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The aim of this project is to gain a basic understanding on the structure of many-objective optimization problems and to develop methods for coping with high-dimensional spaces like dimensionality reduction methods, indicator-based algorithms and the visualization of high-dimensional data
The project focuses on the development of novel concepts and algorithms for the optimization of large-scale network problems arising in two application domains, namely the study of gene regulation in living cells and the design of embedded systems based on wireless communication. Major research topics are preference articulation, hybrid optimization, and interactive decision support.
This project aims at the development of methods for module identification from high-throughput data such as gene expression, protein interaction or metabolic flux measurements.
Testing different search strategies on the same optimization problem, or applying one search method to several benchmark problems is often difficult in practice. Search strategies and application problems may be implemented in different programming languages, even on different platforms, which prevents the arbitrary combination of these components. The PISA project aims at circumventing this problem by introducing a text-based interface between the problem specific parts (e.g., representation, fitness evaluation, variation) and the problem independent parts (e.g., selection) of an application.
The aim of this project is to improve the understanding of the gene regulation in the shoot apical meristem (SAM) of the plant Arabidopsis thaliana. Within a EU project encompassing ten different groups from all over Europe, our group focuses on modeling the gene regulatory networks. Based on data gained by the collaborating groups, the model itself and necessary parameter estimation techniques for the model used are developed and applied to test and explore biological hypotheses about SAM regulation.
