323 research outputs found

    Computational models for inferring biochemical networks

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    Biochemical networks are of great practical importance. The interaction of biological compounds in cells has been enforced to a proper understanding by the numerous bioinformatics projects, which contributed to a vast amount of biological information. The construction of biochemical systems (systems of chemical reactions), which include both topology and kinetic constants of the chemical reactions, is NP-hard and is a well-studied system biology problem. In this paper, we propose a hybrid architecture, which combines genetic programming and simulated annealing in order to generate and optimize both the topology (the network) and the reaction rates of a biochemical system. Simulations and analysis of an artificial model and three real models (two models and the noisy version of one of them) show promising results for the proposed method.The Romanian National Authority for Scientific Research, CNDI–UEFISCDI, Project No. PN-II-PT-PCCA-2011-3.2-0917

    Hybrid optimization method with general switching strategy for parameter estimation

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    This article is available from: http://www.biomedcentral.com/1752-0509/2/26[Background] Modeling and simulation of cellular signaling and metabolic pathways as networks of biochemical reactions yields sets of non-linear ordinary differential equations. These models usually depend on several parameters and initial conditions. If these parameters are unknown, results from simulation studies can be misleading. Such a scenario can be avoided by fitting the model to experimental data before analyzing the system. This involves parameter estimation which is usually performed by minimizing a cost function which quantifies the difference between model predictions and measurements. Mathematically, this is formulated as a non-linear optimization problem which often results to be multi-modal (non-convex), rendering local optimization methods detrimental.[Results] In this work we propose a new hybrid global method, based on the combination of an evolutionary search strategy with a local multiple-shooting approach, which offers a reliable and efficient alternative for the solution of large scale parameter estimation problems.[Conclusion] The presented new hybrid strategy offers two main advantages over previous approaches: First, it is equipped with a switching strategy which allows the systematic determination of the transition from the local to global search. This avoids computationally expensive tests in advance. Second, using multiple-shooting as the local search procedure reduces the multi-modality of the non-linear optimization problem significantly. Because multiple-shooting avoids possible spurious solutions in the vicinity of the global optimum it often outperforms the frequently used initial value approach (single-shooting). Thereby, the use of multiple-shooting yields an enhanced robustness of the hybrid approach.This work was supported by the European Community as part of the FP6 COSBICS Project (STREP FP6-512060), the German Federal Ministry of Education and Research, BMBF-project FRISYS (grant 0313921) and Xunta de Galicia (PGIDIT05PXIC40201PM).Peer reviewe

    Modeling and Analysis of Signal Transduction Networks

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    Biological pathways, such as signaling networks, are a key component of biological systems of each living cell. In fact, malfunctions of signaling pathways are linked to a number of diseases, and components of signaling pathways are used as potential drug targets. Elucidating the dynamic behavior of the components of pathways, and their interactions, is one of the key research areas of systems biology. Biological signaling networks are characterized by a large number of components and an even larger number of parameters describing the network. Furthermore, investigations of signaling networks are characterized by large uncertainties of the network as well as limited availability of data due to expensive and time-consuming experiments. As such, techniques derived from systems analysis, e.g., sensitivity analysis, experimental design, and parameter estimation, are important tools for elucidating the mechanisms involved in signaling networks. This Special Issue contains papers that investigate a variety of different signaling networks via established, as well as newly developed modeling and analysis techniques

    Modeling and Optimization of Dynamical Systems in Epidemiology using Sparse Grid Interpolation

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    Infectious diseases pose a perpetual threat across the globe, devastating communities, and straining public health resources to their limit. The ease and speed of modern communications and transportation networks means policy makers are often playing catch-up to nascent epidemics, formulating critical, yet hasty, responses with insufficient, possibly inaccurate, information. In light of these difficulties, it is crucial to first understand the causes of a disease, then to predict its course, and finally to develop ways of controlling it. Mathematical modeling provides a methodical, in silico solution to all of these challenges, as we explore in this work. We accomplish these tasks with the aid of a surrogate modeling technique known as sparse grid interpolation, which approximates dynamical systems using a compact polynomial representation. Our contributions to the disease modeling community are encapsulated in the following endeavors. We first explore transmission and recovery mechanisms for disease eradication, identifying a relationship between the reproductive potential of a disease and the maximum allowable disease burden. We then conduct a comparative computational study to improve simulation fits to existing case data by exploiting the approximation properties of sparse grid interpolants both on the global and local levels. Finally, we solve a joint optimization problem of periodically selecting field sensors and deploying public health interventions to progressively enhance the understanding of a metapopulation-based infectious disease system using a robust model predictive control scheme

