Your Problems

In medical and biological investigations many different kinds of data are acquired to find significant cause-effect-relationships. Those studies consider for example large patient cohorts or "omics" data (high-throughput data of genes, proteins, etc.). The final application in most cases is of medical nature, in particular new or improved diagnosis or therapy (treatment) for either larger groups of patients or individuals ("personalized medicine").

In such cases bioinformatics plays a major role because this area regards analysis methods (from mathematics and computer science) adapted to biological and medical data. Although most methods are derived from statistics and general data analysis, there are relevant improvements and extensions for the application in the life sciences. Therefore, in this field bioinformatics (as considered here to contain data-based top-down approaches) complements methods of systems biology.

One further focus of bioinformatics is the standardisation, annotation and management of those data.

Our Solutions

The interdisciplinary team of BioControl applies bioinformatics to basic research and applications in the life sciences. Example applications are the analysis of gene expression or patient data. Statistical test models help to find significant effects in different conditions (e.g. changing environment or mutations).

Subsequently, further algorithms (including network inference) can determine relationships or function of the different data components. In this context, biological markers (qualitative or quantitative) or treatment targets can be determined for diagnosis and therapy, respectively. In a similar manner existing methods of diagnosis and therapy can be evaluated. This way alternative (measurement) methods can be found which reduce effort (costs, severity, etc.) or gain better results.

An additional focus of our company is the management of biological and medical data. We offer adapted solutions for data annotation, storage and user access.


  • Omics analysis (e.g. immune response to infections)
  • Promoter analysis (examination of binding sites using ChIP-seq data)
  • Data management (standardisation, annotation, databases, user interfaces)
  • Interaction networks
  • Sequence analysis (mapping)
  • Analysis of evolutionary relationships