Search result: Catalogue data in Autumn Semester 2020

Number	Title	Type	ECTS	Hours	Lecturers
Biology Master
Elective Major Subject Areas
Elective Major: Systems Biology
Elective Compulsory Master Courses II: Biology
551-1153-00L	Systems Biology of Metabolism Number of participants limited to 15.	W	4 credits	2V	U. Sauer, N. Zamboni, M. Zampieri
Abstract	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.
Learning objective	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.
Content	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.
Lecture notes	Script and original publications will be supplied during the course.
Prerequisites / Notice	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) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: BIO336 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/chmobilityin.html l	W	2 credits	2V	A. Hajnal, D. Bopp
Abstract	The evolution of the various body-plans is investigated by means of comparison of developmentally essential control genes of molecularly analysed model organisms.
Learning objective	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 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.
Learning 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)
227-0939-00L	Cell Biophysics	W	6 credits	4G	T. Zambelli
Abstract	A mathematical description is derived for a variety of biological phenomena at the molecular and cellular level applying the two fundamental principles of thermodynamics (entropy maximization and Gibbs energy minimization).
Learning objective	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 concepts 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 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 according to experimental data. By the end of the course, students will also learn to critically evaluate the concepts of making an assumption and making an approximation.
Content	1. Basics of theory of probability 2. Boltzmann's law 3. Entropy maximization and Gibbs free energy minimization 4. Two-state systems and the MWC model 5. Random walks and macromolecular structures 6. Electrostatics for salty solutions 7. Elasticity: fibers and membranes 8. Diffusion and crowding: cell signaling 9. Molecular motors 10. Action potential: Hodgkin-Huxley model 11. Photosynthesis 12. Gene regulation 13. Development: Turing patterns 14. Sequences and evolution
Literature	- 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.
Prerequisites / Notice	Participants need a good command of differentiation and integration of a function with one or more variables (calculus) as well as of Newton's and Coulomb's laws (basics of mechanics and electrostatics). Notions of vectors in 2D and 3D are beneficial. Theory and corresponding exercises are merged together during the classes.

Page 1 of 1