Modeling Reaction Kinetics in Low-dimensional Environments with Conformon P Systems: Comparison with Cellular Automata and New Rate Laws

Recently it has been shown that simulations of complex biological systems using conformon P systems and cellular automata do not necessarily give the same pre- dictions. To further elucidate these di®erences we simulate a simple model of intracellular reactions involving a single bimolecular reaction occurring on a biological membrane us- ing conformon P systems. We ¯nd that the predictions broadly agree with results from both the theory of ran- dom walks in low-dimensional environments and with previously published simulations using cellular automata. Moreover, a re-analysis of the data enables us to deduce novel rate laws for the kinetics of reactions occurring on biological membranes

No Cycles in Compartments. Starting from Conformon-P Systems

Starting from proofs of results about the computing power of conformon- P systems, we infer several results about the power of certain classes of tissue-like P systems with (cooperative) rewriting rules used in an asynchronous way, without cycles in compartments. This last feature is related to an important restriction appearing when dealing with lab implementations of P systems, that of avoiding local evolution loops of objects

Predicting the outcomes of HIV treatment interruptions using computational modelling

In the past 30 years, HIV infection made a transition from fatal to chronic disease due to the emergence of potent treatment largely suppressing viral replication. However, this medication must be administered life-long on a regular basis to maintain viral suppression and is not always well tolerated. Any interruption of treatment causes residual virus to be reactivated and infection to progress, where the underlying processes occurring and consequences for the immune system are still poorly understood. Nonetheless, treatment interruptions are common due to adherence issues or limited access to antiretroviral drugs. Early clinical studies, aiming at application of treatment interruptions in a structured way, gave contradictory results concerning patient safety, discouraging further trials. In-silico models potentially add to knowledge but a review of the Literature indicates most current models used for studying treatment interruptions (equation-based), neglect recent clinical findings of collagen formation in lymphatic tissue due to HIV and its crucial role in immune system stability and efficacy. The aim of this research is the construction and application of so-called ‘Bottom-Up’ models to allow improved assessment of these processes in relation to HIV treatment interruptions. In this regard, a novel computational model based on 2D Cellular Automata for lymphatic tissue depletion and associated damage to the immune system was developed. Hence, (i) using this model, the influence of spatial distribution of collagen formation on HIV infection progression speed was evaluated while discussing aspects of computational performance. Further, (ii) direct Monte Carlo simulations were employed to explore the accumulation of tissue impairment due to repeated treatment interruptions and consequences for long-term prognosis. Finally, (iii) an inverse Monte Carlo approach was used to reconstruct yet unknown characteristics of patient groups. This is based on sparse data from past clinical studies on treatment interruptions with the aim of explaining their contradictory results

Modeling formalisms in systems biology

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.Research supported by grants SFRH/BD/35215/2007 and SFRH/BD/25506/2005 from the Fundacao para a Ciencia e a Tecnologia (FCT) and the MIT-Portugal Program through the project "Bridging Systems and Synthetic Biology for the development of improved microbial cell factories" (MIT-Pt/BS-BB/0082/2008)

Membrane Computing as a Modeling Framework. Cellular Systems Case Studies

Membrane computing is a branch of natural computing aiming to abstract computing models from the structure and functioning of the living cell, and from the way cells cooperate in tissues, organs, or other populations of cells. This research area developed very fast, both at the theoretical level and in what concerns the applications. After a very short description of the domain, we mention here the main areas where membrane computing was used as a framework for devising models (biology and bio-medicine, linguistics, economics, computer science, etc.), then we discuss in a certain detail the possibility of using membrane computing as a high level computational modeling framework for addressing structural and dynamical aspects of cellular systems. We close with a comprehensive bibliography of membrane computing applications

A systems biology approach to multi-scale modelling and analysis of planar cell polarity in drosophila melanogaster wing

