262 research outputs found
Modeling Biological Pathway Dynamics With Timed Automata
Living cells are constantly subjected to a plethora of environmental stimuli that require integration into an appropriate cellular response. This integration takes place through signal transduction events that form tightly interconnected networks. The understanding of these networks requires to capture their dynamics through computational support and models. ANIMO (Analysis of Networks with Interactive MOdelling) is a tool that enables construction and exploration of executable models of biological networks, helping to derive hypotheses and to plan wet-lab experiments. The tool is based on the formalism of Timed Automata, which can be analysed via the UPPAAL model checker. Thanks to Timed Automata, we can provide a formal semantics for the domain-specific language used to represent signalling networks. This enforces precision and uniformity in the definition of signalling pathways, contributing to the integration of isolated signalling events into complex network models. We propose an approach to discretization of reaction kinetics that allows us to efficiently use UPPAAL as the computational engine to explore the dynamic behaviour of the network of interest. A user-friendly interface hides the use of Timed Automata from the user, while keeping the expressive power intact. Abstraction to single-parameter kinetics speeds up construction of models that remain faithful enough to provide meaningful insight. The resulting dynamic behaviour of the network components is displayed graphically, allowing for an intuitive and interactive modelling experience
Integrating Time-Series Data in Large-Scale Discrete Cell-Based Models
International audienceIn this work we propose an automatic way of generating and verifying formal hybrid models of signaling and transcriptional events, gathered in large-scale regulatory networks.This is done by integrating temporal and stochastic aspects of the expression of some biological components. The hybrid approach lies in the fact that measurements take into account both times of lengthening phases and discrete switches between them. The model proposed is based on a real case study of keratinocytes differentiation, in which gene time-series data was generated upon Calcium stimulation. To achieve this we rely on the Process Hitting (PH) formalism that was designed to consider large-scale system analysis. We first propose an automatic way of detecting and translating biological motifs from the Pathway Interaction Database to the PH formalism. Then, we propose a way of estimating temporal and stochas-tic parameters from time-series expression data of action on the PH. Simulations emphasize the interest of synchronizing concurrent events
Simulation of networks of spiking neurons: A review of tools and strategies
We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of
Computational Neuroscience, in press (2007
Dagstuhl News January - December 2001
"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic
Analysis of Biochemical Reaction Networks using Tropical and Polyhedral Geometry Methods
The field of systems biology makes an attempt to realise various biological functions and processes as the emergent properties of the underlying biochemical network model. The area of computational systems biology deals with the computational methods to compute such properties. In this context, the thesis primarily discusses novel computational methods to compute the emergent properties as well as to recognize the essence in complex network models. The computational methods described in the thesis are based on the computer algebra techniques, namely tropical geometry and extreme currents. Tropical geometry is based on ideas of dominance of monomials appearing in a system of differential equations, which are often used to describe the dynamics of the network model. In such differential equation based models, tropical geometry deals with identification of the metastable regimes, defined as low dimensional regions of the phase space close to which the dynamics is much slower compared to the rest of the phase space. The application of such properties in model reduction and symbolic dynamics are demonstrated in the network models obtained from a public database namely Biomodels. Extreme currents are limiting edges of the convex polyhedrons describing the admissible fluxes in biochemical networks, which are helpful to decompose a biochemical network into a set of irreducible pathways. The pathways are shown to be associated with given clinical outcomes thereby providing some mechanistic insights associated with the clinical phenotypes. Similar to the tropical geometry, the method based on extreme currents is evaluated on the network models derived from a public database namely KEGG. Therefore, this thesis makes an attempt to explain the emergent properties of the network model by determining extreme currents or metastable regimes. Additionally, their applicability in the real world network models are discussed
Corporate Smart Content Evaluation
Nowadays, a wide range of information sources are available due to the
evolution of web and collection of data. Plenty of these information are
consumable and usable by humans but not understandable and processable by
machines. Some data may be directly accessible in web pages or via data feeds,
but most of the meaningful existing data is hidden within deep web databases
and enterprise information systems. Besides the inability to access a wide
range of data, manual processing by humans is effortful, error-prone and not
contemporary any more. Semantic web technologies deliver capabilities for
machine-readable, exchangeable content and metadata for automatic processing
of content. The enrichment of heterogeneous data with background knowledge
described in ontologies induces re-usability and supports automatic processing
of data. The establishment of “Corporate Smart Content” (CSC) - semantically
enriched data with high information content with sufficient benefits in
economic areas - is the main focus of this study. We describe three actual
research areas in the field of CSC concerning scenarios and datasets
applicable for corporate applications, algorithms and research. Aspect-
oriented Ontology Development advances modular ontology development and
partial reuse of existing ontological knowledge. Complex Entity Recognition
enhances traditional entity recognition techniques to recognize clusters of
related textual information about entities. Semantic Pattern Mining combines
semantic web technologies with pattern learning to mine for complex models by
attaching background knowledge. This study introduces the afore-mentioned
topics by analyzing applicable scenarios with economic and industrial focus,
as well as research emphasis. Furthermore, a collection of existing datasets
for the given areas of interest is presented and evaluated. The target
audience includes researchers and developers of CSC technologies - people
interested in semantic web features, ontology development, automation,
extracting and mining valuable information in corporate environments. The aim
of this study is to provide a comprehensive and broad overview over the three
topics, give assistance for decision making in interesting scenarios and
choosing practical datasets for evaluating custom problem statements. Detailed
descriptions about attributes and metadata of the datasets should serve as
starting point for individual ideas and approaches
Recent advances in petri nets and concurrency
CEUR Workshop Proceeding
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