Efficient Parallel Statistical Model Checking of Biochemical Networks

TL;DR

This paper introduces a parallel statistical model checking approach for biochemical networks that significantly reduces computational costs by on-the-fly verification, efficient confidence interval estimation, and parallel processing.

Contribution

It presents a novel parallel methodology for stochastic model checking of biochemical networks, combining on-the-fly verification and an efficient Wilson-based confidence interval method.

Findings

Achieved faster convergence in probability estimation.

Implemented a parallel software tool on HPC architecture.

Reduced computational demands compared to traditional methods.

Abstract

We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture.