Before revision:  Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and interaction among these components are given by logical rules representing the biochemical mechanisms governing their regulation ; stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update function guarantee activation or degradation there is a probability that the process will not occur due to stochasticity in the process, for instance, some of the chemical reactions encoded by the function fail to occur which lead to a function failure .
After revision:  Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update function guarantee activation or degradation there is a probability that the process will not occur due to stochasticity in the process, for instance, some of the chemical reactions encoded by the function fail to occur which lead to a function failure .