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Logic-Based Models for the Analysis of Cell Signaling Networks
[Image: see text] Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolec...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2853906/ https://www.ncbi.nlm.nih.gov/pubmed/20225868 http://dx.doi.org/10.1021/bi902202q |
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author | Morris, Melody K. Saez-Rodriguez, Julio Sorger, Peter K. Lauffenburger, Douglas A. |
author_facet | Morris, Melody K. Saez-Rodriguez, Julio Sorger, Peter K. Lauffenburger, Douglas A. |
author_sort | Morris, Melody K. |
collection | PubMed |
description | [Image: see text] Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks. |
format | Text |
id | pubmed-2853906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-28539062010-04-13 Logic-Based Models for the Analysis of Cell Signaling Networks Morris, Melody K. Saez-Rodriguez, Julio Sorger, Peter K. Lauffenburger, Douglas A. Biochemistry [Image: see text] Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks. American Chemical Society 2010-03-12 2010-04-20 /pmc/articles/PMC2853906/ /pubmed/20225868 http://dx.doi.org/10.1021/bi902202q Text en Copyright © 2010 American Chemical Society http://pubs.acs.org This is an open-access article distributed under the ACS AuthorChoice Terms & Conditions. Any use of this article, must conform to the terms of that license which are available at http://pubs.acs.org. |
spellingShingle | Morris, Melody K. Saez-Rodriguez, Julio Sorger, Peter K. Lauffenburger, Douglas A. Logic-Based Models for the Analysis of Cell Signaling Networks |
title | Logic-Based Models for the Analysis of Cell Signaling Networks |
title_full | Logic-Based Models for the Analysis of Cell Signaling Networks |
title_fullStr | Logic-Based Models for the Analysis of Cell Signaling Networks |
title_full_unstemmed | Logic-Based Models for the Analysis of Cell Signaling Networks |
title_short | Logic-Based Models for the Analysis of Cell Signaling Networks |
title_sort | logic-based models for the analysis of cell signaling networks |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2853906/ https://www.ncbi.nlm.nih.gov/pubmed/20225868 http://dx.doi.org/10.1021/bi902202q |
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