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Theoretical biology : ウィキペディア英語版
Mathematical and theoretical biology
Mathematical and theoretical biology is an interdisciplinary scientific research field with a range of applications in biology, biotechnology, and medicine.〔(Mathematical and Theoretical Biology: A European Perspective )〕 The field is also called mathematical biology or biomathematics to stress the mathematical side, or theoretical biology to stress the biological side.〔 "There is a subtle difference between mathematical biologists and theoretical biologists. Mathematical biologists tend to be employed in mathematical departments and to be a bit more interested in math inspired by biology than in the biological problems themselves, and vice versa." (Careers in theoretical biology )〕 Mathematical biology aims at the mathematical representation, treatment and modeling of biological processes, using a variety of applied mathematical techniques and tools. It has both theoretical and practical applications in biological, biomedical and biotechnology research. For example, in cell biology, protein interactions are often represented as "cartoon" models, which, although easy to visualize, do not accurately describe the systems studied. This requires precise mathematical models. Describing systems in a quantitative manner means their behavior can be better simulated, and hence properties can be predicted that might not be evident to the experimenter.
Such mathematical areas as calculus, probability theory, statistics, linear algebra, abstract algebra, graph theory, combinatorics, algebraic geometry, topology, dynamical systems, differential equations and coding theory are now being applied in biology. Some mathematical areas, such as certain methodologies in statistics, were developed as tools during the conduct of research into mathematical biology.
==Importance==
Applying mathematics to biology has a long history, but only recently has there been an explosion of interest in the field. Some reasons for this include:
* The explosion of data-rich information sets, due to the genomics revolution, which are difficult to understand without the use of analytical tools
* Recent development of mathematical tools such as chaos theory to help understand complex, non-linear mechanisms in biology
* An increase in computing power, which facilitates calculations and simulations not previously possible
* An increasing interest in in silico experimentation due to ethical considerations, risk, unreliability and other complications involved in human and animal research

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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