Nicola Zamboni: Catalogue data in Spring Semester 2021
|Name||Prof. Dr. Nicola Zamboni|
Inst. f. Molekulare Systembiologie
ETH Zürich, HPM H 45
|Telephone||+41 44 633 31 41|
Number of participants limited to 15.
The enrolment is done by the D-BIOL study administration.
General safety regulations for all block courses:
-Whenever possible the distance rules have to be respected
-All students have to wear masks throughout the course. Please keep reserve masks ready. Surgical masks (IIR) or medical grade masks (FFP2) without a valve are permitted. Community masks (fabric masks) are not allowed.
-The installation and activation of the Swiss Covid-App is highly encouraged
-Any additional rules for individual courses have to be respected
-Students showing any COVID-19 symptoms are not allowed to enter ETH buildings and have to inform the course responsible
|6 credits||7P||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.|
|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.
Information for UZH students:
Enrolment to this course unit only possible at ETH. No enrolment to module BIO 254 at UZH.
Please mind the ETH enrolment deadlines for UZH students: Link
|3 credits||2V||C. von Mering, C. Beyer, B. Bodenmiller, M. Gstaiger, H. Rehrauer, R. Schlapbach, K. Shimizu, N. Zamboni, further lecturers|
|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.|
|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 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 sequencing, 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.|
|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||Scripts to prepare the lectures will be provided via Moodle|
|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