A Neyman-Pearson Framework for Modeling Cellular Decision Making Using Single-Cell TNF-NF-κB Signaling Data.

Area of Science:

• Cellular biology
• Systems biology
• Statistical modeling

Background:

• Cellular decisions under noisy signaling can lead to pathological conditions.
• The NF-κB pathway is critical for cell survival, apoptosis, immune signaling, and stress response.
• Understanding cellular decision accuracy is crucial for identifying disease mechanisms.

Purpose of the Study:

• To develop a Neyman-Pearson (NP) detection-theory framework for analyzing single-cell measurements of cellular responses.
• To quantify the performance of cellular signaling pathways, specifically the NF-κB response to tumor necrosis factor (TNF).
• To assess how noise and perturbations affect cellular decision-making and potentially lead to pathology.

Main Methods:

• Applied a Neyman-Pearson (NP) detection-theory framework to experimental single-cell measurements of NF-κB responses.
• Modeled log-responses as multivariate Gaussian distributions.
• Computed optimal thresholds, probability of detection (P_D) - probability of false alarm (P_FA) trade-offs, and Receiver Operating Characteristic (ROC) curves at different time points.

Main Results:

• The NP framework successfully captured expected biological responses, showing increased P_D with higher TNF dose.
• Wild-type cells demonstrated superior performance compared to A20-deficient cells.
• Combining data from multiple time points (bivariate analysis) improved detection accuracy.
• The framework identified conditions where decision quality degraded due to perturbations.

Conclusions:

• The NP detection framework provides a quantitative score of pathway performance and failure.
• This approach transforms noisy single-cell data into actionable metrics for comparing conditions and perturbations.
• The framework can help explain how cellular decisions deviate towards pathology.