Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Jörg Stelling: Catalogue data in Autumn Semester 2016

Name Prof. Dr. Jörg Stelling
FieldComputational Systems Biology
Comput. Systems Biology, Stelling
ETH Zürich, D-BSSE, BSA N 800
Mattenstrasse 26
4058 Basel
Telephone+41 61 387 31 94
DepartmentBiosystems Science and Engineering
RelationshipFull Professor

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.
4 credits9RJ. Stelling, N. Beerenwinkel
AbstractThe course introduces concepts of bioinformatics starting from first principles: DNA sequence alignment, phylogenetic tree inference, genome annotation, protein structure and function prediction. Key methods and algorithms are covered, including dynamic programming, Markov and Hidden Markov models, and molecular dynamics simulations. Practical applications and limitations are discussed.
ObjectiveThe course aims at introducing the fundamental concepts and methods of bioinformatics. Emphasis is given to a deep understanding of the methods' foundations and limitations to enable critical evaluations and applications of bioinformatics tools in areas such as biotechnology and systems biology.
ContentFrom "Understanding Bioinformatics":
Chapter 4: Producing and Analyzing Sequence Alignments
Chapter 5: Pairwise Sequence Alignment and Database Searching
Chapter 6: Patterns, Profiles, and Multiple Alignments
Chapter 7: Recovering Evolutionary History
Chapter 8: Building Phylogenetic Trees
Chapter 9: Revealing Genome Features
Chapter 10: Gene Detection and Genome Annotation
Chapter 11: Obtaining Secondary Structure from Sequence
Chapter 12: Predicting Secondary Structures
Chapter 13: Modeling Protein Structure
Chapter 14: Analyzing Structure-Function Relationships

From "Biological Sequence Analysis":
Sections 3.1, 3.2, 3.3, 4.1, 4.2, 4.4, 5.2, 5.3, 5.4, 6.5 (Markov Chains and Hidden Markov Models)

From "A First Course in Systems Biology":
Chapter 1: Biological Systems
Lecture notesCourse material will be made available at:
LiteratureZvelebil M, Baum JO. Understanding Bioinformatics. Garland Science, 2008.
Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge University Press, 2004.
Voit EO. A First Course in Systems Biology. Garland Science, 2012.
Prerequisites / NoticeThere will be two opportunities for tutorials during the semester
636-0007-00LComputational Systems Biology Information 6 credits3V + 2UJ. Stelling
AbstractStudy 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).
ObjectiveThe aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks.
ContentBiology 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
LiteratureU. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006.

Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2006.
636-0301-00LCurrent Topics in Biosystems Science and Engineering2 credits1ST. Stadler, N. Beerenwinkel, Y. Benenson, K. M. Borgwardt, P. S. Dittrich, M. Fussenegger, A. Hierlemann, D. Iber, M. H. Khammash, D. J. Müller, S. Panke, P. Pantazis, R. Paro, R. Platt, S. Reddy, T. Schroeder, J. Stelling
AbstractThis seminar will feature invited lectures about recent advances and developments in systems biology, including topics from biology, bioengineering, and computational biology.
ObjectiveTo provide an overview of current systems biology research.
ContentThe final list of topics will be available at
636-0507-00LSynthetic Biology II Restricted registration - show details 4 credits4AS. Panke, Y. Benenson, J. Stelling
Abstract7 months biological design project, during which the students are required to give presentations on advanced topics in synthetic biology (specifically genetic circuit design) and then select their own biological system to design. The system is subsequently modeled, analyzed, and experimentally implemented. Results are presented at an international student competition at the MIT (Cambridge).
ObjectiveThe students are supposed to acquire a deep understanding of the process of biological design including model representation of a biological system, its thorough analysis, and the subsequent experimental implementation of the system and the related problems.
ContentPresentations on advanced synthetic biology topics (eg genetic circuit design, adaptation of systems dynamics, analytical concepts, large scale de novo DNA synthesis), project selection, modeling of selected biological system, design space exploration, sensitivity analysis, conversion into DNA sequence, (DNA synthesis external,) implementation and analysis of design, summary of results in form of scientific presentation and poster, presentation of results at the iGEM international student competition (
Lecture notesHandouts during course
Prerequisites / NoticeThe final presentation of the project is typically at the MIT (Cambridge, US). Other competing schools include regularly Imperial College, Cambridge University, Harvard University, UC Berkeley, Princeton Universtiy, CalTech, etc.

This project takes place between end of Spring Semester and beginning of Autumn Semester. Registration in April.

Please note that the number of ECTS credits and the actual work load are disconnected.