Evolution of associative learning in chemical networks.

Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning - the ability to detect correlated features of the environment - has been studied extensivel...

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Autores principales:	Simon McGregor, Vera Vasas, Phil Husbands, Chrisantha Fernando
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/822a62184980455b98b67c358ea6ef9b
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spelling	oai:doaj.org-article:822a62184980455b98b67c358ea6ef9b2021-11-18T05:52:45ZEvolution of associative learning in chemical networks.1553-734X1553-735810.1371/journal.pcbi.1002739https://doaj.org/article/822a62184980455b98b67c358ea6ef9b2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23133353/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning - the ability to detect correlated features of the environment - has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the 'memory traces' of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells.Simon McGregorVera VasasPhil HusbandsChrisantha FernandoPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 11, p e1002739 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Simon McGregor Vera Vasas Phil Husbands Chrisantha Fernando Evolution of associative learning in chemical networks.
description	Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning - the ability to detect correlated features of the environment - has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the 'memory traces' of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells.
format	article
author	Simon McGregor Vera Vasas Phil Husbands Chrisantha Fernando
author_facet	Simon McGregor Vera Vasas Phil Husbands Chrisantha Fernando
author_sort	Simon McGregor
title	Evolution of associative learning in chemical networks.
title_short	Evolution of associative learning in chemical networks.
title_full	Evolution of associative learning in chemical networks.
title_fullStr	Evolution of associative learning in chemical networks.
title_full_unstemmed	Evolution of associative learning in chemical networks.
title_sort	evolution of associative learning in chemical networks.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/822a62184980455b98b67c358ea6ef9b
work_keys_str_mv	AT simonmcgregor evolutionofassociativelearninginchemicalnetworks AT veravasas evolutionofassociativelearninginchemicalnetworks AT philhusbands evolutionofassociativelearninginchemicalnetworks AT chrisanthafernando evolutionofassociativelearninginchemicalnetworks
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Evolution of associative learning in chemical networks.

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