Physics-Informed Deep Learning Characterizes Morphodynamics of Asian Soybean Rust Disease

TL;DR

This paper introduces a physics-informed deep learning framework to analyze and interpret the shape dynamics of Asian soybean rust disease development directly from images, combining biophysical models with unsupervised learning.

Contribution

It presents a novel approach that integrates deep learning with biophysical modeling to characterize morphodynamics in plant pathogen development from image data.

Findings

Identified diverse developmental landscapes of soybean rust.

Detected perturbations in tip growth machinery affecting morphology.

Demonstrated the framework's applicability to biological shape analysis.

Abstract

Medicines and agricultural biocides are often discovered using large phenotypic screens across hundreds of compounds, where visible effects of whole organisms are compared to gauge efficacy and possible modes of action. However, such analysis is often limited to human-defined and static features. Here, we introduce a novel framework that can characterize shape changes (morphodynamics) for cell-drug interactions directly from images, and use it to interpret perturbed development of Phakopsora pachyrhizi, the Asian soybean rust crop pathogen. We describe population development over a 2D space of shapes (morphospace) using two models with condition-dependent parameters: a top-down Fokker-Planck model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. We discover a variety of landscapes, describing phenotype transitions during growth, and identify possible perturbations in the tip growth machinery that cause this variation. This demonstrates a widely-applicable integration of unsupervised learning and biophysical modeling.