A new method and application for controlling the steady-state probability distributions of probabilistic Boolean networks

Probabilistic Boolean networks (PBNs) have been proved to be a useful tool for modeling genetic regulatory interactions. The study of the steady-state probability distribution may help to understand the essential long-run behavior of a PBN. In this paper we focus on a type of PBNs derived from gene expression data collected in a study of metastatic melanoma. The metastatic melanoma model is usually described by a PBN containing seven genes among which WNT5A plays a significant role in the development of melanoma and is known to induce the metastasis of melanoma when highly active. This paper investigates the issue of how to drive the corresponding PBN towards desired steady-state probability distributions so as to reduce the WNT5A´s ability to induce a metastatic phenotype.