How to ‘find’ an error minimized genetic code: neutral emergence as an alternative to direct Darwinian selection for evolutionary optimization

  • Steven E. MasseyEmail author


Error minimization (EM) of the standard genetic code (SGC) refers to the assignment of amino acids to codons in such a way that the deleterious impact of mutations is reduced. The SGC is nearly optimal for the property of EM, compared to randomly generated codes, and prompts the question of how the property arose. Brute force searching of alternative genetic codes is unlikely to have occurred, given the high number of alternative codes. Therefore, a heuristic search of ‘code space’, the space of alternative codes, would have been necessary. Uncovering the nature of this heuristic search is key to understanding the evolution of the genetic code, and consequently the origin of life. Scenarios that rely on direct selection for the property of EM require codon reassignments to sample code space, but these are problematic mechanistically. Alternatively, it has been shown that EM may have emerged in a neutral fashion as a byproduct of the process of genetic code expansion. In this scenario, similar amino acids are added to similar codons via the gene duplication of tRNAs and aminoacyl-tRNA synthetases. Mimicking this process via simulation indeed produces high levels of EM in the resulting genetic codes. These observations imply that optimization has occurred by an alternative to direct selection, commonly viewed as the only form of evolutionary optimization followed in nature. I propose that the neutral emergence of EM produced by code expansion is a genetic algorithm but unlike direct selection, the local selection criterion (amino acid and codon similarity) is distant from the global fitness function (EM), leading to the emergent optimization of EM. By presenting this counter example I clarify how evolutionary optimization in biological systems is not restricted to direct selection, and emphasize that additional processes may lead to the production of beneficial traits, via ‘non-Darwinian optimization’.


Error minimization Neutral emergence Heuristic search Optimization process Graph coloring Code expansion 



Error minimization


Standard genetic code


Aminoacyl-tRNA synthetase


Record to record travel


Genetic algorithm



The author would like to thank Dr. Ivan Erill (Department of Biological Sciences, University of Maryland – Baltimore) for discussion regarding emergent scenarios of code expansion, Dr. Heeralal Janwa (Department of Mathematics, UPR) for discussion on the four color theorem and Dr. Julian Velev (Department of Physics, UPR) for advice on optimization algorithms.


This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.


