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Probabilistic Inference with Polymerizing Biochemical Circuits

Probabilistic inference—the process of estimating the values of unobserved variables in probabilistic models—has been used to describe various cognitive phenomena related to learning and memory. While the study of biological realizations of inference has focused on animal nervous systems, single-cel...

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Detalles Bibliográficos
Autores principales: Katz, Yarden, Fontana, Walter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140500/
https://www.ncbi.nlm.nih.gov/pubmed/35626513
http://dx.doi.org/10.3390/e24050629
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author Katz, Yarden
Fontana, Walter
author_facet Katz, Yarden
Fontana, Walter
author_sort Katz, Yarden
collection PubMed
description Probabilistic inference—the process of estimating the values of unobserved variables in probabilistic models—has been used to describe various cognitive phenomena related to learning and memory. While the study of biological realizations of inference has focused on animal nervous systems, single-celled organisms also show complex and potentially “predictive” behaviors in changing environments. Yet, it is unclear how the biochemical machinery found in cells might perform inference. Here, we show how inference in a simple Markov model can be approximately realized, in real-time, using polymerizing biochemical circuits. Our approach relies on assembling linear polymers that record the history of environmental changes, where the polymerization process produces molecular complexes that reflect posterior probabilities. We discuss the implications of realizing inference using biochemistry, and the potential of polymerization as a form of biological information-processing.
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spelling pubmed-91405002022-05-28 Probabilistic Inference with Polymerizing Biochemical Circuits Katz, Yarden Fontana, Walter Entropy (Basel) Article Probabilistic inference—the process of estimating the values of unobserved variables in probabilistic models—has been used to describe various cognitive phenomena related to learning and memory. While the study of biological realizations of inference has focused on animal nervous systems, single-celled organisms also show complex and potentially “predictive” behaviors in changing environments. Yet, it is unclear how the biochemical machinery found in cells might perform inference. Here, we show how inference in a simple Markov model can be approximately realized, in real-time, using polymerizing biochemical circuits. Our approach relies on assembling linear polymers that record the history of environmental changes, where the polymerization process produces molecular complexes that reflect posterior probabilities. We discuss the implications of realizing inference using biochemistry, and the potential of polymerization as a form of biological information-processing. MDPI 2022-04-29 /pmc/articles/PMC9140500/ /pubmed/35626513 http://dx.doi.org/10.3390/e24050629 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Katz, Yarden
Fontana, Walter
Probabilistic Inference with Polymerizing Biochemical Circuits
title Probabilistic Inference with Polymerizing Biochemical Circuits
title_full Probabilistic Inference with Polymerizing Biochemical Circuits
title_fullStr Probabilistic Inference with Polymerizing Biochemical Circuits
title_full_unstemmed Probabilistic Inference with Polymerizing Biochemical Circuits
title_short Probabilistic Inference with Polymerizing Biochemical Circuits
title_sort probabilistic inference with polymerizing biochemical circuits
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140500/
https://www.ncbi.nlm.nih.gov/pubmed/35626513
http://dx.doi.org/10.3390/e24050629
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