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Bioinformatics, Biomathematics and Biostochastics

Bioinformatics does not aim to lay down fundamental mathematical laws that govern biological systems parallel to those laid down in physics. At this stage the main utility of Bioinformatics is in the creation of tools that investigators can use to analyze data. For example, biologists need tools for the statistical assessment of the similarity between two or more DNA or protein sequences, for finding genes in genomic DNA, and for estimating differences in how genes are expressed in different tissues. Important areas in Bioinformatics work on the development and the optimization of corresponding data bases, algorithms and program packages.

Biomathematics works mainly on the mathematical description and investigation of biological phenomena. The aim is a general mathematical modelling and mathematical analysis of biological systems similar to the laws known from physics. The probably most simple example is the exponential growth of populations (N'=cN). In contrast to physics such laws are still long way from being determined for biological systems. In its original meaning Biomathematics mainly considers deterministic approaches, most of them from the area of Differential Equations and Dynamical Systems.

Biostochastics pays attention to the fact that many biological phenomena are partly or even completely determined by randomness. Although the relevance of randomness in biology (for example in evolution) was seen quite early (Darwin, Mendel, Hardy-Weinberg), more complex probabilistic models have been intensively studied only recently, also due to the relative late development of stochastics. Fundamental progress could be made only after the theory of Stochastic Processes and the theory of Stochastic Integration was sufficiently developed. For example the theory of Branching Processes fundamentally contributed to the understanding of biological population growth up to the Polymerase Chain Reaction (PCR). Another typical example is the Coalescent Theory going back to Kingman (1982), a theory on ancestral trees which is still influencing the movement of population genetics. The number of publications in journals on probability theory with connection to biology increased since 1980 drastically and documents the importance of Biostochastics. Moreover, Biostochastics forms the basis for a profound statistical analysis of biological data. Here the connection to Bioinformatics becomes in particular obvious.
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Last Update: April 22, 2022