Nicola Zamboni: Catalogue data in Spring Semester 2015 |  |
| Name | Prof. Dr. Nicola Zamboni |
| Address | Inst. f. Molekulare Systembiologie ETH Zürich, HPM H 45 Otto-Stern-Weg 3 8093 Zürich SWITZERLAND |
| Telephone | +41 44 633 31 41 |
| zamboni@imsb.biol.ethz.ch | |
| Department | Biology |
| Relationship | Adjunct Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 551-0342-00L | Metabolomics   Number of participants limited to 10 | 6 credits | 7G | N. Zamboni, U. Sauer | |
| Abstract | The course covers all basic aspects of metabolome measurements, from sample sampling to mass spectrometry and data analysis. Participants work in groups and independently perform and interpret metabolomic experiments. | ||||
| Learning objective | Performing and reporting a metabolomic experiment, understanding pro and cons of mass spectrometry based metabolomics. Knowledge of workflows and tools to assist experiment interpretation, and metabolite identification. | ||||
| Content | Basics of metabolomics: workflows, sample preparation, targeted and untargeted mass spectrometry, instrumentation, separation techniques (GC, LC, CE), metabolite identification, data interpretation and integration, normalization, QCs, maintenance. Soft skills to be trained: project planning, presentation, reporting, independent working style, team work. | ||||
| 551-0364-00L | Functional Genomics | 5 credits | 3V + 1U | K. Bärenfaller, C. von Mering, C. Beyer, B. Bodenmiller, H. Rehrauer, M. Robinson, R. Schlapbach, K. Shimizu, N. Zamboni | |
| Abstract | Functional genomics is key to understanding the dynamic aspects of genome function and regulation. Functional genomics approaches use the wealth of data produced by large-scale DNA sequencing, gene expression profiling, proteomics and metabolomics. Today functional genomics is becoming increasingly important for the generation and interpretation of quantitative biological data. | ||||
| Learning objective | Functional genomics is key to understanding the dynamic aspects of genome function and regulation. Functional genomics approaches use the wealth of data produced by large-scale DNA sequencing, gene expression profiling, proteomics and metabolomics. Today functional genomics is becoming increasingly important for the generation and interpretation of quantitative biological data. Such data provide the basis for systems biology efforts to elucidate the structure, dynamics and regulation of cellular networks. | ||||
| Content | The Functional Genomics course builds on the training and information students have received in the Bioinformatics I and II courses (prerequisites). The curriculum of the Functional Genomics course emphasizes an in depth understanding of new technology platforms for modern genomics and advanced genetics, including the application of functional genomics approaches such as advanced microarrays, proteomics, metabolomics, clustering and classification. Students will learn quality controls and standards (benchmarking) that apply to the generation of quantitative data and will be able to analyze and interpret these data. The training obtained in the Functional Genomics course will be immediately applicable to experimental research and design of systems biology projects. | ||||
| Prerequisites / Notice | The Functional Genomics course will be taught in English. For the exericse, the presentation and discussion of original research articles will also be in English. Grading The final grade for this course will be based on a written exam, also a grade for the exercise based on the presentation and discussion of an original research paper. | ||||
| 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 learn to program their own analyses/models for data analysis/interpretation. | ||||
| Learning objective | - 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 | No script | ||||
| 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 | ||||