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Systems approaches to computational modeling of the oral microbiome

Current microbiome research has generated tremendous amounts of data providing snapshots of molecular activity in a variety of organisms, environments, and cell types. However, turning this knowledge into whole system level of understanding on pathways and processes has proven to be a challenging ta...

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Autor principal: Dimitrov, Dimiter V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3706740/
https://www.ncbi.nlm.nih.gov/pubmed/23847548
http://dx.doi.org/10.3389/fphys.2013.00172
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author Dimitrov, Dimiter V.
author_facet Dimitrov, Dimiter V.
author_sort Dimitrov, Dimiter V.
collection PubMed
description Current microbiome research has generated tremendous amounts of data providing snapshots of molecular activity in a variety of organisms, environments, and cell types. However, turning this knowledge into whole system level of understanding on pathways and processes has proven to be a challenging task. In this review we highlight the applicability of bioinformatics and visualization techniques to large collections of data in order to better understand the information that contains related diet—oral microbiome—host mucosal transcriptome interactions. In particular, we focus on systems biology of Porphyromonas gingivalis in the context of high throughput computational methods tightly integrated with translational systems medicine. Those approaches have applications for both basic research, where we can direct specific laboratory experiments in model organisms and cell cultures, and human disease, where we can validate new mechanisms and biomarkers for prevention and treatment of chronic disorders.
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spelling pubmed-37067402013-07-11 Systems approaches to computational modeling of the oral microbiome Dimitrov, Dimiter V. Front Physiol Physiology Current microbiome research has generated tremendous amounts of data providing snapshots of molecular activity in a variety of organisms, environments, and cell types. However, turning this knowledge into whole system level of understanding on pathways and processes has proven to be a challenging task. In this review we highlight the applicability of bioinformatics and visualization techniques to large collections of data in order to better understand the information that contains related diet—oral microbiome—host mucosal transcriptome interactions. In particular, we focus on systems biology of Porphyromonas gingivalis in the context of high throughput computational methods tightly integrated with translational systems medicine. Those approaches have applications for both basic research, where we can direct specific laboratory experiments in model organisms and cell cultures, and human disease, where we can validate new mechanisms and biomarkers for prevention and treatment of chronic disorders. Frontiers Media S.A. 2013-07-10 /pmc/articles/PMC3706740/ /pubmed/23847548 http://dx.doi.org/10.3389/fphys.2013.00172 Text en Copyright © 2013 Dimitrov. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
spellingShingle Physiology
Dimitrov, Dimiter V.
Systems approaches to computational modeling of the oral microbiome
title Systems approaches to computational modeling of the oral microbiome
title_full Systems approaches to computational modeling of the oral microbiome
title_fullStr Systems approaches to computational modeling of the oral microbiome
title_full_unstemmed Systems approaches to computational modeling of the oral microbiome
title_short Systems approaches to computational modeling of the oral microbiome
title_sort systems approaches to computational modeling of the oral microbiome
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3706740/
https://www.ncbi.nlm.nih.gov/pubmed/23847548
http://dx.doi.org/10.3389/fphys.2013.00172
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