Section: New Results

Liver organ modelling

Participants : Noémie Boissier, Dirk Drasdo, Géraldine Cellière, Adrian Friebel, Group Heinzle [Univ. Saarbruecken, Germany] , Group Hengstler [IfADo, Germany] , Stefan Hoehme, Tim Johann, Group Klingmueller [German Cancer Center, Heidelberg] , Johannes Neitsch, Group Reo [Inria Paris - Rocquencourt] , Paul Van Liedekerke, Eric Vibert [Hopital Paul Brousse] , Yi Yin, Group Zerial [Max-Planck Inst. for Molecular Genetics, Dresden, Germany] , Groups Iflow, Notox, Vln.

Ammonia detoxification after drug-induced damage.

The model for ammonia detoxification after drug-induced damage (see above) identified a systematic deviation between data and results that would be expected from the current standard model for ammonia detoxification in healthy liver (Haeussinger D., Eur. J. Biochem, 1983; Gebhardt R and Mecke, D. EMBO J 1983) ([17] , [6] ) (see also comments/editorials in (Wierling, C. Hepatology, 60(6) 2014; and: Widera, A., EXCLI Journal, 13, 2014)). The findings triggered a series of new experiments identifying reversibility of the glutamate-dehydrogenase reaction in hepatocytes, and in blood (Ghallab et. al., subm.). It could be shown in an animal model that the newly recognized reactions can be therapeutically used to significantly reduce the concentration of toxic ammonia after drug-induced damage. (Work in close collaboration with partners of the project VLN (BMBF, Germany) and EU-NOTOX.

Systematic analysis strategies permitting quantitative conclusions in systems medicine and biology.

Based on the examples from liver regeneration after drug-induced damage [57] [17] ) systematic iterative strategies can be inferred to enable identification of mechanisms underlying complex processes in spatial temporal tissue organisation and organ functioning. These use an iterative application of a pipeline of imaging, image analysis and modeling, quantitative models by parameterization of model components by measurable parameters for which the physiological ranges are known, and systematic simulated parameter sensitivity analyses [7] .