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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00008469naa a2200601 4500
001oai:DiVA.org:his-22781
003SwePub
008230622s2023 | |||||||||||000 ||eng|
009oai:DiVA.org:oru-106320
009oai:DiVA.org:liu-195286
009oai:gup.ub.gu.se/327926
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-227812 URI
0247 a https://doi.org/10.1038/s41540-023-00282-92 DOI
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-1063202 URI
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1952862 URI
0247 a https://gup.ub.gu.se/publication/3279262 URI
040 a (SwePub)hisd (SwePub)orud (SwePub)liud (SwePub)gu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Lövfors, William,d 1991-u 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, Sweden4 aut0 (Swepub:liu)willo18
2451 0a A comprehensive mechanistic model of adipocyte signaling with layers of confidence
264 1b Springer Nature,c 2023
338 a electronic2 rdacarrier
500 a 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.
500 a Funding agency:Swedish Fund for Research without Animal Experiments F2019-0010 S2021-0008
500 a 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]
520 a 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. 
650 7a NATURVETENSKAPx Data- och informationsvetenskapx Bioinformatik0 (SwePub)102032 hsv//swe
650 7a NATURAL SCIENCESx Computer and Information Sciencesx Bioinformatics0 (SwePub)102032 hsv//eng
650 7a NATURVETENSKAPx Biologix Bioinformatik och systembiologi0 (SwePub)106102 hsv//swe
650 7a NATURAL SCIENCESx Biological Sciencesx Bioinformatics and Systems Biology0 (SwePub)106102 hsv//eng
650 7a MEDICIN OCH HÄLSOVETENSKAPx Medicinsk bioteknologix Biomedicinsk laboratorievetenskap/teknologi0 (SwePub)304022 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Medical Biotechnologyx Biomedical Laboratory Science/Technology0 (SwePub)304022 hsv//eng
653 a Bioinformatik
653 a Bioinformatics
7001 a Magnusson, Rasmus,d 1992-u 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, Sweden4 aut0 (Swepub:his)magr
7001 a Jönsson, Ceciliau Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Avdelningen för medicinsk teknik,Medicinska fakulteten4 aut0 (Swepub:liu)cecka94
7001 a Gustafsson, Mikau Linköpings universitet,Bioinformatik,Tekniska fakulteten4 aut0 (Swepub:liu)mikgu75
7001 a Olofsson, Charlotta S,d 1971u Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för fysiologi,Institute of Neuroscience and Physiology, Department of Physiology4 aut0 (Swepub:gu)xoloch
7001 a Cedersund, Gunnar,c Associate Professor,d 1978-u 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, CMIV4 aut0 (Swepub:liu)gunce57
7001 a Nyman, Elinu Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten4 aut0 (Swepub:liu)eliny61
7102 a Örebro universitetb Institutionen för medicinska vetenskaper4 org
7730 t npj Systems Biology and Applicationsd : Springer Natureg 9:1q 9:1x 2056-7189
8564 u https://doi.org/10.1038/s41540-023-00282-9y Fulltext
8564 u https://his.diva-portal.org/smash/get/diva2:1772871/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 u https://liu.diva-portal.org/smash/get/diva2:1770149/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-22781
8564 8u https://doi.org/10.1038/s41540-023-00282-9
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-106320
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-195286
8564 8u https://gup.ub.gu.se/publication/327926

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