Detecting drought regulators using stochastic inference in Bayesian networks.

Drought is a natural hazard that affects crops by inducing water stress. Water stress, induced by drought accounts for more loss in crop yield than all the other causes combined. With the increasing frequency and intensity of droughts worldwide, it is essential to develop drought-resistant crops to...

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Autores principales:	Aditya Lahiri, Lin Zhou, Ping He, Aniruddha Datta
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/af2cbe046765401ea415668fb558342c
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id	oai:doaj.org-article:af2cbe046765401ea415668fb558342c
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spelling	oai:doaj.org-article:af2cbe046765401ea415668fb558342c2021-12-02T20:18:05ZDetecting drought regulators using stochastic inference in Bayesian networks.1932-620310.1371/journal.pone.0255486https://doaj.org/article/af2cbe046765401ea415668fb558342c2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255486https://doaj.org/toc/1932-6203Drought is a natural hazard that affects crops by inducing water stress. Water stress, induced by drought accounts for more loss in crop yield than all the other causes combined. With the increasing frequency and intensity of droughts worldwide, it is essential to develop drought-resistant crops to ensure food security. In this paper, we model multiple drought signaling pathways in Arabidopsis using Bayesian networks to identify potential regulators of drought-responsive reporter genes. Genetically intervening at these regulators can help develop drought-resistant crops. We create the Bayesian network model from the biological literature and determine its parameters from publicly available data. We conduct inference on this model using a stochastic simulation technique known as likelihood weighting to determine the best regulators of drought-responsive reporter genes. Our analysis reveals that activating MYC2 or inhibiting ATAF1 are the best single node intervention strategies to regulate the drought-responsive reporter genes. Additionally, we observe simultaneously activating MYC2 and inhibiting ATAF1 is a better strategy. The Bayesian network model indicated that MYC2 and ATAF1 are possible regulators of the drought response. Validation experiments showed that ATAF1 negatively regulated the drought response. Thus intervening at ATAF1 has the potential to create drought-resistant crops.Aditya LahiriLin ZhouPing HeAniruddha DattaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255486 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Aditya Lahiri Lin Zhou Ping He Aniruddha Datta Detecting drought regulators using stochastic inference in Bayesian networks.
description	Drought is a natural hazard that affects crops by inducing water stress. Water stress, induced by drought accounts for more loss in crop yield than all the other causes combined. With the increasing frequency and intensity of droughts worldwide, it is essential to develop drought-resistant crops to ensure food security. In this paper, we model multiple drought signaling pathways in Arabidopsis using Bayesian networks to identify potential regulators of drought-responsive reporter genes. Genetically intervening at these regulators can help develop drought-resistant crops. We create the Bayesian network model from the biological literature and determine its parameters from publicly available data. We conduct inference on this model using a stochastic simulation technique known as likelihood weighting to determine the best regulators of drought-responsive reporter genes. Our analysis reveals that activating MYC2 or inhibiting ATAF1 are the best single node intervention strategies to regulate the drought-responsive reporter genes. Additionally, we observe simultaneously activating MYC2 and inhibiting ATAF1 is a better strategy. The Bayesian network model indicated that MYC2 and ATAF1 are possible regulators of the drought response. Validation experiments showed that ATAF1 negatively regulated the drought response. Thus intervening at ATAF1 has the potential to create drought-resistant crops.
format	article
author	Aditya Lahiri Lin Zhou Ping He Aniruddha Datta
author_facet	Aditya Lahiri Lin Zhou Ping He Aniruddha Datta
author_sort	Aditya Lahiri
title	Detecting drought regulators using stochastic inference in Bayesian networks.
title_short	Detecting drought regulators using stochastic inference in Bayesian networks.
title_full	Detecting drought regulators using stochastic inference in Bayesian networks.
title_fullStr	Detecting drought regulators using stochastic inference in Bayesian networks.
title_full_unstemmed	Detecting drought regulators using stochastic inference in Bayesian networks.
title_sort	detecting drought regulators using stochastic inference in bayesian networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/af2cbe046765401ea415668fb558342c
work_keys_str_mv	AT adityalahiri detectingdroughtregulatorsusingstochasticinferenceinbayesiannetworks AT linzhou detectingdroughtregulatorsusingstochasticinferenceinbayesiannetworks AT pinghe detectingdroughtregulatorsusingstochasticinferenceinbayesiannetworks AT aniruddhadatta detectingdroughtregulatorsusingstochasticinferenceinbayesiannetworks
_version_	1718374304368820224

Detecting drought regulators using stochastic inference in Bayesian networks.

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