Edge-based sensitivity analysis of signaling networks by using Boolean dynamics

Trinh, Hung-Cuong; Kwon, Yung-Keun

doi:10.1093/bioinformatics/btw464

Cited by 13 publications

(14 citation statements)

References 60 publications

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“…Whereas nFC measures the similarity of activity between two brain regions -a first-order correlation [49] -eFC measures the similarity of co-fluctuations between edge pairs -a higher-order correlation [36]. Understanding the higherorder organization of networks has proven useful in other disciplines [40,41,50,51]. Here, adopting an edge-centric perspective allows us to link higher-order brain network organization with subject specific features.…”

Section: Edge Functional Connectivity Enhances Subject Identifiabilitymentioning

confidence: 99%

Subject identification using edge-centric functional connectivity

Faskowitz

Esfahlani

et al. 2020

Preprint

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Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed "fingerprinting" analyses on functional connectivity to identify subjects' idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterized subjects' identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.

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Section: Edge Functional Connectivity Enhances Subject Identifiabilitymentioning

confidence: 99%

Subject identification using edge-centric functional connectivity

Faskowitz

Esfahlani

et al. 2020

Preprint

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“…The edgecentric representation shifts focus away from dyadic relationships between nodal activations and onto the interactions between edges (the similarity in their patterns of cofluctuation, a potential hallmark of communication), instead. While similar models have been explored in other scientific domains [19][20][21][22], they require as input sparse node-node connectivity matrices and are poorly suited for continuous-valued time series data, making them suboptimal representations of dynamic neural data.…”

Section: Edge-centric Perspective On Functional Network Organizationmentioning

confidence: 99%

“…A limitation of the node-centric approach is that it cannot capture potentially meaningful features or patterns of interrelationships among edges. In other scientific domains, prioritizing network edges, for example by modeling and analyzing edge-edge interactions as a graph, has provided important insights into the organization and function of complex systems [19][20][21][22]. In contrast, with few exceptions [23], network neuroscience has remained focused on nodal features and partitions, paralleling a * rbetzel@indiana.edu rich history of parceling, mapping, and comparing cortical and subcortical gray matter regions [24].…”

Section: Introductionmentioning

confidence: 99%

Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture

Faskowitz

Esfahlani

et al. 2019

Preprint

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Network neuroscience has relied on a node-centric network model in which cells, populations, and regions are linked to one another via anatomical or functional connections. This model cannot account for interactions of edges with one another. Here, we develop an edge-centric network model, which generates the novel constructs of "edge time series" and "edge functional connectivity" (eFC). Using network analysis, we show that at rest eFC is consistent across datasets and reproducible within the same individual over multiple scan sessions. We demonstrate that clustering eFC yields communities of edges that naturally divide the brain into overlapping clusters, with regions in sensorimotor and attentional networks exhibiting the greatest levels of overlap. We go on to show that eFC is systematically and consistently modulated by variation in sensory input. In future work, the edge-centric approach could be used to map the connectional architecture of brain circuits and for the development of brain-based biomarkers of disease and development.

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“…We need to extensively simulate randomly structure networks to verify that the new findings in real networks are generally conserved. In this study, we employed a shuffling model to generate random networks [ 10 , 18 ]. Given a reference network, it rewires some edges in a way that in-degree and out-degree of every node are conserved.…”

Section: Methodsmentioning

confidence: 99%

“…For example, a few studies showed that the modularity is greatly changed by the removal of hubs [ 9 ] or by stabilizing events in protein–protein interaction networks. Some other studies also proved that the robustness is considerably changeable according to a variety of mutations [ 10 – 13 ]. Additionally, there were some previous studies to investigate a relation between the robustness and the modularity.…”

Section: Introductionmentioning

confidence: 94%

Investigation on changes of modularity and robustness by edge-removal mutations in signaling networks

Truong

Kwon

2017

BMC Syst Biol

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BackgroundBiological networks consisting of molecular components and interactions are represented by a graph model. There have been some studies based on that model to analyze a relationship between structural characteristics and dynamical behaviors in signaling network. However, little attention has been paid to changes of modularity and robustness in mutant networks.ResultsIn this paper, we investigated the changes of modularity and robustness by edge-removal mutations in three signaling networks. We first observed that both the modularity and robustness increased on average in the mutant network by the edge-removal mutations. However, the modularity change was negatively correlated with the robustness change. This implies that it is unlikely that both the modularity and the robustness values simultaneously increase by the edge-removal mutations. Another interesting finding is that the modularity change was positively correlated with the degree, the number of feedback loops, and the edge betweenness of the removed edges whereas the robustness change was negatively correlated with them. We note that these results were consistently observed in randomly structure networks. Additionally, we identified two groups of genes which are incident to the highly-modularity-increasing and the highly-robustness-decreasing edges with respect to the edge-removal mutations, respectively, and observed that they are likely to be central by forming a connected component of a considerably large size. The gene-ontology enrichment of each of these gene groups was significantly different from the rest of genes. Finally, we showed that the highly-robustness-decreasing edges can be promising edgetic drug-targets, which validates the usefulness of our analysis.ConclusionsTaken together, the analysis of changes of robustness and modularity against edge-removal mutations can be useful to unravel novel dynamical characteristics underlying in signaling networks.Electronic supplementary materialThe online version of this article (10.1186/s12918-017-0505-2) contains supplementary material, which is available to authorized users.

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Edge-based sensitivity analysis of signaling networks by using Boolean dynamics

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 13 publications

References 60 publications

Subject identification using edge-centric functional connectivity

Subject identification using edge-centric functional connectivity

Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture

Investigation on changes of modularity and robustness by edge-removal mutations in signaling networks

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