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)

Modelling coordination in biological systems

We present an application of the Reo coordination paradigm to provide a compositional formal model for describing and reasoning about the behaviour of biological systems, such as regulatory gene networks. Reo governs the interaction and flow of data between components by allowing the construction of connector circuits which have a precise formal semantics. When applied to systems biology, the result is a graphical model, which is comprehensible, mathematically precise, and flexibl

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods

A diversity-aware computational framework for systems biology

L'abstract è presente nell'allegato / the abstract is in the attachmen

Integration of Genome Scale Metabolic Networks and gene regulation of metabolic enzymes with Physiologically Based Pharmacokinetics

The scope of Physiologically Based Pharmacokinetic (PBPK) modelling can be expanded by assimilation of the mechanistic models of intracellular processes from Systems Biology field. Genome Scale Metabolic Networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs and metabolic gene regulation. We demonstrate example models

Hybrid Modeling of Cell Signaling and Transcriptional Reprogramming and Its Application in C. elegans Development

Modeling of signal driven transcriptional reprogramming is critical for understanding of organism development, human disease, and cell biology. Many current modeling techniques discount key features of the biological sub-systems when modeling multiscale, organism-level processes. We present a mechanistic hybrid model, GESSA, which integrates a novel pooled probabilistic Boolean network model of cell signaling and a stochastic simulation of transcription and translation responding to a diffusion model of extracellular signals. We apply the model to simulate the well studied cell fate decision process of the vulval precursor cells (VPCs) in C. elegans, using experimentally derived rate constants wherever possible and shared parameters to avoid overfitting. We demonstrate that GESSA recovers (1) the effects of varying scaffold protein concentration on signal strength, (2) amplification of signals in expression, (3) the relative external ligand concentration in a known geometry, and (4) feedback in biochemical networks. We demonstrate that setting model parameters based on wild-type and LIN-12 loss-of-function mutants in C. elegans leads to correct prediction of a wide variety of mutants including partial penetrance of phenotypes. Moreover, the model is relatively insensitive to parameters, retaining the wild-type phenotype for a wide range of cell signaling rate parameters

Current approaches to gene regulatory network modelling

Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model

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