BioDiVinE: A Framework for Parallel Analysis of Biological Models

Barnat, Jǐŕı; Brim, Luboš; Černá, Ivana; Dražan, Sven; Fabrikovà, J.; Láník, Jan; Šafránek, David; Ma, Huimin

doi:10.4204/eptcs.6.3

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…BioDiVinE is a tool for parallel study and analysis of biological systems. The tool analyzes the model based on its chemical reactions, through the following main features: (a) the specificity of models according to their chemical equations through the user interface, (b) the representation of models based on the various ordinary differential equations (ODEs), (c) adjustment of the initial kinetic parameters and conditions, (d) regulation of the discrete abstraction parameters, (e) graphic simulation of model's discrete stages and (f) model controlling [13].…”

Section: Biological Systems and Computational Tools In Systems Biologymentioning

confidence: 99%

Introductory Chapter: Systems Biology Consolidating State of the Art Genetics and Bioinformatics

Papakonstantinou¹,

Pierouli²,

Eliopoulos³

et al. 2019

Systems Biology

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Section: Biological Systems and Computational Tools In Systems Biologymentioning

confidence: 99%

Introductory Chapter: Systems Biology Consolidating State of the Art Genetics and Bioinformatics

Papakonstantinou¹,

Pierouli²,

Eliopoulos³

et al. 2019

Systems Biology

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“…The application of model checking to systems biology has targeted both modeling frameworks. BIOCHAM [18], GNA [15], BioDIVINE [10] are examples of tools providing LTL/CTL model-checking functionalities for the verification of qualitative properties of biological models represented by means of differential equations. Conversely, PRISM [31], MARCIE [35] are examples of tools featuring Continuos Stochastic Logic (CSL) [6] model-checking for the verification of quantitative properties of continuous-time Markov chains (CTMC) models of biological systems.…”

Section: Introductionmentioning

confidence: 99%

Expressive Statistical Model Checking of Genetic Networks with Delayed Stochastic Dynamics

Ballarini

Mäkelä

Ribeiro

2012

Computational Methods in Systems Biology

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Abstract. The recently introduced Hybrid Automata Stochastic Logic (HASL) [8] establishes a powerful framework for the analysis of a broad class of stochastic processes, namely Discrete Event Stochastic Processes (DESPs). Here we demonstrate the potential of HASL based verification in the context of genetic circuits. To this aim we consider the analysis of a model of gene expression with delayed stochastic dynamics, a class of systems whose dynamics includes both Markovian and non-Markovian events. We identify a number of relevant properties related to this model, formally express them in HASL terms and, assess them with COSMOS, a statistical model checker for HASL model checking. We demonstrate that this allows assessing the "performances" of a biological system beyond the capability of other stochastic logics.

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“…model checking [10]) proved to be valuable instruments for a more expressive analysis of stochastic biological models [21,4,5]. Biologically relevant events can be formally characterised as temporal logic formulae that can then be automatically checked against a discrete state model.…”

Section: Introductionmentioning

confidence: 99%

Efficient Parallel Statistical Model Checking of Biochemical Networks

Ballarini

Forlin

Mazza

et al. 2009

Electron. Proc. Theor. Comput. Sci.

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We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property φ holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of φ which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of φ to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture.

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BioDiVinE: A Framework for Parallel Analysis of Biological Models

Cited by 11 publications

References 16 publications

Introductory Chapter: Systems Biology Consolidating State of the Art Genetics and Bioinformatics

Introductory Chapter: Systems Biology Consolidating State of the Art Genetics and Bioinformatics

Expressive Statistical Model Checking of Genetic Networks with Delayed Stochastic Dynamics

Efficient Parallel Statistical Model Checking of Biochemical Networks

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