Fig. 1: Morphodynamics of the Asian soybean rust pathogen, P. pachyrhizi, are characterized through condition-dependent dynamics over a global morphospace.
From: Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease
a P. pachyrhizi burrows into soybean leaves to extract nutrients, as sketched (top). Image sets at nine time points under six conditions (bottom) are processed to yield aligned, single-fungus images. b An autoencoder learns the biophysical degrees of freedom from the images, discovering a 2D morphospace. c Dynamics are characterized using two models: a top-down landscape (U(x)) model, where a physics-informed neural network fits the Fokker–Planck equation to the morphospace embeddings, and a bottom-up persistent random walk model of the growth zone, with parameters fitted using approximate Bayesian computation with a morphospace-derived similarity metric. These yield d interpretable, condition-dependent characterizations in the form of Waddington-type landscapes and tip growth parameter posteriors.
