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Adaptive Responses Limited by Intrinsic Noise.

Shankar P, Nishikawa M, Shibata T - PLoS ONE (2015)

Bottom Line: Such adaptive responses are effective for a wide dynamic range of sensing and perception of temporal change in stimulus.We also identify the condition that yields the upper limit of response for both network motifs.These results may explain the reason of why nFBL seems to be more abundant in nature for the implementation of adaption systems.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematical and Life Sciences, Hiroshima University, Higashi-Hiroshima, Japan; Laboratory for Physical Biology, RIKEN Quantitative Biology Center, Kobe, Japan.

ABSTRACT
Sensory systems have mechanisms to respond to the external environment and adapt to them. Such adaptive responses are effective for a wide dynamic range of sensing and perception of temporal change in stimulus. However, noise generated by the adaptation system itself as well as extrinsic noise in sensory inputs may impose a limit on the ability of adaptation systems. The relation between response and noise is well understood for equilibrium systems in the form of fluctuation response relation. However, the relation for nonequilibrium systems, including adaptive systems, are poorly understood. Here, we systematically explore such a relation between response and fluctuation in adaptation systems. We study the two network motifs, incoherent feedforward loops (iFFL) and negative feedback loops (nFBL), that can achieve perfect adaptation. We find that the response magnitude in adaption systems is limited by its intrinsic noise, implying that higher response would have higher noise component as well. Comparing the relation of response and noise in iFFL and nFBL, we show that whereas iFFL exhibits adaptation over a wider parameter range, nFBL offers higher response to noise ratio than iFFL. We also identify the condition that yields the upper limit of response for both network motifs. These results may explain the reason of why nFBL seems to be more abundant in nature for the implementation of adaption systems.

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The dependence of the average ratio between gain and Fano factor g/f on the adaptation error ϵ.(A) iFFL (B) nFBL with non-oscillatory response.
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pone.0136095.g004: The dependence of the average ratio between gain and Fano factor g/f on the adaptation error ϵ.(A) iFFL (B) nFBL with non-oscillatory response.

Mentions: To see that with the perfect adaptation condition the points in nFBL (Fig 3F) are distributed closer to the line of equality than in iFFL (Fig 3B), we consider the ratio between the gain and Fano factor, g/f. In Fig 4, using the samples in Fig 3A and 3E, we plot the average of the ratio g/f calculated over the samples for which the adaptation error ϵ is less than a given threshold. This threshold decreases from 10−1 to 10−3, implying the selected points show increasing more perfect adaptation. In the plot, the average of ratio g/f of nFBL is larger than that of iFFL for any value of ϵ, indicating that nFBL is closer to the equality line than iFFL. Furthermore, as the perfect adaptation becomes more strict as the adaptation error ϵ decreases, the average value of ratio g/f for nFBL increases approaching to the equality line, whereas that for iFFL decreases. This indicates that in nFBL the perfect adaptation condition takes the gain closer to the limit of Fano factor.


Adaptive Responses Limited by Intrinsic Noise.

Shankar P, Nishikawa M, Shibata T - PLoS ONE (2015)

The dependence of the average ratio between gain and Fano factor g/f on the adaptation error ϵ.(A) iFFL (B) nFBL with non-oscillatory response.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4549281&req=5

pone.0136095.g004: The dependence of the average ratio between gain and Fano factor g/f on the adaptation error ϵ.(A) iFFL (B) nFBL with non-oscillatory response.
Mentions: To see that with the perfect adaptation condition the points in nFBL (Fig 3F) are distributed closer to the line of equality than in iFFL (Fig 3B), we consider the ratio between the gain and Fano factor, g/f. In Fig 4, using the samples in Fig 3A and 3E, we plot the average of the ratio g/f calculated over the samples for which the adaptation error ϵ is less than a given threshold. This threshold decreases from 10−1 to 10−3, implying the selected points show increasing more perfect adaptation. In the plot, the average of ratio g/f of nFBL is larger than that of iFFL for any value of ϵ, indicating that nFBL is closer to the equality line than iFFL. Furthermore, as the perfect adaptation becomes more strict as the adaptation error ϵ decreases, the average value of ratio g/f for nFBL increases approaching to the equality line, whereas that for iFFL decreases. This indicates that in nFBL the perfect adaptation condition takes the gain closer to the limit of Fano factor.

Bottom Line: Such adaptive responses are effective for a wide dynamic range of sensing and perception of temporal change in stimulus.We also identify the condition that yields the upper limit of response for both network motifs.These results may explain the reason of why nFBL seems to be more abundant in nature for the implementation of adaption systems.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematical and Life Sciences, Hiroshima University, Higashi-Hiroshima, Japan; Laboratory for Physical Biology, RIKEN Quantitative Biology Center, Kobe, Japan.

ABSTRACT
Sensory systems have mechanisms to respond to the external environment and adapt to them. Such adaptive responses are effective for a wide dynamic range of sensing and perception of temporal change in stimulus. However, noise generated by the adaptation system itself as well as extrinsic noise in sensory inputs may impose a limit on the ability of adaptation systems. The relation between response and noise is well understood for equilibrium systems in the form of fluctuation response relation. However, the relation for nonequilibrium systems, including adaptive systems, are poorly understood. Here, we systematically explore such a relation between response and fluctuation in adaptation systems. We study the two network motifs, incoherent feedforward loops (iFFL) and negative feedback loops (nFBL), that can achieve perfect adaptation. We find that the response magnitude in adaption systems is limited by its intrinsic noise, implying that higher response would have higher noise component as well. Comparing the relation of response and noise in iFFL and nFBL, we show that whereas iFFL exhibits adaptation over a wider parameter range, nFBL offers higher response to noise ratio than iFFL. We also identify the condition that yields the upper limit of response for both network motifs. These results may explain the reason of why nFBL seems to be more abundant in nature for the implementation of adaption systems.

No MeSH data available.


Related in: MedlinePlus