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Fabian Fröhlich
@frohlichlab.com

Mechanistic ODE models encode pathway topology but are blind to anything outside the pathway. Representation learning compresses data into low-dimensional embeddings but says nothing about mechanism. Deep Mechanistic Models (DMMs) are our attempt at both in one model www.biorxiv.org/content/10.6...

biorxiv.orgDeep Mechanistic Models reveal pathway-extrinsic drivers of mammary MAPK signalling heterogeneityCells sense and respond to their environment through signalling pathways, and the dynamics of these pathways shape cell fate even within genetically identical populations. Two largely separate computa...

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Fabian Fröhlich
@frohlichlab.com

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.

Fabian Fröhlich
@frohlichlab.com

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.

Fabian Fröhlich
@frohlichlab.com

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.

Fabian Fröhlich
@frohlichlab.com

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.