  This article introduces the physics of information in the context of
molecular biology and genomics. Entropy and information, the two central
concepts of Shannon's theory of information and communication, are often
confused with each other but play transparent roles when applied to statistical
ensembles (i.e., identically prepared sets) of symbolic sequences. Such an
approach can distinguish between entropy and information in genes, predict the
secondary structure of ribozymes, and detect the covariation between residues
in folded proteins. We also review applications to molecular sequence and
structure analysis, and introduce new tools in the characterization of
resistance mutations, and in drug design.
