# Suchergebnis: Katalogdaten im Herbstsemester 2016

Computational Biology and Bioinformatics Master More informations at: Link | ||||||

Kernfächer | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
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262-5120-00L | Principles of Evolution: Theory (University of Zurich) Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: BIO351 Beachten Sie die Einschreibungstermine an der UZH: Link | W | 6 KP | 3V | Uni-Dozierende | |

Kurzbeschreibung | "Nothing in Biology Makes Sense Except in the Light of Evolution". Evolutionary theory and methods are essential in all branches of modern biology. | |||||

Lernziel | Subject specific skills: By the end of the course, students will be able to: o describe basic evolutionary theory and its applications o discuss ongoing debates in evolutionary biology o critically assess the presentation of evolutionary research in the popular media Key skills: By the end of the course, students will be able to: o approach biological questions from an evolutionary perspective | |||||

Inhalt | This course will provide a broad overview of current evolutionary thought, including the mechanisms of evolutionary change, adaptation and the history of life and will involve practical field and lab work as well as lecture material. | |||||

401-6282-00L | Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich)Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: STA426 Beachten Sie die Einschreibungstermine an der UZH: Link | W | 5 KP | 3G | H. Rehrauer, M. Robinson | |

Kurzbeschreibung | A range of topics will be covered, including basic molecular biology, genomics technologies and in particular, a wide range of statistical and computational methods that have been used in the analysis of DNA microarray and high throughput sequencing experiments. | |||||

Lernziel | -Understand the fundamental "scientific process" in the field of Statistical Bioinformatics -Be equipped with the skills/tools to preprocess genomic data (Unix, Bioconductor, mapping, etc.) and ensure reproducible research (Sweave) -Have a general knowledge of the types of data and biological applications encountered with microarray and sequencing data -Have the general knowledge of the range of statistical methods that get used with microarray and sequencing data -Gain the ability to apply statistical methods/knowledge/software to a collaborative biological project -Gain the ability to critical assess the statistical bioinformatics literature -Write a coherent summary of a bioinformatics problem and its solution in statistical terms | |||||

Inhalt | Lectures will include: microarray preprocessing; normalization; exploratory data analysis techniques such as clustering, PCA and multidimensional scaling; Controlling error rates of statistical tests (FPR versus FDR versus FWER); limma (linear models for microarray analysis); mapping algorithms (for RNA/ChIP-seq); RNA-seq quantification; statistical analyses for differential count data; isoform switching; epigenomics data including DNA methylation; gene set analyses; classification | |||||

Skript | Lecture notes, published manuscripts | |||||

Voraussetzungen / Besonderes | Prerequisites: Basic knowlegde of the programming language R, sufficient knowledge in statistics Former course title: Statistical Methods for the Analysis of Microarray and Short-Read Sequencing Data | |||||

551-0307-00L | Molecular and Structural Biology I: Protein Structure and Function D-BIOL BSc students are obliged to take part I and part II (next semester) as a two-semester course | W | 3 KP | 2V | R. Glockshuber, K. Locher, E. Weber-Ban | |

Kurzbeschreibung | Biophysik der Proteinfaltung, Membranproteine und Biophysik von Membranen, enzymatischen Katalyse, katalytische RNA und RNAi, aktuelle Themen in Proteinbiophysik und Strukturbiologie. | |||||

Lernziel | Verständnis von Struktur/Funktionsbeziehungen in Proteinen, Proteinfaltung, Vertiefung der Kenntnisse in Biophysik, in physikalischen Messmethoden und modernen Methoden der Proteinreinigung und Protein-Mikroanalytik. | |||||

Skript | Skripte zu einzelnen Themen der Vorlesung sind unter Link abgelegt. | |||||

Literatur | Grundlagen: - Creighton, T.E., Proteins, Freeman, (1993). - Fersht, A., Enzyme, Structure and Mechanism in Protein Science (1999), Freeman. - Berg, Tymoczko, Stryer: Biochemistry (5th edition), Freeman (2001). Aktuelle Themen: Literatur wird jeweils in der Vorlesung angegeben | |||||

636-0007-00L | Computational Systems Biology | W | 6 KP | 3V + 2U | J. Stelling | |

Kurzbeschreibung | Study of fundamental concepts, models and computational methods for the analysis of complex biological networks. Topics: Systems approaches in biology, biology and reaction network fundamentals, modeling and simulation approaches (topological, probabilistic, stoichiometric, qualitative, linear / nonlinear ODEs, stochastic), and systems analysis (complexity reduction, stability, identification). | |||||

