TY - JOUR
T1 - Are Biological Systems Poised at Criticality?
AU - Mora, Thierry
AU - Bialek, William
N1 - Funding Information:
Acknowledgements We thank our many collaborators for the pleasure of working together on these ideas: D. Amodei, M.J. Berry II, C.G. Callan, O. Marre, M. Mézard, S.E. Palmer, R. Ranganathan, E. Schneidman, R. Segev, G.J. Stephens, S. Still, G. Tkacˇik, and A.M. Walczak. In addition, we are grateful to our colleagues who have taken time to explain their own ideas: A. Cavagna, I. Giardina, M.O. Magnasco, and M. Weigt. Speculations, confusions, and errors, of course, remain our fault and not theirs. This work was supported in part by NSF Grants PHY-0650617 and PHY-0957573, by NIH Grant P50 GM071598, and by the Swartz Foundation; T.M. was supported in part by the Human Frontiers Science Program.
PY - 2011/7
Y1 - 2011/7
N2 - Many of life's most fascinating phenomena emerge from interactions among many elements-many amino acids determine the structure of a single protein, many genes determine the fate of a cell, many neurons are involved in shaping our thoughts and memories. Physicists have long hoped that these collective behaviors could be described using the ideas and methods of statistical mechanics. In the past few years, new, larger scale experiments have made it possible to construct statistical mechanics models of biological systems directly from real data. We review the surprising successes of this "inverse" approach, using examples from families of proteins, networks of neurons, and flocks of birds. Remarkably, in all these cases the models that emerge from the data are poised near a very special point in their parameter space-a critical point. This suggests there may be some deeper theoretical principle behind the behavior of these diverse systems.
AB - Many of life's most fascinating phenomena emerge from interactions among many elements-many amino acids determine the structure of a single protein, many genes determine the fate of a cell, many neurons are involved in shaping our thoughts and memories. Physicists have long hoped that these collective behaviors could be described using the ideas and methods of statistical mechanics. In the past few years, new, larger scale experiments have made it possible to construct statistical mechanics models of biological systems directly from real data. We review the surprising successes of this "inverse" approach, using examples from families of proteins, networks of neurons, and flocks of birds. Remarkably, in all these cases the models that emerge from the data are poised near a very special point in their parameter space-a critical point. This suggests there may be some deeper theoretical principle behind the behavior of these diverse systems.
KW - Biological networks
KW - Collective behavior
KW - Critical point
KW - Maximum entropy model
KW - Proteins
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U2 - 10.1007/s10955-011-0229-4
DO - 10.1007/s10955-011-0229-4
M3 - Review article
AN - SCOPUS:79960975007
SN - 0022-4715
VL - 144
SP - 268
EP - 302
JO - Journal of Statistical Physics
JF - Journal of Statistical Physics
IS - 2
ER -