Study on the generalization of Boolean functions in critical Boolean networks

Boolean network has been a major model to study gene regulatory networks. Lots of work have been focused on inferring networks from time-series data and designing potential intervention policies. However, one important problem still remains unsolved, that is the generalization of Boolean function. In general, the inference algorithms always assume a random Boolean value for the unobserved states. As many theoretical and experimental results support that gene regulatory networks lie between the boundary of ordered and disordered regimes, we studied three generalization methods: the majority rule, bias-based and mutual information-based methods. Results both on simulation networks and melanoma network show that reasonable generalization can improve both the steady-state distribution distance and the sensitivity error. And among the three methods, the mutual information-based method performs better than the other two.