Journal articles: 'Genetic code'

1

Cavalcanti, Andre R. O., and Laura F. Landweber. "Genetic code." Current Biology 14, no. 4 (February 2004): R147. http://dx.doi.org/10.1016/j.cub.2004.01.041.

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Fimmel, Elena, and Lutz Strüngmann. "Linear codes and the mitochondrial genetic code." Biosystems 184 (October 2019): 103990. http://dx.doi.org/10.1016/j.biosystems.2019.103990.

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Habrel, Mykola, and Mykhailo Habrel. "GENETIC CODE OF THE CITY." Current problems of architecture and urban planning, no. 63 (April 14, 2022): 18–41. http://dx.doi.org/10.32347/2077-3455.2022.63.18-41.

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Advances in genetics as a science have led the authors to believe that its methods and approaches can be used in urban research and in justifying decisions on spatial organization and urban development. Using the theoretical and methodological tools of genetics revealed the content of the phenomenon of "genetic top" in urban planning and urban development: understood the key provisions of genetics as a science of heredity – the origin and evolution of organisms, substantiated their feasibility for research on urban history and modern cities; the peculiarities of the genetic method for the study of cities and the role of the phenomenon of heredity for their future are determined. The genetic method is presented as a kind of genetic and aimed at studying the phenomena of development not only in time but also in space, determining the transitions from lower forms of organization of territories (places, settlements) to higher – extremely complex urban systems. It covers: the establishment of initial conditions and the origin of the organism, its main stages, main trends and lines (mutations, modifications) of development. On the example of the city of Lviv the application of certain tools for in-depth understanding of the processes of origin and evolution of the city is revealed. The set of "genes" of the city – spiritual-religious, behavioral-activity and psychological-mental in their various combinations, which form the genotype of the city as connections "man–territory–space–time". It has been confirmed that the city’s genetics (genetic space) is rooted in the past, connected with people, the energy of the Earth and the Universe. "Genetic" research shows that inherited urban differences are a factor that determines urban individuality throughout development, should be taken into account in both research and spatial organization and development of urban systems. The article only initiates certain provisions to substantiate the theory of urban genetics. Future research will determine the evidence in support of the formulated provisions or refute them as false.

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KOJIMA, Tatsuya, Yuuki HAYASHI, and Hiroaki SUGA. "Genetic Code Reprogramming." Seibutsu Butsuri 52, no. 1 (2012): 004–9. http://dx.doi.org/10.2142/biophys.52.004.

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OHAMA, Takeshi, Yuji INAGAKI, Yoshitaka BESSHO, and Syozo OSAWA. "Evolving genetic code." Proceedings of the Japan Academy, Series B 84, no. 2 (2008): 58–74. http://dx.doi.org/10.2183/pjab.84.58.

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6

Marshall, J. "The genetic code." Proceedings of the National Academy of Sciences 111, no. 16 (April 22, 2014): 5760. http://dx.doi.org/10.1073/pnas.1405652111.

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7

Helser, Terry L. "Genetic Code Wordsearch." Journal of Chemical Education 80, no. 4 (April 2003): 417. http://dx.doi.org/10.1021/ed080p417.

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8

YARUS, MICHAEL, and ERIC L. CHRISTIAN. "Genetic code origins." Nature 342, no. 6248 (November 1989): 349–50. http://dx.doi.org/10.1038/342349b0.

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9

Osawa, S., A. Muto, T. Ohama, Y. Andachi, R. Tanaka, and F. Yamao. "Prokaryotic genetic code." Experientia 46, no. 11-12 (December 1990): 1097–106. http://dx.doi.org/10.1007/bf01936919.

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10

Giegé, Richard. "Genetic code expansion." Nature Structural & Molecular Biology 10, no. 6 (June 2003): 414–16. http://dx.doi.org/10.1038/nsb0603-414.

