Cell-to-Cell stochastic fluctuations in apoptotic signaling can decide between life and death

Authors: S. Raychaudhuri, E. Willgohs, T. Nguyen, E. M. Khan, T. Goldkorn

Biophysical Journal, 95:3559-3562 (2008)
arXiv: 0707.1569v1 - DOI (q-bio.MN)
6 pages, 3 figures

Abstract: Apoptosis, or genetically programmed cell death, is a crucial cellular process that maintains the balance between life and death in cells. The precise molecular mechanism of apoptosis signaling and how these two pathways are differentially activated under distinct apoptotic stimuli is poorly understood. We developed a Monte Carlo-based stochastic simulation model that can characterize distinct signaling behaviors in the two major pathways of apoptotic signaling using a novel probability distribution-based approach. Specifically, we show that for a weak death signal, such as low levels of death ligand Fas (CD95) binding or under stress conditions, the type 2 mitochondrial pathway dominates apoptotic signaling. Our results also show signaling in the type 2 pathway is stochastic, where the population average over many cells does not capture the cell-to-cell fluctuations in the time course (~1 - 10 hours) of downstream caspase-3 activation. On the contrary, the probability distribution of caspase-3 activation for the mitochondrial pathway shows a distinct bimodal behavior that can be used to characterize the stochastic signaling in type 2 apoptosis. Interestingly, such stochastic fluctuations in apoptosis signaling happen even in the presence of large numbers of signaling molecules. In a fluctuating environment, such stochasticity in the timecourse of caspase-3 activation may be an adaptive mechanism for allowing a competing survival signal to win over a weak death trigger before the critical cell fate decision is made and thus minimizes the risk of pathologies.

Submitted to arXiv on 11 Jul. 2007

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.