Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins co...

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Autores principales:	Yishai Shimoni, Marc Y Fink, Soon-gang Choi, Stuart C Sealfon
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b1b652f6d60c4a7aafb8189bc7f47a3e
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spelling	oai:doaj.org-article:b1b652f6d60c4a7aafb8189bc7f47a3e2021-12-02T19:58:20ZPlato's cave algorithm: inferring functional signaling networks from early gene expression shadows.1553-734X1553-735810.1371/journal.pcbi.1000828https://doaj.org/article/b1b652f6d60c4a7aafb8189bc7f47a3e2010-06-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20585619/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships.Yishai ShimoniMarc Y FinkSoon-gang ChoiStuart C SealfonPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 6, p e1000828 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Yishai Shimoni Marc Y Fink Soon-gang Choi Stuart C Sealfon Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.
description	Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships.
format	article
author	Yishai Shimoni Marc Y Fink Soon-gang Choi Stuart C Sealfon
author_facet	Yishai Shimoni Marc Y Fink Soon-gang Choi Stuart C Sealfon
author_sort	Yishai Shimoni
title	Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.
title_short	Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.
title_full	Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.
title_fullStr	Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.
title_full_unstemmed	Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.
title_sort	plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/b1b652f6d60c4a7aafb8189bc7f47a3e
work_keys_str_mv	AT yishaishimoni platoscavealgorithminferringfunctionalsignalingnetworksfromearlygeneexpressionshadows AT marcyfink platoscavealgorithminferringfunctionalsignalingnetworksfromearlygeneexpressionshadows AT soongangchoi platoscavealgorithminferringfunctionalsignalingnetworksfromearlygeneexpressionshadows AT stuartcsealfon platoscavealgorithminferringfunctionalsignalingnetworksfromearlygeneexpressionshadows
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Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.

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