Abstract:BackgroundNumerous inflammation-related pathways have been shown to play important roles in atherogenesis. Rapid and efficient assessment of the relative influence of each of those pathways is a challenge in the era of “omics” data generation. The aim of the present work was to develop a network model of inflammation-related molecular pathways underlying vascular disease to assess the degree of translatability of preclinical molecular data to the human clinical setting.MethodsWe constructed and evaluated the V… Show more
“…The first versions of 98 network models (Version 1.0) were constructed and evaluated following the strategy reported previously (7)(8)(9)(10)(11)(12). This strategy ( Figure 3A and B) is summarized briefly here:…”
Section: Construction Of the First Version Of The Cbn Modelsmentioning
confidence: 99%
“…Structurally, the CBN collection is organized into the following biological areas (Figure 1): cell response to stress, cell proliferation, cell fate (including DNA damage response, autophagy, senescence, apoptosis and necroptosis), tissue repair and angiogenesis, pulmonary inflammatory processes, vascular inflammatory processes and chronic obstructive pulmonary disease (COPD)-specific networks. Details of these network models have been published previously (7)(8)(9)(10)(11)(12).…”
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. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting V C The Author(s)
“…The first versions of 98 network models (Version 1.0) were constructed and evaluated following the strategy reported previously (7)(8)(9)(10)(11)(12). This strategy ( Figure 3A and B) is summarized briefly here:…”
Section: Construction Of the First Version Of The Cbn Modelsmentioning
confidence: 99%
“…Structurally, the CBN collection is organized into the following biological areas (Figure 1): cell response to stress, cell proliferation, cell fate (including DNA damage response, autophagy, senescence, apoptosis and necroptosis), tissue repair and angiogenesis, pulmonary inflammatory processes, vascular inflammatory processes and chronic obstructive pulmonary disease (COPD)-specific networks. Details of these network models have been published previously (7)(8)(9)(10)(11)(12).…”
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. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting V C The Author(s)
“…The first versions of 98 network models (Version 1.0) were constructed and evaluated following the strategy reported previously ( 7–12 ). This strategy ( Figure 3 A and B) is summarized briefly here: Careful selection of model boundaries, i.e.…”
Section: Construction Of the First Version Of The Cbn Modelsmentioning
confidence: 99%
“…Structurally, the CBN collection is organized into the following biological areas ( Figure 1 ): cell response to stress, cell proliferation, cell fate (including DNA damage response, autophagy, senescence, apoptosis and necroptosis), tissue repair and angiogenesis, pulmonary inflammatory processes, vascular inflammatory processes and chronic obstructive pulmonary disease (COPD)-specific networks. Details of these network models have been published previously ( 7–12 ). Figure 1.…”
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. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. 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.Database URL:
http://causalbionet.com
“…Since 2015, the CBN database website has provided free access to the full collection [27]. In addition to the original focus of inhalation toxicology covering the non-diseased respiratory tract tissues, causal networks for non-diseased vascular tissues, chronic obstructive pulmonary disease, and atherosclerosis plaque destabilization have been assembled and published to enrich the covered biological contexts [58][59][60].…”
We describe how the genome-wide transcriptional profiling can be used in networkbased systems toxicology, an approach leveraging biological networks for assessing the health risks of exposure to chemical compounds. Driven by the technological advances changing the ways in which data are generated, systems toxicology has allowed traditional toxicity endpoints to be enhanced with far deeper levels of analysis. In combination, new experimental and computational methods have offered the potential for more effective, efficient, and reliable toxicological testing strategies. We illustrate these advances by the "network perturbation amplitude" methodology that quantifies the effects of exposure treatments on biological mechanisms represented by causal networks. We also describe recent developments in the assembly of highquality causal biological networks using crowdsourcing and text-mining approaches. We further show how network-based approaches can be integrated into the multiscale modeling framework of response to toxicological exposure. Finally, we combine biological knowledge assembly and multiscale modeling to report on the promising developments of the "quantitative adverse outcome pathway" concept, which spans multiple levels of biological organization, from molecules to population, and has direct relevance in the context of the "Toxicity Testing in the 21st century" vision of the US National Research Council.
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