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11

Sleator, Roy D. "The genetic code." Artificial DNA: PNA & XNA 5, no. 2 (May 2014): e29408. http://dx.doi.org/10.4161/adna.29408.

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12

Nangle, Leslie A., Valérie de Crécy Lagard, Volker Döring, and Paul Schimmel. "Genetic Code Ambiguity." Journal of Biological Chemistry 277, no. 48 (September 18, 2002): 45729–33. http://dx.doi.org/10.1074/jbc.m208093200.

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13

Weiss, Kenneth M., and Anne V. Buchanan. "“The” genetic code?" Evolutionary Anthropology: Issues, News, and Reviews 14, no. 1 (January 25, 2005): 6–11. http://dx.doi.org/10.1002/evan.20033.

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14

Singh, Brijendra. "Perceptions about Genetic Code and its Evolution." Indian Journal of Genetics and Molecular Research 5, no. 2 (2016): 57–60. http://dx.doi.org/10.21088/ijgmr.2319.4782.5216.4.

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15

Omachi, Yuji, Nen Saito, and Chikara Furusawa. "Rare-event sampling analysis uncovers the fitness landscape of the genetic code." PLOS Computational Biology 19, no. 4 (April 17, 2023): e1011034. http://dx.doi.org/10.1371/journal.pcbi.1011034.

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The genetic code refers to a rule that maps 64 codons to 20 amino acids. Nearly all organisms, with few exceptions, share the same genetic code, the standard genetic code (SGC). While it remains unclear why this universal code has arisen and been maintained during evolution, it may have been preserved under selection pressure. Theoretical studies comparing the SGC and numerically created hypothetical random genetic codes have suggested that the SGC has been subject to strong selection pressure for being robust against translation errors. However, these prior studies have searched for random genetic codes in only a small subspace of the possible code space due to limitations in computation time. Thus, how the genetic code has evolved, and the characteristics of the genetic code fitness landscape, remain unclear. By applying multicanonical Monte Carlo, an efficient rare-event sampling method, we efficiently sampled random codes from a much broader random ensemble of genetic codes than in previous studies, estimating that only one out of every 1020 random codes is more robust than the SGC. This estimate is significantly smaller than the previous estimate, one in a million. We also characterized the fitness landscape of the genetic code that has four major fitness peaks, one of which includes the SGC. Furthermore, genetic algorithm analysis revealed that evolution under such a multi-peaked fitness landscape could be strongly biased toward a narrow peak, in an evolutionary path-dependent manner.

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16

Rubino, I. Alex, Anna Saya, and Bianca Pezzarossa. "Percept-Genetic Signs of Repression in Histrionic Personality Disorder." Perceptual and Motor Skills 74, no. 2 (April 1992): 451–64. http://dx.doi.org/10.2466/pms.1992.74.2.451.

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Several types of perceptual distortions of two anxiety-arousing visual stimuli are coded as repression in the Defense Mechanism Test, a tachistoscopic, percept-genetic technique. Given the well-established correspondence between hysteria and repression, the study included a clinical validation of these variants of repression against the diagnosis of histrionic personality disorder. 41 subjects with evidence of this disorder on the Millon Clinical Multiaxial Inventory-II were compared with 41 nonhistrionic controls. Significantly more histrionics were coded for the type of repression in which the threatening figure is transformed into a harmless object (code 1:42), while animal- and statue-repressions, when combined (codes 1:1 and 1:2), were significantly more characteristic of the nonhistrionic group. As an unpredicted finding, significantly more histrionic subjects employed defensive strategies, currently coded as reaction formations (code 4:). Histrionic subjects without concomitant compulsive features were coded more frequently for introaggression (code 6:) compared both with nonhistrionic controls and with histrionic-compulsive subjects. The findings are discussed within the context of the available percept-genetic literature. It is suggested that the Defense Mechanism Test may be further employed to objectify and investigate the defense mechanisms of the DSM-III—R disorders.

