Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling
When modeling cell signaling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a fuzzy logic framework to the analysis of a large, systematic dataset describing the dynamics of cell signaling downstream of TNF, EGF, and insulin receptors...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en_US |
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Public Library of Science
2010
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| Online Access: | http://hdl.handle.net/1721.1/52533 |
| _version_ | 1826206893967671296 |
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| author | Aldridge, Bree B. Saez-Rodriguez, Julio Muhlich, Jeremy L. Sorger, Peter K. Lauffenburger, Douglas A. |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Aldridge, Bree B. Saez-Rodriguez, Julio Muhlich, Jeremy L. Sorger, Peter K. Lauffenburger, Douglas A. |
| author_sort | Aldridge, Bree B. |
| collection | MIT |
| description | When modeling cell signaling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a fuzzy logic framework to the analysis of a large, systematic dataset describing the dynamics of cell signaling downstream of TNF, EGF, and insulin receptors in human colon carcinoma cells. Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation. We uncover a relationship between MK2 and ERK pathways that might account for the previously identified pro-survival influence of MK2. We also find unexpected inhibition of IKK following EGF treatment, possibly due to down-regulation of autocrine signaling. More generally, fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to produce quantitative predictions and new biological insights about the operation of signaling networks. |
| first_indexed | 2024-09-23T13:40:36Z |
| format | Article |
| id | mit-1721.1/52533 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:40:36Z |
| publishDate | 2010 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/525332022-10-01T16:24:57Z Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling Aldridge, Bree B. Saez-Rodriguez, Julio Muhlich, Jeremy L. Sorger, Peter K. Lauffenburger, Douglas A. Massachusetts Institute of Technology. Department of Biological Engineering Lauffenburger, Douglas A. Aldridge, Bree B. Saez-Rodriguez, Julio Sorger, Peter K. Lauffenburger, Douglas A. When modeling cell signaling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a fuzzy logic framework to the analysis of a large, systematic dataset describing the dynamics of cell signaling downstream of TNF, EGF, and insulin receptors in human colon carcinoma cells. Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation. We uncover a relationship between MK2 and ERK pathways that might account for the previously identified pro-survival influence of MK2. We also find unexpected inhibition of IKK following EGF treatment, possibly due to down-regulation of autocrine signaling. More generally, fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to produce quantitative predictions and new biological insights about the operation of signaling networks. 2010-03-12T15:55:24Z 2010-03-12T15:55:24Z 2007-12 2009-04 Article http://purl.org/eprint/type/JournalArticle 1553-7358 http://hdl.handle.net/1721.1/52533 Aldridge, Bree B. et al. “Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling.” PLoS Comput Biol 5.4 (2009): e1000340. 19343194 en_US http://dx.doi.org/10.1371/journal.pcbi.1000340 PLoS Computational Biology Creative Commons Attribution http://creativecommons.org/licenses/by/2.5/ application/pdf Public Library of Science PLoS |
| spellingShingle | Aldridge, Bree B. Saez-Rodriguez, Julio Muhlich, Jeremy L. Sorger, Peter K. Lauffenburger, Douglas A. Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling |
| title | Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling |
| title_full | Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling |
| title_fullStr | Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling |
| title_full_unstemmed | Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling |
| title_short | Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling |
| title_sort | fuzzy logic analysis of kinase pathway crosstalk in tnf egf insulin induced signaling |
| url | http://hdl.handle.net/1721.1/52533 |
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