scholarly journals Structural redundancy in supracellular actomyosin networks enables robust tissue folding

2019 ◽  
Author(s):  
Hannah G. Yevick ◽  
Pearson W. Miller ◽  
Jörn Dunkel ◽  
Adam C. Martin
Keyword(s):  
Large Scale ◽  
Transportation Networks ◽  
Network Connectivity ◽  
Ventral Side ◽  
Local Network ◽  
Scale Function ◽  
Structural Redundancy ◽  
Cytoskeletal Network ◽  
Network Properties ◽  
Proper Orientation

SummaryTissue morphogenesis is strikingly reproducible. Yet, how tissues are robustly sculpted, even under challenging conditions, is unknown. Here, we combined network analysis, experimental perturbations, and computational modeling to determine how network connectivity between hundreds of contractile cells on the ventral side of the Drosophila embryo ensures robust tissue folding. We identified two network properties that mechanically promote robustness. First, redundant supracellular cytoskeletal network paths ensure global connectivity, even with network degradation. By forming many more connections than are required, morphogenesis is not disrupted by local network damage, analogous to the way redundancy guarantees the large-scale function of vasculature and transportation networks. Second, directional stiffening of edges oriented orthogonal to the folding axis promotes furrow formation at lower contractility levels. Structural redundancy and directional network stiffening ensure robust tissue folding with proper orientation.

SIMULATION OF LARGE SCALE CORTICAL NETWORKS BY INDIVIDUAL NEURON DYNAMICS

2010 ◽  
Vol 20 (03) ◽  
pp. 859-867 ◽  
Author(s):  
G. SCHMIDT ◽  
G. ZAMORA-LÓPEZ ◽  
J. KURTHS
Keyword(s):  
Multilevel Model ◽  
Large Scale ◽  
Dynamical Behavior ◽  
Local Network ◽  
Mammalian Brain ◽  
Systematic Analysis ◽  
Functional Dynamics ◽  
Simple Simulation ◽  
Network Properties ◽  
Neuron Dynamics

Understanding the functional dynamics of the mammalian brain is one of the central aims of modern neuroscience. Mathematical modeling and computational simulations of neural networks can help in this quest. In recent publications, a multilevel model has been presented to simulate the resting-state dynamics of the cortico-cortical connectivity of the mammalian brain. In the present work we investigate how much of the dynamical behavior of the multilevel model can be reproduced by a strongly simplified model. We find that replacing each cortical area by a single Rulkov map recreates the patterns of dynamical correlation of the multilevel model, while the outcome of other models and setups mainly depends on the local network properties, e.g. the input degree of each vertex. In general, we find that a simple simulation whose dynamics depends on the global topology of the whole network is far from trivial. A systematic analysis of different dynamical models and coupling setups is required.

scholarly journals Identifying Node Importance in a Complex Network Based on Node Bridging Feature

2018 ◽  
Vol 8 (10) ◽  
pp. 1914 ◽  
Author(s):  
Lincheng Jiang ◽  
Yumei Jing ◽  
Shengze Hu ◽  
Bin Ge ◽  
Weidong Xiao
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Shortest Paths ◽  
Evaluation Criteria ◽  
Local Network ◽  
Network Efficiency ◽  
Network Attack ◽  
Node Importance ◽  
Pass Through ◽  
Large Scale Networks

Identifying node importance in complex networks is of great significance to improve the network damage resistance and robustness. In the era of big data, the size of the network is huge and the network structure tends to change dynamically over time. Due to the high complexity, the algorithm based on the global information of the network is not suitable for the analysis of large-scale networks. Taking into account the bridging feature of nodes in the local network, this paper proposes a simple and efficient ranking algorithm to identify node importance in complex networks. In the algorithm, if there are more numbers of node pairs whose shortest paths pass through the target node and there are less numbers of shortest paths in its neighborhood, the bridging function of the node between its neighborhood nodes is more obvious, and its ranking score is also higher. The algorithm takes only local information of the target nodes, thereby greatly improving the efficiency of the algorithm. Experiments performed on real and synthetic networks show that the proposed algorithm is more effective than benchmark algorithms on the evaluation criteria of the maximum connectivity coefficient and the decline rate of network efficiency, no matter in the static or dynamic attack manner. Especially in the initial stage of attack, the advantage is more obvious, which makes the proposed algorithm applicable in the background of limited network attack cost.

