Title |
Regulatory network features in Listeria monocytogenes—changing the way we talk
|
---|---|
Published in |
Frontiers in Cellular and Infection Microbiology, January 2014
|
DOI | 10.3389/fcimb.2014.00014 |
Pubmed ID | |
Authors |
Veronica Guariglia-Oropeza, Renato H. Orsi, Haiyuan Yu, Kathryn J. Boor, Martin Wiedmann, Claudia Guldimann |
Abstract |
Our understanding of how pathogens shape their gene expression profiles in response to environmental changes is ever growing. Advances in Bioinformatics have made it possible to model complex systems and integrate data from variable sources into one large regulatory network. In these analyses, regulatory networks are typically broken down into regulatory motifs such as feed-forward loops (FFL) or auto-regulatory feedbacks, which serves to simplify the structure, while the functional implications of different regulatory motifs allow to make informed assumptions about the function of a specific regulatory pathway. Here we review the basic concepts of network features and use this language to break down the regulatory networks that govern the interactions between the main regulators of stress response, virulence, and transmission in Listeria monocytogenes. We point out the advantage that taking a "systems approach" could have for our understanding of gene functions, the detection of distant regulatory inputs, interspecies comparisons, and co-expression. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 50% |
Members of the public | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Japan | 1 | 1% |
United Kingdom | 1 | 1% |
Unknown | 68 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 12 | 17% |
Researcher | 12 | 17% |
Student > Master | 10 | 14% |
Student > Doctoral Student | 5 | 7% |
Professor | 5 | 7% |
Other | 18 | 26% |
Unknown | 8 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 32 | 46% |
Biochemistry, Genetics and Molecular Biology | 10 | 14% |
Immunology and Microbiology | 6 | 9% |
Nursing and Health Professions | 2 | 3% |
Veterinary Science and Veterinary Medicine | 2 | 3% |
Other | 3 | 4% |
Unknown | 15 | 21% |