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17

Geyer, Regine, and Amir Madany Mamlouk. "On the efficiency of the genetic code after frameshift mutations." PeerJ 6 (May 21, 2018): e4825. http://dx.doi.org/10.7717/peerj.4825.

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Statistical and biochemical studies of the standard genetic code (SGC) have found evidence that the impact of mistranslations is minimized in a way that erroneous codes are either synonymous or code for an amino acid with similar polarity as the originally coded amino acid. It could be quantified that the SGC is optimized to protect this specific chemical property as good as possible. In recent work, it has been speculated that the multilevel optimization of the genetic code stands in the wider context of overlapping codes. This work tries to follow the systematic approach on mistranslations and to extend those analyses to the general effect of frameshift mutations on the polarity conservation of amino acids. We generated one million random codes and compared their average polarity change over all triplets and the whole set of possible frameshift mutations. While the natural code—just as for the point mutations—appears to be competitively robust against frameshift mutations as well, we found that both optimizations appear to be independent of each other. For both, better codes can be found, but it becomes significantly more difficult to find candidates that optimize all of these features—just like the SGC does. We conclude that the SGC is not only very efficient in minimizing the consequences of mistranslations, but rather optimized in amino acid polarity conservation for all three effects of code alteration, namely translational errors, point and frameshift mutations. In other words, our result demonstrates that the SGC appears to be much more than just “one in a million”.

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18

Ardell, David H., and Guy Sella. "No accident: genetic codes freeze in error–correcting patterns of the standard genetic code." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 357, no. 1427 (November 29, 2002): 1625–42. http://dx.doi.org/10.1098/rstb.2002.1071.

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The standard genetic code poses a challenge in understanding the evolution of information processing at a fundamental level of biological organization. Genetic codes are generally coadapted with, or ‘frozen‘ by, the protein–coding genes that they translate, and so cannot easily change by natural selection. Yet the standard code has a significantly non–random pattern that corrects common errors in the transmission of information in protein–coding genes. Because of the freezing effect and for other reasons, this pattern has been proposed not to be due to selection but rather to be incidental to other evolutionary forces or even entirely accidental. We present results from a deterministic population genetic model of code–message coevolution. We explicitly represent the freezing effect of genes on genetic codes and the perturbative effect of changes in genetic codes on genes. We incorporate characteristic patterns of mutation and translational error, namely, transition bias and positional asymmetry, respectively. Repeated selection over small successive changes produces genetic codes that are substantially, but not optimally, error correcting. In particular, our model reproduces the error–correcting patterns of the standard genetic code. Aspects of our model and results may be applicable to the general problem of adaptation to error in other natural information–processing systems.

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19

Aisah, Isah, B. Subartini, and A. Muhaemin. "Endomorphism Representation Matrix From Standard Genetic Code." JURNAL ILMIAH SAINS 20, no. 1 (April 20, 2020): 26. http://dx.doi.org/10.35799/jis.20.1.2020.27787.

