Abstract:We introduce the Slime Mold Graph Repository, a novel data collection promoting the visibility, accessibility and reuse of experimental data revolving around network-forming slime molds. By making data instantly available for researchers across multiple disciplines, the SMGR promotes novel research as well as the reproduction of original results. While SMGR data may take various forms, we stress the importance of graph representations of slime mold networks due to their ease of handling and their large potenti… Show more
“…7 ), which shows that slime molds in neutral environments build longer networks with thin veins, as they are more ’spread-out’, while nutritive and adverse substrates promote compact, denser networks. Hence, our findings confirm previous observations 22 , 26 that network characteristics depend on the environmental conditions and our results are in agreement with previous observations on vein width 18 , 21 .…”
Section: Discussionsupporting
confidence: 94%
“…Physarum polycephalum cells have been modeled as undirected graphs by a number of authors, which even resulted in the creation of a public repository of slime mold extracted graphs 18 , and different codes for network extraction from raw images 19 , 20 , which describe different segmentation networks based on the characteristics of the acquired images. Different studies on the network dynamics of slime molds suggest that the networks are hierarchical, with the distribution of veins widths and lengths following exponential, gamma or log-normal distributions, with the majority of the veins being short and thin, with a few long and thick veins 21 – 23 .…”
The acellular slime mold Physarum polycephalum provides an excellent model to study network formation, as its network is remodelled constantly in response to mass gain/loss and environmental conditions. How slime molds networks are built and fuse to allow for efficient exploration and adaptation to environmental conditions is still not fully understood. Here, we characterize the network organization of slime molds exploring homogeneous neutral, nutritive and adverse environments. We developed a fully automated image analysis method to extract the network topology and followed the slime molds before and after fusion. Our results show that: (1) slime molds build sparse networks with thin veins in a neutral environment and more compact networks with thicker veins in a nutritive or adverse environment; (2) slime molds construct long, efficient and resilient networks in neutral and adverse environments, whereas in nutritive environments, they build shorter and more centralized networks; and (3) slime molds fuse rapidly and establish multiple connections with their clone-mates in a neutral environment, whereas they display a late fusion with fewer connections in an adverse environment. Our study demonstrates that slime mold networks evolve continuously via pruning and reinforcement, adapting to different environmental conditions.
“…7 ), which shows that slime molds in neutral environments build longer networks with thin veins, as they are more ’spread-out’, while nutritive and adverse substrates promote compact, denser networks. Hence, our findings confirm previous observations 22 , 26 that network characteristics depend on the environmental conditions and our results are in agreement with previous observations on vein width 18 , 21 .…”
Section: Discussionsupporting
confidence: 94%
“…Physarum polycephalum cells have been modeled as undirected graphs by a number of authors, which even resulted in the creation of a public repository of slime mold extracted graphs 18 , and different codes for network extraction from raw images 19 , 20 , which describe different segmentation networks based on the characteristics of the acquired images. Different studies on the network dynamics of slime molds suggest that the networks are hierarchical, with the distribution of veins widths and lengths following exponential, gamma or log-normal distributions, with the majority of the veins being short and thin, with a few long and thick veins 21 – 23 .…”
The acellular slime mold Physarum polycephalum provides an excellent model to study network formation, as its network is remodelled constantly in response to mass gain/loss and environmental conditions. How slime molds networks are built and fuse to allow for efficient exploration and adaptation to environmental conditions is still not fully understood. Here, we characterize the network organization of slime molds exploring homogeneous neutral, nutritive and adverse environments. We developed a fully automated image analysis method to extract the network topology and followed the slime molds before and after fusion. Our results show that: (1) slime molds build sparse networks with thin veins in a neutral environment and more compact networks with thicker veins in a nutritive or adverse environment; (2) slime molds construct long, efficient and resilient networks in neutral and adverse environments, whereas in nutritive environments, they build shorter and more centralized networks; and (3) slime molds fuse rapidly and establish multiple connections with their clone-mates in a neutral environment, whereas they display a late fusion with fewer connections in an adverse environment. Our study demonstrates that slime mold networks evolve continuously via pruning and reinforcement, adapting to different environmental conditions.
