Karsten M. Borgwardt: Catalogue data in Spring Semester 2020

Name Prof. Dr. Karsten M. Borgwardt
Name variantsKarsten Borgwardt
K. Borgwardt
K Borgwardt
Karsten M. Borgwardt
Karsten Michael Borgwardt
K.M. Borgwardt
KM Borgwardt
FieldData Mining
Dep. Biosysteme
ETH Zürich, D-BSSE, BSD G 234
Mattenstrasse 26
4058 Basel
Award: The Golden Owl
Telephone+41 61 387 34 20
DepartmentBiosystems Science and Engineering
RelationshipFull Professor

551-1174-00LSystems Biology4 credits2V + 2UU. Sauer, K. M. Borgwardt, J. Stelling, N. Zamboni
AbstractThe course teaches computational methods and first hands-on applications by starting from biological problems/phenomena that students in the 4th semester are somewhat familiar with. During the exercises, students will obtain first experience with programming their own analyses/models for data analysis/interpretation.
ObjectiveWe will teach little if any novel biological knowledge or analysis methods, but focus on training the ability of use existing knowledge (for example from enzyme kinetics, regulatory mechanisms or analytical methods) to understand biological problems that arise when considering molecular elements in their context and to translate some of these problems into a form that can be solved by computational methods. Specific goals are:
- understand the limitations of intuitive reasoning
- obtain a first overview of computational approaches in systems biology
- train ability to translate biological problems into computational problems
- solve practical problems by programming with MATLAB
- make first experiences in computational interpretation of biological data
- understand typical abstractions in modeling molecular systems
ContentDuring the first 7 weeks, the will focus on mechanistic modeling. Starting from simple enzyme kinetics, we will move through the dynamics of small pathways that also include regulation and end with flux balance analysis of a medium size metabolic network. During the second 7 weeks, the focus will shift to the analysis of larger data sets, such as metabolomics and transcriptomics that are often generated in biology. Here we will go through multivariate statistical methods that include clustering and principal component analysis, ending with first methods to learn networks from data.
Lecture notesScripts to prepare the lectures will be provided via Moodle
LiteratureThe course is not taught by a particular book, but two books are suggested for further reading:
- Systems Biology (Klipp, Herwig, Kowald, Wierling und Lehrach) Wiley-VCH 2009
- A First Course in Systems Biology (Eberhardt O. Voight) Garland Science 2012
636-0019-00LData Mining II
Prerequisites: Basic understanding of mathematics, as taught in basic mathematics courses at the Bachelor`s level. Ideally, students will have attended Data Mining I before taking this class.
6 credits3G + 2AK. M. Borgwardt
AbstractData Mining, the search for statistical dependencies in large databases, is of utmost important in modern society, in particular in biological and medical research. Building on the basic algorithms and concepts of data mining presented in the course "Data Mining I", this course presents advanced algorithms and concepts from data mining and the state-of-the-art in applications of data mining.
ObjectiveThe goal of this course is that the participants gain an advanced understanding of data mining problems and algorithms to solve these problems, in particular in biological and medical applications, and to enable them to conduct their own research projects in the domain of data mining.
ContentThe goal of the field of data mining is to find patterns and statistical dependencies in large databases, to gain an understanding of the underlying system from which the data were obtained. In computational biology, data mining contributes to the analysis of vast experimental data generated by high-throughput technologies, and thereby enables the generation of new hypotheses.

In this course, we will present advanced topics in data mining and its applications in computational biology.

Tentative list of topics:

1. Dimensionality Reduction
2. Association Rule Mining
3. Text Mining
4. Graph Mining
Lecture notesCourse material will be provided in form of slides.
LiteratureWill be provided during the course.
636-0301-00LCurrent Topics in Biosystems Science and Engineering2 credits1SR. Platt, N. Beerenwinkel, Y. Benenson, K. M. Borgwardt, P. S. Dittrich, M. Fussenegger, A. Hierlemann, D. Iber, M. H. Khammash, D. J. Müller, S. Panke, S. Reddy, T. Schroeder, T. Stadler, J. Stelling, B. Treutlein, C. Uhler
AbstractThis seminar will feature invited lectures about recent advances and developments in systems biology, including topics from biology, bioengineering, and computational biology.
ObjectiveTo provide an overview of current systems biology research.
ContentThe final list of topics will be available at http://www.bsse.ethz.ch/education/.