Prediction and Dissipation in Nonequilibrium Molecular Sensors: Conditionally Markovian Channels Driven by Memoryful Environments
Abstract 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 purpo...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer US
2021
|
| Online Access: | https://hdl.handle.net/1721.1/131893 |
| _version_ | 1826197598273273856 |
|---|---|
| author | Marzen, Sarah E Crutchfield, James P |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Marzen, Sarah E Crutchfield, James P |
| author_sort | Marzen, Sarah E |
| collection | MIT |
| description | Abstract
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. |
| first_indexed | 2024-09-23T10:50:05Z |
| format | Article |
| id | mit-1721.1/131893 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:50:05Z |
| publishDate | 2021 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1318932023-12-13T21:33:56Z Prediction and Dissipation in Nonequilibrium Molecular Sensors: Conditionally Markovian Channels Driven by Memoryful Environments Marzen, Sarah E Crutchfield, James P Massachusetts Institute of Technology. Department of Physics Abstract 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. 2021-09-20T17:30:49Z 2021-09-20T17:30:49Z 2020-01-28 2020-09-24T21:40:13Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131893 Bulletin of Mathematical Biology. 2020 Jan 28;82(2):25 en https://doi.org/10.1007/s11538-020-00694-2 Article 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. Society for Mathematical Biology application/pdf Springer US Springer US |
| spellingShingle | Marzen, Sarah E Crutchfield, James P Prediction and Dissipation in Nonequilibrium Molecular Sensors: Conditionally Markovian Channels Driven by Memoryful Environments |
| title | Prediction and Dissipation in Nonequilibrium Molecular Sensors: Conditionally Markovian Channels Driven by Memoryful Environments |
| title_full | Prediction and Dissipation in Nonequilibrium Molecular Sensors: Conditionally Markovian Channels Driven by Memoryful Environments |
| title_fullStr | Prediction and Dissipation in Nonequilibrium Molecular Sensors: Conditionally Markovian Channels Driven by Memoryful Environments |
| title_full_unstemmed | Prediction and Dissipation in Nonequilibrium Molecular Sensors: Conditionally Markovian Channels Driven by Memoryful Environments |
| title_short | Prediction and Dissipation in Nonequilibrium Molecular Sensors: Conditionally Markovian Channels Driven by Memoryful Environments |
| title_sort | prediction and dissipation in nonequilibrium molecular sensors conditionally markovian channels driven by memoryful environments |
| url | https://hdl.handle.net/1721.1/131893 |
| work_keys_str_mv | AT marzensarahe predictionanddissipationinnonequilibriummolecularsensorsconditionallymarkovianchannelsdrivenbymemoryfulenvironments AT crutchfieldjamesp predictionanddissipationinnonequilibriummolecularsensorsconditionallymarkovianchannelsdrivenbymemoryfulenvironments |