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Topics in Computational Biology

Module MA5607

This Module is offered by TUM Department of Mathematics.

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 2018

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 2019SS 2018

Basic Information

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 workloadContact hoursCredits (ECTS)
180 h 60 h 6 CP

Content, Learning Outcome and Preconditions


In all fields of life sciences, ranging from the analysis of genomic data over stem cell research to the treatment of disease, computational methods are employed to deepen our understanding of the respective biological processes and make predictions about the system’s dynamics. As the range of biological questions approached with computational biology is extremely broad, the number of different methods applied is likewise tremendous. In this lecture, we will give an overview of commonly used tools in computational biology, including gene sequence analysis, image computing, statistical network approaches and dynamic pathway modelling. In particular, we will introduce recent applications of deep learning to address biological questions. In parallel to the lecture, we offer an exercise course that gives the students hands-on experience in computational analyses and sharpens their analytic and programming skills.
Topics includes:
- 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

Learning Outcome

After the successful completion of the module, the participants
- understand a selection of methods used in computational biology
- understand advantages and disadvantages of the introduced methods
- can evaluate which methods can be used to approach a given problem.


Bachelor in mathematics, bioinformatics, statistics or related fields.

Courses, Learning and Teaching Methods and Literature

Courses and Schedule

Learning and Teaching Methods

The module consists in multiple lectures in which different research topics and methods in computational biology are introduced, followed by a parallel exercise course that gives students hands-on experience. The exercise course also allows students to interact with lecturers, discussing possible research projects in their research group.
- 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 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.


slides, blackboard


H. Kitano (2002) Computational systems biology. Nature 420 (6912): 206-210.
F. Markowetz (2017) All biology is computational biology. PLOS Biology.

Module Exam

Description of exams and course work

The exam will be in written form (90 minutes). Students demonstrate that they have gained sufficient knowledge of commonly used tools in computational biology, including gene sequence analysis, image computing, statistical network approaches and dynamic pathway modeling presented in the course and their applicability in data analysis. The students are expected to be able to derive the methods, to explain their properties, and to apply them to specific examples. In particular, some problems from weekly exercise could reappear on the exam with small modifications. Students are also allowed to take one page of course notes during the examination.

Exam Repetition

The exam may be repeated at the end of the semester.

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