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Biomolecular Information Gained through In Vitro Evolution on a Fitness Landscape in Sequence Space

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Recent Advances in the Theory and Application of Fitness Landscapes

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 6))

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Abstract

Biological evolution at the molecular level is conceptually regarded as the genetic information gaining process. Analyzing the in vitro evolution process, which is a simplified Darwinian evolution under a well-controlled environment, we can clarify the concept of the information gaining process. This evolution process can be modeled as a hill-climbing or adaptive walk on a fitness landscape in sequence space. Through the hill-climbing process, the evolving biopolymer (as the adaptive walker) stores the following two aspects of information: one stems from the sequences converged in sequence space and the other stems from the fitness increment on the fitness landscape. In Eigen’s words, the former and latter are described as the “extent” and “content” of biological information, respectively [25]. In our approach, these two aspects can be interpreted based on the analogy between evolutionary dynamics and thermodynamics. Several studies introduced the concept of “free fitness” (which is analogous to free energy) as the Lyapunov function for evolution: Free fitness ≡ Fitness + Temperature − like parameter ×Entropy. Furthermore, we focus on the novel quantity of Fitness divided by \(\mbox{\it Temperature-like para\-meter}\), and regard this quantity as the content of information, while we regard Entropy as the extent of information. The quantity of Free fitness divided by Temperature-like parameter is a Lyapunov function of the evolution process, and then it should be called “biomolecular information”, which includes both aspects of information.

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References

  1. Adami, C., Ofria, C., Collier, T.C.: Evolution of biological complexity. Proc. Natl. Acad. Sci. USA 97, 4463–4468 (2000)

    Article  Google Scholar 

  2. Aita, T., Morinaga, S., Husimi, Y.: Thermodynamical interpretation of evolutionary dynamics on a fitness landscape in an evolution reactor, I. Bull. Math. Biol. 66, 1371–1403 (2004)

    Article  MathSciNet  Google Scholar 

  3. Aita, T., Morinaga, S., Husimi, Y.: Thermodynamical interpretation of evolutionary dynamics on a fitness landscape in an evolution reactor, II. Bull. Math. Biol. 66, 1371–1403 (2005)

    Article  MathSciNet  Google Scholar 

  4. Aita, T., Husimi, Y.: An interpretation of evolutionary dynamics in in vitro molecular evolution from thermodynamical and informational viewpoint. Seibutsu Butsuri (Biophysics) 46, 137–143 (2006) (in Japanese with English abstracts)

    Google Scholar 

  5. Aita, T., Hayashi, Y., Toyota, H., Husimi, Y., Urabe, I., Yomo, T.: Extracting characteristic properties of fitness landscape from in vitro molecular evolution: A case study on infectivity of fd phage to E.coli. J. Theor. Biol. 246, 538–550 (2007)

    Article  MathSciNet  Google Scholar 

  6. Aita, T.: A trade-off relationship between energetic cost and entropic cost for in vitro evolution. Biosystems 101, 194–199 (2010)

    Article  Google Scholar 

  7. Aita, T., Husimi, Y.: Biomolecular information gained through in vitro evolution. Biophys. Rev. 2, 1–11 (2010)

    Article  Google Scholar 

  8. Aita, T., Husimi, Y.: Biophysical connection between evolutionary dynamics and thermodynamics in vitro evolution. in vitro 294, 122–129 (2012)

    MathSciNet  Google Scholar 

  9. Ao, P.: Emerging of stochastic dynamical equalities and steady state thermodynamics from Darwinian dynamics. Communications in Theoretical Physics 49, 1073–1090 (2008)

    Article  MathSciNet  Google Scholar 

  10. Atkins, P.W.: Physical Chemistry. Oxford University Press, Oxford (1978)

    Google Scholar 

  11. Barton, N.H., Coe, J.: On the application of statistical physics to evolutionary biology. J. Theor. Biol. 259, 317–324 (2009)

    Article  MathSciNet  Google Scholar 

  12. Barton, N.H., de Vladar, H.P.: Statistical mechanics and the evolution of polygenic quantitative traits. Genetics 181, 997–1011 (2009)

    Article  Google Scholar 

  13. Berg, J., Willmann, S., Lässig, M.: Adaptive evolution of transcription factor binding sites. BMC Evol. Biol. 4, 42-1-12 (2004)

    Google Scholar 

  14. Biebricher, C.K., Eigen, M., Luce, R.: Product analysis of RNA generated de-novo by phage Q-beta replicase. J. Mol. Biol. 148, 369–390 (1981)

    Article  Google Scholar 

  15. Blackburne, B.P., Hirst, J.D.: Population dynamics simulations of functional model proteins. J. Chemical Physics 123, 154907 (2005)

