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Background: Many biological questions can only be addressed with some level of mathematical, statistical or computational expertise. Yet, studies in Life Science traditionally have put less emphasis on mathematical training than other scientific disciplines like physics and engineering. Students of biology should therefore improve their mathematical skills to stay competitive in many fields of biological research.

Goals: This course aims to improve mathematical and computational skills of undergraduate students in the 3rd semester by studying a specific biological questions that requires math.

Contents: The idea behind this course is that for many students a mathematical concept or tool is best understood in the context of an application. Thus rather than learning an abstract mathematical theory, this course is very applied and the learning process is driven by a biological question. For example, students may be confronted with big datasets (such a s gene expression profiles for thousand of genes in hundreds of samples) and learn how to study such data using approaches like principle component analysis or clustering. Topics like regression analysis may be explored to associate different types of biological observation (e.g. genotypes and some phenotypes). Network concepts and analysis methods are taught in the context of protein interactions or other biological networks. All project include real biological data and address questions that are of current research interest, while requiring some non-trivial mathematical analysis whose outcome is open. Students will work in small groups of 2-4 students on projects under the weekly supervision of a teaching assistant (usually a PhD student or post-doc) with monthly joint meetings: In the first joint meeting the project choices will be presented, in the second joint meeting students will present their projects and preliminary results, and in the final meeting the completed projects will be presented.

Prerequisites: All 3rd year students are welcome. Exceptionally we also permit 2nd year students if free places are available. Basic math, stats and programming knowledge is a big plus, but the key requirement is an interest to improve these skills.

Evaluation: 3/4 of the final grade is based on the performance in the final presentation. Here we look at the results, the depth of the student teams' analysis, and how well they are presented. 1/4 of the grade is based on the individual study performance throughout the course.

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