The department of Molecular Genetics is concerned with the elucidation of molecular processes that determine plant performance characteristics. The aim is to develop and test strategies that can be used to specifically improve the performance potential of crops. The performance of plants is particularly reflected in the formation of biomass and seeds and thus their yield. The scientists are investigating the dynamics of vegetative growth and metabolism as well as the processes of seed development and filling.
The department makes important contributions to the long-term goal of enabling predictions of crop performance in specific environmental scenarios. The focus is on using biological knowledge about the molecular mechanisms that control important performance-related regulatory and metabolic processes in plants, and about the genes involved. The aim is to gain knowledge that enables the quantitatively and qualitatively adequate production of food and feed, but also of renewable raw materials and energy sources, and helps to conserve the environment and resources.
To achieve these goals, the department operates globally unique plant phenotyping platforms in facilities such as the Plant Cultivation Hall, where environmental conditions can be precisely controlled and current as well as future field conditions can be simulated. In addition, imaging technologies such as nuclear magnetic resonance (NMR) technologies for high-resolution, non-invasive analysis of structures and constituents as well as their dynamics during development are operated and further developed, as are bioinformatics methods for image analysis, for the investigation of networks, and for modelling, which are essential for the extraction, exploration and interpretation of the extensive systems biology data obtained.
Research activities within the Molecular Genetics Department take place in the Heterosis (HET), Automated Plant Phenotyping (APP), Image Analysis (BA), Metabolic Diversity (MD), Seed Development (SD), Assimilate Allocation and NMR (AAN), Network Analysis and Modelling (NAM) and Integrated Mechanistic Models (IMM).