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Stochastic evolution of Chemical Reactions Networks (CRNs) over time isusually analysed through solving the Chemical Master Equation (CME) orperforming extensive simulations. Analysing stochasticity is often needed,particularly when some molecules occur in low numbers. Unfortunately, bothapproaches become infeasible if the system is complex and/or it cannot beensured that initial populations are small. We develop a probabilistic logicfor CRNs that enables stochastic analysis of the evolution of populations ofmolecular species. We present an approximate model checking algorithm based onthe Linear Noise Approximation (LNA) of the CME, whose computational complexityis independent of the population size of each species and polynomial in thenumber of different species. The algorithm requires the solution of first orderpolynomial differential equations. We prove that our approach is valid for anyCRN close enough to the thermodynamical limit. However, we show on four casestudies that it can still provide good approximation even for low moleculecounts. Our approach enables rigorous analysis of CRNs that are not analyzableby solving the CME, but are far from the deterministic limit. Moreover, it canbe used for a fast approximate stochastic characterization of a CRN.

Stochastic Analysis of Chemical Reaction Networks Using Linear Noise Approximation