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Using biological networks to improve our understanding of infectious diseases

Infectious diseases are the leading cause of death, particularly in developing countries. Although many drugs are available for treating the most common infectious diseases, in many cases the mechanism of action of these drugs or even their targets in the pathogen remain unknown. In addition, the ke...

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Detalles Bibliográficos
Autores principales: Mulder, Nicola J., Akinola, Richard O., Mazandu, Gaston K., Rapanoel, Holifidy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4212278/
https://www.ncbi.nlm.nih.gov/pubmed/25379138
http://dx.doi.org/10.1016/j.csbj.2014.08.006
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author Mulder, Nicola J.
Akinola, Richard O.
Mazandu, Gaston K.
Rapanoel, Holifidy
author_facet Mulder, Nicola J.
Akinola, Richard O.
Mazandu, Gaston K.
Rapanoel, Holifidy
author_sort Mulder, Nicola J.
collection PubMed
description Infectious diseases are the leading cause of death, particularly in developing countries. Although many drugs are available for treating the most common infectious diseases, in many cases the mechanism of action of these drugs or even their targets in the pathogen remain unknown. In addition, the key factors or processes in pathogens that facilitate infection and disease progression are often not well understood. Since proteins do not work in isolation, understanding biological systems requires a better understanding of the interconnectivity between proteins in different pathways and processes, which includes both physical and other functional interactions. Such biological networks can be generated within organisms or between organisms sharing a common environment using experimental data and computational predictions. Though different data sources provide different levels of accuracy, confidence in interactions can be measured using interaction scores. Connections between interacting proteins in biological networks can be represented as graphs and edges, and thus studied using existing algorithms and tools from graph theory. There are many different applications of biological networks, and here we discuss three such applications, specifically applied to the infectious disease tuberculosis, with its causative agent Mycobacterium tuberculosis and host, Homo sapiens. The applications include the use of the networks for function prediction, comparison of networks for evolutionary studies, and the generation and use of host–pathogen interaction networks.
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spelling pubmed-42122782014-11-06 Using biological networks to improve our understanding of infectious diseases Mulder, Nicola J. Akinola, Richard O. Mazandu, Gaston K. Rapanoel, Holifidy Comput Struct Biotechnol J Mini Review Infectious diseases are the leading cause of death, particularly in developing countries. Although many drugs are available for treating the most common infectious diseases, in many cases the mechanism of action of these drugs or even their targets in the pathogen remain unknown. In addition, the key factors or processes in pathogens that facilitate infection and disease progression are often not well understood. Since proteins do not work in isolation, understanding biological systems requires a better understanding of the interconnectivity between proteins in different pathways and processes, which includes both physical and other functional interactions. Such biological networks can be generated within organisms or between organisms sharing a common environment using experimental data and computational predictions. Though different data sources provide different levels of accuracy, confidence in interactions can be measured using interaction scores. Connections between interacting proteins in biological networks can be represented as graphs and edges, and thus studied using existing algorithms and tools from graph theory. There are many different applications of biological networks, and here we discuss three such applications, specifically applied to the infectious disease tuberculosis, with its causative agent Mycobacterium tuberculosis and host, Homo sapiens. The applications include the use of the networks for function prediction, comparison of networks for evolutionary studies, and the generation and use of host–pathogen interaction networks. Research Network of Computational and Structural Biotechnology 2014-08-27 /pmc/articles/PMC4212278/ /pubmed/25379138 http://dx.doi.org/10.1016/j.csbj.2014.08.006 Text en © 2014 Mulder et al. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Biotechnology.
spellingShingle Mini Review
Mulder, Nicola J.
Akinola, Richard O.
Mazandu, Gaston K.
Rapanoel, Holifidy
Using biological networks to improve our understanding of infectious diseases
title Using biological networks to improve our understanding of infectious diseases
title_full Using biological networks to improve our understanding of infectious diseases
title_fullStr Using biological networks to improve our understanding of infectious diseases
title_full_unstemmed Using biological networks to improve our understanding of infectious diseases
title_short Using biological networks to improve our understanding of infectious diseases
title_sort using biological networks to improve our understanding of infectious diseases
topic Mini Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4212278/
https://www.ncbi.nlm.nih.gov/pubmed/25379138
http://dx.doi.org/10.1016/j.csbj.2014.08.006
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