The physical language of molecular codes: A rate-distortion approach to the evolution and emergence of biological codes

The function of the organism hinges on the performance of its information-processing networks, which convey information via molecular recognition. Many paths within these networks utilize molecular codebooks, such as the genetic code, to translate information written in one class of molecules into another molecular “language”. The present paper examines the emergence and evolution of molecular codes in terms of ratedistortion theory and reviews recent results of this approach. We discuss how the biological problem of maximizing the fitness of an organism by optimizing its molecular coding machinery is equivalent to the communication engineering problem of designing an optimal information channel. The fitness of a molecular code takes into account the interplay between the quality of the channel and the cost of resources which the organism needs to invest in its construction and maintenance. We analyze the dynamics of a population of organisms that compete according to the fitness of their codes. The model suggests a generic mechanism for the emergence of molecular codes as a phase transition in an information channel. This mechanism is put into biological context and demonstrated in a simple example.

[1]  F. H. C. CRICK,et al.  Origin of the Genetic Code , 1967, Nature.

[2]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[3]  Tsvi Tlusty,et al.  Casting polymer nets to optimize noisy molecular codes , 2008, Proceedings of the National Academy of Sciences.

[4]  Tsvi Tlusty,et al.  A RELATION BETWEEN THE MULTIPLICITY OF THE SECOND EIGENVALUE OF A GRAPH LAPLACIAN, COURANT'S NODAL LINE THEOREM AND THE SUBSTANTIAL DIMENSION OF TIGHT POLYHEDRAL SURFACES ∗ , 2010, 1007.4132.

[5]  Tsvi Tlusty,et al.  A rate-distortion scenario for the emergence and evolution of noisy molecular codes , 2008, Physical review letters.

[6]  K. Rose Deterministic annealing for clustering, compression, classification, regression, and related optimization problems , 1998, Proc. IEEE.

[7]  Tsvi Tlusty,et al.  A simple model for the evolution of molecular codes driven by the interplay of accuracy, diversity and cost , 2008, Physical biology.

[8]  T. Cover,et al.  Rate Distortion Theory , 2001 .

[9]  Naftali Tishby,et al.  The information bottleneck method , 2000, ArXiv.

[10]  Tsvi Tlusty,et al.  A model for the emergence of the genetic code as a transition in a noisy information channel , 2007, Journal of theoretical biology.

[11]  Uri Alon,et al.  Coding limits on the number of transcription factors , 2006, BMC Genomics.

[12]  Aaron D. Wyner,et al.  Coding Theorems for a Discrete Source With a Fidelity CriterionInstitute of Radio Engineers, International Convention Record, vol. 7, 1959. , 1993 .

[13]  Uri Alon,et al.  Rules for biological regulation based on error minimization. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[14]  S. P. Luttrell,et al.  A Bayesian Analysis of Self-Organizing Maps , 1994, Neural Computation.

[15]  Tsvi Tlusty,et al.  Optimal Design of a Molecular Recognizer: Molecular Recognition as a Bayesian Signal Detection Problem , 2008, IEEE Journal of Selected Topics in Signal Processing.

[16]  K. Obermayer,et al.  PHASE TRANSITIONS IN STOCHASTIC SELF-ORGANIZING MAPS , 1997 .

[17]  Guy Sella,et al.  The Coevolution of Genes and Genetic Codes: Crick’s Frozen Accident Revisited , 2006, Journal of Molecular Evolution.