  Complex interactions between genes or proteins contribute a substantial part
to phenotypic evolution. Here we develop an evolutionarily grounded method for
the cross-species analysis of interaction networks by {\em alignment}, which
maps bona fide functional relationships between genes in different organisms.
Network alignment is based on a scoring function measuring mutual similarities
between networks taking into account their interaction patterns as well as
sequence similarities between their nodes. High-scoring alignments and optimal
alignment parameters are inferred by a systematic Bayesian analysis. We apply
this method to analyze the evolution of co-expression networks between human
and mouse. We find evidence for significant conservation of gene expression
clusters and give network-based predictions of gene function. We discuss
examples where cross-species functional relationships between genes do not
concur with sequence similarity.
