Title |
Systems approaches to computational modeling of the oral microbiome
|
---|---|
Published in |
Frontiers in Physiology, January 2013
|
DOI | 10.3389/fphys.2013.00172 |
Pubmed ID | |
Authors |
Dimiter V Dimitrov, Julia Hoeng |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Switzerland | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Members of the public | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 3% |
Germany | 1 | 1% |
Switzerland | 1 | 1% |
Unknown | 74 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 23 | 29% |
Researcher | 12 | 15% |
Student > Master | 9 | 12% |
Student > Postgraduate | 8 | 10% |
Other | 4 | 5% |
Other | 10 | 13% |
Unknown | 12 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 28 | 36% |
Medicine and Dentistry | 12 | 15% |
Biochemistry, Genetics and Molecular Biology | 5 | 6% |
Chemistry | 3 | 4% |
Nursing and Health Professions | 2 | 3% |
Other | 11 | 14% |
Unknown | 17 | 22% |