Back to top


Biological Expression Language

BEL Features



Makes biological knowledge computable. Drive prior-knowledge-based algorithms using a standard representation. Build your knowledgebase once.

Easy to learn

Easy to learn

BEL is easier than the Chemical Reaction Language which every high-school student learns.

What is BEL?

BEL is a language for representing scientific findings in the life sciences in a computable form. BEL is designed to represent scientific findings by capturing causal and correlative relationships in context, where context can include information about the biological and experimental system in which the relationships were observed, the supporting publications cited and the process of curation.

BEL Example

“Shear stress enhances expression of the gene encoding the endothelial nitric oxide synthase (eNOS) and further stimulates its enzymatic activity, leading to physiologic low concentrations of nitric oxide (NO) within endothelial cells [44–46]. This continuous generation of NO prevents the apoptosis of endothelial cells, thereby protecting the endothelial monolayer from injury [47,48].”

Is converted into BEL Assertions:

  • biologicalProcess(GO:“response to fluid shear stress”) increases rnaAbundance(HGNC:NOS3)
  • biologicalProcess(GO:“response to fluid shear stress”) increases activity(proteinAbundance(HGNC:NOS3), molecularActivity(cat))
  • activity(proteinAbundance(HGNC:NOS3), molecularActivity(cat)) directlyIncreases abundance(CHEBI:“nitric oxide”)
  • abundance(CHEBI:“nitric oxide”) decreases biologicalProcess(GO:“apoptotic process”)

the abbreviated BEL Assertions are:

  • bp(GO:“response to fluid shear stress”) -> r(HGNC:NOS3)
  • bp(GO:“response to fluid shear stress”) -> act(p(HGNC:NOS3), ma(cat))
  • act(p(HGNC:NOS3), ma(cat)) => a(CHEBI:“nitric oxide”)
  • a(CHEBI:“nitric oxide”) -| bp(GO:“apoptotic process”)

BEL Language documentation and tutorials

What are the advantages?

  1. Straightforward representation of network and causal biology
  2. Easy to find and evaluate contradictory biology, e.g. A increases B and A decreases B is easy to find.
  3. The BEL Assertions are triples which lend themselves to network representation. Individual BEL Assertions can be combined/aggregated into networks.
  4. BEL Assertions are packaged in BEL Nanopubs which encapsulate provenance and experimental context (required to evaluate a BEL Assertion).
  5. The BEL Language is parse-able into a computable format that allows transformations and expansions for building a computable knowledge graph which can then be used in algorithms such as Reverse Causal Reasoning, Heat Diffusion or as prior knowledge for deep-learning.