Cell-Fate Decision in Response to Death Receptor Engagement

Taxon: Mammal
Process: Cell fate decision
Submitter: Laurence Calzone

Supporting paper: Calzone, Laurence and Tournier, Laurent and Fourquet, Simon and Thieffry, Denis and Zhivotovsky, Boris and Barillot, Emmanuel and Zinovyev, Andrei (2010). Mathematical Modelling of Cell-Fate Decision in Response to Death Receptor Engagement. PLoS Computational Biology. 10.1371/journal.pcbi.1000702

Model file(s) Description(s)
Calzone__Cell_Fate.zginml The original cell fate model
CellFate_multiscale.zginml Model variant adapted for multiscale analysis

Summary:
This model provides a generic high-level view of the interplays between NFκB pro-survival pathway, RIP1-dependent necrosis, and the apoptosis pathway in response to death receptor-mediated signals.

Wild type simulations demonstrate robust segregation of cellular responses to receptor engagement. Model simulations recapitulate documented phenotypes of protein knockdowns and enable the prediction of the effects of novel knockdowns. In silico experiments simulate the outcomes following ligand removal at different stages, and suggest experimental approaches to further validate and specialise the model for particular cell types.

This analysis gives specific predictions regarding cross-talks between the three pathways, as well as the transient role of RIP1 protein in necrosis, and confirms the phenotypes of novel perturbations. Our wild type and mutant simulations provide novel insights to restore apoptosis in defective cells. The model analysis expands our understanding of how cell fate decision is made.

The original model focuses on the interplay between three pathways activated in response to the same signal.

This model has then been adapted for multiscale analysis 1.

  1. Gaelle Letort, Arnau Montagud, Gautier Stoll, Randy Heiland, Emmanuel Barillot, Paul Macklin, Andrei Zinovyev, and Laurence Calzone. Physiboss: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling. Bioinformatics, 35(7):1188–1196, August 2018. doi:10.1093/bioinformatics/bty766. ↩