We work on the development and application of computational approaches to understand molecular biological systems based on complex experimental data. We pursue this objective using a combination of direct mathematical modeling, machine learning, and statistical / bioinformatics data analysis methods, in close collaboration with specialized experimental collaboration partners.
Biological applications include
- Viral infection and immune response, where we use a combination of mathematical modeling, machine learning and high-throughput screening techniques to decipher virus-host interactions and develop new antiviral strategies.
- Bacterial infection and host-pathogen interactions.
- Ageing and age-related diseases, where based on deep sequencing and other high-throughput data we study regulatory processes underlying cellular ageing.
- Cancer and oncogenic transformations in cells, for example prediction of personalized therapeutic targets based on molecular tumor profiles and personalized mathematical models of mutated cellular pathways in cancerous cells.
- Understanding the regulatory role of microRNAs in secretory trafficking, using machine learning and mathematical modeling based on large-scale image based RNAi screening data.
- Diabetes and Obesity, with the aim to find predictive patterns that can be used for early diagnosis of pre-diabetic patients at high risk for progression to diabetes, and to predict the individual response of different interventional and disease management strategies in these patients.
Research in these biological applications is complemented by the development of novel bioinformatics and biostatistics tools to process complex experimental data. For example, we have developed a pipeline for the automated processing of large-scale, image based RNAi screens. We develop and employ supervised and unsupervised maching learning tools for Bioinformatics and Systems Biology, which we employ for example to predict survival and treatment response of cancer patients based on molecular data, or to reconstruct genetic regulatory and signal transduction networks from high-throughput experimental data.