Dagmar Iber: Catalogue data in Autumn Semester 2016

Name Prof. Dr. Dagmar Iber
FieldComputational Biology
Professur f. Computational Biology
ETH Zürich, D-BSSE, BSD G 204.2
Mattenstrasse 26
4058 Basel
Award: The Golden Owl
Telephone+41 61 387 32 10
DepartmentBiosystems Science and Engineering
RelationshipAssociate Professor

636-0021-00LMathematical Modelling in Systems Biology Information 5 credits3GD. Iber
AbstractBasic concepts and mathematical tools to explore biochemical reaction kinetics and biological network dynamics.
ObjectiveThe aim of the course is to provide an introductory overview of mathematical and computational methods to analyse biological network dynamics.
Content1. Introduction to Mathematical Modeling
2. Introduction to Biochemical Reaction Modeling
3. Model Analysis: Phase Plane
4. Model Analysis: Linear Stability Analysis
5. Model Analysis: Bifurcation Analysis
6. Regulatory Feedback: Switches
7. Regulatory Feedback: Adaptation
8. Regulatory Feedback: Oscillations and Delay Equations
9. Receptor Signaling and Signaling Cascades
10. Network Properties: Sensitivity and Robustness
11. Introduction to Parameter Estimation
Lecture noteshttps://www.bsse.ethz.ch/cobi/education/626-0005-00l-mathematical-modelling-in-systems-biology.html
Literature- Wolkenhauer, Systems Biology, http: // www.sbi.uni-rockstock.de/files/p_sb.pdf - Keener and Sneyd, Mathematical Physiology, Springer - Klipp et al, Systems Biology in Practice, Wiley - Kreyszig, Engineering Mathematics, Wiley
Prerequisites / NoticeIntroductory courses in Mathematics (Linear Algebra, Differential Equations, Numerics) and basic concepts of programming.
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 http://www.bsse.ethz.ch/education/.
636-0706-00LSpatio-Temporal Modelling in Biology Information 5 credits3GD. Iber
AbstractThis course focuses on modeling spatio-temporal problems in biology, in particular on the cell and tissue level. A wide range of mathematical techniques will be presented as part of the course, including concepts from non-linear dynamics (ODE and PDE models), stochastic techniques (SDE, Master equations, Monte Carlo simulations), and thermodynamic descriptions.
ObjectiveThe aim of the course is to introduce students to state-of-the-art mathematical modelling of spatio-temporal problems in biology. Students will learn how to chose from a wide range of modelling techniques and how to apply these to further our understanding of biological mechanisms. The course aims at equipping students with the tools and concepts to conduct successful research in this area; both classical as well as recent research work will be discussed.
Content1. Introduction to Modelling in Biology
2. Morphogen Gradients
3. Turing Pattern
4. Travelling Waves & Wave Pinning
5. Application Example 1: Dorso-ventral axis formation
6. Chemotaxis, Cell Adhesion & Migration
7. Introduction to Numerical Methods
8. Simulations on Growing Domains
9. Image-Based Modelling
10. Branching Processes
11. Cell-based Simulation Frameworks
12. Application Example 2: Limb Development
13. Summary
Lecture notesAll lecture material will be made available online
LiteratureMurray, 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 / NoticeThe course builds on introductory courses in Computational Biology. The course assumes no background in biology but a good foundation regarding mathematical and computational techniques.