Topics in Computational Biology (Selected Topics in Machine Learning and Modelling in Biology)
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 2019 (current)
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|
|SS 2019||SS 2018|
MA5607 is a semester module in English language at Master’s level which is offered irregular.
This Module is included in the following catalogues within the study programs in physics.
- Elective Modules Natural Sciences in the Master Program Matter to Life
|Total workload||Contact hours||Credits (ECTS)|
|180 h||60 h||6 CP|
Content, Learning Outcome and Preconditions
questions approached using computational biology is rather broad, the number of different machine learning, biostatistics and modelling methods applied in this field is tremendous. Commonly used tools include gene sequence analysis, image analysis, statistical network modeling and dynamic pathway modelling. All of these tools span one or more fields of
mathematics, e.g., statistics, differential equations and optimization.
This lecture series aims at providing the participants with an overview about different fields of computational biology and the methods / algorithms used in this field. To complement the theoretical part, concrete application and ongoing research projects will be presented.
- Statistical inference for dynamical biological systems
- Models of Stem Cell Decision Making
- Quantitative models of transcriptional gene regulation
- Hidden Markov Models for the analysis of epigenomics data
- Polygenic Risk Analysis
- Imputing single-cell gene expression
- explore a selection of machine learning & modelling methods used in computational biology and biomedicine
- understand advantages and limitations of each method / algorithm
- to select suitable computational biology / systems biology approaches for a given biological / biomedical problem.
Furthermore, they have an overview in computational biology.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||2||Selected Topics in Machine Learning & Modelling in Biology [MA5607]||Marr, C. Peng, T. Theis, F.||
Wed, 14:15–15:45, MI 03.06.011
|UE||2||Selected Topics in Machine Learning & Modelling in Biology (Exercise Session) [MA5607]||Marr, C. Peng, T. Theis, F.||
Wed, 16:00–18:00, MI 02.06.011
Learning and Teaching Methods
- The weekly lecture includes one introductory lecture and 12 lectures introducing specific research questions and computational approaches, given by group leaders from the ICB (see http://icb.helmholtz-muenchen.de for an overview).
- In parallel, each lecture will be accompanied by an exercise course that gives students hands-on experience of the research topics addressed in the lecture. Participation to the exercise
course is compulsory and should be registered to the exercise group via Moodle and TUMonline. All participants should bring their own laptop for the exercises and should install the latest version of the Jupyter Notebook with Python kernel. The lecturer and teaching assistant are present during each exercise course to give professional advices.
The individual lectures will be taught by researchers from the: - M12 Biomathematics, Center of Mathematical Sciences, TUM - Institute of Computational Biology, Helmholtz Center Munich
F. Markowetz (2017) All biology is computational biology. PLOS Biology.
Description of exams and course work
The exam may be repeated at the end of the semester.