Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal...

Area of Science:

• Systems biology
• Toxicology
• Genomics

Background:

• High-throughput technologies like microarrays generate complex data requiring sophisticated analysis.
• Cause-and-effect network models are increasingly used to interpret biological mechanisms.
• Systems toxicology necessitates quantitative network-level dose-response assessment.

Purpose of the Study:

• To develop a method for quantifying network response from transcriptomics data.
• To integrate cause-and-effect network models with high-throughput measurements.
• To enable network-based signatures for phenotype prediction and patient stratification.

Main Methods:

• Developed a quantitative method leveraging signed graph structures of cause-and-effect networks.
• Integrated transcriptomics measurements with network models.
• Applied the methodology to in vivo and in vitro datasets, including ulcerative colitis drug efficacy.

Main Results:

• Quantified network response in an interpretable manner.
• Extracted network-based signatures for robust phenotype prediction.
• Provided mechanistic insights into anti-inflammatory drug efficacy and developed a predictive diagnosis tool for treatment response.

Conclusions:

• The framework integrates transcriptomics and cause-and-effect networks for quantitative assessment.
• Enables data interpretation and patient stratification for diagnostic purposes.
• Offers a mathematically coherent approach for systems toxicology and personalized medicine.