Modelling of the SARS-CoV-2 life cycle and interactions with host cellular defence systems

Abstract: SARS-CoV-2 is a recently emerged zoonotic beta-coronavirus that has become the cause of a global pandemic. In response to this pandemic, an international collaborative effort has been established to produce models of the SARS-CoV-2 lifecycle and host responses to infection, the COVID-19 Disease Mapping Consortium. Alongside this group we have produced a pair of graphical and computational models of the SARS-CoV-2 lifecycle covering viral entry into a host cell, the replication cycle of the virus, interactions between viral replication and host defence pathways and the release of daughter virions in both lung epithelial and macrophages cells. These models have been built in the yEd network editing software using the modified Edinburgh Pathway Notation (mEPN) scheme. In addition to supporting the graphical representation of biological pathways, the mEPN scheme supports computational modelling without simplification of the graphical elements. The modelling system is based on stochastic Petri nets where place nodes represent biological entities and transition nodes represent the interactions between them. Activity is represented by the rule-based flow of tokens. Simulations are performed using the tool BioLayout (http://biolayout.org/). The events depicted within these models have been produced from information available in the literature and Informed by models produced by other members of the Disease Mapping Consortium. The models produced by this project are sufficiently computationally detailed for use in drug target identification and elimination, with sufficient graphical detail and clarity to function as educational tools covering the SARS-CoV-2 lifecycle and its interaction with the Toll-like Receptor 3 and Interferon signalling pathways. We have also demonstrated that these host defence systems and the three currently included drug species significantly influence the behaviour of these models. The effects of these systems are comparable to those expected from their in vivo counterpoints, indicating that this model is suitable as a tool for in silico experimentation with SARS-CoV-2 replication