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AutoAnalyze in Systems Biology
AutoAnalyze is a highly customizable framework for the visualization and analysis of large-scale model graphs. Originally developed for use in the automotive domain, it also supports efficient computation within molecular networks represented by reaction equations. A static analysis approach is used...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6328952/ https://www.ncbi.nlm.nih.gov/pubmed/30670917 http://dx.doi.org/10.1177/1177932218818458 |
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author | Saad, Christian Bauer, Bernhard Mansmann, Ulrich R Li, Jian |
author_facet | Saad, Christian Bauer, Bernhard Mansmann, Ulrich R Li, Jian |
author_sort | Saad, Christian |
collection | PubMed |
description | AutoAnalyze is a highly customizable framework for the visualization and analysis of large-scale model graphs. Originally developed for use in the automotive domain, it also supports efficient computation within molecular networks represented by reaction equations. A static analysis approach is used for efficient treatment-condition-specific simulation. The chosen method relies on the computation of a global network data-flow resulting from the evaluation of individual genetic data. The approach facilitates complex analyses of biological components from a molecular network under specific therapeutic perturbations, as demonstrated in a case study. In addition to simulating the complex networks in a stable and reproducible way, kinetic constants can also be fine-tuned using a genetic algorithm and built-in statistical tools. |
format | Online Article Text |
id | pubmed-6328952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-63289522019-01-22 AutoAnalyze in Systems Biology Saad, Christian Bauer, Bernhard Mansmann, Ulrich R Li, Jian Bioinform Biol Insights Technical Advances AutoAnalyze is a highly customizable framework for the visualization and analysis of large-scale model graphs. Originally developed for use in the automotive domain, it also supports efficient computation within molecular networks represented by reaction equations. A static analysis approach is used for efficient treatment-condition-specific simulation. The chosen method relies on the computation of a global network data-flow resulting from the evaluation of individual genetic data. The approach facilitates complex analyses of biological components from a molecular network under specific therapeutic perturbations, as demonstrated in a case study. In addition to simulating the complex networks in a stable and reproducible way, kinetic constants can also be fine-tuned using a genetic algorithm and built-in statistical tools. SAGE Publications 2019-01-09 /pmc/articles/PMC6328952/ /pubmed/30670917 http://dx.doi.org/10.1177/1177932218818458 Text en © The Author(s) 2019 http://www.creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Technical Advances Saad, Christian Bauer, Bernhard Mansmann, Ulrich R Li, Jian AutoAnalyze in Systems Biology |
title | AutoAnalyze in Systems Biology |
title_full | AutoAnalyze in Systems Biology |
title_fullStr | AutoAnalyze in Systems Biology |
title_full_unstemmed | AutoAnalyze in Systems Biology |
title_short | AutoAnalyze in Systems Biology |
title_sort | autoanalyze in systems biology |
topic | Technical Advances |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6328952/ https://www.ncbi.nlm.nih.gov/pubmed/30670917 http://dx.doi.org/10.1177/1177932218818458 |
work_keys_str_mv | AT saadchristian autoanalyzeinsystemsbiology AT bauerbernhard autoanalyzeinsystemsbiology AT mansmannulrichr autoanalyzeinsystemsbiology AT lijian autoanalyzeinsystemsbiology |