Data-driven integration calls breast cancer subtype important; supply EGFR levels to the ODE instead and the subtype signal vanishes. Two explanations with same predictive power so the data cannot arbitrate between a molecular and a systems lens. DMMs surface the ambiguity and let us be the judge.

Per usual, mechanistic models stay informative where they fail: DMMs systematically underpredict phospho-ERK under MEK inhibition, which could reflect unmodelled crosstalk with AMPK signalling.

The unexpected result: across 63 mammary cell lines, most heterogeneity sits at the inputs and outputs rather than in the core machinery. Baseline ERBB2 activation and ERK-to-RSK gain emerge as the major axes, likely set by endocytic, cytoskeletal and calcium programmes.

DMMs couple semi-supervised representation learning to an ODE model of EGFR/MAPK signalling, end-to-end. The encoder proposes cell-line-specific parameters; the ODE model tests them against dynamic perturbation data. Representation and mechanism constrain each other rather than sitting side by side.

Not all those who wander are lost.