Limits...
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.

No MeSH data available.


Related in: MedlinePlus

Stochastic reactions with perfect adaptation response.For a randomly selected network in steady state, a unit step input stimulus was applied at t = 10. Fig 2(A-B) shows the response for iFFL, while Fig 2(C-D) shows the non-oscillatory and damped oscillatory response for nFBL respectively. The green lines show the response of the network obtained using Gillespie Algorithm, while red lines show the corresponding deterministic response obtained from differential equations. The parameters for each case are provided in the section Methods.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4549281&req=5

pone.0136095.g002: Stochastic reactions with perfect adaptation response.For a randomly selected network in steady state, a unit step input stimulus was applied at t = 10. Fig 2(A-B) shows the response for iFFL, while Fig 2(C-D) shows the non-oscillatory and damped oscillatory response for nFBL respectively. The green lines show the response of the network obtained using Gillespie Algorithm, while red lines show the corresponding deterministic response obtained from differential equations. The parameters for each case are provided in the section Methods.

Mentions: When a step stimulus is applied, the adaptive network shows a response by changing its activity A, which is followed by adaptation through returning towards the prestimulus level. The magnitude of this response varies for different parameters. In Fig 2, the time series of four different parameter sets are plotted for iFFLs (A-B) and nFBL (C-D). For each of these networks in steady state, a unit step perturbation of magnitude ΔS = S was provided at time t = 10. The green lines show the time series obtained by stochastic simulation using Gillespie Algorithm [37]. For a guide, the time series obtained by the corresponding differential equation are plotted as well(red line). Among the two examples from iFFLs in Fig 2(A) and 2(B), the response in Fig 2(A) is slightly distinguishable, while in Fig 2(B), it is completely blurred by noise. In contrast, the response in Fig 2(C) is clearly distinguishable over the noise. Depending on the parameter value, nFBL can show both non-oscillatory as well as oscillatory response, as shown in Fig 2(C) and 2(D) respectively. iFFL shows only non-oscillatory responses.


Adaptive Responses Limited by Intrinsic Noise.

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

Stochastic reactions with perfect adaptation response.For a randomly selected network in steady state, a unit step input stimulus was applied at t = 10. Fig 2(A-B) shows the response for iFFL, while Fig 2(C-D) shows the non-oscillatory and damped oscillatory response for nFBL respectively. The green lines show the response of the network obtained using Gillespie Algorithm, while red lines show the corresponding deterministic response obtained from differential equations. The parameters for each case are provided in the section Methods.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0136095.g002: Stochastic reactions with perfect adaptation response.For a randomly selected network in steady state, a unit step input stimulus was applied at t = 10. Fig 2(A-B) shows the response for iFFL, while Fig 2(C-D) shows the non-oscillatory and damped oscillatory response for nFBL respectively. The green lines show the response of the network obtained using Gillespie Algorithm, while red lines show the corresponding deterministic response obtained from differential equations. The parameters for each case are provided in the section Methods.
Mentions: When a step stimulus is applied, the adaptive network shows a response by changing its activity A, which is followed by adaptation through returning towards the prestimulus level. The magnitude of this response varies for different parameters. In Fig 2, the time series of four different parameter sets are plotted for iFFLs (A-B) and nFBL (C-D). For each of these networks in steady state, a unit step perturbation of magnitude ΔS = S was provided at time t = 10. The green lines show the time series obtained by stochastic simulation using Gillespie Algorithm [37]. For a guide, the time series obtained by the corresponding differential equation are plotted as well(red line). Among the two examples from iFFLs in Fig 2(A) and 2(B), the response in Fig 2(A) is slightly distinguishable, while in Fig 2(B), it is completely blurred by noise. In contrast, the response in Fig 2(C) is clearly distinguishable over the noise. Depending on the parameter value, nFBL can show both non-oscillatory as well as oscillatory response, as shown in Fig 2(C) and 2(D) respectively. iFFL shows only non-oscillatory responses.

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