Abstract
We revisit previous analyses on the computation of the maximum mutual information between a genetic sequence and its mutated versions down the generations, taking into account the protein translation mechanism of the genetic machinery. This amounts to the application of Shannon’s capacity to the study of the transmission of genetic information. Studies on this subject were started by Yockey and then followed by a number of researchers. Here we refine prior analyses employing the Kimura model of base substitution mutations, which is more realistic than the Jukes-Cantor model used by all previous research on this topic. Furthermore we undertake exact computations where prior works just used approximations, and we propose two practical applications of genetic capacity.
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References
Quastler, H. (ed.): Information Theory in Biology. University of Illinois Press, Urbana (1953)
Yockey, H.P.: Information Theory, Evolution, and the Origin of Life. Cambridge University Press (2005)
Battail, G.: Does information theory explain biological evolution? Europhys. Lett. 40(3), 343–348 (1997)
Guiaşu, S.: Information Theory with Applications. McGraw-Hill (1977)
Battail, G.: Information theory and error-correcting codes in genetics and biological evolution. In: Barbieri, M. (ed.) Introduction to Biosemiotics. Springer (2007)
May, E.E.: Bits and bases: An analysis of genetic information paradigms. In: 41st Asilomar Conf. on Signals, Systems and Computers (ACSSC), Asilomar, USA, pp. 165–169 (November 2007)
Gong, L., Bouaynaya, N., Schonfeld, D.: Information-theoretic model of evolution over protein communication channel. IEEE/ACM Trans. on Comp. Biol. and Bioinformat. 8(1), 143–151 (2011)
Shannon, C.E.: A mathematical theory of communication. Bell System Tech. J. 27, 379–423, 623–656 (1948)
Li, W.: Molecular Evolution. Sinauer Associates (1997)
Yockey, H.P.: An application of information theory to the central dogma and the sequence hypothesis. J. Theor. Biol. (46), 369–406 (1974)
Yu, Z., Mao, Z., Zhou, L.-Q., Anh, V.: A mutual information based sequence distance for vertebrate phylogeny using complete mitochondrial genomes. In: Procs. of the IEEE 3rd Intl. Conf. on Natural Computation, Haikou, China, pp. 253–257 (2007)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley-Interscience (1991)
Kimura, M.: A simple method for estimating evolutionary rate in a finite population due to mutational production of neutral and nearly neutral base substitution through comparative studies of nucleotide sequences. J. Mol. Biol. 16, 111–120 (1980)
Purvis, A., Bromham, L.: Estimating the transition/transversion ratio from independent pairwise comparisons with an assumed phylogeny. J. of Mol. Evol. 44, 112–119 (1997)
Magnus, J.R., Neudecker, H.: Matrix Differential Calculus with Applications in Statistics and Econometrics, 3rd edn. John Wiley & Sons (1999)
Mackiewicz, P., Biecek, P., Mackiewicz, D., Kiraga, J., Baczkowski, K., Sobczynski, M., Cebrat, S.: Optimisation of Asymmetric Mutational Pressure and Selection Pressure Around the Universal Genetic Code. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part III. LNCS, vol. 5103, pp. 100–109. Springer, Heidelberg (2008)
Kimura, M.: The Neutral Theory of Molecular Evolution. Cambridge University Press (1983)
Dayhoff, M., Schwartz, R., Orcutt, B.: A model of evolutionary change in proteins. Atlas of Protein Sequence and Structure 5(3), 345–352 (1978)
Blahut, R.: Computation of channel capacity and rate-distortion functions. IEEE Trans. on Inf. Theory 18(4), 460–473 (1972)
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Balado, F. (2012). Genetic Channel Capacity Revisited. In: Hart, E., Timmis, J., Mitchell, P., Nakamo, T., Dabiri, F. (eds) Bio-Inspired Models of Networks, Information, and Computing Systems. BIONETICS 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32711-7_7
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DOI: https://doi.org/10.1007/978-3-642-32711-7_7
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