Abstract
This paper focuses on a major improvement on the analysis of reachability properties in large-scale dynamical biological models. To tackle such models, where classical model checkers fail due to state space explosion led by exhaustive search. Alternative static analysis approaches have been proposed, but they may also fail in certain cases due to non-exhaustive search. In this paper, we introduce a hybrid approach ASPReach, which combines static analysis and stochastic search to break the limits of both approaches. We tackle this issue on a modeling framework we recently introduced, Asynchronous Binary Automata Network (ABAN). We show that ASPReach is able to analyze efficiently some reachability properties which could not be solved by existing methods. We studied also various cases from biological literature, emphasizing the merits of our approach in terms of conclusiveness and performance.
Highlights
With increasing quantities of available data provided by new technologies, e.g. DNA microarray [22], there is a growing need for expressive modelings and their related high-performance analytic tools
We have developed a heuristic approach PermReach to attack reachability problem [7] which is more conclusive than pure static analysis but time-consuming and still not able to solve reachability problems under certain conditions
We propose a hybrid approach ASPReach based on the former Local Causality Graph (LCG) reasoning and a non-exhaustive search in the LCG to obtain a more conclusive solution of reachability problems
Summary
With increasing quantities of available data provided by new technologies, e.g. DNA microarray [22], there is a growing need for expressive modelings and their related high-performance analytic tools. Various modeling frameworks and semantics in bioinformatics have been studied: Boolean network [2], Petri nets [23, 14], timedautomata [12, 36] These approaches rely on global search and face state explosion problem as the state space grows exponentially with the number of variables. Bounded Model Checking (BMC) [9] is an efficient approach but generally not complete as its searching depth is limited to a given integer k Beside these approaches, abstraction is an efficient strategy to deal with such models of big scale. In ABAN, we applied the approach developed by Pauleve et al [25, 15, 26] to address reachability problem This approach refers to a static abstraction of the reachability (with an over-approximation and an under-approximation of the real dynamics). This paper is organized as follows: Section 2 introduces the formal background and the formalization necessary to the understanding of the work; Section 3 presents the concrete methods and algorithms composing the whole approach; Section 4 shows the benchmarks evaluating our approach and other alternatives; Section 5 concludes this paper
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