Name | Herr Prof. Dr. Niko Beerenwinkel |

Lehrgebiet | Rechnergestützte Biologie |

Adresse | Professur f. Computational Biology ETH Zürich, BSS G 57.2 Klingelbergstrasse 48 4056 Basel SWITZERLAND |

Telefon | +41 61 387 31 69 |

niko.beerenwinkel@bsse.ethz.ch | |

URL | http://www.bsse.ethz.ch/cbg/people/nikob |

Departement | Biosysteme |

Beziehung | Ordentlicher Professor |

Nummer | Titel | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|

626-0002-AAL | BioinformaticsBelegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben. Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen. | 4 KP | 9R | J. Stelling, N. Beerenwinkel | |

Kurzbeschreibung | The course introduces concepts of bioinformatics starting from first principles: DNA sequence alignment, phylogenetic tree inference, genome annotation, protein structure and function prediction. Key methods and algorithms are covered, including dynamic programming, Markov and Hidden Markov models, and molecular dynamics simulations. Practical applications and limitations are discussed. | ||||

Lernziel | The course aims at introducing the fundamental concepts and methods of bioinformatics. Emphasis is given to a deep understanding of the methods' foundations and limitations to enable critical evaluations and applications of bioinformatics tools in areas such as biotechnology and systems biology. | ||||

Inhalt | From "Understanding Bioinformatics": Chapter 4: Producing and Analyzing Sequence Alignments Chapter 5: Pairwise Sequence Alignment and Database Searching Chapter 6: Patterns, Profiles, and Multiple Alignments Chapter 7: Recovering Evolutionary History Chapter 8: Building Phylogenetic Trees Chapter 9: Revealing Genome Features Chapter 10: Gene Detection and Genome Annotation Chapter 11: Obtaining Secondary Structure from Sequence Chapter 12: Predicting Secondary Structures Chapter 13: Modeling Protein Structure Chapter 14: Analyzing Structure-Function Relationships From "Biological Sequence Analysis": Sections 3.1, 3.2, 3.3, 4.1, 4.2, 4.4, 5.2, 5.3, 5.4, 6.5 (Markov Chains and Hidden Markov Models) From "A First Course in Systems Biology": Chapter 1: Biological Systems | ||||

Skript | Course material will be made available at: http://www.csb.ethz.ch | ||||

Literatur | Zvelebil M, Baum JO. Understanding Bioinformatics. Garland Science, 2008. Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge University Press, 2004. Voit EO. A First Course in Systems Biology. Garland Science, 2012. | ||||

Voraussetzungen / Besonderes | There will be two opportunities for tutorials during the semester http://www.csb.ethz.ch/teaching | ||||

636-0005-00L | Systems Biology | 6 KP | 3G | R. Paro, N. Beerenwinkel | |

Kurzbeschreibung | This lecture course is an introduction to systems biology. It explores how complex biological networks are experimentally studied and how the resulting data is mathematically evaluated in order to derive predictive models. The biology of selected cellular processes, ranging from protein interaction networks to gene controlling systems and signaling cascades will be discussed in detail. | ||||

Lernziel | 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. | ||||

Inhalt | Sessions will alternate between a thorough introduction into the basic biology of defined 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. Topics: - Control of Gene Expression: DNA binding proteins, gene activation in chromatin, posttranscriptional control - Genetic Switches: combinatorial gene control, transcriptional circuits, transcriptional noise/robustness - Analysis of Gene Expression Data: normalization, differential gene expression, multiple testing, PCA, clustering - Large-scale Genomic Profiling: mapping genomes/epigenomes, high throughput sequencing technologies - Analysis of Deep Sequencing Data: quality control, genome assembly, read mapping, RNA-seq, ChIP-seq - Biological Networks: signaling networks and protein-protein interaction networks - Network Biology: basic graph theory, motifs, dense subgraphs, power laws - Boolean Network Dynamics: Boolean algebra, Boolean networks, random Boolean networks, yeast cell cycle - Cellular Communication: signal transduction cascades, regulatory mechanisms - Probabilistic Graphical Models: probabilities, statistical inference, Bayesian networks, nested effects models - Evolutionary Mechanisms: RNA world, origin of life, ribozyme selection, genome evolution, SNP mapping, evolution & development - Genome-wide association studies As part of the tutorial you will work on a real set of data, elaborate the experimental strategy to produce the data and use bioinformatics tools to analyze the data. | ||||

Skript | The Powerpoint presentations of the lectures as well as other course material relevant for an active participation will be made available online. | ||||

Literatur | - Alberts B et al. (2008) Molecular Biology of the Cell, Fifth Edition, Garland Science - Klipp E. et al (2009) Systems Biology, Wiley-Blackwell - 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 | ||||

636-0009-00L | Evolutionary Dynamics | 5 KP | 2V + 1U | N. Beerenwinkel | |

Kurzbeschreibung | 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. | ||||

Lernziel | The goal of this course is to understand and to appreciate mathematical models and computational methods that provide insight into the evolutionary process. | ||||

Inhalt | 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. | ||||

Skript | No. | ||||

Literatur | - 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. | ||||

Voraussetzungen / Besonderes | Prerequisites: Basic mathematics (linear algebra, calculus, probability) | ||||

636-0301-00L | Current Topics in Biosystems Science and Engineering | 2 KP | 1S | T. Stadler, N. Beerenwinkel, Y. Benenson, K. M. Borgwardt, P. S. Dittrich, M. Fussenegger, A. Hierlemann, D. Iber, M. H. Khammash, D. J. Müller, S. Panke, P. Pantazis, R. Paro, R. Platt, S. Reddy, T. Schroeder, J. Stelling | |

Kurzbeschreibung | This seminar will feature invited lectures about recent advances and developments in systems biology, including topics from biology, bioengineering, and computational biology. | ||||

Lernziel | To provide an overview of current systems biology research. | ||||

Inhalt | The final list of topics will be available at http://www.bsse.ethz.ch/education/. |