AD BEL Model
The discovery and development of new treatments for Alzheimer's disease (AD) requires a profound mechanistic understanding of the disease. We used a model-driven approach supporting the systematic identification of putative disease mechanisms. By systematically curating knowledge from the AD literature, we have developed a knowledge assembly using Biological Expression Language (BEL) that comprises causal and correlative relationships between biomolecules, pathways, and clinical readouts. This AD knowledge assembly is represented as a network, that contains 9.645 nodes, 10.251 edges and comprises the knowledge of almost 35.266 citations, resulting in 44.437 BEL statements. Furthermore, this knowledge assembly is the foundation of NeuroMMSig, the catalog of mechanisms in AETIONOMY.
The network consists of 1502 proteins, 567 genes, 496 RNAs, 514 molecules, 48 miRNA, 710 biological processes, 136 pathologies, 16 reactions, 72 composites and 377 other entities, which include BEL functions like translocation, transcription, degradation and secretion.
Most of the edges are causal assertion like increases (4450) or decreases (1818). It is also note- worthy to mention that association relationships occur regularly since it is often the case that there is not a causal relationship described in the article but only a known connection between entities. Thus, association relationships were the only non-casual relationships.
More detailed information regarding incorporated subgraphs in the PD BEL model can be found here:
Tom-Kodamullil A., Younesi, E., Naz, M., Bagewadi, S., & Hofmann-Apitius, M. (2015). Computable cause-and-effect models of healthy and Alzheimer's disease states and their mechanistic differential analysis. Alzheimer's & Dementia, 11(11), 1329-1339.