Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures

Neural development features: Spatio-temporal development of the Caenorhabditis elegans neuronal network

The nematode Caenorhabditis elegans, with information on neural connectivity, three-dimensional position and cell linage provides a unique system for understanding the development of neural networks. Although C. elegans has been widely studied in the past, we present the first statistical study from a developmental perspective, with findings that raise interesting suggestions on the establishment of long-distance connections and network hubs. Here, we analyze the neuro-development for temporal and spatial features, using birth times of neurons and their three-dimensional positions. Comparisons of growth in C. elegans with random spatial network growth highlight two findings relevant to neural network development. First, most neurons which are linked by long-distance connections are born around the same time and early on, suggesting the possibility of early contact or interaction between connected neurons during development. Second, early-born neurons are more highly connected (tendency to form hubs) than later born neurons. This indicates that the longer time frame available to them might underlie high connectivity. Both outcomes are not observed for random connection formation. The study finds that around one-third of electrically coupled long-range connections are late forming, raising the question of what mechanisms are involved in ensuring their accuracy, particularly in light of the extremely invariant connectivity observed in C. elegans. In conclusion, the sequence of neural network development highlights the possibility of early contact or interaction in securing long-distance and high-degree connectivity

Information inequalities and Generalized Graph Entropies

In this article, we discuss the problem of establishing relations between information measures assessed for network structures. Two types of entropy based measures namely, the Shannon entropy and its generalization, the R\'{e}nyi entropy have been considered for this study. Our main results involve establishing formal relationship, in the form of implicit inequalities, between these two kinds of measures when defined for graphs. Further, we also state and prove inequalities connecting the classical partition-based graph entropies and the functional-based entropy measures. In addition, several explicit inequalities are derived for special classes of graphs.Comment: A preliminary version. To be submitted to a journa

Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitud

Networks of Emotion Concepts

The aim of this work was to study the similarity network and hierarchical clustering of Finnish emotion concepts. Native speakers of Finnish evaluated similarity between the 50 most frequently used Finnish words describing emotional experiences. We hypothesized that methods developed within network theory, such as identifying clusters and specific local network structures, can reveal structures that would be difficult to discover using traditional methods such as multidimensional scaling (MDS) and ordinary cluster analysis. The concepts divided into three main clusters, which can be described as negative, positive, and surprise. Negative and positive clusters divided further into meaningful sub-clusters, corresponding to those found in previous studies. Importantly, this method allowed the same concept to be a member in more than one cluster. Our results suggest that studying particular network structures that do not fit into a low-dimensional description can shed additional light on why subjects evaluate certain concepts as similar. To encourage the use of network methods in analyzing similarity data, we provide the analysis software for free use (http://www.becs.tkk.fi/similaritynets/)

A Network of Conserved Damage Survival Pathways Revealed by a Genomic RNAi Screen

Damage initiates a pleiotropic cellular response aimed at cellular survival when appropriate. To identify genes required for damage survival, we used a cell-based RNAi screen against the Drosophila genome and the alkylating agent methyl methanesulphonate (MMS). Similar studies performed in other model organisms report that damage response may involve pleiotropic cellular processes other than the central DNA repair components, yet an intuitive systems level view of the cellular components required for damage survival, their interrelationship, and contextual importance has been lacking. Further, by comparing data from different model organisms, identification of conserved and presumably core survival components should be forthcoming. We identified 307 genes, representing 13 signaling, metabolic, or enzymatic pathways, affecting cellular survival of MMS–induced damage. As expected, the majority of these pathways are involved in DNA repair; however, several pathways with more diverse biological functions were also identified, including the TOR pathway, transcription, translation, proteasome, glutathione synthesis, ATP synthesis, and Notch signaling, and these were equally important in damage survival. Comparison with genomic screen data from Saccharomyces cerevisiae revealed no overlap enrichment of individual genes between the species, but a conservation of the pathways. To demonstrate the functional conservation of pathways, five were tested in Drosophila and mouse cells, with each pathway responding to alkylation damage in both species. Using the protein interactome, a significant level of connectivity was observed between Drosophila MMS survival proteins, suggesting a higher order relationship. This connectivity was dramatically improved by incorporating the components of the 13 identified pathways within the network. Grouping proteins into “pathway nodes” qualitatively improved the interactome organization, revealing a highly organized “MMS survival network.” We conclude that identification of pathways can facilitate comparative biology analysis when direct gene/orthologue comparisons fail. A biologically intuitive, highly interconnected MMS survival network was revealed after we incorporated pathway data in our interactome analysis

