# Search result: Catalogue data in Autumn Semester 2018

Computational Biology and Bioinformatics Master More informations at: https://www.cbb.ethz.ch/ | ||||||

Master Studies (Programme Regulations 2017) | ||||||

Core Courses 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. | ||||||

Bioinformatics Please note that all Bioinformatics core courses are offered in the autumn semester | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
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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. | |||||

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) | |||||

636-0017-00L | Computational Biology | W | 6 credits | 3G + 2A | T. Stadler, C. Magnus, T. Vaughan | |

Abstract | The 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. | |||||

Objective | Attendees 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 | |||||

Content | The 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. | |||||

Lecture notes | Lecture slides will be available on moodle. | |||||

Literature | The 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. | |||||

Prerequisites / Notice | Basic 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 http://www.cbb.ethz.ch/news-events.html 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. | |||||

262-5120-00L | Principles of Evolution: Theory (University of Zurich)No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: BIO351 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | W | 6 credits | 3V | University lecturers | |

Abstract | "Nothing in Biology Makes Sense Except in the Light of Evolution". Evolutionary theory and methods are essential in all branches of modern biology. | |||||

Objective | Subject 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 | |||||

Content | This 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. | |||||

262-6100-00L | Evolutionary Genetics | W | 6 credits | 5G | external organisers | |

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262-6110-00L | Bioinformatics Algorithms | W | 4 credits | 3G | external organisers | |

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Biophysics | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

551-0307-00L | Molecular and Structural Biology I: Protein Structure and Function D-BIOL students are obliged to take part I and part II (next semester) as a two-semester course | W | 3 credits | 2V | R. Glockshuber, K. Locher, E. Weber-Ban | |

Abstract | Biophysics of protein folding, membrane proteins and biophysics of membranes, enzymatic catalysis, catalytic RNA and RNAi, current topics in protein biophysics and structural biology. | |||||

Objective | Understanding of structure-function relationships in proteins and in protein folding, detailed understanding of biophysics and physical methods as well as modern methods for protein purification and microanalytics. | |||||

Lecture notes | Scripts on the individual topics can be found under http://www.mol.biol.ethz.ch/teaching. | |||||

Literature | Basics: - 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). Current topics: References will be given during the lectures. . | |||||

262-6120-00L | Molecular Biophysics IDoes not take place this semester. | W | 2 credits | 2V | external organisers | |

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262-6160-00L | Theoretical Biophysics | W | 4 credits | 2G | external organisers | |

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Biosystems | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

636-0007-00L | Computational Systems Biology | W | 6 credits | 3V + 2U | J. Stelling | |

Abstract | 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). | |||||

Objective | 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. | |||||

Content | 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. | |||||

Lecture notes | http://www.csb.ethz.ch/education/lectures.html | |||||

Literature | 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 credits | 3G | D. Iber | |

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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 | |||||

Lecture notes | All lecture material will be made available online Link | |||||

Literature | 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 | |||||

Prerequisites / Notice | 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 credits | 3G | external organisers | |

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Data Science | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

636-0018-00L | Data Mining I | W | 6 credits | 3G + 2A | K. M. Borgwardt | |

Abstract | Data Mining, the search for statistical dependencies in large databases, is of utmost important in modern society, in particular in biological and medical research. This course provides an introduction to the key problems, concepts, and algorithms in data mining, and the applications of data mining in computational biology. | |||||

Objective | The goal of this course is that the participants gain an understanding of data mining problems and algorithms to solve these problems, in particular in biological and medical applications. | |||||

Content | The goal of the field of data mining is to find patterns and statistical dependencies in large databases, to gain an understanding of the underlying system from which the data were obtained. In computational biology, data mining contributes to the analysis of vast experimental data generated by high-throughput technologies, and thereby enables the generation of new hypotheses. In this course, we will present the algorithmic foundations of data mining and its applications in computational biology. The course will feature an introduction to popular data mining problems and algorithms, reaching from classification via clustering to feature selection. This course is intended for both students who are interested in applying data mining algorithms and students who would like to gain an understanding of the key algorithmic concepts in data mining. Tentative list of topics: 1. Distance functions 2. Classification 3. Clustering 4. Feature Selection | |||||

Lecture notes | Course material will be provided in form of slides. | |||||

Literature | Will be provided during the course. | |||||

Prerequisites / Notice | Basic understanding of mathematics, as taught in basic mathematics courses at the Bachelor's level. | |||||

401-6282-00L | Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich)No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: STA426 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | W | 5 credits | 3G | H. Rehrauer, M. Robinson | |

Abstract | A 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. | |||||

Objective | -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 | |||||

Content | Lectures 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 | |||||

Lecture notes | Lecture notes, published manuscripts | |||||

Prerequisites / Notice | Prerequisites: 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 | |||||

252-0535-00L | Advanced Machine Learning | W | 8 credits | 3V + 2U + 2A | J. M. Buhmann | |

Abstract | Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects. | |||||

Objective | Students will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data. | |||||

Content | The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data. Topics covered in the lecture include: Fundamentals: What is data? Bayesian Learning Computational learning theory Supervised learning: Ensembles: Bagging and Boosting Max Margin methods Neural networks Unsupservised learning: Dimensionality reduction techniques Clustering Mixture Models Non-parametric density estimation Learning Dynamical Systems | |||||

Lecture notes | No lecture notes, but slides will be made available on the course webpage. | |||||

Literature | C. Bishop. Pattern Recognition and Machine Learning. Springer 2007. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004. | |||||

Prerequisites / Notice | The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments. Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution. | |||||

Seminar Compulsory seminar. | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

636-0704-00L | Computational Biology and Bioinformatics SeminarThe Seminar will be offered in autumn semester in Basel and in spring semester in Zürich. | O | 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. | |||||

Advanced Courses A total of 30 ECTS needs to be acquired in the Advanced Courses category. Thereof 18 ECTS in the Theory and 12 ECTS in the Biology category. | ||||||

Theory At least 18 ECTS need to be acquired in this category. | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

401-0663-00L | Numerical Methods for CSE | W | 8 credits | 4V + 2U + 1P | R. Alaifari | |

Abstract | The course gives an introduction into fundamental techniques and algorithms of numerical mathematics which play a central role in numerical simulations in science and technology. The course focuses on fundamental ideas and algorithmic aspects of numerical methods. The exercises involve actual implementation of numerical methods in C++. | |||||

Objective | * Knowledge of the fundamental algorithms in numerical mathematics * Knowledge of the essential terms in numerical mathematics and the techniques used for the analysis of numerical algorithms * Ability to choose the appropriate numerical method for concrete problems * Ability to interpret numerical results * Ability to implement numerical algorithms afficiently | |||||

Content | 1. Direct Methods for linear systems of equations 2. Least Squares Techniques 3. Data Interpolation and Fitting 4. Filtering Algorithms 8. Approximation of Functions 9. Numerical Quadrature 10. Iterative Methods for non-linear systems of equations 11. Single Step Methods for ODEs 12. Stiff Integrators | |||||

Lecture notes | Lecture materials (PDF documents and codes) will be made available to the participants through the course web page: https://metaphor.ethz.ch/x/2018/hs/401-0663-00L/ | |||||

Literature | U. ASCHER AND C. GREIF, A First Course in Numerical Methods, SIAM, Philadelphia, 2011. A. QUARTERONI, R. SACCO, AND F. SALERI, Numerical mathematics, vol. 37 of Texts in Applied Mathematics, Springer, New York, 2000. W. Dahmen, A. Reusken "Numerik für Ingenieure und Naturwissenschaftler", Springer 2006. M. Hanke-Bourgeois "Grundlagen der Numerischen Mathematik und des wissenschaftlichen Rechnens", BG Teubner, 2002 P. Deuflhard and A. Hohmann, "Numerische Mathematik I", DeGruyter, 2002 | |||||

Prerequisites / Notice | The course will be accompanied by programming exercises in C++ relying on the template library EIGEN. Familiarity with C++, object oriented and generic programming is an advantage. Participants of the course are expected to learn C++ by themselves. | |||||

263-5210-00L | Probabilistic Artificial Intelligence | W | 4 credits | 2V + 1U | A. Krause | |

Abstract | This course introduces core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. | |||||

Objective | How can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for upper-level undergraduate and graduate students. | |||||

