DeFiNe: an optimisation-based method for robust disentangling of filamentous networks

TL;DR

DeFiNe is an automated, optimization-based method for accurately disentangling filamentous structures in networks, applicable across biological and cosmic systems, enabling detailed analysis of individual filaments.

Contribution

The paper introduces DeFiNe, a fully automated and robust approach for filament detection in networks, overcoming parameter tuning issues of previous methods.

Findings

Accurately detects filaments with consistent intensities and angles.

Validated on biological and cosmic filamentous structures.

Provides an open-source tool for network filament decomposition.

Abstract

Thread-like structures are pervasive across scales, from polymeric proteins to root systems to galaxy filaments, and their characteristics can be readily investigated in the network formalism. Yet, network links usually represent only parts of filaments, which, when neglected, may lead to erroneous conclusions from network-based analyses. The existing alternatives to detect filaments in network representations require tuning of parameters over a large range of values and treat all filaments equally, thus, precluding automated analysis of diverse filamentous systems. Here, we propose a fully automated and robust optimisation-based approach to detect filaments of consistent intensities and angles in a given network. We test and demonstrate the accuracy of our solution with contrived, biological, and cosmic filamentous structures. In particular, we show that the proposed approach provides powerful automated means to study properties of individual actin filaments in their network context. Our solution is made publicly available as an open-source tool, DeFiNe, facilitating decomposition of any given network into individual filaments.