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Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

Boué S, Talikka M, Westra JW, Hayes W, Di Fabio A, Park J, Schlage WK, Sewer A, Fields B, Ansari S, Martin F, Veljkovic E, Kenney R, Peitsch MC, Hoeng J - Database (Oxford) (2015)

Bottom Line: With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format.Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed.Moreover, networks may be downloaded for further visualization and evaluation.

View Article: PubMed Central - PubMed

Affiliation: Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA.

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Possible application of network models. Network models may be useful in diverse applications ranging from mechanistic investigation of biology to clinical relevance and personalized medicine.
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bav030-F7: Possible application of network models. Network models may be useful in diverse applications ranging from mechanistic investigation of biology to clinical relevance and personalized medicine.

Mentions: The biological information contained in the CBN models can be used in a variety of research applications, some of which are summarized in Figure 7. In a simple case, a list of differentially expressed genes could be mapped to network models to help identify perturbations in biological pathways that may be modulated following an experimental challenge, similar to the pathway enrichment approach using KEGG maps. The inherent computability conferred by encoding networks in BEL allows them to be interrogated and traversed to explore relationships and identify pathways that connect biological entities. We have described previously the development of statistical approaches to predict and interpret biological hypotheses from high-dimensional data sets such as those derived from microarray profiling studies (14). One such approach is the use of RCR to identify hypothetical causes of downstream transcriptional changes. The open tool Whistle together with a sample open-source knowledgebase are available to perform such a task from the OpenBel consortium webpage (www.openbel.org) (14). In a more complex example, we recently used the set of CBN models (∼v 1.0) with internally developed algorithms and the Selventa Knowledgebase to compute the network perturbation amplitude (NPA) following simple experimental exposures (25). In this example, we were able to detect dynamic changes in the amplitude of perturbation in a network model describing the tumor necrosis factor-nuclear factor kappa B (TNF-NFkB) signaling pathway following TNF treatment of normal human bronchial epithelial (NHBE) cells in vitro as measured by gene expression data. Importantly, the quantitated changes in network amplitude corresponded to direct experimental measurement of NFkB nuclear translocation following TNF treatment. In similar examples, we have used the CBN models to identify changes in the cell cycle following exposure of NHBEs to a cell cycle inhibitor (26), and to identify changes in cell proliferation, inflammation and necrosis in the rat nasal epithelium following exposure to formaldehyde (27). Of interest, specific subsets of biological networks may be applied to different experimental settings ranging from cigarette smoke exposure of rats (28), to environmental toxicants analyses in vitro (29), or to translational biology of xenobiotic metabolism (30). These examples illustrate how the network models contained in the CBN database (or similar network databases) can identify and even quantitate biological changes induced by diverse stimuli. Moreover, as the networks are causal, the backbone topology can be leveraged by algorithms to quantitate even more precisely the overall impact of a stimulus on a biological system (31). In addition, downloading the network models and using basic Cytoscape tools provides the ability to compute a set of topological parameters for a given network, including network connectivity measures, characteristic path lengths, and neighborhood connectivities (32), enabling the formulation of hypotheses and possible target design for experimental follow-up.Figure 7.


Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

Boué S, Talikka M, Westra JW, Hayes W, Di Fabio A, Park J, Schlage WK, Sewer A, Fields B, Ansari S, Martin F, Veljkovic E, Kenney R, Peitsch MC, Hoeng J - Database (Oxford) (2015)

Possible application of network models. Network models may be useful in diverse applications ranging from mechanistic investigation of biology to clinical relevance and personalized medicine.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401337&req=5

bav030-F7: Possible application of network models. Network models may be useful in diverse applications ranging from mechanistic investigation of biology to clinical relevance and personalized medicine.
Mentions: The biological information contained in the CBN models can be used in a variety of research applications, some of which are summarized in Figure 7. In a simple case, a list of differentially expressed genes could be mapped to network models to help identify perturbations in biological pathways that may be modulated following an experimental challenge, similar to the pathway enrichment approach using KEGG maps. The inherent computability conferred by encoding networks in BEL allows them to be interrogated and traversed to explore relationships and identify pathways that connect biological entities. We have described previously the development of statistical approaches to predict and interpret biological hypotheses from high-dimensional data sets such as those derived from microarray profiling studies (14). One such approach is the use of RCR to identify hypothetical causes of downstream transcriptional changes. The open tool Whistle together with a sample open-source knowledgebase are available to perform such a task from the OpenBel consortium webpage (www.openbel.org) (14). In a more complex example, we recently used the set of CBN models (∼v 1.0) with internally developed algorithms and the Selventa Knowledgebase to compute the network perturbation amplitude (NPA) following simple experimental exposures (25). In this example, we were able to detect dynamic changes in the amplitude of perturbation in a network model describing the tumor necrosis factor-nuclear factor kappa B (TNF-NFkB) signaling pathway following TNF treatment of normal human bronchial epithelial (NHBE) cells in vitro as measured by gene expression data. Importantly, the quantitated changes in network amplitude corresponded to direct experimental measurement of NFkB nuclear translocation following TNF treatment. In similar examples, we have used the CBN models to identify changes in the cell cycle following exposure of NHBEs to a cell cycle inhibitor (26), and to identify changes in cell proliferation, inflammation and necrosis in the rat nasal epithelium following exposure to formaldehyde (27). Of interest, specific subsets of biological networks may be applied to different experimental settings ranging from cigarette smoke exposure of rats (28), to environmental toxicants analyses in vitro (29), or to translational biology of xenobiotic metabolism (30). These examples illustrate how the network models contained in the CBN database (or similar network databases) can identify and even quantitate biological changes induced by diverse stimuli. Moreover, as the networks are causal, the backbone topology can be leveraged by algorithms to quantitate even more precisely the overall impact of a stimulus on a biological system (31). In addition, downloading the network models and using basic Cytoscape tools provides the ability to compute a set of topological parameters for a given network, including network connectivity measures, characteristic path lengths, and neighborhood connectivities (32), enabling the formulation of hypotheses and possible target design for experimental follow-up.Figure 7.

Bottom Line: With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format.Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed.Moreover, networks may be downloaded for further visualization and evaluation.

View Article: PubMed Central - PubMed

Affiliation: Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA.

Show MeSH
Related in: MedlinePlus