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1.
Background
Community structure is one of the key properties of complex networks and plays a crucial role in their topology and function. While an impressive amount of work has been done on the issue of community detection, very little attention has been so far devoted to the investigation of communities in real networks.Methodology/Principal Findings
We present a systematic empirical analysis of the statistical properties of communities in large information, communication, technological, biological, and social networks. We find that the mesoscopic organization of networks of the same category is remarkably similar. This is reflected in several characteristics of community structure, which can be used as “fingerprints” of specific network categories. While community size distributions are always broad, certain categories of networks consist mainly of tree-like communities, while others have denser modules. Average path lengths within communities initially grow logarithmically with community size, but the growth saturates or slows down for communities larger than a characteristic size. This behaviour is related to the presence of hubs within communities, whose roles differ across categories. Also the community embeddedness of nodes, measured in terms of the fraction of links within their communities, has a characteristic distribution for each category.Conclusions/Significance
Our findings, verified by the use of two fundamentally different community detection methods, allow for a classification of real networks and pave the way to a realistic modelling of networks'' evolution. 相似文献2.
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Background
Recent experimental studies have demonstrated the importance of invasion history for evolutionary formation of community. However, only few theoretical studies on community evolution have focused on such views.Methodology and Principal Findings
We used a tri-trophic food web model to analyze the coevolutionary effects of ecological invasions by a mutant and by a predator and/or resource species of a native consumer species community and found that ecological invasions can lead to various evolutionary histories. The invasion of a predator makes multiple evolutionary community histories possible, and the evolutionary history followed can determine both the invasion success of the predator into the native community and the fate of the community. A slight difference in the timing of an ecological invasion can lead to a greatly different fate. In addition, even greatly different community histories can converge as a result of environmental changes such as a predator trait shift or a productivity change. Furthermore, the changes to the evolutionary history may be irreversible.Conclusions and Significance
Our modeling results suggest that the timing of ecological invasion of a species into a focal community can largely change the evolutionary consequences of the community. Our approach based on adaptive dynamics will be a useful tool to understand the effect of invasion history on evolutionary formation of community. 相似文献4.
Background
The evolutionary origin of cooperation among unrelated individuals remains a key unsolved issue across several disciplines. Prominent among the several mechanisms proposed to explain how cooperation can emerge is the existence of a population structure that determines the interactions among individuals. Many models have explored analytically and by simulation the effects of such a structure, particularly in the framework of the Prisoner''s Dilemma, but the results of these models largely depend on details such as the type of spatial structure or the evolutionary dynamics. Therefore, experimental work suitably designed to address this question is needed to probe these issues.Methods and Findings
We have designed an experiment to test the emergence of cooperation when humans play Prisoner''s Dilemma on a network whose size is comparable to that of simulations. We find that the cooperation level declines to an asymptotic state with low but nonzero cooperation. Regarding players'' behavior, we observe that the population is heterogeneous, consisting of a high percentage of defectors, a smaller one of cooperators, and a large group that shares features of the conditional cooperators of public goods games. We propose an agent-based model based on the coexistence of these different strategies that is in good agreement with all the experimental observations.Conclusions
In our large experimental setup, cooperation was not promoted by the existence of a lattice beyond a residual level (around 20%) typical of public goods experiments. Our findings also indicate that both heterogeneity and a “moody” conditional cooperation strategy, in which the probability of cooperating also depends on the player''s previous action, are required to understand the outcome of the experiment. These results could impact the way game theory on graphs is used to model human interactions in structured groups. 相似文献5.
Background
Simple models inspired by processes shaping consumer-resource interactions have helped to establish the primary processes underlying the organization of food webs, networks of trophic interactions among species. Because other ecological interactions such as mutualisms between plants and their pollinators and seed dispersers are inherently based in consumer-resource relationships we hypothesize that processes shaping food webs should organize mutualistic relationships as well.Methodology/Principal Findings
We used a likelihood-based model selection approach to compare the performance of food web models and that of a model designed for mutualisms, in reproducing the structure of networks depicting mutualistic relationships. Our results show that these food web models are able to reproduce the structure of most of the mutualistic networks and even the simplest among the food web models, the cascade model, often reproduce overall structural properties of real mutualistic networks.Conclusions/Significance
Based on our results we hypothesize that processes leading to feeding hierarchy, which is a characteristic shared by all food web models, might be a fundamental aspect in the assembly of mutualisms. These findings suggest that similar underlying ecological processes might be important in organizing different types of interactions. 相似文献6.
