题 目：Boolean and Probabilistic Boolean Networks
报 告 人：Wai-Ki CHING (邀请人：刘秋丽)
时 间：2018-10-15 16:30--17:30
Boolean Networks (BNs) and its extension Probabilistic Boolean Networks (PBNs) are useful and effective tools for studying genetic regulatory networks. A PBN is essentially a collection of BNs driven by a Markov chain (a random process). A BN is characterized by its attractor cycles and a PBN is characterized by its steady-state distribution. We review some algorithms for finding attractor cycles and steady-state distributions for BNs and PBNs, respectively. We then discuss an inverse problem, the problem of constructing a PBN given a set of BNs. It is well-known that the control of a genetic regulatory network is useful for avoiding undesirable states associated with diseases and this results in a control problem. We formulate both problems as optimization problems and efficient algorithms are also presented to solve them. Other applications of PBNs will also be discussed.