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Biogrammar Summary of Nicotinic receptors: allosteric transitions and therapeutic targets in the nervous system 1.0.0

Table of Contents

  1. Contradictory Triplets
  2. Causal Pathologies

Contradictory Triplets 1

Analysis of triple stability comes from a deep graph theoretic background. It identifies triangles within the graph that have logically inconsistent relations.

Separately Unstable Triplet
When both A positiveCorrelation B, B negativeCorrelation C, but C positiveCorrelation A.
Mutually Unstable Triplets
When both A negativeCorrelation B, B negativeCorrelation C, and C negativeCorrelation A.
Jens Contradictory Triplet
When A increases B, A decreases C, and C positiveCorrelation A.
Increase Mismatch Triplet
When A increases B, A increases C, and C negativeCorrelation A.
Decrease Mismatch Triplet
When A decreases B, A decreases C, and C negativeCorrelation A.
Type Node A Node B Node C
Jens a(CHEBI:"amyloid-beta polypeptide 42") a(MESH:"3-(2,4-dimethoxybenzylidene)anabaseine") bp(MESH:Neuroprotection)

Causal Pathologies 6

Pathologies are more dogmatically the result of molecular and physical processes, and do not necessarily make sense as the subject of causal statements.

Source Relation Target
path(MESH:"Alzheimer Disease") decreases a(MESH:Neurons)
path(MESH:"Alzheimer Disease") decreases p(HBP:"alpha-4 beta-2 nAChR")
path(MESH:"Alzheimer Disease") decreases p(HGNCGENEFAMILY:"Cholinergic receptors muscarinic")
path(MESH:"Alzheimer Disease") decreases p(HGNCGENEFAMILY:"Cholinergic receptors nicotinic subunits")
path(MESH:Depression) increases a(CHEBI:choline)
path(MESH:Schizophrenia) decreases p(HGNC:CHRNA7)

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 PyBEL, an open source project. Please feel free to contact us here to give us feedback or report any issues. Also, see our Publishing Notes and Data Protection information.

If you find BEL Commons useful in your work, please consider citing: Hoyt, C. T., Domingo-Fernández, D., & Hofmann-Apitius, M. (2018). BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language. Database, 2018(3), 1–11.