  We present a data-driven verification approach that determines whether or not
a given chemical reaction network (CRN) satisfies a given property, expressed
as a formula in a modal logic. Our approach consists of three phases,
integrating formal verification over models with learning from data. First, we
consider a parametric set of possible models based on a known stoichiometry and
classify them against the property of interest. Secondly, we utilise Bayesian
inference to update a probability distribution of the parameters within a
parametric model with data gathered from the underlying CRN. In the third and
final stage, we combine the results of both steps to compute the probability
that the underlying CRN satisfies the given property. We apply the new approach
to a case study and compare it to Bayesian statistical model checking.

Computational Engineering, Finance, and Science