LTP.................................................... 35 3.2.4 Experimental induction........................................... 36 3.2.5 Phases of LTP................................................... 37 3.3 Molecular mechanisms of LTP 38 3.3.1 The CaMKII pathway and early LTP.................................. 39 3.3.2 The PKC pathway............................................... 42 3.3.3 The PKA pathway............................................... 42 3.3.4 The Ras-Erk1/2 pathway........................................... 43 3.3.5 The presynaptic component....................................... 43 3.3.6 Gene expression in LTP............................................ 43 4 Models of gene regulatory networks ........................... 51 4.1 Introduction 51 4.2 Logic-based models 52 4.2.1 Boolean models................................................. 52 4.2.2 Other logic-based models......................................... 58 4.2.3 Continuous and single-molecule models............................. 58 4.2.4 Other continuous models......................................... 60 4.3 LTP in the context of functional genomics 61 4.4 Justification for the choice of models 61 5 Long-term potentiation microarray
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