“…We run our model on three datasets of images covering various types of network-like topologies observed in biology and ecology. The images represent: (i) the slime mould Physarum polycephalum ( Physarum polycephalum ) [ 30 ], which is also the inspiration of our dynamics; (ii) the retinal vascular system ( retina ) [ 31 ]; (iii) river networks ( rivers ) obtained by extracting images from [ 32 ]. The number of images taken from the Physarum polycephalum , retina and rivers sources is 25, 20 and 10, respectively, see table 1 .…”
Images of natural systems may represent patterns of network-like structure, which could reveal important information about the topological properties of the underlying subject. However, the image itself does not automatically provide a formal definition of a network in terms of sets of nodes and edges. Instead, this information should be suitably extracted from the raw image data. Motivated by this, we present a principled model to extract network topologies from images that is scalable and efficient. We map this goal into solving a routing optimization problem where the solution is a network that minimizes an energy function which can be interpreted in terms of an operational and infrastructural cost. Our method relies on recent results from optimal transport theory and is a principled alternative to standard image-processing techniques that are based on heuristics. We test our model on real images of the retinal vascular system, slime mould and river networks and compare with routines combining image-processing techniques. Results are tested in terms of a similarity measure related to the amount of information preserved in the extraction. We find that our model finds networks from retina vascular network images that are more similar to hand-labelled ones, while also giving high performance in extracting networks from images of rivers and slime mould for which there is no ground truth available. While there is no unique method that fits all the images the best, our approach performs consistently across datasets, its algorithmic implementation is efficient and can be fully automatized to be run on several datasets with little supervision.
“…To conclude, we demonstrate the flexibility of our graph extraction method on a more general input than the one extracted from DMK-Solver . Specifically, we consider as example an image of P. Polycephalum taken from data publicly available in the Slime Mold Graph Repository (SMGR) repository 58 . We first downsample an image of the SMGR’s KIST Europe data set , using OpenCV (left) and a color scale defined on the pixels as an artificial function.…”
Section: Application: Network Analysis Of a Vein Networkmentioning
confidence: 99%
“… Figure 7 Application to images. We take an image of the P. Polycephalum from the SMGR repository 58 .The picture used is a 1200x1200-pixel section of an original image of size 5184x3456 pixels (see Supplementary S4 for details) and extract a network with step 2 and 3 of our protocol. As a pre-processing step, we downsample the image using OpenCV (left) and use the color scale defined on the pixels as an artificial function.…”
Section: Application: Network Analysis Of a Vein Networkmentioning
Routing optimization is a relevant problem in many contexts. Solving directly this type of optimization problem is often computationally intractable. Recent studies suggest that one can instead turn this problem into one of solving a dynamical system of equations, which can instead be solved efficiently using numerical methods. This results in enabling the acquisition of optimal network topologies from a variety of routing problems. However, the actual extraction of the solution in terms of a final network topology relies on numerical details which can prevent an accurate investigation of their topological properties. In fact, in this context, theoretical results are fully accessible only to an expert audience and ready-to-use implementations for non-experts are rarely available or insufficiently documented. In particular, in this framework, final graph acquisition is a challenging problem in-and-of-itself. Here we introduce a method to extract network topologies from dynamical equations related to routing optimization under various parameters’ settings. Our method is made of three steps: first, it extracts an optimal trajectory by solving a dynamical system, then it pre-extracts a network, and finally, it filters out potential redundancies. Remarkably, we propose a principled model to address the filtering in the last step, and give a quantitative interpretation in terms of a transport-related cost function. This principled filtering can be applied to more general problems such as network extraction from images, thus going beyond the scenarios envisioned in the first step. Overall, this novel algorithm allows practitioners to easily extract optimal network topologies by combining basic tools from numerical methods, optimization and network theory. Thus, we provide an alternative to manual graph extraction which allows a grounded extraction from a large variety of optimal topologies. The analysis of these may open up the possibility to gain new insights into the structure and function of optimal networks. We provide an open source implementation of the code online.
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