  When analysing gene expression time series data an often overlooked but
crucial aspect of the model is that the regulatory network structure may change
over time. Whilst some approaches have addressed this problem previously in the
literature, many are not well suited to the sequential nature of the data. Here
we present a method that allows us to infer regulatory network structures that
may vary between time points, utilising a set of hidden states that describe
the network structure at a given time point. To model the distribution of the
hidden states we have applied the Hierarchical Dirichlet Process Hideen Markov
Model, a nonparametric extension of the traditional Hidden Markov Model, that
does not require us to fix the number of hidden states in advance. We apply our
method to exisiting microarray expression data as well as demonstrating is
efficacy on simulated test data.

Molecular Networks;Applications