    Mathematical Analysis and Modeling of Signaling Networks

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    Mathematical models are in focus of modern systems biology and increasingly important to understand and manipulate complex biological systems. At the same time, new and improved techniques in metabolomics and proteomics enhance the ability to measure cellular states and molecular concentrations. In consequence, this leads to important biological insights and novel potential drug targets. Model development in systems biology can be described as an iterative process of model refinement to match the observed properties. The resulting research cycle is based on a well-defined initial model and requires careful model revision in each step. {As an initial step, a stoichiometry-based mathematical model of the muscarinic acetylcholine receptor subtype 2 (M2 receptor)-induced signaling in Chinese hamster ovary (CHO) cells was derived. To validate the obtained initial model based on spatially accessible, not neces-sarily time-resolved data, the novel constrained flux sampling (CFS) is proposed in this work. The thus verified static model was then translated into a dynamical system based on ordinary differential equations (ODEs) by incorporating time-dependent experimental data. To learn from the errors of systems biological models, the dynamic elastic-net (DEN), a novel approach based on optimal control theory, is proposed in this thesis. Next, the Bayesian dy-namic elastic-net (BDEN), a systematic, fully algorithmic method based on the Markov chain Monte Carlo method was derived, which allows to detect hidden influences as well as missed reactions in ODE-based models. The BDEN allows for further validation of the developed M2 receptor-induced signaling pathway and thus provides evidence for the completeness of the obtained dynamical system. This thesis introduces the first comprehensive model of the M2 receptor-induced signaling in CHO cells. Furthermore, this work presents several novel algorithms to validate and correct static and dynamic models of biological systems in a semi-automatic manner. These novel algorithms are expected to simplify the development of further mathematical models in systems biology.Mathematische Modellierung und Analyse von Signalnetzwerken Mathematische Modelle stehen im Zentrum der modernen Systembiologie und werden immer wichtiger, um komplexe biologische Systeme verstehen und manipulieren zu können. Gleichzeitig erweitern neue und verbesserte Verfahren der Metabolomik und Proteomik die Möglichkeiten, Zellzustände und Molekülkonzentrationen zu bestimmen. Dies ermöglicht die Gewinnung neuer und wichtiger biologischer Erkenntnisse und die Identifizierung neuer potentieller Ansatzpunkte für medizinische Wirkstoffe. Die Modellentwicklung in der Systembiologie kann als ein iterativer Prozess der permanenten Modellverbesserung beschrieben werden, der das Ziel hat, die beobachteten Eigenschaften korrekt wiederzugeben. Der resultierende Modellierungskreislauf basiert auf einem klar bestimmten Anfangsmodell und erfordert das sorgfältige Anpassen des Modells in jedem einzelnen Modellierungsschritt. In einem ersten Schritt wurde ein auf stöchiometrischen Daten basierendes mathematisches Modell für die durch den muskarinischen Acetylcholinrezeptor des Subtyps 2 (M2-Rezeptor) induzierte Signalübertragung in CHO-Zellen aufgestellt. Zur Validierung des ursprünglichen Modells auf der Grundlage von räumlich erfassbaren, nicht notwendigerweise zeitaufgelösten Daten wird in dieser Arbeit das neu entwickelte Constrained Flux Sampling (CFS) vorgestellt. Das auf diese Weise verifizierte statische Modell wurde dann unter Einbeziehung zeitabhängiger experimenteller Messdaten in ein dynamisches Modell basierend auf gewöhnlichen Differentialgleichungen (DGL) umgewandelt. Um aus den mathematischen Unsicherheiten systembiologischer Modelle zu lernen, wird in dieser Arbeit das Dynamic Elastic-Net (DEN) eingeführt, ein neuer Ansatz basierend auf der Theorie der optimalen Steuerungen. Als nächster Schritt wurde das Bayesian Dynamic Elastic-Net (BDEN) entwickelt, eine systematische, vollständig algorithmische Methode basierend auf dem Markov-Chain-Monte-Carlo-Verfahren, die es erlaubt, sowohl verborgene Einflussfaktoren als auch übersehene Reaktionen in DGL-basierten Modellen aufzuspüren. Das BDEN ermöglicht die weitere Validierung des durch den M2-Rezeptor induzierten Signalwegs und liefert so den Beweis für die Vollständigkeit des modellierten dynamischen Systems. In dieser Arbeit wird das erste vollständige Modell für den durch den M2-Rezeptor induzierten Signalweg in CHO-Zellen eingeführt. Des Weiteren werden in dieser Arbeit verschiedene neue Algorithmen zur halbautomatischen Validierung und Korrektur statischer und dynamischer Modelle biologischer Systeme vorgestellt. Es wird erwartet, dass diese neuen Algorithmen die Entwicklung weiterer mathematischer Modelle in der Systembiologie stark vereinfachen

