Optimal inference strategies and their implications for the linear noise approximation

Hartich, David; Seifert, Udo

doi:10.1103/physreve.94.042416

Cited by 9 publications

(5 citation statements)

References 48 publications

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“…In other words, can miRNA noise regulation approach an optimal WK filter? This type of comparison has recently proven fruitful in a variety of biological contexts ( 17 , 27 – 33 ), for example, yielding tight bounds on the fidelity of information transmission in signaling networks.…”

Section: Noise Reduction Can Approach Optimal Linear Filter Performancementioning

confidence: 99%

Bioenergetic costs and the evolution of noise regulation by microRNAs

Ilker,

Hinczewski

2024

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

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Noise control, together with other regulatory functions facilitated by microRNAs (miRNAs), is believed to have played important roles in the evolution of multicellular eukaryotic organisms. miRNAs can dampen protein fluctuations via enhanced degradation of messenger RNA (mRNA), but this requires compensation by increased mRNA transcription to maintain the same expression levels. The overall mechanism is metabolically expensive, leading to questions about how it might have evolved in the first place. We develop a stochastic model of miRNA noise regulation, coupled with a detailed analysis of the associated metabolic costs. Additionally, we calculate binding free energies for a range of miRNA seeds, the short sequences which govern target recognition. We argue that natural selection may have fine-tuned the Michaelis–Menten constant K M describing miRNA–mRNA affinity and show supporting evidence from analysis of experimental data. K M is constrained by seed length, and optimal noise control (minimum protein variance at a given energy cost) is achievable for seeds of 6 to 7 nucleotides in length, the most commonly observed types. Moreover, at optimality, the degree of noise reduction approaches the theoretical bound set by the Wiener–Kolmogorov linear filter. The results illustrate how selective pressure toward energy efficiency has potentially shaped a crucial regulatory pathway in eukaryotes.

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Section: Noise Reduction Can Approach Optimal Linear Filter Performancementioning

confidence: 99%

Bioenergetic costs and the evolution of noise regulation by microRNAs

Ilker,

Hinczewski

2024

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

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“…In other words, can miRNA noise regulation approach an optimal WK filter? This type of comparison has recently proven fruitful in a variety of biological contexts [17,[27][28][29][30][31][32][33], for example yielding tight bounds on the fidelity of information transmission in signaling networks.…”

Section: On the Other Hand K *mentioning

confidence: 99%

“…In other words, can miRNA noise regulation approach an optimal WK filter? This type of comparison has recently proven fruitful in a variety of biological contexts [17,[25][26][27][28][29][30][31], for example yielding 3. The discrepancy between E * and E * wk , the error of the actual system and the WK optimal system respectively, at the most energetically efficient KM for the single target case (Fig.…”

mentioning

confidence: 99%

Bioenergetic costs and the evolution of noise regulation by microRNAs

İlker

Hinczewski

2023

Preprint

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Noise control, together with other regulatory functions facilitated by microRNAs (miRNAs), is believed to have played important roles in the evolution of multicellular eukaryotic organisms. miRNAs can dampen protein fluctuations via enhanced degradation of mRNAs, but this requires compensation by increased mRNA transcription to maintain the same expression levels. The overall mechanism is metabolically expensive, leading to questions about how it might have evolved in the first place. We develop a stochastic model of miRNA noise regulation, coupled with a detailed analysis of the associated metabolic costs. Additionally we calculate binding free energies for a range of miRNA seeds, the short sequences which govern target recognition. We argue that natural selection may have fine-tuned the Michaelis-Menten constant KM describing miRNA-mRNA affinity. KM is constrained by seed length, and we show that optimal noise control (minimum protein variance at a given energy cost) is achievable for seeds of 6-7 nucleotides in length, the most commonly observed types. Moreover, at optimality the degree of noise reduction approaches the theoretical bound set by the Wiener-Kolmogorov linear filter. The results illustrate how selective pressure toward energy efficiency has potentially shaped a crucial regulatory pathway in eukaryotes.

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“…These systems employ the mechanism of time integration, in which the ligand concentration is inferred from the average receptor occupancy over the past integration time [12]. We thus do not consider the sensing strategy of maximum-likelihood estimation, in which the concentration is estimated from the duration of the unbound receptor state [7,9,17,26,27].…”

Section: Introductionmentioning

confidence: 99%

Theory for the optimal detection of time-varying signals in cellular sensing systems

Malaguti¹,

Wolde²

2019

Preprint

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Living cells often need to measure chemical concentrations that vary in time. To this end, they deploy many resources such as receptors, downstream signaling molecules, time and energy. Here, we present a theory for the optimal design of a large class of sensing systems that need to detect time-varying signals, a receptor driving a push-pull network. The theory is based on the concept of the dynamic input-output relation, which describes the mapping between the current ligand concentration and the average receptor occupancy over the past integration time. This concept is used to develop the idea that the cell employs its push-pull network to estimate the receptor occupancy and then uses this estimate to infer the current ligand concentration by inverting the dynamic input-output relation. The theory reveals that the sensing error can be decomposed into two terms: the sampling error in the estimate of the receptor occupancy and the dynamical error that arises because the average ligand concentration over the past integration time may not reflect the current ligand concentration. The theory generalizes the design principle of optimal resource allocation previously identified for static signals, which states that in an optimally designed sensing system the three fundamental resource classes of receptors and their integration time, readout molecules, and energy are equally limiting so that no resource is wasted. However, in contrast to static signals, receptors and power cannot be traded freely against time to reach a desired sensing precision: there exists an optimal integration time that maximizes the sensing precision, which depends on the number of receptors, the receptor correlation time, and the correlation time and variance of the input signal. Applying our theory to the chemotaxis system of Escherichia coli indicates that this bacterium has evolved to optimally sense shallow gradients.

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Optimal inference strategies and their implications for the linear noise approximation

Cited by 9 publications

References 48 publications

Bioenergetic costs and the evolution of noise regulation by microRNAs

Bioenergetic costs and the evolution of noise regulation by microRNAs

Bioenergetic costs and the evolution of noise regulation by microRNAs

Theory for the optimal detection of time-varying signals in cellular sensing systems

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