A comprehensive mechanistic model of adipocyte signaling with layers of confidence

Abstract

Adipocyte cellular signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for a number of well-studied, and partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. Here, we have created a new method where we first establish a core model by connecting our partially overlapping models of adipocyte cellular signaling with focus on: 1) lipolysis and fatty acid release, 2) glucose uptake, and 3) the release of adiponectin. We then use prior knowledge on protein interactions to identify phosphosites downstream of the core model. Using publicly available phosphoproteome data for the insulin response in adipocytes, we test if the identified downstream phosphosites can be added to the model. The additions of the downstream phosphosites are tested in a parallel pairwise approach with low computation time. We iteratively collect the accepted additions into a layer, and use the newly added layer to find new downstream phosphosites. We find that the first 15 layers (60 added phosphosites) with the highest confidence can correctly predict independent inhibitor-data (70-90 \% correct), and that this ability decrease when we add layers of decreasing confidence. In total, 60 layers (3,926 phosphosites) can be added to the model and still keep predictive ability. Finally, we use this large layered model to simulate systems-wide alterations in adipocytes in type 2 diabetes -- simulations made possible for the first time -- with the method developed herein.