# Niko Beerenwinkel: Catalogue data in Autumn Semester 2018

Name | Prof. Dr. Niko Beerenwinkel |

Field | Computational Biology |

Address | Professur f. Computational Biology ETH Zürich, D-BSSE, BSD G 264 Mattenstrasse 26 4058 Basel SWITZERLAND |

Telephone | +41 61 387 31 69 |

niko.beerenwinkel@bsse.ethz.ch | |

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

Department | Biosystems Science and Engineering |

Relationship | Full Professor |

Number | Title | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|

636-0009-00L | Evolutionary Dynamics | 6 credits | 2V + 1U + 2A | N. Beerenwinkel | |

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. | ||||

Lecture notes | No. | ||||

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. | ||||

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

636-0101-00L | Systems GenomicsDoes not take place this semester. This lecture will take place again in Spring Semester 2019. | 4 credits | 3G | N. Beerenwinkel, S. Reddy | |

Abstract | This lecture course is an introduction to Systems Genomics. It addresses how fundamental questions in biological systems are studied and how the resulting data is statistically analyzed in order to derive predictive mathematical models. The focus is on viewing biology from a genomic perspective, which requires high-throughput experimental methods (e.g., RNA-seq, genome-scale screening, single-cell | ||||

Objective | The goal of this course is to learn how a detailed quantitative description of genome biology can be employed for a better understanding of molecular and cellular processes and function. Students will learn fundamental questions driving the field of Systems Genomics. They will also be introduced to traditional and advanced state-of-the-art technologies (e.g., CRISPR-Cas9 screening, droplet-microfluidic sequencing, cellular genetic barcoding) that are used to obtain quantitative data in Systems Genomics. They will learn how to use these data to develop mathematical models and efficient statistical inference algorithms to recognize patterns, molecular interrelationships, and systems behavior. Finally, students will gain a perspective of how Systems Genomics can be used for applied biological sciences (e.g., drug discovery and screening, bio-production, cell line engineering, biomarker discovery, and diagnostics). | ||||

Content | Lectures in Systems Genomics will alternate between lectures on (i) biological questions, experimental technologies, and applications, and (ii) statistical data analysis and mathematical modeling. Selected complex biological systems and the respective experimental tools for a quantitative analysis will be presented. Some specific examples are the use of RNA-sequencing to do quantitative gene expression profiling, CRISPR-Cas9 genome scale screening to identify genes responsible for drug resistance, single-cell measurements to identify novel cellular phenotypes, and genetic barcoding of cells to dissect development and lineage differentiation. Main Topics: -- Next-generation sequencing -- Transcriptomics -- Biological network analysis -- Functional and perturbation genomics -- Single-cell biology and analysis -- Genomic profiling of the immune system -- Genomic profiling of cancer -- Evolutionary genomics -- Genome-wide association studies Selected genomics datasets will be analyzed by students in the tutorials using the statistical programming language R and dedicated Bioconductor packages. | ||||

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 | -- Do K-A, Qin ZS & Vannucci M (2013) Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput Data, Cambridge University Press -- Klipp E. et al (2009) Systems Biology, Wiley-Blackwell -- Alon U (2007) An Introduction to Systems Biology, Chapman & Hall -- Zvelebil M & Baum JO (2008) Understanding Bioinformatics, Garland Science | ||||

636-0301-00L | Current Topics in Biosystems Science and EngineeringFor doctoral students only. Master's students cannot receive credits for the seminar. | 2 credits | 1S | R. Platt, N. Beerenwinkel, Y. Benenson, K. M. Borgwardt, P. S. Dittrich, M. Fussenegger, A. Hierlemann, D. Iber, M. H. Khammash, D. J. Müller, S. Panke, R. Paro, S. Reddy, T. Schroeder, T. Stadler, J. Stelling | |

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

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

Content | The final list of topics will be available at https://www.bsse.ethz.ch/news-and-events/seminar-series.html | ||||

636-0704-00L | Computational Biology and Bioinformatics SeminarThe Seminar will be offered in autumn semester in Basel and in spring semester in Zürich. | 2 credits | 2S | N. Beerenwinkel, M. Claassen, D. Iber, T. Stadler, J. Stelling | |

Abstract | Computational biology and bioinformatics aim at an understanding of living systems through computation. The seminar combines student presentations and current research project presentations to review the rapidly developing field from a computer science perspective. Areas: DNA sequence analysis, proteomics, optimization and bio-inspired computing, and systems modeling, simulation and analysis. | ||||

Objective | Studying and presenting fundamental papers of Computational Biology and Bioinformatics. Learning how to make a scientific presentation and how classical methods are used or further developed in current research. | ||||

Content | Computational biology and bioinformatics aim at advancing the understanding of living systems through computation. The complexity of these systems, however, provides challenges for software and algorithms, and often requires entirely novel approaches in computer science. The aim of the seminar is to give an overview of this rapidly developing field from a computer science perspective. In particular, it will focus on the areas of (i) DNA sequence analysis, sequence comparison and reconstruction of phylogenetic trees, (ii) protein identification from experimental data, (iii) optimization and bio-inspired computing, and (iv) systems analysis of complex biological networks. The seminar combines the discussion of selected research papers with a major impact in their domain by the students with the presentation of current active research projects / open challenges in computational biology and bioinformatics by the lecturers. Each week, the seminar will focus on a different topic related to ongoing research projects at ETHZ, University of Basel and University of Zurich, thus giving the students the opportunity of obtaining knowledge about the basic research approaches and problems as well as of gaining insight into (and getting excited about) the latest developments in the field. | ||||

Literature | Original papers to be presented by the students will be provided in the first week of the seminar. |