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Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research

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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.

No MeSH data available.


Interaction networks (Left) represent direct interactions between biological molecules (e.g. transcripts, proteins, and ligands). The interactions represented include direct physical interaction (e.g. protein–protein, and gene regulatory networks) or transition (e.g. metabolic network). Association networks (Right) represent biological molecules that are linked based on their shared and/or common properties (e.g. co-expression)
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Fig1: Interaction networks (Left) represent direct interactions between biological molecules (e.g. transcripts, proteins, and ligands). The interactions represented include direct physical interaction (e.g. protein–protein, and gene regulatory networks) or transition (e.g. metabolic network). Association networks (Right) represent biological molecules that are linked based on their shared and/or common properties (e.g. co-expression)

Mentions: We first outline the fundamental concepts of a network representation. In general, a network represents the presence of objects or entities in a system as “nodes”, and the relationships or interactions among the nodes are called “edges” (Fig. 1). In biology, nodes can represent biological molecules such as genes, proteins, and ligands, or even larger entities such as cells or individual humans. Edges represent physical interactions or contacts between biological molecules, biochemical processes between substrates and products, genetic interactions between genes, and in some cases, interactions between cells or individual organisms.Fig. 1


Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research
Interaction networks (Left) represent direct interactions between biological molecules (e.g. transcripts, proteins, and ligands). The interactions represented include direct physical interaction (e.g. protein–protein, and gene regulatory networks) or transition (e.g. metabolic network). Association networks (Right) represent biological molecules that are linked based on their shared and/or common properties (e.g. co-expression)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5120462&req=5

Fig1: Interaction networks (Left) represent direct interactions between biological molecules (e.g. transcripts, proteins, and ligands). The interactions represented include direct physical interaction (e.g. protein–protein, and gene regulatory networks) or transition (e.g. metabolic network). Association networks (Right) represent biological molecules that are linked based on their shared and/or common properties (e.g. co-expression)
Mentions: We first outline the fundamental concepts of a network representation. In general, a network represents the presence of objects or entities in a system as “nodes”, and the relationships or interactions among the nodes are called “edges” (Fig. 1). In biology, nodes can represent biological molecules such as genes, proteins, and ligands, or even larger entities such as cells or individual humans. Edges represent physical interactions or contacts between biological molecules, biochemical processes between substrates and products, genetic interactions between genes, and in some cases, interactions between cells or individual organisms.Fig. 1

View Article: PubMed Central - PubMed

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.

No MeSH data available.