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8.
Background
Compartmentalization and nestedness are common patterns in ecological networks. The aim of this study was to elucidate some of the processes shaping these patterns in a well resolved network of host/pathogen interactions.Methology/Principal Findings
Based on a long-term (1972–2005) survey of forest health at the regional scale (all French forests; 15 million ha), we uncovered an almost fully connected network of 51 tree taxa and 157 parasitic fungal species. Our analyses revealed that the compartmentalization of the network maps out the ancient evolutionary history of seed plants, but not the ancient evolutionary history of fungal species. The very early divergence of the major fungal phyla may account for this asymmetric influence of past evolutionary history. Unlike compartmentalization, nestedness did not reflect any consistent phylogenetic signal. Instead, it seemed to reflect the ecological features of the current species, such as the relative abundance of tree species and the life-history strategies of fungal pathogens. We discussed how the evolution of host range in fungal species may account for the observed nested patterns.Conclusion/Significance
Overall, our analyses emphasized how the current complexity of ecological networks results from the diversification of the species and their interactions over evolutionary times. They confirmed that the current architecture of ecological networks is not only dependant on recent ecological processes. 相似文献9.
Background
Protein kinases (PKs) have emerged as the largest family of signaling proteins in eukaryotic cells and are involved in every aspect of cellular regulation. Great progresses have been made in understanding the mechanisms of PKs phosphorylating their substrates, but the detailed mechanisms, by which PKs ensure their substrate specificity with their structurally conserved catalytic domains, still have not been adequately understood. Correlated mutation analysis based on large sets of diverse sequence data may provide new insights into this question.Methodology/Principal Findings
Statistical coupling, residue correlation and mutual information analyses along with clustering were applied to analyze the structure-based multiple sequence alignment of the catalytic domains of the Ser/Thr PK family. Two clusters of highly coupled sites were identified. Mapping these positions onto the 3D structure of PK catalytic domain showed that these two groups of positions form two physically close networks. We named these two networks as θ-shaped and γ-shaped networks, respectively.Conclusions/Significance
The θ-shaped network links the active site cleft and the substrate binding regions, and might participate in PKs recognizing and interacting with their substrates. The γ-shaped network is mainly situated in one side of substrate binding regions, linking the activation loop and the substrate binding regions. It might play a role in supporting the activation loop and substrate binding regions before catalysis, and participate in product releasing after phosphoryl transfer. Our results exhibit significant correlations with experimental observations, and can be used as a guide to further experimental and theoretical studies on the mechanisms of PKs interacting with their substrates. 相似文献10.
Background
Mate preference behavior is an essential first step in sexual selection and is a critical determinant in evolutionary biology. Previously an environmental compound (the fungicide vinclozolin) was found to promote the epigenetic transgenerational inheritance of an altered sperm epigenome and modified mate preference characteristics for three generations after exposure of a gestating female.Results
The current study investigated gene networks involved in various regions of the brain that correlated with the altered mate preference behavior in the male and female. Statistically significant correlations of gene clusters and modules were identified to associate with specific mate preference behaviors. This novel systems biology approach identified gene networks (bionetworks) involved in sex-specific mate preference behavior. Observations demonstrate the ability of environmental factors to promote the epigenetic transgenerational inheritance of this altered evolutionary biology determinant.Conclusions
Combined observations elucidate the potential molecular control of mate preference behavior and suggests environmental epigenetics can have a role in evolutionary biology.Electronic supplementary material
The online version of this article (doi:10.1186/1471-2164-15-377) contains supplementary material, which is available to authorized users. 相似文献11.
Introduction
Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods.Aim
To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R).Methods
434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis.Results
At facet level, NCS showed a best match (96.2%) with a ‘confirmatory’ 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with ‘confirmatory’ 5-FS and ‘exploratory’ 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks.Conclusion
We present the first optimized network graph of personality traits according to the NEO-PI-R: a ‘Personality Web’. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network. 相似文献12.
