Inferring Probabilistic Boolean Networks from Steady-State Gene Data Samples

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 costly to collect and/or noisy, e.g., in case expression profile data. In this paper, we present a reproducible method inferring real measurements taken system was at steady state. The steady-state dynamics special interest analysis biological machinery. approach does not rely on reconstructing state evolution network, which computationally intractable larger networks. We demonstrate samples profiling well-known study metastatic melanoma. pipeline implemented using Python and make it publicly available.