Computing vs. Genetics

Computing vs. Genetics

José M. Barreiro, Juan Pazos
Copyright: © 2009 |Pages: 17
DOI: 10.4018/978-1-59904-996-0.ch010
OnDemand:
(Individual Chapters)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

This chapter first presents the interrelations between computing and genetics, which both are based on information and, particularly, self-reproducing artificial systems. It goes on to examine genetic code from a computational viewpoint. This raises a number of important questions about genetic code. These questions are stated in the form of an as yet unpublished working hypothesis. This hypothesis suggests that many genetic alterations are caused by the last base of certain codons. If this conclusive hypothesis were to be confirmed through experiementation if would be a significant advance for treating many genetic diseases.
Chapter Preview
Top

Introduction

The mutual, two-way relationships between genetics and computing (see Table 1) go back a long way and are more wide-ranging, closer and deeper than what they might appear to be at first sight. The best-known contribution of genetics to computing is perhaps evolutionary computation. Evolutionary computation’s most noteworthy representatives are genetic algorithms and genetic programs as search strategies. The most outstanding inputs from computing to genetics are reproductive automata and genetic code deciphering. Therefore, section 2 will deal with von Neumann reproductive automata. Section 3 will discuss genetic code. Section 4 will introduce the well-know χ2 test because of this importance in establishing the working hypothesis. Later, section, will address genome deciphering. And finally section 6 will establish the conjecture or working hypothesis, which is the central conclusion of the paper, and define the future research lines.

Table 1.
Computing vs. genetics
From genetics to computingFrom computing to genetics
Natural Computation (NC) ≡ Evolutionary Computation (EC) [Genetics Algorithms (GA) + Evolution Strategies (ES) + Evolutionary Programming (EP)] + Neural Networks (NN) + Genetic Programming
1966 Fogel, Owens and Walsh (1966) establish how finite state automata can be evolved by means of unit transformations and two genetic operators: selection and mutation.
1973 Rechemberg (1973) defined the evolutionary strategies of finite state machine populations.
1974 Holland (1975) and disciples defined genetic algorithms.
1992 Koza (1992) proposed the use of the evolutionary computation technique to find the best procedure for solving problems, which was the root of genetic programming.
1994 Michalewitz (1992) established evolutionary programs as a way of naturally representing genetic algorithms and context-sensitive genetic operators.		1940 Claude Elwood Shannon (1940) defended his PhD thesis titled “An Algebra for Theoretical Genetics”.
1944 Erwin Schrödinger (1983) conjectured that genetic code existed.
1948 John Von Neumann (1966) established the principles underlying a self-reproducing machine.
1953 Crick (Watson, 1953) luckily but mistakenly named the small dictionary that shows the relationship between the four DNA bases and the 20 amino acids that are the letters of protein language genetic code.
1955 John G. Kemeny (1955) defined the characteristics of machine reproduction and how it could take place.
1975 Roger and Lionel S. Penrose (Penrose, 1974) tackled the mechanical problems of self-reproduction based on Homer Jacobson’s and Kemeny’s work.
1982 Tipler (1982) justified the use of self-reproducing automata.

Complete Chapter List

Search this Book:
Reset