1. Catalog
  2. Networks
  3. Activation of M1 and M4 muscarinic receptors as potential treatments for Alzheimer's disease and schizophrenia. v1.0.0
  4. Biogrammar

Biogrammar Summary of Activation of M1 and M4 muscarinic receptors as potential treatments for Alzheimer's disease and schizophrenia. 1.0.0

Table of Contents

  1. Unstable Pairs
  2. Contradictory Triplets
  3. Causal Pathologies

Unstable Pairs 1

Chaotic Pairs
Nodes that mutually increase each other, such as when both A increases B and B increases A.
Dampened Pairs
Nodes that mutually decrease each other, such as when both A decreases B and B decreases A.

While neither chaotic nor dampened pairs are biologically invalid, they require additional context to understand their regulation.

Type Node A Node B
Chaotic a(CHEBI:acetylcholine) p(HGNC:CHRM1)

Contradictory Triplets 2

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:scopolamine) a(MESH:"1-(4-methoxybenzyl)-4-oxo-1,4-dihydroquinoline-3-carboxylic acid") bp(GO:"behavioral fear response")
Jens a(CHEBI:scopolamine) a(MESH:VU0357017) bp(GO:"behavioral fear response")

Causal Pathologies 1

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:"Cholinergic Neurons")

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.