AICES - Data driven modelling in computational engineering sciences
Head of the Institute
Univ.-Prof. Dr. rer. nat. Andreas Schuppert
Field of Study
CES, Biomedical Engineering, Biotechnology
Hybrid modelling, Systems biomedicine, Bioinformatics
Researvch activities are focussed on data driven modelling of complex systems. The applications span a wide area from chemical and biotechnological process analysis and -modelling up to gene diagnostics in oncology and cellular reengineering in stem cell technology. The joint scientific backbone is the application of inverse problem methods on the high-dimensional data spaces arising in biological applications, such as micro-array gene- or protein expressions analysis or next generation sequencing.
- Hybrid modeling of complex systems with functional networks Reengineering of functional network structures from data, with applications in unravelling of mechanisms of drug action.
- Modeling of regulation structures of biological systems by integration of statistical thermodynamics approaches, data mining and hybrid models, with applications in cellular reengineering.
- Data driven modeling of clinical parameters from –omics data with applications in gene diagnostics, e.g. in oncology.