Automated analysis of networks of interactions between proteins has become pervasive in molecular biology. Each node in such a network represents a protein and each edge an interaction between two proteins. Nearly every publication that uses network analysis includes a visualization of a graph in which the nodes and edges are laid out in two dimensions. Several systems implement multiple types of graph layout algorithms and make them easily accessible to scientists. Despite the existence of these systems, interdisciplinary research teams in computational biology face several challenges in sharing computed networks and interpreting them. This thesis presents two systemsGraphSpace and GraphCrowdthat together enhance network-based collaboration. GraphSpace users can automatically and rapidly share richly- annotated networks, irrespective of the algorithms or software used to generate them. A user may search for networks that contain a specific node or edge, or a collection of nodes and edges. Users can manually modify a layout, save it, and share it with other users. Users can create private groups, invite other users to join groups, and share networks with group members. Upon publication, researchers may make networks public and provide a URL in the paper. GraphCrowd addresses the challenging posed by automated layout algorithms, which incorporate almost no knowledge of the biological information underlying the networks. These algorithms compel researchers to use their knowledge and intuition to modify the node and edge positions manually to bring out salient features. GraphCrowd focuses on signaling networks, which connect proteins that represent a cells response to external signals. Treating network layout as a design problem, GraphCrowd explores the feasibility of leveraging human computation via crowdsourcing to create simplified and meaningful visualizations. GraphCrowd provides a streamlined interface that enables crowd workers to easily manipulate networks to create layouts that follow a specific set of guidelines. GraphCrowd also implements an interface to allow a user (e.g., an expert or a crowd worker) to evaluate how well a layout conforms to the guidelines. We use GraphCrowd to address two research questions: (i) Can we harness the power of crowdsourcing to create simplified, biologically meaningful visualizations of signaling networks?(ii) Can crowd workers rate layouts similarly to how an expert with domain knowledge would rate them? We design two systematic experiments that enable us to answer both questions in the affirmative. This thesis establishes crowdsourcing as a powerful methodology for laying out complex signaling networks. Moreover, by developing appropriate domain-specific guidelines for crowd workers, GraphCrowd can be generalized to a variety of applications. / Master of Science

Signaling pathways are widely studied in systems biology. Several databases catalog our knowledge of these pathways, including the proteins and interactions that comprise them. However, high-quality curation of this information is slow and painstaking. As a result, many interactions still lack annotation concerning the pathways they participate in. A natural question that arises is whether or not it is possible to automatically leverage existing annotations to identify new interactions for inclusion in a given pathway. Here, we present RegLinker, an algorithm that achieves this purpose by computing multiple short paths from pathway receptors to transcription factors (TFs) within a background interaction network. The key idea underlying RegLinker is the use of regular-language constraints to control the number of non-pathway edges present in the computed paths. We systematically evaluate RegLinker and alternative approaches against a comprehensive set of 15 signaling pathways and demonstrate that RegLinker exhibits superior recovery of withheld pathway proteins and interactions. These results show the promise of our approach for prioritizing candidates for experimental study and the broader potential of automated analysis to attenuate difficulties of traditional manual inquiry. / Master of Science / Cells in the human body are constantly receiving signals that inform their response to a variety of conditions. These signals serve as cues to a cell, allowing it to make informed decisions that impact cellular processes such as movement, growth, and death. Cells employ proteins and the interactions between them to achieve these capabilities. Signals manifest as molecules that interact with proteins bound to membrane of a cell. When this happens, a cascade of interactions between the proteins inside the cell will be set off. Ultimately, this cascade activate or inhibit the cell’s production of new proteins, constituting a response to the signal received. The proteins and interactions involved in such a cascade together form what is known as a signaling pathway. Experiments have uncovered the interactions that are present in many signaling pathways, and researchers have carefully cataloged this information in publicly available databases. However, high-quality curation is slow and painstaking, and many known interactions have not been annotated as belonging to any pathway. A natural question that arises is whether or not it is possible to leverage existing annotations to automatically determine which new interactions to include in a given pathway. In this thesis, we present an efficient algorithm, RegLinker, for this purpose. We evaluate this method and alternative approaches on a comprehensive set of 15 signaling pathways and demonstrate that RegLinker is better at recovering interactions withheld from these pathways. In particular, we show RegLinker’s superior ability to identify interactions that utilize proteins that were not previously considered part of a pathway. These results underscore the promise of our approach for prioritizing candidates for experimental study and the broader potential of automated analysis to attenuate difficulties of traditional manual inquiry.

