Maximum power efficiency and criticality in random Boolean networks.

Random Boolean networks are models of disordered causal systems that can occur in cells and the biosphere. These are open thermodynamic systems exhibiting a flow of energy that is dissipated at a finite rate. Life does work to acquire more energy, then uses the available energy it has gained to perform more work. It is plausible that natural selection has optimized many biological systems for power efficiency: useful power generated per unit fuel. In this Letter, we begin to investigate these questions for random Boolean networks using Landauer's erasure principle, which defines a minimum entropy cost for bit erasure. We show that critical Boolean networks maximize available power efficiency, which requires that the system have a finite displacement from equilibrium. Our initial results may extend to more realistic models for cells and ecosystems.

[1]  A. J. Lotka Contribution to the Energetics of Evolution. , 1922, Proceedings of the National Academy of Sciences of the United States of America.

[2]  K. Hellingwerf,et al.  Thermodynamic efficiency of microbial growth is low but optimal for maximal growth rate. , 1983, Proceedings of the National Academy of Sciences of the United States of America.

[3]  R. Rosenfeld Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[4]  Charles A. S. Hall,et al.  Maximum power : the ideas and applications of H.T. Odum , 1996 .

[5]  Jason Lloyd-Price,et al.  Mutual information in random Boolean models of regulatory networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  S. Kauffman Metabolic stability and epigenesis in randomly constructed genetic nets. , 1969, Journal of theoretical biology.

[8]  Charles H. Bennett,et al.  Logical reversibility of computation , 1973 .

[9]  L. Hood,et al.  Gene expression dynamics in the macrophage exhibit criticality , 2008, Proceedings of the National Academy of Sciences.

[10]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[11]  W. H. Zurek,et al.  Thermodynamic cost of computation, algorithmic complexity and the information metric , 1989, Nature.

[12]  奥仲 哲弥,et al.  肺門部早期肺癌に対する光線力学的治療法(肺門部早期癌の診断と治療)(第 18 回日本気管支学会総会特集号) , 1995 .

[13]  A. Bejan Entropy generation minimization: The new thermodynamics of finite-size devices and finite-time processes , 1996 .

[14]  S. Kauffman,et al.  Activities and sensitivities in boolean network models. , 2004, Physical review letters.

[15]  B. Derrida,et al.  Random networks of automata: a simple annealed approximation , 1986 .

[16]  Charles H. Bennett Time/Space Trade-Offs for Reversible Computation , 1989, SIAM J. Comput..

[17]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[18]  Charles H. Bennett,et al.  The thermodynamics of computation—a review , 1982 .

[19]  Anthony J. G. Hey,et al.  Feynman Lectures on Computation , 1996 .

[20]  Jorgensen,et al.  The growth rate of zooplankton at the edge of chaos: ecological models , 1995, Journal of theoretical biology.

[21]  Arantxa Etxeverria The Origins of Order , 1993 .

[22]  Ming Li,et al.  Reversibility and adiabatic computation: trading time and space for energy , 1996, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[23]  Stuart A. Kauffman,et al.  The origins of order , 1993 .

[24]  Harry Buhrman,et al.  Time and Space Bounds for Reversible Simulation , 2001, ICALP.