    Assessment of network module identification across complex diseases

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    Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology

    Unraveling the intricacies of spatial organization of the ErbB receptors and downstream signaling pathways

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    Faced with the complexity of diseases such as cancer which has 1012 mutations, altering gene expression, and disrupting regulatory networks, there has been a paradigm shift in the biological sciences and what has emerged is a much more quantitative field of biology. Mathematical modeling can aid in biological discovery with the development of predictive models that provide future direction for experimentalist. In this work, I have contributed to the development of novel computational approaches which explore mechanisms of receptor aggregation and predict the effects of downstream signaling. The coupled spatial non-spatial simulation algorithm, CSNSA is a tool that I took part in developing, which implements a spatial kinetic Monte Carlo for capturing receptor interactions on the cell membrane with Gillespies stochastic simulation algorithm, SSA, for temporal cytosolic interactions. Using this framework we determine that receptor clustering significantly enhances downstream signaling. In the next study the goal was to understand mechanisms of clustering. Cytoskeletal interactions with mobile proteins are known to hinder diffusion. Using a Monte Carlo approach we simulate these interactions, determining at what cytoskeletal distribution and receptor concentration optimal clustering occurs and when it is inhibited. We investigate oligomerization induced trapping to determine mechanisms of clustering, and our results show that the cytoskeletal interactions lead to receptor clustering. After exploring the mechanisms of clustering we determine how receptor aggregation effects downstream signaling. We further proceed by implementing the adaptively coarse grained Monte Carlo, ACGMC to determine if \u27receptor-sharing\u27 occurs when receptors are clustered. In our proposed \u27receptor-sharing\u27 mechanism a cytosolic species binds with a receptor then disassociates and rebinds a neighboring receptor. We tested our hypothesis using a novel computational approach, the ACGMC, an algorithm which enables the spatial temporal evolution of the system in three dimensions by using a coarse graining approach. In this framework we are modeling EGFR reaction-diffusion events on the plasma membrane while capturing the spatial-temporal dynamics of proteins in the cytosol. From this framework we observe \u27receptor-sharing\u27 which may be an important mechanism in the regulation and overall efficiency of signal transduction. In summary, I have helped to develop predictive computational tools that take systems biology in a new direction.\u2

    Quantitative Immunology for Physicists

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    The adaptive immune system is a dynamical, self-organized multiscale system that protects vertebrates from both pathogens and internal irregularities, such as tumours. For these reason it fascinates physicists, yet the multitude of different cells, molecules and sub-systems is often also petrifying. Despite this complexity, as experiments on different scales of the adaptive immune system become more quantitative, many physicists have made both theoretical and experimental contributions that help predict the behaviour of ensembles of cells and molecules that participate in an immune response. Here we review some recent contributions with an emphasis on quantitative questions and methodologies. We also provide a more general methods section that presents some of the wide array of theoretical tools used in the field.Comment: 78 page revie
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