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Mutations are changes in genetic material that can occur at the level of genes or chromosomes. Mutations at the gene level are structural changes in the genetic code. In this paper we will investigate the necessary and sufficient conditions for an endomorphism called autumorphism. The method used in this research paper is a review of literature conducted by collecting literature from previous studies in accordance with the study discussed. Mathematically, genetic mutations can be viewed with an endomorphism and automorphism f in the vector space which maps the standard genetic code sequence of wild-type genes into mutant genes. In the endomorphism there is a matrix called the endomorphism representation matrix.Keywords: authomorphism, endomophism, mutation Matriks Representasi Endomorfisma Dari Kode Genetik StandarABSTRAKMutasi merupakan perubahan pada materi genetik yang dapat terjadi pada tingkat gen maupun kromosom. Mutasi pada tingkat gen merupakan perubahan struktur dalam kode genetik. Pada penelitian ini akan dibahas syarat perlu dan syarat cukup sebuah endomorfisma disebut automorfisma. Metode yang digunakan dalam makalah penelitian ini adalah tinjauan literatur yang dilakukan dengan mengumpulkan literatur dari penelitian-penelitian sebelumnya sesuai dengan penelitian yang dibahas. Secara matematis, mutasi genetik dapat ditinjau dengan sebuah endomorfisma dan automorfisma pada ruang vektor yang memetakan barisan kode genetik standar gen wild-type ke dalam gen mutan. Pada endomorfisma tersebut terdapat sebuah matriks yang disebut matriks representasi endomorfisma.Kata kunci: Automorfisma, endomorfisma, mutasi GenetikMutations are changes in genetic material that can occur at the level of genes or chromosomes. Mutations at the gene level are structural changes in the genetic code. In this paper we will investigate the necessary and sufficient conditions for an endomorphism called autumorphism. The method used in this research paper is a review of literature conducted by collecting literature from previous studies in accordance with the study discussed. Mathematically, genetic mutations can be viewed with an endomorphism and automorphism f in the vector space which maps the standard genetic code sequence of wild-type genes into mutant genes. In the endomorphism there is a matrix called the endomorphism representation matrix.Keywords: authomorphism, endomophism, mutation Matriks Representasi Endomorfisma Dari Kode Genetik StandarABSTRAKMutasi merupakan perubahan pada materi genetik yang dapat terjadi pada tingkat gen maupun kromosom. Mutasi pada tingkat gen merupakan perubahan struktur dalam kode genetik. Pada penelitian ini akan dibahas syarat perlu dan syarat cukup sebuah endomorfisma disebut automorfisma. Metode yang digunakan dalam makalah penelitian ini adalah tinjauan literatur yang dilakukan dengan mengumpulkan literatur dari penelitian-penelitian sebelumnya sesuai dengan penelitian yang dibahas. Secara matematis, mutasi genetik dapat ditinjau dengan sebuah endomorfisma dan automorfisma pada ruang vektor yang memetakan barisan kode genetik standar gen wild-type ke dalam gen mutan. Pada endomorfisma tersebut terdapat sebuah matriks yang disebut matriks representasi endomorfisma.Kata kunci: Automorfisma, endomorfisma, mutasi Genetik

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20

Jewel, Delilah, and Abhishek Chatterjee. "Rewriting the genetic code." Science 372, no. 6546 (June 3, 2021): 1040–41. http://dx.doi.org/10.1126/science.abi9892.

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21

Gorman, Jessica. "Amending the Genetic Code." Science News 164, no. 7 (August 16, 2003): 102. http://dx.doi.org/10.2307/3982036.

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22

Wang, Lei, Jianming Xie, and Peter G. Schultz. "EXPANDING THE GENETIC CODE." Annual Review of Biophysics and Biomolecular Structure 35, no. 1 (June 2006): 225–49. http://dx.doi.org/10.1146/annurev.biophys.35.101105.121507.

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23

Chin, J. W. "Reprogramming the Genetic Code." Science 336, no. 6080 (April 26, 2012): 428–29. http://dx.doi.org/10.1126/science.1221761.

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24

Zahn, Laura M. "Expanding the genetic code." Science 363, no. 6429 (February 21, 2019): 831.7–832. http://dx.doi.org/10.1126/science.363.6429.831-g.

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25

Wang, Lei, and Peter G. Schultz. "Expanding the genetic code." Chemical Communications, no. 1 (December 17, 2001): 1–11. http://dx.doi.org/10.1039/b108185n.

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26

Chin, Jason W. "Reprogramming the genetic code." EMBO Journal 30, no. 12 (May 20, 2011): 2312–24. http://dx.doi.org/10.1038/emboj.2011.160.

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27

de Duve, Christian. "The second genetic code." Nature 333, no. 6169 (May 1988): 117–18. http://dx.doi.org/10.1038/333117a0.

