| Abstract: | Powerful new experimental methods have been used to monitor changes in gene expression levels as a result of a variety of metabolic, xenobiotic or pathogenic challenges. Here we present a general approach to both statistical analysis and dynamic modeling of gene expression profiles that uses linear response theory.The formalism leads naturally to Markov models of the network reflected in the data, and provides a direct method of classifying genes according to their place in the resulting network. Non-linear and higher order Markov behavior of the network can also be included by a self-consistent method. In a sample data set from yeast, we calculate the Markov matrix and the gene classes based on the linear Markov network. |
