Skip to main content

Advertisement

SpringerLink
Log in

Genetic code optimality studied by means of simulated evolution and within the coevolution theory of the canonical code organization

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

We have studied the canonical genetic code optimality by means of simulated evolution. A genetic algorithm is used to search for better adapted hypothetical codes and as a method to guess the difficulty in finding such alternative codes. Such analysis is performed within the coevolution theory of the genetic code organization. We have studied the progression of the canonical genetic code optimality within such theory, considering a possible scenario of a previous code with two-letter codons as well as the current organization of the canonical code. Moreover, we have analysed the particular optimality and progression of adaptability of the individual nucleotide bases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Di Giulio M (1989) The extension reached by the minimization of the polarity distances during the evolution of the genetic code. J Mol Evol 29:288–293

    Article  Google Scholar 

  • Di Giulio M (2000) The origin of the genetic code. Trends Biochem Sci 25(2):44

    Article  Google Scholar 

  • Di Giulio M (2005) The origin of the genetic code: theories and their relationship, a review. Biosystems 80: 175–184

    Article  Google Scholar 

  • Di Giulio M, Medugno M (1999) Physicochemical optimization in the genetic code origin as the number of codified amino acids increases. J Mol Evol 49(1):1–10

    Article  Google Scholar 

  • Di Giulio M, Capobianco M, Medugno M (1994) On the optimization of the physicochemical distances between amino acids in the evolution of the genetic code. J Theor Biol 168:43–51

    Article  Google Scholar 

  • Freeland S (2002) The Darwinian genetic code: an adaptation for adapting? Genet Prog Evolvable Mach Kluwer Acad Publ 3:113–127

    Article  MATH  Google Scholar 

  • Freeland S, Hurst L (1998a) The genetic code is one in a million. J Mol Evol 47(3):238–248

    Article  Google Scholar 

  • Freeland S, Hurst L (1998b) Load minimization of the genetic code: History does not explain the pattern. Proc R Soc 265:2111–2119

    Article  Google Scholar 

  • Freeland S, Knight R, Landweber L, Hurst L (2000a) Early fixation of an optimal genetic code. Mol Biol Evol 17(4):511–518

    Google Scholar 

  • Freeland S, Knight R, Landweber L (2000b) Measuring adaptation within the genetic code. Trends Biochem Sci 25(2):44–45

    Article  Google Scholar 

  • Gilis D, Massar S, Cerf N, Rooman M (2001) Optimality of the genetic code with respect to protein stability and amino-acid frequencies. Genome Biol 2(11)

  • Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co., Inc., Boston

  • Goodarzi H, Najafabadi H, Nejad H, Torabi N (2006) The impact of including tRNA content on the optimality of the genetic code. Bull Math Biol 67(6):1355–1368

    Article  MathSciNet  Google Scholar 

  • Haig D, Hurst L (1991) A quantitative measure of error minimization in the genetic code. J Mol Evol 33:412–417

    Article  Google Scholar 

  • Jukes T (1965) Coding triplets and their possible evolutionary implications. Biochem Biophys Res Commun 19:391–396

    Article  Google Scholar 

  • Jukes T (1973) Possibilities for the evolution of the genetic code from a preceding form. Nature 246:22–26

    Article  Google Scholar 

  • Knight R, Landweber L (2000) The early evolution of the genetic code. Cell 101:569–572

    Article  Google Scholar 

  • Knight R, Freeland S, Landweber L (1999) Selection, history and chemistry: the three faces of the genetic code. Trends Biochem Sci 24:241–247

    Article  Google Scholar 

  • Marquez R, Smit S, Knight R (2005) Do universal codon-usage patterns minimize the effects of mutation and translation error? Genome Biol 6(11):R91

    Article  Google Scholar 

  • Monteagudo A, Santos J (2007) Simulated evolution of the adaptability of the genetic code using genetic algorithms. Bio-Inspired Model Cognitive Tasks Lect Notes Comp Sci 4527:478–487

    Article  Google Scholar 

  • Schönauer S, Clote P (1997) How optimal is the genetic code? In: Frishman D, Mewes H (eds) Computer science and biology, German conference on bioinformatics (GCB 97), pp 65–67

  • Sella G, Ardell D (2001) The coevolution of genes and the genetic code. SFI Working Paper 01-03-015

  • Torabi N, Goodarzi H, Najafabadi H (2007) The case for an error minimizing set of coding amino acids. J Theor Biol 244(4):737–744

    Article  MathSciNet  Google Scholar 

  • Woese C (1965) On the evolution of the genetic code. Proc Natl Acad Sci USA 54:1546–1552

    Article  Google Scholar 

  • Wong J (1975) A co-evolution theory of the genetic code. Proc Natl Acad Sci USA 72:1909–1912

    Article  Google Scholar 

  • Wong J (2005) Coevolution theory of the genetic code at age thirty. BioEssays 27:416–425

    Article  Google Scholar 

  • Yockey H (2005) Information theory, evolution, and the origin of life. Cambridge University Press, NY

    MATH  Google Scholar 

  • Zhu C-T, Zeng X-B, Huang W-D (2003) Codon usage decreases the error minimization within the genetic code. J Mol Evol 57:533–537

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of A Coruna, Campus de Elviña s/n, A Coruna, 15071, Spain

    José Santos & Ángel Monteagudo

Authors
  1. José Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ángel Monteagudo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José Santos.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Santos, J., Monteagudo, Á. Genetic code optimality studied by means of simulated evolution and within the coevolution theory of the canonical code organization. Nat Comput 8, 719–738 (2009). https://doi.org/10.1007/s11047-008-9092-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-008-9092-x

Keywords

Search

Navigation