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UPMaBoSS: A Novel Framework for Dynamic Cell Population Modeling.

Gautier Stoll1,2, Aurélien Naldi3,4, Vincent Noël5,6,7

  • 1Equipe Labellisée Par La Ligue Contre Le Cancer, Université de Paris, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Paris, France.

Frontiers in Molecular Biosciences
|March 21, 2022
PubMed
Summary
This summary is machine-generated.

A new computational framework, UPMaBoSS, models dynamic cell populations to understand disease mechanisms. Simulations revealed a TNF-induced resistance mechanism, offering insights for cancer and autoimmune disorder research.

Keywords:
cell interactionsheterogeneous cell populationlogical modelpathway modelingstochastic simulation

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Area of Science:

  • Computational biology
  • Systems biology
  • Mathematical modeling

Background:

  • Mathematical modeling is crucial for understanding cellular processes in diseases like cancer.
  • Modeling complex cell populations, including interactions and dynamics, presents significant challenges.
  • Existing models often struggle to incorporate intra-cellular details, cell-cell interactions, and population dynamics simultaneously.

Purpose of the Study:

  • To introduce UPMaBoSS (Update Population MaBoSS), a novel computational framework for modeling dynamic, interacting cell populations.
  • To provide a software layer that accounts for cell interactions and population dynamics, building upon the MaBoSS tool.
  • To offer an intermediate modeling approach that bridges detailed cellular network simulations with population-level dynamics.

Main Methods:

  • Developed UPMaBoSS, a novel software layer integrated with the MaBoSS probabilistic simulation tool.
  • Focused on modeling cell interactions and population dynamics without incorporating spatial dimensions.
  • Utilized Jupyter notebooks within the CoLoMoTo Docker image for simulation reproducibility.

Main Results:

  • Simulations using UPMaBoSS revealed a previously unknown mechanism of resistance triggered by TNF treatment.
  • The framework successfully modeled TNF-induced cell death dynamics in a case study.
  • UPMaBoSS simulations demonstrated efficiency, requiring moderate computational power and execution time.

Conclusions:

  • UPMaBoSS provides an accessible and efficient method for modeling dynamic cell populations and their interactions.
  • The framework can uncover complex biological mechanisms, such as TNF-induced resistance, relevant to diseases.
  • UPMaBoSS facilitates reproducible research through provided Jupyter notebooks and a comprehensive Docker image.