Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.

It is generally believed that associative memory in the brain depends on multistable synaptic dynamics, which enable the synapses to maintain their value for extended periods of time. However, multistable dynamics are not restricted to synapses. In particular, the dynamics of some genetic regulatory...

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Autores principales:	Matan Sorek, Nathalie Q Balaban, Yonatan Loewenstein
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/7d3777b4d5cc48089e3d0d6a575c709a
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spelling	oai:doaj.org-article:7d3777b4d5cc48089e3d0d6a575c709a2021-11-18T05:53:39ZStochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.1553-734X1553-735810.1371/journal.pcbi.1003179https://doaj.org/article/7d3777b4d5cc48089e3d0d6a575c709a2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23990765/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358It is generally believed that associative memory in the brain depends on multistable synaptic dynamics, which enable the synapses to maintain their value for extended periods of time. However, multistable dynamics are not restricted to synapses. In particular, the dynamics of some genetic regulatory networks are multistable, raising the possibility that even single cells, in the absence of a nervous system, are capable of learning associations. Here we study a standard genetic regulatory network model with bistable elements and stochastic dynamics. We demonstrate that such a genetic regulatory network model is capable of learning multiple, general, overlapping associations. The capacity of the network, defined as the number of associations that can be simultaneously stored and retrieved, is proportional to the square root of the number of bistable elements in the genetic regulatory network. Moreover, we compute the capacity of a clonal population of cells, such as in a colony of bacteria or a tissue, to store associations. We show that even if the cells do not interact, the capacity of the population to store associations substantially exceeds that of a single cell and is proportional to the number of bistable elements. Thus, we show that even single cells are endowed with the computational power to learn associations, a power that is substantially enhanced when these cells form a population.Matan SorekNathalie Q BalabanYonatan LoewensteinPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 8, p e1003179 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Matan Sorek Nathalie Q Balaban Yonatan Loewenstein Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.
description	It is generally believed that associative memory in the brain depends on multistable synaptic dynamics, which enable the synapses to maintain their value for extended periods of time. However, multistable dynamics are not restricted to synapses. In particular, the dynamics of some genetic regulatory networks are multistable, raising the possibility that even single cells, in the absence of a nervous system, are capable of learning associations. Here we study a standard genetic regulatory network model with bistable elements and stochastic dynamics. We demonstrate that such a genetic regulatory network model is capable of learning multiple, general, overlapping associations. The capacity of the network, defined as the number of associations that can be simultaneously stored and retrieved, is proportional to the square root of the number of bistable elements in the genetic regulatory network. Moreover, we compute the capacity of a clonal population of cells, such as in a colony of bacteria or a tissue, to store associations. We show that even if the cells do not interact, the capacity of the population to store associations substantially exceeds that of a single cell and is proportional to the number of bistable elements. Thus, we show that even single cells are endowed with the computational power to learn associations, a power that is substantially enhanced when these cells form a population.
format	article
author	Matan Sorek Nathalie Q Balaban Yonatan Loewenstein
author_facet	Matan Sorek Nathalie Q Balaban Yonatan Loewenstein
author_sort	Matan Sorek
title	Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.
title_short	Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.
title_full	Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.
title_fullStr	Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.
title_full_unstemmed	Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.
title_sort	stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/7d3777b4d5cc48089e3d0d6a575c709a
work_keys_str_mv	AT matansorek stochasticitybistabilityandthewisdomofcrowdsamodelforassociativelearningingeneticregulatorynetworks AT nathalieqbalaban stochasticitybistabilityandthewisdomofcrowdsamodelforassociativelearningingeneticregulatorynetworks AT yonatanloewenstein stochasticitybistabilityandthewisdomofcrowdsamodelforassociativelearningingeneticregulatorynetworks
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Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.

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