Décrypter le fonctionnement des réseaux biologiques est une des missions centrales de la biologie des systèmes. En particulier, les réseaux de transduction du signal sont essentiels pour la compréhension de la réponse cellulaire à des perturbations externes ou internes. Pour faire face à la complexité de ces réseaux, des modélisations aussi bien numériques que formelles sont nécessaires. Nous proposons un cadre de modélisation formelle, dans le cadre de réseaux logiques, afin d'obtenir des prédictions robustes sur le comportement et le contrôle des voies de signalisation. Nous modélisons la réponse des réseaux logiques de signalisation par du raisonnement automatique à l'aide de Programmation par Ensembles-Réponses (Answer Set Programming, ASP). ASP fournit un langage déclaratif pour la modélisation de divers problèmes de représentation des connaissances et de raisonnement. Des solveurs permettent plusieurs modes de raisonnement pour étudier la multitude d'ensembles réponses. En s'appuyant sur la richesse du langage de modélisation et ses capacités de résolution très efficaces, nous utilisons ASP pour modéliser et résoudre trois problèmes dans le contexte des réseaux logiques de signalisation: apprentissage de réseaux booléens, calculs de plan d'expériences, et l'identification des contrôleurs. Globalement, la contribution de cette thèse est de trois ordres. Premièrement, nous introduisons un cadre formel pour la caractérisation et le raisonnement sur la réponse des réseaux logiques de signalisation. Deuxièmement, nous contribuons à une liste croissante d'applications réussies d'ASP en biologie des systèmes. Troisièmement, nous présentons un logiciel fournissant un pipeline complet de raisonnement automatisé sur la réponse des réseaux logiques de signalisation. / Deciphering the functioning of biological networks is one of the central tasks in systems biology. In particular, signal transduction networks are crucial for the understanding of the cellular response to external and internal perturbations. Importantly, in order to cope with the complexity of these networks, mathematical and computational modeling is required. We propose a computational modeling framework in order to achieve more robust discoveries in the context of logical signaling networks. More precisely, we focus on modeling the response of logical signaling networks by means of automated reasoning using Answer Set Programming (ASP). ASP provides a declarative language for modeling various knowledge representation and reasoning problems. Moreover, available ASP solvers provide several reasoning modes for assessing the multitude of answer sets. Therefore, leveraging its rich modeling language and its highly efficient solving capacities, we use ASP to address three challenging problems in the context of logical signaling networks: learning of (Boolean) logical networks, experimental design, and identification of intervention strategies. Overall, the contribution of this thesis is three-fold. Firstly, we introduce a mathematical framework for characterizing and reasoning on the response of logical signaling networks. Secondly, we contribute to a growing list of successful applications of ASP in systems biology. Thirdly, we present a software providing a complete pipeline for automated reasoning on the response of logical signaling networks.

