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Determinants of Cell-to-Cell Variability in Protein Kinase Signaling

Abstract:

Cells reliably sense environmental changes despite internal and external fluctuations, but the mechanisms underlying robustness remain unclear. We analyzed how fluctuations in signaling protein concentrations give rise to cell-to-cell variability in protein kinase signaling using analytical theory and numerical simulations. We characterized the dose-response behavior of signaling cascades by calculating the stimulus level at which a pathway responds ('pathway sensitivity') and the maximal activation level upon strong stimulation. Minimal kinase cascades with gradual dose-response behavior show strong variability, because the pathway sensitivity and the maximal activation level cannot be simultaneously invariant. Negative feedback regulation resolves this trade-off and coordinately reduces fluctuations in the pathway sensitivity and maximal activation. Feedbacks acting at different levels in the cascade control different aspects of the dose-response curve, thereby synergistically reducing the variability. We also investigated more complex, ultrasensitive signaling cascades capable of switch-like decision making, and found that these can be inherently robust to protein concentration fluctuations. We describe how the cell-to-cell variability of ultrasensitive signaling systems can be actively regulated, e.g., by altering the expression of phosphatase(s) or by feedback/feedforward loops. Our calculations reveal that slow transcriptional negative feedback loops allow for variability suppression while maintaining switch-like decision making. Taken together, we describe design principles of signaling cascades that promote robustness. Our results may explain why certain signaling cascades like the yeast pheromone pathway show switch-like decision making with little cell-to-cell variability.

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Projects: A2.4: Linking signalling pathways regulating liver regeneration and orga..., B1.2: Reciprocal effect of cell-cell communication on information proces..., B1.3: Linking modulation of iron metabolism with the impact of macrophag...

PLoS Comput. Biol.
PLoS Comput. Biol. 9(12): e1003357
5th Dec 2013

Matthias Jeschke, Stephan Baumgärtner, Stefan Legewie

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[Stefan Legewie]

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Views: 1474
  • Created: 20th Jan 2014 at 16:29

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