  1. Alff-Steinberger C (1969) The genetic code and error transmission. Proc Natl Acad Sci USA 64:584–591CrossRefGoogle Scholar
  2. Archetti M (2004) Selection on codon usage for error minimization at the protein level. J Mol Evol 59:400–415CrossRefGoogle Scholar
  3. Bergthorsson U, Andersson DI, Roth JR (2007) Ohno’s dilemma: evolution of new genes under continuous selection. Proc Natl Acad Sci USA 104:17004–17009CrossRefGoogle Scholar
  4. Bilgin T, Kurnaz IA, Wagner A (2013) Selection shapes the robustness of ligand-binding amino acids. J Mol Evol 76:343–349CrossRefGoogle Scholar
  5. Bornberg-Bauer E, Chan HS (1999) Modelling evolutionary landscapes: mutational stability, topology and superfunnels in sequence space. Proc Natl Acad Sci USA 96:10689–10694CrossRefGoogle Scholar
  6. Buhrman H, van der Gulik PT, Kelk SM, Koolen WM, Stougle L (2011) Some mathematical refinements concerning error minimization in the genetic code. IEEE/ACM Trans Comput Biol Bioinform 8:1358–1372CrossRefGoogle Scholar
  7. Buhrman H, van der Gulik PTS, Klau GW, Schaffner C, Speijer D, Stougie L (2013) A realistic model under which the code is optimal. J Mol Evol 77:170–184CrossRefGoogle Scholar
  8. Butler T, Goldenfeld N, Mathew D, Luthey-Schulten Z (2009) Extreme genetic code optimality from a molecular dynamics calculation of amino acid polar requirement. Phys Rev 79:060901Google Scholar
  9. Cavalcanti ARO, De Barros Neto B, Ferreira R (2000) On the classes of aminoacyl-tRNA synthetases and the error minimization in the genetic code. J Theor Biol 204:15–20CrossRefGoogle Scholar
  10. Codoner FM, Daros J, Sole RV, Elena SF (2006) The fittest versus the flattest: experimental confirmation of the quasispecies effect with subviral pathogens. PLoS Pathog 2:2136CrossRefGoogle Scholar
  11. Conant GC, Wolfe KH (2008) Turning a hobby into a job: how duplicated genes find new functions. Nat Rev Genet 9:938–950CrossRefGoogle Scholar
  12. Crick FH (1968) The origin of the genetic code. J Mol Biol 38:367–379CrossRefGoogle Scholar
  13. Cusack BP, Arndt PF, Duret L, Crollius HR (2011) Preventing dangerous nonsense: selection for robustness to transcriptional error in human genes. PLoS Genet 7:e1002276CrossRefGoogle Scholar
  14. de Oliveira LL, de Oliveira PSL, Tinos R (2015) A multiobjective approach to the genetic code adaptability problem. BMC Bioinform 16:52CrossRefGoogle Scholar
  15. De Visser JAGM et al (2003) Perspective: evolution and detetion of genetic robustness. Evolution 57:1959–1972Google Scholar
  16. Di Giulio M (1989) The extension reached by the minimization of the polarity distances during the evolution of the genetic code. J Mol Evol 29:288–293CrossRefGoogle Scholar
  17. Di Giulio M, Medugno M (2001) The level and landscape of optimization in the origin of the genetic code. J Mol Evol 52:372–382CrossRefGoogle Scholar
  18. Di Giulio M, Capobianco MR, Medugno M (1994) On the optimization of the physicochemical distances between amino acids in the evolution of the genetic code. J Theor Biol 186:43–51CrossRefGoogle Scholar
  19. Dueck G (1993) New optimization heuristics: the great deluge algorithm and the record-to-record trave. J Comput Phys 104:86–92CrossRefzbMATHGoogle Scholar
  20. Ellis N, Gallant J (1982) An estimate of the global error frequency in translation. Mol Gen Genet 188:169–172CrossRefGoogle Scholar
  21. Epstein CJ (1966) Role of the amino-acid “code” and of selection for conformation in the evoluiton of proteins. Nature 210:26–28CrossRefGoogle Scholar
  22. Fitch WM, Upper K (1987) The phylogeny of tRNA sequences provides evidence for ambiguity reduction in the origin of the genetic code. Cold Spring Harb Symp Quant Biol 52:759–767CrossRefGoogle Scholar
  23. Freeland SJ, Knight RD, Landweber LF, Hurst LD (2000) Early fixation of an optimal genetic code. Mol Biol Evol 17:511–518CrossRefGoogle Scholar
  24. Freeland SJ, Wu T, Keulmann N (2003) The case for an error minimizing standard genetic code. Origins Life Evol Biosphere 33:457–477CrossRefGoogle Scholar