O estudo da evolução da concentração de elementos de uma reação química, conhecida como Cinética Química, é de extrema importância para a compreensão das complexas interações em sistemas biológicos. Uma maneira de descrever a cinética de uma reação química é utilizando um sistema de equações diferenciais ordinárias (EDOs). Uma vez que para resolver um sistema de equações diferenciais ordinárias pode ser uma tarefa difícil (ou mesmo inviável), métodos numéricos são utilizados para realizar simulações, ou seja, para obter concentrações aproximadas das espécies químicas envolvidas durante um determinado período de tempo. No entanto, quanto maior for o sistema simulado de EDOs, mais os métodos numéricos estão sujeitos a erros. Além disso, o aumento do tamanho do sistema muitas vezes resulta em simulações que são mais exigentes do ponto de vista computacional. Assim, o objetivo deste projeto de mestrado é o desenvolvimento de regras para simplificar os sistemas de equações diferenciais ordinárias que modelam a cinética de reações químicas e, portanto, a obtenção de um algoritmo para executar simulações numéricas de um modo mais rápido e menos propenso a erros. Mais do que diminuir o erro e o tempo de execução, esta simplificação possibilita o biólogo escolher a solução mais factível do ponto de vista de medida. Isso porque, a identificação dos sistemas (i.e., inferência dos parâmetros) requer que a concentração de todas as espécies químicas seja conhecida, ao menos em um certo intervalo de tempo. Contudo, em muitos casos, não é possível medir a concentração de todas as espécies químicas consideradas. Esta simplificação gera sistemas equivalentes ao original, mas que dispensa a utilização de certas concentrações de espécies químicas. Um sistema de equações diferenciais ordinárias pode ser simplificado considerando as relações de conservação de massa, que são equações algébricas. Além disso, no caso de reações enzimáticas, o sistema de equações diferenciais ordinárias pode ser simplificado pelo pressuposto de que a concentração do complexo enzima-substrato mantém-se constante, o que permite a utilização da equação de Michaelis-Menten. De todas as combinações possíveis das equações algébricas com as equações diferenciais, uma família de sistemas simplificados de EDOs foi construída, permitindo a escolha do sistema mais simples. Esta escolha segue um critério guloso que favorece a minimização do número de equações diferenciais e do número total de termos. As regras em desenvolvimento de simplificação dos sistemas de equações diferenciais ordinárias foram utilizados para projetar um algoritmo, que foi implementado usando a linguagem de programação Python. O algoritmo concebido foi testado utilizando instâncias artificiais. / The study of the evolution of the concentration of species in a chemical reaction, known as Chemical Kinetics, is of paramount importance for the understanding of complex interactions in biological systems. One way to describe the kinetics of a chemical reaction is using a system of ordinary differential equations (ODEs). Once to solve a system of ODEs can be a difficult (or even unfeasible) task, numerical methods are employed to carry out simulations, that is, to obtain approximated concentrations of the involved chemical species for a certain time frame. However, the larger is the simulated system of ODEs, the more numerical methods are subject to error. Moreover, the increase of the system size often results in simulations that are more demanding from the computational point of view. Thus, the objective is the development of rules to simplify systems of ODEs that models the kinetics of chemical reactions, hence obtaining an algorithm to execute numerical simulations in a faster way and less prone to error. More than decrease error and run time, this simplification allows the biologist to choose the most feasible solution from the point of view of measurement. This is because the identification of systems (i.e., inferring parameters) requires that the concentration of all chemical species is known, at least in a certain time interval. However, in many cases it is not possible to measure the concentration of all chemical species considered. This simplification creates systems equivalent to the original, but that does not require the use of certain concentrations of chemical species. A system of ODEs can be simplified considering the relations of mass conservation, which are algebraic equations. Furthermore, in the case of enzymatic reactions, the system of ODEs can be simplified under the assumption that the concentration of enzyme-substrate complex remains constant, which allows us to use the Michaelis-Menten equation. From all possible combinations of the algebraic equations with differential equations, a family of simplified systems of ODEs will be built, allowing the choice of a simplest system. This choice will follow a greedy criterion which favors the minimization of number of differential equations and the total number of terms. The rules under development to simplify systems of ODEs will be used to design an algorithm, which will be implemented using Python programming language. The designed algorithm will be tested using synthetic data.

