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

Abstract

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.

Petri Net modelling approach for analysing the behaviour of Wnt/ ‐catenin and Wnt/ Ca 2+ signalling pathways in arrhythmogenic right ventricular cardiomyopathy

“…Petri net is a very dynamic mathematical formalism for modelling of real systems initially utilised for the biochemical systems in 1939 [21]. After that many biological systems have been modelled using petri net approach on the basis of its simplicity and flexibility (some recent examples are [10,12,[23][24][25]). These models facilitate us to present the dynamic behaviour of time-dependent systems.…”

“…These models are based on our extensive literature survey effort conducted to first gather all molecular links in these signalling pathways which was then used to create biological regulatory networks (BRNs). The BRNs which encompass the regulatory feedback loops among key molecular entities in these signalling pathways were then transformed into respective petri net models (the employed methodologies have been comprehensively described in our previous work [9][10][11][12] and Section 2 below). These models were then used to simulate and predict the dynamical behaviour of the associated BRNs during homeostasis and pathological conditions that may underpin ARVC.…”