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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.


Related in: MedlinePlus

Biological networks of healthy (left panel) and diseased (right panel) individuals. Biological components in healthy individuals are represented as green nodes in a network. Pathological perturbation, represented by red nodes that lead to morbidity, can occur at different stages of the regulation of key components: a presence and absence of key component (green for presence and red for absence), b mis-regulated gene expression, leading to over- or under-expression (node sizes represent expression levels), c absence or erroneous interactions with interacting partners (dotted lines represent erroneous interactions), d mis-regulated directions (mis-directed arrows), or e strengths of interactions (thicknesses of arrows and accompanying numbers denote interaction strengths)
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Fig2: Biological networks of healthy (left panel) and diseased (right panel) individuals. Biological components in healthy individuals are represented as green nodes in a network. Pathological perturbation, represented by red nodes that lead to morbidity, can occur at different stages of the regulation of key components: a presence and absence of key component (green for presence and red for absence), b mis-regulated gene expression, leading to over- or under-expression (node sizes represent expression levels), c absence or erroneous interactions with interacting partners (dotted lines represent erroneous interactions), d mis-regulated directions (mis-directed arrows), or e strengths of interactions (thicknesses of arrows and accompanying numbers denote interaction strengths)

Mentions: If regulatory circuits that control biological activities in a human body can be represented using a complex network, then a diseased state would be expected to occur when the normal state of the network is perturbed. Failure of key components (e.g. mutations in hub genes in genetic diseases) or external stimuli (e.g. invasion of pathogens in infectious diseases) would lead to loss of network integrity. Diseased perturbations can occur at different regulatory levels, as illustrated in Fig. 2. Firstly, the absence or malfunction in important network components can lead to diseases, such as the loss of a particular gene. The absence of TBX1, in 22q11.2 deletion syndrome (DiGeorge syndrome) is responsible for the majority of characteristic features of this disease [84] (Fig. 2a, the absence of node is illustrated in red). Similarly, inappropriate levels of gene expression can cause disorders (Fig. 2b, altered node size). For example, specific mutations in the FGFR3 gene result in an overactive receptor and lead to the short stature phenotype observed in achondroplasia [85]. Some diseased states can be explained by mis-regulation of the interactions between key components of the network (Fig. 2c, missing edge), as well as mis-direction (Fig. 2d, mis-directed edge) or strength (Fig. 2e, altered edge’s thickness) of interactions. The diseases that can be linked to erroneous interactions include neurodegenerative and neurodevelopmental diseases, genetic disorders, and cancers. In these cases, mutations in multiple relevant genes lead to abnormal protein interactions, and disrupt networks (see [29, 30, 36, 37] for details).Fig. 2


Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research
Biological networks of healthy (left panel) and diseased (right panel) individuals. Biological components in healthy individuals are represented as green nodes in a network. Pathological perturbation, represented by red nodes that lead to morbidity, can occur at different stages of the regulation of key components: a presence and absence of key component (green for presence and red for absence), b mis-regulated gene expression, leading to over- or under-expression (node sizes represent expression levels), c absence or erroneous interactions with interacting partners (dotted lines represent erroneous interactions), d mis-regulated directions (mis-directed arrows), or e strengths of interactions (thicknesses of arrows and accompanying numbers denote interaction strengths)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: Biological networks of healthy (left panel) and diseased (right panel) individuals. Biological components in healthy individuals are represented as green nodes in a network. Pathological perturbation, represented by red nodes that lead to morbidity, can occur at different stages of the regulation of key components: a presence and absence of key component (green for presence and red for absence), b mis-regulated gene expression, leading to over- or under-expression (node sizes represent expression levels), c absence or erroneous interactions with interacting partners (dotted lines represent erroneous interactions), d mis-regulated directions (mis-directed arrows), or e strengths of interactions (thicknesses of arrows and accompanying numbers denote interaction strengths)
Mentions: If regulatory circuits that control biological activities in a human body can be represented using a complex network, then a diseased state would be expected to occur when the normal state of the network is perturbed. Failure of key components (e.g. mutations in hub genes in genetic diseases) or external stimuli (e.g. invasion of pathogens in infectious diseases) would lead to loss of network integrity. Diseased perturbations can occur at different regulatory levels, as illustrated in Fig. 2. Firstly, the absence or malfunction in important network components can lead to diseases, such as the loss of a particular gene. The absence of TBX1, in 22q11.2 deletion syndrome (DiGeorge syndrome) is responsible for the majority of characteristic features of this disease [84] (Fig. 2a, the absence of node is illustrated in red). Similarly, inappropriate levels of gene expression can cause disorders (Fig. 2b, altered node size). For example, specific mutations in the FGFR3 gene result in an overactive receptor and lead to the short stature phenotype observed in achondroplasia [85]. Some diseased states can be explained by mis-regulation of the interactions between key components of the network (Fig. 2c, missing edge), as well as mis-direction (Fig. 2d, mis-directed edge) or strength (Fig. 2e, altered edge’s thickness) of interactions. The diseases that can be linked to erroneous interactions include neurodegenerative and neurodevelopmental diseases, genetic disorders, and cancers. In these cases, mutations in multiple relevant genes lead to abnormal protein interactions, and disrupt networks (see [29, 30, 36, 37] for details).Fig. 2

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.


Related in: MedlinePlus