RSI

RSI infers regulostat molecular networks from basal transcriptomic data to identify gene-pair rheostat-like cooperativity that modulates continuous cellular responses to stressors and drugs.

Key Features:

• Novel algorithm: Processes basal transcriptomic data to extract gene expression patterns for network inference.
• Regulostat identification: Detects constituent gene pairs that operate in a rheostat-like mode-of-cooperation.
• Continuous-response focus: Captures continuous (rheostat-like) rather than binary cellular response patterns.
• Drug–regulostat interaction analysis: Analyzes context-specific interactions between drugs and regulostats that influence drug response phenotypes.
• Candidate prioritization: Prioritizes gene candidates that could shift resistant phenotypes toward sensitivity to specific treatments.
• Computational proof-of-concept: Provided computational evidence for intrinsic regulostat devices and their role in predetermining phenotypic responses in cancer cells.

Scientific Applications:

• Molecular network mapping: Identifying networks that determine cellular response phenotypes to stressors and drugs.
• Drug-response prediction: Predicting how context-specific drug–regulostat interactions influence drug response phenotypes in cancer cells.
• Target prioritization for resistance reversal: Prioritizing gene targets to convert resistant phenotypes into sensitive ones.
• Quantitative phenotyping: Studying continuous cellular response variation prior to external stimulus exposure.
• Support for bioengineering and medical research: Informing experimental prioritization and intervention strategies based on inferred regulostats.

Methodology:

Processes basal transcriptomic data to extract gene expression patterns, identifies regulostat constituent gene pairs operating in a rheostat-like mode-of-cooperation, and analyzes context-specific drug–regulostat interactions to link regulostats to drug response phenotypes.