Content | Topics covered: - Search (BFS, DFS, A*), constraint satisfaction and optimization - Tutorial in logic (propositional, first-order) - Probability - Bayesian Networks (models, exact and approximative inference, learning) - Temporal models (Hidden Markov Models, Dynamic Bayesian Networks) - Probabilistic palnning (MDPs, POMPDPs) - Reinforcement learning - Combining logic and probability | |||||

Prerequisites / Notice | Solid basic knowledge in statistics, algorithms and programming | |||||

401-0647-00L | Introduction to Mathematical Optimization | W | 5 credits | 2V + 1U | D. Adjiashvili | |

Abstract | Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering. | |||||

Objective | The goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering. | |||||

Content | Topics covered in this course include: - Linear programming (simplex method, duality theory, shadow prices, ...). - Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...). - Modelling with mathematical optimization: applications of mathematical programming in engineering. | |||||

Literature | Information about relevant literature will be given in the lecture. | |||||

Prerequisites / Notice | This course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics. Compared to "Mathematical Optimization", this course has a stronger focus on modeling and applications. | |||||

227-0225-00L | Linear System Theory | W | 6 credits | 5G | M. Kamgarpour | |

Abstract | The class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems. | |||||

Objective | Students should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems. | |||||

Content | - Proof techniques and practices. - Linear spaces, normed linear spaces and Hilbert spaces. - Ordinary differential equations, existence and uniqueness of solutions. - Continuous and discrete-time, time-varying linear systems. Time domain solutions. Time invariant systems treated as a special case. - Controllability and observability, duality. Time invariant systems treated as a special case. - Stability and stabilization, observers, state and output feedback, separation principle. | |||||

Lecture notes | Available on the course Moodle platform. | |||||

Prerequisites / Notice | Sufficient mathematical maturity with special focus on logic, linear algebra, analysis. | |||||

151-0575-01L | Signals and Systems | W | 4 credits | 2V + 2U | A. Carron, G. Ducard | |

Abstract | Signals arise in most engineering applications. They contain information about the behavior of physical systems. Systems respond to signals and produce other signals. In this course, we explore how signals can be represented and manipulated, and their effects on systems. We further explore how we can discover basic system properties by exciting a system with various types of signals. | |||||

Objective | Master the basics of signals and systems. Apply this knowledge to problems in the homework assignments and programming exercise. | |||||

Content | Discrete-time signals and systems. Fourier- and z-Transforms. Frequency domain characterization of signals and systems. System identification. Time series analysis. Filter design. | |||||

Lecture notes | Lecture notes available on course website. | |||||

Prerequisites / Notice | Control Systems I is helpful but not required. | |||||

529-0004-01L | Computer Simulation in Chemistry, Biology and Physics | W | 6 credits | 4G | P. H. Hünenberger | |

Abstract | Molecular models, Force fields, Boundary conditions, Electrostatic interactions, Molecular dynamics, Analysis of trajectories, Quantum-mechanical simulation, Structure refinement, Application to real systems. Exercises: Analysis of papers on computer simulation, Molecular simulation in practice, Validation of molecular dynamics simulation. | |||||

Objective | Introduction to computer simulation of (bio)molecular systems, development of skills to carry out and interpret computer simulations of biomolecular systems. | |||||

Content | Molecular models, Force fields, Spatial boundary conditions, Calculation of Coulomb forces, Molecular dynamics, Analysis of trajectories, Quantum-mechanical simulation, Structure refinement, Application to real systems. Exercises: Analysis of papers on computer simulation, Molecular simulation in practice, Validation of molecular dynamics simulation. | |||||

Lecture notes | Available (copies of powerpoint slides distributed before each lecture) | |||||

Literature | See: www.csms.ethz.ch/education/CSCBP | |||||

Prerequisites / Notice | Since the exercises on the computer do convey and test essentially different skills as those being conveyed during the lectures and tested at the oral exam, the results of the exercises are taken into account when evaluating the results of the exam (learning component, possible bonus of up to 0.25 points on the exam mark). For more information about the lecture: www.csms.ethz.ch/education/CSCBP | |||||

252-0237-00L | Concepts of Object-Oriented Programming | W | 6 credits | 3V + 2U | P. Müller | |

Abstract | Course that focuses on an in-depth understanding of object-oriented programming and compares designs of object-oriented programming languages. Topics include different flavors of type systems, inheritance models, encapsulation in the presence of aliasing, object and class initialization, program correctness, reflection | |||||

Objective | After this course, students will: Have a deep understanding of advanced concepts of object-oriented programming and their support through various language features. Be able to understand language concepts on a semantic level and be able to compare and evaluate language designs. Be able to learn new languages more rapidly. Be aware of many subtle problems of object-oriented programming and know how to avoid them. | |||||

Content | The main goal of this course is to convey a deep understanding of the key concepts of sequential object-oriented programming and their support in different programming languages. This is achieved by studying how important challenges are addressed through language features and programming idioms. In particular, the course discusses alternative language designs by contrasting solutions in languages such as C++, C#, Eiffel, Java, Python, and Scala. The course also introduces novel ideas from research languages that may influence the design of future mainstream languages. The topics discussed in the course include among others: The pros and cons of different flavors of type systems (for instance, static vs. dynamic typing, nominal vs. structural, syntactic vs. behavioral typing) The key problems of single and multiple inheritance and how different languages address them Generic type systems, in particular, Java generics, C# generics, and C++ templates The situations in which object-oriented programming does not provide encapsulation, and how to avoid them The pitfalls of object initialization, exemplified by a research type system that prevents null pointer dereferencing How to maintain the consistency of data structures | |||||

Literature | Will be announced in the lecture. | |||||

Prerequisites / Notice | Prerequisites: Mastering at least one object-oriented programming language (this course will NOT provide an introduction to object-oriented programming); programming experience | |||||

262-6140-00L | Random Processes: Theory and Applications from Physics to Finance | W | 4 credits | 3G | external organisers | |

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Objective | ||||||

262-6150-00L | Programming for Life Sciences | W | 4 credits | 2P | external organisers | |

Abstract | ||||||

Objective | ||||||

636-0015-00L | An Introduction to Probability Theory and Stochastic Processes with Applications to BiologyDoes not take place this semester. | W | 4 credits | 3G | ||

Abstract | Biology is becoming increasingly quantitative and mathematical modeling is now an integral part of biological research. In many biological processes, ranging from gene-expression to evolution, randomness plays an important role that can only be understood using stochastic models. This course will provide the students with a theoretical foundation for developing such stochastic models and analyzing | |||||

Objective | The aim of this course is to introduce certain topics in Probability Theory and Stochastic Processes that have been specifically selected with an eye on biological applications. This course will teach students the tools and techniques for modeling and analyzing random phenomena. Throughout the course, several biological applications will be discussed and students will be encouraged to do additional reading based on their research interests. | |||||

Content | The first half of the course will cover the basics of Probability Theory while the second half will delve into the theory of Stochastic Processes. Below is the list of topics that will be covered in the course. 1. The mathematical representation of random phenomena: The probability space, properties of the probability measure, Independence of events, Conditional probability and Bayes formula, applications to parameter inference. 2. Random Variables and their distributions: Discrete and continuous random variables, Expectation and Variance, Important Examples of Random Variables, Independent random variables and their sums, Conditional Distribution and Conditional Expectation, Markov and Chebyshev inequalities. Law of total variation, estimation of intrinsic and extrinsic noise in biological systems. 3. Convergence of Random Variables: Modes of convergence, Laws of large numbers, the central limit theorem, the law of the iterated logarithm, Applications to the analysis of cell population data. 4. Generating functions and their applications: Definition and important examples, Random Walks, Branching processes, Coalescent processes, Modeling epidemic processes and stem-cell differentiation. 5. Markov chains: Transition functions and related computations, Classification of states and classification of chains. Concepts of recurrence, transience, irreducibility and periodicity, Stationary distributions, Continuous time Markov Chain model of a biochemical reaction network. 6. Stochastic Processes: Existence and Construction, Stationary Processes, Renewal Processes, The Wiener Process, The Ergodic Theorem, Leveraging experimental techniques in Biology. 7. Introduction to the theory of Martingales: Basic definitions, Martingale differences and Hoeffding's inequality, Martingale Convergence Theorem, Crossings and convergence, Stopping times and the optional sampling theorem, Doob's maximal inequalities, Applications to the analysis of stochastic biochemical reaction networks. | |||||

