Modelling and Validating an Engineering Application in Kernel P Systems

Lefticaru, Raluca; Bakir, Mehmet E.; Konur, Savas; Stannett, Mike; Ipate, Florentin

doi:10.1007/978-3-319-73359-3_12

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…Formal verification is a well-established research field, and it has many applications, e.g. , safety-critical systems, concurrent systems, distributed systems, network protocols, stochastic systems, multiagent systems, , pervasive systems, − swarm robotics, and biology as well as some engineering applications. − …”

Section: Results and Discussionmentioning

confidence: 99%

Toward Full-Stack In Silico Synthetic Biology: Integrating Model Specification, Simulation, Verification, and Biological Compilation

et al. 2021

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We present the Infobiotics Workbench (IBW), a user-friendly, scalable, and integrated computational environment for the computer-aided design of synthetic biological systems. It supports an iterative workflow that begins with specification of the desired synthetic system, followed by simulation and verification of the system in high-performance environments and ending with the eventual compilation of the system specification into suitable genetic constructs. IBW integrates modeling, simulation, verification, and biocompilation features into a single software suite. This integration is achieved through a new domain-specific biological programming language, the Infobiotics Language (IBL), which tightly combines these different aspects of in silico synthetic biology into a full-stack integrated development environment. Unlike existing synthetic biology modeling or specification languages, IBL uniquely blends modeling, verification, and biocompilation statements into a single file. This allows biologists to incorporate design constraints within the specification file rather than using decoupled and independent formalisms for different in silico analyses. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the burden of doing manual translations for standalone applications. We demonstrate the features, usability, and effectiveness of IBW and IBL using well-established synthetic biological circuits.

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Section: Results and Discussionmentioning

confidence: 99%

Toward Full-Stack In Silico Synthetic Biology: Integrating Model Specification, Simulation, Verification, and Biological Compilation

et al. 2021

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“…Verification, in particular model checking, has been widely applied to the analysis of various systems, e.g. safety-critical systems [34,36], concurrent systems [6], distributed systems [54], network protocols [37], systems and synthetic biology [10,39], multi-agent systems [1] and pervasive systems [9,38], as well as some engineering applications [14,44,45].…”

Section: Simulation and Verification Using Kpworkbenchmentioning

confidence: 99%

Spiking neural P systems: matrix representation and formal verification

et al. 2021

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Structural and behavioural properties of models are very important in development of complex systems and applications. In this paper, we investigate such properties for some classes of SN P systems. First, a class of SN P systems associated to a set of routing problems are investigated through their matrix representation. This allows to make certain connections amongst some of these problems. Secondly, the behavioural properties of these SN P systems are formally verified through a natural and direct mapping of these models into kP systems which are equipped with adequate formal verification methods and tools. Some examples are used to prove the effectiveness of the verification approach.

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“…simulation and testing, allowing us to infer Model checking has been widely used in computing and engineering applications in the last two decades to verify various systems, e.g. safety-critical systems [23], concurrent systems [24], distributed systems [25], network protocols [26], stochastic systems [27], multi-agent systems [28,29], pervasive systems [30][31][32], swarm robotics [33,34] and some engineering applications [35][36][37]. Due to its novel features to infer information about system behaviour, there is growing interest in applying this technique in systems biology [38,39].…”

Section: Introductionmentioning

confidence: 99%

Verifiable biology

Konur

Gheorghe

Krasnogor

2023

J. R. Soc. Interface.

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The formalization of biological systems using computational modelling approaches as an alternative to mathematical-based methods has recently received much interest because computational models provide a deeper mechanistic understanding of biological systems. In particular, formal verification, complementary approach to standard computational techniques such as simulation, is used to validate the system correctness and obtain critical information about system behaviour. In this study, we survey the most frequently used computational modelling approaches and formal verification techniques for computational biology. We compare a number of verification tools and software suites used to analyse biological systems and biochemical networks, and to verify a wide range of biological properties. For users who have no expertise in formal verification, we present a novel methodology that allows them to easily apply formal verification techniques to analyse their biological or biochemical system of interest.

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Modelling and Validating an Engineering Application in Kernel P Systems

Cited by 6 publications

References 23 publications

Toward Full-Stack In Silico Synthetic Biology: Integrating Model Specification, Simulation, Verification, and Biological Compilation

Toward Full-Stack In Silico Synthetic Biology: Integrating Model Specification, Simulation, Verification, and Biological Compilation

Spiking neural P systems: matrix representation and formal verification

Verifiable biology

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