FIGURE 1 

Model overview. (A) Plots of time series expression profiles generated to illustrate the model. (B) Static TF-DNA binding data—DREM integrates TF-gene regulatory relationships derived from ChIP-chip or motif data with the time series expression data. For this example a majority of the pink genes in (A) are regulated by TF A, the blue genes by TF B and the red genes by TF C and D. (C) The model structure inferred by DREM for the data in (A) and (B). After the model is derived genes are assigned to their most likely paths based on their expression profile as well as on the set of TFs that regulate them. TF labels appear on some of the paths out of splits. (D) IOHMM model—each state has a Gaussian emission distribution for the expression values and the transition probabilities for a gene depend on the set of TFs that regulates it. A logistic regression classifier (Krishnapuram et al, 2005) maps the set of regulating TFs to transition probabilities. The classifiers are denoted by question marks in the figure. Example transition probabilities are given for a gene which is regulated by TF B. These probabilities are greater for the states with distributions similar to those of TF B regulated genes. The TF information also affects the structure of the resulting IOHMM model. Based on this information some splits can be added and some splits are removed from the model.