Made available in DSpace on 2014-10-09T12:49:21Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:02:30Z (GMT). No. of bitstreams: 1 09994.pdf: 3905779 bytes, checksum: 7cdc8d8ae9585729a6a818ed202c59c8 (MD5) / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP

Made available in DSpace on 2014-10-09T12:49:21Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:02:30Z (GMT). No. of bitstreams: 1 09994.pdf: 3905779 bytes, checksum: 7cdc8d8ae9585729a6a818ed202c59c8 (MD5) / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP

No description available.

Quantitative exploration of biological pathway networks must begin with a qualitative understanding of them. Often researchers aggregate and disseminate experimental data using regulatory diagrams with ad hoc notations leading to ambiguous interpretations of presented results. This thesis has two main aims. First, it develops software to allow researchers to aggregate pathway data diagrammatically using the Molecular Interaction Map (MIM) notation in order to gain a better qualitative understanding of biological systems. Secondly, it develops a quantitative biological model to study the effect of DNA damage on circadian rhythms. The second aim benefits from the first by making use of visual representations to identify potential system boundaries for the quantitative model. I focus first on software for the MIM notation - a notation to concisely visualize bioregulatory complexity and to reduce ambiguity for readers. The thesis provides a formalized MIM specification for software implementation along with a base layer of software components for the inclusion of the MIM notation in other software packages. It also provides an implementation of the specification as a user-friendly tool, PathVisio-MIM, for creating and editing MIM diagrams along with software to validate and overlay external data onto the diagrams. I focus secondly on the application of the MIM software to the quantitative exploration of the poorly understood role of SIRT1 and PARP1, two NAD+-dependent enzymes, in the regulation of circadian rhythms during DNA damage response. SIRT1 and PARP1 participate in the regulation of several key DNA damage-repair proteins and are the subjects of study as potential cancer therapeutic targets. In this part of the thesis, I present an ordinary differential equation (ODE) model that simulates the core circadian clock and the involvement of SIRT1 in both the positive and negative arms of circadian regulation. I then use this model is then used to predict a potential role for the competition for NAD+ supplies by SIRT1 and PARP1 leading to the observed behavior of primarily phase advancement of circadian oscillations during DNA damage response. The model further predicts a potential mechanism by which multiple forms of post-transcriptional modification may cooperate to produce a primarily phase advancement.

Made available in DSpace on 2014-10-09T12:48:15Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T13:56:55Z (GMT). No. of bitstreams: 1 09455.pdf: 4871108 bytes, checksum: cdb33a154f97f7b1de400d0a95b20a24 (MD5) / Tese (Doutoramento) / IPEN/T / Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP

Vias biológicas representam interações entre entidades químicas complexas (proteínas, substratos, metabólitos etc.) que ocorrem no nível molecular das células. A representação e compreensão do comportamento destas vias é o principal alvo de estudos da Biologia Sistêmica. Esta área de estudos envolve a construção de modelos matemáticos que possam simular in silico (computacionalmente) o comportamento destes sistemas biológicos verificados in vivo (experimentalmente). Do ponto de vista computacional é evidente que tais sistemas são complexos para abordar e descrever de modo intuitivo. São necessários modelos com valor preditivo, isto é, que permitam descrever os comportamentos do sistema que são experimentalmente verificáveis. Algumas notações gráficas foram propostas para descrever vias biológicas. Entre elas, os diagramas de processos tem sido amplamente utilizados. Um diagrama de processos é essencialmente um grafo no qual vértices e arestas representam componentes biológicos, e há uma notação gráfica associada com cada elemento. Nesta dissertação propomos uma fundamentação formal para a linguagem dos diagramas de processos definindo a sintaxe usando gramática de grafos. Nós definimos primeiramente um grafo chamado BioProc, descrevendo o meta-modelo dos diagramas de processos. Instâncias do grafo BioProc são portanto diagramas de processos modelando vias biológicas. Para descrever a semântica foi proposta uma tradução algébrica dos grafos BioProc para redes de Petri estocásticas generalizadas (GSPNs) já amplamente utilizadas na modelagem de processos biológicos. O uso de gramática de grafos como formalismo intermediário na tradução habilita a verificação sintática da via com a checagem dos tipos válidos que podem ser definidos para cada reação antes da simulação na rede de Petri e usá-las posteriormente para explorar propriedades estruturais e estocásticas do modelo. Além disso serve como base para a evolução do modelo proposto. Isto é relevante já que modelos frequentemente são construídos incrementalmente para se adaptar a novos requisitos e/ou incluir novas características. / Biological pathways represent interactions between complex chemical entities (proteins, substrates, metabolites, etc.) that occur at the molecular level of cells. The representation and comprehension of biological pathways behavior is the main target of research in the field of Systems Biology. This area investigates the construction of mathematical models that can simulate in silico (computationally) the behavior of biological systems checked in vivo (experimentally). From a computational view point it is clear that such systems are too complex to analyze and describe in an intuitiveway. Models with predictive value are needed, describing the behaviors that are experimentally verifiable. There are some graphical notations to describe biological pathways. Among them, process diagrams have been widely used. A process diagram is essentially a graph in which vertices and edges represent biological components, and there is a graphical notation associated with each element. In this master thesis we give a formal foundation for biological process diagrams, by defining their (concrete and abstract) syntax and semantics using a formalism called graph grammars. We first build a graph called BioProc Graph, describing the meta-model of process diagrams. Instances of this BioProc graph are concrete process diagrams modeling biological pathways. To describe the semantics we proposed a translation of BioProc diagrams to generalized stochastic Petri networks (GSPNs) already widely used in modeling biological processes. The use of graph grammar formalism as a basis for translation enables the syntatic verification to check the valid types that can be defined for each reaction after the simulation of Petri net and before that to explore structural and stochastic properties of the model. In addition it serves as the basis for model evolution proposed. This is relevant because models are often built incrementally to adapt to new requirements and/or include new features.