Inference of gene regulatory networks from time series by Tsallis entropy

Background: The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results: In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions: A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 <= q <= 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/.Fundacao de Amparo e Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Coordenacao de Aperfeicofamento de Pessoal de Nivel Superior (CAPES)Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq

Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest

Efeitos da terapia ultrassônica de baixa intensidade sobre o infarto agudo do miocárdio em ratos

Introdução. O infarto agudo do miocárdio (IAM) é considerado importante causa de morbidade e mortalidade no mundo e no Brasil. Novas intervenções terapêuticas estão sendo testadas isoladas ou em associação com as já existentes com o intuito de impedir a progressão ou atenuar o remodelamento no coração infartado. Dentre elas destaca-se a aplicação do Ultra-som (US) conjunto com agentes trombolíticos. Entretanto, na aplicação da energia ultrassônica como terapêutica pós-infarto é avaliado somente o seu possível efeito como agente trombolítico, não sendo investigado a sua possível implicação no processo de cicatrização da área infartada e parâmetros funcionais cardíacos. Objetivos. Diante dessas informações, nós objetivamos avaliar os efeitos da terapia ultrassônica transtorácica não-invasiva de baixa intensidade (NITUS) sobre a morfologia e função do músculo cardíaco de ratos infartados cirurgicamente após o 5° e 30° dia. Metodologia. Ratos machos Wistar (200-250g) foram pesados e divididos aleatoriamente em oito grupos com oito animais em cada grupo. Quatro grupos de animais foram submetidos à indução do IAM através da oclusão permanente da artéria coronária descendente anterior esquerda, sendo que dois destes grupos foram sacrificados no 5° dia após o IAM e as cinco aplicações da terapia ultrassônica e os outros dois grupos foram sacrificados no 30° dia após o IAM e as 5 aplicações da terapia ultrassônica. Quatro grupos de animais foram submetidos à cirurgia fictícia (Sham), sendo que dois destes grupos foram sacrificados no 5° dia após a cirurgia fictícia e as 5 aplicações da terapia ultrassônica e os outros dois grupos foram sacrificados no 30° dia após a cirurgia fictícia e as 5 aplicações da terapia ultrassônica. Os parâmetros da terapia ultrassônica foram freqüência de 1MHz, potência de 1W/cm2, modo pulsado e tempo de aplicação de 5 minutos. Para avaliação dos parâmetros funcionais foi realizado registros hemodinâmicos de todos os grupos e após a coleta dos registros os corações foram retirados para análise morfométrica a fim de avaliar a área da cicatriz do infarto. Os corações foram cortados em 4 fatias sendo retirados 3 cortes com espessura de 8 micrômetros da terceira fatia do ápice para a base, e estes foram corados com picrosírius. Foi utilizada uma câmera de vídeo para capturar uma área que contivesse todo o corte. A imagem era capturada com a utilização do programa AMCap e após a captura, esta era arquivada. A imagem arquivada era transferida para o programa ImageJ 1.42q/java no qual era marcada a área da cicatriz. De modo semelhante, era marcada toda a área da parede ventricular, para se obter a relação entre a área da cicatriz e a área total da parede ventricular. Resultados. No que concerne aos parâmetros hemodinâmicos, observamos que 30 dias após o IAM houve redução na pressão diastólica final (PDF) (mmHg) do grupo IAM+US quando comparado com grupo IAM (15±1.9 e 26±1.4; p<0.01 respectivamente). Não houve diferença significativa na área da cicatriz do infarto entre os grupos IAM e IAM+US no 5º. dia após infarto (31.6%±3.1% e 34.5%±1.6, respectivamente). Houve redução da área da cicatriz do infarto no grupo IAM+US quando comparado ao grupo IAM (21.5%±1.4% e 26.2%±1.7%; p<0.05, respectivamente) no 30º dia após IAM. Conclusão. A terapia com US dentro dos parâmetros estabelecidos, reduziu a área da cicatriz do infarto no grupo IAM+US (30 dias) bem como manteve a PDF dentro de valores fisiológicos, provavelmente por exercer influência nas fases inflamatória, proliferativa e de remodelamento, o que favorece um aumento na velocidade da resposta inflamatória por meio da mobilização de células inflamatórias como neutrófilos, macrófagos, ao mesmo tempo em que estimulou à degranulação dos mastócitos, bem como interferiu na mobilização leucocitária