Temporal Boolean Network Models of Genetic Networks and their Inference from Gene Expression Time Series

摘要: Identification of genetic regulatory networks and signal transduction pathways from gene expression data is one the key problems in computational molecular biology. Boolean offer a discrete time model expression. In this model, each can be two states (on or off) at any given time, t " 1 modeled by function most k genes t. Typically # n, where n total number under consideration. This paper motivates introduces generalization network to address dependencies among activity that span for more than unit time. The resulting called temporal TBN(n, k, T) allows controlled levels times $t . % (T 1)&. We apply an adaptation popular machine learning algorithm decision tree induction inference artificially generated data. Preliminary experiments with synthetic known demonstrate feasibility approach. conclude discussion some limitations proposed approach directions further research.