Deep learning of dynamic signatures resolves mechanistic ambiguity in complex signaling networks
TL;DR
An AI-driven framework treating model dynamics as learnable signatures is introduced, using deep learning autoencoders to embed the dynamic signatures of thousands of competing models into a low-dimensional latent space and provides a rigorous, full-state alternative to scalar metrics.
Abstract
Sparse experimental data often yields vast mechanistic hypothesis spaces with numerous equally probable models. Traditional model selection metrics like the Akaike Information Criterion fall short because they reduce models’ nonlinear dynamics to a scalar score that masks crucial mechanistic details. Here, we introduce an AI-driven framework treating model dynamics as learnable signatures. Using deep learning autoencoders, we embed the dynamic signatures of thousands of competing models into a low-dimensional latent space. Iterative clustering and physiological constraints systematically refine this space to a manageable number of testable hypotheses. Applying this to the Integrated Stress Response, a fundamental cellular homeostasis mechanism, we converged on 12 compelling mechanistic hypotheses from over 12,000 candidates. Notably, this framework confidently rejected structure-derived kinetic assumptions regarding higher-order PKR activation that traditional metrics could not resolve. Ultimately, this method provides a rigorous, full-state alternative to scalar metrics, accelerating the discovery of driving principles in complex biological systems.