Les voies de signalisation canoniques traditionnelles aident à comprendre l'ensemble des processus de signalisation à l'intérieur de la cellule. Les données phosphoprotéomiques à grande échelle donnent un aperçu des altérations entre différentes protéines dans différents contextes expérimentaux. Notre objectif est de combiner les réseaux de signalisation traditionnels avec des données de séries temporelles phosphoprotéomiques complexes afin de démêler les réseaux de signalisation spécifiques aux cellules. Côté application, nous appliquons et améliorons une méthode de séries temporelles caspo conçue pour intégrer des données phosphoprotéomiques de séries temporelles dans des réseaux de signalisation de protéines. Nous utilisons une étude de cas réel à grande échelle tirée du défi HPN-DREAM BreastCancer. Nous déduisons une famille de modèles booléens à partir de données de séries temporelles de perturbations multiples de quatre lignées cellulaires de cancer du sein, compte tenu d'un réseau de signalisation protéique antérieur. Les résultats obtenus sont comparables aux équipes les plus performantes du challenge HPN-DREAM. Nous avons découvert que les modèles similaires sont regroupés dans l'espace de solutions. Du côté informatique, nous avons amélioré la méthode pour découvrir diverses solutions et améliorer le temps de calcul. / Traditional canonical signaling pathways help to understand overall signaling processes inside the cell. Large scale phosphoproteomic data provide insight into alterations among different proteins under different experimental settings. Our goal is to combine the traditional signaling networks with complex phosphoproteomic time-series data in order to unravel cell specific signaling networks. On the application side, we apply and improve a caspo time series method conceived to integrate time series phosphoproteomic data into protein signaling networks. We use a large-scale real case study from the HPN-DREAM BreastCancer challenge. We infer a family of Boolean models from multiple perturbation time series data of four breast cancer cell lines given a prior protein signaling network. The obtained results are comparable to the top performing teams of the HPN-DREAM challenge. We also discovered that the similar models are clustered to getherin the solutions space. On the computational side, we improved the method to discover diverse solutions and improve the computational time.

Deciphering the functioning of biological networks is one of the central tasks in systems biology. In particular, signal transduction networks are crucial for the understanding of the cellular response to external and internal perturbations. Importantly, in order to cope with the complexity of these networks, mathematical and computational modeling is required. We propose a computational modeling framework in order to achieve more robust discoveries in the context of logical signaling networks. More precisely, we focus on modeling the response of logical signaling networks by means of automated reasoning using Answer Set Programming (ASP). ASP provides a declarative language for modeling various knowledge representation and reasoning problems. Moreover, available ASP solvers provide several reasoning modes for assessing the multitude of answer sets. Therefore, leveraging its rich modeling language and its highly efficient solving capacities, we use ASP to address three challenging problems in the context of logical signaling networks: learning of (Boolean) logical networks, experimental design, and identification of intervention strategies. Overall, the contribution of this thesis is three-fold. Firstly, we introduce a mathematical framework for characterizing and reasoning on the response of logical signaling networks. Secondly, we contribute to a growing list of successful applications of ASP in systems biology. Thirdly, we present a software providing a complete pipeline for automated reasoning on the response of logical signaling networks. / Deciphering the functioning of biological networks is one of the central tasks in systems biology. In particular, signal transduction networks are crucial for the understanding of the cellular response to external and internal perturbations. Importantly, in order to cope with the complexity of these networks, mathematical and computational modeling is required. We propose a computational modeling framework in order to achieve more robust discoveries in the context of logical signaling networks. More precisely, we focus on modeling the response of logical signaling networks by means of automated reasoning using Answer Set Programming (ASP). ASP provides a declarative language for modeling various knowledge representation and reasoning problems. Moreover, available ASP solvers provide several reasoning modes for assessing the multitude of answer sets. Therefore, leveraging its rich modeling language and its highly efficient solving capacities, we use ASP to address three challenging problems in the context of logical signaling networks: learning of (Boolean) logical networks, experimental design, and identification of intervention strategies. Overall, the contribution of this thesis is three-fold. Firstly, we introduce a mathematical framework for characterizing and reasoning on the response of logical signaling networks. Secondly, we contribute to a growing list of successful applications of ASP in systems biology. Thirdly, we present a software providing a complete pipeline for automated reasoning on the response of logical signaling networks.

