Information transmission in a two-step cascade: interplay of activation and repression

Roy, Tuhin Subhra; Nandi, Mintu; Biswas, Ayan; Chaudhury, Pinaki; Banik, Suman Kumar

doi:10.1007/s12064-021-00357-3

Cited by 5 publications

(5 citation statements)

References 41 publications

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“…We exclude results for n > 2, say n = 3, etc as the functions f x and f y are highly nonlinear in this regime. The results obtained using higher-order nonlinear functions show disagreement between theory and simulation [26].…”

Section: J Stat Mech (2023) 093502mentioning

confidence: 72%

“…In many cases, however, one observes a non-degenerate response. In a recent study [26], AA and AR motifs were found to collate with respect to the fidelity of signal transduction (measured as signal-to-noise ratio). In addition, RA and RR motifs were also found to have a collating nature with respect to the said metric.…”

Section: Introductionmentioning

confidence: 99%

“…Mech. (2023) 093502 been observed [26]. In the next section, we discuss the stochastic model of CFFLs and the corresponding mathematical analysis to compute fluctuations associated with the output Y.…”

Section: Introductionmentioning

confidence: 99%

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Interplay of degeneracy and non-degeneracy in fluctuation propagation in coherent feed-forward loop motif

Subhra Roy,

Nandi,

Chaudhury

et al. 2023

J. Stat. Mech.

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We present a stochastic framework to decipher fluctuation propagation in classes of coherent feed-forward loops (CFFLs). The systematic contribution of the direct (one-step) and indirect (two-step) pathways is considered to quantify fluctuations of the output node. We also consider both additive and multiplicative integration mechanisms of the two parallel pathways (one-step and two-step). Analytical expression of the output node’s coefficient of variation shows contributions of intrinsic, one-step, two-step, and cross-interaction in closed form. We observe a diverse range of degeneracy and non-degeneracy in each of the decomposed fluctuation terms and their contribution to the overall output fluctuations of each CFFL motif. The analysis of output fluctuations reveals a maximal level of fluctuations of the CFFL motif of type 1.

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Section: J Stat Mech (2023) 093502mentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interplay of degeneracy and non-degeneracy in fluctuation propagation in coherent feed-forward loop motif

Subhra Roy,

Nandi,

Chaudhury

et al. 2023

J. Stat. Mech.

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“…Other approaches are the following: (i) Gaussian approximations of the input to make use of analytic results [12], (ii) Monte Carlo estimators [13], [32], [33]. (iii) Not rarely, the intractability decoyed researchers to resort to other channels such as the Gaussian channel [34], [35]. We contribute to information theory in biology by presenting a Monte Carlo-free numerical computation of the path mutual information rate of a Poisson channel for Markovian input with low state number.…”

Section: B Path Mutual Information In Cell Signalingmentioning

confidence: 99%

ACID: A Low Dimensional Characterization of Markov-Modulated and Self-Exciting Counting Processes

Sinzger-D'Angelo¹,

Koeppl²

2022

Preprint

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The conditional intensity (CI) of a counting process Yt is based on the minimal knowledge F Y t , i.e., on the observation of Yt alone. Prominently, the mutual information rate of a signal and its Poisson channel output is a difference functional between the CI and the intensity that has full knowledge about the input. While the CI of Markov-modulated Poisson processes evolves according to Snyder's filter, self-exciting processes, e.g., Hawkes processes, specify the CI via the history of Yt. The emergence of the CI as a self-contained stochastic process prompts us to bring its statistical ensemble into focus. We investigate the asymptotic conditional intensity distribution (ACID) and emphasize its rich information content. We assume the case in which the CI is determined from a sufficient statistic that progresses as a Markov process. We present a simulation-free method to compute the ACID when the dimension of the sufficient statistic is low. The method is made possible by introducing a backward recurrence time parametrization, which has the advantage to align all probability inflow in a boundary condition for the master equation. Case studies illustrate the usage of ACID for three primary examples: 1) the Poisson channels with binary Markovian input (as an example of a Markov-modulated Poisson process), 2) the standard Hawkes process with exponential kernel (as an example of a self-exciting counting process) and 3) the Gamma filter (as an example of an approximate filter to a Markov-modulated Poisson process).

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“…Another study characterises the signalling fidelity in the TSC motif by measuring redundancy based on PID formalism [21]. The architecture dependent information transmission on four different variants of TSC motifs has been studied by Roy et al [22]. In one of our previous studies [23], we showed the role of intermediate node X in the TSC motif in information transmission where we used the formalism of Shannon's MI and Schreiber's TE for bivariate and multivariate information transfer along with the formalism of PID.…”

Section: J Stat Mech (2022) 123502mentioning

confidence: 99%

Information restriction in two-step cascade: role of fidelity and fluctuations

Nandi¹

2022

J. Stat. Mech.

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Information retained by the middle node X in a generic two-step cascade (TSC) motif (S → X → Y) is quantified by the metric restricted information (RI). Restricted efficiency, a normalised measure of RI, computes the ability of X in resisting the information flow from S to Y. We incorporate two types of autoregulatory edges, positive and negative, at X of the TSC framework, denoted as +TSC and −TSC, respectively. The role of autoregulatory edges (positive and negative) on the behaviour of X in information restriction is studied with reference to the TSC motif without autoregulation. Our theoretical analysis supplemented by numerical simulation shows that the information restriction imposed by X decreases (increases) with positive (negative) autoregulation. We introduce a dimensionless measure, relative fidelity, to understand the variation in RI due to autoregulatory edges. The relative fidelity is the signal-to-noise ratio for S–X regulation divided by the same for S–Y regulation. We find a specific operating range of the binding coefficient due to the autoregulatory edge at which a minimum (maximum) effect on information restriction is observed in the +TSC (−TSC) motif for high autoregulation strength.

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Information transmission in a two-step cascade: interplay of activation and repression

Cited by 5 publications

References 41 publications

Interplay of degeneracy and non-degeneracy in fluctuation propagation in coherent feed-forward loop motif

Interplay of degeneracy and non-degeneracy in fluctuation propagation in coherent feed-forward loop motif

ACID: A Low Dimensional Characterization of Markov-Modulated and Self-Exciting Counting Processes

Information restriction in two-step cascade: role of fidelity and fluctuations

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