Integrative systems biology: inferring from massive heterogeneous data



If you have any questions or comments. Please use contacts available at home pages of selected group members.


Modern biotechnology offers efficient techniques for large-scale measurements of molecular responses to targeted interventions into cellular processes. Besides experimental techniques for high-throughput analysis, various formal methods and algorithmic approaches were proposed for molecular modelling. These are,e.g., logical inference techniques for studying possible system evolution, statistical data mining for gene expression and proteomic studies, and stochastic simulations for dynamic system behavior.

Our approach aims to interrelate heterogeneous and often noisy ''-omics'' data by relying on mathematics, statistics and computer science. It is our objective to address the specific challenges and obtain deep biological insights relating to the following tasks:

  • Modelling peptide degradation process from LC-MS/MS data.
  • Determining the regulatory mechanism of gene expression pattern.
  • Sensitivity analysis of signalling pathways.
  • Detecting aberrations in diseased genomes.