  The behaviour of gene regulatory networks (GRNs) is typically analysed using
simulation-based statistical testing-like methods. In this paper, we
demonstrate that we can replace this approach by a formal verification-like
method that gives higher assurance and scalability. We focus on Wagner weighted
GRN model with varying weights, which is used in evolutionary biology. In the
model, weight parameters represent the gene interaction strength that may
change due to genetic mutations. For a property of interest, we synthesise the
constraints over the parameter space that represent the set of GRNs satisfying
the property. We experimentally show that our parameter synthesis procedure
computes the mutational robustness of GRNs -an important problem of interest in
evolutionary biology- more efficiently than the classical simulation method. We
specify the property in linear temporal logics. We employ symbolic bounded
model checking and SMT solving to compute the space of GRNs that satisfy the
property, which amounts to synthesizing a set of linear constraints on the
weights.