O estudo da evolução da concentração de elementos de uma reação química, conhecida como Cinética Química, é de extrema importância para a compreensão das complexas interações em sistemas biológicos. Uma maneira de descrever a cinética de uma reação química é utilizando um sistema de equações diferenciais ordinárias (EDOs). Uma vez que para resolver um sistema de equações diferenciais ordinárias pode ser uma tarefa difícil (ou mesmo inviável), métodos numéricos são utilizados para realizar simulações, ou seja, para obter concentrações aproximadas das espécies químicas envolvidas durante um determinado período de tempo. No entanto, quanto maior for o sistema simulado de EDOs, mais os métodos numéricos estão sujeitos a erros. Além disso, o aumento do tamanho do sistema muitas vezes resulta em simulações que são mais exigentes do ponto de vista computacional. Assim, o objetivo deste projeto de mestrado é o desenvolvimento de regras para simplificar os sistemas de equações diferenciais ordinárias que modelam a cinética de reações químicas e, portanto, a obtenção de um algoritmo para executar simulações numéricas de um modo mais rápido e menos propenso a erros. Mais do que diminuir o erro e o tempo de execução, esta simplificação possibilita o biólogo escolher a solução mais factível do ponto de vista de medida. Isso porque, a identificação dos sistemas (i.e., inferência dos parâmetros) requer que a concentração de todas as espécies químicas seja conhecida, ao menos em um certo intervalo de tempo. Contudo, em muitos casos, não é possível medir a concentração de todas as espécies químicas consideradas. Esta simplificação gera sistemas equivalentes ao original, mas que dispensa a utilização de certas concentrações de espécies químicas. Um sistema de equações diferenciais ordinárias pode ser simplificado considerando as relações de conservação de massa, que são equações algébricas. Além disso, no caso de reações enzimáticas, o sistema de equações diferenciais ordinárias pode ser simplificado pelo pressuposto de que a concentração do complexo enzima-substrato mantém-se constante, o que permite a utilização da equação de Michaelis-Menten. De todas as combinações possíveis das equações algébricas com as equações diferenciais, uma família de sistemas simplificados de EDOs foi construída, permitindo a escolha do sistema mais simples. Esta escolha segue um critério guloso que favorece a minimização do número de equações diferenciais e do número total de termos. As regras em desenvolvimento de simplificação dos sistemas de equações diferenciais ordinárias foram utilizados para projetar um algoritmo, que foi implementado usando a linguagem de programação Python. O algoritmo concebido foi testado utilizando instâncias artificiais. / The study of the evolution of the concentration of species in a chemical reaction, known as Chemical Kinetics, is of paramount importance for the understanding of complex interactions in biological systems. One way to describe the kinetics of a chemical reaction is using a system of ordinary differential equations (ODEs). Once to solve a system of ODEs can be a difficult (or even unfeasible) task, numerical methods are employed to carry out simulations, that is, to obtain approximated concentrations of the involved chemical species for a certain time frame. However, the larger is the simulated system of ODEs, the more numerical methods are subject to error. Moreover, the increase of the system size often results in simulations that are more demanding from the computational point of view. Thus, the objective is the development of rules to simplify systems of ODEs that models the kinetics of chemical reactions, hence obtaining an algorithm to execute numerical simulations in a faster way and less prone to error. More than decrease error and run time, this simplification allows the biologist to choose the most feasible solution from the point of view of measurement. This is because the identification of systems (i.e., inferring parameters) requires that the concentration of all chemical species is known, at least in a certain time interval. However, in many cases it is not possible to measure the concentration of all chemical species considered. This simplification creates systems equivalent to the original, but that does not require the use of certain concentrations of chemical species. A system of ODEs can be simplified considering the relations of mass conservation, which are algebraic equations. Furthermore, in the case of enzymatic reactions, the system of ODEs can be simplified under the assumption that the concentration of enzyme-substrate complex remains constant, which allows us to use the Michaelis-Menten equation. From all possible combinations of the algebraic equations with differential equations, a family of simplified systems of ODEs will be built, allowing the choice of a simplest system. This choice will follow a greedy criterion which favors the minimization of number of differential equations and the total number of terms. The rules under development to simplify systems of ODEs will be used to design an algorithm, which will be implemented using Python programming language. The designed algorithm will be tested using synthetic data.

