Search result: Catalogue data in Spring Semester 2021
|Computational Science and Engineering Master|
|Fields of Specialization|
|636-0702-00L||Statistical Models in Computational Biology||W||6 credits||2V + 1U + 2A||N. Beerenwinkel|
|Abstract||The 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.|
|Objective||The 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.|
|Content||Graphical 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.|
|Literature||- 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
|701-1708-00L||Infectious Disease Dynamics||W||4 credits||2V||S. Bonhoeffer, R. D. Kouyos, R. R. Regös, T. Stadler|
|Abstract||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.|
|Objective||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").
|Content||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.|
|Lecture notes||Slides and script of the lecture will be available online.|
|Literature||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.|
|262-0200-00L||Bayesian Phylodynamics – Taming the BEAST||W||4 credits||2G + 2A||T. Stadler, T. Vaughan|
|Abstract||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.|
|Objective||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
|Content||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.
|Lecture notes||All material will be available on https://taming-the-beast.org/.|
|Literature||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.|
|227-0973-00L||Translational Neuromodeling||W||8 credits||3V + 2U + 1A||K. Stephan|
|Abstract||This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). It focuses on a generative modeling strategy and teaches (hierarchical) Bayesian models of neuroimaging data and behaviour, incl. exercises.|
|Objective||To obtain an understanding of the goals, concepts and methods of Translational Neuromodeling and Computational Psychiatry/Psychosomatics, particularly with regard to Bayesian models of neuroimaging (fMRI, EEG) and behavioural data.|
|Content||This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for inferring mechanisms of brain diseases from neuroimaging and behavioural data) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). The first part of the course will introduce disease concepts from psychiatry and psychosomatics, their history, and clinical priority problems. The second part of the course concerns computational modeling of neuronal and cognitive processes for clinical applications. A particular focus is on Bayesian methods and generative models, for example, dynamic causal models for inferring neuronal processes from neuroimaging data, and hierarchical Bayesian models for inference on cognitive processes from behavioural data. The course discusses the mathematical and statistical principles behind these models, illustrates their application to various psychiatric diseases, and outlines a general research strategy based on generative models. |
Lecture topics include:
1. Introduction to Translational Neuromodeling and Computational Psychiatry/Psychosomatics
2. Psychiatric nosology
3. Pathophysiology of psychiatric disease mechanisms
4. Principles of Bayesian inference and generative modeling
5. Variational Bayes (VB)
6. Bayesian model selection
7. Markov Chain Monte Carlo techniques (MCMC)
8. Bayesian frameworks for understanding psychiatric and psychosomatic diseases
9. Generative models of fMRI data
10. Generative models of electrophysiological data
11. Generative models of behavioural data
12. Computational concepts of schizophrenia, depression and autism
13. Model-based predictions about individual patients
Practical exercises include mathematical derivations and the implementation of specific models and inference methods. In additional project work, students are required to use one of the examples discussed in the course as a basis for developing their own generative model and use it for simulations and/or inference in application to a clinical question. Group work (up to 3 students) is required.
|Literature||See TNU website:|
|Prerequisites / Notice||Good knowledge of principles of statistics, good programming skills (MATLAB, Julia, or Python)|
|701-1418-00L||Modelling Course in Population and Evolutionary Biology |
Number of participants limited to 20.
Priority is given to MSc Biology and Environmental Sciences students.
|W||4 credits||6P||S. Bonhoeffer, V. Müller|
|Abstract||This course provides a "hands-on" introduction into mathematical/computational modelling of biological processes with particular emphasis on evolutionary and population-biological questions. The models are developed using the Open Source software R.|
|Objective||The aim of this course is to provide a practical introduction into the modelling of fundamental biological questions. The participants will receive guidance to develop mathematical/computational models in small teams. The participants chose two modules with different levels of difficulty from a list of projects.|
The participant shall get a sense of the utility of modelling as a tool to investigate biological problems. The simpler modules are based mostly on examples from the earlier lecture "Ecology and evolution: populations" (script accessible at the course webpage). The advanced modules address topical research questions. Although being based on evolutionary and population biological methods and concepts, these modules also address topics from other areas of biology.
|Lecture notes||Detailed handouts describing both the modelling and the biological background are available to each module at the course website. In addition, the script of the earlier lecture "Ecology and evolution: populations" can also be downloaded, and contains further background information.|
|Prerequisites / Notice||The course is based on the open source software R. Experience with R is useful but not required for the course. Similarly, the course 701-1708-00L Infectious Disease Dynamics is useful but not required.|
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