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Title:
A Multi-Compartment Disease Map Approach to MASLD

Authors:
Matti Hoch, Ronja Müller, Ali Canbay, Andreas Geier, Jörn Schattenberg, Konstantin Cesnulevicius, Myron Schultz, David Lescheid, Olaf Wolkenhauer and Shailendra Gupta

Abstract:
Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), has a complex pathogenesis that requires an in-depth understanding of diagnostic and prognostic molecular patterns to develop effective clinical treatment strategies. Due to the heterogeneity of the liver, approaches that focus on small-scale models of a few signaling pathways inadequately represent the spatial aspects of disease progression. To this end, we created a disease map and collected experimentally validated molecular interactions to study macro-scale heterogeneity and disease progression. We developed a multicompartmental agent-based model (ABM) of autonomous but interacting Boolean networks representing heterogeneous liver compartments and cell types. The stochastic distribution of nutrient supply and metabolic activities between compartments allows the simulation of variations in liver metabolism under different conditions. We present the model as an interactive web-based platform comprising over 30 interconnected subnetworks following SBML standards. Network models of digestive processes enable the study of disease progression following changes in nutritional status. Finally, integrating models of the immune response, such as from the Atlas of Inflammation Resolution (AIR), establishes links between metabolic changes in liver compartments and inflammation, bridging the gap to metabolic dysfunction-associated steatohepatitis (MASH). The disease map allows researchers to perform in silico simulations of MASLD development, defining conditions such as dietary conditions and drug interactions. The impact of changing conditions on MASLD progression can be visualized through simulations, providing a better understanding of the disease. Our resource supports the simulation of clinical interventions, potentially facilitating the development of improved treatment strategies.