Establishment of spatio-temporal multiscale multispecies liver tissue models by analysis of experimental images for systems medicine

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The proposed project is envisioned as being part of the search for understanding of the integrated physiological function of the human body in terms of structure and function of tissues, cells and proteins. There are two main objectives of the proposed project: (1) Automatization and standardization of creation of individual-cell-based spatio-temporal multi-scale models for biological tissues directly from experimental image modalities(2) Application of these tissue models to elucidate the mechanisms behind liver regeneration after partial liver resection in different species to better understand liver failure in humanManual construction of predictive tissue models where realistic structures and dynamics are parameterized directly by experimental data is still very challenging. Therefore, automatization of tissue model construction has the potential to open up a whole new group of possible model users in the scientific community. Future large-scale use of such complex tissue models in science and especially in patient therapy will only be possible if robust automatization of model construction can successfully be established.Along this path, standardization of models, for example by establishment of comprehensive model description languages, will play an important role. Only unambiguous model description will ensure model reproducibly that is an important foundation for their continuous improvement by building on previous work. The spatio-temporal liver models that will be established in this project exemplify this approach. Moreover, the general concepts and techniques will be widely applicable in systems biology and systems medicine. Furthermore, we will utilize the developed model for liver regeneration after PHx to address the clinically relevant question of why the fascinating ability of liver to regenerate after partial liver resection in some patients fails leading to high mortality rates. Understanding the processes leading to this liver failure is of utmost importance to improve for example liver transplantation and treatment of liver cancer for example by better assessing the risk of liver resection surgery for individual patients based on model simulations. The developed models will integrate genetic, molecular, tissue and organ scales and therefore will allow for a truly multiscale representation of liver tissue structures and dynamics in healthy and damaged fatty liver. Moreover, we will study and model a novel treatment using mesenchymal stem cells that, as preliminary results suggest, has the potential to be a significant step forward in patient care.