A short introduction to the BoolNet package

one for each variable. These transition functions map an input of the Boolean variables in X to a Boolean value (0 or 1). We call a Boolean vector x(t) = (x1(t), . . . , xn(t)) the state of the network at time t. Then, the next state of the network x(t+ 1) is calculated by applying all transition functions fi(x(t)). In a biological context, genes can be modeled as Boolean variables (active/expressed or inactive/not expressed), and the transition functions model the dependencies among these genes. In the synchronous model, the assumption is that all genes are updated at the same time. This simplification facilitates the analysis of the networks.