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Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data
We present a method for automatically discovering signaling pathways from time-resolved phosphoproteomic data. The Temporal Pathway Synthesizer (TPS) algorithm uses constraint-solving techniques first developed in the context of formal verification to explore paths in an interaction network. It syst...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6295338/ https://www.ncbi.nlm.nih.gov/pubmed/30257219 http://dx.doi.org/10.1016/j.celrep.2018.08.085 |
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author | Kӧksal, Ali Sinan Beck, Kirsten Cronin, Dylan R. McKenna, Aaron Camp, Nathan D. Srivastava, Saurabh MacGilvray, Matthew E. Bodík, Rastislav Wolf-Yadlin, Alejandro Fraenkel, Ernest Fisher, Jasmin Gitter, Anthony |
author_facet | Kӧksal, Ali Sinan Beck, Kirsten Cronin, Dylan R. McKenna, Aaron Camp, Nathan D. Srivastava, Saurabh MacGilvray, Matthew E. Bodík, Rastislav Wolf-Yadlin, Alejandro Fraenkel, Ernest Fisher, Jasmin Gitter, Anthony |
author_sort | Kӧksal, Ali Sinan |
collection | PubMed |
description | We present a method for automatically discovering signaling pathways from time-resolved phosphoproteomic data. The Temporal Pathway Synthesizer (TPS) algorithm uses constraint-solving techniques first developed in the context of formal verification to explore paths in an interaction network. It systematically eliminates all candidate structures for a signaling pathway where a protein is activated or inactivated before its upstream regulators. The algorithm can model more than one hundred thousand dynamic phosphosites and can discover pathway members that are not differentially phosphorylated. By analyzing temporal data, TPS defines signaling cascades without needing to experimentally perturb individual proteins. It recovers known pathways and proposes pathway connections when applied to the human epidermal growth factor and yeast osmotic stress responses. Independent kinase mutant studies validate predicted substrates in the TPS osmotic stress pathway. |
format | Online Article Text |
id | pubmed-6295338 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
record_format | MEDLINE/PubMed |
spelling | pubmed-62953382018-12-16 Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data Kӧksal, Ali Sinan Beck, Kirsten Cronin, Dylan R. McKenna, Aaron Camp, Nathan D. Srivastava, Saurabh MacGilvray, Matthew E. Bodík, Rastislav Wolf-Yadlin, Alejandro Fraenkel, Ernest Fisher, Jasmin Gitter, Anthony Cell Rep Article We present a method for automatically discovering signaling pathways from time-resolved phosphoproteomic data. The Temporal Pathway Synthesizer (TPS) algorithm uses constraint-solving techniques first developed in the context of formal verification to explore paths in an interaction network. It systematically eliminates all candidate structures for a signaling pathway where a protein is activated or inactivated before its upstream regulators. The algorithm can model more than one hundred thousand dynamic phosphosites and can discover pathway members that are not differentially phosphorylated. By analyzing temporal data, TPS defines signaling cascades without needing to experimentally perturb individual proteins. It recovers known pathways and proposes pathway connections when applied to the human epidermal growth factor and yeast osmotic stress responses. Independent kinase mutant studies validate predicted substrates in the TPS osmotic stress pathway. 2018-09-25 /pmc/articles/PMC6295338/ /pubmed/30257219 http://dx.doi.org/10.1016/j.celrep.2018.08.085 Text en This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kӧksal, Ali Sinan Beck, Kirsten Cronin, Dylan R. McKenna, Aaron Camp, Nathan D. Srivastava, Saurabh MacGilvray, Matthew E. Bodík, Rastislav Wolf-Yadlin, Alejandro Fraenkel, Ernest Fisher, Jasmin Gitter, Anthony Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data |
title | Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data |
title_full | Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data |
title_fullStr | Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data |
title_full_unstemmed | Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data |
title_short | Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data |
title_sort | synthesizing signaling pathways from temporal phosphoproteomic data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6295338/ https://www.ncbi.nlm.nih.gov/pubmed/30257219 http://dx.doi.org/10.1016/j.celrep.2018.08.085 |
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