Computational Biology is a discipline of biology that uses computers and computer science to better understand and simulate biological structures and processes.

Computational biology is a branch of biology that seeks to address the question, "How can we learn and use models of biological systems derived from experimental data?" These models could explain what biological tasks specific nucleic acid or peptide sequences perform, which gene (or genes) produce a specific phenotype or behaviour when expressed, what sequence of changes in gene or protein expression or localization leads to a specific disease, and how changes in cell organisation influence cell behaviour.

Computational Biology

The development and implementation of data-analytical and theoretical approaches, mathematical modelling, and computational simulation techniques in life sciences is the emphasis of computational biology.

A variety of variables contribute to the misconception, including the fact that one of the leading journals in computational biology is called "Bioinformatics," and that computer science is called "informatik" in German whereas computational biology is called "bioinformatik." Bioinformatics, according to some, stresses the flow of information in biology. In any event, the two subjects are intertwined because "bioinformatics" systems are frequently used to feed data to "computational biology" systems that construct models, and the models' findings are frequently returned for storage in "bioinformatics" databases.

Current computational biology research is organised into many major categories, dependent on the sort of experimental data studied or modelled. Structure and function of proteins and nucleic acids, gene and protein sequence, evolutionary genomics and proteomics, population genomics, regulatory and metabolic networks, biomedical image analysis and modelling, gene-disease associations, and disease development and spread are just a few examples.

Computational biology is a broad field that aims to develop models for a wide range of experimental data (e.g., concentrations, sequences, images, and so on) and biological systems (e.g., molecules, cells, tissues, organs, and so on), and it employs methods from a variety of mathematical and computational fields (e.g., complexity theory, algorithmics, machine learning, robotics, etc.).

The ability to conceptualise biological problems as computer challenges is perhaps the most critical task that computational biologists perform (and that future computational biologist should be prepared to accomplish). This frequently entails taking a fresh look at a biological system, questioning current assumptions or hypotheses about the system's interactions, or combining several sources of data to create a more thorough model than has previously been done.