  Probabilistic Boolean Networks have been proposed for estimating the
behaviour of dynamical systems as they combine rule-based modelling with
uncertainty principles. Inferring PBNs directly from gene data is challenging
however, especially when data is costly to collect and/or noisy, e.g., in the
case of gene expression profile data. In this paper, we present a reproducible
method for inferring PBNs directly from real gene expression data measurements
taken when the system was at a steady state. The steady-state dynamics of PBNs
is of special interest in the analysis of biological machinery. The proposed
approach does not rely on reconstructing the state evolution of the network,
which is computationally intractable for larger networks. We demonstrate the
method on samples of real gene expression profiling data from a well-known
study on metastatic melanoma. The pipeline is implemented using Python and we
make it publicly available.