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Systems biology aims to describe and understand biology at a global scale where biological systems function as a result of complex mechanisms that happen at several scales. Modelling and simulation are computational tools that are invaluable for description, understanding and prediction these mechanisms in a quantitative and integrative way. Thus multi-scale methods that couple the design, simulation and analysis of models spanning several spatial and temporal scales is becoming a new emerging focus of systems biology. This thesis uses an exemplar – Planar cell polarity (PCP) signalling – to illustrate a generic approach to model biological systems at different spatial scales, using the new concept of Hierarchically Coloured Petri Nets (HCPN). PCP signalling refers to the coordinated polarisation of cells within the plane of various epithelial tissues to generate sub-cellular asymmetry along an axis orthogonal to their apical-basal axes. This polarisation is required for many developmental events in both vertebrates and non-vertebrates. Defects in PCP in vertebrates are responsible for developmental abnormalities in multiple tissues including the neural tube, the kidney and the inner ear. In Drosophila wing, PCP is seen in the parallel orientation of hairs that protrude from each of the approximately 30,000 epithelial cells to robustly point toward the wing tip. This work applies HCPN to model a tissue comprising multiple cells hexagonally packed in a honeycomb formation in order to describe the phenomenon of Planar Cell Polarity (PCP) in Drosophila wing. HCPN facilitate the construction of mathematically tractable, compact and parameterised large-scale models. Different levels of abstraction that can be used in order to simplify such a complex system are first illustrated. The PCP system is first represented at an abstract level without modelling details of the cell. Each cell is then sub-divided into seven virtual compartments with adjacent cells being coupled via the formation of intercellular complexes. A more detailed model is later developed, describing the intra- and inter-cellular signalling mechanisms involved in PCP signalling. The initial model is for a wild-type organism, and then a family of related models, permitting different hypotheses to be explored regarding the mechanisms underlying PCP, are constructed. Among them, the largest model consists of 800 cells which when unfolded yields 164,000 places (each of which is described by an ordinary differential equation). This thesis illustrates the power and validity of the approach by showing how the models can be easily adapted to describe well-documented genetic mutations in the Drosophila wing using the proposed approach including clustering and model checking over time series of primary and secondary data, which can be employed to analyse and check such multi-scale models similar to the case of PCP. The HCPN models support the interpretation of biological observations reported in literature and are able to make sensible predictions. As HCPN model multi-scale systems in a compact, parameterised and scalable way, this modelling approach can be applied to other large-scale or multi-scale systems.This study was funded by Brunel University

