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      PhysiBoSS: a multi-scale agent based modelling framework integrating physical dimension and cell signalling

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          Abstract

          Due to the complexity of biological systems, their heterogeneity, and the internal regulation of each cell and its surrounding, mathematical models that take into account cell signalling, cell population behaviour and the extracellular environment are particularly helpful to understand such complex systems. However, very few of these tools, freely available and computationally efficient, are currently available. To fill this gap, we present here our open-source software, PhysiBoSS, which is built on two available software packages that focus on different scales: intracellular signalling using continuous-time markovian Boolean modelling (MaBoSS) and multicellular behaviour using agent-based modelling (PhysiCell). The multi-scale feature of PhysiBoSS --- its agent-based structure and the possibility to integrate any Boolean network to it --- provide a flexible and computationally efficient framework to study heterogeneous cell population growth in diverse experimental set-ups. This tool allows one to explore the effect of environmental and genetic alterations of individual cells at the population level, bridging the critical gap from genotype to phenotype. PhysiBoSS thus becomes very useful when studying population response to treatment, mutations effects, cell modes of invasion or isomorphic morphogenesis events. To illustrate potential use of PhysiBoSS, we studied heterogeneous cell fate decisions in response to TNF treatment in a 2-D cell population and in a tumour cell 3-D spheroid. We explored the effect of different treatment regimes and the behaviour and selection of several resistant mutants. We highlighted the importance of spatial information on the population dynamics by considering the effect of competition for resources like oxygen. PhysiBoSS is freely available on GitHub (https://github.com/gletort/PhysiBoSS), and is distributed open source under the BSD 3-clause license. It is compatible with most Unix systems, and a Docker package (https://hub.docker.com/r/gletort/physiboss/) is provided to ease its deployment in other systems.

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          Author and article information

          Journal
          bioRxiv
          February 17 2018
          Article
          10.1101/267070
          9ad0f1fe-5253-46ab-863e-5ec4355b7d63
          © 2018
          History

          Quantitative & Systems biology
          Quantitative & Systems biology

          Comments

          2019-04-12 13:52 UTC
          +1

          Please note that this is now published at http://dx.doi.org/10.1093/bioinformatics/bty766

          2019-02-17 19:05 UTC
          +1

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