Our research projects and publications


  • Complex Networks
    We develop novel methods for the analysis of large-scale complex networks.
  • DNA methylation models
    Together with the group of Prof. Dr. Jörn Walter we develop stochastic models that describe epigenetic modifications of the DNA.
    In this project we develop novel modeling and simulation methods for discrete-space biological models that exhibit multimodal behaviour.


  • Geobound
    Overview Geobound takes a transition class model and a polynomial Lyapunov function as input and symbolically computes the drift, i.e. a multivariate polynomial expressing the expected change in the Lyapunov function for each state.
  • H(O)TA
    Overview H(O)TA is a Matlab based tool that allows biologists to accurately measure the methylation and hydroxylation levels at a certain locus of the DNA and to determine the efficiencies of the enzymes that are responsible for maintenance (Dnmt1) and de novo (Dnmt3a/b) methylation as well as hydroxylation (Tets) at this locus over time.
  • LumPy
    The LumPy toolset implements lumping for Pair-Approximation (PA) and Degree-Based Mean Field (DBMF) equations for contact processes on complex networks. It reduces the large number of ODEs given by the PA or DBMF equations by clustering them and solving instead just a single ODE per cluster.
  • STAR
    STAR is a tool for the stochastic hybrid analysis of Markov population models, that is, Markov processes with an underlying population structure. It efficiently computes an accurate approximation of the probability distribution at a particular time instant based on a stochastic hybrid model that combines moment-based and state-based representations of probability distributions.