Novel modeling formalisms and simulation tools in computational biosystems

Tese de doutoramento em BioengenhariaThe goal of Systems Biology is to understand the complex behavior that emerges from the interaction among the cellular components. Industrial biotechnology is one of the areas of application, where new approaches for metabolic engineering are developed, through the creation of new models and tools for simulation and optimization of the microbial metabolism. Although whole-cell modeling is one of the goals of Systems Biology, so far most models address only one kind of biological network independently. This work explores the integration of di erent kinds of biological networks with a focus on the improvement of simulation of cellular metabolism. The bacterium Escherichia coli is the most well characterized model organism and is used as our case-study. An extensive review of modeling formalisms that have been used in Systems Biology is presented in this work. It includes several formalisms, including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, di erential equations, rule-based models, interacting state machines, cellular automata and agent-based models. We compare the features provided by these formalisms and classify the most suitable ones for the creation of a common framework for modeling, analysis and simulation of integrated biological networks. Currently, there is a separation between dynamic and constraint-based modeling of metabolism. Dynamic models are based on detailed kinetic reconstructions of central metabolic pathways, whereas constraint-based models are based on genome-scale stoichiometric reconstructions. Here, we explore the gap between both formulations and evaluate how dynamic models can be used to reduce the solution space of constraint-based models in order to eliminate kinetically infeasible solutions. The limitations of both kinds of models are leading to new approaches to build kinetic models at the genome-scale. The generation of kinetic models from stoichiometric reconstructions can be performed within the same framework as a transformation from discrete to continuous Petri nets. However, the size of these networks results in models with a large number of parameters. In this scope, we develop and implement structural reduction methods that adjust the level of detail of metabolic networks without loss of information, which can be applied prior to the kinetic inference to build dynamic models with a smaller number of parameters. In order to account for enzymatic regulation, which is not present in constraint-based models, we propose the utilization of Extended Petri nets. This results in a better sca old for the kinetic inference process. We evaluate the impact of accounting for enzymatic regulation in the simulation of the steady-state phenotype of mutant strains by performing knockouts and adjustment of enzyme expression levels. It can be observed that in some cases the impact is signi cant and may reveal new targets for rational strain design. In summary, we have created a solid framework with a common formalism and methods for metabolic modeling. This will facilitate the integration with gene regulatory networks, as we have already addressed many issues also associated with these networks, such as the trade-o between size and detail, and the representation of regulatory interactions.O objectivo da Biologia de Sistemas é compreender os comportamentos que resultam das complexas interacções entre todos os componentes celulares. A biotecnologia industrial é uma das áreas de aplicação, onde novas abordagens para a engenharia metabólica são desenvolvidas através da criação de novos modelos e ferramentas de simulação e optimização do metabolismo microbiano. Apesar de um dos principais objectivos da Biologia de Sistemas ser a criação de um modelo completo de uma célula, até ao momento a maioria dos modelos desenvolvidos incorpora de forma separada cada tipo de rede biológica. Este trabalho explora a integração de diferentes tipos de redes biológicas, focando melhorar a simulação do metabolismo celular. A bactéria Escherichia coli é o organismo modelo que estáa melhor caracterizado e é usado como caso de estudo. Neste trabalho é elaborada uma extensa revisão dos formalismos de modela ção que têm sido utilizados em Biologia de Sistemas. São considerados vários formalismos tais como, redes Booleanas, redes Bayesianas, redes de Petri, álgebras de processos, modelos baseados em restrições, equações diferenciais, modelos baseados em regras, máquinas de interacção de estados, autómatos celulares e modelos baseados em agentes. As funcionalidades inerentes a estes formalismos são analisadas de forma a classificar os mesmos pelo seu potencial em servir de base à criação de uma plataforma para modela ção, análise e simulação de redes biológicas integradas. Actualmente, existe uma separação entre modelação dinâmica e modelação baseada em restrições para o metabolismo celular. Os modelos dinâmicos consistem em reconstruções cinéticas detalhadas de vias centrais do metabolismo, enquanto que os modelos baseados em restrições são construídos à escala genómica com base apenas na estequiometria das reacçõoes. Neste trabalho explora-se a separação entre os dois tipos de formulação e é avaliada a forma como os modelos dinâmicos podem ser utilizados para reduzir o espaço de soluções de modelos baseados em restrições de forma a eliminar soluções inalcançáveis. As limitações impostas por ambos os tipos de modelos estão a conduzir à criação de novas abordagens para a construção de modelos cinéticos à escala genómica. A geração de modelos cinéticos a partir de reconstruções estequiométricas pode ser feita dentro de um mesmo formalismo através da transformação de redes de Petri discretas em redes de Petri contínuas. No entanto, devido ao tamanho destas redes, os modelos resultantes incluem um número extremamente grande de parâmetros. Neste trabalho são implementados métodos para a redução estrutural de redes metabólicas sem perda de informação, que permitem ajustar o nível de detalhe das redes. Estes métodos podem ser aplicados à inferência de cinéticas, de forma a gerar modelos dinâmicos com um menor número de parâmetros. De forma a considerar efeitos de regulação enzimática, os quais não são representados em modelos baseados em restrições, propõe-se a utilização de redes de Petri complementadas com arcos regulatórios. Este formalismo é utilizado como base para o processo de inferência cinética. A influência da regulação enzimática na determinação do estado estacionário de estirpes mutantes é avaliada através da análise da remoção de reacções e da variação dos níveis de expressão enzimática. Observa-se que em alguns casos esta influência é significativa e pode ser utilizada para obter novas estratégias de manipulação de estirpes. Em suma, neste trabalho foi criada uma plataforma sólida para modelação do metabolismo baseada num formalismo comum. Esta plataforma facilitará a integração com redes de regulação genética, pois foram abordados vários problemas que também se colocam nestas redes, tais como o ajuste entre o tamanho da rede e o seu nível de detalhe, bem como a representação de interacções regulatórias entre componentes da rede

