CoInDiVinE: Parallel Distributed Model Checker for Component-Based Systems

CoInDiVinE is a tool for parallel distributed model checking of interactions among components in hierarchical component-based systems. The tool extends the DiVinE framework with a new input language (component-interaction automata) and a property specification logic (CI-LTL). As the language differs from the input language of DiVinE, our tool employs a new state space generation algorithm that also supports partial order reduction. Experiments indicate that the tool has good scaling properties when run in parallel setting.Comment: In Proceedings PDMC 2011, arXiv:1111.006

Modal Transition Systems: Composition and LTL Model Checking

Modal transition systems (MTS) is a~well established formalism used for specification and for abstract interpretation. We consider its disjunctive extension (DMTS) and we provide algorithms showing that refinement problems for DMTS are not harder than in the case of MTS. There are two main results in the paper. Firstly, we identify an error in a~previous attempt at LTL model checking of MTS and provide algorithms for LTL model checking of MTS and DMTS. Moreover, we show how to apply this result to compositional verification and circumvent the general incompleteness of the MTS composition. Secondly, we give a~solution to the common implementation and conjunctive composition problems lowering the complexity from EXPTIME to PTIME

Refinement checking on parametric modal transition systems

Modal transition systems (MTS) is a well-studied specification formalism of reactive systems supporting a step-wise refinement methodology. Despite its many advantages, the formalism as well as its currently known extensions are incapable of expressing some practically needed aspects in the refinement process like exclusive, conditional and persistent choices. We introduce a new model called parametric modal transition systems (PMTS) together with a general modal refinement notion that overcomes many of the limitations. We investigate the computational complexity of modal and thorough refinement checking on PMTS and its subclasses and provide a direct encoding of the modal refinement problem into quantified Boolean formulae, allowing us to employ state-of-the-art QBF solvers for modal refinement checking. The experiments we report on show that the feasibility of refinement checking is more influenced by the degree of nondeterminism rather than by the syntactic restrictions on the types of formulae allowed in the description of the PMTS

BDD-Based Algorithm for SCC Decomposition of Edge-Coloured Graphs

Edge-coloured directed graphs provide an essential structure for modellingand analysis of complex systems arising in many scientific disciplines (e.g.feature-oriented systems, gene regulatory networks, etc.). One of thefundamental problems for edge-coloured graphs is the detection of stronglyconnected components, or SCCs. The size of edge-coloured graphs appearing inpractice can be enormous both in the number of vertices and colours. The largenumber of vertices prevents us from analysing such graphs using explicit SCCdetection algorithms, such as Tarjan's, which motivates the use of a symbolicapproach. However, the large number of colours also renders existing symbolicSCC detection algorithms impractical. This paper proposes a novel algorithmthat symbolically computes all the monochromatic strongly connected componentsof an edge-coloured graph. In the worst case, the algorithm performs O(p \cdotn \cdot log~n) symbolic steps, where $p$ is the number of colours and $n$ isthe number of vertices. We evaluate the algorithm using an experimentalimplementation based on binary decision diagrams (BDDs). Specifically, we useour implementation to explore the SCCs of a large collection of coloured graphs(up to $2^{48}$) obtained from Boolean networks -- a modelling frameworkcommonly appearing in systems biology

EXPTIME-Completeness of Thorough Refinement on Modal Transition Systems

AbstractModal transition systems (MTS), a specification formalism introduced more than 20 years ago, has recently received a considerable attention in several different areas. Many of the fundamental questions related to MTSs have already been answered. However, the problem of the exact computational complexity of thorough refinement checking between two finite MTSs remained unsolved.We settle down this question by showing EXPTIME-completeness of thorough refinement checking on finite MTSs. The upper-bound result relies on a novel algorithm running in single exponential time providing a direct goal-oriented way to decide thorough refinement. If the right-hand side MTS is moreover deterministic, or has a fixed size, the running time of the algorithm becomes polynomial. The lower-bound proof is achieved by reduction from the acceptance problem of alternating linear bounded automata and the problem remains EXPTIME-hard even if the left-hand side MTS is fixed and deterministic

Boolean network sketches: A unifying framework for logical model inference

Motivation: The problem of model inference is of fundamental importance to systems biology. Logical models (e.g. Boolean networks; BNs) represent a computationally attractive approach capable of handling large biological networks. The models are typically inferred from experimental data. However, even with a substantial amount of experimental data supported by some prior knowledge, existing inference methods often focus on a small sample of admissible candidate models only. Results: We propose Boolean network sketches as a new formal instrument for the inference of Boolean networks. A sketch integrates (typically partial) knowledge about the network’s topology and the update logic (obtained through, e.g. a biological knowledge base or a literature search), as well as further assumptions about the properties of the network’s transitions (e.g. the form of its attractor landscape), and additional restrictions on the model dynamics given by the measured experimental data. Our new BNs inference algorithm starts with an ‘initial’ sketch, which is extended by adding restrictions representing experimental data to a ‘data-informed’ sketch and subsequently computes all BNs consistent with the data-informed sketch. Our algorithm is based on a symbolic representation and coloured model-checking. Our approach is unique in its ability to cover a broad spectrum of knowledge and efficiently produce a compact representation of all inferred BNs. We evaluate the method on a non-trivial collection of real-world and simulated data

LNCS

Partially specified Boolean networks (PSBNs) represent a promising framework for the qualitative modelling of biological systems in which the logic of interactions is not completely known. Phenotype control aims to stabilise the network in states exhibiting specific traits. In this paper, we define the phenotype control problem in the context of asynchronous PSBNs and propose a novel semi-symbolic algorithm for solving this problem with permanent variable perturbations