Cell-Cycle Process and Regulatory Power of Canalizing Genes Modeled by Context-Sensitive Probabilistic Boolean Networks

A context-sensitive probabilistic Boolean network (cPBN) has been introduced in order to model biolog-ical systems. Essentially, a cPBN is a finite collection of Boolean Networks (BNs) with perturbation (each gene is allowed to randomly change its value at each instant time with a small perturbation probability). This modeling makes the resulting system behave as an ergodic Markov chain possessing a steady-state probability distribution. Switching a BN to another one corresponds to switching the wiring diagram of the network. In this way, taking the perspective that this switch corresponds to a change in context for the cell, this PBN model is called context-sensitive PBN. Under this model, we can investigate the cell-cycle process and the regulatory power of canalizing genes. A complex molecular network is responsible for the cell-cycle process by which a cell grows and divides into two daughter cells. In order to model and understand the cell-cycle process, a considerable amount of attention has been paid to the budding yeast cell cycle regulation [1,2,3]. In particular, after an exhaustive literature investigation, Li et al. [1] modeled the yeast cell-cycle system under the Boolean network (BN) model and studied its dynamics and structural properties. Zhang et al. [2] extended Li’s model including in it effects of stochasticity and noise. Trepode et al. [3] introduced amore sophisticate