Noisy signal amplification in ultrasensitive signal transduction

Shibata, Tatsuo; Fujimoto, Koichi

doi:10.1073/pnas.0403350102

Cited by 156 publications

(164 citation statements)

References 37 publications

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“…3F, which demonstrates that the chemotactic efficiency is a function of the correlation time. In particular, the chemotactic efficiency decreases as the correlation time increases, consistent with our findings in 1D (24) and theoretical work on the the propagation of noise in signal transduction networks (22,30). This correlation time is the sum of two terms, one that describes the receptor dynamics and the other one that arises from the ligand diffusion.…”

Section: Discussionsupporting

confidence: 89%

“…A number of other studies have also recognized the importance of noise in directional sensing (20)(21)(22)(23)(24). Several of these attempt to estimate the SNR of the input signal, taken to be the difference between the number of bound receptors at the front and at the back of the cell (20,23).…”

Section: Discussionmentioning

confidence: 99%

“…Several recent studies have investigated this issue, by using different approaches, including information theoretical ones (21), general considerations (20,22,23) and 1D caricatures of the cell (24). What has been lacking to date, however, is a treatment of the directional sensing problem that (i) exactly quantifies the fluctuations in the number of bound receptors taking into account the spatial extent of the cell and (ii) couples this noisy signal to a downstream intracellular directional sensing pathway.…”

Section: Uring Eukaryotic Chemotaxis Chemical Gradients Determinementioning

confidence: 99%

“…For these shallow gradients the difference in the number of bound receptors at the front and the back of the cell can be as little as 20. Comparing this with the total number of bound receptors, which is of the order of several hundred, leads to the question of noise in directional sensing.Several recent studies have investigated this issue, by using different approaches, including information theoretical ones (21), general considerations (20,22,23) and 1D caricatures of the cell (24). What has been lacking to date, however, is a treatment of the directional sensing problem that (i) exactly quantifies the fluctuations in the number of bound receptors taking into account the spatial extent of the cell and (ii) couples this noisy signal to a downstream intracellular directional sensing pathway.…”

mentioning

confidence: 99%

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Receptor noise limitations on chemotactic sensing

Rappel

Levine

2008

Proc. Natl. Acad. Sci. U.S.A.

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Chemotactic eukaryotic cells are able to detect chemoattractant gradients that are both shallow and have a low background concentration. Under these conditions, the noise in the number of bound receptors can be significant and needs to be taken into account in determining the directional sensing process. Here, we quantify numerically the number of bound receptors on the membrane of a disk-shaped cell by using a numerical Monte Carlo tool. The obtained time traces of the receptor occupancy can be used as inputs for any directional sensing model. We investigate the response of the local excitation global inhibition model and a recently developed balanced inactivation model. We determine a measure for the motility of the cell for each model, based on the relevant output variable, as a function of experimental parameters, resulting in several experimentally testable predictions. Furthermore, we show that these two models behave in a qualitatively different fashion when the background concentration is varied. Thus, to properly characterize the sensitivity of cells to receptor occupancy, it is not sufficient to examine the input signal. Rather, one needs to take into account the response of the second messenger pathway.chemotaxis | motility D uring eukaryotic chemotaxis, chemical gradients determine and guide the crawling motion of cells. Chemotaxis plays a fundamental role in development and in immune responses and is implicated in the spreading of cancer (1-3). The external gradient of the chemoattractant results in an asymmetric distribution of bound receptors on the cell's membrane and the directional sensing process translates this receptor asymmetry into directed cell motion. A number of biochemical components of the directional sensing pathway of several model systems have been determined (4-7) and an array of different mechanisms have been proposed, including Turing-type instability mechanisms (8), phase separation mechanisms (9), bistable mechanisms (10), depletion mechanisms (11, 12), and communication through diffusible inhibitors (13)(14)(15)(16). Despite the recent experimental progress and theoretical efforts, however, the precise mechanisms underlying chemotaxis, in general, and directional sensing, in particular, are poorly understood (17,18).Further complicating the directional sensing problem is the potential role of stochasticity. It has been shown that cells are able to chemotax in very shallow gradients with a large range of background concentrations (19,20). For these shallow gradients the difference in the number of bound receptors at the front and the back of the cell can be as little as 20. Comparing this with the total number of bound receptors, which is of the order of several hundred, leads to the question of noise in directional sensing.Several recent studies have investigated this issue, by using different approaches, including information theoretical ones (21), general considerations (20,22,23) and 1D caricatures of the cell (24). What has been lacking to date, however, is a treatment of...

