PhysiBoSS simulation of COVID19 infection

By Vincent Noel1; Miguel Ponce de Leon2; Anna Niarakis3; Laurence Calzone1; Alfonso Valencia2; Arnau Montagud2

1. Institut Curie, INSERM, Mines ParisTech, University PSL, Paris, France 2. Barcelona Supercomputing Center, Barcelona, Spain 3. Univ. Evry, University of Paris-Saclay, Evry, France

PhysiBoSS-COVID: the Boolean modelling of COVID-19 signalling pathways in a multicellular simulation framework

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Version 1.0.5 - published on 12 Nov 2020

doi:10.21981/TQ16-VG65 cite this

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Abstract

PhysiBoSS-COVID is an effort to integrate MaBoSS, a stochastic Boolean modelling software, into PhysiCell-COVID to allow the leverage of cell- and pathway-specific Boolean models. To obtain these COVID-19-specific models, we have taken advantage of CaSQ 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 to our prototype a model of apoptosis on human epithelial host cells as a consequence of SARS-CoV-2 infection or T cell induction and hereby present preliminary results.

PhysiBoSS-COVID, which is based on our previous work PhysiBoSS, provides a framework that enables testing of combined genetic and environmental perturbations, and can offer mechanistic insights of SARS-CoV-2 infection, its dissemination among human host cells and its competition against immune 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 cell-level simulation tools to supercomputing  environments and to provide an easy-to-use interface to systems biology end users.

A poster and a short presentation of this model is available at : https://doi.org/10.5281/zenodo.4266778

 

 

 

Publications

  1. G. Letort, A. Montagud, G. Stoll, R. Heiland, E. Barillot, P. Macklin, A. Zinovyev, L. Calzone, PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling, Bioinformatics, Volume 35, Issue 7, 01 April 2019, Pages 1188–1196, DOI: 10.1093/bioinformatics/bty766
  2. A. Ghaffarizadeh, R. Heiland, S.H. Friedman, S.M. Mumenthaler, and P. Macklin. PhysiCell: an open source physics-based cell simulator for 3-D multicellular systems. PLoS Comput. Biol. 14(2):e1005991, 2018. DOI: 10.1371/journal.pcbi.1005991.
  3. R. Heiland, D. Mishler, T. Zhang, E. Bower, and P. Macklin. xml2jupyter: Mapping parameters between XML and Jupyter widgets. Journal of Open Source Software 4(39):1408, 2019. DOI: 10.21105/joss.01408.
  4. Michael Getz, Yafei Wang, Gary An, Andrew Becker, Chase Cockrell, Nicholson Collier, Morgan Craig, Courtney L. Davis, James Faeder, Ashlee N. Ford Versypt, Juliano F. Gianlupi, James A. Glazier, Sara Hamis, Randy Heiland, Thomas Hillen, Dennis Hou, Mohammad Aminul Islam, Adrianne Jenner, Furkan Kurtoglu, Bing Liu, Fiona Macfarlane, Pablo Maygrundter, Penelope A Morel, Aarthi Narayanan, Jonathan Ozik, Elsje Pienaar, Padmini Rangamani, Jason Edward Shoemaker, Amber M. Smith, Paul Macklin, Rapid community-driven development of a SARS-CoV-2 tissue simulator, bioRxiv 2020.04.02.019075; doi: https://doi.org/10.1101/2020.04.02.019075

Cite this work

Researchers should cite this work as follows:

  • Vincent Noel, Miguel Ponce de Leon, Anna Niarakis, Laurence Calzone, Alfonso Valencia, Arnau Montagud (2020), "PhysiBoSS simulation of COVID19 infection," https://nanohub.org/resources/pb4covid19. (DOI: 10.21981/TQ16-VG65).

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Tags

  1. COVID19
  2. nano/bio
  3. PhysiBoSS
  4. PhysiCell
  5. SARS-Cov-2