Jörg Stelling: Catalogue data in Spring Semester 2019

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

NumberTitleECTSHoursLecturers
551-1174-00LSystems Biology4 credits2V + 2UU. Sauer, K. M. Borgwardt, J. Stelling, N. Zamboni
AbstractThe course teaches computational methods and first hands-on applications by starting from biological problems/phenomena that students in the 4th semester are somewhat familiar with. During the exercises, students will obtain first experience with programming their own analyses/models for data analysis/interpretation.
ObjectiveWe will teach little if any novel biological knowledge or analysis methods, but focus on training the ability of use existing knowledge (for example from enzyme kinetics, regulatory mechanisms or analytical methods) to understand biological problems that arise when considering molecular elements in their context and to translate some of these problems into a form that can be solved by computational methods. Specific goals are:
- understand the limitations of intuitive reasoning
- obtain a first overview of computational approaches in systems biology
- train ability to translate biological problems into computational problems
- solve practical problems by programming with MATLAB
- make first experiences in computational interpretation of biological data
- understand typical abstractions in modeling molecular systems
ContentDuring the first 7 weeks, the will focus on mechanistic modeling. Starting from simple enzyme kinetics, we will move through the dynamics of small pathways that also include regulation and end with flux balance analysis of a medium size metabolic network. During the second 7 weeks, the focus will shift to the analysis of larger data sets, such as metabolomics and transcriptomics that are often generated in biology. Here we will go through multivariate statistical methods that include clustering and principal component analysis, ending with first methods to learn networks from data.
Lecture notesKein Skript
LiteratureThe course is not taught by a particular book, but two books are suggested for further reading:
- Systems Biology (Klipp, Herwig, Kowald, Wierling und Lehrach) Wiley-VCH 2009
- A First Course in Systems Biology (Eberhardt O. Voight) Garland Science 2012
636-0006-00LComputational Systems Biology: Deterministic Approaches Restricted registration - show details 4 credits3GJ. Stelling, D. Iber
AbstractThe course introduces computat. methods for systems biology under ‘real-world’ conditions of limiting biological knowledge, uncertain model scopes and predictions, and spatial effects. Focus is on systems identification for mechanistic, deterministic models and the corresponding numerical approaches. Topics include uncertainty evaluation, experim. design, and numerical methods for spatial models
ObjectiveThe aim of the course is to provide students with mathematical and computational methods for the analysis of biological systems in a ‘real world’ setting. This implies (i) incomplete knowledge of components, interactions, and their quantitative features in cellular networks, (ii) resulting uncertainties in model predictions and iterations between models and experiments, and (iii) spatial effects. All these factors make direct representations of biological mechanisms in mechanistic, deterministic mathematical models challenging. Based on general concepts of systems identification and on corresponding numerical methods, the course aims at providing an in-depth understanding of computational approaches that enable the analysis of mechanisms of biological network operation in detail, using iterations between experimental and theoretical systems analysis.
ContentLecture topics: (1) Mechanistic mathematical models and systems identification challenges; (2-4) Structural models and data integration; (5-8) Identification and experimental design for ODE models; (9-10) Uncertainty quantification; (11-13) Numerical methods for partial differential equation (PDE) models to describe spatial effects.
Lecture notesCourse material will be made available at: http://www.csb.ethz.ch
LiteratureBackground literature will be available on-line at the start of the course.
Prerequisites / NoticeFor this advanced course, participants are expected to have a solid background in the mathematical modelling of biological systems, as conveyed by the combination of the following two courses in the MSc Computational Biology and Bioinformatics: ‘Computational systems biology’ and ‘Spatio-temporal modeling in biology’.
636-0111-00LSynthetic Biology I
Attention: This course was offered in previous semesters with the number: 636-0002-00L "Synthetic Biology I". Students that already passed course 636-0002-00L cannot receive credits for course 636-0111-00L.
4 credits3GS. Panke, J. Stelling
AbstractTheoretical & practical introduction into the design of dynamic biological systems at different levels of abstraction, ranging from biological fundamentals of systems design (introduction to bacterial gene regulation, elements of transcriptional & translational control, advanced genetic engineering) to engineering design principles (standards, abstractions) mathematical modelling & systems desig
ObjectiveAfter the course, students will be able to theoretically master the biological and engineering fundamentals required for biological design to be able to participate in the international iGEM competition (see www.syntheticbiology.ethz.ch).
ContentThe overall goal of the course is to familiarize the students with the potential, the requirements and the problems of designing dynamic biological elements that are of central importance for manipulating biological systems, primarily (but not exclusively) prokaryotic systems. Next, the students will be taken through a number of successful examples of biological design, such as toggle switches, pulse generators, and oscillating systems, and apply the biological and engineering fundamentals to these examples, so that they get hands-on experience on how to integrate the various disciplines on their way to designing biological systems.
Lecture notesHandouts during classes.
LiteratureMark Ptashne, A Genetic Switch (3rd ed), Cold Spring Haror Laboratory Press
Uri Alon, An Introduction to Systems Biology, Chapman & Hall
Prerequisites / Notice1) Though we do not place a formal requirement for previous participation in particular courses, we expect all participants to be familiar with a certain level of biology and of mathematics. Specifically, there will be material for self study available on http://www.bsse.ethz.ch/bpl/education/index as of mid January, and everybody is expected to be fully familiar with this material BEFORE THE CLASS BEGINS to be able to follow the different lectures. Please contact sven.panke@bsse.ethz.ch for access to material
2) The course is also thought as a preparation for the participation in the international iGEM synthetic biology summer competition (www.syntheticbiology.ethz.ch, http://www.igem.org). This competition is also the contents of the course Synthetic Biology II. http://www.bsse.ethz.ch/bpl/education/index
636-0301-00LCurrent Topics in Biosystems Science and Engineering2 credits1SR. Platt, N. Beerenwinkel, Y. Benenson, K. M. Borgwardt, P. S. Dittrich, M. Fussenegger, A. Hierlemann, D. Iber, M. H. Khammash, D. J. Müller, S. Panke, R. Paro, S. Reddy, T. Schroeder, T. Stadler, 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 http://www.bsse.ethz.ch/education/.
636-0704-00LComputational Biology and Bioinformatics Seminar2 credits2SJ. Stelling, M. Claassen, D. Iber, T. Stadler
AbstractComputational 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.
ObjectiveStudying 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.
ContentComputational 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, 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.
LiteratureOriginal papers to be presented by the students will be provided in the first week of the seminar.