Modelling of Biological Networks
Many biological processes including animal development are coordinated by cell-to-cell communication. Genome sequencing and high-throughput measurement techniques led to the identification of hundreds of molecular species involved in sensing external cues. However, the dynamic interplay between signaling proteins is highly complex and cannot be understood by mere intuition. Mathematical models can be a valuable tool for the identification of mechanisms shaping the dynamics of regulatory networks. Typically, modeling aims at guiding the design of experiments, which are then used to further refine the model (iterative experiment-theory cycle).
Our research focuses on quantitative modeling of biological regulatory networks controlling gene expression. External stimuli typically induce expression of hundreds of genes. Post-translational signaling mechanisms convey the signal into the nucleus, where signal integration occurs by complex transcription factor networks that are incompletely understood. Based on large-scale perturbation data sets, we apply network inference algorithms to obtain insights into the wiring of the transcription factor network. Mechanistic ordinary differential equation models are developed for prototypical target genes to understand the dynamics of transcription factor recruitment. In particular, we focus on interdependent gene regulation by multiple transcription factors and its modulation by epigenetic events such as DNA methylation. Integrative models linking signaling and gene expression levels are employed to study signaling crosstalk at the gene expression level and transcriptional feedback regulation of signal transduction. Furthermore, scientists at IMB use bioinformatics to model regulatory networks.
Such systems biology approaches yield a more complete and quantitative understanding of biological processes.
We are always interested in applications from PhD students, postdocs or students who want to write their Master's thesis in our group.