Computação evolucionária para indução de regras de autômatos celulares multidimensionais

A cellular automata is a discrete dynamic system that evolves thought interactions of rules and can be applied to solve several complex problems. The task to find the transition rule to solve a problem can be generalized as a problem of rule induction for cellular automata. Several approaches, based on evolutionary computation techniques, have been proposed to solve this problem. However, there is no generic methodology capable of being applied to a large range of problems. The main contribution of this work is a generic methodology for rule induction for cellular automata. This research was done in four steps to achieve this objective. In the first step we evaluated the performance of some dynamic behavior forecasting parameters calculated as function of a transition rule. The obtained results indicated that those parameters can be used in a careful way. This is due to the possibility of obtaining valid, but insatisfactory solutions. We stress the importance of considering reference parameters, which for the majority of real problems, are not available. In the second research step we proposed a new method to forecast the dynamic behavior. This method considers the transition rule and the initial configuration of the cellular automata. We used the qualitative dynamic behavior patterns described by Wolfram as reference to the forecast. This method was efficient for null behavior rules. Since the process of dynamic simulation can have a high computational cost, we developed a third methodology: an architecture based on the concept of hardware/software co-design to accelerate the processing time. This architecture implements the evolution of cellular automata using reconfigurable logic and was able to decrease hundreds of times the processing time. In the fourth step we developed a new parallel architecture based on the master-slave paradigm. In this paradigm, the master process implements the evolutionary algorithm and a set of slaves processes divide the task of validating the obtained rules. The system runs in a cluster with 120 processing cores connected by a local area network. The co-evolutionary strategy based on an insular model allowed the search for high quality solutions. The generic system implemented over a parallel environment was able to solve the problems proposed. A task distribution analyses among several processors emphasized the benefits of parallel processing. The experiments also indicated a set of reference parameters that can be used to configure the system. The contributions of this work were theoretical and methodological. The former refers to the evaluations done and the different methods for dynamic behavior forecasting parameters. The latter is about the development of two architectures for processing.Um autômato celular é um sistema dinâmico discreto que evolui pela iteração de regras. Os valores das variáveis do sistema mudam em função de seus valores correntes. Os autômatos celulares podem ser aplicados na resolução de diversos problemas. A tarefa de encontrar uma regra de transição que solucione um determinado problema pode ser generalizada como um problema de indução de regras para autômatos celulares. Várias abordagens baseadas em técnicas de computação evolucionária vêm sendo empregadas neste problema. No entanto, estas restringem-se a aplicações específicas. A principal contribuição deste trabalho é a proposição de uma metodologia genérica para indução de regras de autômatos celulares. Para alcançar este objetivo a pesquisa foi segmentada em quatro etapas. Na primeira etapa avaliou-se o desempenho de alguns parâmetros de previsão de comportamento calculados em função de regras de transição. Os resultados obtidos nesta etapa indicaram que os parâmetros de previsão de comportamento dinâmico devem ser utilizados de forma criteriosa. Este cuidado reside na possibilidade de se obter soluções válidas, porém, não satisfatórias. Ressalta-se também a necessidade da existência de parâmetros de referência que para a maioria dos problemas reais, não está disponível. Na segunda etapa apresentou-se um novo método para a previsão do comportamento dinâmico. Este método considera a regra de transição e a configuração inicial do autômato celular. Para a previsão utilizou-se como referência os padrões de comportamento dinâmico qualitativos descritos por Wolfram. O método mostrou-se eficiente para regras de comportamento nulo. Como o processo de simulação da dinâmica de um sistema pode ter um custo computacional elevado, desenvolveu-se uma terceira metodologia. Nesta metodologia implementou-se uma arquitetura baseada no conceito de hardware/software co-design com a finalidade de contornar problemas referentes a tempo de processamento. Esta arquitetura realiza a evolução de autômatos celulares utilizando lógica reconfigurável. A arquitetura diminuiu o tempo de processamento por centenas de vezes, mas algumas restrições do modelo, como número limitado de células lógicas e reprogramações do hardware inviabilizaram seu uso. Considerando-se as restrições impostas pela arquitetura implementada, iniciou-se a quarta etapa da pesquisa onde foi desenvolvida uma nova arquitetura paralela fundamentada no paradigma mestre-escravo. Neste paradigma um processo mestre implementa o algoritmo evolucionário e um conjunto de processos escravos dividem a tarefa de validação das regras obtidas. O sistema é executado em um cluster composto por 120 núcleos de processamento que se interligam por meio de uma rede ethernet. A estratégia co-evolucionária baseada em um modelo insular permitiu a busca por soluções que apresentam um melhor valor para função de fitness. O sistema genérico implementado sobre um ambiente paralelo foi capaz de solucionar os problemas abordados. Uma análise de distribuição de tarefas entre vários processadores enfatizou os benefícios do processamento paralelo. Os experimentos também indicaram um conjunto de parâmetros evolucionários de referência que podem ser utilizados para configurar o sistema. As contribuições deste trabalho foram tanto teóricas, com as avaliações realizadas sobre os parâmetros e os diferentes métodos de previsão de comportamento dinâmico, quanto metodológicas, pois desenvolveu-se a proposta de duas arquiteturas de processamento distintas

