Integration of Proteomic, Transcriptional, and Interactome Data Reveals Hidden Signaling Components
Cellular signaling and regulatory networks underlie fundamental biological processes such as growth, differentiation, and response to the environment. Although there are now various high-throughput methods for studying these processes, knowledge of them remains fragmentary. Typically, the majority o...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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American Association for the Advancement of Science (AAAS)
2012
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| Online Access: | http://hdl.handle.net/1721.1/75444 https://orcid.org/0000-0001-9249-8181 |
| _version_ | 1826197833077751808 |
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| author | Huang, Shao-shan Carol Fraenkel, Ernest |
| author2 | Massachusetts Institute of Technology. Computational and Systems Biology Program |
| author_facet | Massachusetts Institute of Technology. Computational and Systems Biology Program Huang, Shao-shan Carol Fraenkel, Ernest |
| author_sort | Huang, Shao-shan Carol |
| collection | MIT |
| description | Cellular signaling and regulatory networks underlie fundamental biological processes such as growth, differentiation, and response to the environment. Although there are now various high-throughput methods for studying these processes, knowledge of them remains fragmentary. Typically, the majority of hits identified by transcriptional, proteomic, and genetic assays lie outside of the expected pathways. These unexpected components of the cellular response are often the most interesting, because they can provide new insights into biological processes and potentially reveal new therapeutic approaches. However, they are also the most difficult to interpret. We present a technique, based on the Steiner tree problem, that uses previously reported protein-protein and protein-DNA interactions to determine how these hits are organized into functionally coherent pathways, revealing many components of the cellular response that are not readily apparent in the original data. Applied simultaneously to phosphoproteomic and transcriptional data for the yeast pheromone response, it identifies changes in diverse cellular processes that extend far beyond the expected pathways. |
| first_indexed | 2024-09-23T10:54:00Z |
| format | Article |
| id | mit-1721.1/75444 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:54:00Z |
| publishDate | 2012 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | dspace |
| spelling | mit-1721.1/754442022-09-27T15:47:46Z Integration of Proteomic, Transcriptional, and Interactome Data Reveals Hidden Signaling Components Integrating Proteomic, Transcriptional, and Interactome Data Reveals Hidden Components of Signaling and Regulatory Networks Huang, Shao-shan Carol Fraenkel, Ernest Massachusetts Institute of Technology. Computational and Systems Biology Program Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Biological Engineering Huang, Shao-shan Carol Fraenkel, Ernest Cellular signaling and regulatory networks underlie fundamental biological processes such as growth, differentiation, and response to the environment. Although there are now various high-throughput methods for studying these processes, knowledge of them remains fragmentary. Typically, the majority of hits identified by transcriptional, proteomic, and genetic assays lie outside of the expected pathways. These unexpected components of the cellular response are often the most interesting, because they can provide new insights into biological processes and potentially reveal new therapeutic approaches. However, they are also the most difficult to interpret. We present a technique, based on the Steiner tree problem, that uses previously reported protein-protein and protein-DNA interactions to determine how these hits are organized into functionally coherent pathways, revealing many components of the cellular response that are not readily apparent in the original data. Applied simultaneously to phosphoproteomic and transcriptional data for the yeast pheromone response, it identifies changes in diverse cellular processes that extend far beyond the expected pathways. 2012-12-12T21:52:50Z 2012-12-12T21:52:50Z 2009-07 Article http://purl.org/eprint/type/JournalArticle 1945-0877 1937-9145 http://hdl.handle.net/1721.1/75444 Huang, S.-s. C., and E. Fraenkel. “Integrating Proteomic, Transcriptional, and Interactome Data Reveals Hidden Components of Signaling and Regulatory Networks.” Science Signaling 2.81 (2009): ra40–ra40. https://orcid.org/0000-0001-9249-8181 en_US http://dx.doi.org/10.1126/scisignal.2000350 Science Signaling Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf American Association for the Advancement of Science (AAAS) PMC |
| spellingShingle | Huang, Shao-shan Carol Fraenkel, Ernest Integration of Proteomic, Transcriptional, and Interactome Data Reveals Hidden Signaling Components |
| title | Integration of Proteomic, Transcriptional, and Interactome Data Reveals Hidden Signaling Components |
| title_full | Integration of Proteomic, Transcriptional, and Interactome Data Reveals Hidden Signaling Components |
| title_fullStr | Integration of Proteomic, Transcriptional, and Interactome Data Reveals Hidden Signaling Components |
| title_full_unstemmed | Integration of Proteomic, Transcriptional, and Interactome Data Reveals Hidden Signaling Components |
| title_short | Integration of Proteomic, Transcriptional, and Interactome Data Reveals Hidden Signaling Components |
| title_sort | integration of proteomic transcriptional and interactome data reveals hidden signaling components |
| url | http://hdl.handle.net/1721.1/75444 https://orcid.org/0000-0001-9249-8181 |
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