Qualitative and Quantitative Analysis of Systems and Synthetic Biology Constructs using P Systems

Konur, Savas; Gheorghe, Marian; Dragomir, Ciprian; Mierlă, Laurenţiu; Ipate, Florentin; Krasnogor, Natalio

doi:10.1021/sb500134w

Cited by 17 publications

(12 citation statements)

References 34 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Namely, they allow one to describe all chains of reactions, observe various interactions between species and determine various dependencies between molecules. In [22], two biological systems, the quorum sensing in P. aeruginosas and the synthetic pulse generator, and in [21] some genetic Boolean gates have been qualitatively analysed using the NuSMV and Spin model checkers. However, the ideas and methodology presented in these papers were not fully automated.…”

Section: Discussionmentioning

confidence: 99%

“…The model checking framework now works in a fully automated fashion and is integrated into the kPWorkbench platform. Thus, this work can be considered progress on the conceptually presented methodology introduced in [22,21].…”

Section: Discussionmentioning

confidence: 99%

“…The kP systems have been also used to specify and analyse, through formal verification, synthetic biology systems, e.g. genetic gates [22,21].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Integrated Model Checking Toolset for Kernel P Systems

et al. 2015

Self Cite

View full text Add to dashboard Cite

Abstract. P systems are the computational models introduced in the context of membrane computing, a computational paradigm within the more general area of unconventional computing. Kernel P (kP) systems are defined to unify the specification of different variants of P systems, motivated by challenging theoretical aspects and the need to model different problems. kP systems are supported by a software framework, called kPWorkbench, which integrates a set of related simulation and verification methodologies and tools. In this paper, we present an extension to kPWorkbench with a new model checking framework supporting the formal verification of kP system models. This framework supports both LTL and CTL properties. To make the property specification an easier task, we propose a property language, composed of natural language statements. We demonstrate our proposed methodology with an example.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

An Integrated Model Checking Toolset for Kernel P Systems

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…The basis of such models is state machines, which can be used to model numerous variables and relate different system states (configurations) to one another [21]. There have been various attempts to model biological systems from a computational point of view, including the use of Boolean networks [31], Petri nets [45], the π-calculus [39], interacting state machines [25], L-systems [38] and variants of P systems (membrane systems) [5,17,23,29,33,42]. These techniques are useful for investigating the qualitative features, as are their stochastic counterparts (e.g., stochastic Petri Nets [26] and stochastic P systems [8,43]) are useful for investigating the quantitative features of computation models.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Statistical Model Checking Tools

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract. Statistical model checking is a powerful and flexible approach for formal verification of computational models, e.g. P systems, which can have very large search spaces. Various statistical model checking tools have been developed, but choosing the most efficient and appropriate tool requires a significant degree of experience, not only because different tools have different modelling and property specification languages, but also because they may be designed to support only a certain subset of property types. Furthermore, their performance can vary depending on the property types and membrane systems being verified. In this paper, we evaluate the performance of various common statistical model checkers based on a pool of biological models. Our aim is to help users select the most suitable SMC tools from among the available options, by comparing their modelling and property specification languages, capabilities and performances.

show abstract

“…quorum sensing [18], genetic Boolean gates [23] and synthetic pulse generators [1], to some classical computational problems, e.g. sorting [6], broadcasting [10] and subset sum [5].…”

Section: Introductionmentioning

confidence: 99%

Modelling and Validating an Engineering Application in Kernel P Systems

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract. This paper illustrates how kernel P systems (kP systems) can be used for modelling and validating an engineering application, in this case a cruise control system of an electric bike. The validity of the system is demonstrated via formal verification, carried out using the kPWorkbench tool. Furthermore, we show how the kernel P system model can be tested using automata and X-machine based techniques.

show abstract

Qualitative and Quantitative Analysis of Systems and Synthetic Biology Constructs using P Systems

Cited by 17 publications

References 34 publications

An Integrated Model Checking Toolset for Kernel P Systems

An Integrated Model Checking Toolset for Kernel P Systems

Comparative Analysis of Statistical Model Checking Tools

Modelling and Validating an Engineering Application in Kernel P Systems

Contact Info

Product

Resources

About