Inferring probabilistic Boolean networks from steady-state gene data samples
This work addresses a domain-specific problem for computational biologists by providing an incremental method to infer PBNs from steady-state gene data, which is crucial for analyzing biological machinery but often limited by data constraints.
The authors tackled the challenge of inferring Probabilistic Boolean Networks (PBNs) directly from noisy and costly gene expression data at steady state, presenting a reproducible method that avoids computationally intractable state evolution reconstruction and demonstrating it on real metastatic melanoma data with a publicly available Python implementation.
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.