Noise Filtering and Prediction in Biological Signaling Networks

Hathcock, David; Sheehy, James L.; Weisenberger, Casey; İlker, Efe; Hinczewski, Michael

doi:10.1109/tmbmc.2016.2633269

Cited by 24 publications

(54 citation statements)

References 57 publications

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“…Since ρc > b with our choice of the parameters, the discussion in the preceding subsection tells us that the spectral formula is valid up to τ = τ * ≈ 1.209. Indeed, we see in the figure that the (53). Note that this function is not equal to 1 at t = 0 (it is also nonzero for t < 0).…”

Section: B Influence Of τ and ρmentioning

confidence: 88%

“…[1], which is regarded as the "minimal" continuous-time version of a VAR process. Interestingly, this also corresponds to the model of a cellular signaling pathway considered in [53], in which X 1 (t) and X 2 (t) represent the deviations from the mean of active kinase populations (these quantities can be treated as continuous variables by assuming a chemical Langevin description [54]). Of course, the fact that X 2 is now an autonomous process implies that T 1→2 (h) and thus T 1→2 vanish identically.…”

Section: Spectral Expression Of the Te Ratementioning

confidence: 99%

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Influence of time delay on information exchanges between coupled linear stochastic systems

2018

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Time lags are ubiquitous in biophysiological processes and more generally in real-world complex networks. It has been recently proposed to use information-theoretic tools such as transfer entropy to detect and estimate a possible delay in the couplings. In this work, we focus on stationary linear stochastic processes in continuous time and compute the transfer entropy in the presence of delay and correlated noises, using an approximate but numerically effective solution to the relevant Wiener-Hopf factorization problem. Our results rectify and complete the recent study of [1].

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Section: B Influence Of τ and ρmentioning

confidence: 88%

Section: Spectral Expression Of the Te Ratementioning

confidence: 99%

Influence of time delay on information exchanges between coupled linear stochastic systems

2018

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“…In the steady state, the entropy production rate in system 1 is equal to the rate of entropy change in the environment, and is computed from Eq (91) where F X,t , D XX,t , and J XX,t are replaced by F 1 (x) = −a 11 x 1 − a 12 x 2 , D 11 , and J 1 (x) = F 1 (x)P (x) − D 11 ∂ x1 P (x) − D 12 ∂ x2 P (x), respectively. This yields…”

Section: Entropy Production Ratementioning

confidence: 99%

Information thermodynamics for interacting stochastic systems without bipartite structure

Chétrite¹,

Rosinberg²,

Sagawa³

et al. 2019

J. Stat. Mech.

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Fluctuations in biochemical networks, e.g., in a living cell, have a complex origin that precludes a description of such systems in terms of bipartite or multipartite processes, as is usually done in the framework of stochastic and/or information thermodynamics. This means that fluctuations in each subsystem are not independent: subsystems jump simultaneously if the dynamics is modeled as a Markov jump process, or noises are correlated for diffusion processes. In this paper, we consider information and thermodynamic exchanges between a pair of coupled systems that do not satisfy the bipartite property. The generalization of information-theoretic measures, such as learning rates and transfer entropy rates, to this situation is non-trivial and also involves introducing several additional rates. We describe how this can be achieved in the framework of general continuoustime Markov processes, without restricting the study to the steady-state regime. We illustrate our general formalism on the case of diffusion processes and derive an extension of the second law of information thermodynamics in which the difference of transfer entropy rates in the forward and backward time directions replaces the learning rate. As a side result, we also generalize an important relation linking information theory and estimation theory. To further obtain analytical expressions we treat in detail the case of Ornstein-Uhlenbeck processes, and discuss the ability of the various information measures to detect a directional coupling in the presence of correlated noises. Finally, we apply our formalism to the analysis of the directional influence between cellular processes in a concrete example, which also requires considering the case of a non-bipartite and non-Markovian process. Contents

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“…Discovering optimality principles in biological function has been a major goal of biophysics [1][2][3][4][5][6], but the competition between genetic drift and natural selection means that evolution is not purely an optimization process [7][8][9]. A necessary complement to elucidating optimality is clarifying under what circumstances selection is actually strong enough relative to drift in order to drive systems toward local optima in the fitness landscape.…”

Section: Introductionmentioning

confidence: 99%

Modeling the growth of organisms validates a general relation between metabolic costs and natural selection

İlker

Hinczewski

2018

Preprint

Self Cite

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Metabolism and evolution are closely connected: if a mutation incurs extra energetic costs for an organism, there is a baseline selective disadvantage that may or may not be compensated for by other adaptive effects. A long-standing, but to date unproven, hypothesis is that this disadvantage is equal to the fractional cost relative to the total resting metabolic expenditure. This hypothesis has found a recent resurgence as a powerful tool for quantitatively understanding the strength of selection among different classes of organisms. Our work explores the validity of the hypothesis from first principles through a generalized metabolic growth model, versions of which have been successful in describing organismal growth from single cells to higher animals. We build a mathematical framework to calculate how perturbations in maintenance and synthesis costs translate into contributions to the selection coefficient, a measure of relative fitness. This allows us to show that the hypothesis is an approximation to the actual baseline selection coefficient. Moreover we can directly derive the correct prefactor in its functional form, as well as analytical bounds on the accuracy of the hypothesis for any given realization of the model. We illustrate our general framework using a special case of the growth model, which we show provides a quantitative description of overall metabolic synthesis and maintenance expenditures in data collected from a wide array of unicellular organisms (both prokaryotes and eukaryotes). In all these cases we demonstrate that the hypothesis is an excellent approximation, allowing estimates of baseline selection coefficients to within 15% of their actual values. Even in a broader biological parameter range, covering growth data from multicellular organisms, the hypothesis continues to work well, always within an order of magnitude of the correct result. Our work thus justifies its use as a versatile tool, setting the stage for its wider deployment.Discovering optimality principles in biological function has been a major goal of biophysics [1][2][3][4][5][6], but the competition between genetic drift and natural selection means that evolution is not purely an optimization process [7][8][9]. A necessary complement to elucidating optimality is clarifying under what circumstances selection is actually strong enough relative to drift in order to drive systems toward local optima in the fitness landscape. In this work we focus on one key component of this . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/358440 doi: bioRxiv preprint first posted online Jun. 29, 2018; 2 problem: quantifying the selective pressure on the extra metabolic costs associated with a genetic variant. We validate a long hypothesized relation [10][11][12] between this pressure and the fractional change in the total restin...

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Noise Filtering and Prediction in Biological Signaling Networks

Cited by 24 publications

References 57 publications

Influence of time delay on information exchanges between coupled linear stochastic systems

Influence of time delay on information exchanges between coupled linear stochastic systems

Information thermodynamics for interacting stochastic systems without bipartite structure

Modeling the growth of organisms validates a general relation between metabolic costs and natural selection

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