Recent development on distributed systems has shown that a variety of fairness constraints (some of which are only recently defined) play vital roles in designing self-stabilizing population protocols. Current practice of system analysis is, however, deficient under fairness. In this work, we present PAT, a toolkit for flexible and efficient system analysis under fairness. A unified algorithm is proposed to model check systems with a variety of fairness effectively in two different settings. Empirical evaluation shows that PAT complements existing model checkers in terms of fairness. We report that previously unknown bugs have been revealed using PAT against systems functioning under strong global fairness.

International audienceThis paper defines action-labelled quantitative transition systems as a general framework for combining qualitative and quantitative analysis. We define state-metrics as a natural extension of bisimulation from non-quantitative systems to quantitative ones. We then prove that any single state-metric corresponds to a bisimulation and that the greatest state-metric corresponds to bisimilarity. Furthermore, we provide two extended examples which show that our results apply to both probabilistic and weighted automata as special cases of action-labelled quantitative transition systems

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered.A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed.A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels.

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be controlled to drive its dynamics from an initial steady state (or attractor) to a target steady state. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network, may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking such an approach, we derive a decomposition-based solution to the minimal control problem which can be significantly faster than the existing approaches on large networks. We apply our solution to both real-life biological networks and randomly generated networks, demonstrating promising results.

Abstract. We propose algorithms significantly extending the limits for maintaining exact representations in the verification of linear hybrid systems with large discrete state spaces. We use AND-Inverter Graphs (AIGs) extended with linear constraints (LinAIGs) as symbolic representation of the hybrid state space, and show how methods for maintaining compactness of AIGs can be lifted to support model-checking of linear hybrid systems with large discrete state spaces. This builds on a novel approach for eliminating sets of redundant constraints in such rich hybrid state representations by a suitable exploitation of the capabilities of SMT solvers, which is of independent value beyond the application context studied in this paper. We used a benchmark derived from an Airbus flap control system (containing 2 20 discrete states) to demonstrate the relevance of the approach.

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GPS P-Code has a higher chipping rate, better accuracy, and anti-jamming property than C/A code. Traditionally, GPS P-Code acquisition depends on handover from C/A code. This potentially needs long acquisition time. Moreover, when C/A code is not available, it is no longer possible to acquire GPS P-Code through handover from C/A code. The purpose of this paper is to describe a new overlap average method to facilitate hardware design of fast direct P-Code acquisition. It allows the rapid code phase search to acquire GPS P-Code signals, and also decreases the hardware resource requirement. The small size FFT in the proposed methods is very promising for fast FPGA hardware system design using FFT cores. The simulation results and theoretical analysis are included demonstrating the overall performance of the proposed method.

BackgroundThere exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only limited quantitative insights into the complexity of biological systems because of the inherited qualitative nature of Boolean networks.ResultsWe introduce optPBN, a Matlab-based toolbox for the optimisation of probabilistic Boolean networks (PBN) which operates under the framework of the BN/PBN toolbox. optPBN offers an easy generation of probabilistic Boolean networks from rule-based Boolean model specification and it allows for flexible measurement data integration from multiple experiments. Subsequently, optPBN generates integrated optimisation problems which can be solved by various optimisers.In term of functionalities, optPBN allows for the construction of a probabilistic Boolean network from a given set of potential constitutive Boolean networks by optimising the selection probabilities for these networks so that the resulting PBN fits experimental data. Furthermore, the optPBN pipeline can also be operated on large-scale computational platforms to solve complex optimisation problems. Apart from exemplary case studies which we correctly inferred the original network, we also successfully applied optPBN to study a large-scale Boolean model of apoptosis where it allows identifying the inverse correlation between UVB irradiation, NFκB and Caspase 3 activations, and apoptosis in primary hepatocytes quantitatively. Also, the results from optPBN help elucidating the relevancy of crosstalk interactions in the apoptotic network.SummaryThe optPBN toolbox provides a simple yet comprehensive pipeline for integrated optimisation problem generation in the PBN formalism that can readily be solved by various optimisers on local or grid-based computational platforms. optPBN can be further applied to various biological studies such as the inference of gene regulatory networks or the identification of the interaction's relevancy in signal transduction networks.