Literature | While no specific textbook will be followed, much of the material and homework problems will be taken from the following books: An Introduction to Stochastic Processes with Applications to Biology, Linda Allen, Second Edition, Chapman and Hall, 2010. Probability And Random Processes, Grimmett and Stirzaker, Third Edition, Oxford University Press, 2001. | |||||

Prerequisites / Notice | The course will involve a healthy balance between mathematical rigor (theorem proving) and biological applications. Students are expected to have a good grasp of Linear Algebra and Multivariable Calculus. Basic knowledge of set theory will also be needed. Students should be prepared for abstract reasoning. | |||||

263-3010-00L | Big Data | W | 8 credits | 3V + 2U + 2A | G. Fourny | |

Abstract | The key challenge of the information society is to turn data into information, information into knowledge, knowledge into value. This has become increasingly complex. Data comes in larger volumes, diverse shapes, from different sources. Data is more heterogeneous and less structured than forty years ago. Nevertheless, it still needs to be processed fast, with support for complex operations. | |||||

Objective | This combination of requirements, together with the technologies that have emerged in order to address them, is typically referred to as "Big Data." This revolution has led to a completely new way to do business, e.g., develop new products and business models, but also to do science -- which is sometimes referred to as data-driven science or the "fourth paradigm". Unfortunately, the quantity of data produced and available -- now in the Zettabyte range (that's 21 zeros) per year -- keeps growing faster than our ability to process it. Hence, new architectures and approaches for processing it were and are still needed. Harnessing them must involve a deep understanding of data not only in the large, but also in the small. The field of databases evolves at a fast pace. In order to be prepared, to the extent possible, to the (r)evolutions that will take place in the next few decades, the emphasis of the lecture will be on the paradigms and core design ideas, while today's technologies will serve as supporting illustrations thereof. After visiting this lecture, you should have gained an overview and understanding of the Big Data landscape, which is the basis on which one can make informed decisions, i.e., pick and orchestrate the relevant technologies together for addressing each business use case efficiently and consistently. | |||||

Content | This course gives an overview of database technologies and of the most important database design principles that lay the foundations of the Big Data universe. The material is organized along three axes: data in the large, data in the small, data in the very small. A broad range of aspects is covered with a focus on how they fit all together in the big picture of the Big Data ecosystem. - physical storage: distributed file systems (HDFS), object storage(S3), key-value stores - logical storage: document stores (MongoDB), column stores (HBase), graph databases (neo4j), data warehouses (ROLAP) - data formats and syntaxes (XML, JSON, RDF, Turtle, CSV, XBRL, YAML, protocol buffers, Avro) - data shapes and models (tables, trees, graphs, cubes) - type systems and schemas: atomic types, structured types (arrays, maps), set-based type systems (?, *, +) - an overview of functional, declarative programming languages across data shapes (SQL, XQuery, JSONiq, Cypher, MDX) - the most important query paradigms (selection, projection, joining, grouping, ordering, windowing) - paradigms for parallel processing, two-stage (MapReduce) and DAG-based (Spark) - resource management (YARN) - what a data center is made of and why it matters (racks, nodes, ...) - underlying architectures (internal machinery of HDFS, HBase, Spark, neo4j) - optimization techniques (functional and declarative paradigms, query plans, rewrites, indexing) - applications. Large scale analytics and machine learning are outside of the scope of this course. | |||||

Literature | Papers from scientific conferences and journals. References will be given as part of the course material during the semester. | |||||

Prerequisites / Notice | This course, in the autumn semester, is only intended for: - Computer Science students - Data Science students - CBB students with a Computer Science background Mobility students in CS are also welcome and encouraged to attend. If you experience any issue while registering, please contact the study administration and you will be gladly added. Another version of this course will be offered in Spring for students of other departments. However, if you would like to already start learning about databases now, a course worth taking as a preparation/good prequel to the Spring edition of Big Data is the "Information Systems for Engineers" course, offered this Fall for other departments as well, and introducing relational databases and SQL. | |||||

636-0117-00L | Mathematical Modelling for Bioengineering and Systems Biology | W | 4 credits | 3G | D. Iber | |

Abstract | Basic concepts and mathematical tools to explore biochemical reaction kinetics and biological network dynamics. | |||||

Objective | The course enables students to formulate, analyse, and simulate mathematical models of biochemical networks. To this end, the course covers basic mathematical concepts and tools to explore biochemical reaction dynamics as well as basic concepts from dynamical systems theory. The exercises serve to deepen the understanding of the presented concepts and the mathematical methods, and to train students to numerically solve and simulate mathematical models. | |||||

Content | Biochemical Reaction Modelling Basic Concepts from Linear Algebra & Differential Equations Mathematical Methods: Linear Stability Analysis, Phase Plane Analysis, Bifurcation Analysis Dynamical Systems: Switches, Oscillators, Adaptation Signal Propagation in Signalling Networks Parameter Estimation | |||||

261-5112-00L | Advanced Approaches for Population Scale Compressive GenomicsNumber of participants limited to 30. | W | 3 credits | 2G | A. Kahles | |

Abstract | Research in Biology and Medicine have been transformed into disciplines of applied data science over the past years. Not only size and inherentcomplexity of the data but also requirements on data privacy and complexity of search and access pose a wealth of new research questions. | |||||

Objective | This interactive course will explore the latest research on algorithms and data structures for population scale genomics applications and give insights into both the technical basis as well as the domain questions motivating it. | |||||

Content | Over the duration of the semester, the course will cover three main topics. Each of the topics will consist of 70-80% lecture content and 20-30% seminar content. 1) Algorithms and data structures for text and graph compression. Motivated through applications in compressive genomics, the course will cover succinct indexing schemes for strings, trees and general graphs, compression schemes for binary matrices as well as the efficient representation of haplotypes and genomic variants. 2) Stochastic data structures and algorithms for approximate representation of strings and graphs as well as sets in general. This includes winnowing schemes and minimizers, sketching techniques, (minimal perfect) hashing and approximate membership query data structures. 3) Data structures supporting encryption and data privacy. As an extension to data structures discussed in the earlier topics, this will include secure indexing using homomorphic encryption as well as design for secure storage and distribution of data. | |||||

252-0834-00L | Information Systems for Engineers | W | 4 credits | 2V + 1U | G. Fourny | |

Abstract | This course provides the basics of relational databases from the perspective of the user. We will discover why tables are so incredibly powerful to express relations, learn the SQL query language, and how to make the most of it. The course also covers support for data cubes (analytics). After this course, you will be ready for Big Data for Engineers. | |||||

Objective | After visiting this course, you will be capable to: 1. Explain, in the big picture, how a relational database works and what it can do in your own words. 2. Explain the relational data model (tables, rows, attributes, primary keys, foreign keys), formally and informally, including the relational algebra operators (select, project, rename, all kinds of joins, division, cartesian product, union, intersection, etc). 3. Perform non-trivial reading SQL queries on existing relational databases, as well as insert new data, update and delete existing data. 4. Design new schemas to store data in accordance to the real world's constraints, such as relationship cardinality 5. Explain what bad design is and why it matters. 6. Adapt and improve an existing schema to make it more robust against anomalies, thanks to a very good theoretical knowledge of what is called "normal forms". 7. Understand how indices work (hash indices, B-trees), how they are implemented, and how to use them to make queries faster. 8. Access an existing relational database from a host language such as Java, using bridges such as JDBC. 9. Explain what data independence is all about and didn't age a bit since the 1970s. 10. Explain, in the big picture, how a relational database is physically implemented. 11. Know and deal with the natural syntax for relational data, CSV. 12. Explain the data cube model including slicing and dicing. 13. Store data cubes in a relational database. 14. Map cube queries to SQL. 15. Slice and dice cubes in a UI. And of course, you will think that tables are the most wonderful object in the world. | |||||

Content | Using a relational database ================= 1. Introduction 2. The relational model 3. Data definition with SQL 4. The relational algebra 5. Queries with SQL Taking a relational database to the next level ================= 6. Database design theory 7. Databases and host languages 8. Databases and host languages 9. Indices and optimization 10. Database architecture and storage Analytics on top of a relational database ================= 12. Data cubes Outlook ================= 13. Outlook | |||||