Background
Physiologically stressful environments tend to host depauperate and specialized biological communities. Serpentine soils exemplify this phenomenon by imposing well-known constraints on plants; however, their effect on other organisms is still poorly understood.Methodology/Principal Findings
We used a combination of field and molecular approaches to test the hypothesis that serpentine fungal communities are species-poor and specialized. We conducted surveys of ectomycorrhizal fungal diversity from adjacent serpentine and non-serpentine sites, described fungal communities using nrDNA Internal Transcribed Spacer (ITS) fragment and sequence analyses, and compared their phylogenetic community structure. Although we detected low fungal overlap across the two habitats, we found serpentine soils to support rich fungal communities that include representatives from all major fungal lineages. We failed to detect the phylogenetic signature of endemic clades that would result from specialization and adaptive radiation within this habitat.Conclusions/Significance
Our results indicate that serpentine soils do not constitute an extreme environment for ectomycorrhizal fungi, and raise important questions about the role of symbioses in edaphic tolerance and the maintenance of biodiversity. 相似文献13.
Background
Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system’s response after systematic perturbations are available.Results
We present a novel method to infer signaling networks from time-course perturbation data. We utilize dynamic Bayesian networks with probabilistic Boolean threshold functions to describe protein activation. The model posterior distribution is analyzed using evolutionary MCMC sampling and subsequent clustering, resulting in probability distributions over alternative networks. We evaluate our method on simulated data, and study its performance with respect to data set size and levels of noise. We then use our method to study EGF-mediated signaling in the ERBB pathway.Conclusions
Dynamic Probabilistic Threshold Networks is a new method to infer signaling networks from time-series perturbation data. It exploits the dynamic response of a system after external perturbation for network reconstruction. On simulated data, we show that the approach outperforms current state of the art methods. On the ERBB data, our approach recovers a significant fraction of the known interactions, and predicts novel mechanisms in the ERBB pathway.Electronic supplementary material
The online version of this article (doi:10.1186/1471-2105-15-250) contains supplementary material, which is available to authorized users. 相似文献14.
Background
Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network.Methodology/Principal Findings
Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm''s locomotion.Conclusions/Significance
The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex networks into its functional (rather than topological) motifs as long as both wiring and functional information is available. 相似文献15.
N. Legay C. Baxendale K. Grigulis U. Krainer E. Kastl M. Schloter R. D. Bardgett C. Arnoldi M. Bahn M. Dumont F. Poly T. Pommier J. C. Clément S. Lavorel 《Annals of botany》2014,114(5):1011-1021
Background and Aims
Abiotic properties of soil are known to be major drivers of the microbial community within it. Our understanding of how soil microbial properties are related to the functional structure and diversity of plant communities, however, is limited and largely restricted to above-ground plant traits, with the role of below-ground traits being poorly understood. This study investigated the relative contributions of soil abiotic properties and plant traits, both above-ground and below-ground, to variations in microbial processes involved in grassland nitrogen turnover.Methods
In mountain grasslands distributed across three European sites, a correlative approach was used to examine the role of a large range of plant functional traits and soil abiotic factors on microbial variables, including gene abundance of nitrifiers and denitrifiers and their potential activities.Key Results
Direct effects of soil abiotic parameters were found to have the most significant influence on the microbial groups investigated. Indirect pathways via plant functional traits contributed substantially to explaining the relative abundance of fungi and bacteria and gene abundances of the investigated microbial communities, while they explained little of the variance in microbial activities. Gene abundances of nitrifiers and denitrifiers were most strongly related to below-ground plant traits, suggesting that they were the most relevant traits for explaining variation in community structure and abundances of soil microbes involved in nitrification and denitrification.Conclusions
The results suggest that consideration of plant traits, and especially below-ground traits, increases our ability to describe variation in the abundances and the functional characteristics of microbial communities in grassland soils. 相似文献16.
