Two-level modeling approach to identify the regulatory dynamics capturing drug response heterogeneity in single-cells

Area of Science:

• Cellular and Molecular Biology
• Systems Biology
• Cancer Research

Background:

• Cellular heterogeneity impacts cancer treatment efficacy.
• Understanding drug resistance mechanisms requires analyzing cell response dynamics.
• Single-cell technologies offer insights into phenotypic variation.

Purpose of the Study:

• To develop a modeling approach for identifying regulatory reactions driving population heterogeneity in drug response.
• To pinpoint fate-determining reactions responsible for cell response diversity.
• To uncover potential therapeutic targets for overcoming drug resistance.

Main Methods:

• Utilized single-cell response dynamics data from HeLa cells treated with TNF-related apoptosis-inducing ligand (TRAIL).
• Developed a modeling approach incorporating kinetic parameters of an apoptosis receptor reaction model.
• Employed a positive feedback loop motif to identify critical reaction sets.

Main Results:

• Identified specific reactions that drive population heterogeneity in drug response.
• Demonstrated that caspase-8 encapsulates heterogeneity by introducing response delays and amplifying initial expression variability.
• Characterized the kinetic parameters of fate-determining reactions.

Conclusions:

• The developed modeling approach successfully identifies key regulatory reactions driving cellular heterogeneity.
• Caspase-8 plays a crucial role in generating and amplifying cell response diversity.
• Findings provide potential regulatory targets to modulate cell dynamics and combat drug resistance in cancer therapy.