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  A logical model provides insights into T cell receptor signaling

Saez-Rodriguez, J., Simeoni, L., Lindquist, J., Hemenway, R., Bommhardt, U., Arndt, B., et al. (2007). A logical model provides insights into T cell receptor signaling. PLoS Computational Biology, 3: e163. doi:10.1371/journal.pcbi.0030163.

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Copyright: 2007 Saez-Rodriguez et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Saez-Rodriguez, J.1, Autor           
Simeoni, L.2, Autor
Lindquist, J.2, Autor
Hemenway, R.1, Autor           
Bommhardt, U.2, Autor
Arndt, B.2, Autor
Haus, U. U.3, Autor
Weismantel, R.3, Autor
Gilles, E. D.1, Autor           
Klamt, S.1, Autor           
Schraven, B.2, Autor
Affiliations:
1Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738155              
2Otto-von-Guericke University, Institute of Immunology, Magdeburg, ou_persistent22              
3Otto-von-Guericke University, Institute for Mathematical Optimization, Magdeburg, ou_persistent22              

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 Zusammenfassung: Cellular decisions are determined by complex molecular interaction networks. Large-scale signaling networks are currently being reconstructed, but the kinetic parameters and quantitative data that would allow for dynamic modeling are still scarce. Therefore, computational studies based upon the structure of these networks are of great interest. Here, a methodology relying on a logical formalism is applied to the functional analysis of the complex signaling network governing the activation of T cells via the T cell receptor, the CD4/CD8 co-receptors, and the accessory signaling receptor CD28. Our large-scale Boolean model, which comprises 94 nodes and 123 interactions and is based upon well-established qualitative knowledge from primary T cells, reveals important structural features (e.g., feedback loops and network-wide dependencies) and recapitulates the global behavior of this network for an array of published data on T cell activation in wild-type and knock-out conditions. More importantly, the model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated. Finally, we show that the logical model reveals key elements and potential failure modes in network functioning and provides candidates for missing links. In summary, our largescale logical model for T cell activation proved to be a promising in silico tool, and it inspires immunologists to ask new questions. We think that it holds valuable potential in foreseeing the effects of drugs and network modifications.

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Sprache(n): eng - English
 Datum: 2007
 Publikationsstatus: Erschienen
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1371/journal.pcbi.0030163
eDoc: 322167
Anderer: 29/07
 Art des Abschluß: -

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Titel: PLoS Computational Biology
Genre der Quelle: Zeitschrift
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Seiten: - Band / Heft: 3 Artikelnummer: e163 Start- / Endseite: - Identifikator: -