Suchergebnis: Katalogdaten im Frühjahrssemester 2021

MAS in Medical Physics Information
Fachrichtung: General Medical Physics
Vertiefung Molecular Biology and Biophysics
Kernfächer
NummerTitelTypECTSUmfangDozierende
551-1402-00LMolecular and Structural Biology VI: Biophysical Analysis of Macromolecular Mechanisms
This course is strongly recommended for the Masters Major "Biology and Biophysics".
W4 KP2VR. Glockshuber, T. Ishikawa, S. Jonas, B. Schuler, E. Weber-Ban
KurzbeschreibungThe course is focussed on biophysical methods for characterising conformational transitions and reaction mechanisms of proteins and biological mecromolecules, with focus on methods that have not been covered in the Biology Bachelor Curriculum.
LernzielThe goal of the course is to give the students a broad overview on biopyhsical techniques available for studying conformational transitions and complex reaction mechanisms of biological macromolecules. The course is particularly suited for students enrolled in the Majors "Structural Biology and Biophysics", "Biochemistry" and "Chemical Biology" of the Biology MSc curriculum, as well as for MSc students of Chemistry and Interdisciplinary Natural Sciences".
InhaltThe biophysical methods covered in the course include advanced reaction kinetics, methods for the thermodynamic and kinetic analysis of protein-ligand interactions, static and dynamic light scattering, analytical ultracentrifugation, spectroscopic techniques such as fluorescence anisotropy, fluorescence resonance energy transfer (FRET) and single molecule fluorescence spectrosopy, modern electron microscopy techniques, atomic force microscopy, and isothermal and differential scanning calorimetry.
SkriptCourse material from the individual lecturers wil be made available at the sharepoint website

Link
Voraussetzungen / BesonderesFinished BSc curriculum in Biology, Chemistry or Interdisciplinary Natural Sciences. The course is also adequate for doctoral students with research projects in structural biology, biophysics, biochemistry and chemical biology.
551-1556-00LMacromolecular Structure Determination Using Modern Methods Belegung eingeschränkt - Details anzeigen
Number of participants limited to 11 in the 3rd semester quarter of the spring semester

Number of participants limited to 12 in the 4th semester quarter of the spring semester

The block course will only take place with a minimum of 4 participants.

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
W6 KP7PK. Locher, R. Irobalieva, J. Kowal, G. Schertler
KurzbeschreibungThis course will expose the students to two prominent techniques for high-resolution structural characterization of biological macromolecules. The students will have the opportunity to get hands-on experience in either cryo-electron microscopy (ETH) or X-ray crystallography (PSI).
LernzielThe goal of this course is to introduce the students to the principles of high-resolution structure determination. Students will conduct hands-on experiments and use computational techniques for data processing.
InhaltAt the ETH the students will prepare and vitrify a protein and then image it on a cryo-TEM. Next, the students will process the data and build an atomic model into the EM map.

At the PSI the students will purify and crystallize a membrane protein, collect X-ray diffraction data using synchrotron X-ray source or with cryo-EM, analyze and build an atomic model into a density map. They will refine this model and interpret and illustrate the determined structure. The course work is trying to present insights in the use of structural information. The course also includes a demonstration of the Synchrotron capabilities at the Paul Scherrer Institute (SLS).
Voraussetzungen / BesonderesThe students will be split into two groups for the practical part of the work: One group will work at ETH Hönggerberg, the other at the Paul Scherrer Institute (PSI) at Villigen. All students will spend one full day at the PSI for a tour of the facilities, including a visit of the synchrotron beam lines of the Swiss Light Source SLS.

The students joining the ETH Hönggerberg group will spend the majority of the time on data processing and are therefore expected to have some basic knowledge of bash terminal commands. Basic physics, optics and linear algebra knowledge is also helpful. By the end of the course, the students will be expected to understand concepts such as the difference between Fourier and real space, image formation, contrast transfer, fast Fourier transfer and Fourier shell correlation.
262-5100-00LProtein Biophysics (University of Zurich)
Der Kurs muss direkt an der UZH belegt werden.
UZH Modulkürzel: BCH304

Beachten Sie die Einschreibungstermine an der UZH: Link
W6 KP3V + 1UUni-Dozierende
KurzbeschreibungThe course includes a general introduction into protein structure and biophysics as well as into the usage of molecular dynamics simulations and other computational methods, protein structure and X-ray techniques, protein NMR for determining protein structure and dynamics as well as for folding studies and protein thermodynamics.
LernzielA 4 hour/week course on all aspects of protein biophysics. The course includes a general introduction into protein structure and biophysics as well as into the usage of molecular dynamics simulations and other computational methods, protein structure and X-ray techniques, protein NMR for determining protein structure and dynamics as well as for folding studies and protein thermodynamics.
InhaltThe lecture course consists of four parts:
1) non-covalent interactions, properties of water and hydrophobic
effect, protein folding and misfolding, molecular dynamics simulations;
2) atomistic simulations of proteins
3) thermodynamics and kinetics of protein folding;
4) single molecule biophysics: single molecule fluorescence
spectroscopy, fluorescence correlation spectroscopy, and applications to
stochastic processes in biology.
636-0702-00LStatistical Models in Computational BiologyW6 KP2V + 1U + 2AN. Beerenwinkel
KurzbeschreibungThe course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods.
LernzielThe goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets.
InhaltGraphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises.
Skriptno
Literatur- Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252
- Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007.
- Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004
  •  Seite  1  von  1