|Prof. Dr. Tanja Stadler
ETH Zürich, BSS J 6.3
|Biosystems Science and Engineering
|Bayesian Phylodynamics – Taming the BEAST
|2G + 2A
|T. Stadler, T. Vaughan
|How fast is COVID-19 spreading at the moment? How fast was Ebola spreading in West Africa? Where and when did these epidemic outbreak start? How can we construct the phylogenetic tree of great apes, and did gene flow occur between different apes? At the end of the course, students will have designed, performed, presented, and discussed their own phylodynamic data analysis to answer such questions.
|Attendees will extend their knowledge of Bayesian phylodynamics obtained in the “Computational Biology” class (636-0017-00L) and will learn how to apply this theory to real world data. The main theoretical concepts introduced are:
* Bayesian statistics
* Phylogenetic and phylodynamic models
* Markov Chain Monte Carlo methods
Attendees will apply these concepts to a number of applications yielding biological insight into:
* Pathogen evolution
* Macroevolution of species
|During the first part of the block course, the theoretical concepts of Bayesian phylodynamics will be presented by us as well as leading international researchers in that area. The presentations will be followed by attendees using the software package BEAST v2 to apply these theoretical concepts to empirical data. We will use previously published datasets on e.g. COVID-19, Ebola, Zika, Yellow Fever, Apes, and Penguins for analysis. Examples of these practical tutorials are available on https://taming-the-beast.org/.
In the second part of the block course, students choose an empirical dataset of genetic sequencing data and possibly some non-genetic metadata. They then design and conduct a research project in which they perform Bayesian phylogenetic analyses of their dataset. A final written report on the research project has to be submitted after the block course for grading.
|All material will be available on https://taming-the-beast.org/.
|The following books provide excellent background material:
• Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST.
• Yang, Z. 2014. Molecular Evolution: A Statistical Approach.
• Felsenstein, J. 2003. Inferring Phylogenies.
More detailed information is available on https://taming-the-beast.org/.
|Prerequisites / Notice
|This class builds upon the content which we teach in the Computational Biology class (636-0017-00L). Attendees must have either taken the Computational Biology class or acquired the content elsewhere.
|Current Topics in Biosystems Science and Engineering
|R. Platt, N. Beerenwinkel, Y. Benenson, K. M. Borgwardt, P. S. Dittrich, M. Fussenegger, A. Hierlemann, D. Iber, M. H. Khammash, A. Moor, D. J. Müller, S. Panke, S. Reddy, T. Schroeder, T. Stadler, J. Stelling, B. Treutlein
|This seminar will feature invited lectures about recent advances and developments in systems biology, including topics from biology, bioengineering, and computational biology.
|To provide an overview of current systems biology research.
|The final list of topics will be available at http://www.bsse.ethz.ch/education/.
|Computational Biology and Bioinformatics Seminar
|J. Stelling, D. Iber, M. H. Khammash, J. Payne, T. Stadler
|Computational Biology und Bioinformatik analysieren lebende Systeme mit Methoden der Informatik. Das Seminar kombiniert Präsentationen von Studierenden und Forschenden, um das sich schnell entwickelnde Gebiet aus der Informatikperspektive zu skizzieren. Themenbereiche sind Sequenzanalyse, Proteomics, Optimierung und Bio-inspired computing, Systemmodellierung, -simulation und -analyse.
|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.
|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, 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.
|Original papers to be presented by the students will be provided in the first week of the seminar.
|Infectious Disease Dynamics
|S. Bonhoeffer, R. D. Kouyos, R. R. Regös, T. Stadler
|This course introduces into current research on the population biology of infectious diseases. The course discusses the most important mathematical tools and their application to relevant diseases of human, natural or managed populations.
|Attendees will learn about:
* the impact of important infectious pathogens and their evolution on human, natural and managed populations
* the population biological impact of interventions such as treatment or vaccination
* the impact of population structure on disease transmission
Attendees will learn how:
* the emergence spread of infectious diseases is described mathematically
* the impact of interventions can be predicted and optimized with mathematical models
* population biological models are parameterized from empirical data
* genetic information can be used to infer the population biology of the infectious disease
The course will focus on how the formal methods ("how") can be used to derive biological insights about the host-pathogen system ("about").
|After an introduction into the history of infectious diseases and epidemiology the course will discuss basic epidemiological models and the mathematical methods of their analysis. We will then discuss the population dynamical effects of intervention strategies such as vaccination and treatment. In the second part of the course we will introduce into more advanced topics such as the effect of spatial population structure, explicit contact structure, host heterogeneity, and stochasticity. In the final part of the course we will introduce basic concepts of phylogenetic analysis in the context of infectious diseases.
|Slides and script of the lecture will be available online.
|The course is not based on any of the textbooks below, but they are excellent choices as accompanying material:
* Keeling & Rohani, Modeling Infectious Diseases in Humans and Animals, Princeton Univ Press 2008
* Anderson & May, Infectious Diseases in Humans, Oxford Univ Press 1990
* Murray, Mathematical Biology, Springer 2002/3
* Nowak & May, Virus Dynamics, Oxford Univ Press 2000
* Holmes, The Evolution and Emergence of RNA Viruses, Oxford Univ Press 2009
|Prerequisites / Notice
|Basic knowledge of population dynamics and population genetics as well as linear algebra and analysis will be an advantage.