  We describe the time evolution of gene expression levels by using a time
translational matrix to predict future expression levels of genes based on
their expression levels at some initial time. We deduce the time translational
matrix for previously published DNA microarray gene expression data sets by
modeling them within a linear framework using the characteristic modes obtained
by singular value decomposition. The resulting time translation matrix provides
a measure of the relationships among the modes and governs their time
evolution. We show that a truncated matrix linking just a few modes is a good
approximation of the full time translation matrix. This finding suggests that
the number of essential connections among the genes is small.