    Article  Google Scholar 

  16. Brillouin, L.: Negentropy principle of information. J. of Applied Physics 24, 1152–1163 (1953)

    Article  MATH  Google Scholar 

  17. Brillouin, L.: Science and Information Theory. Academic Press, New York (1956)

    MATH  Google Scholar 

  18. Demetrius, L.: Directionality principles in thermodynamics and evolution. Proc. Natl. Acad. Sci. USA 94, 3491–3498 (1997)

    Article  Google Scholar 

  19. Demetrius, L.: Thermodynamics and evolution. J. Theor. Biol. 206, 1–16 (2000)

    Article  Google Scholar 

  20. de Vladar, H.P., Barton, N.H.: The contribution of statistical physics to evolutionary biology. Trends Ecol. Evol. 26, 424–432 (2011)

    Article  Google Scholar 

  21. Durston, K.K., Chiu, D.K., Abel, D.L., Trevors, J.T.: Measuring the functional sequence complexity of proteins. Theor. Biol. Med. Model. 4, 47-1-14 (2007)

    Google Scholar 

  22. Eigen, M.: Self-organization of matter and the evolution of biological macromolecules. Naturwissenschaften 58, 465–523 (1971)

    Article  Google Scholar 

  23. Eigen, M., Schuster, P.: The Hypercycle. Springer, Berlin (1979)

    Book  Google Scholar 

  24. Eigen, M., Gardiner, W.: Evolutionary Molecular Engineering Based on RNA Replication. Pure and Appl. Chem. 56, 967–978 (1984)

    Article  Google Scholar 

  25. Eigen, M.: Natural selection: a phase transition? Biophysical Chemistry 85, 101–123 (2000)

    Article  Google Scholar 

  26. Einstein, A.: Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen (On the movement of small particles suspended in stationary liquids required by the molecular-kinetic theory of heat.). Ann. D. Phys. 17, 549–560 (1905)

    Article  MATH  Google Scholar 

  27. Ellington, A.D., Szostak, J.W.: in Vitro selection of RNA molecules that bind specific ligands. selection of RNA molecules that bind specific ligands 346, 818–822 (1990)

    Google Scholar 

  28. Fisher, R.A.: The Genetical Theory of Natural Selection. Clarendon, Oxford (1930)

    MATH  Google Scholar 

  29. Husimi, Y., Nishigaki, K., Kinoshita, Y., Tanaka, T.: Cellstat-a continuous culture system of a bacteriophage for the study of the mutation rate and the selection process of the DNA level. Rev. Sci. Instrum. 53, 517–522 (1982)

    Article  Google Scholar 

  30. Husimi, Y.: Selective value landscape on the base sequence space and concept of free selective value: a model of quasi-species. Viva Origino 16, 136–141 (1988) (in Japanese with English abstracts)

    Google Scholar 

  31. Iguchi, K.: Reciprocal relations in evolutionary processes. Prog. Theor. Phys. Suppl. 173, 235–242 (2008)

    Article  Google Scholar 

  32. Iwasa, Y.: Free fitness that always increases in evolution. J. Theor. Biol. 135, 265–281 (1988)

    Article  Google Scholar 

  33. Kauffman, S.A.: The Origin of Order. Oxford University Press, Oxford (1993)

    Google Scholar 

  34. Kim, J.T., Martinetz, T., Polani, D.: Bioinformatic principles underlying the information content of transcription factor binding sites. J. Theor. Biol. 220, 529–544 (2003)

    Article  MathSciNet  Google Scholar 

  35. Kimura, M.: Natural selection as the process of accumulating genetic information in adaptive evolution. Genet. Res. 2, 127–140 (1961)

    Article  Google Scholar 

  36. Kimura, M.: Genetic variability maintained in a finite population due to mutational production of neutral or nearly neutral isoalleles. Genet. Res. Camb. 11, 247–269 (1968)

    Article  Google Scholar 

  37. Maynard-Smith, J.: Natural selection and the concept of a protein space. Nature 225, 563–564 (1970)

    Article  Google Scholar 

  38. Maynard-Smith, J.: The concept of information in biology. Philos. Sci. 67, 177–194 (2000)

    Article  MathSciNet  Google Scholar 

  39. Mills, D.R., Peterson, R.L., Spiegelman, S.: An extracellular Darwinian experiment with a self-duplicating nucleic acid molecule. Proc. Natl. Acad. Sci. USA 58, 217–224 (1967)

    Article  Google Scholar 

  40. Nemoto, N., Miyamoto-Sato, E., Husimi, Y., Yanagawa, H.: In vitro virus: bonding of mRNA bearing puromycin at the 3’-terminal end to the C-terminal end of its encoded protein on the ribosome in vitro. FEBS Lett. 414, 405–408 (1997)

    Article  Google Scholar 

  41. Nicolis, G., Prigogine, I.: Self-Organization in Nonequilibrium Systems: From Dissipative Structures to Order through Fluctuations. Wiley, Chichester (1977)

