Computational biology

Computational biology, also known as bioinformatics, is an interdisciplinary field that applies computational techniques to solve biological problems. It involves the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems.

Overview

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.

History

The term "computational biology" was first used in the late 1980s, but the concept of using computers to assist in biological research dates back to the 1960s. The field has grown significantly since the late 20th century, driven by advances in computing power and the explosion of biological data generated by various genome projects.

Applications

Computational biology has been used in a wide range of applications, from the analysis of genetic sequences to the modeling of evolutionary processes. Some of the major applications of computational biology include:

• Genomics and Genetic Sequencing
• Protein Structure Prediction
• Drug Discovery
• Evolutionary Biology
• Systems Biology
• Bioinformatics

Challenges

Despite its many successes, computational biology also faces many challenges. These include the need for better algorithms to handle the vast amounts of biological data, the integration of different types of data, and the development of new computational tools to analyze and interpret this data.

See also

• Bioinformatics
• Systems biology
• Computational genomics
• Computational neuroscience
• Computational pharmacology
• Computational evolutionary biology
• Computational systems biology
• Computational anatomy

References

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  Timeline of the Human Genome Project

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  Screenshot of a genome viewer

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  Jmatrix visualization

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  Explanation of a random forest model