Topics in Computational Biology [MA5607]

Vortragende/r (Mitwirkende/r)
  • Fabian Theis
  • Carsten Marr
  • Tingying Peng
Umfang2 SWS
SemesterWintersemester 2018/19
Stellung in StudienplänenSiehe TUMonline
TermineSiehe TUMonline




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


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 based bioimage processing

Inhaltliche Voraussetzungen

Bachelor in mathematics, bioinformatics, statistics or related fields.