Tanja Stadler: Catalogue data in Autumn Semester 2023 |  |
| Name | Prof. Dr. Tanja Stadler |
| Field | Computational Evolution |
| Address | Computational Evolution ETH Zürich, BSS J 6.3 Klingelbergstrasse 48 4056 Basel SWITZERLAND |
| Telephone | +41 61 387 34 10 |
| tanja.stadler@bsse.ethz.ch | |
| URL | http://www.bsse.ethz.ch/cevo |
| Department | Biosystems Science and Engineering |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||||||||
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| 262-6260-00L | Summer School: Computational Approaches for Epidemiology and Public Health (ETH-EPFL) Students need to register directly at the summer school - registration modalities are specified solely by the organizers of the summer school. Contact: summerschool23@digitalepidemiologylab.org Submission of interest form: https://forms.gle/FcUe5EeVfBP4WLCAA. Deadline 31 May 2023, with feedback expected before 30 June 2023. | 2 credits | 3K | T. Stadler | |||||||||||||||||||||||
| Abstract | In this summer school, our main objective is to bring PhD and master students with a strong quantitative background closer to the challenges of epidemiology and public health, and work with them on developing proposals for concrete applications. | ||||||||||||||||||||||||||
| Learning objective | In this summer school, our main objective is to bring PhD and master students with a strong quantitative background closer to the challenges of epidemiology and public health, and work with them on developing proposals for concrete applications. | ||||||||||||||||||||||||||
| Content | The COVID-19 pandemic has highlighted once again the need of integrating computational approaches more deeply into the practice of public health. Computational approaches like viral phylogenetics, digital contact tracing, and others have had a clear and direct impact on the pandemic. For further implementation of these approaches in local public health practices, collaboration between state-of-the-art science and technology and public health has to be strengthened. In this summer school, our main objective is to bring PhD and master students with a strong quantitative background closer to the challenges of epidemiology and public health, and work with them on developing proposals for concrete applications. Venue: Sunstar Hotel Grindelwald, Grindelwald (Switzerland) Dates: 25-29 September 2023 Speakers & Guest Scientists: - Marcel Salathé, EPFL, Switzerland - Laura Espinosa Montalban, EPFL and ECDC, Switzerland, Sweden - Chaoran Chen, ETH, Switzerland - Cecilia Valenzuela Agui, ETH, Switzerland - Theo Sanderson, Francis Crick Institute, UK - Daniela Paolotti, ISI, Italy - Ciro Cattuto, ISI, Italy - Leonidas Alexakis, ECDC, Sweden Organizers: Laura Espinosa (EPFL), Chaoran Chen (ETH) and Cecilia Valenzuela Agüí (ETH) Supporting Professors: Marcel Salathé (EPFL) and Tanja Stadler (ETH) https://summerschool.digitalepidemiologylab.org | ||||||||||||||||||||||||||
| Prerequisites / Notice | Participation fee: 250 CHF (PhD students), 100 CHF (master students). This fee covers attendance to the summer school, accommodation (24-29 September 2023) in single rooms in the venue, and lunches and morning coffee breaks. Contact: summerschool23@digitalepidemiologylab.org Submission of interest form: https://forms.gle/FcUe5EeVfBP4WLCAA. Deadline 31 May 2023, with feedback expected before 30 June 2023. | ||||||||||||||||||||||||||
| 636-0017-00L | Computational Biology | 6 credits | 3G + 2A | T. Vaughan, C. Magnus, T. Stadler | |||||||||||||||||||||||
| Abstract | The aim of the course is to provide up-to-date knowledge on how we can study biological processes using genetic sequencing data. Computational algorithms extracting biological information from genetic sequence data are discussed, and statistical tools to understand this information in detail are introduced. | ||||||||||||||||||||||||||
| Learning objective | Attendees will learn which information is contained in genetic sequencing data and how to extract information from this data using computational tools. The main concepts introduced are: * stochastic models in molecular evolution * phylogenetic & phylodynamic inference * maximum likelihood and Bayesian statistics Attendees will apply these concepts to a number of applications yielding biological insight into: * epidemiology * pathogen evolution * macroevolution of species | ||||||||||||||||||||||||||
