Comparative Analysis of Statistical Model Checking Tools

Bakir, Mehmet E.; Gheorghe, Marian; Konur, Savas; Stannett, Mike

doi:10.1007/978-3-319-54072-6_8

Cited by 7 publications

(9 citation statements)

References 44 publications

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“…This is a significant extension of our previous work (Bakir et al , 2017), where we only considered a small subset of models and property patterns with a significantly less number of experiments. Since all models are available in the SBML format, we have developed a tool translating the SBML model into the syntax that these SMC tools accept as input.…”

Section: Resultsmentioning

confidence: 98%

“…The performance of stochastic model checking depends primarily on such model features as well as the property type being queried (Bakir et al , 2017). In our work, we aim to increase the predictive accuracy without compromising on computation time.…”

Section: Resultsmentioning

confidence: 99%

“…These tools have been used for analysing a wide range of systems, including computer, network and biological systems. The applicability of these SMC tools to a broad range of biological systems has been intensively investigated (Jansen et al , 2008; Bakir et al , 2017; Boyer et al , 2013; Donaldson and Gilbert, 2008; Zuliani, 2015). …”

Section: Methodsmentioning

confidence: 99%

“…probabilistic and symbolic model checker (PRISM) (Hinton et al , 2006), Ymer (Younes, 2005), Markov reward model checker (MRMC) (Katoen et al , 2009), Monte Carlo Model Checker (MC2) (Donaldson and Gilbert, 2008) and PLASMA-Lab (Boyer et al , 2013). In order to facilitate the model checking process, SMC tools have also been employed as third party tools in a number of integrated software suites, such as SMBioNet (Khalis et al , 2009), Biocham (Faeder et al , 2009), Bio-PEPA Eclipse Workbench (Ciocchetta and Hillston, 2009), genetic network analyser (Batt et al , 2012), kPWorkbench (Bakir et al , 2014; Dragomir et al , 2014) and Infobiotics Workbench (Blakes et al , 2011, 2014). …”

Section: Introductionmentioning

confidence: 99%

“…The model features and characteristics can affect the verification performance hugely (as well as simulation performance, as shown in Sanassy et al , 2015); in particular, verification of models with more complex network structures tend to be more challenging. As we showed in a recent work (Bakir et al , 2017), the type of biological property (i.e. requirement) can also significantly affect the verification time, as each property type can involve different computational processes on the network working at different levels of complexity (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Automatic selection of verification tools for efficient analysis of biochemical models

et al. 2018

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Motivation 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 article 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.ResultsWe 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.Availability and implementationSMC Predictor tool is available at http://www.smcpredictor.com.Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%