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

Letort, Gaëlle; Montagud, Arnau; Stoll, Gautier; Heiland, Randy; Barillot, Emmanuel; Macklin, Paul; Zinovyev, Andreï; Calzone, Laurence

doi:10.1093/bioinformatics/bty766

Cited by 105 publications

(142 citation statements)

References 77 publications

(113 reference statements)

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“…Contacts between agents were modeled as a hard-core repulsive force, F r , effective as soon as spheres overlapped (Ghaffarizadeh et al, 2018; Letort et al, 2019):trueF→r=cr)(1−‖trued→‖deqtrued→‖trued→‖,where trued→ is the vector between the two sphere centers and d eq is the equilibrium distance (i.e., the sum of the two sphere radii). Thus, when two spheres came into physical contact, the increasing overlap, which generated an increasing deformation of the spheres, created a repulsive force (Drasdo et al, 2007).…”

Section: Methodsmentioning

confidence: 99%

Active diffusion in oocytes nonspecifically centers large objects during prophase I and meiosis I

Colin

Letort

Razin

et al. 2020

Journal of Cell Biology

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Nucleus centering in mouse oocytes results from a gradient of actin-positive vesicle activity and is essential for developmental success. Here, we analyze 3D model simulations to demonstrate how a gradient in the persistence of actin-positive vesicles can center objects of different sizes. We test model predictions by tracking the transport of exogenous passive tracers. The gradient of activity induces a centering force, akin to an effective pressure gradient, leading to the centering of oil droplets with velocities comparable to nuclear ones. Simulations and experimental measurements show that passive particles subjected to the gradient exhibit biased diffusion toward the center. Strikingly, we observe that the centering mechanism is maintained in meiosis I despite chromosome movement in the opposite direction; thus, it can counteract a process that specifically off-centers the spindle. In conclusion, our findings reconcile how common molecular players can participate in the two opposing functions of chromosome centering versus off-centering.

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Section: Methodsmentioning

confidence: 99%

Active diffusion in oocytes nonspecifically centers large objects during prophase I and meiosis I

Colin

Letort

Razin

et al. 2020

Journal of Cell Biology

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“…Its modular design allows further customization by open source contributors. For example, Letort et al recently integrated Boolean signalling networks into each cell agent to model molecular-scale processes 31 .…”

Section: Physicell: a Platform For Multicellular Systems Biologymentioning

confidence: 99%

Learning-accelerated Discovery of Immune-Tumour Interactions

Ozik

Collier

Heiland

et al. 2019

Preprint

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We present an integrated framework for enabling dynamic exploration of design spaces for cancer immunotherapies with detailed dynamical simulation models on high-performance computing resources. Our framework combines PhysiCell, an open source agent-based simulation platform for cancer and other multicellular systems, and EMEWS, an open source platform for extreme-scale model exploration. We build an agent-based model of immunosurveillance against heterogeneous tumours, which includes spatial dynamics of stochastic tumour-immune contact interactions. We implement active learning and genetic algorithms using high-performance computing workflows to adaptively sample the model parameter space and iteratively discover optimal cancer regression regions within biological and clinical constraints.

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“…Some modeling approaches have tackled this issue using an agent-based formalism: each agent corresponds to a cell, whose activity depends on that of its neighbors [9,10] (i.e., CellSys [11], PhysiCell [12] ). Attempts to explicitly include regulatory networks into these agents [13] have shown that this can easily become computationally costly or would increase considerably the number of parameters to tune (e.g., PhysiBoSS [14]). Other applications rely on cellular automata, e.g., Epilog [15], or yet on the Potts model, which can be also considered as an agent-based approach [16].…”

Section: Introductionmentioning

confidence: 99%

UPMaBoSS: a novel framework for dynamic cell population modeling

Stoll

Naldi

Noël

et al. 2020

Preprint

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One of the aims of mathematical modeling is to understand and simulate the e↵ects of biological perturbations and suggest ways to intervene and reestablish proper cell functioning. However, it remains a challenge, especially when considering the dynamics at the level of a cell population, with cells dying, dividing and interacting. Here, we introduce a novel framework for the dynamical modelling of cell populations packaged into a dedicated tool, UPMaBoSS. We rely on the preexisting tool MaBoSS, which enables probabilistic simulations of cellular networks, and add a novel layer to account for cell interactions and population dynamics. We illustrate our methodology by means of a case study dealing with TNF-induced cell death. Interestingly, the simulation of cell population dynamics with UPMaBoSS reveals a mechanism of resistance triggered by TNF treatment. This appoach can be applied to diverse models of cellular networks, for example to study the impact of ligand release or drug treatments on cell fate decisions, such as commitment to proliferation, di↵erentiation, apoptosis, etc. Relatively easy to encode, UPMaBoSS simulations require only moderate computational power and execution time.To ease the reproduction of simulations, we provide several Jupyter notebooks that can be accessed within a new release of the CoLoMoTo Docker image, which contains all required software and the example models.

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

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 105 publications

References 77 publications

Active diffusion in oocytes nonspecifically centers large objects during prophase I and meiosis I

Active diffusion in oocytes nonspecifically centers large objects during prophase I and meiosis I

Learning-accelerated Discovery of Immune-Tumour Interactions

UPMaBoSS: a novel framework for dynamic cell population modeling

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