Designing probabilistic reaction models and determining their stochastic kinetic parameters are major issues in systems biology. In order to assist in the construction of reaction network models, we introduce a logic that allows one to express asymp-totic properties about the steady-state stochastic dynamics of a reaction network. Basically, the formulas can express properties on expectancies, variances and co-variances. If a formula encoding for experimental observations on the system is not sat-isfiable then the reaction network model can be rejected. We demonstrate that deciding the satisfi-ability of a formula is NP-hard but we provide a decision method based on solving systems of polynomial constraints. We illustrate our method on a toy example.
['Vincent Picard', 'Anne Siegel', 'Jérémie Bourdon']
A Logic for Checking the Probabilistic Steady-State Properties of Reaction Networks