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

ABSTRACT: Cells make hard calls under noise. When signaling is abnormal, those calls can go wrong and drive pathological conditions and diseases. In this research, we develop a Neyman-Pearson (NP) detection-theory framework that maximizes probability of detection (P _D) for a chosen false alarm probability (P _FA), without requiring prior probabilities, using experimental single-cell measurements of NF-κB responses to tumor necrosis factor (TNF), a critical pathway involved in cell survival, apoptosis, immune signaling, and stress response, in wild-type and A20-deficient fibroblasts. We model log-responses as (multi)variate Gaussian and compute optimal thresholds, P _D-P _FA trade-offs, and ROC curves at 30 minutes and 4 hours. The

SUBMITTER: Emadi A 

PROVIDER: S-EPMC12776388 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature