ingest cdrApp 2017-07-06T12:04:54.880Z 082b3de9-6030-4a3e-a983-035a47fc699e modifyDatastreamByValue RELS-EXT cdrApp 2017-07-06T12:26:57.061Z Setting exclusive relation modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-07-06T12:33:54.715Z Setting exclusive relation modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-07-06T12:33:55.210Z Setting exclusive relation addDatastream MD_TECHNICAL fedoraAdmin 2017-07-06T12:34:03.483Z Adding technical metadata derived by FITS modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-07-06T12:34:04.870Z Setting exclusive relation addDatastream MD_FULL_TEXT fedoraAdmin 2017-07-06T12:34:07.528Z Adding full text metadata extracted by Apache Tika modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-07-06T12:34:15.805Z Setting exclusive relation modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-01-25T06:40:30.246Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-01-27T07:11:29.540Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-03-14T03:27:41.535Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-05-17T15:15:46.451Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-07-11T01:56:54.409Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-07-17T22:14:34.408Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-08-08T21:18:58.424Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-08-15T18:29:01.807Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-09-21T18:52:03.600Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-09-26T22:08:24.074Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-10-11T22:43:12.491Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2019-03-20T16:24:27.953Z Katrina Kutchko Author Curriculum in Bioinformatics and Computational Biology School of Medicine From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system. Spring 2017 2017 Bioinformatics Biology computational neuroscience, cortical oscillations, covariation, evolution, RNA structure, viruses eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Alain Laederach Thesis advisor Terry Furey Thesis advisor Mark Heise Thesis advisor Sarah Linnstaedt Thesis advisor Flavio Frohlich Thesis advisor text Katrina Kutchko Creator Curriculum in Bioinformatics and Computational Biology School of Medicine From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system. Spring 2017 2017 Bioinformatics Biology computational neuroscience, cortical oscillations, covariation, evolution, RNA structure, viruses eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Alain Laederach Thesis advisor Terry Furey Thesis advisor Mark Heise Thesis advisor Sarah Linnstaedt Thesis advisor Flavio Frohlich Thesis advisor text Katrina Kutchko Creator Curriculum in Bioinformatics and Computational Biology School of Medicine From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system. Spring 2017 2017 Bioinformatics Biology computational neuroscience, cortical oscillations, covariation, evolution, RNA structure, viruses eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Alain Laederach Thesis advisor Terry Furey Thesis advisor Mark Heise Thesis advisor Sarah Linnstaedt Thesis advisor Flavio Frohlich Thesis advisor text Katrina Kutchko Creator Curriculum in Bioinformatics and Computational Biology School of Medicine From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system. 2017-05 2017 Bioinformatics Biology computational neuroscience, cortical oscillations, covariation, evolution, RNA structure, viruses eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Alain Laederach Thesis advisor Terry Furey Thesis advisor Mark Heise Thesis advisor Sarah Linnstaedt Thesis advisor Flavio Frohlich Thesis advisor text Katrina Kutchko Creator Curriculum in Bioinformatics and Computational Biology School of Medicine From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system. 2017 Bioinformatics Biology computational neuroscience, cortical oscillations, covariation, evolution, RNA structure, viruses eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Alain Laederach Thesis advisor Terry Furey Thesis advisor Mark Heise Thesis advisor Sarah Linnstaedt Thesis advisor Flavio Frohlich Thesis advisor text 2017-05 Katrina Kutchko Creator Curriculum in Bioinformatics and Computational Biology School of Medicine From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system. 2017 Bioinformatics Biology computational neuroscience, cortical oscillations, covariation, evolution, RNA structure, viruses eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Alain Laederach Thesis advisor Terry Furey Thesis advisor Mark Heise Thesis advisor Sarah Linnstaedt Thesis advisor Flavio Frohlich Thesis advisor text 2017-05 Katrina Kutchko Creator Curriculum in Bioinformatics and Computational Biology School of Medicine From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system. 2017 Bioinformatics Biology computational neuroscience, cortical oscillations, covariation, evolution, RNA structure, viruses eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Alain Laederach Thesis advisor Terry Furey Thesis advisor Mark Heise Thesis advisor Sarah Linnstaedt Thesis advisor Flavio Frohlich Thesis advisor text 2017-05 Katrina Kutchko Creator Curriculum in Bioinformatics and Computational Biology School of Medicine From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system. 2017 Bioinformatics Biology computational neuroscience, cortical oscillations, covariation, evolution, RNA structure, viruses eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Alain Laederach Thesis advisor Terrence Furey Thesis advisor Mark Heise Thesis advisor Sarah Linnstaedt Thesis advisor Flavio Frohlich Thesis advisor text 2017-05 Katrina Kutchko Creator Curriculum in Bioinformatics and Computational Biology School of Medicine From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system. 2017 Bioinformatics Biology computational neuroscience, cortical oscillations, covariation, evolution, RNA structure, viruses eng Doctor of Philosophy Dissertation Bioinformatics and Computational Biology Alain Laederach Thesis advisor Terrence Furey Thesis advisor Mark Heise Thesis advisor Sarah Linnstaedt Thesis advisor Flavio Frohlich Thesis advisor text 2017-05 University of North Carolina at Chapel Hill Degree granting institution Katrina Kutchko Creator Curriculum in Bioinformatics and Computational Biology School of Medicine From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system. 2017 Bioinformatics Biology computational neuroscience, cortical oscillations, covariation, evolution, RNA structure, viruses eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Alain Laederach Thesis advisor Terry Furey Thesis advisor Mark Heise Thesis advisor Sarah Linnstaedt Thesis advisor Flavio Frohlich Thesis advisor text 2017-05 Katrina Kutchko Creator Curriculum in Bioinformatics and Computational Biology School of Medicine From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system. 2017 Bioinformatics Biology computational neuroscience; cortical oscillations; covariation; evolution; RNA structure; viruses eng Doctor of Philosophy Dissertation Bioinformatics and Computational Biology Alain Laederach Thesis advisor Terrence Furey Thesis advisor Mark Heise Thesis advisor Sarah Linnstaedt Thesis advisor Flavio Frohlich Thesis advisor text 2017-05 University of North Carolina at Chapel Hill Degree granting institution Katrina Kutchko Creator Curriculum in Bioinformatics and Computational Biology School of Medicine From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system. 2017 Bioinformatics Biology computational neuroscience, cortical oscillations, covariation, evolution, RNA structure, viruses eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Alain Laederach Thesis advisor Terrence Furey Thesis advisor Mark Heise Thesis advisor Sarah Linnstaedt Thesis advisor Flavio Frohlich Thesis advisor text 2017-05 Katrina Kutchko Creator Curriculum in Bioinformatics and Computational Biology School of Medicine Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system. 2017 Bioinformatics Biology computational neuroscience; cortical oscillations; covariation; evolution; RNA structure; viruses eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Alain Laederach Thesis advisor Terrence Furey Thesis advisor Mark Heise Thesis advisor Sarah Linnstaedt Thesis advisor Flavio Frohlich Thesis advisor text 2017-05 Kutchko_unc_0153D_17007.pdf uuid:db47da37-aa07-4938-8323-05d12344660a proquest 2019-07-06T00:00:00 2017-04-21T00:21:04Z yes application/pdf 16200044