  25. Gilis D, Massar S, Cerf NJ, Rooman M (2001) Optimality of the genetic code with respect to protein stability and amino-acid frequencies. Genome Biol 2:11CrossRefGoogle Scholar
  26. Goldberg AL, Wittes RE (1966) Genetic code: aspects of organization. Science 153:420–424CrossRefGoogle Scholar
  27. Goldman N (1993) Further results on error minimization in the genetic code. J Mol Evol 37:662–664Google Scholar
  28. Goodarzi H, Nejad HA, Torabi N (2004) On the optimality of the genetic code, with consideration of termination codons. BioSystems 77:163–173CrossRefGoogle Scholar
  29. Gould SJ, Lewontin RC (1979) The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme. Proc R Soc Lond B 205:581–598CrossRefGoogle Scholar
  30. Grantham R (1974) Amino acid difference formula to help explain protein evolution. Science 185:862–864CrossRefGoogle Scholar
  31. Higgs PG (2009) A four-column thoery for the origin of the genetic code: tracing the evolutioonary pathways that gave rise to an optimized code. Biol Direct 4:16CrossRefGoogle Scholar
  32. Judson OP, Haydon D (1999) The genetic code: what is it good for? An analysis of the effects of selection pressures on genetic codes. J Mol Evol 49:539–550CrossRefGoogle Scholar
  33. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680MathSciNetCrossRefzbMATHGoogle Scholar
  34. Knight RD, Freeland SJ, Landweber LF (1999) Selection, history and chemistry: the three faces of the genetic code. Trends Biochem Sci 24:242–247CrossRefGoogle Scholar
  35. Koonin EV, Novozhilov AS (2009) Origin and evolution of the genetic code: the universal enigma. IUBMB Life 61:99–111CrossRefGoogle Scholar
  36. Kurnaz ML, Bilgin T, Kurnaz IA (2010) Certain non-standard coding tables appear to be more robust to error than the standard genetic code. J Mol Evol 70:13–28CrossRefGoogle Scholar
  37. Lauring AS, Andino R (2010) Quasispecies theory and the behavior of RNA viruses. PLoS Pathog 6:e10010005CrossRefGoogle Scholar
  38. Lee S, Cho S (2001) Emergent behaviors of a fuzzy sensory-motor controller evolved by genetic algorithm. IEEE Trans Syst Man Cybern Part B 31:919–929CrossRefGoogle Scholar
  39. Lenstra R (2014) Evolution of the genetic code through progressive symmetry breaking. J Theor Biol 347:95–108MathSciNetCrossRefGoogle Scholar
  40. Marquez R, Smit S, Knight R (2005) Do universal codon-usage patterns minimize the effects of mutation and translation error? Genome Biol 6:R91CrossRefGoogle Scholar
  41. Massey SE (2006) A sequential ‘2-1-3’ model of genetic code evolution that explains codon constraints. J Mol Evol 62:809–810CrossRefGoogle Scholar
  42. Massey SE (2008) A neutral origin of error minimization in the genetic code. J Mol Evol 67:510–516CrossRefGoogle Scholar
  43. Massey SE (2010a) Searching of code space for an error minimized genetic code via Codon Capture leads to failure, or requires at least 20 improving codon reassignments via the Ambiguous Intermediate mechanism. J Mol Evol 70:106–115CrossRefGoogle Scholar
  44. Massey SE (2010b) Pseudaptations and the emergence of beneficial traits. In: Pontarotti P (ed) Evolutionary biology: concepts, molecular and morphological evolution. Springer, Berlin, pp 81–100CrossRefGoogle Scholar
  45. Massey SE (2015) Genetic code evolution reveals the neutral emergence of mutational robustness, and information as an evolutionary constraint. Life 5:1301–1332CrossRefGoogle Scholar
  46. Massey SE (2016) Neutral emergence of error minimized genetic codes superior to the standard genetic code. J Theor Biol 408:237–242CrossRefGoogle Scholar
  47. Massey SE (2017) The identities of stop codon reassignments support ancestral tRNA stop codon decoding activity as a facilitator of gene duplication and evolution of novel function. Gene 619:37–43CrossRefGoogle Scholar
  48. Massey SE, Garey JR (2007) A comparative genomics analysis of codon reassignments reveals a link with mitochondrial proteome size and a mechanism of genetic code change via suppressor tRNAs. J Mol Evol 64:399–410CrossRefGoogle Scholar
  49. McKay BD (2013) A note on the history of the four-colour conjecture. J Graph Theory 72:361–363MathSciNetCrossRefzbMATHGoogle Scholar
