Bio-Inspired Algorithms in Bioinformatics I

Bio-Inspired Algorithms in Bioinformatics I

José Antonio Seoane Fernández (University of A Coruña, Spain) and Mónica Miguélez Rico (University of A Coruña, Spain)
Copyright: © 2009 |Pages: 5
DOI: 10.4018/978-1-59904-849-9.ch037
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Abstract

Large worldwide projects like the Human Genome Project, which in 2003 successfully concluded the sequencing of the human genome, and the recently terminated Hapmap Project, have opened new perspectives in the study of complex multigene illnesses: they have provided us with new information to tackle the complex mechanisms and relationships between genes and environmental factors that generate complex illnesses (Lopez, 2004; Dominguez, 2006). Thanks to these new genomic and proteomic data, it becomes increasingly possible to develop new medicines and therapies, establish early diagnoses, and even discover new solutions for old problems. These tasks however inevitably require the analysis, filtration, and comparison of a large amount of data generated in a laboratory with an enormous amount of data stored in public databases, such as the NCBI and the EBI. Computer sciences equip biomedicine with an environment that simplifies our understanding of the biological processes that take place in each and every organizational level of live matter (molecular level, genetic level, cell, tissue, organ, individual, and population) and the intrinsic relationships between them. Bioinformatics can be described as the application of computational methods to biological discoveries (Baldi, 1998). It is a multidisciplinary area that includes computer sciences, biology, chemistry, mathematics, and statistics. The three main tasks of bioinformatics are the following: develop algorithms and mathematical models to test the relationships between the members of large biological datasets, analyze and interpret heterogeneous data types, and implement tools that allow the storage, retrieve, and management of large amounts of biological data.
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Background

The following section describes some of the problems that are most commonly found in bioinformatics.

Interpretation of Gene Expression

The expression of genes is the process by which the codified information of a gene is transformed into the necessary proteins for the development and functioning of the cell. In the course of this process, small sequences of ARN, also called ARN messengers, are formed by transcription and subsequently translated into proteins.

The amount of expressed mARN can be measured with various methods, such as gel electrophoresis, but large numbers of simultaneous expression analyses are usually carried out with microarrays (Quackenbush, 2001), which make it possible to obtain the simultaneous expression of tens of thousands of genes; such an amount of data can only be analyzed with the help of an informatic process.

Among the most common tasks in this type of analysis is the task to find the differences between, for instance, a patient and a test that determines whether a gene is expressed or not. These tasks can be divided into classical problems of classification and clustering. Clustering is used not only in experiments of microarrays (to identify groups of genes with similar expressions), but also suggests functional relationships between the members of the cluster.

Key Terms in this Chapter

Translation: The process of converting RNA to protein by the assembly of a polypeptide chain from an mRNA molecule at the ribosome.

Messenger RNA: The complementary copy of DNA formed from a single-stranded DNA template during the transcription that migrates from the nucleus to the cytoplasm where it is processed into a sequence carrying the information to code for a polypeptide domain.

Swarm Intelligence: An artificial intelligence technique based on the study of collective behaviour in decentralised, self-organised systems.

Electroforesis: The use of an external electric field to separate large biomolecules on the basis of their charge by running them through acrylamide or agarose gel.

Nucleotid: A nucleic acid unit composed of a five carbon sugar joined to a phosphate group and a nitrogen base.

Microarray: A 2D array, typically on a glass, filter, or silicon wafer, upon which genes or gene fragments are deposited or synthesized in a predetermined spatial order allowing them to be made available as probes in a high-throughput, parallel manner.

Amino Acid: One of the 20 chemical building blocks that are joined by amide (peptide) linkages to form a polypeptide chain of a protein.

Artificial Immune System: Biologically inspired computer algorithms that can be applied to various domains, including fault detection, function optimization, and intrusion detection. Also called computer immune system.

Transcription: The assembly of complementary single-stranded RNA on a DNA template.

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