Parallel Model Checking Large-Scale Genetic Regulatory Networks with DiVinE

Barnat, Jǐŕı; Brim, Luboš; Černá, Ivana; Dražan, Sven; Šafránek, David

doi:10.1016/j.entcs.2007.12.001

Cited by 13 publications

(21 citation statements)

References 32 publications

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“…[3,1] [2,2] → [1,2]. For all other domains in the example, all ↑-vectors are zero, as [2,2] → [1,3] is the only case of a dimension-increasing multi-dimensional transition.…”

Section: Proposition 10 Let π Dd Be a By-passing Sequence For D Dmentioning

confidence: 96%

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On algorithmic analysis of transcriptional regulation by LTL model checking

Barnat

Brim

erná

et al. 2009

Theoretical Computer Science

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a b s t r a c tStudies of cells in silico can greatly reduce the need for expensive and prolonged laboratory experimentation. The use of model checking for the analysis of biological networks has attracted much attention recently. The practical limitations are still the size of the model, and the time needed to generate the state space. This paper is focused on the model checking approach for analysis of piecewise-linear deterministic models of genetic regulatory networks. Firstly, the qualitative simulation algorithm of de Jong et al. that builds the heart of Genetic Network Analyzer (GNA) is revisited and its time complexity is studied in detail. Secondly, a novel algorithm that reduces the state space generation time is introduced. The new algorithm is developed as an abstraction of the original GNA algorithm. Finally, a fragment of linear time temporal logic for which the provided abstraction is conservative is identified. Efficiency of the new algorithm when implemented in the parallel model checking environment is demonstrated on a set of experiments performed on randomly modified biological models. In general, the achieved results bring a new insight into the field of qualitative simulation emerging in the context of systems biology.

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“…[3,1] [2,2] → [1,2]. For all other domains in the example, all ↑-vectors are zero, as [2,2] → [1,3] is the only case of a dimension-increasing multi-dimensional transition.…”

Section: Proposition 10 Let π Dd Be a By-passing Sequence For D Dmentioning

confidence: 96%

“…is set to 0, as the transitions operating in the first dimension have been already employed in [1,1] (resp. [3,1] [2,2] → [1,2].…”

Section: Proposition 10 Let π Dd Be a By-passing Sequence For D Dmentioning

confidence: 99%

On algorithmic analysis of transcriptional regulation by LTL model checking

Barnat

Brim

erná

et al. 2009

Theoretical Computer Science

Self Cite

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“…And as is well-known, abstraction techniques and distributed model checking (see e.g. [1]) can help to alleviate the state explosion problem. However, in view of the very large scale and complexity of biological systems, so far even these optimisation techniques cannot push model checking applications in this area beyond toy examples.…”

Section: Formal Models Of Biological Systemsmentioning

confidence: 99%

What Can Formal Methods Bring to Systems Biology?

Bonzanni

Feenstra

Fokkink

et al. 2009

FM 2009: Formal Methods

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Abstract. This position paper argues that the operational modelling approaches from the formal methods community can be applied fruitfully within the systems biology domain. The results can be complementary to the traditional mathematical descriptive modelling approaches used in systems biology. We discuss one example: a recent Petri net analysis of C. elegans vulval development. Systems BiologySystems biology studies complex interactions in biological systems, with the aim to understand better the entirety of processes that happen in such a system, as well as to grasp the emergent properties of such a system as a whole. This can for instance be at the level of metabolic or interaction networks, signal transduction, genetic regulatory networks, multi-cellular development, or social behaviour of insects.The last decade has seen a rapid and successful development in the collaboration between biologists and computer scientists in the area of systems biology and bioinformatics. It has turned out that formal modelling and analysis techniques that have been developed for distributed computer systems, are applicable to biological systems as well. Namely, both kinds of systems have a lot in common. Biological systems are built from separate components that communicate with each other and thus influence each other's behaviour. Notably, signal transduction within a cell consists of cascades of biochemical reactions, by which for instance genes are activated or down-regulated. The genes themselves produce the proteins that drive signal transduction, and cells can be connected in a multicellular organism, making this basically one large, complex distributed system. Another, very different, example at the organism level is how ants in one colony send stimuli to each other in the form of pheromones.Biological systems are reactive systems, as they continuously interact with their environment. In November 2002, David Harel [11] put forward a grand challenge to computer science, to build a fully animated model of a multi-cellular organism as a reactive system; specifically, he suggested to build such a model of the C. elegans nematode worm, which serves as a one of the model organisms in developmental biology.Open questions in biology that could be addressed in such a modelling framework include the following, listed in order from a detailed, molecular viewpoint to a more global view of whole organisms: -How complete is our knowledge of metabolic, signalling and regulatory processes at a molecular level?

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“…No doubt, a number of applications is placing great demands on model checkers -the models have become very large and vast resources are required to verify the desired properties. This is the case, for example, if we want to apply model checking algorithms to verification of specific properties of a complex biological model in order to verify consistency of predictions of the system behaviour with experimental data [1].…”

Section: Introductionmentioning

confidence: 99%