Statistical Inference for Dynamical Systems [MA5612]

Vortragende/r (Mitwirkende/r)
  • Jan Hasenauer
Umfang2 SWS
SemesterSommersemester 2018
Stellung in StudienplänenSiehe TUMonline
TermineSiehe TUMonline

Teilnahmekriterien

Lernziele

After the course, the participants can: 1. model biochemical reaction networks using ODEs. 2. solve parameter estimation problems for ODEs using Matlab/Python. 3. analyze the uncertainty of parameter estimates using Matlab/Python. 4. critically evaluate parameter estimation procedures.

Beschreibung

Mathematical models are nowadays essential for the quantitative assessment of technical, physical, chemical, and biological processes. While a broad class of models is used in the different field, almost all models share one common property: the need for accurate parameter values. Due to experimental constraints, many parameters cannot be measured directly, but have to be estimated from the available experimental data. In this course, we will introduce deterministic modeling approaches for biochemical reaction networks. These modeling approaches can be used to describe, e.g., signal transduction and metabolic processes. For these models the respective parameter estimation problem will be formulated and methods will be presented to solve these problems. As parameter estimates carry uncertainties due to limited amounts of data and measurement noise, we furthermore provide methods for a rigorous analysis of parameter uncertainties. This is crucial to evaluate the model uncertainties as well as the predictive power of models. The participants will gather hands-on experiences with parameter estimation and uncertainty analysis, including the implementation of own models and estimation procedures in MATLAB or Python. The estimation methods are presented in the context of biological processes, but the approaches are applicable in many other fields.

Inhaltliche Voraussetzungen

Bachelor in mathematics, bioinformatics, statistics or related fields. Basic MATLAB programming skills.

Lehr- und Lernmethoden

classical lectures & computer exercises and small homework assignments

Links