Provenance

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charles.hoyt@scai.fraunhofer.de at 2018-04-03 15:19:14
Authors
Causal Biological Networks Database
Contact
CausalBiologicalNetworks.RD@pmi.com
Description
The Tissue Damage network depicts the causal mechanisms leading to the secretion of damage-associated molecular patterns (DAMPs) and pathogen associated molecular patterns (PAMPs) following tissue injury. The network includes pathways related to the detection of DAMPs, which serve to perpetuate heightened immune responses to noninfectious tissue insult.
License
Please cite: - www.causalbionet.com - https://bionet.sbvimprover.com as well as any relevant publications. The sbv IMPROVER project, the website and the Symposia are part of a collaborative project designed to enable scientists to learn about and contribute to the development of a new crowd sourcing method for verification of scientific data and results. The current challenges, website and biological network models were developed and are maintained as part of a collaboration among Selventa, OrangeBus and ADS. The project is led and funded by Philip Morris International. For more information on the focus of Philip Morris International’s research, please visit www.pmi.com.
Number Nodes
68
Number Edges
209
Number Components
24
Network Density
0.0458736
Average Degree
3.07353
Number Citations
141
Number BEL Errors
0

Content Statistics

Network Overlap

The node-based overlap between this network and other networks is calculated as the Szymkiewicz-Simpson coefficient of their respective nodes. Up to the top 10 are shown below.

Network Overlap
Dendritic Cell Signaling-2.0-Rn v2.0 53%
Macrophage Signaling-2.0-Rn v2.0 49%
Tissue Damage-2.0-Hs v2.0 47%
Tissue Damage-2.0-Mm v2.0 46%
BEL Framework Large Corpus Document v20170611 44%
Neutrophil Signaling-2.0-Rn v2.0 44%
Epithelial Innate Immune Activation-2.0-Rn v2.0 40%
Immune Regulation of Tissue Repair-2.0-Rn v2.0 38%
Epithelial Mucus Hypersecretion-2.0-Rn v2.0 34%
Megakaryocyte Differentiation-2.0-Rn v2.0 32%

Sample Edges

act(complex(SCOMP:"IkappaB Kinase Complex"), ma(kin)) directlyIncreases p(HGNC:NFKBIA, pmod(Ph, Ser, 36))

In the classical NF-{kappa}B signaling pathway, IKK{beta} is both necessary and sufficient for phosphorylation of I{kappa}B{alpha} on Ser 32 and Ser 36, and I{kappa}B{beta} on Ser 19 and Ser 23. PubMed:15371334

act(complex(SCOMP:"IkappaB Kinase Complex"), ma(kin)) directlyIncreases p(HGNC:NFKBIA, pmod(Ph, Ser, 36))

The IKK immunoprecipitates from thrombin-treated cells showed increased phosphorylation of GST-IkBalpha compared with the immunoprecipitates from control cells (Fig. 6), indicating the activation of IKK by thrombin. PubMed:15843586

act(complex(SCOMP:"IkappaB Kinase Complex"), ma(kin)) directlyIncreases p(HGNC:NFKBIA, pmod(Ph, Ser, 32))

In the classical NF-{kappa}B signaling pathway, IKK{beta} is both necessary and sufficient for phosphorylation of I{kappa}B{alpha} on Ser 32 and Ser 36, and I{kappa}B{beta} on Ser 19 and Ser 23. PubMed:15371334

Sample Nodes

a(CHEBI:lipopolysaccharide)

In-Edges: 277 | Out-Edges: 1541 | Explore Neighborhood | Download JSON

p(RGD:Il1a)

In-Edges: 8 | Out-Edges: 14 | Explore Neighborhood | Download JSON

p(RGD:Nfkbia)

In-Edges: 129 | Out-Edges: 96 | Explore Neighborhood | Download JSON

a(CHEBI:adenosine)

In-Edges: 18 | Out-Edges: 21 | Explore Neighborhood | Download JSON

p(RGD:Ikbkb)

In-Edges: 14 | Out-Edges: 5 | Explore Neighborhood | Download JSON

About

BEL Commons is developed and maintained in an academic capacity by Charles Tapley Hoyt and Daniel Domingo-Fernández at the Fraunhofer SCAI Department of Bioinformatics with support from the IMI project, AETIONOMY. It is built on top of the open source project, PyBEL. Please feel free to contact us here to give us feedback or report any issues.