A machine learning method for the prediction of receptor activation in the simulation of synapses.

Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transm...

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Autores principales:	Jesus Montes, Elena Gomez, Angel Merchán-Pérez, Javier Defelipe, Jose-Maria Peña
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/443f184763c546e58c8a9f71a21ad60c
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spelling	oai:doaj.org-article:443f184763c546e58c8a9f71a21ad60c2021-11-18T09:03:33ZA machine learning method for the prediction of receptor activation in the simulation of synapses.1932-620310.1371/journal.pone.0068888https://doaj.org/article/443f184763c546e58c8a9f71a21ad60c2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23894367/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of synapses and it is therefore an excellent tool for multi-scale simulations.Jesus MontesElena GomezAngel Merchán-PérezJavier DefelipeJose-Maria PeñaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 7, p e68888 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jesus Montes Elena Gomez Angel Merchán-Pérez Javier Defelipe Jose-Maria Peña A machine learning method for the prediction of receptor activation in the simulation of synapses.
description	Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of synapses and it is therefore an excellent tool for multi-scale simulations.
format	article
author	Jesus Montes Elena Gomez Angel Merchán-Pérez Javier Defelipe Jose-Maria Peña
author_facet	Jesus Montes Elena Gomez Angel Merchán-Pérez Javier Defelipe Jose-Maria Peña
author_sort	Jesus Montes
title	A machine learning method for the prediction of receptor activation in the simulation of synapses.
title_short	A machine learning method for the prediction of receptor activation in the simulation of synapses.
title_full	A machine learning method for the prediction of receptor activation in the simulation of synapses.
title_fullStr	A machine learning method for the prediction of receptor activation in the simulation of synapses.
title_full_unstemmed	A machine learning method for the prediction of receptor activation in the simulation of synapses.
title_sort	machine learning method for the prediction of receptor activation in the simulation of synapses.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/443f184763c546e58c8a9f71a21ad60c
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A machine learning method for the prediction of receptor activation in the simulation of synapses.

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