Perturbation biology: inferring signaling networks in cellular systems

Area of Science:

• Systems biology
• Computational biology
• Cancer research

Background:

• Understanding complex cellular signaling networks is crucial for developing effective cancer therapies.
• Existing methods often rely on prior pathway knowledge, limiting discovery of novel interactions.
• Predicting cellular responses to single or combination drug perturbations remains a challenge.

Purpose of the Study:

• To present a novel experimental-computational technology for inferring de novo network models of cellular responses.
• To enable the prediction of cell line responses to targeted drug perturbations for combinatorial therapy design.
• To identify novel molecular interactions and drug targets in cancer cell lines.

Main Methods:

• Systematic perturbation of cancer cell lines with targeted drugs (singly and in combination).
• Quantification of cellular response via protein, phospho-protein, and phenotype measurements (e.g., viability).
• De novo computational network modeling using non-linear differential equations and Belief Propagation (BP) for efficient exploration of the solution space.

Main Results:

• Developed executable network models for SKMEL-133 melanoma cell lines resistant to RAF kinase inhibitors.
• Validated models reproduce and extend known cancer signaling pathway biology.
• Identified and experimentally verified novel drug perturbations, including PLK1 inhibition, for potential therapeutic efficacy.

Conclusions:

• The presented technology offers a powerful, data-driven approach to deciphering cellular signaling networks.
• This method facilitates the discovery of new molecular interactions and predicts effective combinatorial cancer therapies.
• The technology is scalable and applicable to diverse areas within molecular biology and drug discovery.