  Two recent streams of work suggest that pairwise interactions may be
sufficient to capture the complexity of biological systems ranging from protein
structure to networks of neurons. In one approach, possible amino acid
sequences in a family of proteins are generated by Monte Carlo annealing of a
"Hamiltonian" that forces pairwise correlations among amino acid substitutions
to be close to the observed correlations. In the other approach, the observed
correlations among pairs of neurons are used to construct a maximum entropy
model for the states of the network as a whole. We show that, in certain
limits, these two approaches are mathematically equivalent, and we comment on
open problems suggested by this framework
