We provide expert-solutions for analysis, modelling and interpretation of data generated by groups of the Molecular Genetics department and our external collaboration partners. Our routinely used methods include:

1.    RNAseq data processing and differential expression analysis
2.    Metabolomic data processing, annotation and differential analysis
3.    Data mining
4.    GWAS and QTL analysis
5.    Statistical and model-based data integration
6.    Inference and modelling of signalling cascades
7.    Kinetic and constraint-based modelling of metabolism


BioModelKit is a versatile modelling framework for modular biomodel-engineering with multi-scale, multi-level and multi-space support. The purpose of the modular modelling concept within BioModelKit is to represent knowledge about molecular mechanisms by consistent executable sub-models (modules)  equipped with defined interfaces facilitating their reuse and recombination. This concept allows composing complex and integrative models from an ad hoc chosen set of modules including different omic and abstraction levels with the option to integrate spatial aspects. The modular design of models within BioModelKit promotes the construction of alternative models by either the exchange of competing module versions or the algorithmic mutation of the composed model. Furthermore, BioModelKit offers concepts for (omic) data integration and integration of existing resources i.e. models, and thus facilitate their reuse. BioModelKit is a web-based tool (www.biomodelkit.org), where users can interact with the modules stored in a database, and make use of the model composition features. BMK facilitates and encourages model-driven predictions and hypotheses supporting experimental research in a multilateral exchange.