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28

Mukai, Takahito, Marc J. Lajoie, Markus Englert, and Dieter Söll. "Rewriting the Genetic Code." Annual Review of Microbiology 71, no. 1 (September 8, 2017): 557–77. http://dx.doi.org/10.1146/annurev-micro-090816-093247.

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29

ARNAUD, CELIA. "EXPANDING THE GENETIC CODE." Chemical & Engineering News 88, no. 8 (February 22, 2010): 9. http://dx.doi.org/10.1021/cen-v088n008.p009.

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30

Ibba, Michael, and Dieter Söll. "Genetic Code: Introducing Pyrrolysine." Current Biology 12, no. 13 (July 2002): R464—R466. http://dx.doi.org/10.1016/s0960-9822(02)00947-8.

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31

Xie, Jianming, and Peter G. Schultz. "An expanding genetic code." Methods 36, no. 3 (July 2005): 227–38. http://dx.doi.org/10.1016/j.ymeth.2005.04.010.

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32

Pohlmeyer, Roland. "The genetic code revisited." Journal of Theoretical Biology 253, no. 3 (August 2008): 623–24. http://dx.doi.org/10.1016/j.jtbi.2008.04.028.

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33

Calder, Nigel. "The genetic code explained." New Scientist 192, no. 2578 (November 2006): s12—s13. http://dx.doi.org/10.1016/s0262-4079(06)61014-2.

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34

Kubli, E. "Genetic code 1990. Introduction." Experientia 46, no. 11-12 (December 1990): 1089. http://dx.doi.org/10.1007/bf01936917.

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35

Jukes, T. H. "Genetic code 1990. Outlook." Experientia 46, no. 11-12 (December 1990): 1149–57. http://dx.doi.org/10.1007/bf01936925.

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36

North, Geoffrey. "Third Genetic Code Anyone?" Current Biology 21, no. 6 (March 2011): R203. http://dx.doi.org/10.1016/j.cub.2011.02.024.

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37

Schimmel, Paul, and Kirk Beebe. "Genetic code seizes pyrrolysine." Nature 431, no. 7006 (September 2004): 257–58. http://dx.doi.org/10.1038/431257a.

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38

Jukes, T. H. "Genetic Code 1990. Outlook." Experientia 47, no. 4 (April 1991): 399. http://dx.doi.org/10.1007/bf01972083.

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39

Cropp, T. Ashton, and Peter G. Schultz. "An expanding genetic code." Trends in Genetics 20, no. 12 (December 2004): 625–30. http://dx.doi.org/10.1016/j.tig.2004.09.013.

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40

Ninio, Jacques. "The revised genetic code." Origins of Life and Evolution of the Biosphere 20, no. 2 (March 1990): 167–71. http://dx.doi.org/10.1007/bf01808278.

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41

Doty, Paul. "Translating the genetic code." Journal of Polymer Science Part C: Polymer Symposia 12, no. 1 (March 7, 2007): 235–48. http://dx.doi.org/10.1002/polc.5070120118.

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42

Lemke, Edward A. "The Exploding Genetic Code." ChemBioChem 15, no. 12 (July 30, 2014): 1691–94. http://dx.doi.org/10.1002/cbic.201402362.

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43

Findley, G. L., and S. P. McGlynn. "A generalized genetic code." International Journal of Quantum Chemistry 16, S6 (June 19, 2009): 313–27. http://dx.doi.org/10.1002/qua.560160720.

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44

Wang, Lei, and Peter G. Schultz. "Expanding the Genetic Code." Angewandte Chemie International Edition 44, no. 1 (January 2005): 34–66. http://dx.doi.org/10.1002/anie.200460627.

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45

Celia Henry Arnaud. "Rewriting bacteria’s genetic code." C&EN Global Enterprise 99, no. 21 (June 7, 2021): 7. http://dx.doi.org/10.1021/cen-09921-scicon3.

