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Bioinformatics and Computational Biology

(Photo: Princeton University, Office of Communications)


The terms computational biology and bioinformatics are often used interchangeably. However, computational biology sometimes connotes the development of algorithms, mathematical models, and methods for statistical inference, while bioinformatics is more associated with the development of software tools, databases, and visualization methods.

Computational biologists and bioinformaticists typically leverage data generated by modern high-throughput assays including microarrays, mass spectrometry, confocal microscopy, sequencing and other advances in biotechnology. 




  • Bioinformatics refers to the data management and processing of biomolecular data often collected on a genome-wide scale. 
  • Bioinformatics, the key to a precision medicine future, aims to store, organize, explore, extract, analyze, interpret, and utilize information from biological data. Translational bioinformatics is a rapidly emerging field of biomedical data sciences and informatics technologies that efficiently translate basic molecular, genetic, cellular, and clinical data into clinical products or health implications. Its focus is on applying informatics methodology to the increasing amount of biomedical and genomic data to formulate knowledge and medical tools, which can be utilized by scientists, clinicians, and patients. Furthermore, it involves applying biomedical research to improve human health through the use of computer-based information system. Translational bioinformatics employs data mining and analyzing biomedical informatics in order to generate clinical knowledge for application. Clinical knowledge includes finding similarities in patient populations, interpreting biological information to suggest therapy treatments and predict health outcomes.
  • The vast molecular biology information resulting from high throughput genomic, proteomic and other ‘omic’ projects challenges the understanding of the role of genes, proteins and other molecules and the use of this knowledge in applications related to health, well-being, agriculture, society, and environment. 


Computational Biology


  • [Cornell University]: Broadly speaking, computational biology is the application of computer science, statistics, and mathematics to problems in biology. Computational biology spans a wide range of fields within biology, including genomics/genetics, biophysics, cell biology, biochemistry, and evolution. Likewise, it makes use of tools and techniques from many different quantitative fields, including algorithm design, machine learning, Bayesian and frequentist statistics, and statistical physics. Much of computational biology is concerned with the analysis of molecular data, such as biosequences (DNA, RNA, or protein sequences), three-dimensional protein structures, gene expression data, or molecular biological networks (metabolic pathways, protein-protein interaction networks, or gene regulatory networks). A wide variety of problems can be addressed using these data, such as the identification of disease-causing genes, the reconstruction of the evolutionary histories of species, and the unlocking of the complex regulatory codes that turn genes on and off. Computational biology can also be concerned with non-molecular data, such as clinical or ecological data.


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