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Computational Biology

The University of Chicago_052921B
[The University of Chicago]



- Computational Biology and Molecular Data

Broadly speaking, computational biology is the application of computer science, statistics, and mathematics to biological problems. Computational biology covers a wide range of fields in biology, including genomics/genetics, biophysics, cell biology, biochemistry, and evolution. Likewise, it draws on tools and techniques from many different quantitative fields, including algorithm design, machine learning, Bayesian and frequentist statistics, and statistical physics. 

Much computational biology is concerned with the analysis of molecular data, such as biological sequences (DNA, RNA, or protein sequences), three-dimensional protein structures, gene expression data, or molecular biology networks (metabolic pathways, protein-protein interaction networks, or gene regulatory networks). Problems as diverse as the identification of disease-causing genes, reconstruction of a species' evolutionary history, and unlocking of complex regulatory codes that turn genes on and off can be addressed using this data. Computational biology can also focus on non-molecular data, such as clinical or ecological data.


- When To Use Computational Biology

Computational biology can be used to support or replace laboratory procedures, helping organizations save money and sometimes producing more accurate results. It is most effective when working with smaller specific datasets, such as projects involving conducting population genetics and protein analysis or understanding specific pathways in larger genomes.  It is also used to answer more general biological questions rather than pinpoint highly specific information.

Computational biologists are more concerned with what's going on in biology. As a result, many take on more academic roles than laboratory or field work. 

Scientists who choose to study larger datasets or need multi-server networks should turn to bioinformatics, which provides resources better suited to organizing and understanding big data.



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