A capacidade das células para responder corretamente a sinais externos e perceber mudanças no seu microambiente é a base do desenvolvimento, reparação de tecidos e de imunidade, bem como a homeostase do tecido normal. Transdução de sinal é o principal meio pelo qual as células respondem a sinais externos de seu ambiente e coordenam alterações celulares complexas. O estudo das vias de sinalização molecular permite-nos tentar compreender o funcionamento dessas transduções de sinais e, consequentemente, as respostas celulares a estímulos externos. Uma abordagem adequada para tais estudos é o uso de modelos matemáticos para simular a cinética das reações químicas que descrevem uma dada via de sinalização, o que nos permite gerar predições testáveis de processos celulares. Construir modelos cinéticos preditivos de vias de sinalização molecular através de dados de alto rendimento produzidos utilizando técnicas ômicas (i.e., genômica, transcriptômica, (fosfo-)proteômica) constitui um dos atuais desafios enfrentados pelos pesquisadores na área de Biologia Molecular. Recentemente, para lidar com este desafio, o arcabouço de e-Science SigNetSim foi introduzido pelo Grupo de Biologia Computacional e de Bioinformática do Instituto Butantan. Esse arcabouço permite fazer a descrição de vias de sinalização molecular através da descrição da estrutura de um modelo através de um conjunto de reações químicas, que por sua vez é mapeado para um sistema de Equações Diferencias Ordinárias (EDOs), numericamente simuladas e avaliadas. Todavia, modificações na estrutura das vias precisam ser feitas manualmente, o qual restringe severamente o número de estruturas da via que precisam ser testadas, especialmente no caso de modelos grandes. Portanto, diante desse panorama, este trabalho propõe o desenvolvimento de um método para modificar vias de sinalização molecular. Esse método se baseia no uso de bancos de dados de interatomas para fornecer um conjunto de espécies químicas candidatas para serem incluídas na via de sinalização. Um componente integrado ao arcabouço SigNetSim capaz de testar diferentes hipóteses de modificação de vias foi desenvolvido neste projeto utilizando a metodologia de heurística incremental. Para avaliar a eficiência do componente implementado, utilizamos como estudo de caso um modelo de vias sinalização de MAPKs e PI3K/Akt para realizar testes experimentais e analisar os resultados obtidos. / The ability of cells to respond correctly external signals and to perceive changes in their microenvironment is the basis for development, tissue repair and immunity as well as normal tissue homeostasis. Signal transduction is the primary means by which cells respond to external signals from their environment and coordinate complex cellular changes. The study of molecular signaling pathways allows us to understand the operation of each process of cellular signal transduction. The use of mathematical models to simulate the kinetics of chemical reactions that describe a given signaling pathway, allow us to generate testable predictions of the cell processos. To Build Kinetic predictive models to molecular signaling pathways through massive data omics produced using modern techniques, Genomics, transcriptomics, (Phospho) proteomics, is one of the current challenges faced by researchers in the field of molecular biology. Recently, the \\textit SigNetSim e-Science was introduced by the Biological Computacional and Bioinformatical Group from the Butantan Institute to face this challenge. This \\textit makes the description of molecular signaling pathways through a set of chemical reactions, which are mapped into a system of ordinary differential equations, this system will be numerically simulated and evaluated . However, changes in the structure of the pathways need to be updated manually presented in this work, which severely restricts the number of track structures that need to be tested, especially for the large models. Therefore, given this background, we present the method to modify the molecular signaling pathways. This method relies on the use of interactome database to provide a set of chemical species candidates to be included in the signaling pathway. An component integrated to SigNetSim framework able to test different hypotheses of pathways modification was developed in this project using the incremental heuristic methodology. To evaluate the implemented component, we used the MAPKs and PI3K/Akt pathways model as case study, in order to perform experimental tests and to analyze the obtained results.