Literature | - Lecture material (slides). - Book: "Database Systems: The Complete Book", H. Garcia-Molina, J.D. Ullman, J. Widom (It is not required to buy the book, as the library has it) | |||||

Prerequisites / Notice | For non-CS/DS students only, BSc and MSc Elementary knowledge of set theory and logics Knowledge as well as basic experience with a programming language such as Pascal, C, C++, Java, Haskell, Python | |||||

Biology At least 12 ECTS need to be acquired in this category. | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

529-0733-01L | Enzymes | W | 6 credits | 3G | D. Hilvert | |

Abstract | Principles of enzymatic catalysis, enzyme kinetics, mechanisms of enzyme-catalyzed reactions (group transfer reactions, carbon-carbon bond formation, eliminations, isomerisations and rearrangements), cofactor chemistry, enzymes in organic synthesis and the biosynthesis of natural products, catalytic antibodies. | |||||

Objective | Overview of enzymes, enzyme-catalyzed reactions and metabolic processes. | |||||

Content | Principles of enzymatic catalysis, enzyme kinetics, mechanisms of enzyme catalyzed reactions (group transfer reactions, carbon-carbon bond formation, eliminations, isomerisations and rearrangements), cofactor chemistry, enzymes in organic synthesis and the biosynthesis of natural products, catalytic antibodies. | |||||

Lecture notes | A script will not be handed out. | |||||

Literature | General: T. Bugg, An Introduction to Enzyme and Coenzyme Chemistry, Blackwell Science Ltd., Oxford, 1997. In addition, citations from the original literature relevant to the individual lectures will be assigned weekly. | |||||

551-0309-00L | Concepts in Modern Genetics | W | 6 credits | 4V | Y. Barral, D. Bopp, A. Hajnal, M. Stoffel, O. Voinnet | |

Abstract | Concepts of modern genetics and genomics, including principles of classical genetics; yeast genetics; gene mapping; forward and reverse genetics; structure and function of eukaryotic chromosomes; molecular mechanisms and regulation of transcription, replication, DNA-repair and recombination; analysis of developmental processes; epigenetics and RNA interference. | |||||

Objective | This course focuses on the concepts of classical and modern genetics and genomics. | |||||

Content | The topics include principles of classical genetics; yeast genetics; gene mapping; forward and reverse genetics; structure and function of eukaryotic chromosomes; molecular mechanisms and regulation of transcription, replication, DNA-repair and recombination; analysis of developmental processes; epigenetics and RNA interference. | |||||

Lecture notes | Scripts and additional material will be provided during the semester. | |||||

551-0313-00L | Microbiology (Part I) | W | 3 credits | 2V | W.‑D. Hardt, L. Eberl, H.‑M. Fischer, J. Piel, M. Pilhofer | |

Abstract | Advanced lecture class providing a broad overview on bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis. | |||||

Objective | This concept class will be based on common concepts and introduce to the enormous diversity among bacteria and archaea. It will cover the current research on bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis. | |||||

Content | Advanced class covering the state of the research in bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis. | |||||

Lecture notes | Updated handouts will be provided during the class. | |||||

Literature | Current literature references will be provided during the lectures. | |||||

Prerequisites / Notice | English The lecture "Grundlagen der Biologie II: Mikrobiologie" is the basis for this advanced lecture. | |||||

551-0317-00L | Immunology I | W | 3 credits | 2V | M. Kopf, A. Oxenius | |

Abstract | Introduction into structural and functional aspects of the immune system. Basic knowledge of the mechanisms and the regulation of an immune response. | |||||

Objective | Introduction into structural and functional aspects of the immune system. Basic knowledge of the mechanisms and the regulation of an immune response. | |||||

Content | - Introduction and historical background - Innate and adaptive immunity, Cells and organs of the immune system - B cells and antibodies - Generation of diversity - Antigen presentation and Major Histoincompatibility (MHC) antigens - Thymus and T cell selection - Autoimmunity - Cytotoxic T cells and NK cells - Th1 and Th2 cells, regulatory T cells - Allergies - Hypersensitivities - Vaccines, immune-therapeutic interventions | |||||

Lecture notes | Electronic access to the documentation will be provided. The link can be found at "Lernmaterialien" | |||||

Literature | - Kuby, Immunology, 7th edition, Freemen + Co., New York, 2009 | |||||

Prerequisites / Notice | Immunology I (WS) and Immunology II (SS) will be examined as one learning entity in a "Sessionsprüfung". | |||||

636-0105-00L | Introduction to Biological ComputersAttention: This course was offered in previous semesters with the number: 636-0011-00L "Introduction to Biological Computers". Students that already passed course 636-0011-00L cannot receive credits for course 636-0105-00L. | W | 4 credits | 3G | Y. Benenson | |

Abstract | Biological computers are man-made biological networks that interrogate and control cells and organisms in which they operate. Their key features, inspired by computer science, are programmability, modularity, and versatility. The course will show how to rationally design, implement and test biological computers using molecular engineering, DNA nanothechnology and synthetic biology. | |||||

Objective | The course has the following objectives: * Familiarize students with parallels between theories in computer science and engineering and information-processing in live cells and organisms * Introduce basic theories of computation * Introduce approaches to creating novel biological computing systems in non-living environment and in living cells including bacteria, yeast and mammalian/human cells. The covered approaches will include - Nucleic acids engineering - DNA and RNA nanotechnology - Synthetic biology and gene circuit engineering - High-throughput genome engineering and gene circuit assembly * Equip the students with computer-aided design (CAD) tools for biocomputing circuit engineering. A number of tutorials will introduce MATLAB SimBiology toolbox for circuit design and simulations * Foster creativity, research and communication skills through semester-long "Design challenge" assignment in the broad field of biological computing and biological circuit engineering. | |||||

Content | Note: the exact subjects can change, the details below should only serve for general orientation Lecture 1. Introduction: what is molecular computation (part I)? * What is computing in general? * What is computing in the biological context (examples from development, chemotaxis and gene regulation) * The difference between natural computing and engineered biocomputing systems Lecture 2: What is molecular computation (part II) + State machines 1st hour * Detailed definition of an engineered biocomputing system * Basics of characterization * Design challenge presentation 2nd hour * Theories of computation: state machines (finite automata and Turing machines) Lecture 3: Additional models of computation * Logic circuits * Analog circuits * RAM machines Basic approaches to computer science notions relevant to molecular computation. (i) State machines; (ii) Boolean networks; (iii) analog computing; (iv) distributed computing. Design Challenge presentation. Lecture 4. Classical DNA computing * Adleman experiment * Maximal clique problem * SAT problem Lecture 5: Molecular State machines through self-assembly * Tiling implementation of state machine * DNA-based tiling system * DNA/RNA origami as a spin-off of self-assembling state machines Lecture 6: Molecular State machines that use DNA-encoded tapes * Early theoretical work * Tape extension system * DNA and enzyme-based finite automata for diagnostic applications Lecture 7: Introduction to cell-based logic and analog circuits * Computing with (bio)chemical reaction networks * Tuning computation with ultrasensitivity and cooperativity * Specific examples Lecture 8: Transcriptional circuits I * Introducing transcription-based circuits * General features and considerations * Guidelines for large circuit construction Lecture 9: Transcriptional circuits II * Large-scale distributed logic circuits in bacteria * Toward large-scale circuits in mammalian cells Lecture 10: RNA circuits I * General principles of RNA-centered circuit design * Riboswitches and sRNA regulation in bacteria * Riboswitches in yeast and mammalian cells * General approach to RNAi-based computing Lecture 11: RNA circuits II * RNAi logic circuits * RNAi-based cell type classifiers * Hybrid transcriptional/posttranscriptional approaches Lecture 12: In vitro DNA-based logic circuits * DNAzyme circuits playing tic-tac-toe against human opponents * DNA brain Lecture 13: Advanced topics * Engineered cellular memory * Counting and sequential logic * The role of evolution * Fail-safe design principles Lecture 14: Design challenge presentation | |||||

Lecture notes | Lecture notes will be available online | |||||

Literature | As a way of general introduction, the following two review papers could be useful: Benenson, Y. RNA-based computation in live cells. Current Opinion in Biotechnology 2009, 20:471:478 Benenson, Y. Biocomputers: from test tubes to live cells. Molecular Biosystems 2009, 5:675:685 Benenson, Y. Biomolecular computing systems: principles, progress and potential (Review). Nature Reviews Genetics 13, 445-468 (2012). | |||||

