Suchergebnis: Katalogdaten im Herbstsemester 2018

Computational Biology and Bioinformatics Master Information
More informations at: Link
Master-Studium (Studienreglement 2011)
Kernfächer
NummerTitelTypECTSUmfangDozierende
262-5120-00LPrinciples 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
W6 KP3VUni-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.
LernzielSubject 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
InhaltThis 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.
401-6282-00LStatistical 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
W5 KP3GH. Rehrauer, M. Robinson
KurzbeschreibungA 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
InhaltLectures 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
SkriptLecture notes, published manuscripts
Voraussetzungen / BesonderesPrerequisites: 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
551-0307-00LMolecular and Structural Biology I: Protein Structure and Function Information
D-BIOL students are obliged to take part I and part II (next semester) as a two-semester course
W3 KP2VR. Glockshuber, K. Locher, E. Weber-Ban
KurzbeschreibungBiophysik der Proteinfaltung, Membranproteine und Biophysik von Membranen, enzymatischen Katalyse, katalytische RNA und RNAi, aktuelle Themen in Proteinbiophysik und Strukturbiologie.
LernzielVerständnis von Struktur/Funktionsbeziehungen in Proteinen, Proteinfaltung, Vertiefung der Kenntnisse in Biophysik, in physikalischen Messmethoden und modernen Methoden der Proteinreinigung und Protein-Mikroanalytik.
SkriptSkripte zu einzelnen Themen der Vorlesung sind unter Link abgelegt.
LiteraturGrundlagen:
- 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
636-0007-00LComputational Systems Biology Information W6 KP3V + 2UJ. Stelling
KurzbeschreibungStudy 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).
LernzielThe aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks.
InhaltBiology 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.
SkriptLink
LiteraturU. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006.

Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2010.

B. Ingalls, Mathematical modeling in systems biology: an introduction. MIT Press, 2013
636-0009-00LEvolutionary DynamicsW6 KP2V + 1U + 2AN. Beerenwinkel
KurzbeschreibungEvolutionary 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.
LernzielThe goal of this course is to understand and to appreciate mathematical models and computational methods that provide insight into the evolutionary process.
InhaltEvolution 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.
SkriptNo.
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 / BesonderesPrerequisites: Basic mathematics (linear algebra, calculus, probability)
636-0017-00LComputational Biology Information W6 KP3G + 2AT. Stadler, C. Magnus, T. Vaughan
KurzbeschreibungThe 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.
LernzielAttendees will learn which information is contained in genetic sequencing data and how to extract information from this data 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
InhaltThe 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 for direct alignment analysis using approaches such as BLAST and GWAS. 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. Lastly, we introduce the field of phylodynamics, the aim of which is to understand and quantify 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.
SkriptLecture slides will be available on moodle.
LiteraturThe 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 / BesonderesBasic knowledge in linear algebra, analysis, and statistics will be helpful. Programming in R will be required for the project work (compulsory continuous performance assessments). We provide an R tutorial and help sessions during the first two weeks of class to learn the required skills. However, in case you do not have any previous experience with R, we strongly recommend to get familiar with R prior to the semester start. For the D-BSSE students, we highly recommend the voluntary course „Introduction to Programming“, which takes place at D-BSSE from Wednesday, September 12 to Friday, September 14, i.e. BEFORE the official semester starting date Link
For the Zurich-based students without R experience, we recommend the R course Link, or working through the script provided as part of this R course.
636-0706-00LSpatio-Temporal Modelling in Biology Information W4 KP3GD. Iber
KurzbeschreibungThis course focuses on modeling spatio-temporal problems in biology, in particular on the cell and tissue level. The main focus is on mechanisms and concepts, but mathematical and numerical techniques are introduced as required. Biological examples discussed in the course provide an introduction to key concepts in developmental biology.
LernzielStudents will learn state-of-the-art approaches to modelling spatial effects in dynamical biological systems. The course provides an introduction to dynamical system, and covers the mathematical analysis of pattern formation in growing, developing systems, as well as the description of mechanical effects at the cell and tissue level. The course also provides an introduction to image-based modelling, i.e. the use of microscopy data for model development and testing. The course covers classic as well as current approaches and exposes students to open problems in the field. In this way, the course seeks to prepare students to conduct research in the field. The course prepares students for research in developmental biology, as well as for applications in tissue engineering, and for biomedical research.
Inhalt1. Introduction to Modelling in Biology
2. Morphogen Gradients
3. Dynamical Systems
4. Cell-cell Signalling (Dr Boareto)
5. Travelling Waves
6. Turing Patterns
7. Chemotaxis
8. Mathematical Description of Growing Biological Systems
9. Image-Based Modelling
10. Tissue Mechanics
11. Cell-based Tissue Simulation Frameworks
12. Plant Development (Dr Dumont)
13. Growth Control
14. Summary
SkriptAll lecture material will be made available online
Link
LiteraturThe lecture course is not based on any textbook. The following textbooks are related to some of its content. The textbooks may be of interest for further reading, but are not necessary to follow the course:

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 / BesonderesThe course is self-contained. The course assumes no background in biology but a good foundation regarding mathematical and computational techniques.
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