title: 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 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.
authors.affiliation: Integrative Computational BioScience (ICBS) Center, Mahidol University, Nakhon Pathom, Thailand.|Program in Translational Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
authors.forename: Natini
authors.initials: N
authors.lastname: Jinawath
authors.affiliation: Program in Translational Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
authors.forename: Sacarin
authors.initials: S
authors.lastname: Bunbanjerdsuk
authors.affiliation: Department of Physiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.|Division of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
authors.forename: Maneerat
authors.initials: M
authors.lastname: Chayanupatkul
authors.affiliation: Medical Oncology Unit, Department of Medicine Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
authors.forename: Nuttapong
authors.initials: N
authors.lastname: Ngamphaiboon
authors.affiliation: Department of Physiology, Faculty of Science, Mahidol University, Bangkok, Thailand.
authors.forename: Nithi
authors.initials: N
authors.lastname: Asavapanumas
authors.affiliation: Integrative Computational BioScience (ICBS) Center, Mahidol University, Nakhon Pathom, Thailand.|Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, Thailand.|Laboratory of Biochemistry, Chulabhorn Research Institute, Bangkok, Thailand.
authors.forename: Jisnuson
authors.initials: J
authors.lastname: Svasti
authors.affiliation: Integrative Computational BioScience (ICBS) Center, Mahidol University, Nakhon Pathom, Thailand. varodom.cha@mahidol.ac.th.|Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, Thailand. varodom.cha@mahidol.ac.th.|Systems Biology of Diseases Research Unit, Faculty of Science, Mahidol University, Bangkok, Thailand. varodom.cha@mahidol.ac.th.
authors.forename: Varodom
authors.identifier: 0000-0002-2199-4126
authors.initials: V
authors.lastname: Charoensawan
mesh_terms: D006801:Humans; D009369:Neoplasms / Q000378:metabolism; D010820:Physicians; D057285:Precision Medicine; D049490:Systems Biology; D057170:Translational Research, Biomedical; D000078329:Workforce
keywords: Biomedical research; Cancers; Network biology; Personalized therapy; Systems biology