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


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.


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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