Vias biológicas representam interações entre entidades químicas complexas (proteínas, substratos, metabólitos etc.) que ocorrem no nível molecular das células. A representação e compreensão do comportamento destas vias é o principal alvo de estudos da Biologia Sistêmica. Esta área de estudos envolve a construção de modelos matemáticos que possam simular in silico (computacionalmente) o comportamento destes sistemas biológicos verificados in vivo (experimentalmente). Do ponto de vista computacional é evidente que tais sistemas são complexos para abordar e descrever de modo intuitivo. São necessários modelos com valor preditivo, isto é, que permitam descrever os comportamentos do sistema que são experimentalmente verificáveis. Algumas notações gráficas foram propostas para descrever vias biológicas. Entre elas, os diagramas de processos tem sido amplamente utilizados. Um diagrama de processos é essencialmente um grafo no qual vértices e arestas representam componentes biológicos, e há uma notação gráfica associada com cada elemento. Nesta dissertação propomos uma fundamentação formal para a linguagem dos diagramas de processos definindo a sintaxe usando gramática de grafos. Nós definimos primeiramente um grafo chamado BioProc, descrevendo o meta-modelo dos diagramas de processos. Instâncias do grafo BioProc são portanto diagramas de processos modelando vias biológicas. Para descrever a semântica foi proposta uma tradução algébrica dos grafos BioProc para redes de Petri estocásticas generalizadas (GSPNs) já amplamente utilizadas na modelagem de processos biológicos. O uso de gramática de grafos como formalismo intermediário na tradução habilita a verificação sintática da via com a checagem dos tipos válidos que podem ser definidos para cada reação antes da simulação na rede de Petri e usá-las posteriormente para explorar propriedades estruturais e estocásticas do modelo. Além disso serve como base para a evolução do modelo proposto. Isto é relevante já que modelos frequentemente são construídos incrementalmente para se adaptar a novos requisitos e/ou incluir novas características. / Biological pathways represent interactions between complex chemical entities (proteins, substrates, metabolites, etc.) that occur at the molecular level of cells. The representation and comprehension of biological pathways behavior is the main target of research in the field of Systems Biology. This area investigates the construction of mathematical models that can simulate in silico (computationally) the behavior of biological systems checked in vivo (experimentally). From a computational view point it is clear that such systems are too complex to analyze and describe in an intuitiveway. Models with predictive value are needed, describing the behaviors that are experimentally verifiable. There are some graphical notations to describe biological pathways. Among them, process diagrams have been widely used. A process diagram is essentially a graph in which vertices and edges represent biological components, and there is a graphical notation associated with each element. In this master thesis we give a formal foundation for biological process diagrams, by defining their (concrete and abstract) syntax and semantics using a formalism called graph grammars. We first build a graph called BioProc Graph, describing the meta-model of process diagrams. Instances of this BioProc graph are concrete process diagrams modeling biological pathways. To describe the semantics we proposed a translation of BioProc diagrams to generalized stochastic Petri networks (GSPNs) already widely used in modeling biological processes. The use of graph grammar formalism as a basis for translation enables the syntatic verification to check the valid types that can be defined for each reaction after the simulation of Petri net and before that to explore structural and stochastic properties of the model. In addition it serves as the basis for model evolution proposed. This is relevant because models are often built incrementally to adapt to new requirements and/or include new features.

