Bioinformatics is interdisciplinary field for studying biological data.
Bio-Informatics
Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. Broadly speaking, bioinformatics (and the related field of computational biology) is the application of mathematical and information-science techniques to biology. This undertaking is inherently difficult because a living organism represents such a complex interaction of chemical processes. Understanding any one process in isolation gives little understanding of the role it plays in physiology. Similarly, as more has been learned about the genome of humans and other organisms, it has become increasingly clear that the “programs” represented by gene sequences are “interpreted” through complex interactions of genes and the environment. Given this complexity, the great strides that have been made in genetics and the detailed study of metabolic and other biological processes would have been impossible without advances in computing and computer science.
Application to Genetics
Since information in the form of DNA sequences is the heart of genetics, information science plays a key role in understanding its significance and expression. The sequences of genes that determine the makeup and behavior of organisms can be represented and manipulated as strings of symbols using, for example, indexing and search algorithms. It is thus natural that the advent of powerful computer workstations and automated lab equipment would lead to the automation of gene sequencing (determining the order of nucleotides), comparing or determining the relationship between corresponding sequences, and identifying and annotating regions of interest. The completion of the sequencing of the human genome well ahead of schedule was thus a triumph of computer science as much as biology. Today the systematic search for genetic and metabolic interactions has been greatly sped up by the use of microarrays, silicon chips with grids of tiny holes that each contain a specified material that can be automatically tested for reaction to a given sample.
Evolutionary Biology
The ability to compare genes and to account for the effects of mutation has also established evolutionary biology on a firm foundation. Given a good estimate of the mutation rate (a “molecular clock”) in mitochondrial DNA, the chronology of species and common ancestors can be determined with considerable accuracy using statistical methods and appropriate data structures (see tree). The results of such research have cast intriguing if sometimes controversial light on such issues in paleontology as the relationship between early modern humans and Neanderthals. Computational genetics can also measure the biodiversity of a present-day ecosystem and predict the likely future of particular species in it.
From Genes to Proteins
Gene sequences are only half of many problems in biology. Computational techniques are also being increasingly applied to the analysis and simulation of the many intricate chemical steps that link genetic information to expression in the form of a particular protein and its three-dimensional structure in the process known as protein folding. Already molecular simulations and predictive techniques are being used to determine which of thousands of possible molecular configurations might have promising pharmaceutical applications. The development of better algorithms and more powerful computing architectures for such analysis can further speed up research, avoid wasteful “dead ends,” and bring effective treatments for cancer and other serious diseases to market sooner. Recently, the unlikely platform of a Sony PlayStation 3 and its powerful new processor has been harnessed to turn gamers’ idle time to the processing of protein-folding data in the Folding@Home project.
Simulation
A variety of other types of biological computer simulation have been employed. Examples include the chemical components (metabolites and enzymes) that are responsible for metabolic activity in organisms, the structure of the nervous system and the brain (see neural network), and the interaction of multiple predators and food sources in an ecosystem. Simulations can also incorporate algorithms first devised by artificial intelligence researchers (see genetic algorithms and artificial life). Simulations are combined with sophisticated graphics to enable researchers to visualize structure. Such visualization can provide insight and encourage intuitive “leaps” that might be missed when working only with formulas. Visualization algorithms developed for biomedical research can also be applied to the development of advanced MRI and other scans for use in diagnosis and therapy.
A Fruitful Relationship
Bioinformatics has been one of the “hottest” areas in computing in recent years, often following trends in the broader “biotech” sector. This challenging field involves such diverse subjects as genetics, biochemistry, physiology, mathematics (structural and statistical), database analysis and search techniques (see data mining), simulation, modeling, graphics, and image analysis. Major projects often involve close cooperation between bioinformatics specialists and other researchers. Many computer scientists may find it profitable to study biology just as biologists will need to learn about and master the latest software tools. Researchers must also consider how the availability of ever-increasing computing power might make previously impossible projects feasible (see supercomputer and grid computing). (The National Institutes of Health (NIH) currently funds seven biomedical computation centers, including the National Center for Physics-based Simulation of Biological Structures at Stanford University. ) The relationship between biology and computer science seems destined to be even more fruitful in coming years. As software tools allow researchers to probe ever more deeply into biological processes and to bridge the gap between physics, biochemistry, and the emergent behavior of living organisms, understanding of those processes may in turn inspire the creation of new architectures and algorithms in areas such as artificial intelligence and robotics.
References:
- Bader, David A. “Computational Biology and High-Performance Computing.” Communications of the ACM 47, 11 (2004): 34–41.
- Brent, Roger, and Jehoshua Bruck. “Can Computers Help to Explain Biology?” Nature 440 (March 23, 2006): 416.
- Campbell, A. Malcolm, and Laurie J. Heyer. Discovering Genomics, Proteomics, and Bioinformatics. 2nd ed. San Francisco: Benjamin Cummings, 2006.
- Claverie, Jean-Michel, and Cedric Notredame. Bioinformatics for Dummies. 2nd ed. Indianapolis: Wiley, 2006.
- Cohen, Jacques. “Computer Science and Bioinformatics.” Communications of the ACM 48 (2005): 72–78.
- “Just the Facts: A Basic Introduction to the Science Underlying NCBI Resources: Bioinformatics.” National Center for Biotechnology Information. Available online. URL: http://www. ncbi.nlm.nih.gov/About/primer/bioinformatics.html. Accessed April 24, 2007.
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