S. Kathleen Bandt David T. Bundy Ammar H. Hawasli Kareem W. Ayoub Mohit Sharma Carl D. Hacker Mrinal Pahwa Eric C. Leuthardt 《PloS one》2014,9(9)
Objective
The role of resting state functional networks in epilepsy is incompletely understood. While some pathologic diagnoses have been shown to have maintained but altered resting state connectivity, others have implicated resting state connectivity in disease progression. However little is known about how these resting state networks influence the behavior of a focal neocortical seizure.Methods
Using data taken from invasively monitored patients with intractable focal neocortical epilepsy, we evaluated network connectivity (as determined by oscillatory covariance of the slow cortical potential (<0.5 Hz)) as it relates to neocortical seizure foci both in the interictal and ictal states.Results
Similar to what has been shown in the past for sleep and anesthesia, electophysiologic resting state networks that are defined by this slow cortical potential covariance maintain their topographic correlation structure throughout an ictal event. Moreover, in the context of focal epilepsy in which the seizure has a specific site of onset, seizure propagation is not chaotic or random. Rather, the seizure (reflected by an elevation of high frequency power) preferentially propagates along the network that contains the seizure onset zone.Significance
Taken together, these findings further undergird the fundamental role of resting state networks, provide novel insights into the network-influenced behavior of seizures, and potentially identify additional targets for surgical disconnection including informing the location for the completion of multiple subpial transections (MSPTs). 相似文献17.
Background
The von Hippel-Lindau (VHL) tumor suppressor gene encodes a component of a ubiquitin ligase complex, which is best understood as a negative regulator of hypoxia inducible factor (HIF). VHL ubiquitinates and degrades the α subunits of HIF, and this is proposed to suppress tumorigenesis and tumor angiogenesis. However, several lines of evidence suggest that there are unidentified substrates or targets for VHL that play important roles in tumor suppression.Methodology/Principal Findings
Employing quantitative proteomics, we developed an approach to systematically identify the substrates of ubiquitin ligases and using this method, we identified the Myb-binding protein p160 as a novel substrate of VHL.Conclusions/Significance
A major barrier to understanding the functions of ubiquitin ligases has been the difficulty in pinpointing their ubiquitination substrates. The quantitative proteomics approach we devised for the identification of VHL substrates will be widely applicable to other ubiquitin ligases. 相似文献18.
Background
Understanding protein complexes is important for understanding the science of cellular organization and function. Many computational methods have been developed to identify protein complexes from experimentally obtained protein-protein interaction (PPI) networks. However, interaction information obtained experimentally can be unreliable and incomplete. Reconstructing these PPI networks with PPI evidences from other sources can improve protein complex identification.Results
We combined PPI information from 6 different sources and obtained a reconstructed PPI network for yeast through machine learning. Some popular protein complex identification methods were then applied to detect yeast protein complexes using the new PPI networks. Our evaluation indicates that protein complex identification algorithms using the reconstructed PPI network significantly outperform ones on experimentally verified PPI networks.Conclusions
We conclude that incorporating PPI information from other sources can improve the effectiveness of protein complex identification. 相似文献19.
Background
Guide-trees are used as part of an essential heuristic to enable the calculation of multiple sequence alignments. They have been the focus of much method development but there has been little effort at determining systematically, which guide-trees, if any, give the best alignments. Some guide-tree construction schemes are based on pair-wise distances amongst unaligned sequences. Others try to emulate an underlying evolutionary tree and involve various iteration methods.Results
We explore all possible guide-trees for a set of protein alignments of up to eight sequences. We find that pairwise distance based default guide-trees sometimes outperform evolutionary guide-trees, as measured by structure derived reference alignments. However, default guide-trees fall way short of the optimum attainable scores. On average chained guide-trees perform better than balanced ones but are not better than default guide-trees for small alignments.Conclusions
Alignment methods that use Consistency or hidden Markov models to make alignments are less susceptible to sub-optimal guide-trees than simpler methods, that basically use conventional sequence alignment between profiles. The latter appear to be affected positively by evolutionary based guide-trees for difficult alignments and negatively for easy alignments. One phylogeny aware alignment program can strongly discriminate between good and bad guide-trees. The results for randomly chained guide-trees improve with the number of sequences.Electronic supplementary material
The online version of this article (doi:10.1186/1471-2105-15-338) contains supplementary material, which is available to authorized users. 相似文献20.
Diego P. V��zquez Nico Bl��thgen Luciano Cagnolo Natacha P. Chacoff 《Annals of botany》2009,103(9):1445-1457