Computational Biology 2

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The second course in this field aims at giving a basic understanding of computational biology and theoretical models in molecular biology. This will include models of the origin of life, molecular evolution, and molecular genetics.

As a consequence of new measurement techniques, our knowledge of structure and function of biological macromolecules has increased significantly in recent years. The amount of data is now so large that it has become necessary to use computational and statistical methods of analysis. The new empirical data now allow statistically significant testing of models for genetic evolution. This has led to a renewed interest in evolution models on the genetic and molecular level. New numerical algorithms and mathematical models have been developed describing population genetics. It is the aim of this course to introduce the mathematical models and computational methods used in the analysis and modelling of genetical data and their evolution.

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Bernhard Mehlig

A fundamental question in the statistical physics of  complex systems is how spatial and temporal randomness may generate patterns and dynamics.

The dynamics of complex systems can be systematically analysed using diffusion equations and random-matrix theory.  This approach may yield, as experience shows, surprisingly universal, and in several cases analytically exact results. More importantly, the results show that at first sight unrelated phenomena observed in complex systems in a wide range of different disciplines (Biology, Condensed Matter Physics, and Fluid Dynamics) can be understood in terms of simple and thus general mechanisms. | This email address is being protected from spambots. You need JavaScript enabled to view it.

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