The document discusses representing and handling causal statements from biological data sources. It summarizes existing standards and terms for describing causality, including Molecular Interactions (MI), the Relation Ontology (RO), and Biological Expression Language (BEL). It also outlines next steps for the DrugLogics project, including developing a common format for collecting and storing causal statements and generated logical models, and a planned exchange with collaborators to work on the input/output of the DrugLogics pipeline.

1. Representation of Causality
Statements
State of the art
Vasundra Touré
on behalf of the DrugLogics team
Contact: vasundra.toure@ntnu.no
A
B
C
A up-regulates activity of B
B up-regulates activity of C
A down-regulates activity of C
A -> B
B -> C
A -| C
A activates B
B activates C
A inhibits C
@Vasundra_T
@DrugLogics
European Bioinformatics Institute, May 2017, Wellcome Trust Genome Campus, UK.

2. Background: DrugLogics and logical modeling
DrugLogics pipeline
A -> B
B -> C
A -| C
internal optimal
input format
primary
resources
models
GO
Signor ACSN
OpenBEL
Common Knowledge Resources
GOIntAct
Signor ACSN
OpenBELcollect causal
statements
https://www.ntnu.edu/health/druglogics

3. Background: DrugLogics and logical modeling
DrugLogics pipeline
A -> B
B -> C
A -| C
internal optimal
input format
primary
resources
models
?
?
How to minimise the effort to handle causal statements?
GO
Signor ACSN
OpenBEL
Common Knowledge Resources
GOIntAct
Signor ACSN
OpenBELcollect causal
statements
https://www.ntnu.edu/health/druglogics

4. State of the art: How is causality handled?
Several databases were built to depict causal relationships
IntAct Signor Reactome CBN
Data
Type
molecular interaction
data
signaling
information
knowledgebase of
biological pathways
causal network models
Data
Source
literature & user
submissions
literature literature literature
CV
terms
MI, GO, ChEBI,
InterPro, UniProt
Taxonomy
MI - causal
interaction
NCBI, UniProt,
Ensembl, GO, KEGG,
ChEBI, PubMed, …
BEL statements
Export MITAB, CSV causalTAB,
CSV, XLS
MITAB, SBML,
BioPAX, Protege
JSON, SIF

5. State of the art: Existing terms representing causal relationships
Activation Direct activation Inhibition Direct inhibition
MI MI:2235 MI:2254 MI:2240 MI:2255
RO RO:0002213 RO:0002406 RO:0002212 RO:0002408
BEL
increases -> directlyIncreases => Decreases -| directlyDecreases =|
● Molecular Interactions (MI) http://www.ebi.ac.uk/ols/ontologies/mi
● Relation Ontology (RO) http://obofoundry.org/ontology/ro.html
● BEL https://wiki.openbel.org/display/BLD/Causal+Relationships

6. Agreement during PSI-MI Spring meeting 2017 in Beijing
Merge CausalTAB and MITAB: MITAB should be extended to support
causality statements
A checklist will be generated for describing causality statements
(MICAST?)
RO could extend PSI-MI CV terms for causal relations
1
2
3

7. Next steps in the project: open questions
DrugLogics pipeline
A -> B
B -> C
A -| C
internal optimal
input format
models
collect causal
statements
storage platform?
format?
How and where to store: 1) the collected causal statements, 2) our generated models?
Common Knowledge
Resources
…
Collected causal statements Generated boolean models
Export MITAB - PSIMI XML? SBMLqual, RDF/OWL
Storage SIGNOR DB? Biomodels? new platform?

8. Create a repository of causality statements that logical models can be built from
Institut Curie (Team Barillot) and ENS Paris (Team Thieffry)
Work on input and output format of the DrugLogics pipeline
Strategy for extracting causal statements from ACSN
Use of GINSIM for building minimal logic models
Next steps in the project: 6 months exchange in Paris