Development and validation of computational models of cellular interaction

In this paper we take the view that computational models of biological systems should satisfy two conditions – they should be able to predict function at a systems biology level, and robust techniques of validation against biological models must be available. A modelling paradigm for developing a predictive computational model of cellular interaction is described, and methods of providing robust validation against biological models are explored, followed by a consideration of software issues

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

Background: Study of drug-target interaction networks is an important topic for drug development. It is both timeconsuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner. Methods/Principal Findings: To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively. Conclusion/Significance: Our results indicate that the network prediction system thus established is quite promising an

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)

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

04281 Abstracts Collection -- Integrative Bioinformatics - Aspects of the Virtual Cell

From 04.07.04 to 09.07.04, the Dagstuhl Seminar 04281 ``Integrative Bioinformatics - Aspects of the Virtual Cell\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors

[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order kth (Wk). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the Wk(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation)

A Novel Method to Detect Functional Subgraphs in Biomolecular Networks

Several biomolecular pathways governing the control of cellular processes have been discovered over the last several years. Additionally, advances resulting from combining these pathways into networks have produced new insights into the complex behaviors observed in cell function assays. Unfortunately, identification of important subnetworks, or “motifs”, in these networks has been slower in development. This study focused on identifying important network motifs and their rate of occurrence in two different biomolecular networks. The two networks evaluated for this study represented both ends of the spectrum of interaction knowledge by comparing a well defined network (apoptosis) with and poorly studied network that was early in development (autism). This study identified several motifs that could be important in governing and controlling cellular processes in healthy and diseased cells. Additionally, this study revealed an inverse relationship when comparing the occurrence rate of these motifs in apoptosis and autism

Computational shelf-life dating : complex systems approaches to food quality and safety

Shelf-life is defined as the time that a product is acceptable and meets the consumers expectations regarding food quality. It is the result of the conjunction of all services in production, distribution, and consumption. Shelf-life dating is one of the most difficult tasks in food engineering. Market pressure has lead to the implementation of shelf-life by sensory analyses, which may not reflect the full quality spectra. Moreover, traditional methods for shelf-life dating and small-scale distribution chain tests cannot reproduce in a laboratory the real conditions of storage, distribution, and consumption on food quality. Today, food engineers are facing the challenges to monitor, diagnose, and control the quality and safety of food products. The advent of nanotechnology, multivariate sensors, information systems, and complex systems will revolutionize the way we manage, distribute, and consume foods. The informed consumer demands foods, under the legal standards, at low cost, high standards of nutritional, sensory, and health benefits. To accommodate the new paradigms, we herein present a critical review of shelf-life dating approaches with special emphasis in computational systems and future trends on complex systems methodologies applied to the prediction of food quality and safety.Fundo Europeu de Desenvolvimento Regional (FEDER) - Programa POS-ConhecimentoFundação para a Ciência e a Tecnologia (FCT) - SFRH/BPD/26133/2005, SFRH/ BPD/20735/200