Computational Biology

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• Computational biology involves the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, ecological, behavioral, and social systems.

• The field is broadly defined and includes foundations in biology, applied mathematics, statistics, biochemistry, chemistry, biophysics, molecular biology, genetics, genomics, computer science and evolution.

• Computational biology is different from biological computing, which is a subfield of computer science and computer engineering using bioengineering and biology to build computers, but is similar to bioinformatics, which is an interdisciplinary science using computers to store and process biological data.

• Computational Biology, which includes many aspects of bioinformatics, is the science of using biological data to develop algorithms or models to understand biological systems and relationships. Until recently, biologists did not have access to very large amounts of data.

• This data has now become commonplace, particularly in molecular biology and genomics. Researchers were able to develop analytical methods for interpreting biological information, but were unable to share them quickly among colleagues.

• Bioinformatics began to develop in the early 1970s. It was considered the science of analyzing informatics processes of various biological systems. At this time, research in artificial intelligence was using network models of the human brain in order to generate new algorithms.

• This use of biological data to develop other fields pushed biological researchers to revisit the idea of using computers to evaluate and compare large data sets. By 1982, information was being shared among researchers through the use of punch cards.

• Once the problem has been framed, the second major task of computational biologists begins.  This is to borrow, refine, or invent methods to solve the problem.Current computational biology research can be divided into a number of broad areas, mainly based on the type of experimental data that is analyzed or modeled.

• Among these are analysis of protein and nucleic acid structure and function, gene and protein sequence, evolutionary genomics and proteomics, population genomics, regulatory and metabolic networks, biomedical image analysis and modeling, gene-disease associations, and development and spread of disease.

         

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