Chemical systems can store and process information in several ways. We focus on finite systems of molecules interacting in a well-mixed solution under mass-action kinetics and emulate Boolean circuits by encoding information through molecular concentrations reaching a particular threshold. The computation proceeds by transforming input species concentrations into outputs according to the reactions of a finite CRN. It is known that the computational power of CRNs is affected by the choice of the semantics, deterministic or stochastic. In particular, assuming a small probability of error, (finite) stochastic CRNs have been shown to be Turing universal (Soloveichik et al. 2008). The deterministic semantics interprets the reactions as a system of differential equations, which describe the evolution of the system as a vector of real-valued species concentrations over time (Chen et al. 2013). The stochastic semantics, on the other hand, views the state of the system as a vector of (non-negative) integer molecular counts and state transitions as a reaction which has a non-zero probability of occurring (Cook et al. 2009). The stochastic evolution of the system over time is obtained as a solution of the Chemical Master Equation (CME) (Kampen 1992). It is well known that the deterministic semantics is not accurate for small populations. While the stochastic semantics is exact, it is infeasible for large molecular counts. One scalable alternative is the Linear Noise Approximation, which is a real-valued approximation of the CME (Cardelli et al. 2015). The correctness of the behaviour of a circuit described by a finite CRN can be analysed by inspecting its stochastic and deterministic evolution over time. In addition, techniques such as model checking can be employed to analyse the temporal ordering of events.