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46

José, Marco, and Gabriel Zamudio. "Symmetrical Properties of Graph Representations of Genetic Codes: From Genotype to Phenotype." Symmetry 10, no. 9 (September 8, 2018): 388. http://dx.doi.org/10.3390/sym10090388.

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It has long been claimed that the mitochondrial genetic code possesses more symmetries than the Standard Genetic Code (SGC). To test this claim, the symmetrical structure of the SGC is compared with noncanonical genetic codes. We analyzed the symmetries of the graphs of codons and their respective phenotypic graph representation spanned by the RNY (R purines, Y pyrimidines, and N any of them) code, two RNA Extended codes, the SGC, as well as three different mitochondrial genetic codes from yeast, invertebrates, and vertebrates. The symmetry groups of the SGC and their corresponding phenotypic graphs of amino acids expose the evolvability of the SGC. Indeed, the analyzed mitochondrial genetic codes are more symmetrical than the SGC.

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47

Yarus, Michael. "The Genetic Code Assembles via Division and Fusion, Basic Cellular Events." Life 13, no. 10 (October 17, 2023): 2069. http://dx.doi.org/10.3390/life13102069.

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Standard Genetic Code (SGC) evolution is quantitatively modeled in up to 2000 independent coding ‘environments’. Environments host multiple codes that may fuse or divide, with division yielding identical descendants. Code division may be selected—sophisticated gene products could be required for an orderly separation that preserves the coding. Several unforeseen results emerge: more rapid evolution requires unselective code division rather than its selective form. Combining selective and unselective code division, with/without code fusion, with/without independent environmental coding tables, and with/without wobble defines 25 = 32 possible pathways for SGC evolution. These 32 possible histories are compared, specifically, for evolutionary speed and code accuracy. Pathways differ greatly, for example, by ≈300-fold in time to evolve SGC-like codes. Eight of thirty-two pathways employing code division evolve quickly. Four of these eight that combine fusion and division also unite speed and accuracy. The two most precise, swiftest paths; thus the most likely routes to the SGC are similar, differing only in fusion with independent environmental codes. Code division instead of fusion with unrelated codes implies that exterior codes can be dispensable. Instead, a single ancestral code that divides and fuses can initiate fully encoded peptide biosynthesis. Division and fusion create a ‘crescendo of competent coding’, facilitating the search for the SGC and also assisting the advent of otherwise uniformly disfavored wobble coding. Code fusion can unite multiple codon assignment mechanisms. However, via code division and fusion, an SGC can emerge from a single primary origin via familiar cellular events.

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Kumar, Balaji, and Supreet Saini. "Analysis of the optimality of the standard genetic code." Molecular BioSystems 12, no. 8 (2016): 2642–51. http://dx.doi.org/10.1039/c6mb00262e.

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Many theories have been proposed attempting to explain the origin of the genetic code. In this work, we compare performance of the standard genetic code against millions of randomly generated codes. On left, ability of genetic codes to encode additional information and their robustness to frameshift mutations.

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Rozhoňová, Hana, Carlos Martí-Gómez, David M. McCandlish, and Joshua L. Payne. "Robust genetic codes enhance protein evolvability." PLOS Biology 22, no. 5 (May 16, 2024): e3002594. http://dx.doi.org/10.1371/journal.pbio.3002594.

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The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability—the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether such robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively parallel sequence-to-function assays to construct and analyze 6 empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. However, the standard genetic code is rarely exceptional in this regard, because many alternative codes render smoother landscapes than the standard code. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we show that such alternative codes radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering nonstandard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the bio-containment of synthetic organisms, respectively.

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50

Wang, Kaihang, Wolfgang H. Schmied, and Jason W. Chin. "Reprogramming the Genetic Code: From Triplet to Quadruplet Codes." Angewandte Chemie International Edition 51, no. 10 (January 19, 2012): 2288–97. http://dx.doi.org/10.1002/anie.201105016.

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Journal articles on the topic 'Genetic code'

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