    MATH  Google Scholar 

  42. Pande, V.S., Grosberg, A.Y., Tanaka, T.: Statistical mechanics of simple models of protein folding and design. Biophys. J. 73, 3192–3210 (1997)

    Article  Google Scholar 

  43. Sato, K., Ito, Y., Yomo, T., Kaneko, K.: On the relation between fluctuation and response in biological systems. Proc. Natl. Acad. Sci. USA 100, 14086–14090 (2003)

    Article  Google Scholar 

  44. Schneider, T.D.: Evolution of biological information. Nucleic Acid Research 28, 2794–2799 (2000)

    Article  Google Scholar 

  45. Schuster, P., Swetina, J.: Stationary mutant distributions and evolutionary optimization. Bull. Math. Biol. 50, 635–660 (1988)

    MathSciNet  MATH  Google Scholar 

  46. Scott, J.K., Smith, G.P.: Searching for peptide ligands with an epitope library. Science 249, 386–390 (1990)

    Article  Google Scholar 

  47. Sella, G., Hirsh, A.E.: The application of statistical physics to evolutionary biology. Proc. Natl. Acad. Sci. USA 102, 9541–9546 (2005)

    Article  Google Scholar 

  48. Shannon, C.E., Weaver, W.: The Mathematical Theory of Communication. Univ. Illinois Press, Champaign (1949)

    MATH  Google Scholar 

  49. Smith, E.: Thermodynamics of natural selection I: Energy flow and the limits on organization. J. Theor. Biol. 252, 185–197 (2008)

    Article  Google Scholar 

  50. Smith, E.: Thermodynamics of natural selection II: Chemical Carnot cycles. J. Theor. Biol. 252, 198–212 (2008)

    Article  Google Scholar 

  51. Smith, E.: Thermodynamics of natural selection III: Landauer’s principle in computation and chemistry. J. Theor. Biol. 252, 213–220 (2008)

    Article  Google Scholar 

  52. Stemmer, W.P.C.: The evolution of molecular computation. Science 270, 1510 (1995)

    Article  Google Scholar 

  53. Szostak, J.W.: Functional information: Molecular messages. Nature 423, 689 (2003)

    Article  Google Scholar 

  54. Tuerk, C., Gold, L.: Systematic evolution of nucleic acids by exponential enrichment: RNA nucleic acids to bacteriophage T4 DNA polymerase. Science 249, 505–510 (1990)

    Article  Google Scholar 

  55. Voigt, C.A., Kauffman, S., Wang, Z.G.: Rational evolutionary design: the theory of in vitro protein evolution. Advances in Protein Chemistry 55, 79–160 (2000)

    Article  Google Scholar 

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

    Article  Google Scholar 

  57. Wilke, C.O., Wang, J.L., Ofria, C., Lenski, R.E., Adami, C.: Evolution of digital organisms at high mutation rates leads to survival of the flattest. Nature 412, 331–333 (2001)

    Article  Google Scholar 

  58. Wolynes, P.G., Luthey-Schulten, Z.: The energy landscape theory of protein folding. In: Flyvbjerg, H., Hertz, J., Jensen, M.H., Mouritsen, O.G., Sneppen, K. (eds.) Physics of Biological Systems: From Molecules to Species, pp. 61–79. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  59. Wright, S.: Evolution in Mendelian populations. Genetics 16, 97–159 (1931)

    Google Scholar 

  60. Yang, X., Liu, X., Lou, C., Chen, J., Ouyang, Q.: A case study of the dynamics of in vitro DNA evolution under constant selection pressure. J. Mol. Evol. 68, 14–27 (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. Graduate School of Science and Engineering, Saitama University, Saitama, 338-8570, Japan

    Takuyo Aita

  2. Innovative Research Organization, Saitama University, Saitama, 338-8570, Japan

    Yuzuru Husimi

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  1. Takuyo Aita
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  2. Yuzuru Husimi
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Correspondence to Takuyo Aita .

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Editors and Affiliations

  1. Faculty of Electrical Engineering & Information Technology, HTWK Leipzig University of Applied Sciences, Leipzig, Germany

    Hendrik Richter

  2. Department of Computer Science, University of Pretoria, Pretoria, South Africa

    Andries Engelbrecht

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Aita, T., Husimi, Y. (2014). Biomolecular Information Gained through In Vitro Evolution on a Fitness Landscape in Sequence Space. In: Richter, H., Engelbrecht, A. (eds) Recent Advances in the Theory and Application of Fitness Landscapes. Emergence, Complexity and Computation, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41888-4_3

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  • DOI: https://doi.org/10.1007/978-3-642-41888-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41887-7

  • Online ISBN: 978-3-642-41888-4

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