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  • Lövfors, William,1991-Linköpings universitet,Örebro universitet,Institutionen för medicinska vetenskaper,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,Avdelningen för medicinsk teknik,Matematiska institutionen,Tekniska fakulteten,Orebro Univ, Sweden; Orebro Univ, Sweden (författare)

A comprehensive mechanistic model of adipocyte signaling with layers of confidence

  • Artikel/kapitelEngelska2023

Förlag, utgivningsår, omfång ...

  • Springer Nature,2023
  • electronicrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:his-22781
  • https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-22781URI
  • https://doi.org/10.1038/s41540-023-00282-9DOI
  • https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-106320URI
  • https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-195286URI
  • https://gup.ub.gu.se/publication/327926URI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

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Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • 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.
  • Funding agency:Swedish Fund for Research without Animal Experiments F2019-0010 S2021-0008
  • Funding Agencies|Swedish Research Council [2018-05418]; Swedish Research Council; CENIIT; Swedish Foundation for Strategic Research [2018-05418, 2018-03319, S2021-0008]; SciLifeLab National COVID-19 Research Program - Knut and Alice Wallenberg Foundation [Dnr 2019-03767, 2020-04711]; H2020 project PRECISE4Q [15.09]; Swedish Fund for Research without Animal Experiments [20.08]; ELLIIT [ITM17-0245]; VINNOVA (VisualSweden) [2020.0182, 20200017]; Heart and Lung Foundation [777107]; Ake Wibergs Stiftelse [F2019-0010]; Knowledge Foundation; [2020-A12]; [M19-0449]; [M21-0030]; [M22-0027]
  • 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. 

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Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Magnusson, Rasmus,1992-Högskolan i Skövde,Institutionen för biovetenskap,Forskningsmiljön Systembiologi,Translationell Bioinformatik, Translational Bioinformatics,School of Bioscience, Systems Biology Research Center, University of Skövde, Skövde, Sweden,Univ Skovde, Sweden(Swepub:his)magr (författare)
  • Jönsson, CeciliaLinköpings universitet,Avdelningen för diagnostik och specialistmedicin,Avdelningen för medicinsk teknik,Medicinska fakulteten(Swepub:liu)cecka94 (författare)
  • Gustafsson, MikaLinköpings universitet,Bioinformatik,Tekniska fakulteten(Swepub:liu)mikgu75 (författare)
  • Olofsson, Charlotta S,1971Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för fysiologi,Institute of Neuroscience and Physiology, Department of Physiology(Swepub:gu)xoloch (författare)
  • Cedersund, Gunnar,Associate Professor,1978-Linköpings universitet,Örebro universitet,Institutionen för medicinska vetenskaper,Department of Biomedical Engineering, 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; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden,Avdelningen för medicinsk teknik,Tekniska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV(Swepub:liu)gunce57 (författare)
  • Nyman, ElinLinköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten(Swepub:liu)eliny61 (författare)
  • Örebro universitetInstitutionen för medicinska vetenskaper (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:npj Systems Biology and Applications: Springer Nature9:12056-7189

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