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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...

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Detalles Bibliográficos
Autores principales: Saad, Christian, Bauer, Bernhard, Mansmann, Ulrich R, Li, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2019
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.
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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
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