Suchergebnis: Katalogdaten im Herbstsemester 2022
Biologie Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Wahlvertiefung: Systembiologie | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Wahlpflicht Masterkurse II: Biologie | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 551-1153-00L | Systems Biology of Metabolism Number of participants limited to 15. | W | 4 KP | 2V | U. Sauer, N. Zamboni, M. Zampieri | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Starting from contemporary biological problems related to metabolism, the course focuses on systems biological approaches to address them. In a problem-oriented, this-is-how-it-is-done manner, we thereby teach modern methods and concepts. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Develop a deeper understanding of how relevant biological problems can be solved, thereby providing advanced insights to key experimental and computational methods in systems biology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course will be given as a mixture of lectures, studies of original research and guided discussions that focus on current research topics. For each particular problem studied, we will work out how the various methods work and what their capabilities/limits are. The problem areas range from microbial metabolism to cancer cell metabolism and from metabolic networks to regulation networks in populations and single cells. Key methods to be covered are various modeling approaches, metabolic flux analyses, metabolomics and other omics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Script and original publications will be supplied during the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The course extends many of the generally introduced concepts and methods of the Concept Course in Systems Biology. It requires a good knowledge of biochemistry and basics of mathematics and chemistry. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 551-0571-00L | From DNA to Diversity (University of Zurich) Der Kurs muss direkt an der UZH als incoming student belegt werden. UZH Modulkürzel: BIO336 Beachten Sie die Einschreibungstermine an der UZH: https://www.uzh.ch/cmsssl/de/studies/application/deadlines.html | W | 2 KP | 2V | A. Hajnal, D. Bopp | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The evolution of the various body-plans is investigated by means of comparison of developmentally essential control genes of molecularly analysed model organisms. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | By the end of this module, each student should be able to - recognize the universal principles underlying the development of different animal body plans. - explain how the genes encoding the molecular toolkit have evolved to create animal diversity. - relate changes in gene structure or function to evolutionary changes in animal development. Key skills: By the end of this module, each student should be able to - present and discuss a relevant evolutionary topic in an oral presentation - select and integrate key concepts in animal evolution from primary literature - participate in discussions on topics presented by others | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 636-0009-00L | Evolutionary Dynamics | W | 6 KP | 2V + 1U + 2A | 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, with an emphasis on tumor evolution. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The goal of this course is to understand and to appreciate mathematical models and computational methods that provide insight into the evolutionary process in general and tumor evolution in particular. Students should analyze and evaluate models and their application critically and be able to design new models. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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, evolutionary stability, evolutionary graph theory, tumor evolution, stochastic tunneling, genetic progression of cancer, diffusion theory, fitness landscapes, branching processes, and evolutionary escape. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 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) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 227-0939-00L | Cell Biophysics | W | 6 KP | 4G | T. Zambelli | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Applying two fundamental principles of thermodynamics (entropy maximization and Gibbs energy minimization), an analytical model is derived for a variety of biological phenomena at the molecular as well as cellular level, and critically compared with the corresponding experimental data in the literature. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Engineering uses the laws of physics to predict the behavior of a system. Biological systems are so diverse and complex prompting the question whether we can apply unifying concepts of theoretical physics coping with the multiplicity of life’s mechanisms. Objective of this course is to show that biological phenomena despite their variety can be analytically described using only two principles from statistical mechanics: maximization of the entropy and minimization of the Gibbs free energy. Starting point of the course is the probability theory, which enables to derive step-by-step the two pillars of thermodynamics from the perspective of statistical mechanics: the maximization of entropy according to the Boltzmann’s law as well as the minimization of the Gibbs free energy. Then, an assortment of biological phenomena at the molecular and cellular level (e.g. cytoskeletal polymerization, action potential, photosynthesis, gene regulation, morphogen patterning) will be examined at the light of these two principles with the aim to derive a quantitative expression describing their behavior. Each analytical model is finally validated by comparing it with the corresponding available experimental results. By the end of the course, students will also learn to critically evaluate the concepts of making an assumption and making an approximation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | • Basics of theory of probability • Boltzmann's law • Entropy maximization and Gibbs free energy minimization • Ligand-receptor: two-state systems and the MWC model • Random walks, diffusion, crowding • Electrostatics for salty solutions • Elasticity: fibers and membranes • Molecular motors • Action potential: Hodgkin-Huxley model • Photosynthesis and vision • Gene regulation • Development: Turing patterns • Sequences and evolution Theory and corresponding exercises are merged together during the classes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | No lecture notes because the two proposed textbooks are more than exhaustive! An extra hour (Mon 17.00 o'clock - 18.00) will be proposed via ZOOM to solve together the exercises of the previous week. !!!!! I am using OneNote. All lectures and exercises will be broadcast via ZOOM and correspondingly recorded (link in Moodle) !!!!! | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | • (Statistical Mechanics) K. Dill, S. Bromberg, "Molecular Driving Forces", 2nd Edition, Garland Science, 2010. • (Biophysics) R. Phillips, J. Kondev, J. Theriot, H. Garcia, "Physical Biology of the Cell", 2nd Edition, Garland Science, 2012. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Participants need a good command of • differentiation and integration of a function with one or more variables (basics of Analysis), • Newton's and Coulomb's laws (basics of Mechanics and Electrostatics). Notions of vectors in 2D and 3D are beneficial. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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