| Content | The course consists of four parts. We first introduce modern genetic sequencing technology, and algorithms to obtain sequence alignments from the output of the sequencers. We then present methods for direct alignment analysis using approaches such as BLAST and GWAS. Second, we introduce mechanisms and concepts of molecular evolution, i.e. we discuss how genetic sequences change over time. Third, we employ evolutionary concepts to infer ancestral relationships between organisms based on their genetic sequences, i.e. we discuss methods to infer genealogies and phylogenies. Lastly, we introduce the field of phylodynamics, the aim of which is to understand and quantify population dynamic processes (such as transmission in epidemiology or speciation & extinction in macroevolution) based on a phylogeny. Throughout the class, the models and methods are illustrated on different datasets giving insight into the epidemiology and evolution of a range of infectious diseases (e.g. HIV, HCV, influenza, Ebola). Applications of the methods to the field of macroevolution provide insight into the evolution and ecology of different species clades. Students will be trained in the algorithms and their application both on paper and in silico as part of the exercises. | ||||||||||||||||||||||||||
| Lecture notes | Lecture slides will be available on moodle. | ||||||||||||||||||||||||||
| Literature | The course is not based on any of the textbooks below, but they are excellent choices as accompanying material: * Yang, Z. 2006. Computational Molecular Evolution. * Felsenstein, J. 2004. Inferring Phylogenies. * Semple, C. & Steel, M. 2003. Phylogenetics. * Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST. | ||||||||||||||||||||||||||
| Prerequisites / Notice | Basic knowledge in linear algebra, analysis, and statistics will be helpful. Programming in R will be required for the project work (compulsory continuous performance assessments). In case you do not have any previous experience with R, we strongly recommend to get familiar with R prior to the semester start. For the D-BSSE students, we highly recommend the voluntary course „Introduction to Programming“, which takes place in Basel before the start of the semester. | ||||||||||||||||||||||||||
Competencies |
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| 636-0704-00L | Computational Biology and Bioinformatics Seminar  Number of participants limited to 30. The seminar is addressed primarily at students enrolled in the MSc CBB programme. Students of other ETH study programmes interested in this course need to ask the lecturer for permission to enrol in the course. The Seminar will be offered in autumn semester in Basel (involving professors and lecturers from the University of Basel) and in spring semester in Zurich (involving professors and lecturers from the University of Zurich). Professors and lecturers from ETH Zurich are involved in both semesters. | 2 credits | 2S | N. Beerenwinkel, D. Iber, M. H. Khammash, T. Stadler, J. Stelling, to be announced | |||||||||||||||||||||||
| Abstract | Computational biology and bioinformatics aim at an understanding of living systems through computation. The seminar combines student presentations and current research project presentations to review the rapidly developing field from a computer science perspective. Areas: DNA sequence analysis, proteomics, optimization and bio-inspired computing, and systems modeling, simulation and analysis. | ||||||||||||||||||||||||||
| Learning objective | Studying and presenting fundamental papers of Computational Biology and Bioinformatics. Learning how to make a scientific presentation and how classical methods are used or further developed in current research. | ||||||||||||||||||||||||||
| Content | Computational biology and bioinformatics aim at advancing the understanding of living systems through computation. The complexity of these systems, however, provides challenges for software and algorithms, and often requires entirely novel approaches in computer science. The aim of the seminar is to give an overview of this rapidly developing field from a computer science perspective. In particular, it will focus on the areas of (i) DNA sequence analysis, sequence comparison and reconstruction of phylogenetic trees, (ii) protein identification from experimental data, (iii) optimization and bio-inspired computing, and (iv) systems analysis of complex biological networks. The seminar combines the discussion of selected research papers with a major impact in their domain by the students with the presentation of current active research projects / open challenges in computational biology and bioinformatics by the lecturers. Each week, the seminar will focus on a different topic related to ongoing research projects at ETHZ, University of Basel and University of Zurich, thus giving the students the opportunity of obtaining knowledge about the basic research approaches and problems as well as of gaining insight into (and getting excited about) the latest developments in the field. | ||||||||||||||||||||||||||
| Literature | Original papers to be presented by the students will be provided in the first week of the seminar. | ||||||||||||||||||||||||||