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dc.contributor.authorBakir, M.E.
dc.contributor.authorKonur, Savas
dc.contributor.authorGheorghe, Marian
dc.contributor.authorKrasnogor, N.
dc.contributor.authorStannett, M.
dc.date.accessioned2018-05-15T14:59:25Z
dc.date.available2018-05-15T14:59:25Z
dc.date.issued2018
dc.identifier.citationBakir ME, Konur S, Gheorghe M et al (2018) Automatic selection of verification tools for efficient analysis of biochemical models. Bioinformatics. Accepted for publication.en_US
dc.identifier.urihttp://hdl.handle.net/10454/15885
dc.descriptionYesen_US
dc.description.abstractMotivation: Formal verification is a computational approach that checks system correctness (in relation to a desired functionality). It has been widely used in engineering applications to verify that systems work correctly. Model checking, an algorithmic approach to verification, looks at whether a system model satisfies its requirements specification. This approach has been applied to a large number of models in systems and synthetic biology as well as in systems medicine. Model checking is, however, computationally very expensive, and is not scalable to large models and systems. Consequently, statistical model checking (SMC), which relaxes some of the constraints of model checking, has been introduced to address this drawback. Several SMC tools have been developed; however, the performance of each tool significantly varies according to the system model in question and the type of requirements being verified. This makes it hard to know, a priori, which one to use for a given model and requirement, as choosing the most efficient tool for any biological application requires a significant degree of computational expertise, not usually available in biology labs. The objective of this paper is to introduce a method and provide a tool leading to the automatic selection of the most appropriate model checker for the system of interest. Results: We provide a system that can automatically predict the fastest model checking tool for a given biological model. Our results show that one can make predictions of high confidence, with over 90% accuracy. This implies significant performance gain in verification time and substantially reduces the “usability barrier” enabling biologists to have access to this powerful computational technology.en_US
dc.description.sponsorshipEPSRC, Innovate UKen_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1093/bioinformatics/bty282en_US
dc.rights© 2018 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/)en_US
dc.subjectFormal verificationen_US
dc.subjectModel checkingen_US
dc.subjectSystems biologyen_US
dc.subjectMachine learningen_US
dc.subjectModellingen_US
dc.titleAutomatic Selection of Verification Tools for Efficient Analysis of Biochemical Modelsen_US
dc.status.refereedYesen_US
dc.date.Accepted2018-04-20
dc.date.application2018-04-24
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
refterms.dateFOA2018-07-29T01:37:44Z


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