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Evolutionary Dynamics
News
May 1
Our paper Reliable detection of subclonal
single-nucleotide variants in tumor cell populations appeared in Nature Communications today (see also ETH Life). In this work, we present the deepSNV algorithm and demonstrate its capability to detect subclonal mutations present in only 1/10,000 cells.
Gerstung et al. (2012) Nat Commun 3:811. DOI: 10.1038/ncomms1814.
| 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. |
Course Details
Number and title: 636-0009-00L Evolutionary Dynamics
All lectures will be given in English and are accompanied by a bi-weekly 2h tutorial. For each tutorial, there will be assignments that need to be handed in on Thursdays before each tutorial 12pm (noon). This can be done either electronically to teaching.cbg(at)bsse.ethz.ch, or as a hardcopy to Madeleine Bernard, Institute for Computational Science, CAB H 81.1. Each student has to obtain at least 60% of the available points in the assignments to be admitted to the final exams. These will be held during the winter exam session as a 20 min oral exam for each student.
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.
Schedule
|
Date |
Title |
Slides |
Assignment |
Tutorial | |
| 1 |
Sep 24 |
What is evolution? [Lecture given by Moritz Gerstung] |
Lecture 01 |
Assignment 01 |
1 |
| 2 |
Oct 1 |
Genetic diversity of HIV |
Lecture 02 |
- |
|
| 3 |
Oct 8 |
Antigenic variation of HIV, Evolution of virulence |
Lecture 03 | Assignment 02 | 2 |
| 4 |
Oct 15 |
Stochastic models of finite populations |
Lecture 04 |
- |
|
| 5 |
Oct 22 |
Evolutionary dynamics of cancer |
Lecture 05 |
Assignment 03 |
3 |
| 6 |
Oct 29 |
Cancer progression: the speed of adaptation |
Lecture 06 |
- |
|
| 7 |
Nov 5 |
Hematopoiesis and cancer | Lecture 07 |
Assignment 04 |
4 |
| 8 |
Nov 12 |
Diffusion theory |
Lecture 08 |
- |
|
| 9 |
Nov 19 |
Evolutionary game theory | Lecture 09 |
Assignment 05 |
5 |
|
10 |
Nov 26 |
Evolutionary games in finite populations | Lecture 10 |
- |
|
| 11 |
Dec 3 |
Evolutionary graph theory, Spatial games | Lecture 11 |
Assignment 06 |
6 |
| 12 |
Dec 10 |
Branching processes | Lecture 12 |
- |
|
| 13 |
Dec 17 |
Evolutionary escape | Lecture 13 | Assignment 07 | 7 |
| 14 |
Dec 24 |
Coalescent theory | Lecture 14 |
Solution 07 |
- |
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