Mesoscale analyses of fungal networks as an approach for quantifying phenotypic traits

TL;DR

This study applies mesoscopic response functions to analyze fungal and slime mould networks, creating taxonomies that distinguish phenotypic traits and behaviors across species and treatments, aiding biological understanding.

Contribution

It introduces the use of mesoscopic response functions for characterizing and clustering fungal networks based on structural and functional properties, providing a new quantitative tool for phenotypic analysis.

Findings

Functional network clustering shows lower intra-group variation.

Both network types produce sensible taxonomies across species and treatments.

MRFs effectively summarize complex biological network behaviors.

Abstract

We investigate the application of mesoscopic response functions (MRFs) to characterize a large set of networks of fungi and slime moulds grown under a wide variety of different experimental treatments, including inter-species competition and attack by fungivores. We construct 'structural networks' by estimating cord conductances (which yield edge weights) from the experimental data, and we construct 'functional networks' by calculating edge weights based on how much nutrient traffic is predicted to occur along each edge. Both types of networks have the same topology, and we compute MRFs for both families of networks to illustrate two different ways of constructing taxonomies to group the networks into clusters of related fungi and slime moulds. Although both network taxonomies generate intuitively sensible groupings of networks across species, treatments and laboratories, we find that clustering using the functional-network measure appears to give groups with lower intra-group variation in species or treatments. We argue that MRFs provide a useful quantitative analysis of network behaviour that can (1) help summarize an expanding set of increasingly complex biological networks and (2) help extract information that captures subtle changes in intra- and inter-specific phenotypic traits that are integral to a mechanistic understanding of fungal behaviour and ecology. As an accompaniment to our paper, we also make a large data set of fungal networks available in the public domain.