A capacidade das células para responder corretamente a sinais externos e perceber mudanças no seu microambiente é a base do desenvolvimento, reparação de tecidos e de imunidade, bem como a homeostase do tecido normal. Transdução de sinal é o principal meio pelo qual as células respondem a sinais externos de seu ambiente e coordenam alterações celulares complexas. O estudo das vias de sinalização molecular permite-nos tentar compreender o funcionamento dessas transduções de sinais e, consequentemente, as respostas celulares a estímulos externos. Uma abordagem adequada para tais estudos é o uso de modelos matemáticos para simular a cinética das reações químicas que descrevem uma dada via de sinalização, o que nos permite gerar predições testáveis de processos celulares. Construir modelos cinéticos preditivos de vias de sinalização molecular através de dados de alto rendimento produzidos utilizando técnicas ômicas (i.e., genômica, transcriptômica, (fosfo-)proteômica) constitui um dos atuais desafios enfrentados pelos pesquisadores na área de Biologia Molecular. Recentemente, para lidar com este desafio, o arcabouço de e-Science SigNetSim foi introduzido pelo Grupo de Biologia Computacional e de Bioinformática do Instituto Butantan. Esse arcabouço permite fazer a descrição de vias de sinalização molecular através da descrição da estrutura de um modelo através de um conjunto de reações químicas, que por sua vez é mapeado para um sistema de Equações Diferencias Ordinárias (EDOs), numericamente simuladas e avaliadas. Todavia, modificações na estrutura das vias precisam ser feitas manualmente, o qual restringe severamente o número de estruturas da via que precisam ser testadas, especialmente no caso de modelos grandes. Portanto, diante desse panorama, este trabalho propõe o desenvolvimento de um método para modificar vias de sinalização molecular. Esse método se baseia no uso de bancos de dados de interatomas para fornecer um conjunto de espécies químicas candidatas para serem incluídas na via de sinalização. Um componente integrado ao arcabouço SigNetSim capaz de testar diferentes hipóteses de modificação de vias foi desenvolvido neste projeto utilizando a metodologia de heurística incremental. Para avaliar a eficiência do componente implementado, utilizamos como estudo de caso um modelo de vias sinalização de MAPKs e PI3K/Akt para realizar testes experimentais e analisar os resultados obtidos. / The ability of cells to respond correctly external signals and to perceive changes in their microenvironment is the basis for development, tissue repair and immunity as well as normal tissue homeostasis. Signal transduction is the primary means by which cells respond to external signals from their environment and coordinate complex cellular changes. The study of molecular signaling pathways allows us to understand the operation of each process of cellular signal transduction. The use of mathematical models to simulate the kinetics of chemical reactions that describe a given signaling pathway, allow us to generate testable predictions of the cell processos. To Build Kinetic predictive models to molecular signaling pathways through massive data omics produced using modern techniques, Genomics, transcriptomics, (Phospho) proteomics, is one of the current challenges faced by researchers in the field of molecular biology. Recently, the \\textit SigNetSim e-Science was introduced by the Biological Computacional and Bioinformatical Group from the Butantan Institute to face this challenge. This \\textit makes the description of molecular signaling pathways through a set of chemical reactions, which are mapped into a system of ordinary differential equations, this system will be numerically simulated and evaluated . However, changes in the structure of the pathways need to be updated manually presented in this work, which severely restricts the number of track structures that need to be tested, especially for the large models. Therefore, given this background, we present the method to modify the molecular signaling pathways. This method relies on the use of interactome database to provide a set of chemical species candidates to be included in the signaling pathway. An component integrated to SigNetSim framework able to test different hypotheses of pathways modification was developed in this project using the incremental heuristic methodology. To evaluate the implemented component, we used the MAPKs and PI3K/Akt pathways model as case study, in order to perform experimental tests and to analyze the obtained results.

Deciphering the functioning of biological networks is one of the central tasks in systems biology. In particular, signal transduction networks are crucial for the understanding of the cellular response to external and internal perturbations. Importantly, in order to cope with the complexity of these networks, mathematical and computational modeling is required. We propose a computational modeling framework in order to achieve more robust discoveries in the context of logical signaling networks. More precisely, we focus on modeling the response of logical signaling networks by means of automated reasoning using Answer Set Programming (ASP). ASP provides a declarative language for modeling various knowledge representation and reasoning problems. Moreover, available ASP solvers provide several reasoning modes for assessing the multitude of answer sets. Therefore, leveraging its rich modeling language and its highly efficient solving capacities, we use ASP to address three challenging problems in the context of logical signaling networks: learning of (Boolean) logical networks, experimental design, and identification of intervention strategies. Overall, the contribution of this thesis is three-fold. Firstly, we introduce a mathematical framework for characterizing and reasoning on the response of logical signaling networks. Secondly, we contribute to a growing list of successful applications of ASP in systems biology. Thirdly, we present a software providing a complete pipeline for automated reasoning on the response of logical signaling networks.