# Suchergebnis: Katalogdaten im Herbstsemester 2019

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

Master-Studium (Studienreglement 2017) | ||||||

Kernfächer Please note that the list of core courses is a closed list. Other courses cannot be added to the core course category in the study plan. Also the assignments of courses to core subcategories cannot be changed. Students need to pass at least one course in each core subcategory. A total of 40 ECTS needs to be acquired in the core course category. | ||||||

Biosystems | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
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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, 2010. B. Ingalls, Mathematical modeling in systems biology: an introduction. MIT Press, 2013 | |||||

636-0706-00L | Spatio-Temporal Modelling in Biology | W | 4 KP | 3G | D. Iber | |

Kurzbeschreibung | This 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. | |||||

Lernziel | Students 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. | |||||

Inhalt | 1. 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 | |||||

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

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

262-6130-00L | Computational Systems Biology | W | 6 KP | 3G | externe Veranstalter | |

Kurzbeschreibung | ||||||

Lernziel |

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