Poster Open Access

PhysiBoSS-COVID: the Boolean modelling of COVID-19 signalling pathways in a multicellular simulation framework allows for the uncovering of mechanistic insights

Noël, Vincent; Carbonell, Jose; Ponce de Leon, Miguel; Soliman, Sylvain; Niarakis, Anna; Calzone, Laurence; Barillot, Emmanuel; Valencia, Alfonso; Montagud, Arnau

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has experienced an extremely fast worldwide spreading, producing an unprecedented pandemic situation. As a result, a strong response from the research community resulted in a vast amount of new COVID-19-related publications, covering different clinical and molecular aspects of the disease. In spite of all these efforts, most of the underlying molecular events that lead SARS-CoV-2 infection to the observed clinical traits of COVID-19 remain unclear, since multiple biological pathways, cell types and organs are involved through a set of complex interactions.

 

In this context, COVID-19 Disease Map (C19DM) initiative[1] was launched in order to provide a systems perspective to the COVID-19 infection, by integrating and formalising mechanistic knowledge, using current systems biology standards (https://covid.pages.uni.lu/).

 

This open-access collection of curated pathways is a valuable resource which allows the conversion into different mathematical models, which in turn, can provide  interesting mechanistic insights by using an array of simulation methods. One of these methods, PhysiCell[2] is an agent-based flexible cell simulator that can be used to perform diverse multiscale modelling. In fact, PhysiCell has been adapted to the COVID-19 problem recently by incorporating some signalling models of the interactions of virus and human host cells.


In this work we present, PhysiBoSS-COVID, an effort to integrate MaBoSS[3], a stochastic Boolean modelling software, into PhysiCell-COVID[6] to allow the leverage  of  cell- and pathway-specific Boolean models in this framework. To obtain these COVID-19-specific models, we have taken advantage of CaSQ[4] ability to convert all C19DM maps into SBML-qual files, that can subsequently be transformed to MaBoSS-format Boolean models, ready-to-use with PhysiBoSS-COVID. As a proof of concept, we have incorporated models from several immune cells, as well as human epithelial host cells infected by SARS-CoV-2 to our prototype and hereby present preliminary results.

PhysiBoSS-COVID, which is based on our previous work PhysiBoSS[5], provides a framework that enables testing of combined genetic and environmental perturbations, and can offer mechanistic insights of SARS-CoV-2 infection and dissemination among human host cells. Finally, PhysiBoSS-COVID was incorporated as a use case into the European HPC/Exascale Centre of Excellence in Personalized Medicine (PerMedCoE, http://permedcoe.eu/), whose purpose is to adapt multiscale modelling to supercomputing environments and to provide an easy-to-use interface to systems biology end users.

European Commission grants: INFORE project(H2020-ICT-825070) and PerMedCoE project(H2020-ICT-951773)
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