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A genetic code Boolean structure. II. The genetic information system as a Boolean information system

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Abstract

A Boolean structure of the genetic code where Boolean deductions have biological and physicochemical meanings was discussed in a previous paper. Now, from these Boolean deductions we propose to define the value of amino acid information in order to consider the genetic information system as a communication system and to introduce the semantic content of information ignored by the conventional information theory. In this proposal, the value of amino acid information is proportional to the molecular weight of amino acids with a proportional constant of about 1.96×1025 bits per kg. In addition to this, for the experimental estimations of the minimum energy dissipation in genetic logic operations, we present two postulates: (1) the energy E i (i = 1, 2, ..., 20) of amino acids in the messages conveyed by proteins is proportional to the value of information, and (2) amino acids are distributed according to their energy E i so the amino acid population in proteins follows a Boltzmann distribution. Specifically, in the genetic message carried by the DNA from the genomes of living organisms, we found that the minimum energy dissipation in genetic logic operations was close to kTLn(2) joules per bit.

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References

  • Bennett, C.H., 1973. Logical reversibility of computation. IBM J. Res. Dev. 17, 525–532.

    MATH  Google Scholar 

  • Bennett, C.H., 1982. The thermodynamics of computation—A review. Internat. J. Theoret. Phys. 21, 905–940.

    Article  Google Scholar 

  • Bennett, C.H., Landauer, R., 1985. The fundamental physical limits of computation. Sci. Am. 253, 48–56.

    Google Scholar 

  • Crick, F.H.C., 1968. The origin of the genetic code. J. Mol. Biol. 38, 367–379.

    Article  Google Scholar 

  • Darlington, R.B., 1990. Regression and Linear Models. McGraw-Hill, New York.

    Google Scholar 

  • Epstein, C.J., 1966. Role of the amino-acid “code” and of selection for conformation in the evolution of proteins. Nature 210, 25–28.

    Google Scholar 

  • Frank, M.P., 2002. Physical limits of computing. IEEE Comput. Sci. Eng. Magazine 4, 16–26.

    Google Scholar 

  • Friedman, S.M., Weinstein, I.B., 1964. Lack of fidelity in the translation of ribopolynucleotides. Proc. Natl. Acad. Sci. USA 52, 988–996.

    Article  Google Scholar 

  • Gillis, D., Massar, S., Cerf, N.J., Rooman, M., 2001. Optimality of the genetic code with respect to protein stability and amino acid frequencies. Genome Biol. 2, 1–12.

    Google Scholar 

  • Jiménez-Montaño, M.A., 1996. The hypercube structure of the genetic code explains conservative and nonconservative amino acid substitutions in vivo and in vitro. Biosystems 39, 117–125.

    Article  Google Scholar 

  • Landauer, R., 1961. Irreversibility and heat generation in the computing process. IBM J. Res. Dev. 5, 183–191.

    Article  MathSciNet  MATH  Google Scholar 

  • Landauer, R., 1991. Information is physical. Phys. Today 44, 23–29.

    Google Scholar 

  • Landauer, R., 1996. Minimal energy requirements in communication. Science 272, 1914–1918.

    MathSciNet  Google Scholar 

  • Landauer, R., 1998. Energy needed to send a bit. Proc. R. Soc. Lond. A 454, 305–311.

    Article  MATH  Google Scholar 

  • Neumann, J., 1966. In: Burks, A. (Ed.), Theory of Self-Reproducing Automata. University of Illinois Press, Urbana-Champaign, IL.

    Google Scholar 

  • Sanchez, R., Grau, R., Morgado, E., 2005. A genetic code Boolean structure. I. Meaning of Boolean deductions. Bull. Math. Biol. 67, 1–14.

    Article  MathSciNet  Google Scholar 

  • Schneider, R.W., 1991. Theory of molecular machines. II. Energy dissipation from molecular machines. J. Theor. Biol. 148, 125–137.

    Google Scholar 

  • Shannon, C.E., 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656.

    MathSciNet  MATH  Google Scholar 

  • Shannon, C.E., Weaver, W., 1949. The Mathematical Theory of Communication. University of Illinois, Urbana, IL.

    MATH  Google Scholar 

  • Taylor, J.D.T., Thornton, J.M., 1991. Recompilation of the mutation matrices. CABIOS 8, 275–282.

    Google Scholar 

  • Volkenshtein, M.V. 1985. Biofísica. Editorial MIR, Moscú, Capítulo 17, 621–639.

  • Weinberger, E.D., 2002. A theory of pragmatic information and its application to the quasi-species model of biological evolution. Biosystems 66, 105–119.

    Article  Google Scholar 

  • Yockey, H.P., 2000. Origin of life on earth and Shannon’s theory of communication. Comput. Chem. 24, 105–123.

    Article  MATH  Google Scholar 

  • Yockey, H.P., 2002. Fundamentals of life. In: Information Theory, Evolution and the Origin of Life. Éditions scientifiques et médicales Elsevier SAS (Chapter II.10).

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Correspondence to Robersy Sanchez.

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Sanchez, R., Grau, R. A genetic code Boolean structure. II. The genetic information system as a Boolean information system. Bull. Math. Biol. 67, 1017–1029 (2005). https://doi.org/10.1016/j.bulm.2004.12.004

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  • DOI: https://doi.org/10.1016/j.bulm.2004.12.004

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