  50. Mitchell M, Crutchfield JP, Das R (1996) Evolving cellular automata with genetic algorithms: a review of recent work. In: Proceedings of the first international conference on evolutionary computation and its applications. Russian Academy of SciencesGoogle Scholar
  51. Morgens DW, Cavalcanti ARO (2013) An alternative look at code evolution: using non-canonical codes to evaulate adaptive and historic models for the origin of the genetic code. J Mol Evol 76:71–80CrossRefGoogle Scholar
  52. Nagel GM, Doolittle RF (1995) Phylogenetic analysis of hte aminoacyl-tRNA synthetases. J Mol Evol 40:487–498CrossRefGoogle Scholar
  53. Najafabadi HS, Lehmann J, Omidi M (2007) Error minimization explains the codon usage of highly expressed genes in Escherichia coli. Gene 387:150–155CrossRefGoogle Scholar
  54. Novozhilov AS, Wolf YI, Koonin EV (2007) Evolution of the genetic code: partial optimization of a random code for robustness to translation error in a rugged fitness landscape. Biol Direct 2:24CrossRefGoogle Scholar
  55. Osawa S, Jukes TH (1988) Evolution of the genetic code as affected by anticodon content. Trends Genet 4:191–197CrossRefGoogle Scholar
  56. Osawa S, Jukes TH (1989) Codon reassignment (codon capture) in evolution. J Mol Evol 28:271–278CrossRefGoogle Scholar
  57. Pagan RF, Massey SE (2014) A nonadaptive origin of a beneficial trait. in silico selection for free energy of folding leads to the neutral emergence of mutational robustness in single domain proteins. J Mol Evol 78:130–139CrossRefGoogle Scholar
  58. Pokarowski P, Kloczkowski A, Nowakowski S, Pokarowska M, Jernigan RL, Kolinksi A (2007) Ideal amino acid exchange forms for approximating substitution matrices. Proteins Struct Funct Bioinform 69:379–393CrossRefGoogle Scholar
  59. Santos J, Monteagudo A (2010) Study of the genetic code adaptability by means of a genetic algorithm. J Theor Biol 264:854–865MathSciNetCrossRefGoogle Scholar
  60. Schultz DW, Yarus M (1994) Transfer RNA mutation and the malleability of the genetic code. J Mol Biol 235:1377–1380CrossRefGoogle Scholar
  61. Schultz DW, Yarus M (1996) On malleability in the genetic code. J Mol Evol 42:597–601CrossRefGoogle Scholar
  62. Sonneborn TM (1965) Degeneracy of the genetic code: extent, nature, and genetic implications. Vogel V, Bryson HJ (eds). Academic Press, New YorkGoogle Scholar
  63. Stoletzki N, Eyre-Walker A (2007) Synonymous codon usage in Escherichia coli: selection for translational accuracy. J Mol Evol 24:374–381CrossRefGoogle Scholar
  64. Stoltzfus A, Yampolsky LY (2007) Amino acid exchangeability and the adaptive code hypothesis. J Mol Evol 65:456–462CrossRefGoogle Scholar
  65. van der Gulik PTS, Hoff WDJ (2011) Unassigned codons, nonsense suppression, and anticodon modifications in the evolution of the genetic code. J Mol Evol 73:59CrossRefGoogle Scholar
  66. van Nimwegen E, Crutchfield JP, Huynen M (1999) Neutral evolution of mutational robustness. Proc Natl Acad Sci 96:9716–9720CrossRefGoogle Scholar
  67. Wilke CO, Wang JL, Ofria C, Lenski RE, Adami C (2001) Evolution of digital organisms at high mutation rates leads to survival of the flattest. Nature 412:331–333CrossRefGoogle Scholar
  68. Woese CR (1965) On the evolution of the genetic code. Proc Natal Acad Sci USA 54:1546–1552CrossRefGoogle Scholar
  69. Woese C (1967) The genetic code: the molecular basis for genetic expression. Harper, New YorkGoogle Scholar
  70. Wong JTF (1980) Role of minimization of chemical distances between amino acids in the evolution of the genetic code. Proc Natl Acad Sci USA 77:1083–1086CrossRefGoogle Scholar
  71. Xue H, Tong K, Marck C, Grosjean H, Wong JT (2003) Transfer RNA paralogs: evidence of genetic code-amino acid biosynthesis coevolution and an archaeal root of life. Gene 310:59–66CrossRefGoogle Scholar
  72. Zhang J (2003) Evolution by gene duplication: an update. Trends Ecol Evol 18:292–298CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of BiologyUniversity of Puerto Rico – Rio PiedrasSan JuanUSA

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