Prerequisites / Notice | Basic knowledge of molecular biology is assumed. | |||||

636-0510-00L | Proteomics and Drug Discovery ResearchDoes not take place this semester. | W | 2 credits | 2V | external organisers | |

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636-0511-00L | Developmental Neuroscience (HS)Does not take place this semester. | W | 2 credits | 2V | external organisers | |

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636-0515-00L | Molecular Medicine I | W | 2 credits | 2V | external organisers | |

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262-6170-00L | Molecular Mechanisms of DevelopmentDoes not take place this semester. | W | 2 credits | 2V | external organisers | |

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262-6180-00L | Molecular Control of Vertebrate Development and Organogenesis | W | 2 credits | 2V | external organisers | |

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262-5130-00L | Evolutionary Medicine (University of Zurich)No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: BIO201 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | W | 6 credits | 5G | University lecturers | |

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262-6101-00L | Antibiotic Drug Targets and Resistance | W | 1 credit | 1V | external organisers | |

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262-6102-00L | Functional Organization of the Cell Nucleus | W | 2 credits | 2V | external organisers | |

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262-6103-00L | Cellular Signalling | W | 2 credits | 2V | external organisers | |

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262-6104-00L | Molecular Structure, Function, and Dynamics of Membranes and Membrane Proteins | W | 2 credits | 2V | external organisers | |

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262-6105-00L | Frontiers in RNA Biology | W | 2 credits | 2V | external organisers | |

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Lab Rotations Students need to acquire a total of 18 ECTS in this category. At least two lab rotations need to be completed in two different research groups. Either choose Lab Rotation Short 1 (6 ECTS), Lab Rotation Short 2 (6 ECTS) and Lab Rotation Short 3 (6 ECTS) Or choose Lab Rotation Long 1 (9 ECTS) and Lab Rotation Long 2 (9 ECTS) Or choose Lab Rotation Short 1 (6 ECTS) and Industry Internship (12 ECTS) | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

262-0100-00L | Lab Rotation Short 1 Only for Computational Biology and Bioinformatics MSc, Programme Regulations 2017. | W | 6 credits | 13A | Lecturers | |

Abstract | Flexible short research project of 4 weeks, completed with a written report. | |||||

Objective | Students gain an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||

262-0101-00L | Lab Rotation Short 2 Only for Computational Biology and Bioinformatics MSc, Programme Regulations 2017. | W | 6 credits | 13A | Lecturers | |

Abstract | Flexible short research project of 4 weeks, completed with a written report. | |||||

Objective | Students gain an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||

262-0102-00L | Lab Rotation Short 3 Only for Computational Biology and Bioinformatics MSc, Programme Regulations 2017. | W | 6 credits | 13A | Lecturers | |

Abstract | Flexible short research project of 4 weeks, completed with a written report. | |||||

Objective | Students gain an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||

262-0103-00L | Lab Rotation Long 1 Only for Computational Biology and Bioinformatics MSc, Programme Regulations 2017. | W | 9 credits | 19A | Lecturers | |

Abstract | Flexible short research project of 6 weeks, completed with a written report. | |||||

Objective | ||||||

262-0104-00L | Lab Rotation Long 2 Only for Computational Biology and Bioinformatics MSc, Programme Regulations 2017. | W | 9 credits | 19A | Lecturers | |

Abstract | Flexible short research project of 6 weeks, completed with a written report. | |||||

Objective | ||||||

262-0105-00L | Industry Internship Only for Computational Biology and Bioinformatics MSc, Programme Regulations 2017. | W | 12 credits | 26A | Lecturers | |

Abstract | Industry internship of at least 8 weeks, completed with a written report. | |||||

Objective | Students gain experience in an industrial environment and an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||

Prerequisites / Notice | The students look for a placement themselves. | |||||

Master Studies (Programme Regulations 2011) | ||||||

Core Courses | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

262-5120-00L | Principles of Evolution: Theory (University of Zurich)No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: BIO351 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | W | 6 credits | 3V | University lecturers | |

Abstract | "Nothing in Biology Makes Sense Except in the Light of Evolution". Evolutionary theory and methods are essential in all branches of modern biology. | |||||

Objective | Subject 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 | |||||

Content | This 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. | |||||

401-6282-00L | Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich)No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: STA426 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | W | 5 credits | 3G | H. Rehrauer, M. Robinson | |

Abstract | A 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. | |||||

Objective | -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 | |||||

Content | Lectures 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 | |||||

Lecture notes | Lecture notes, published manuscripts | |||||

Prerequisites / Notice | Prerequisites: 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 | |||||

551-0307-00L | Molecular and Structural Biology I: Protein Structure and Function D-BIOL students are obliged to take part I and part II (next semester) as a two-semester course | W | 3 credits | 2V | R. Glockshuber, K. Locher, E. Weber-Ban | |

Abstract | Biophysics of protein folding, membrane proteins and biophysics of membranes, enzymatic catalysis, catalytic RNA and RNAi, current topics in protein biophysics and structural biology. | |||||

Objective | Understanding of structure-function relationships in proteins and in protein folding, detailed understanding of biophysics and physical methods as well as modern methods for protein purification and microanalytics. | |||||

Lecture notes | Scripts on the individual topics can be found under http://www.mol.biol.ethz.ch/teaching. | |||||

Literature | Basics: - 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). Current topics: References will be given during the lectures. . | |||||

636-0007-00L | Computational Systems Biology | W | 6 credits | 3V + 2U | J. Stelling | |

Abstract | 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). | |||||

Objective | 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. | |||||

Content | 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. | |||||

Lecture notes | http://www.csb.ethz.ch/education/lectures.html | |||||

Literature | 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 | |||||

636-0009-00L | 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. | |||||

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) | |||||

636-0017-00L | Computational Biology | W | 6 credits | 3G + 2A | T. Stadler, C. Magnus, T. Vaughan | |

Abstract | The 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. | |||||

Objective | Attendees 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 | |||||

Content | The 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. | |||||

Lecture notes | Lecture slides will be available on moodle. | |||||

Literature | The 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. | |||||

Prerequisites / Notice | Basic 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 http://www.cbb.ethz.ch/news-events.html 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. | |||||

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

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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 | |||||

Lecture notes | All lecture material will be made available online Link | |||||

Literature | 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 | |||||

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

Advanced Courses and Methods of Computer Science | ||||||

Advanced Courses | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0025-00L | Discrete Mathematics | W | 7 credits | 4V + 2U | U. Maurer | |

Abstract | Content: Mathematical reasoning and proofs, abstraction. Sets, relations (e.g. equivalence and order relations), functions, (un-)countability, number theory, algebra (groups, rings, fields, polynomials, subalgebras, morphisms), logic (propositional and predicate logic, proof calculi). | |||||

Objective | The primary goals of this course are (1) to introduce the most important concepts of discrete mathematics, (2) to understand and appreciate the role of abstraction and mathematical proofs, and (3) to discuss a number of applications, e.g. in cryptography, coding theory, and algorithm theory. | |||||

Content | See course description. | |||||

Lecture notes | available (in english) | |||||

227-1033-00L | Neuromorphic Engineering I Registration in this class requires the permission of the instructors. Class size will be limited to available lab spots. Preference is given to students that require this class as part of their major. | W | 6 credits | 2V + 3U | T. Delbrück, G. Indiveri, S.‑C. Liu | |

Abstract | This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, silicon cochlea) with an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions. | |||||

Objective | Understanding of the characteristics of neuromorphic circuit elements. | |||||

Content | Neuromorphic circuits are inspired by the organizing principles of biological neural circuits. Their computational primitives are based on physics of semiconductor devices. Neuromorphic architectures often rely on collective computation in parallel networks. Adaptation, learning and memory are implemented locally within the individual computational elements. Transistors are often operated in weak inversion (below threshold), where they exhibit exponential I-V characteristics and low currents. These properties lead to the feasibility of high-density, low-power implementations of functions that are computationally intensive in other paradigms. Application domains of neuromorphic circuits include silicon retinas and cochleas for machine vision and audition, real-time emulations of networks of biological neurons, and the development of autonomous robotic systems. This course covers devices in CMOS technology (MOS transistor below and above threshold, floating-gate MOS transistor, phototransducers), static circuits (differential pair, current mirror, transconductance amplifiers, etc.), dynamic circuits (linear and nonlinear filters, adaptive circuits), systems (silicon neuron, silicon retina and cochlea) and an introduction to multi-chip systems that communicate events analogous to spikes. The lectures are accompanied by weekly laboratory sessions on the characterization of neuromorphic circuits, from elementary devices to systems. | |||||

