Automated analysis of Physarum network structure and dynamics

Fricker, Mark D.; Akita, Dai; Heaton, Luke L.M.; Jones, Nick; Obara, Boguslaw; Nakagaki, Toshiyuki

Abstract

We evaluate different ridge-enhancement and segmentation methods to automatically extract the network architecture from time-series of Physarum plasmodia withdrawing from an arena via a single exit. Whilst all methods gave reasonable results, judged by precision-recall analysis against a ground-truth skeleton, the mean phase angle (Feature Type) from intensity-independent, phase-congruency edge enhancement and watershed segmentation was the most robust to variation in threshold parameters. The resultant single pixel-wide segmented skeleton was converted to a graph representation as a set of weighted adjacency matrices containing the physical dimensions of each vein, and the inter-vein regions. We encapsulate the complete image processing and network analysis pipeline in a downloadable software package, and provide an extensive set of metrics that characterise the network structure, including hierarchical loop decomposition to analyse the nested structure of the developing network. In addition, the change in volume for each vein and intervening plasmodial sheet was used to predict the net flow across the network. The scaling relationships between predicted current, speed and shear force with vein radius were consistent with predictions from Murray's law. This work was presented at PhysNet 2015.

Citation

Fricker, M. D., Akita, D., Heaton, L. L., Jones, N., Obara, B., & Nakagaki, T. (2017). Automated analysis of Physarum network structure and dynamics. Journal of Physics D: Applied Physics, 50(25), Article 254005. https://doi.org/10.1088/1361-6463/aa72b9

• Journal Article Type: Article
• Acceptance Date: May 12, 2017
• Online Publication Date: Jun 6, 2017
• Publication Date: Jun 6, 2017
• Deposit Date: Jun 19, 2017
• Publicly Available Date: Jun 6, 2018
• Journal: Journal of Physics D: Applied Physics
• Print ISSN: 0022-3727
• Electronic ISSN: 1361-6463
• Publisher: IOP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 50
• Issue: 25
• Article Number: 254005
• DOI: https://doi.org/10.1088/1361-6463/aa72b9
• Public URL: https://durham-repository.worktribe.com/output/1376624