Department of Mathematics
On computation of the steady-state probability distribution of probabilistic Boolean networks with gene perturbation
Given a Probabilistic Boolean Network (PBN), an important problem is to study its steady-state probability distribution for network analysis. In this paper, we present a new perturbation bound of the steady-state probability distribution of PBNs with gene perturbation. The main contribution of our results is that this new bound is established without additional condition required by the existing method. The other contribution of this paper is to propose a fast algorithm based on the special structure of a transition probability matrix of PBNs with gene perturbation to compute its steady-state probability distribution. Experimental results are given to demonstrate the effectiveness of the new bound, and the efficiency of the proposed method. © 2012 Elsevier B.V. All rights reserved.
Iterative methods, Perturbation bound, Probabilistic Boolean networks, Steady-state probability distribution, Structured matrices
Source Publication Title
Journal of Computational and Applied Mathematics
Li, Wen, Lu-Bin Cui, and Michael K. Ng. "On computation of the steady-state probability distribution of probabilistic Boolean networks with gene perturbation." Journal of Computational and Applied Mathematics 236.16 (2012): 4067-4081.