One of our main missions is to let modelling and bioinformatics become a standard component of biological research. Only by doing so can we make biology a more quantitative science, and integrate large data bases of knowledge into an understanding of how complex biological systems function and evolve. Our bachelor and master programs aim to introduce students interested in biology to the basic concepts and techniques of computational biology. Some of the students will proceed and attend our PhD program, and others will benefit from these theoretical skills in their future experimental work.
Computational Biology enriches our mechanistic understanding of complex living systems at all levels, e.g., from molecules to cells, organisms, and ecosystems, and from cell signalling to gene expression, development, and evolution. Computational methods are necessary for understanding living organisms, because our intuition typically falls short on capturing the complexity at all these levels, and struggles with integrating the large amounts of data that we nowadays have on these systems. Examples of computational methods are:
- sequence analysis, phylogeny, and genomics
- analysis of biological high throughput data
- differential equation models
- cellular automata
- agent based computer simulation models