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Two recent streams of work suggest that pairwise interactions may besufficient to capture the complexity of biological systems ranging from proteinstructure to networks of neurons. In one approach, possible amino acidsequences in a family of proteins are generated by Monte Carlo annealing of a"Hamiltonian" that forces pairwise correlations among amino acid substitutionsto be close to the observed correlations. In the other approach, the observedcorrelations among pairs of neurons are used to construct a maximum entropymodel for the states of the network as a whole. We show that, in certainlimits, these two approaches are mathematically equivalent, and we comment onopen problems suggested by this framework

Rediscovering the power of pairwise interactions