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dc.contributor.authorMarzen, Sarah E
dc.contributor.authorCrutchfield, James P
dc.date.accessioned2021-09-20T17:30:49Z
dc.date.available2021-09-20T17:30:49Z
dc.date.issued2020-01-28
dc.identifier.urihttps://hdl.handle.net/1721.1/131893
dc.description.abstractAbstract Biological sensors must often predict their input while operating under metabolic constraints. However, determining whether or not a particular sensor is evolved or designed to be accurate and efficient is challenging. This arises partly from the functional constraints being at cross purposes and partly since quantifying the prediction performance of even in silico sensors can require prohibitively long simulations, especially when highly complex environments drive sensors out of equilibrium. To circumvent these difficulties, we develop new expressions for the prediction accuracy and thermodynamic costs of the broad class of conditionally Markovian sensors subject to complex, correlated (unifilar hidden semi-Markov) environmental inputs in nonequilibrium steady state. Predictive metrics include the instantaneous memory and the total predictable information (the mutual information between present sensor state and input future), while dissipation metrics include power extracted from the environment and the nonpredictive information rate. Success in deriving these formulae relies on identifying the environment’s causal states, the input’s minimal sufficient statistics for prediction. Using these formulae, we study large random channels and the simplest nontrivial biological sensor model—that of a Hill molecule, characterized by the number of ligands that bind simultaneously—the sensor’s cooperativity. We find that the seemingly impoverished Hill molecule can capture an order of magnitude more predictable information than large random channels.en_US
dc.publisherSpringer USen_US
dc.relation.isversionofhttps://doi.org/10.1007/s11538-020-00694-2en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceSpringer USen_US
dc.titlePrediction and Dissipation in Nonequilibrium Molecular Sensors: Conditionally Markovian Channels Driven by Memoryful Environmentsen_US
dc.typeArticleen_US
dc.identifier.citationBulletin of Mathematical Biology. 2020 Jan 28;82(2):25en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Physics
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-09-24T21:40:13Z
dc.language.rfc3066en
dc.rights.holderSociety for Mathematical Biology
dspace.embargo.termsY
dspace.date.submission2020-09-24T21:40:13Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Needed


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