Estudio, Modelado e Implementación Paralela de Sistemas Celulares Utilizados en Microfabricación

La presente tesis toma como eje central el modelado de sistemas dinámicos mediante Autómatas Celulares (ACs). Los ACs permiten modelar un sistema enunciando el comportamiento microscópico a fin de obtener un comportamiento macroscópico correcto. Una de los principales campos donde esta metodología ha sido aplicada (y la cual forma otro de los puntos centrales de esta tesis) es el modelado del Grabado Anisótropo Húmedo (GAH). El GAH es un proceso químico el cual permite realizar microestructuras de silicio tridimensionales, lo que le ha permitido convertirse en una importante técnica de microfabricación. El GAH se utiliza para el micromaquinado de Sistemas Micro-Electro-Mecánicos (MEMS). Los MEMS consisten en la integración de elementos mecánicos, sensores, actuadores y electrónica en un substrato de silicio común a través de la tecnología de microfabricación. Los MEMS tienen una gran influencia en la industria puesto que dispositivos fabricados mediante esta tecnología se utilizan de forma intensiva en diversos campos tales como: sistemas de seguridad en automoción, sensores de movimiento en electrónica de consumo o inyectores en sistemas de impresión. El GAH es un proceso complejo cuyo resultado depende en gran medida de los diversos parámetros del proceso: (disolución, temperatura, tiempo), por lo que la utilización de un simulador previo a la realización del experimento puede suponer un gran ahorro en cuestión de tiempo y material. Los simuladores actuales de GAH basados en ACs poseen diversas limitaciones: Tiempos de computación muy elevados debido a los altos requisitos computacionales de los ACs, un reducido conjunto de calibraciones existentes, así como la imposibilidad de simular el GAH basado en nuevos atacantes tales como TMAH+Triton. La resolución de estas limitaciones es abordada en diversos capítulos de la tesis.Ferrando Jódar, N. (2011). Estudio, Modelado e Implementación Paralela de Sistemas Celulares Utilizados en Microfabricación [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10984Palanci