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One of the most fundamental problems in biology and artificial life is the definition and understanding of "the gene". As pointed out by Carl Woese, whose work provides a very strong motivation for this study, this problem continues to contribute to much debate between classical biologists who understand "the gene to be defined by the genotype-phenotype relationship, by gene expression as well as gene replication" and many molecular biologists who declared the problem to be solved when the Watson-Crick structure of DNA clearly revealed the mechanism of gene replication [1]. Woese strongly argues against fundamentalist reductionism and presents the real problem of the gene as "how the genotype-phenotype relationship had come to be". In other words, the main question is how the mechanism of translation evolved.

The studies of Piraveenan et al. [2], Polani et al. [3], and Prokopenko et al. [4] considered a model for evolutionary dynamics in the vicinity of the "coding threshold": the transition when the capacity to symbolically represent nucleic acid sequences emerged in response to a change in environmental conditions. The model allows to identify conditions under which a separation between a proto-cell and its symbolic encoding becomes beneficial in terms of preserving the information within a noisy environment. Although evolutionary processes involve a large number of drives and constraints, information fidelity (i.e. preservation) is a consistent motif throughout biology: e.g., modern evolution operates close to the error threshold [5], and biological sensorimotor equipment typically exhausts the available informatory capacity (under given constraints) close to the limit [6]. Adami, in fact, argues that the evolutionary process extracts valuable information and stores it in the genes [5]. Since this process is relatively slow [7], it is a selective advantage to preserve this information, once captured.

Selected references

1. Woese, C.R.: A new biology for a new century. Microbiology and Molecular Biology Reviews 68(2) (2004) 173–186.

2. Piraveenan, M., Polani, D. Prokopenko, M. Emergence of Genetic Coding: an Information-theoretic Model, in F. Almeida e Costa, L. M. Rocha, E. Costa, I. Harvey, A. Coutinho (eds.) Advances in Artificial Life: 9th European Conference on Artificial Life (ECAL-2007), Lisbon, Portugal, September 10-14, Lecture Notes in Artificial Intelligence, vol. 4648, pp. 42-52, Springer, Berlin, 2007.  

3. Polani, D., Prokopenko, M., and Chadwick, M. Modelling Stigmergic Gene Transfer, in S. Bullock, J. Noble, R. Watson, and M. A. Bedau (eds.) Artificial Life XI - Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, pp. 490-497, MIT Press, 2008.

4. Prokopenko, M., Polani, D., Chadwick, M. Stigmergic gene transfer and emergence of universal coding, HFSP Journal, 3(5), 317-327, DOI: 10.2976/1.3175813, 2009.

5. Adami, C.. Introduction to Artificial Life. Springer, 1998.

6. Laughlin, S.B., de Ruyter van Steveninck, R.R., Anderson, J.C. The metabolic cost of neural information. Nature Neuroscience 1(1), 36–41, 1998.

7. Zurek, W.H., ed., Valuable Information. In Zurek, W.H., ed., Complexity, Entropy and the Physics of Information. Santa Fe Studies in the Sciences of Complexity, Reading, Mass., Addison-Wesley, 1990.

 

 


 
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