Literature | S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications. | |||||

Prerequisites / Notice | Particular: The course is highly recommended for those who intend to take the spring semester course 'Neuromorphic Engineering II', that teaches the conception, simulation, and physical layout of such circuits with chip design tools. Prerequisites: Background in basics of semiconductor physics helpful, but not required. | |||||

227-1037-00L | Introduction to Neuroinformatics | W | 6 credits | 2V + 1U | V. Mante, M. Cook, B. Grewe, G. Indiveri, D. Kiper, W. von der Behrens | |

Abstract | The course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented. | |||||

Objective | Understanding computation by neurons and neuronal circuits is one of the great challenges of science. Many different disciplines can contribute their tools and concepts to solving mysteries of neural computation. The goal of this introductory course is to introduce the monocultures of physics, maths, computer science, engineering, biology, psychology, and even philosophy and history, to discover the enchantments and challenges that we all face in taking on this major 21st century problem and how each discipline can contribute to discovering solutions. | |||||

Content | This course considers the structure and function of biological neural networks at different levels. The function of neural networks lies fundamentally in their wiring and in the electro-chemical properties of nerve cell membranes. Thus, the biological structure of the nerve cell needs to be understood if biologically-realistic models are to be constructed. These simpler models are used to estimate the electrical current flow through dendritic cables and explore how a more complex geometry of neurons influences this current flow. The active properties of nerves are studied to understand both sensory transduction and the generation and transmission of nerve impulses along axons. The concept of local neuronal circuits arises in the context of the rules governing the formation of nerve connections and topographic projections within the nervous system. Communication between neurons in the network can be thought of as information flow across synapses, which can be modified by experience. We need an understanding of the action of inhibitory and excitatory neurotransmitters and neuromodulators, so that the dynamics and logic of synapses can be interpreted. Finally, the neural architectures of feedforward and recurrent networks will be discussed in the context of co-ordination, control, and integration of sensory and motor information in neural networks. | |||||

529-0004-01L | Computer Simulation in Chemistry, Biology and Physics | W | 6 credits | 4G | P. H. Hünenberger | |

Abstract | Molecular models, Force fields, Boundary conditions, Electrostatic interactions, Molecular dynamics, Analysis of trajectories, Quantum-mechanical simulation, Structure refinement, Application to real systems. Exercises: Analysis of papers on computer simulation, Molecular simulation in practice, Validation of molecular dynamics simulation. | |||||

Objective | Introduction to computer simulation of (bio)molecular systems, development of skills to carry out and interpret computer simulations of biomolecular systems. | |||||

Content | Molecular models, Force fields, Spatial boundary conditions, Calculation of Coulomb forces, Molecular dynamics, Analysis of trajectories, Quantum-mechanical simulation, Structure refinement, Application to real systems. Exercises: Analysis of papers on computer simulation, Molecular simulation in practice, Validation of molecular dynamics simulation. | |||||

Lecture notes | Available (copies of powerpoint slides distributed before each lecture) | |||||

Literature | See: www.csms.ethz.ch/education/CSCBP | |||||

Prerequisites / Notice | Since the exercises on the computer do convey and test essentially different skills as those being conveyed during the lectures and tested at the oral exam, the results of the exercises are taken into account when evaluating the results of the exam (learning component, possible bonus of up to 0.25 points on the exam mark). For more information about the lecture: www.csms.ethz.ch/education/CSCBP | |||||

529-0733-01L | Enzymes | W | 6 credits | 3G | D. Hilvert | |

Abstract | Principles of enzymatic catalysis, enzyme kinetics, mechanisms of enzyme-catalyzed reactions (group transfer reactions, carbon-carbon bond formation, eliminations, isomerisations and rearrangements), cofactor chemistry, enzymes in organic synthesis and the biosynthesis of natural products, catalytic antibodies. | |||||

Objective | Overview of enzymes, enzyme-catalyzed reactions and metabolic processes. | |||||

Content | Principles of enzymatic catalysis, enzyme kinetics, mechanisms of enzyme catalyzed reactions (group transfer reactions, carbon-carbon bond formation, eliminations, isomerisations and rearrangements), cofactor chemistry, enzymes in organic synthesis and the biosynthesis of natural products, catalytic antibodies. | |||||

Lecture notes | A script will not be handed out. | |||||

Literature | General: T. Bugg, An Introduction to Enzyme and Coenzyme Chemistry, Blackwell Science Ltd., Oxford, 1997. In addition, citations from the original literature relevant to the individual lectures will be assigned weekly. | |||||

535-0810-00L | Gene Technology | W | 2 credits | 2G | D. Neri | |

Abstract | The course will provide a solid overview of the science and issues in gene technology and its pharmaceutical applications. | |||||

Objective | The aim of the lecture course is to provide a solid overview of gene technology, with a special focus on drug development. Topics: Antibody phage technology, DNA-encoded chemistry, protein modification technology, genome sequencing, transcriptomics, proteomics, functional genomics, principle of drug discovery. The course is suited for advanced undergraduate and early graduate students in pharmaceutical sciences or related fields. | |||||

Content | 1. Antibody phage technology The antibody molecule V genes, CDRs, basics of antibody engineering Principles of phage display Phagemid and phage vectors Antibody libraries Phage display selection methodologies Other phage libraries (peptides, globular proteins, enzymes) Alternative screening/selection methodologies DNA-encoded chemical libraries 2. Proteins: chemical modification and detection of biomolecular interactions Homo- and hetero-dimerization of proteins Chemical modifications of proteins Antibody-drug conjugates Radioactive labeling of proteins Kinetic association and dissociation constants Affinity constant: definition and its experimental measurement 3. Genomics: Applications to Human Biology Protein cloning and expression DNA sequencing Some foundations of genetic analysis Knock-out technologies Transcriptomics Proteomics Recombinant vaccines 4: Pharmaceuticals: Focus on Discovery Ligand Discovery Half-life extension Cancer therapy Gene therapy | |||||

Lecture notes | Skript "Gene Technology" by Prof. Dario Neri and slides of the lecture | |||||

551-0307-00L | Molecular and Structural Biology I: Protein Structure and Function D-BIOL students are obliged to take part I and part II (next semester) as a two-semester course | W | 3 credits | 2V | R. Glockshuber, K. Locher, E. Weber-Ban | |

Abstract | Biophysics of protein folding, membrane proteins and biophysics of membranes, enzymatic catalysis, catalytic RNA and RNAi, current topics in protein biophysics and structural biology. | |||||

Objective | Understanding of structure-function relationships in proteins and in protein folding, detailed understanding of biophysics and physical methods as well as modern methods for protein purification and microanalytics. | |||||

Lecture notes | Scripts on the individual topics can be found under http://www.mol.biol.ethz.ch/teaching. | |||||

Literature | Basics: - 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). Current topics: References will be given during the lectures. . | |||||

551-0309-00L | Concepts in Modern Genetics | W | 6 credits | 4V | Y. Barral, D. Bopp, A. Hajnal, M. Stoffel, O. Voinnet | |

Abstract | Concepts of modern genetics and genomics, including principles of classical genetics; yeast genetics; gene mapping; forward and reverse genetics; structure and function of eukaryotic chromosomes; molecular mechanisms and regulation of transcription, replication, DNA-repair and recombination; analysis of developmental processes; epigenetics and RNA interference. | |||||

Objective | This course focuses on the concepts of classical and modern genetics and genomics. | |||||

Content | The topics include principles of classical genetics; yeast genetics; gene mapping; forward and reverse genetics; structure and function of eukaryotic chromosomes; molecular mechanisms and regulation of transcription, replication, DNA-repair and recombination; analysis of developmental processes; epigenetics and RNA interference. | |||||

Lecture notes | Scripts and additional material will be provided during the semester. | |||||

