English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Two distinct mechanisms for filtering noise in an input signal are identifiedin a class of adaptive sensory networks. We find that the high frequency noiseis filtered by the output degradation process through time-averaging; while thelow frequency noise is damped by adaptation through negative feedback. Bothfiltering processes themselves introduce intrinsic noises, which are found tobe unfiltered and can thus amount to a significant internal noise floor evenwithout signaling. These results are applied to E. coli chemotaxis. We showunambiguously that the molecular mechanism for the Berg-Purcell time-averagingscheme is the dephosphorylation of the response regulator CheY-P, not thereceptor adaptation process as previously suggested. The high frequency noisedue to the stochastic ligand binding-unbinding events and the random ligandmolecule diffusion is averaged by the CheY-P dephosphorylation process to anegligible level in E.coli. We identify a previously unstudied noise sourcecaused by the random motion of the cell in a ligand gradient. We show that thisrandom walk induced signal noise has a divergent low frequency component, whichis only rendered finite by the receptor adaptation process. For gradientswithin the E. coli sensing range, this dominant external noise can becomparable to the significant intrinsic noise in the system. The dependence ofthe response and its fluctuations on the key time scales of the system arestudied systematically. We show that the chemotaxis pathway may have evolved tooptimize gradient sensing, strong response, and noise control in different timescales

Noise Filtering Strategies of Adaptive Signaling Networks: The Case of E. Coli Chemotaxis