Lernziel | The aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks. | |||||

Inhalt | Biology has witnessed an unprecedented increase in experimental data and, correspondingly, an increased need for computational methods to analyze this data. The explosion of sequenced genomes, and subsequently, of bioinformatics methods for the storage, analysis and comparison of genetic sequences provides a prominent example. Recently, however, an additional area of research, captured by the label "Systems Biology", focuses on how networks, which are more than the mere sum of their parts' properties, establish biological functions. This is essentially a task of reverse engineering. The aim of this course is to provide an introductory overview of corresponding computational methods for the modeling, simulation and analysis of biological networks. We will start with an introduction into the basic units, functions and design principles that are relevant for biology at the level of individual cells. Making extensive use of example systems, the course will then focus on methods and algorithms that allow for the investigation of biological networks with increasing detail. These include (i) graph theoretical approaches for revealing large-scale network organization, (ii) probabilistic (Bayesian) network representations, (iii) structural network analysis based on reaction stoichiometries, (iv) qualitative methods for dynamic modeling and simulation (Boolean and piece-wise linear approaches), (v) mechanistic modeling using ordinary differential equations (ODEs) and finally (vi) stochastic simulation methods. | |||||

Skript | Link | |||||

Literatur | U. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006. Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2006. | |||||

636-0009-00L | Evolutionary Dynamics | W | 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) | |||||

636-0017-00L | Computational Biology | W | 4 KP | 3G | T. Stadler, C. Magnus | |

Kurzbeschreibung | The aim of the course is to provide up-to-date knowledge on how we can study biological processes using genetic sequencing data. Computational algorithms extracting biological information from genetic sequence data are discussed, and statistical tools to understand this information in detail are introduced. | |||||

Lernziel | Attendees will learn which information is contained in genetic sequencing data and how to extract information from them using computational tools. The main concepts introduced are: * stochastic models in molecular evolution * phylogenetic & phylodynamic inference * maximum likelihood and Bayesian statistics Attendees will apply these concepts to a number of applications yielding biological insight into: * epidemiology * pathogen evolution * macroevolution of species | |||||

Inhalt | The course consists of four parts. We first introduce modern genetic sequencing technology, and algorithms to obtain sequence alignments from the output of the sequencers. We then present methods to directly analyze this alignment (such as BLAST algorithm, GWAS approaches). Second, we introduce mechanisms and concepts of molecular evolution, i.e. we discuss how genetic sequences change over time. Third, we employ evolutionary concepts to infer ancestral relationships between organisms based on their genetic sequences, i.e. we discuss methods to infer genealogies and phylogenies. We finally introduce the field of phylodynamics. The aim of that field is to understand and quantify the population dynamic processes (such as transmission in epidemiology or speciation & extinction in macroevolution) based on a phylogeny. Throughout the class, the models and methods are illustrated on different datasets giving insight into the epidemiology and evolution of a range of infectious diseases (e.g. HIV, HCV, influenza, Ebola). Applications of the methods to the field of macroevolution provide insight into the evolution and ecology of different species clades. Students will be trained in the algorithms and their application both on paper and in silico as part of the exercises. | |||||

Skript | Slides of the lecture will be available online. Link | |||||

Literatur | The course is not based on any of the textbooks below, but they are excellent choices as accompanying material: * Yang, Z. 2006. Computational Molecular Evolution. * Felsenstein, J. 2004. Inferring Phylogenies. * Semple, C. & Steel, M. 2003. Phylogenetics. * Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST | |||||

Voraussetzungen / Besonderes | Basic knowledge in linear algebra, analysis, and statistics will be helpful. Some programming experience will be useful for the exercises, but is not required. Programming skills will not be tested in the examination. | |||||

636-0706-00L | Spatio-Temporal Modelling in Biology | W | 5 KP | 3G | D. Iber | |

Kurzbeschreibung | This course focuses on modeling spatio-temporal problems in biology, in particular on the cell and tissue level. A wide range of mathematical techniques will be presented as part of the course, including concepts from non-linear dynamics (ODE and PDE models), stochastic techniques (SDE, Master equations, Monte Carlo simulations), and thermodynamic descriptions. | |||||

Lernziel | The aim of the course is to introduce students to state-of-the-art mathematical modelling of spatio-temporal problems in biology. Students will learn how to chose from a wide range of modelling techniques and how to apply these to further our understanding of biological mechanisms. The course aims at equipping students with the tools and concepts to conduct successful research in this area; both classical as well as recent research work will be discussed. | |||||

Inhalt | 1. Introduction to Modelling in Biology 2. Morphogen Gradients 3. Turing Pattern 4. Travelling Waves & Wave Pinning 5. Application Example 1: Dorso-ventral axis formation 6. Chemotaxis, Cell Adhesion & Migration 7. Introduction to Numerical Methods 8. Simulations on Growing Domains 9. Image-Based Modelling 10. Branching Processes 11. Cell-based Simulation Frameworks 12. Application Example 2: Limb Development 13. Summary | |||||

Skript | All lecture material will be made available online Link | |||||

Literatur | Murray, Mathematical Biology, Springer Forgacs and Newman, Biological Physics of the Developing Embryo, CUP Keener and Sneyd, Mathematical Physiology, Springer Fall et al, Computational Cell Biology, Springer Szallasi et al, System Modeling in Cellular Biology, MIT Press Wolkenhauer, Systems Biology Kreyszig, Engineering Mathematics, Wiley | |||||

Voraussetzungen / Besonderes | The course builds on introductory courses in Computational Biology. The course assumes no background in biology but a good foundation regarding mathematical and computational techniques. |

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