551-0313-00L | Microbiology (Part I) | W | 3 credits | 2V | W.‑D. Hardt, L. Eberl, H.‑M. Fischer, J. Piel, M. Pilhofer | |

Abstract | Advanced lecture class providing a broad overview on bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis. | |||||

Objective | This concept class will be based on common concepts and introduce to the enormous diversity among bacteria and archaea. It will cover the current research on bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis. | |||||

Content | Advanced class covering the state of the research in bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis. | |||||

Lecture notes | Updated handouts will be provided during the class. | |||||

Literature | Current literature references will be provided during the lectures. | |||||

Prerequisites / Notice | English The lecture "Grundlagen der Biologie II: Mikrobiologie" is the basis for this advanced lecture. | |||||

551-0317-00L | Immunology I | W | 3 credits | 2V | M. Kopf, A. Oxenius | |

Abstract | Introduction into structural and functional aspects of the immune system. Basic knowledge of the mechanisms and the regulation of an immune response. | |||||

Objective | Introduction into structural and functional aspects of the immune system. Basic knowledge of the mechanisms and the regulation of an immune response. | |||||

Content | - Introduction and historical background - Innate and adaptive immunity, Cells and organs of the immune system - B cells and antibodies - Generation of diversity - Antigen presentation and Major Histoincompatibility (MHC) antigens - Thymus and T cell selection - Autoimmunity - Cytotoxic T cells and NK cells - Th1 and Th2 cells, regulatory T cells - Allergies - Hypersensitivities - Vaccines, immune-therapeutic interventions | |||||

Lecture notes | Electronic access to the documentation will be provided. The link can be found at "Lernmaterialien" | |||||

Literature | - Kuby, Immunology, 7th edition, Freemen + Co., New York, 2009 | |||||

Prerequisites / Notice | Immunology I (WS) and Immunology II (SS) will be examined as one learning entity in a "Sessionsprüfung". | |||||

401-0647-00L | Introduction to Mathematical Optimization | W | 5 credits | 2V + 1U | D. Adjiashvili | |

Abstract | Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering. | |||||

Objective | The goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering. | |||||

Content | Topics covered in this course include: - Linear programming (simplex method, duality theory, shadow prices, ...). - Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...). - Modelling with mathematical optimization: applications of mathematical programming in engineering. | |||||

Literature | Information about relevant literature will be given in the lecture. | |||||

Prerequisites / Notice | This course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics. Compared to "Mathematical Optimization", this course has a stronger focus on modeling and applications. | |||||

Methods of Computer Science | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0057-00L | Theoretical Computer Science | W | 7 credits | 4V + 2U | J. Hromkovic, H.‑J. Böckenhauer | |

Abstract | Concepts to cope with: a) what can be accomplished in a fully automated fashion (algorithmically solvable) b) How to measure the inherent difficulty of tasks (problems) c) What is randomness and how can it be useful? d) What is nondeterminism and what role does it play in CS? e) How to represent infinite objects by finite automata and grammars? | |||||

Objective | Learning the basic concepts of computer science along their historical development | |||||

Content | This lecture gives an introduction to theoretical computer science, presenting the basic concepts and methods of computer science in its historical context. We present computer science as an interdisciplinary science which, on the one hand, investigates the border between the possible and the impossible and the quantitative laws of information processing, and, on the other hand, designs, analyzes, verifies, and implements computer systems. The main topics of the lecture are: - alphabets, words, languages, measuring the information content of words, representation of algorithmic tasks - finite automata, regular and context-free grammars - Turing machines and computability - complexity theory and NP-completeness - design of algorithms for hard problems | |||||

Lecture notes | The lecture is covered in detail by the textbook "Theoretical Computer Science". | |||||

Literature | Basic literature: 1. J. Hromkovic: Theoretische Informatik. 5th edition, Springer Vieweg 2014. 2. J. Hromkovic: Theoretical Computer Science. Springer 2004. Further reading: 3. M. Sipser: Introduction to the Theory of Computation, PWS Publ. Comp.1997 4. J.E. Hopcroft, R. Motwani, J.D. Ullman: Introduction to Automata Theory, Languages, and Computation (3rd Edition), Addison-Wesley 2006. 5. I. Wegener: Theoretische Informatik. Teubner. More exercises and examples in: 6. A. Asteroth, Ch. Baier: Theoretische Informatik | |||||

Prerequisites / Notice | During the semester, two non-obligatory test exams will be offered. | |||||

252-0535-00L | Advanced Machine Learning | W | 8 credits | 3V + 2U + 2A | J. M. Buhmann | |

Abstract | Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects. | |||||

Objective | Students will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data. | |||||

Content | The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data. Topics covered in the lecture include: Fundamentals: What is data? Bayesian Learning Computational learning theory Supervised learning: Ensembles: Bagging and Boosting Max Margin methods Neural networks Unsupservised learning: Dimensionality reduction techniques Clustering Mixture Models Non-parametric density estimation Learning Dynamical Systems | |||||

Lecture notes | No lecture notes, but slides will be made available on the course webpage. | |||||

Literature | C. Bishop. Pattern Recognition and Machine Learning. Springer 2007. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004. | |||||

Prerequisites / Notice | The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments. Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution. | |||||

401-0663-00L | Numerical Methods for CSE | W | 8 credits | 4V + 2U + 1P | R. Alaifari | |

Abstract | The course gives an introduction into fundamental techniques and algorithms of numerical mathematics which play a central role in numerical simulations in science and technology. The course focuses on fundamental ideas and algorithmic aspects of numerical methods. The exercises involve actual implementation of numerical methods in C++. | |||||

Objective | * Knowledge of the fundamental algorithms in numerical mathematics * Knowledge of the essential terms in numerical mathematics and the techniques used for the analysis of numerical algorithms * Ability to choose the appropriate numerical method for concrete problems * Ability to interpret numerical results * Ability to implement numerical algorithms afficiently | |||||

Content | 1. Direct Methods for linear systems of equations 2. Least Squares Techniques 3. Data Interpolation and Fitting 4. Filtering Algorithms 8. Approximation of Functions 9. Numerical Quadrature 10. Iterative Methods for non-linear systems of equations 11. Single Step Methods for ODEs 12. Stiff Integrators | |||||

Lecture notes | Lecture materials (PDF documents and codes) will be made available to the participants through the course web page: https://metaphor.ethz.ch/x/2018/hs/401-0663-00L/ | |||||

Literature | U. ASCHER AND C. GREIF, A First Course in Numerical Methods, SIAM, Philadelphia, 2011. A. QUARTERONI, R. SACCO, AND F. SALERI, Numerical mathematics, vol. 37 of Texts in Applied Mathematics, Springer, New York, 2000. W. Dahmen, A. Reusken "Numerik für Ingenieure und Naturwissenschaftler", Springer 2006. M. Hanke-Bourgeois "Grundlagen der Numerischen Mathematik und des wissenschaftlichen Rechnens", BG Teubner, 2002 P. Deuflhard and A. Hohmann, "Numerische Mathematik I", DeGruyter, 2002 | |||||

Prerequisites / Notice | The course will be accompanied by programming exercises in C++ relying on the template library EIGEN. Familiarity with C++, object oriented and generic programming is an advantage. Participants of the course are expected to learn C++ by themselves. | |||||

Applications (Research Projects) | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

262-0500-00L | Lab Rotation in Experimental Biology Only for Computational Biology and Bioinformatics MSc, Programme Regulations 2011. | O | 3 credits | 6A | Lecturers | |

Abstract | Flexible, short research project (lab rotation) with an emphasis on experimental biology. | |||||

Objective | The course provides a practical overview of an experimental biology research area, applying concepts taught in the General and Core courses, and preparing for further specialization through the Master thesis. | |||||

262-0600-00L | Lab Rotation in Computer Science Only for Computational Biology and Bioinformatics MSc, Programme Regulations 2011. | O | 3 credits | 6A | Lecturers | |

Abstract | Flexible, short research project (lab rotation) with emphasis on computer science/theory | |||||

Objective | The course provides a practical overview of a computer science research area, applying concepts taught in the General and Core courses, and preparing for further specialization through the Master thesis. | |||||

262-0700-00L | Lab Rotation in Bioinformatics Only for Computational Biology and Bioinformatics MSc, Programme Regulations 2011. | O | 3 credits | 6A | Lecturers | |

Abstract | Flexible, short research project within the field of computational biology/bioinformatics. | |||||

