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...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
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Elsevier
2018
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| Online Access: | http://hdl.handle.net/1721.1/118655 https://orcid.org/0000-0001-9249-8181 https://orcid.org/0000-0002-5324-9833 |
| _version_ | 1811084781070319616 |
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| author | Köksal, Ali Sinan Beck, Kirsten Cronin, Dylan R. Camp, Nathan D. MacGilvray, Matthew E. Bodík, Rastislav Fisher, Jasmin McKenna, Aaron Srivastava, Saurabh Wolf Yadlin, Alejandro Marcelo Fraenkel, Ernest Gitter, Anthony |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Köksal, Ali Sinan Beck, Kirsten Cronin, Dylan R. Camp, Nathan D. MacGilvray, Matthew E. Bodík, Rastislav Fisher, Jasmin McKenna, Aaron Srivastava, Saurabh Wolf Yadlin, Alejandro Marcelo Fraenkel, Ernest Gitter, Anthony |
| author_sort | Köksal, Ali Sinan |
| collection | MIT |
| 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. Köksal et al. present a computational technique, the temporal pathway synthesizer (TPS), that combines time series global phosphoproteomic data and protein-protein interaction networks to reconstruct the vast signaling pathways that control post-translational modifications. |
| first_indexed | 2024-09-23T12:57:30Z |
| format | Article |
| id | mit-1721.1/118655 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:57:30Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1186552022-10-01T12:08:23Z Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data Köksal, Ali Sinan Beck, Kirsten Cronin, Dylan R. Camp, Nathan D. MacGilvray, Matthew E. Bodík, Rastislav Fisher, Jasmin McKenna, Aaron Srivastava, Saurabh Wolf Yadlin, Alejandro Marcelo Fraenkel, Ernest Gitter, Anthony Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Biology McKenna, Aaron Srivastava, Saurabh Wolf Yadlin, Alejandro Marcelo Fraenkel, Ernest Gitter, Anthony 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. Köksal et al. present a computational technique, the temporal pathway synthesizer (TPS), that combines time series global phosphoproteomic data and protein-protein interaction networks to reconstruct the vast signaling pathways that control post-translational modifications. National Science Foundation (U.S.) ( grant DBI-1553206) National Institutes of Health (U.S.) (training grant T32-HL007312) National Institutes of Health (U.S.) (grant U01-CA184898) National Institutes of Health (U.S.) (grant U54-NS09104) 2018-10-22T18:08:46Z 2018-10-22T18:08:46Z 2018-09 2018-10-22T17:07:41Z Article http://purl.org/eprint/type/JournalArticle 22111247 http://hdl.handle.net/1721.1/118655 Köksal, Ali Sinan, Kirsten Beck, Dylan R. Cronin, Aaron McKenna, Nathan D. Camp, Saurabh Srivastava, Matthew E. MacGilvray, et al. “Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data.” Cell Reports 24, no. 13 (September 2018): 3607–3618. https://orcid.org/0000-0001-9249-8181 https://orcid.org/0000-0002-5324-9833 http://dx.doi.org/10.1016/j.celrep.2018.08.085 Cell Reports Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/ application/pdf Elsevier Elsevier |
| spellingShingle | Köksal, Ali Sinan Beck, Kirsten Cronin, Dylan R. Camp, Nathan D. MacGilvray, Matthew E. Bodík, Rastislav Fisher, Jasmin McKenna, Aaron Srivastava, Saurabh Wolf Yadlin, Alejandro Marcelo Fraenkel, Ernest 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 |
| url | http://hdl.handle.net/1721.1/118655 https://orcid.org/0000-0001-9249-8181 https://orcid.org/0000-0002-5324-9833 |
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