Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research
Abstract With the wealth of data accumulated from completely sequenced genomes and other high-throughput experiments, global studies of biological systems, by simultaneously investigating multiple biological entities (e.g. genes, transcripts, proteins), has become a routine. Network representation i...
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| Format: | Article |
| Language: | English |
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BMC
2016-11-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12967-016-1078-3 |
| _version_ | 1818138120511553536 |
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| author | Natini Jinawath Sacarin Bunbanjerdsuk Maneerat Chayanupatkul Nuttapong Ngamphaiboon Nithi Asavapanumas Jisnuson Svasti Varodom Charoensawan |
| author_facet | Natini Jinawath Sacarin Bunbanjerdsuk Maneerat Chayanupatkul Nuttapong Ngamphaiboon Nithi Asavapanumas Jisnuson Svasti Varodom Charoensawan |
| author_sort | Natini Jinawath |
| collection | DOAJ |
| description | Abstract With the wealth of data accumulated from completely sequenced genomes and other high-throughput experiments, global studies of biological systems, by simultaneously investigating multiple biological entities (e.g. genes, transcripts, proteins), has become a routine. Network representation is frequently used to capture the presence of these molecules as well as their relationship. Network biology has been widely used in molecular biology and genetics, where several network properties have been shown to be functionally important. Here, we discuss how such methodology can be useful to translational biomedical research, where scientists traditionally focus on one or a small set of genes, diseases, and drug candidates at any one time. We first give an overview of network representation frequently used in biology: what nodes and edges represent, and review its application in preclinical research to date. Using cancer as an example, we review how network biology can facilitate system-wide approaches to identify targeted small molecule inhibitors. These types of inhibitors have the potential to be more specific, resulting in high efficacy treatments with less side effects, compared to the conventional treatments such as chemotherapy. Global analysis may provide better insight into the overall picture of human diseases, as well as identify previously overlooked problems, leading to rapid advances in medicine. From the clinicians’ point of view, it is necessary to bridge the gap between theoretical network biology and practical biomedical research, in order to improve the diagnosis, prevention, and treatment of the world’s major diseases. |
| first_indexed | 2024-12-11T10:07:09Z |
| format | Article |
| id | doaj.art-b8f7b32a1aa14e9ba553a763213895a3 |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-12-11T10:07:09Z |
| publishDate | 2016-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-b8f7b32a1aa14e9ba553a763213895a32022-12-22T01:11:55ZengBMCJournal of Translational Medicine1479-58762016-11-0114111310.1186/s12967-016-1078-3Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical researchNatini Jinawath0Sacarin Bunbanjerdsuk1Maneerat Chayanupatkul2Nuttapong Ngamphaiboon3Nithi Asavapanumas4Jisnuson Svasti5Varodom Charoensawan6Integrative Computational BioScience (ICBS) Center, Mahidol UniversityProgram in Translational Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol UniversityDepartment of Physiology, Faculty of Medicine, Chulalongkorn UniversityMedical Oncology Unit, Department of Medicine Faculty of Medicine, Ramathibodi Hospital, Mahidol UniversityDepartment of Physiology, Faculty of Science, Mahidol UniversityIntegrative Computational BioScience (ICBS) Center, Mahidol UniversityIntegrative Computational BioScience (ICBS) Center, Mahidol UniversityAbstract With the wealth of data accumulated from completely sequenced genomes and other high-throughput experiments, global studies of biological systems, by simultaneously investigating multiple biological entities (e.g. genes, transcripts, proteins), has become a routine. Network representation is frequently used to capture the presence of these molecules as well as their relationship. Network biology has been widely used in molecular biology and genetics, where several network properties have been shown to be functionally important. Here, we discuss how such methodology can be useful to translational biomedical research, where scientists traditionally focus on one or a small set of genes, diseases, and drug candidates at any one time. We first give an overview of network representation frequently used in biology: what nodes and edges represent, and review its application in preclinical research to date. Using cancer as an example, we review how network biology can facilitate system-wide approaches to identify targeted small molecule inhibitors. These types of inhibitors have the potential to be more specific, resulting in high efficacy treatments with less side effects, compared to the conventional treatments such as chemotherapy. Global analysis may provide better insight into the overall picture of human diseases, as well as identify previously overlooked problems, leading to rapid advances in medicine. From the clinicians’ point of view, it is necessary to bridge the gap between theoretical network biology and practical biomedical research, in order to improve the diagnosis, prevention, and treatment of the world’s major diseases.http://link.springer.com/article/10.1186/s12967-016-1078-3Network biologySystems biologyBiomedical researchCancersPersonalized therapy |
| spellingShingle | Natini Jinawath Sacarin Bunbanjerdsuk Maneerat Chayanupatkul Nuttapong Ngamphaiboon Nithi Asavapanumas Jisnuson Svasti Varodom Charoensawan Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research Journal of Translational Medicine Network biology Systems biology Biomedical research Cancers Personalized therapy |
| title | Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research |
| title_full | Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research |
| title_fullStr | Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research |
| title_full_unstemmed | Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research |
| title_short | Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research |
| title_sort | bridging the gap between clinicians and systems biologists from network biology to translational biomedical research |
| topic | Network biology Systems biology Biomedical research Cancers Personalized therapy |
| url | http://link.springer.com/article/10.1186/s12967-016-1078-3 |
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