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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2014-08-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S200103701400021X |
| _version_ | 1818929055993430016 |
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| author | Nicola J. Mulder Richard O. Akinola Gaston K. Mazandu Holifidy Rapanoel |
| author_facet | Nicola J. Mulder Richard O. Akinola Gaston K. Mazandu Holifidy Rapanoel |
| author_sort | Nicola J. Mulder |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-20T03:38:44Z |
| format | Article |
| id | doaj.art-0729092ac43446398360cf30d8538624 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-12-20T03:38:44Z |
| publishDate | 2014-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-0729092ac43446398360cf30d85386242022-12-21T19:54:48ZengElsevierComputational and Structural Biotechnology Journal2001-03702014-08-01111811010.1016/j.csbj.2014.08.006Using biological networks to improve our understanding of infectious diseasesNicola J. MulderRichard O. AkinolaGaston K. MazanduHolifidy RapanoelInfectious 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.http://www.sciencedirect.com/science/article/pii/S200103701400021XBiological networksTuberculosisPathogenEvolutionProtein–protein interaction |
| spellingShingle | Nicola J. Mulder Richard O. Akinola Gaston K. Mazandu Holifidy Rapanoel Using biological networks to improve our understanding of infectious diseases Computational and Structural Biotechnology Journal Biological networks Tuberculosis Pathogen Evolution Protein–protein interaction |
| 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 | Biological networks Tuberculosis Pathogen Evolution Protein–protein interaction |
| url | http://www.sciencedirect.com/science/article/pii/S200103701400021X |
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