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Gene expression and scalable genetic search
Kargupta H. In Advances in evolutionary computing. New York, NY,  Springer-Verlag New York, Inc.,  2003. Type:Book Chapter
Date Reviewed: Dec 30 2003

This is an excellent review of the school of thought that considers gene expression from the perspective of computation and scalability. Kargupta is an expert in this area.

The chapter includes a concise, but effective introduction to the ideas associated with the study of gene expression. This means that a very minimal knowledge of genetics, if any, is really needed to understand the chapter; the primary audiences of the chapter are specialists in gene expression, and computer scientists and engineers who have an interest in these topics. However, to be able to appreciate the algorithm in its full flexibility, some knowledge of transformation calculus is needed.

Readers who need a short and well-written introduction to DNA can read the three-page article published in Nature in 1974 [1]. For a more comprehensive and popularized version of the story, read the book authored Watson [2].

This chapter is 28 pages long, and is composed of six sections. The introduction is about two pages long, and introduces the problem. The problem is essentially scalable genetic search in gene expression. It is known that a sequence of transformations occurs in the transcription of DNA to mRNA, and eventually to protein. One way of looking at this process is that gene expression evaluates the genetic fitness of an organism through these transformations.

Section 2, “Gene Expression and Evaluation of Genetic Fitness,” is about three pages long, and is a review of the gene expression process from the point of view of genetic fitness evaluation. The fitness of an organism is traditionally viewed as being evaluated in its phenotype. DNA plays a significant role in the phenotypic organization of the organism. Thus, phenotype construction can be considered as a form of fitness evaluation. The notions of transcription, translation, and protein folding are explained well enough here to provide a fairly good idea of these processes to engineers.

Section 3 reviews previous work. The survey covers techniques including fast Fourier transform (FFT) and algebraic models, as well as the author’s alternate approach. Traditional evolutionary computation, based on selection, crossover, and mutation, has some scalability problems. This computational roadblock provides the motivation for Kargupta’s approach. The question becomes: What is the mechanism in nature that makes the genetic search so efficient and scalable?

Section 4, “Construction of Decomposed Representation Using Fourier Basis,” is the primary substance of the chapter. It is composed of several subsections, covering a total of about seven pages. This section explains the polynomial time construction of decomposed representation using Fourier basis.

Section 5, “Exploring Genetic Code-like Transformations,” continues exploring genetic code-like transformations. More specifically, the translation process is considered (transforming the mRNA representation to a protein sequence). A summary of recent results indicates that such a transformation may play a critical role in the polynomial time computation of a decomposed representation in the Fourier basis.

The chapter ends with a one-page conclusion, and 59 references. I congratulate the author for such a readable piece of work, in such an interdisciplinary area.

Reviewer:  M. M. Tanik Review #: CR128821 (0405-0663)
1) Hoey, M. Patterns of Lexis in text. Oxford University Press, Oxford, UK, 1991.
2) Morris, J.; Hirst, G. Lexical cohesion, the thesaurus, and the structure of text. Computational Linguistics 17, 1(1991), 21–48.
3) Hearst, M. Multi-paragraph segmentation of expository text. In Proceedings of the 32nd Meeting of the Association for Computational Linguistics (Los Cruces, NM, June, 1994), Association for Computational Linguistics, Morristown, NJ, 1994, 9–16.
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Heuristic Methods (I.2.8 ... )
 
 
Biology And Genetics (J.3 ... )
 
 
Evolutionary Prototyping (D.2.2 ... )
 
 
Permutations And Combinations (G.2.1 ... )
 
 
Combinatorics (G.2.1 )
 
 
Design Tools and Techniques (D.2.2 )
 
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