A critical review on modelling formalisms and simulation tools in computational biosystems

Integration of different kinds of biological processes is an ultimate goal for whole-cell modelling. We briefly review modelling formalisms that have been used in Systems Biology and identify the criteria that must be addressed by an integrating framework capable of modelling, analysing and simulating different biological networks. Aware that no formalism can fit all purposes we realize Petri nets as a suitable model for Metabolic Engineering and take a deeper perspective on the role of this formalism as an integrating framework for regulatory and metabolic networks.Research supported by PhD grant SFRH/BD/35215/2007 from the Fundacao para a Ciencia e a Tecnologia (FCT) and the MIT-Portugal program

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)

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

Modelling gene regulatory networks: systems biology to complex systems

Draft literature review on approaches to modelling gene regulatory networks

A review of modelling and verification approaches for computational biology

This paper reviews most frequently used computational modelling approaches and formal verification techniques in computational biology. The paper also compares a number of model checking tools and software suits used in analysing biological systems and biochemical networks and verifiying a wide range of biological properties

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

SBML Level 3: an extensible format for the exchange and reuse of biological models

Abstract Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

A Method to Identify and Analyze Biological Programs through Automated Reasoning.

Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function