The development and application of data-analytical and theoretical methods, mathematical modelling, and computational simulation techniques in life sciences is the focus of computational biology. Computational biology is the science that seeks to answer the question, "How can we learn and apply models of biological systems built from experimental measurements?" These models may describe 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. Many scientists use the term bioinformatics to describe the field that answers the question, "How can I efficiently store, annotate, search, and compare information from biological measurements and observations?" According to Coherent Market Insights, The Global Computational Biology Market was valued at US$ 3,453.2 Mn in 2019 and is forecast to reach a value of US$ 12,601.1 Mn by 2027 at a CAGR of 17.6% between 2020 and 2027. A number of factors contribute to the confusion, including the fact that one of the top journals in computational biology is called "Bioinformatics," and that in German, computer science is called "informatik," while computational biology is called "bioinformatik." Some people believe that bioinformatics emphasises the flow of information in biology. In any case, the two fields are inextricably linked, because "bioinformatics" systems are frequently required to provide data to "computational biology" systems that create models, and the results of those models are frequently returned for storage in "bioinformatics" databases. Computational biology is a very broad discipline in that it seeks to build models for various types 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 wide range of mathematical and computational fields (e.g., complexity theory, algorithmics, machine learning, robotics, etc.). Perhaps the most important task that computational biologists perform (and that prospective computational biologists should be prepared to do) is to frame biomedical problems as computational problems. This frequently entails looking at a biological system in a new light, challenging current assumptions or theories about the relationships between system components, or combining different sources of information to create a more comprehensive model than has previously been attempted. It is worth noting in this context that the primary goal does not have to be to improve human understanding of the system; even small biological systems can be so complex that scientists cannot fully comprehend or predict their properties. Thus, the goal could be to create the model itself, with the model accounting for as much currently available experimental data as possible. Even if the model makes one or more correct predictions about new experiments, this does not imply that the model has been proven. Except in very specific cases, it is not possible to prove that a model is correct; instead, it is only possible to disprove it and then improve it by modifying it to incorporate the new results.
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