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Uring Eukaryotic Chemotaxis Chemical Gradients Determinementioning

confidence: 99%

mentioning

confidence: 99%

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Receptor noise limitations on chemotactic sensing

Rappel

Levine

2008

Proc. Natl. Acad. Sci. U.S.A.

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“…Most studies have focused on the variance of the protein concentration that has been calculated for models of single unregulated genes (26,27,32), simple gene circuits (32), cascades of genes (23), and signaling cascades (28,42,43). A result for the variance of the output of a gene regulated combinatorially by multiple transcription factors has also been reported recently (44), and this statistic, together with the half-autocorrelation time has been suggested to be useful for discriminating between competing models (45).…”

Section: Discussionmentioning

confidence: 99%

Signatures of combinatorial regulation in intrinsic biological noise

Warmflash

Dinner

2008

Proc. Natl. Acad. Sci. U.S.A.

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Gene expression is controlled by the action of transcription factors that bind to DNA and influence the rate at which a gene is transcribed. The quantitative mapping between the regulator concentrations and the output of the gene is known as the cisregulatory input function (CRIF). Here, we show how the CRIF shapes the form of the joint probability distribution of molecular copy numbers of the regulators and the product of a gene. Namely, we derive a class of fluctuation-based relations that relate the moments of the distribution to the derivatives of the CRIF. These relations are useful because they enable statistics of naturally arising cell-to-cell variations in molecular copy numbers to substitute for traditional manipulations for probing regulatory mechanisms. We demonstrate that these relations can distinguish super-and subadditive gene regulatory scenarios (molecular analogs of AND and OR logic operations) in simulations that faithfully represent bacterial gene expression. Applications and extensions to other regulatory scenarios are discussed.gene expression | mathematical modeling | noise analysis inference | flow cytometry T ranscription factors regulate the expression of genes by binding to specific sites on the DNA that are typically spatially close to, sometimes even in, sequences that code for proteins. A group of such binding sites is collectively known as a cis-regulatory region. When a gene processes the effects of multiple transcription factors, one can view it as performing a computation. The inputs are the occupancies of the binding sites in the cis-regulatory region and the output is the rate of transcription. The simplest method of describing the relationship between the transcription factors and the output is by using boolean logic (1, 2). For example, 2 activators can regulate a gene with AND logic in which both are required or with OR logic in which either transcription factor is sufficient for transcription. Knowing which mode of combinatorial regulation a gene employs can be important for determining its function in regulatory networks. For example, the coherent feed forward loop, one of the most common motifs in gene regulatory networks (3), filters noise in upstream signals differently depending on how one of the participating genes integrates its inputs (4, 5).In general, the output of a gene will not be binary, and will depend on the concentrations of the transcription factors in a complex manner. The notion of logic operations can be generalized by introducing a continuous function that encodes the dependence of the rate of transcription on the concentrations of the inputs. Such "cis-regulatory input functions (CRIFs)" (5) have been evaluated experimentally for the well-studied lac operon. The CRIF of the wild type is complex (6, 7), but those of certain mutant operons represent molecular analogs of binary logic gates (8). In higher organisms, many genes are regulated by a large number of transcription factors, often with multiple binding sites for each factor (9-11). It is increa...

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Opportunities in the Area of Noise in Biological Reaction Networks

Dinner

2014

Advances in Chemical Physics

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Noisy signal amplification in ultrasensitive signal transduction

Cited by 156 publications

References 37 publications

Receptor noise limitations on chemotactic sensing

Receptor noise limitations on chemotactic sensing

Signatures of combinatorial regulation in intrinsic biological noise

Opportunities in the Area of Noise in Biological Reaction Networks

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