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Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, 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. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70–90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes. CC BY 4.0© 2023, The Author(s)Correspondence and requests for materials should be addressed to William Lövfors, Gunnar Cedersund or Elin Nyman.GC acknowledges support from the Swedish Research Council (2018-05418, 2018-03319), CENIIT (15.09), the Swedish Foundation for Strategic Research (ITM17-0245), SciLifeLab National COVID-19 Research Program financed by the Knut and Alice Wallenberg Foundation (2020.0182), the H2020 project PRECISE4Q (777107), the Swedish Fund for Research without Animal Experiments (F2019-0010), ELLIIT (2020-A12), and VINNOVA (VisualSweden, 2020-04711). EN acknowledges support from the Swedish Research Council (Dnr 2019-03767), the Heart and Lung Foundation, CENIIT (20.08), Åke Wibergs Stiftelse (M19-0449, M21-0030, M22-0027), and the Swedish Fund for Research without Animal Experiments (S2021-0008). GC and WL acknowledge scientific support from the Exploring Inflammation in Health and Disease (XHiDE) Consortium, which is a strategic research profile at Örebro University funded by the Knowledge Foundation (20200017). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.