Information Theories in Molecular Biology and Genomics

Abstract

Information theory, statistical theory of signal transmission, communication theory and Shannon theory are synonymous names for the mathematical theory first published by Claude Shannon in the 1940s. The concept of entropy in this theory can be viewed as a measure of ‘randomness’ of sequences of symbols. Shannon entropy and its variants have been widely used in molecular biology and bioinformatics as statistical tools of choice for sequence and structure analyses.

Keywords: Shannon communication theory; information; probability; coding; sequence analysis; cryptanalysis; uniform distribution

Figure 1.

Meanings of ‘information’ in biology. A view from the perspective of the machine metaphor. A clearly reductionist view of living things based on the machine metaphor leads to two different general concepts of ‘information’. Each of these concepts has a different rationale and implicit meaning derived from it. Only a very few aspects of these meanings can be modeled by Shannon theory (these are indicated by the symbol *SH* on the diagram).

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References

Bar‐Hillel Y and Carnap R (1953) Semantic information. British Journal of the Philosophy of Science 4(August): 147–157.

Campbell J (1982) Grammatical Man: Information, Entropy, Language, and Life. New York: Simon & Schuster.

Garnier J, Gibrat J‐F and Robson B (1996) GOR method for predicting protein secondary structure from amino acid sequence. In: Doolittle RF, Abelson JN and Simon MI (eds.) Computer Methods for Macromolecular Sequence Analysis, pp. 540–553. San Diego, CA: Academic Press.

Gatlin LL (1972) Information Theory and the Living System. New York: Columbia University Press.

Hartley RVL (1928) Transmission of information. Bell Systems Technical Journal 7: 535–563.

Konopka AK (1984) Is the information content of DNA evolutionarily significant? Journal of Theoretical Biology 107: 697–704.

Konopka AK (1985) Theory of degenerate coding and informational parameters of protein coding genes. Biochemie 67: 455–468.

Konopka AK (1994) Sequences and codes: fundamentals of biomolecular cryptology. In: Smith D (ed.) Biocomputing: Informatics and Genome Projects, pp. 119–174. San Diego, CA: Academic Press.

Konopka AK (1997) Theoretical molecular biology. In: Meyers RA (ed.) Encyclopedia of Molecular Biology and Molecular Medicine, vol. 6, pp. 37–53. Weinheim: VCH.

Konopka AK (2002) Grand metaphors of biology in the genome era. Computers and Chemistry 26: 397–401.

Kuppers B‐O (1990) Information and the Origin of Life. Cambridge, MA: MIT Press.

Lipman DJ and Wilbur WJ (1983) Contextual constraints on synonymous codon choice. Journal of Molecular Biology 163: 363–376.

MacKay DM (1969) Information, Mechanism and Meaning. Cambridge, MA: MIT Press.

Pattee HH (1969) How does a molecule become a message? In: Lang A (ed.) Proceedings of the 28th Symposium of the Society of Developmental Biology, pp. 1–16. New York: Academic Press.

Shannon CE (1948) A mathematical theory of communication. Bell Systems Technical Journal 27: 379–423, 623–656.

Shannon CE (1949) Communication theory of secrecy systems. Bell Systems Technical Journal 28: 657–715.

Shannon CE (1951) Prediction and entropy of printed English. Bell Systems Technical Journal 30: 50–64.

Tarski A (1944) The semantic conception of truth and the foundation of semantics. Journal of Philosophy and Phenomenological Research 4: 341–375.

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How to Cite close
Konopka, Andrzej K(Jan 2006) Information Theories in Molecular Biology and Genomics. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1038/npg.els.0005927]