scholarly journals Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Pengshuo Yang ◽  
Chongyang Tan ◽  
Maozhen Han ◽  
Lin Cheng ◽  
Xuefeng Cui ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Large Scale ◽  
Network Connectivity ◽  
Small Range ◽  
Environmental Disturbance ◽  
Target Species ◽  
Important Species ◽  
Metagenome Analysis ◽  
Occurrence Patterns ◽  
Edge Networks

Abstract Mainstream studies of microbial community focused on critical organisms and their physiology. Recent advances in large-scale metagenome analysis projects initiated new researches in the complex correlations between large microbial communities. Specifically, previous studies focused on the nodes (i.e. species) of the Species-Centric Networks (SCNs). However, little was understood about the change of correlation between network members (i.e. edges of the SCNs) when the network was disturbed. Here, we introduced a Correlation-Centric Network (CCN) to the microbial research based on the concept of edge networks. In CCN, each node represented a species–species correlation, and edge represented the species shared by two correlations. In this research, we investigated the CCNs and their corresponding SCNs on two large cohorts of microbiome. The results showed that CCNs not only retained the characteristics of SCNs, but also contained information that cannot be detected by SCNs. In addition, when the members of microbial communities were decreased (i.e. environmental disturbance), the CCNs fluctuated within a small range in terms of network connectivity. Therefore, by highlighting the important species correlations, CCNs could unveil new insights when studying not only the functions of target species, but also the stabilities of their residing microbial communities.

scholarly journals Emergent properties in microbiome networks reveal the anthropogenic disturbance of farming practices in vineyard soil fungal communities

2020 ◽  
Author(s):  
Rüdiger Ortiz-Álvarez ◽  
Hector Ortega-Arranz ◽  
Vicente J. Ontiveros ◽  
Charles Ravarani ◽  
Alberto Acedo ◽  
...  
Keyword(s):  
Small World ◽  
Fungal Communities ◽  
Local Network ◽  
Emergent Properties ◽  
Farming Practices ◽  
Natural Systems ◽  
Vineyard Soil ◽  
Global Agriculture ◽  
Network Properties ◽  
The Impact

AbstractAgro-ecosystems are human-managed natural systems, and therefore are subject to generalized ecological rules. A deeper understanding of the factors impacting on the biotic component of ecosystem stability is needed for promoting the sustainability and productivity of global agriculture. Here we propose a method to determine ecological emergent properties through the inference of network properties in local microbial communities, and to use them as biomarkers of the anthropogenic impact of different farming practices on vineyard soil ecosystem functioning. In a dataset of 350 vineyard soil samples from USA and Spain we observed that fungal communities ranged from random to small-world network arrangements with differential levels of niche specialization. Some of the network properties studied were strongly correlated, defining patterns of ecological emergent properties that are influenced by the intensification level of the crop management. Low-intervention practices (from organic to biodynamic approaches) promoted densely clustered networks, describing an equilibrium state based on mixed (generalist-collaborative) communities. Contrary, in conventionally managed vineyards, we observed highly modular (niche-specialized) low clustered communities, supported by a higher degree of selection (more co-exclusion proportion). We also found that, although geographic factors can explain the different fungal community arrangements in both countries, the relationship between network properties in local fungal communities better capture the impact of farming practices regardless of the location. Thus, we hypothesize that local network properties can be globally used to evaluate the effect of ecosystem disturbances in crops, but also in when evaluating the effect of clinical interventions or to compare microbiomes of healthy vs. disturbed conditions.

scholarly journals Next Generation Middleware Technology for Mobile Computing

2011 ◽  
pp. 74-80
Author(s):  
B. Darsana ◽  
Karabi Konar
Keyword(s):  
Mobile Computing ◽  
Large Scale ◽  
Network Connectivity ◽  
Distributed Applications ◽  
Mobile Systems ◽  
Portable Devices ◽  
Next Generation ◽  
Mobile Middleware ◽  
Mobile Computing Environment ◽  
Middleware Technology

Current advances in portable devices, wireless technologies, and distributed systems have created a mobile computing environment that is characterized by a large scale of dynamism. Diversities in network connectivity, platform capability, and resource availability can significantly affect the application performance. Traditional middleware systems are not prepared to offer proper support for addressing the dynamic aspects of mobile systems. Modern distributed applications need a middleware that is capable of adapting to environment changes and that supports the required level of quality of service. This paper represents the experience of several research projects related to next generation middleware systems. We first indicate the major challenges in mobile computing systems and try to identify the main requirements for mobile middleware systems. The different categories of mobile middleware technologies are reviewed and their strength and weakness are analyzed.

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