<i>Genview</i> and <i>Gencode</i> : a pair of programs to

  test theories of genetic code evolution

Ronneberg, Terres A.; Freeland, Stephen J.; Landweber, Laura F.

doi:10.1093/bioinformatics/17.3.280

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…When they used weightings to allow for biases in translation, they found that only 1 in every million randomly generated codes was more efficient than the standard genetic code. In (Ronneberg et al, 2001) the authors presented a graphical tool for testing the adaptive nature of the genetic code under those assumptions about patterns of genetic error and the nature of amino acid similarity. Additionally, as the adaptability of the genetic code does not hold for other amino acid properties other than polar requirement, in (Freeland et al, 2000b) the authors applied point accepted mutation (PAM) 74-100 matrix data, which derives from frequently observed substitution patterns of amino acids in naturally occurring pairs of homologous proteins.…”

Section: Previous Workmentioning

confidence: 99%

Study of the genetic code adaptability by means of a genetic algorithm

Santos¹,

Monteagudo²

2010

Journal of Theoretical Biology

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Cite this article as: José Santos and Ángel Monteagudo, Study of the genetic code adaptability by means of a genetic algorithm, Journal of Theoretical Biology, doi:10.1016Biology, doi:10. /j.jtbi.2010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractWe used simulated evolution to study the adaptability level of the canonical geneticcode. An adapted genetic algorithm (GA) searches for optimal hypothetical codes.Adaptability is measured as the average variation of the hydrophobicity that the encoded amino acids undergo when errors or mutations are present in the codons of the hypothetical codes. Different types of mutations and point mutation rates that depend on codon base number are considered in this study. Previous works have used statistical approaches based on randomly generated alternative codes or have used local search techniques to determine an optimum value. In this work, we emphasize what can be concluded from the use of simulated evolution considering the results of previous works. The GA provides more information about the difficulty of the evolution of codes, without contradicting previous studies using statistical or engineering approaches. The GA also shows that, within the coevolution theory, the third base clearly improves the adaptability of the current genetic code.

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Section: Previous Workmentioning

confidence: 99%

Study of the genetic code adaptability by means of a genetic algorithm

Santos¹,

Monteagudo²

2010

Journal of Theoretical Biology

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“…The standard genetic code table is published in a multitude of scientific papers and books. Numerous papers have been published in an attempt to explain the origin of the genetic code (Barbieri, 2003;Beebe et al, 2003b;Crick, 1968;Di Giulio, 1997, 1999a,b, 2000, 2001a,b, 2002Di Giulio and Medugno, 1998, 1999, 2001Freeland et al, 2003;Guimaraes, 2004;Jukes, 1993;Jukes and Osawa, 1993;Knight et al, 1999;Osawa et al, 1992;Ribas de Pouplana et al, 1998;Schimmel, 2000, 2001c;Ronneberg et al, 2000Ronneberg et al, , 2001Schimmel and Ribas de Pouplana, 1999;Schimmel and Wang, 1999;Seligmann and Amzallag, 2002;shCherbak, 2003;Skouloubris et al, 2003;Stevenson, 2002;Trevors, 2003;Wang and Schultz, 2002;Woese et al, 2000;Wong, 1975Wong, , 1988Xue et al, 2003;Yarian et al, 2002;Yarus and Christian, 1989;Yarus, 2000). All code origin models have problems and lack detail.…”

Section: The Role Of Natural Selectionmentioning

confidence: 99%

Chance and necessity do not explain the origin of life

Trevors

Abel

2004

Cell Biology International

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Where and how did the complex genetic instruction set programmed into DNA come into existence? The genetic set may have arisen elsewhere and was transported to the Earth. If not, it arose on the Earth, and became the genetic code in a previous lifeless, physical-chemical world. Even if RNA or DNA were inserted into a lifeless world, they would not contain any genetic instructions unless each nucleotide selection in the sequence was programmed for function. Even then, a predetermined communication system would have had to be in place for any message to be understood at the destination. Transcription and translation would not necessarily have been needed in an RNA world. Ribozymes could have accomplished some of the simpler functions of current protein enzymes. Templating of single RNA strands followed by retemplating back to a sense strand could have occurred. But this process does not explain the derivation of "sense" in any strand. "Sense" means algorithmic function achieved through sequences of certain decision-node switch-settings. These particular primary structures determine secondary and tertiary structures. Each sequence determines minimum-free-energy folding propensities, binding site specificity, and function. Minimal metabolism would be needed for cells to be capable of growth and division. All known metabolism is cybernetic--that is, it is programmatically and algorithmically organized and controlled.

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2001

Comp Funct Genom

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Genview and Gencode : a pair of programs to test theories of genetic code evolution

Cited by 6 publications

References 11 publications

Study of the genetic code adaptability by means of a genetic algorithm

Study of the genetic code adaptability by means of a genetic algorithm

Chance and necessity do not explain the origin of life

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