Intrinsic Fluctuations, Robustness and Tunability in
Signaling Cycles
Joseph Henry Levine 1, Hao Yuan Kueh 2 and Leonid Mirny 3*
1 Caltech
2 Harvard University
3 Massachusets Institute of Technology
* To whom correspondence should be addressed. E-mail: leonid{at}mit.edu.
Submitted on May 12, 2006
Revised on July 21, 2006
Accepted on 16 January 2007
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Abstract |
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Covalent modification cycles (e.g. phosphorylation-dephosphorylation) underlie most cellular signaling and control processes. Low molecular copy number, arising from compartmental segregation and slow diffusion between compartments, potentially renders these cycles vulnerable to intrinsic chemical fluctuations. How can a cell operate reliably in the presence of this inherent stochasticity? How do changes in extrinsic parameters lead to variability of response? Can cells exploit these parameters to tune cycles to different ranges of stimuli? We study the dynamics of an isolated phosphorylation cycle. Our model shows that the cycle transmits information reliably if it is tuned to an optimal parameter range, in spite of intrinsic fluctuations and even for small input signal amplitudes. At the same time, the cycle is sensitive to changes in the concentration and activity of kinases and phosphatases. This sensitivity can lead to significant cell-to-cell response variability. It also provides a mechanism to tune the cycle to transmit signals in various amplitude ranges. Our results show that signaling cycles possess a surprising combination of robustness and tunability. This combination makes them ubiquitous in eukaryotic signaling, optimizing signaling in the presence of fluctuations using their inherent flexibility. On the other hand, cycles tuned to suppress intrinsic fluctuations can be fragile to changes in the number and activity of kinases and phosphatases. Such trade-offs in robustness to intrinsic and extrinsic fluctuations can influence the evolution of signaling cascades, making them the weakest links in cellular circuits.
Key Words:
Michaelis-Menten, evolution, noise, signaling, simulations, stochastic
