Search result: Catalogue data in Autumn Semester 2018
|Computational Biology and Bioinformatics Master |
More informations at: https://www.cbb.ethz.ch/
|Master Studies (Programme Regulations 2017)|
| Core Courses|
Please note that the list of core courses is a closed list. Other courses cannot be added to the core course category in the study plan. Also the assignments of courses to core subcategories cannot be changed.
Students need to pass at least one course in each core subcategory.
A total of 40 ECTS needs to be acquired in the core course category.
Please note that all Bioinformatics core courses are offered in the autumn semester
|636-0009-00L||Evolutionary Dynamics||W||6 credits||2V + 1U + 2A||N. Beerenwinkel|
|Abstract||Evolutionary dynamics is concerned with the mathematical principles according to which life has evolved. This course offers an introduction to mathematical modeling of evolution, including deterministic and stochastic models.|
|Objective||The goal of this course is to understand and to appreciate mathematical models and computational methods that provide insight into the evolutionary process.|
|Content||Evolution is the one theory that encompasses all of biology. It provides a single, unifying concept to understand the living systems that we observe today. We will introduce several types of mathematical models of evolution to describe gene frequency changes over time in the context of different biological systems, focusing on asexual populations. Viruses and cancer cells provide the most prominent examples of such systems and they are at the same time of great biomedical interest. The course will cover some classical mathematical population genetics and population dynamics, and also introduce several new approaches. This is reflected in a diverse set of mathematical concepts which make their appearance throughout the course, all of which are introduced from scratch. Topics covered include the quasispecies equation, evolution of HIV, evolutionary game theory, birth-death processes, evolutionary stability, evolutionary graph theory, somatic evolution of cancer, stochastic tunneling, cell differentiation, hematopoietic tumor stem cells, genetic progression of cancer and the speed of adaptation, diffusion theory, fitness landscapes, neutral networks, branching processes, evolutionary escape, and epistasis.|
|Literature||- Evolutionary Dynamics. Martin A. Nowak. The Belknap Press of Harvard University Press, 2006.|
- Evolutionary Theory: Mathematical and Conceptual Foundations. Sean H. Rice. Sinauer Associates, Inc., 2004.
|Prerequisites / Notice||Prerequisites: Basic mathematics (linear algebra, calculus, probability)|
|636-0017-00L||Computational Biology||W||6 credits||3G + 2A||T. Stadler, C. Magnus, T. Vaughan|
|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.|
|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:
* 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). We provide an R tutorial and help sessions during the first two weeks of class to learn the required skills. However, 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 at D-BSSE from Wednesday, September 12 to Friday, September 14, i.e. BEFORE the official semester starting date http://www.cbb.ethz.ch/news-events.html |
For the Zurich-based students without R experience, we recommend the R course Link, or working through the script provided as part of this R course.
|262-5120-00L||Principles of Evolution: Theory (University of Zurich)|
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: BIO351
Mind the enrolment deadlines at UZH:
|W||6 credits||3V||University lecturers|
|Abstract||"Nothing in Biology Makes Sense Except in the Light of Evolution".|
Evolutionary theory and methods are essential in all branches of modern
|Objective||Subject specific skills:|
By the end of the course, students will be able to:
o describe basic evolutionary theory and its applications
o discuss ongoing debates in evolutionary biology
o critically assess the presentation of evolutionary research in
the popular media
By the end of the course, students will be able to:
o approach biological questions from an evolutionary perspective
|Content||This course will provide a broad overview of current evolutionary thought, including the mechanisms of evolutionary change,|
adaptation and the history of life and will involve practical field and lab work as well as lecture material.
|262-6100-00L||Evolutionary Genetics||W||6 credits||5G||external organisers|
|262-6110-00L||Bioinformatics Algorithms||W||4 credits||3G||external organisers|
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