Savas Konur

An Integrated Model Checking Toolset for Kernel P Systems

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.

Kernel P Systems and Stochastic P Systems for Modelling and Formal Verification of Genetic Logic Gates

P systems are the computational models of membrane computing, a computing paradigm within natural computing area inspired by the structure and behaviour of the living cell. In this chapter, we discuss two variants of this model, a non-deterministic case, called kernel P (kP) systems, and a stochastic one, called stochastic P (sP) systems. For both we present specification languages and associated tools, including simulation and verification components. The expressivity and analysis power of these natural computing models will be used to illustrate the behaviour of two genetic logic gates.

Agent-Based High-Performance Simulation of Biological Systems on the GPU

Simulation of biological systems are computationally demanding due to the large scale reaction networks of bacterial cells. This scalability issue escalates, in particular, when bacterial colonies, formed by many individual cells, are simulated. Agent-based modelling environments on parallel architectures, such as the FLAME (Flexible Large-scale Modelling Environment) framework, are good candidates to simulate such systems, but due to the complex nature of cellular systems more advance technology is needed. In this paper, we utilise FLAME GPU, extending FLAME with a high performance graphics processing unit, to simulate a pulse generator, a typical multicellular synthetic biology system. This system is specified using a membrane computing model. We also illustrate the performance improvement of FLAME GPU over FLAME.

Kernel P Systems Modelling, Testing and Verification - Sorting Case Study

A kernel P system (kP system, for short) integrates in a coherent and elegant manner many of the P system features most successfully used for modelling various applications and, consequently, it provides a framework for analyzing these models. In this paper, we illustrate the modelling capacity of kernel P systems by providing a number of kP system models for sorting algorithms. Furthermore, the problem of testing systems modelled as kP systems is also discussed and a test generation method based on automata is proposed. We also demonstrate how formal verification can be used to validate that the given models work as desired.

Testing based on identifiable P Systems using cover automata and X-machines

We represent a significant advance on the issue of testing for implementations specified by P systems with transformation and communicating rules.Using the X-machine framework and the concept of cover automaton, we devise a testing approach for such systems, that, under well defined conditions, our approach ensures that the implementation conforms to the specification.We investigate the issue of identifiability for P systems, that is an essential prerequisite for testing these systems and establishes a fundamental set of properties for identifiable P systems. This paper represents a significant advance on the issue of testing for implementations specified by P systems with transformation and communicating rules. Using the X-machine framework and the concept of cover automaton, it devises a testing approach for such systems, that, under well defined conditions, it ensures that the implementation conforms to the specification. It also investigates the issue of identifiability for P systems, that is an essential prerequisite for testing implementations based on such specifications and establishes a fundamental set of properties for identifiable P systems.

An Integrated In Silico Simulation and Biomatter Compilation Approach to Cellular Computation

Recent advances Synthetic Biology are ushering a new practical computational substrate based on programmable information processing via biological cells. Due to the difficulties in orchestrating complex programmes using myriads of relatively simple, limited and highly stochastic processors such as living cells, robust computational technology to specify, simulate, analyse and compile cellular programs are in demand. We provide the Infobiotics Workbench (Ibw) tool, a software platform developed to model and analyse stochastic compartmentalized systems, which permits using various computational techniques, such as modelling, simulation, verification and biocompilation. We report here the details of our work for modelling, simulation and, for the first time, biocompilation, while verification is reported elsewhere in this book. We consider some basic genetic logic gates to illustrate the main features of the Ibw platform. Our results show that membrane computing provides a suitable formalism for building synthetic biology models. The software platform we developed permits analysing biological systems through the computational methods integrated into the workbench, providing significant advantages in terms of time, and enhanced understanding of biological functionality.

Comparative Analysis of Statistical Model Checking Tools

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.

Automatic selection of verification tools for efficient analysis of biochemical models

Abstract 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. 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. Availability and implementation SMC Predictor tool is available at http://www.smcpredictor.com. Supplementary information Supplementary data are available at Bioinformatics online.

Modelling and Validating an Engineering Application in Kernel P Systems

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.

Kernel P systems: from modelling to verification and testing

Abstract A kernel P system integrates in a coherent and elegant manner some of the most successfully used features of the P systems employed in modelling various applications. It also provides a theoretical framework for analysing these applications and a software environment for simulating and verifying them. In this paper, we illustrate the modelling capabilities of kernel P systems by showing how other classes of P systems can be represented with this formalism and providing a number of kernel P system models for a sorting algorithm and a broadcasting problem. We also show how formal verification can be used to validate that the given models work as desired. Finally, a test generation method based on automata is extended to non-deterministic kernel P systems.