The IBIMA is a preclinical institute. We are specifically interested in health and prevention, investigating the molecular basis of aging processes. Generally, we strive to better understand diseases, to propose means for their prevention, to support existing treatments and to develop new strategies to help patients.

A core element of our work is to identify new biomarkers. In population studies this may be genetic variations. For patients this may be a particular profile of the transcriptome that is seen in conjunction with clinical phenotypes. The challenge is to constrain an avalanche of data from patients and from public resources towards a much smaller set of molecular interactions that can be experimentally investigated.

Along these lines, the IBIMA has developed concepts (“FocusHeuristics”, “ExprEssence”) to map disease expression data to public molecular network data and then derive a condensed network (Ernst et al., Scientific Reports, 2017). We are also interested to learn how to adopt these principles to best compare changes to the transcriptome of diseases with changes known to be induced by drugs, and further by combinations of drugs.

We have a local pipeline of tools for the semi-automated processing of Next-Generation Sequencing data that feeds into above routines for condensing networks. These we apply on data from clinical collaborators, for example, on Parkinson data (Hamed et al., Scientific Reports, 2018). We strive to integrate image analyses towards “radiogenomics” and have supported investigations of the immune repertoire (Fähnrich et al., 2017).

For details on our methods please see our repository at https://bitbucket.org/ibima and respective associated publications.

One particular clinical feature connecting the majority of disease is that these occur more frequently in older populations. We want to find out, what it is on a molecular level that makes some people age more quickly and less healthy than others.  In the EU project “Ageing with Elegans” (2015-2020) we investigate the effect of ageing on the transcriptome and the metabolome and how this can be modulated. Ongoing work is a community effort to map healthspan pathways (Moeller et al., 2018, preprint) and to specify the terminology to describe health (Fuellen et al., 2018, preprint). On a molecular level we are interested in molecular pathways contributing to senescence as an age-associated cellular phenotype (Möller et al., in preparation).


Expert Systems in Medicine