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Information flow and optimization in transcriptional control

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arxiv 0705.0313 v1 pith:74PT5GBN submitted 2007-05-02 q-bio.MN

classification q-bio.MN
keywords informationexpressiongenelevelsnoiserecentregulatorysystem
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In the simplest view of transcriptional regulation, the expression of a gene is turned on or off by changes in the concentration of a transcription factor (TF). We use recent data on noise levels in gene expression to show that it should be possible to transmit much more than just one regulatory bit. Realizing this optimal information capacity would require that the dynamic range of TF concentrations used by the cell, the input/output relation of the regulatory module, and the noise levels of binding and transcription satisfy certain matching relations. This parameter-free prediction is in good agreement with recent experiments on the Bicoid/Hunchback system in the early Drosophila embryo, and this system achieves ~90% of its theoretical maximum information transmission.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Discrete turn strategies emerge in information-limited navigation

    physics.bio-ph 2026-02 conditional novelty 8.0 of 10

    In an information-limited navigation model, the optimal non-directional strategy uses discrete turn angles, transitioning from reversals to tumbles as information grows.

  2. Invariant non-equilibrium dynamics of transcriptional regulation optimize information flow

    q-bio.MN 2025-07 conditional novelty 7.0 of 10

    A four-state non-equilibrium promoter model reproduces the invariant switching correlation time seen in Drosophila and links it to maximized information transmission under a switching speed limit.

  3. Mutual Information Rate -- Linear Noise Approximation and Exact Computation

    q-bio.MN 2025-08 conditional novelty 6.0 of 10

    Even in almost-Gaussian discrete signaling systems, the Gaussian approximation underestimates the true information rate, and for nonlinear continuous systems its accuracy depends on how it is applied.

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