Creative destruction: Sparse activity emerges on the mammal connectome under a simulated communication strategy with collisions and redundancy

ABSTRACTSignal interactions in brain network communication have been little studied. We describe how nonlinear collision rules on simulated mammal brain networks can result in sparse activity dynamics characteristic of mammalian neural systems. We tested the effects of collisions in “information spreading” (IS) routing models and in standard random walk (RW) routing models. Simulations employed synchronous agents on tracer-based mesoscale mammal connectomes at a range of signal loads. We find that RW models have high average activity that increases with load. Activity in RW models is also densely distributed over nodes: a substantial fraction is highly active in a given time window, and this fraction increases with load. Surprisingly, while IS models make many more attempts to pass signals, they show lower net activity due to collisions compared to RW, and activity in IS increases little as function of load. Activity in IS also shows greater sparseness than RW, and sparseness decreases slowly with load. Results hold on two networks of the monkey cortex and one of the mouse whole-brain. We also find evidence that activity is lower and more sparse for empirical networks compared to degree-matched randomized networks under IS, suggesting that brain network topology supports IS-like routing strategies.

Creative destruction: Sparse activity emerges on the mammal connectome under a simulated communication strategy with collisions and redundancy

Signal interactions in brain network communication have been little studied. We describe how nonlinear collision rules on simulated mammal brain networks can result in sparse activity dynamics characteristic of mammalian neural systems. We tested the effects of collisions in “information spreading” (IS) routing models and in standard random walk (RW) routing models. Simulations employed synchronous agents on tracer-based mesoscale mammal connectomes at a range of signal loads. We find that RW models have high average activity that increases with load. Activity in RW models is also densely distributed over nodes: a substantial fraction is highly active in a given time window, and this fraction increases with load. Surprisingly, while IS models make many more attempts to pass signals, they show lower net activity due to collisions compared to RW, and activity in IS increases little as function of load. Activity in IS also shows greater sparseness than RW, and sparseness decreases slowly with load. Results hold on two networks of the monkey cortex and one of the mouse whole-brain. We also find evidence that activity is lower and more sparse for empirical networks compared to degree-matched randomized networks under IS, suggesting that brain network topology supports IS-like routing strategies.

Uncovering the Dependence of Cascading Failures on Network Topology by Constructing Null Models

Cascading failures are the significant cause of network breakdowns in a variety of complex infrastructure systems. Given such a system, uncovering the dependence of cascading failures on its underlying topology is essential but still not well explored in the field of complex networks. This study offers an original approach to systematically investigate the association between cascading failures and topological variation occurring in realistic complex networks by constructing different types of null models. As an example of its application, we study several standard Internet networks in detail. The null models first transform the original network into a series of randomized networks representing alternate realistic topologies, while taking its basic topological characteristics into account. Then considering the routing rule of shortest-path flow, it is sought to determine the implications of different topological circumstances, and the findings reveal the effects of micro-scale (such as degree distribution, assortativity, and transitivity) and meso-scale (such as rich-club and community structure) features on the cascade damage caused by deliberate node attacks. Our results demonstrate that the proposed method is suitable and promising to comprehensively analyze realistic influence of various topological properties, providing insight into designing the networks to make them more robust against cascading failures.

Revealing hidden insect–fungus interactions; moderately specialized, modular and anti-nested detritivore networks

Ecological networks are composed of interacting communities that influence ecosystem structure and function. Fungi are the driving force for ecosystem processes such as decomposition and carbon sequestration in terrestrial habitats, and are strongly influenced by interactions with invertebrates. Yet, interactions in detritivore communities have rarely been considered from a network perspective. In the present study, we analyse the interaction networks between three functional guilds of fungi and insects sampled from dead wood. Using DNA metabarcoding to identify fungi, we reveal a diversity of interactions differing in specificity in the detritivore networks, involving three guilds of fungi. Plant pathogenic fungi were relatively unspecialized in their interactions with insects inhabiting dead wood, while interactions between the insects and wood-decay fungi exhibited the highest degree of specialization, which was similar to estimates for animal-mediated seed dispersal networks in previous studies. The low degree of specialization for insect symbiont fungi was unexpected. In general, the pooled insect–fungus networks were significantly more specialized, more modular and less nested than randomized networks. Thus, the detritivore networks had an unusual anti-nested structure. Future studies might corroborate whether this is a common aspect of networks based on interactions with fungi, possibly owing to their often intense competition for substrate.

Randomization Strategies Affect Motif Significance Analysis in TF-miRNA-Gene Regulatory Networks

Gene-regulatory networks are an abstract way of capturing the regulatory connectivity between transcription factors, microRNAs, and target genes in biological cells. Here, we address the problem of identifying enriched co-regulatory three-node motifs that are found significantly more often in real network than in randomized networks. First, we compare two randomization strategies, that either only conserve the degree distribution of the nodes’ in- and out-links, or that also conserve the degree distributions of different regulatory edge types. Then, we address the issue how convergence of randomization can be measured. We show that after at most 10 × |E| edge swappings, converged motif counts are obtained and the memory of initial edge identities is lost.

Creating, generating and comparing random network models with Network Randomizer

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. We have developed an app for the Cytoscape platform which allows the creation of randomized networks and the randomization of existing, real networks. Since there is a lack of tools for generating and randomizing networks, our app helps researchers to exploit different, well known random network models which could be used as a benchmark for validating real datasets. We also propose a novel methodology for creating random weighted networks starting from experimental data. Finally the app provides a statistical tool which compares real versus random attributes, in order to validate all the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

Degree-Based Sampling Method with Partition-Based Subgraph Finder for Larger Motif Detection

Network motifs are subnetworks that appear in the network far more frequently than in randomized networks. They have gathered much attention for uncovering structural design principles of complex networks. One of the previous approaches for motif detection is sampling method, in- troduced to perform the computational challenging task. However, it suffers from sampling bias and probability assignment. In addition, subgraph search, being very time-consuming, is a critical process in motif detection as we need to enumerate subgraphs of given sizes in the original input graph and an ensemble of random generated graphs. Therefore, we present a Degree-based Sampling Method with Partition-based Subgraph Finder for larger motif detection. Inspired by the intrinsic feature of real biological networks, Degree-based Sampling is a new solution for probability assignment based on degree. And, Partition-based Subgraph Finder takes its inspiration from the idea of partition, which improves computational efficiency and lowers space consumption. Experimental study on UETZ and E.COLI data set shows that the proposed method achieves more accuracy and efficiency than previous methods and scales better with increasing subgraph size.

COMPACTNESS AND CYCLES IN SIGNAL TRANSDUCTION AND TRANSCRIPTIONAL REGULATION NETWORKS: A SIGNATURE OF NATURAL SELECTION?

We ask whether natural selection has shaped three biologically important features of 15 signal transduction networks and two genome-scale transcriptional regulation networks. These features are regulatory cycles, the lengths of the longest pathways through a network — a measure of network compactness — and the abundance of node pairs connected by many alternative regulatory pathways. We determine whether these features are significantly more or less abundant in biological networks than in randomized networks with the same distribution of incoming and outgoing connections per network node. We find that autoregulatory cycles are of exceptionally high abundance in transcriptional regulation networks. All other cycles, however, are significantly less abundant in several signal transduction networks. This suggests that the multistability caused by complex feedback loops in a network may interfere with the functioning of such networks. We also find that several of the networks we examine are more compact than expected by chance alone. This raises the possibility that the transmission of information through such networks, which is fastest in compact networks, is a biologically important characteristic of such networks.