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Many of life's most fascinating phenomena emerge from interactions among manyelements--many amino acids determine the structure of a single protein, manygenes determine the fate of a cell, many neurons are involved in shaping ourthoughts and memories. Physicists have long hoped that these collectivebehaviors could be described using the ideas and methods of statisticalmechanics. In the past few years, new, larger scale experiments have made itpossible to construct statistical mechanics models of biological systemsdirectly from real data. We review the surprising successes of this "inverse"approach, using examples form families of proteins, networks of neurons, andflocks of birds. Remarkably, in all these cases the models that emerge from thedata are poised at a very special point in their parameter space--a criticalpoint. This suggests there may be some deeper theoretical principle behind thebehavior of these diverse systems.

Are biological systems poised at criticality?