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Coles Mathematics and the Mysterious Genetic Code at Bayshore Shopping Centre in Ottawa, ON
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Mathematics and the Mysterious Genetic Code brings the tools of modern probability theory and mathematical statistics to one of the central problems of molecular biology: the analysis and recognition of the genetic code. Building on the Bayesian approach as a foundation for inductive inference, the author develops formal recognition procedures and applies them systematically to the study of biological sequences, the structure of DNA and RNA, and the reading frames generated by the code.
The book opens by situating modern mathematics between its inductive and deductive traditions, drawing on the theory of algorithmic complexity and the work of Gödel, before turning to the recognition of complex biological systems in their deterministic, probabilistic, logical, structural, and intelligent forms. It then examines the structure of DNA and the genetic code in detail and shows how statistical analysis, principles of symmetry and harmony, and Markov-chain models illuminate the organization of genetic information. A central worked example demonstrates a Bayesian procedure for predicting the secondary structure of proteins on the basis of Boolean functions, together with an assessment of the reliability of recognition and classification procedures and the logical methods used to identify patterns in genetic data.
Rigorous yet application-oriented, the book connects probability theory, statistical machine learning, bioinformatics, and genetics into a single line of reasoning about the mathematical structure underlying life. It is intended for researchers and graduate students in the biological and mathematical sciences, as well as for a broader readership interested in the deep connection between mathematics and the genetic code. This second edition consolidates and extends the author's approach.
Mathematics and the Mysterious Genetic Code brings the tools of modern probability theory and mathematical statistics to one of the central problems of molecular biology: the analysis and recognition of the genetic code. Building on the Bayesian approach as a foundation for inductive inference, the author develops formal recognition procedures and applies them systematically to the study of biological sequences, the structure of DNA and RNA, and the reading frames generated by the code.
The book opens by situating modern mathematics between its inductive and deductive traditions, drawing on the theory of algorithmic complexity and the work of Gödel, before turning to the recognition of complex biological systems in their deterministic, probabilistic, logical, structural, and intelligent forms. It then examines the structure of DNA and the genetic code in detail and shows how statistical analysis, principles of symmetry and harmony, and Markov-chain models illuminate the organization of genetic information. A central worked example demonstrates a Bayesian procedure for predicting the secondary structure of proteins on the basis of Boolean functions, together with an assessment of the reliability of recognition and classification procedures and the logical methods used to identify patterns in genetic data.
Rigorous yet application-oriented, the book connects probability theory, statistical machine learning, bioinformatics, and genetics into a single line of reasoning about the mathematical structure underlying life. It is intended for researchers and graduate students in the biological and mathematical sciences, as well as for a broader readership interested in the deep connection between mathematics and the genetic code. This second edition consolidates and extends the author's approach.
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