Parameter Inference for Stochastic and Deterministic Dynamic Biological Processes
Module MA5603
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2012
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
| available module versions | ||
|---|---|---|
| WS 2014/5 | SS 2012 | WS 2011/2 |
Basic Information
MA5603 is a semester module in English language at Master’s level which is offered every semester.
This module description is valid from WS 2011/2 to WS 2012/3.
| Total workload | Contact hours | Credits (ECTS) |
|---|---|---|
| 90 h | 30 h | 3 CP |
Content, Learning Outcome and Preconditions
Content
Scientific skills:
1. Modelling
a. Ordinary differential equation
b. Partial differential equation
c. Markov jump processes
d. Chemical Master Equation
2. Probability theory
3. Nonlinear optimization
4. Parameter inference
a. Bayesian
b. Maximum likelihood
5. Uncertainty analysis
a. Asymptotic methods
b. Bootstrapping
c. Profile likelihood
d. Bayesian analysis
Programming skills:
The participants will develop several MATLAB programs for parameter estimation.
Soft skills:
The participants will work in groups and present recent advances in the field. This will improve team working and presentation skills.
1. Modelling
a. Ordinary differential equation
b. Partial differential equation
c. Markov jump processes
d. Chemical Master Equation
2. Probability theory
3. Nonlinear optimization
4. Parameter inference
a. Bayesian
b. Maximum likelihood
5. Uncertainty analysis
a. Asymptotic methods
b. Bootstrapping
c. Profile likelihood
d. Bayesian analysis
Programming skills:
The participants will develop several MATLAB programs for parameter estimation.
Soft skills:
The participants will work in groups and present recent advances in the field. This will improve team working and presentation skills.
Learning Outcome
1) The participants can model deterministic and stochastic biological processes and simulate them using MATLAB.
2) The participants can independently solve common parameter estimation problems for in dynamical systems.
3) The participants can analyze the uncertainty of parameter estimates using different methods.
4) The participants can present own results and critically evaluate them.
2) The participants can independently solve common parameter estimation problems for in dynamical systems.
3) The participants can analyze the uncertainty of parameter estimates using different methods.
4) The participants can present own results and critically evaluate them.
Preconditions
Bachelor in mathematics, bioinformatics, statistics or related fields.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VO | 2 | Parameter Inference for Stochastic and Deterministic Dynamic Biological Processes | Hasenauer, J. Theis, F. |
documents |
Learning and Teaching Methods
Project seminar, active participation in lecture
Media
Blackboard and slides
Literature
A. Tarantola. Inverse Problem Theory and Methods for Model Parameter Estimation. SIAM. Url: http://www.ipgp.fr/~tarantola/Files/Professional/Books/InverseProblemTheory.pdf
Module Exam
Description of exams and course work
Presentation. The lecturers will provide each student with two papers on a certain lecture-related topic. The content of these papers has to be summarized and discussed in a presentation (20-30 minutes). The students are allowed to use the beamer as well as the blackboard.
Exam Repetition
The exam may be repeated at the end of the semester.