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A comprehensive mechanistic model of adipocyte signaling with layers of confidence
Örebro University, School of Medical Sciences. Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Department of Mathematics, Linköping University, Linköping, Sweden; School of Medical Sciences and Inflammatory Response and Infection Susceptibility Centre (iRiSC), Faculty of Medicine and Health, Örebro University, Örebro, Sweden.ORCID iD: 0000-0002-9058-7049
School of Bioscience, Systems Biology Research Center, University of Skövde, Skövde, Sweden.
Department of Biomedical Engineering, Linköping University, Linköping, Sweden. 2 Department of Mathematics, Linköping University, Linköping, Sweden; Department of Biomedical Engineering, Linköping University, Linköping, Sweden. 2 Department of Mathematics, Linköping University, Linköping, Sweden.
Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
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2023 (English)In: npj Systems Biology and Applications, E-ISSN 2056-7189, Vol. 9, no 1, article id 24Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Nature, 2023. Vol. 9, no 1, article id 24
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:oru:diva-106320DOI: 10.1038/s41540-023-00282-9ISI: 001003005100001PubMedID: 37286693Scopus ID: 2-s2.0-85161187432OAI: oai:DiVA.org:oru-106320DiVA, id: diva2:1770102
Funder
Swedish Research Council, 2018-05418 2018-03319 2019-03767Swedish Foundation for Strategic Research, ITM17-0245Knut and Alice Wallenberg Foundation, 2020.0182ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications, 2020-A12Vinnova, 2020-04711Swedish Heart Lung FoundationÅke Wiberg Foundation, M19-0449 M21-0030 M22-0027Knowledge Foundation, 20200017
Note

Funding agency:

Swedish Fund for Research without Animal Experiments F2019-0010 S2021-0008

Available from: 2023-06-19 Created: 2023-06-19 Last updated: 2023-06-19Bibliographically approved

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Lövfors, WilliamCedersund, Gunnar

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