Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data

Area of Science:

• Genomics
• Systems Biology
• Computational Biology

Background:

• Genome-scale measurement technologies like gene expression profiling offer comprehensive molecular data.
• Interpreting this large-scale data to identify specific biological mechanisms remains a critical challenge.
• Understanding disease mechanisms and drug actions requires experimentally verifiable hypotheses.

Purpose of the Study:

• To introduce Reverse Causal Reasoning (RCR), a novel reverse engineering methodology.
• To infer mechanistic hypotheses from molecular profiling data using prior biological knowledge.
• To provide a computational tool for analyzing gene expression data.

Main Methods:

• RCR utilizes small, literature-curated networks linking upstream controllers to downstream measurements.
• These networks are generated from a knowledge base of qualitative biological cause-and-effect relationships.
• The Whistle software implements RCR for gene expression analysis using Biological Expression Language (BEL).

Main Results:

• Whistle was applied to three transcriptomic datasets using a public knowledge base.
• The inferred mechanisms were consistent with the known biology for each dataset.
• RCR successfully generated mechanistic hypotheses from gene expression data.

Conclusions:

• Reverse Causal Reasoning provides mechanistic insights complementary to ontology or pathway analyses.
• This reverse engineering approach aids in developing models for disease, drug action, and toxicity.
• RCR offers an evidence-driven method for interpreting complex biological data.