Discrete logic modelling as a means to link protein signalling networks functional analysis of mammalian signal transduction
Large-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach—implemented in the free CNO...
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| Format: | Article |
| Language: | en_US |
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EMBO and Macmillan Publishers Limited
2011
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| Online Access: | http://hdl.handle.net/1721.1/62180 |
| _version_ | 1826191261385621504 |
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| author | Saez-Rodriguez, Julio Alexopoulos, Leonidas G. Epperlein, Jonathan Samaga, Regina Lauffenburger, Douglas A. Klamt, Steffen Sorger, Peter K. |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Saez-Rodriguez, Julio Alexopoulos, Leonidas G. Epperlein, Jonathan Samaga, Regina Lauffenburger, Douglas A. Klamt, Steffen Sorger, Peter K. |
| author_sort | Saez-Rodriguez, Julio |
| collection | MIT |
| description | Large-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach—implemented in the free CNO software—for turning signalling networks into logical models and calibrating the models against experimental data. When a literature-derived network of 82 proteins covering the immediate-early responses of human cells to seven cytokines was modelled, we found that training against experimental data dramatically increased predictive power, despite the crudeness of Boolean approximations, while significantly reducing the number of interactions. Thus, many interactions in literature-derived networks do not appear to be functional in the liver cells from which we collected our data. At the same time, CNO identified several new interactions that improved the match of model to data. Although missing from the starting network, these interactions have literature support. Our approach, therefore, represents a means to generate predictive, cell-type-specific models of mammalian signalling from generic protein signalling networks. |
| first_indexed | 2024-09-23T08:53:08Z |
| format | Article |
| id | mit-1721.1/62180 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:53:08Z |
| publishDate | 2011 |
| publisher | EMBO and Macmillan Publishers Limited |
| record_format | dspace |
| spelling | mit-1721.1/621802022-09-26T08:57:15Z Discrete logic modelling as a means to link protein signalling networks functional analysis of mammalian signal transduction Saez-Rodriguez, Julio Alexopoulos, Leonidas G. Epperlein, Jonathan Samaga, Regina Lauffenburger, Douglas A. Klamt, Steffen Sorger, Peter K. Massachusetts Institute of Technology. Department of Biological Engineering Lauffenburger, Douglas A. Saez-Rodriguez, Julio Alexopoulos, Leonidas G. Lauffenburger, Douglas A. Sorger, Peter K. Large-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach—implemented in the free CNO software—for turning signalling networks into logical models and calibrating the models against experimental data. When a literature-derived network of 82 proteins covering the immediate-early responses of human cells to seven cytokines was modelled, we found that training against experimental data dramatically increased predictive power, despite the crudeness of Boolean approximations, while significantly reducing the number of interactions. Thus, many interactions in literature-derived networks do not appear to be functional in the liver cells from which we collected our data. At the same time, CNO identified several new interactions that improved the match of model to data. Although missing from the starting network, these interactions have literature support. Our approach, therefore, represents a means to generate predictive, cell-type-specific models of mammalian signalling from generic protein signalling networks. Germany. Federal Ministry of Education and Research ('HepatoSys' and the FORSYS-Centre MaCS) National Institutes of Health. (U.S.) (P50-GM68762) National Institutes of Health. (U.S.) (U54-CA112967) 2011-04-08T19:44:52Z 2011-04-08T19:44:52Z 2009-12 2009-03 Article http://purl.org/eprint/type/JournalArticle 1744-4292 http://hdl.handle.net/1721.1/62180 Saez-Rodriguez, Julio et al. “Discrete Logic Modelling as a Means to Link Protein Signalling Networks with Functional Analysis of Mammalian Signal Transduction.” Mol Syst Biol 5 (2009) : 1-19. © 2009 EMBO and Macmillan Publishers Limited. en_US http://dx.doi.org/10.1038/msb.2009.87 Molecular Systems Biology Creative Commons Attribution-Non-Commercial-Share Alike 3.0 http://creativecommons.org/licenses/by/3.0 application/pdf EMBO and Macmillan Publishers Limited Molecular Systems Biology |
| spellingShingle | Saez-Rodriguez, Julio Alexopoulos, Leonidas G. Epperlein, Jonathan Samaga, Regina Lauffenburger, Douglas A. Klamt, Steffen Sorger, Peter K. Discrete logic modelling as a means to link protein signalling networks functional analysis of mammalian signal transduction |
| title | Discrete logic modelling as a means to link protein signalling networks functional analysis of mammalian signal transduction |
| title_full | Discrete logic modelling as a means to link protein signalling networks functional analysis of mammalian signal transduction |
| title_fullStr | Discrete logic modelling as a means to link protein signalling networks functional analysis of mammalian signal transduction |
| title_full_unstemmed | Discrete logic modelling as a means to link protein signalling networks functional analysis of mammalian signal transduction |
| title_short | Discrete logic modelling as a means to link protein signalling networks functional analysis of mammalian signal transduction |
| title_sort | discrete logic modelling as a means to link protein signalling networks functional analysis of mammalian signal transduction |
| url | http://hdl.handle.net/1721.1/62180 |
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