Objective | Flexible, short research project within the field of computational biology/bioinformatics (can be chosen within any department participating in the CBB-Master). The course provides a practical overview of a bioinformatics research area, applying concepts taught in the General and Core courses, and preparing for further specialization through the Master thesis. | |||||

Content | Students learn to transfer and apply their knowledge by working independently in the laboratory or on projects. By applying knowledge acquired from the core and advanced courses, and the Methods of Computer Science course, students gain insight into different research areas. | |||||

GESS Science in Perspective | ||||||

» Recommended GESS Science in Perspective (Type B) for D-INFK. | ||||||

» see GESS Science in Perspective: Language Courses ETH/UZH | ||||||

» see GESS Science in Perspective: Type A: Enhancement of Reflection Capability | ||||||

Master's Thesis | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

262-0800-00L | Master's Thesis Only students who fulfill the following criteria are allowed to begin with their master thesis: a. successful completion of the bachelor programme; b. fulfilling of any additional requirements necessary to gain admission to the master programme. | O | 30 credits | 64D | Professors | |

Abstract | The Master Thesis is the result of an independent scientific research and/or constructive development project in the chosen area of specialization. | |||||

Objective | The Master thesis concludes the Master programme. By writing up the Master thesis, students show their ability to independently produce a coherent and scientific piece of work. | |||||

Content | The program concludes with a Master thesis of 6 months duration that includes a written report and an oral presentation. The topic of the thesis can be chosen according to the student's interests in the field of computational biology & bioinformatics. | |||||

Course Units for Additional Admission Requirements The courses below are only available for MSc students with additional requirements. | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0002-AAL | Data Structures and Algorithms Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 7 credits | 15R | F. O. Friedrich | |

Abstract | This course is about fundamental algorithm design paradigms (such as induction, divide-and-conquer, backtracking, dynamic programming), classic algorithmic problems (such as sorting and searching), and data structures (such as lists, hashing, search trees). Moreover, an introduction to parallel programming is provided. The programming model of C++ will be discussed in some depth. | |||||

Objective | An understanding of the design and analysis of fundamental algorithms and data structures. Knowledge regarding chances, problems and limits of parallel and concurrent programming. Deeper insight into a modern programming model by means of the programming language C++. | |||||

Content | Fundamental algorithms and data structures are presented and analyzed. Firstly, this comprises design paradigms for the development of algorithms such as induction, divide-and-conquer, backtracking and dynamic programming and classical algorithmic problems such as searching and sorting. Secondly, data structures for different purposes are presented, such as linked lists, hash tables, balanced search trees, heaps and union-find structures. The relationship and tight coupling between algorithms and data structures is illustrated with geometric problems and graph algorithms. In the part about parallel programming, parallel architectures are discussed conceptually (multicore, vectorization, pipelining). Parallel programming concepts are presented (Amdahl's and Gustavson's laws, task/data parallelism, scheduling). Problems of concurrency are analyzed (Data races, bad interleavings, memory reordering). Process synchronisation and communication in a shared memory system is explained (mutual exclusion, semaphores, monitors, condition variables). Progress conditions are analysed (freedom from deadlock, starvation, lock- and wait-freedom). The concepts are underpinned with examples of concurrent and parallel programs and with parallel algorithms. The programming model of C++ is discussed in some depth. The RAII (Resource Allocation is Initialization) principle will be explained. Exception handling, functors and lambda expression and generic prorgamming with templates are further examples of this part. The implementation of parallel and concurrent algorithm with C++ is also part of the exercises (e.g. threads, tasks, mutexes, condition variables, promises and futures). | |||||

Literature | Cormen, Leiserson, Rivest, and Stein: Introduction to Algorithms, 3rd ed., MIT Press, 2009. ISBN 978-0-262-03384-8 (recommended text) Maurice Herlihy, Nir Shavit, The Art of Multiprocessor Programming, Elsevier, 2012. B. Stroustrup, The C++ Programming Language (4th Edition) Addison-Wesley, 2013. B. Stroustrup, The C++ Programming Language (4th Edition) Addison-Wesley, 2013. | |||||

Prerequisites / Notice | Prerequisites: Lecture Series 252-0835-00L Informatik I or equivalent knowledge in programming with C++. Please note that this is a self study (virtual) course, which implies that (in the autumn semester) there are no physical lectures or exercise sessions offered. If you want to attend the real course, please go to 252-0002-00L in the spring semester. | |||||

252-0835-AAL | Computer Science I Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 4 credits | 9R | F. O. Friedrich | |

Abstract | The course covers the fundamental concepts of computer programming with a focus on systematic algorithmic problem solving. Teached language is C++. No programming experience is required. | |||||

Objective | Primary educational objective is to learn programming with C++. When successfully attended the course, students have a good command of the mechanisms to construct a program. They know the fundamental control and data structures and understand how an algorithmic problem is mapped to a computer program. They have an idea of what happens "behind the secenes" when a program is translated and executed. Secondary goals are an algorithmic computational thinking, undestanding the possibilities and limits of programming and to impart the way of thinking of a computer scientist. | |||||

Content | The course covers fundamental data types, expressions and statements, (Limits of) computer arithmetic, control statements, functions, arrays, structural types and pointers. The part on object orientiation deals with classes, inheritance and polymorphy, simple dynamic data types are introduced as examples. In general, the concepts provided in the course are motivated and illustrated with algorithms and applications. | |||||

Literature | Bjarne Stroustrup: Programming:Principles and Practice Using C++, Addison-Wesley, 2014 Stephen Prata: C++ Primer Plus, Sixth Edition, Addison Wesley, 2012 Andrew Koenig and Barbara E. Moo: Accelerated C++, Addison-Wesley, 2000 Bjarne Stroustrup: The C++ Programming Language (4th Edition) Addison-Wesley, 2013 Bjarne Stroustrup: The Design and Evolution of C++, Addison-Wesley, 1994 | |||||

406-0603-AAL | Stochastics (Probability and Statistics)Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 4 credits | 9R | M. Kalisch | |

Abstract | Introduction to basic methods and fundamental concepts of statistics and probability theory for non-mathematicians. The concepts are presented on the basis of some descriptive examples. Learning the statistical program R for applying the acquired concepts will be a central theme. | |||||

Objective | The objective of this course is to build a solid fundament in probability and statistics. The student should understand some fundamental concepts and be able to apply these concepts to applications in the real world. Furthermore, the student should have a basic knowledge of the statistical programming language "R". | |||||

Content | From "Statistics for research" (online) Ch 1: The Role of Statistics Ch 2: Populations, Samples, and Probability Distributions Ch 3: Binomial Distributions Ch 6: Sampling Distribution of Averages Ch 7: Normal Distributions Ch 8: Student's t Distribution Ch 9: Distributions of Two Variables From "Introductory Statistics with R (online)" Ch 1: Basics Ch 2: The R Environment Ch 3: Probability and distributions Ch 4: Descriptive statistics and tables Ch 5: One- and two-sample tests Ch 6: Regression and correlation | |||||

Literature | - "Statistics for research" by S. Dowdy et. al. (3rd edition); Print ISBN: 9780471267355; Online ISBN: 9780471477433; DOI: 10.1002/0471477435 From within the ETH, this book is freely available online under: http://onlinelibrary.wiley.com/book/10.1002/0471477435 - "Introductory Statistics with R" by Peter Dalgaard; ISBN 978-0-387-79053-4; DOI: 10.1007/978-0-387-79054-1 From within the ETH, this book is freely available online under: http://www.springerlink.com/content/m17578/ | |||||

227-0945-00L | Cell and Molecular Biology for Engineers IThis course is part I of a two-semester course. | W | 3 credits | 2G | C. Frei | |

Abstract | The course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology. | |||||

Objective | After completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested. | |||||

Content | Lectures will include the following topics (part I and II): DNA, chromosomes, RNA, protein, genetics, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer, development and stem cells. In addition, 4 journal clubs will be held, where recent publications will be discussed (2 journal clubs in part I and 2 journal clubs in part II). For each journal club, students (alone or in groups of up to three students) have to write a summary and discussion of the publication. These written documents will be graded and count as 40% for the final grade. | |||||

Lecture notes | Scripts of all lectures will be available. | |||||

Literature | "Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Raff, Roberts, and Walter. |