Topics in Computational Biology WS19/20

Fabian TheisCarsten MarrTingying Peng
Tel.: +49 89 2891-7961Tel.: +49 89 3187-2158Tel.: +49 89 3187-4217
E-mail: E-mail:  E-mail:  


Room:TUM Garching, MI 02.06.011 
Date & Time:     Wednesday, 2.00 pm - 5.30 pm
Prerequisites:Bachelor in mathematics, bioinformatics, statistics or related fields.
Number of participants:< 50
Language:      English


Topic: 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
  • Deep learning techniques

Prerequisites: Bachelor in mathematics, bioinformatics, statistics and related fields.

Aims of the course: 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

Description of study/examination: 

The course consists of a weekly lecture and a parallel weekly exercise course, followed by a final written exam.

  • 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.
  • A final written exam will cover the whole material of the course. Some problems from weekly exercise could reappear on the exam with small modifications. Time and place of the exam will be announced later.


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

Course on TUM-online: Topics in Computational Biology  (4SWS, WS 2019/20), Theis F, Marr C, Peng T