Probabilistic Boolean networks (PBNs) is a widely used computational framework for modelling biological systems. The steady-state dynamics of PBNs is of special interest in the analysis of biological systems. However, obtaining the steady-state distributions for such systems poses a significant challenge due to the state space explosion problem which often arises in the case of large PBNs. The ...

Reconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interac...

Probabilistic Boolean Networks (PBNs) were recently introduced as models of gene regulatory networks. The dynamical behavior of PBNs, which are probabilistic generalizations of Boolean networks, can be studied using Markov chain theory. In particular, the steady-state or long-run behavior of PBNs may reflect the phenotype or functional state of the cell. Approaches to alter the steady-state beh...

Probabilistic Boolean networks (PBNs) comprise a model describing a directed graph with rule-based dependences between its nodes. The rules are selected, based on a given probability distribution which provides a flexibility when dealing with the uncertainty which is typical for genetic regulatory networks. Given the computational complexity of the model, the characterization of mappings reduci...

In the post-genomic era, the construction and control of genetic regulatory networks using gene expression data is a hot research topic. Boolean networks (BNs) and its extension Probabilistic Boolean Networks (PBNs) have been served as an effective tool for this purpose. However, PBNs are difficult to be used in practice when the number of genes is large because of the huge computational cost. ...

Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs), which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs) are introduced. First, the outline of PBNs is e...

In this paper, we analyze the synchronization problem of master-slave probabilistic Boolean networks (PBNs). The master Boolean network (BN) is a deterministic BN, while the slave BN is determined by a series of possible logical functions with certain probability at each discrete time point. In this paper, we firstly define the synchronization of master-slave PBNs with probability one, and then...

A Boolean network is one of the models of biological networks such as gene regulatory networks, and has been extensively studied. In particular, a probabilistic Boolean network (PBN) is well known as an extension of Boolean networks, but in the existing methods to solve the optimal control problem of PBNs, it is necessary to compute the state transition diagram with 2 nodes for a given PBN with...

Probabilistic Boolean Networks (PBNs) were introduced as a computational model for the study of complex dynamical systems, such Gene Regulatory (GRNs). Controllability in this context is process making strategic interventions to state network order drive it towards some other that exhibits favourable biological properties. In paper we ability Double Deep Q-Network with Prioritized Experience Re...

Probabilistic Boolean networks (PBNs) is a well-established computational framework for modelling biological systems. The steady-state dynamics of PBNs is of crucial importance in the study of such systems. However, for large PBNs, which often arise in systems biology, obtaining the steady-state distribution poses a significant challenge. In this paper, we revive the two-state Markov chain appr...