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      Deciphering the expression dynamics of ANGPTL8 associated regulatory network in insulin resistance using formal modelling approaches


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          ANGPTL8 is a recently identified novel hormone which regulates both glucose and lipid metabolism. The increase in ANGPTL8 during compensatory insulin resistance has been recently reported to improve glucose tolerance and a part of cytoprotective metabolic circuit. However, the exact signalling entities and dynamics involved in this process have remained elusive. Therefore, the current study was conducted with a specific aim to model the regulation of ANGPTL8 with emphasis on its role in improving glucose tolerance during insulin resistance. The main contribution of this study is the construction of a discrete model (based on kinetic logic of René Thomas) and its equivalent Stochastic Petri Net model of ANGPTL8 associated Biological Regulatory Network (BRN) which can predict its dynamic behaviours. The predicted results of these models are in‐line with the previous experimental observations and provide comprehensive insights into the signalling dynamics of ANGPTL8 associated BRN. The authors’ results support the hypothesis that ANGPTL8 plays an important role in supplementing the insulin signalling pathway during insulin resistance and its loss can aggravate the pathogenic process by quickly leading towards Diabetes Mellitus. The results of this study have potential therapeutic implications for treatment of Diabetes Mellitus and are suggestive of its potential as a glucose‐lowering agent.

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                Author and article information

                IET Syst Biol
                IET Syst Biol
                IET Systems Biology
                The Institution of Engineering and Technology
                01 April 2020
                April 2020
                : 14
                : 2 ( doiID: 10.1049/syb2.v14.2 )
                : 47-58
                [ 1 ] Research Center for Modelling and Simulation (RCMS), National university of Sciences and Technology (NUST) Sector H‐12 Islamabad 46000 Pakistan
                [ 2 ] Department of Computer Science and Information Technology University of Malakand Chakdara, Dir Lower, Khyber Pakhtunkhwa 18800 Pakistan
                [ 3 ] Atta‐ur‐Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST) Sector H‐12 Islamabad 46000 Pakistan
                [ 4 ] School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST) Pakistan
                © 2020 The Institution of Engineering and Technology

                This is an open access article published by the IET under the Creative Commons Attribution‐NoDerivs License ( http://creativecommons.org/licenses/by-nd/3.0/)

                : 03 August 2019
                : 19 February 2019
                : 28 August 2019
                Page count
                Figures: 12, Tables: 3, References: 107, Pages: 12, Words: 13032
                Research Article
                Research Article
                Custom metadata
                April 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.9 mode:remove_FC converted:11.11.2021

                molecular biophysics,biomembranes,diseases,stochastic processes,biochemistry,patient treatment,petri nets,genetics,sugar,cellular biophysics,biology computing,angptl8 associated regulatory network,formal modelling approaches,lipid metabolism,compensatory insulin resistance,glucose tolerance,equivalent stochastic petri net model,angptl8 associated brn


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