Name | Dr. Karsten Michael Borgwardt |

Name variants | Karsten Borgwardt K. Borgwardt K Borgwardt Karsten M. Borgwardt Karsten Michael Borgwardt K.M. Borgwardt KM Borgwardt |

Field | Data Mining |

Address | Dep. Biosysteme ETH Zürich, D-BSSE, BSD G 234 Mattenstrasse 26 4058 Basel SWITZERLAND |

borgwardt@bsse.ethz.ch | |

URL | https://bsse.ethz.ch/mlcb |

Department | Biosystems Science and Engineering |

Relationship | Full Professor |

Number | Title | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|

551-1174-00L | Systems Biology | 4 credits | 2V + 2U | U. Sauer, K. M. Borgwardt, J. Stelling, N. Zamboni | |

Abstract | The 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. | ||||

Objective | We 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 | ||||

Content | During 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 notes | Kein Skript | ||||

Literature | The 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 | ||||

636-0019-00L | Data Mining IIPrerequisites: 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 credits | 3G + 2A | K. M. Borgwardt | |

Abstract | Data 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. | ||||

Objective | The 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. | ||||

Content | The 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 notes | Course material will be provided in form of slides. | ||||

Literature | Will be provided during the course. | ||||

636-0301-00L | Current Topics in Biosystems Science and Engineering | 2 credits | 1S | T. Stadler, N. Beerenwinkel, Y. Benenson, K. M. Borgwardt, P. S. Dittrich, M. Fussenegger, A. Hierlemann, D. Iber, M. H. Khammash, D. J. Müller, S. Panke, R. Paro, R. Platt, S. Reddy, T. Schroeder, J. Stelling | |

Abstract | This seminar will feature invited lectures about recent advances and developments in systems biology, including topics from biology, bioengineering, and computational biology. | ||||

Objective | To provide an overview of current systems biology research. | ||||

Content | The final list of topics will be available at http://www.bsse.ethz.ch/education/. |