Computational Biology

Computational Biology

Andrew LaBrunda (GTA, Guam) and Michelle LaBrunda (Cabrini Medical Center, USA)
DOI: 10.4018/978-1-60566-026-4.ch104
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It is impossible to pinpoint the exact moment at which computational biology became a discipline of its own, but one could say that it was in 1997 when the society of computational biology was formed. Regardless of its exact birthday, the research community has rapidly adopted computational biology and its applications are being vigorously explored. The study and application of medicine is a dynamic challenge. Changes in medicine usually take place as a result of new knowledge acquired through observation and experimentation. When a tamping rod 1-inch thick went through Phineas Gage’s head in 1848, his survival gave the medical field an unusual opportunity to observe behavior of a person missing their prefrontal cortex. This observation lead to the short-lived psychosurgical procedure known as a lobotomy, which attempted to change a person’s behavior by separating two portions of a person’s brain (Pols, 2001). Countless observations, experiments and mistakes represent how almost all medical knowledge has been acquired. The relatively new field of computational biology offers a nontraditional approach to contribute to the medical body of knowledge. Computational biology is a new field combining biology, computer science, and mathematics to solve problems that are unworkable with traditional biological techniques. It includes traditional areas such as systems biology, molecular biology, biochemistry, biophysics, statistics, and computer science, as well as recently developed disciplines including bioinformatics and computational genomics. Algorithms, which are able to closely model biological behavior, validate the medical understanding of the observed processes and can be used to model scenarios that might not be able to be physically reproduced. The goal of computational biology is to use mathematics and computer science to model biological systems on the molecular level. Instead of taking on large complex systems, computational biology is starting small, literally. Modeling problems in molecular biology and biochemistry is a far less daunting task. At a microscopic level, patient’s characteristics drop out of the equation and all information behavior affecting is known. This creates a deterministic model which, given the same input, will always produce the same output. Some of the major subdisciplines of computational biology are computational genomics, systems biology, protein structure prediction, and evolutionary biology, all of which model microscopic structures.
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Computational Genomics

An organism’s heredity is stored as deoxyribonucleic acid (DNA) or ribonucleic acid (RNA). Each of the storage methods contains a linear chain of finite elements called bases. In the case of DNA the chain is composed of four bases, adenine, cytosine, guanine, and thymine, and RNA is composed of four bases, adenine, cytosine, guanine, and uracil. This information can be representing in a computer through a series of linked-lists or arrays. The order of bases in DNA/RNA is 99.9% the same between members of the same species and genetic sequencing is the way of determining the order of the bases. Certain regions of DNA, called genes, are used by the body to create proteins. These proteins are used to construct and maintain the organism. Genes can be thought of as blueprint instructions for how to make each unique person. There are long stretches of DNA between the genes the function of which is not well understood. The sum of all the genetic information about an organism is called a genome. When a computer is applied to deciphering an organism’s genome, this is known is computational genomics. Computational genomics are used to better understand and compare sequences, identify related organisms, and measure biodiversity.

There are two major challenges in genomic studies. The first is genetic sequencing, (determining the order of the bases that make a strand of genetic material) and the second is localizing the genes within the genome. Sequence comparison is probably the most useful computational tool for molecular biologists. Repositories containing hundreds of genomes have been established and are available for public access. A biologist now has the ability to compare a unique sequence of DNA with the already known genetic sequences from this massive repository. Prior to the application of computers to the problems genomics scientist had to manually attempt to align sequences using ill suited tools such as word processors.

Key Terms in this Chapter

Translation: The process by which a protein is produced from a strand of mRNA.

Fuzzy Logic: Fuzzy logic breaks input into variables and assigns each input a probability of being correct on a scale of 0 to 1 with 0 being false This is different from classical discrete computational systems which only allow inputs of false (0) and true (1).

Computational Evolution (Artificial Evolution): The application of computational and mathematical techniques to retrospectively and prospectively model evolutionary processes.

Phylogenic Tree: A diagram illustrating how various species are interconnected.

DNA (Deoxyribonucleic Acid): Comprises the genetic material of humans and most other organisms. It can be thought of as the blueprint to create a unique organism.

Computational Biology: A new field combining biology, computer science, mathematics and physics to model and understand complex biological processes at the molecular level.

Grid System: Utilization of networked computers to solve problems involving data sets too large to be handled by one computer.

Computational Genetics: The application of computational biology to genetics.

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