Computational Biology

Computational Biology

Michelle LaBrunda (Health First Medical Group, USA), Mary Jane Miller (University of Guam, Guam) and Andrew La Brunda (University of Guam, Guam)
Copyright: © 2015 |Pages: 10
DOI: 10.4018/978-1-4666-5888-2.ch043
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Computational biology is a relatively new branch of modern biology. It employs aspects of biology, computer science, and mathematics to solve problems that are unworkable with traditional biological techniques. It is only in the last 30 years that the tools needed by computational biologist have been available. With the advent and advancement of complex data processing systems, computational biology has emerged.

The term “computational biology” is often confused with “bioinformatics” because of the similarity of tools which are employed to solve problems in their respective areas. Both use advanced computational processing and mathematical modeling to explain phenomena and predict outcomes. They both involve the use of techniques from computer science, statistics, and applied mathematics to model living systems and solve biological problems. The primary difference between computational biology and bioinformatics is that the former tends to focus on the testing of hypotheses and new discoveries within the realm of biology and the latter tends to focus on the development of mathematical techniques and algorithms that can be applied to the simulation of biological systems (Ouzounis, 2012).

The field of computational biology offers a nontraditional approach to investigating complex biological problems. Typical topics within the field include gene finding, genome assembly, protein structure prediction and alignment, and the modeling of biological systems and processes over long periods of time. Examples of processes over time suitable for such models would include evolutionary trends, gene expression through multiple generations, and perhaps long-term biological consequences of climate change (Ouzounis, 2012).

Computational biology includes many 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 help to 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 ultimate goal of computational biology would be to create a high level software based organism that is comprised of a collection of biological subsystems which would include circulatory, digestive, endocrine, integumentary, lymphatic, muscular, nervous, reproductive, respiratory, skeletal, reproductive, and urinary systems. Within each of these systems, software based cells and their biological, mechanical, and chemical behaviors would be programmed to interact with the environment and subsystems with which the cell functions.

Key Terms in this Chapter

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

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.

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

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

Computational Genetics: The application of computational biology to genetics.

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

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

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