UPMaBoSS: a novel framework for dynamic cell population modeling.

Stoll, G.; Naldi, A.; Noël, V.; Viara, E.; Barillot, E.; Kroemer, G.; Thieffry, D.; Calzone, L.

doi:10.1101/2020.05.31.126094

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bioRxiv · 10.1101/2020.05.31.126094

UPMaBoSS: a novel framework for dynamic cell population modeling.

Stoll, G.·Naldi, A.·Noël, V.·Viara, E.·Barillot, E.·Kroemer, G.·Thieffry, D.·Calzone, L.

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Abstract

One of the aims of mathematical modeling is to understand and simulate the effects 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, differentiation, 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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Stoll, G., Naldi, A., Noël, V., Viara, E., Barillot, E., Kroemer, G., Thieffry, D., Calzone, L.. 2020-06-01. UPMaBoSS: a novel framework for dynamic cell population modeling.. https://doi.org/10.1101/2020.05.31.126094

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