Courses offered by Bioinspired Communication Systems Lab Summer Semester 2018
Computational Methods for Systems and Synthetic Biology – vl
Course No: 18-kp-2080-vl
Time: Wednesdays 1:30 pm- 3:10 pm
Location: S3/20, room 4
The course covers mathematical methods used in the area of systems and synthetic biology. On the one hand it deals with practical modeling of molecular processes but also with theoretical investigations that reveal general properties of those processes. The course follows a microscopic approach and introduces those processes using probabilistic methods. For that, necessary pre-requists are recapitulated, such as definition of Markov processes in different spaces and their properties. With this background, the dynamics of stochastic reaction kinetics in terms of popula-tion models is investigated. Limiting cases are introduced, such as the diffusion approximation or the deterministic approximation (fluid approximations) of those systems. Often methods from statistical physics are applied. Numerical methods for solving the corresponding Fokker-Planck and Master equations are discussed. For the limiting case of a deterministic approximation, tradi-tional methods for the stability analysis of nonlinear differential equations are introduced and methods are discussed that just rely on the topology of the reaction network to determine stabil-ity properties. In this context, a derivation of the moment dynamics and approximation methods based on moment closure are given. Connections to queueing theory models are shown. Furthermore, the question is addressed of how the introduced dynamical models are calibrated to data from molecular biology. For that, general methods of statistical inference from statistics and of machine learning from computer science are discussed and specialized algorithms for the con-sidered system class are presented. Additionally, a short introduction to the theory of nonlinear optimal filtering is given and special cases such as hidden Markov models are discussed. Beyond reaction kinetics, the course provides a basic introduction to the modeling and numerical methods used in molecular dynamics. Newtonian multi-body simulations and classical potentials and their use in molecular dynamics are discussed. Most of the topics in this course are intro-duced through practical examples from applied modeling in the domain of systems biology. The applicability of the respective methods in synthetic biology is highlighted.
Computational Methods for Systems and Synthetic Biology – ue – exercise
Course No: 18-kp-2080-ue
Time: Fridays 9:50 – 11:30 a.m.
Location: S3/20, room 4
Practical modeling of molecular processes and to determine dynamical properties of model using mathematical methods.
It relies on the understanding of the following topics:
• Mathematical abstraction of molecular mechanisms
• General properties of stochastic processes
• Approximation methods for Markovian population models
• Stability analysis of nonlinear differential equations
• Numerical methods for solving/simulating stochastic systems
• System identification/machine learning for stochastic systems
Machine Learning in Information and Communication Technology (ICT) – lecture
Course No: 18-kp-2110-vl
Time: every Monday 16:15 – 17:55 p.m.
Location: S3/06, room 52
Contents: Introduction into machine learning from the perspective of an engineer.
Most important models and learning procedures will be presented and explained by problems in information and communication systems.
Kevin P. Murphy: Machine Learning – A probabilistic perspective, MIT Press, 2012
Christopher M. Bishop: Pattern recognition and Machine Learning, Springer, 2006
Peter Bühlmann und Sara van de Geer: Statistics of high-dimensional data – Methods, theory and applications, Springer, 2011
Machine Learning in Information and Communication Technologies – exercise
Course No: 18-kp-2110-ue
Time: every Wednesday 3:20 p.m. – 5:00 p.m.
Location: S3/06, room 053
Machine Learning in ICT – practical training
Course No: 18-kp-2110-pr
Time: please fix a date
Please register via TUCaN