Morphological parameters can capture emergent properties of active disordered cytoskeletal networks

The actin cytoskeleton is an inherently disordered active system. The actomyosin cortex and reconstituted actomyosin gels remain globally disordered, yet undergo transitions between distinct disordered states as parameters like motor and crosslinker concentration and filament length change. In real biological systems, these changes are related to genetic mutations or differences in cell state and dictate fundamental biological processes. However, we don’t have well-established methods to detect and classify differences in disordered dynamic biopolymer networks. Image-based morphology techniques provide a non-invasive, high-throughput method of extracting information about a system. In this work we simulate biopolymer networks under varying conditions and develop and use morphological descriptors to construct trajectories in morphospace. Using statistical and machine learning analysis we find that morphological descriptors are able to distinguish between different trajectories of the system, including differences not apparent to the eye. However, no single descriptor is able to capture differences in the simulated trajectories. Nematic order parameters typically perform the worst and texture descriptors (taken together) typically perform the best. This work paves the way for the development of data-driven modelling in morphospace for predicting the temporal trajectories of disordered polymer networks.