ETH - BSSE - CBG Systems Biology

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	This lecture course is an introduction to systems biology. It explores how complex biological networks are experimentally analyzed and how the resulting data is mathematically analyzed in order to derive predic- tive models. The biology of selected cellular processes, ranging from protein interaction networks to gene controlling systems and signaling cascades will be presented at a high level.
Objective	The goal of this course is to learn how a detailed quantitative description of complex biological processes can be employed for a better understanding of molecular interactions, the power and efficiency of regulatory networks, and the evolution of biological complexity. Students will learn how to identify techniques producing quantitative data and how to develop mathematical models and efficient statistical inference algorithms to recognize patterns, molecular interrelationships and systems behavior.
Content	Sessions will alternate between a thorough introduction into the basic biology of particular cellular processes and a corresponding mathematical and statistical analysis of the experimental data. Selected complex biological systems and the respective experimental tools for a quantitative analysis will be presented. Examples include the identification of protein interaction networks required for specific physiological processes in yeast based on graph theoretic methods, including the identification of network motifs and the global statistical analysis of graph properties (power laws); the comparative analysis of gene expressions data from cancer and normal cells involving data normalization techniques, multiple testing procedures, clustering algorithms, Bayesian networks, and linear dynamical systems; the definition of hierarchies of kinase signaling cascades employing Bayesian networks and their causal interpretation and nested effects models for the analysis of perturbed systems; analysis of deep sequencing data derived from studies of chromatin control and gene expression.
Lecture notes	The Powerpoint presentations of the lectures as well as other course material relevant for an active participation will be made available online.
Literature	Alberts B et al. (2008) Molecular Biology of the Cell, Fifth Edition, Garland Science Alon U (2007) An Introduction to Systems Biology, Chapman & Hall Wolkenhauer O (2008) Systems Biology: Dynamic Pathway Modeling Zvelebil M & Baum JO (2008) Understanding Bioinformatics, Garland Science

Course Details

Number and title: 636-0005-00 L Systems Biology

	Date	Room
Lecture	Wednesday, 15-18h	BSA E 46

Note: All lectures take place at the Department of Biosystems Science and Engineering, D-BSSE, in Basel.

Schedule

	Date	Title	Slides	Course Material
0	Sep 22	Introductory week (NO LECTURE)
1	Sep 29	Introduction Biological Networks	00_Introduction 01_Biological_Networks
2	Oct 6	Network Biology	02_Network_Biology
3	Oct 13	Boolean Network Dynamics	03_Boolean_Network_Dynamics
4	Oct 20	Control of Gene Expression	04_Control_of_Gene_Expression
5	Oct 27	Genetic Switches	05_Genetic_Switches
6	Nov 3	Analysis of Gene Expression Data	06_Analysis_of_Microarray_Data
7	Nov 10	Probabilistic Graphical Models	07_Probabilistic_Graphical_Models
8	Nov 17	Cellular Communication	08_Cellular_Communication
9	Nov 24	Large-scale Genomic Profiling	09_Large_Scale_Genomic_Profiling
10	Dec 1	Computational Analysis of Next-generation Sequencing Data	10_Computational_Analysis_of_NGS_Data
11	Dec 8	Evolutionary Mechanisms	11_Evolutionary_Mechanisms
12	Dec 15	Genome-wide Association Studies	12_Genome-wide_association_studies
13	Dec 22	Wrap-up

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