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PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

View ORCID ProfileMiguel Ponce-de-Leon, View ORCID ProfileArnau Montagud, View ORCID ProfileVincent Noël, Gerard Pradas, Annika Meert, View ORCID ProfileEmmanuel Barillot, View ORCID ProfileLaurence Calzone, View ORCID ProfileAlfonso Valencia
doi: https://doi.org/10.1101/2022.01.06.468363
Miguel Ponce-de-Leon
1Barcelona Supercomputing Center, Barcelona, Spain
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Arnau Montagud
1Barcelona Supercomputing Center, Barcelona, Spain
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Vincent Noël
2Institut Curie, Université PSL, Paris, France
3INSERM U900, Paris, France
4Mines ParisTech, Université PSL, Paris, France
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Gerard Pradas
1Barcelona Supercomputing Center, Barcelona, Spain
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Annika Meert
1Barcelona Supercomputing Center, Barcelona, Spain
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Emmanuel Barillot
2Institut Curie, Université PSL, Paris, France
3INSERM U900, Paris, France
4Mines ParisTech, Université PSL, Paris, France
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Laurence Calzone
2Institut Curie, Université PSL, Paris, France
3INSERM U900, Paris, France
4Mines ParisTech, Université PSL, Paris, France
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Alfonso Valencia
1Barcelona Supercomputing Center, Barcelona, Spain
5Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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  • For correspondence: alfonso.valencia@bsc.es
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Abstract

Motivation Cancer progression is a complex phenomenon that spans multiple scales from molecular to cellular and intercellular. Simulations can be used to perturb the underlying mechanisms of those systems and to generate hypotheses on novel therapies. We present a new version of PhysiBoSS, a multiscale modelling framework designed to cover multiple temporal and spatial scales, that improves its integration with PhysiCell, decoupling the cell agent simulations with the internal Boolean model in an easy-to-maintain computational framework.

Results PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS, conceived as an add-on that expands the PhysiCell agent-based functionalities with intracellular cell signalling using MaBoSS having a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 successfully reproduces simulations reported in the former PhysiBoSS and expands its functionalities such as using user-defined models and cells’ specifications, having mechanistic submodels of substrate internalisation with ODEs and enabling the study of drug synergies.

Availability and implementation PhysiBoSS 2.0 is open-source and publicly available on GitHub (https://github.com/PhysiBoSS/PhysiBoSS) under the BSD 3-clause license. Additionally, a nanoHUB tool has been set up to ease the use of PhysiBoSS 2.0 (https://nanohub.org/tools/pba4tnf/).

Contact alfonso.valencia{at}bsc.es

Supplementary information Supplementary Information, figures, and tables are available at Bioinformatics online.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/PhysiBoSS/PhysiBoSS

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 06, 2022.
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PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks
Miguel Ponce-de-Leon, Arnau Montagud, Vincent Noël, Gerard Pradas, Annika Meert, Emmanuel Barillot, Laurence Calzone, Alfonso Valencia
bioRxiv 2022.01.06.468363; doi: https://doi.org/10.1101/2022.01.06.468363
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PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks
Miguel Ponce-de-Leon, Arnau Montagud, Vincent Noël, Gerard Pradas, Annika Meert, Emmanuel Barillot, Laurence Calzone, Alfonso Valencia
bioRxiv 2022.01.06.468363; doi: https://doi.org/10.1101/2022.01.06.468363

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