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

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

A simplified diagram of the therapeutic breast cancer network. The main targetable hubs are ER and HER2 receptor. The PI3K/Akt/mTOR hub was relatively recently identified to be the common mechanism of targeted therapy resistance. Circles and rectangles represent cellular receptors and signaling pathways, respectively. The pentagons represent other unspecified molecules interacted with the hubs. Arrows represent the directions of signals. (E estrogen, ER estrogen receptor, PR progesterone receptor, HER2 HER2 receptor, RTKs receptor tyrosine kinases)
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Fig3: A simplified diagram of the therapeutic breast cancer network. The main targetable hubs are ER and HER2 receptor. The PI3K/Akt/mTOR hub was relatively recently identified to be the common mechanism of targeted therapy resistance. Circles and rectangles represent cellular receptors and signaling pathways, respectively. The pentagons represent other unspecified molecules interacted with the hubs. Arrows represent the directions of signals. (E estrogen, ER estrogen receptor, PR progesterone receptor, HER2 HER2 receptor, RTKs receptor tyrosine kinases)

Mentions: As shown in Fig. 3, ER and HER2 can be considered as hubs of the breast cancer network. The ER+ breast cancer cells depend on activation of ER by estrogen, a sex steroid hormone. ER acts as a transcription factor in the nucleus when bound by estrogen in the genomic (nuclear) pathway, resulting in tumor cell proliferation [94]. The signal can also be activated through the non-genomic (non-nuclear) pathway, where estrogen binds to membrane-associated ER. Endocrine therapy against the ER hubs is one of the cornerstones of treatment for ER+/HER2- breast cancers (luminal-A and B) [95]. The predominant endocrine therapies are a selective ER modulator (SERM), an aromatase inhibitor (AI), and selective ER down-regulators (SERD), such as tamoxifen, anastrozole, and fulvestrant [96].Fig. 3


Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research
A simplified diagram of the therapeutic breast cancer network. The main targetable hubs are ER and HER2 receptor. The PI3K/Akt/mTOR hub was relatively recently identified to be the common mechanism of targeted therapy resistance. Circles and rectangles represent cellular receptors and signaling pathways, respectively. The pentagons represent other unspecified molecules interacted with the hubs. Arrows represent the directions of signals. (E estrogen, ER estrogen receptor, PR progesterone receptor, HER2 HER2 receptor, RTKs receptor tyrosine kinases)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: A simplified diagram of the therapeutic breast cancer network. The main targetable hubs are ER and HER2 receptor. The PI3K/Akt/mTOR hub was relatively recently identified to be the common mechanism of targeted therapy resistance. Circles and rectangles represent cellular receptors and signaling pathways, respectively. The pentagons represent other unspecified molecules interacted with the hubs. Arrows represent the directions of signals. (E estrogen, ER estrogen receptor, PR progesterone receptor, HER2 HER2 receptor, RTKs receptor tyrosine kinases)
Mentions: As shown in Fig. 3, ER and HER2 can be considered as hubs of the breast cancer network. The ER+ breast cancer cells depend on activation of ER by estrogen, a sex steroid hormone. ER acts as a transcription factor in the nucleus when bound by estrogen in the genomic (nuclear) pathway, resulting in tumor cell proliferation [94]. The signal can also be activated through the non-genomic (non-nuclear) pathway, where estrogen binds to membrane-associated ER. Endocrine therapy against the ER hubs is one of the cornerstones of treatment for ER+/HER2- breast cancers (luminal-A and B) [95]. The predominant endocrine therapies are a selective ER modulator (SERM), an aromatase inhibitor (AI), and selective ER down-regulators (SERD), such as tamoxifen, anastrozole, and fulvestrant [96].Fig. 3

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