abstract: Biological organisms are made up of cells containing numerous interconnected biochemical processes. Diseases occur when normal functionality of these processes is disrupted, manifesting as disease symptoms. Thus, understanding these biochemical processes and their interrelationships is a primary task in biomedical research and a prerequisite for activities including diagnosing diseases and drug development. Scientists studying these interconnected processes have identified various pathways involved in drug metabolism, diseases, and signal transduction, etc. High-throughput technologies, new algorithms and speed improvements over the last decade have resulted in deeper knowledge about biological systems, leading to more refined pathways. Such pathways tend to be large and complex, making it difficult for an individual to remember all aspects. Thus, computer models are needed to represent and analyze them. The refinement activity itself requires reasoning with a pathway model by posing queries against it and comparing the results against the real biological system. Many existing models focus on structural and/or factoid questions, relying on surface-level information. These are generally not the kind of questions that a biologist may ask someone to test their understanding of biological processes. Examples of questions requiring understanding of biological processes are available in introductory college level biology text books. Such questions serve as a model for the question answering system developed in this thesis. Thus, the main goal of this thesis is to develop a system that allows the encoding of knowledge about biological pathways to answer questions demonstrating understanding of the pathways. To that end, a language is developed to specify a pathway and pose questions against it. Some existing tools are modified and used to accomplish this goal. The utility of the framework developed in this thesis is illustrated with applications in the biological domain. Finally, the question answering system is used in real world applications by extracting pathway knowledge from text and answering questions related to drug development. / Dissertation/Thesis / Ph.D. Computer Science 2014

Vias biológicas representam interações entre entidades químicas complexas (proteínas, substratos, metabólitos etc.) que ocorrem no nível molecular das células. A representação e compreensão do comportamento destas vias é o principal alvo de estudos da Biologia Sistêmica. Esta área de estudos envolve a construção de modelos matemáticos que possam simular in silico (computacionalmente) o comportamento destes sistemas biológicos verificados in vivo (experimentalmente). Do ponto de vista computacional é evidente que tais sistemas são complexos para abordar e descrever de modo intuitivo. São necessários modelos com valor preditivo, isto é, que permitam descrever os comportamentos do sistema que são experimentalmente verificáveis. Algumas notações gráficas foram propostas para descrever vias biológicas. Entre elas, os diagramas de processos tem sido amplamente utilizados. Um diagrama de processos é essencialmente um grafo no qual vértices e arestas representam componentes biológicos, e há uma notação gráfica associada com cada elemento. Nesta dissertação propomos uma fundamentação formal para a linguagem dos diagramas de processos definindo a sintaxe usando gramática de grafos. Nós definimos primeiramente um grafo chamado BioProc, descrevendo o meta-modelo dos diagramas de processos. Instâncias do grafo BioProc são portanto diagramas de processos modelando vias biológicas. Para descrever a semântica foi proposta uma tradução algébrica dos grafos BioProc para redes de Petri estocásticas generalizadas (GSPNs) já amplamente utilizadas na modelagem de processos biológicos. O uso de gramática de grafos como formalismo intermediário na tradução habilita a verificação sintática da via com a checagem dos tipos válidos que podem ser definidos para cada reação antes da simulação na rede de Petri e usá-las posteriormente para explorar propriedades estruturais e estocásticas do modelo. Além disso serve como base para a evolução do modelo proposto. Isto é relevante já que modelos frequentemente são construídos incrementalmente para se adaptar a novos requisitos e/ou incluir novas características. / Biological pathways represent interactions between complex chemical entities (proteins, substrates, metabolites, etc.) that occur at the molecular level of cells. The representation and comprehension of biological pathways behavior is the main target of research in the field of Systems Biology. This area investigates the construction of mathematical models that can simulate in silico (computationally) the behavior of biological systems checked in vivo (experimentally). From a computational view point it is clear that such systems are too complex to analyze and describe in an intuitiveway. Models with predictive value are needed, describing the behaviors that are experimentally verifiable. There are some graphical notations to describe biological pathways. Among them, process diagrams have been widely used. A process diagram is essentially a graph in which vertices and edges represent biological components, and there is a graphical notation associated with each element. In this master thesis we give a formal foundation for biological process diagrams, by defining their (concrete and abstract) syntax and semantics using a formalism called graph grammars. We first build a graph called BioProc Graph, describing the meta-model of process diagrams. Instances of this BioProc graph are concrete process diagrams modeling biological pathways. To describe the semantics we proposed a translation of BioProc diagrams to generalized stochastic Petri networks (GSPNs) already widely used in modeling biological processes. The use of graph grammar formalism as a basis for translation enables the syntatic verification to check the valid types that can be defined for each reaction after the simulation of Petri net and before that to explore structural and stochastic properties of the model. In addition it serves as the basis for model evolution proposed. This is relevant because models are often built incrementally to adapt to new requirements and/or include new features.

Made available in DSpace on 2014-10-09T12:48:15Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T13:56:55Z (GMT). No. of bitstreams: 1 09455.pdf: 4871108 bytes, checksum: cdb33a154f97f7b1de400d0a95b20a24 (MD5) / Tese (Doutoramento) / IPEN/T / Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP