How directed is a directed network?
Ontology highlight
ABSTRACT:
SUBMITTER: MacKay RS
PROVIDER: S-EPMC7540772 | biostudies-literature | 2020 Sep
REPOSITORIES: biostudies-literature
Publications
How directed is a directed network?
MacKay R S RS Johnson S S Sansom B B
Royal Society open science 20200909 9
The trophic levels of nodes in directed networks can reveal their functional properties. Moreover, the trophic coherence of a network, defined in terms of trophic levels, is related to properties such as cycle structure, stability and percolation. The standard definition of trophic levels, however, borrowed from ecology, suffers from drawbacks such as requiring basal nodes, which limit its applicability. Here we propose simple improved definitions of trophic levels and coherence that can be comp ...[more]
Similar Datasets
Project description:Molecular dynamics simulations contain detailed kinetic information related to the functional states of proteins and macromolecules, but this information is obscured by the high dimensionality of configurational space. Markov state models and transition network models are widely applied to extract kinetic descriptors from equilibrium molecular dynamics simulations. In this study, we developed the Directed Kinetic Transition Network (DKTN)-a graph representation of a master equation which is appropriate for describing nonequilibrium kinetics. DKTN models the transition rate matrix among different states under detailed balance. Adopting the mixing time from the Markov chain, we use the half mixing time as the criterion to identify critical state transition regarding the protein conformational change. The similarity between the master equation and the Kolmogorov equation suggests that the DKTN model can be reformulated into the continuous-time Markov chain model, which is a general case of the Markov chain without a specific lag time. We selected a photo-sensitive protein, vivid, as a model system to illustrate the usage of the DKTN model. Overall, the DKTN model provides a graph representation of the master equation based on chemical kinetics to model the protein conformational change without the underlying assumption of the Markovian property.
| S-EPMC6800283 | biostudies-literature
Project description:BackgroundNetwork Component Analysis (NCA) has shown its effectiveness in discovering regulators and inferring transcription factor activities (TFAs) when both microarray data and ChIP-on-chip data are available. However, a NCA scheme is not applicable to many biological studies due to limited topology information available, such as lack of ChIP-on-chip data. We propose a new approach, motif-directed NCA (mNCA), to integrate motif information and gene expression data to infer regulatory networks.ResultsWe develop motif-directed NCA (mNCA) to incorporate motif information into NCA for regulatory network inference. While motif information is readily available from knowledge databases, it is a "noisy" source of network topology information consisting of many false positives. To overcome this problem, we develop a stability analysis procedure embedded in mNCA to resolve the inconsistency between motif information and gene expression data, and to enable the identification of stable TFAs. The mNCA approach has been applied to a time course microarray data set of muscle regeneration. The experimental results show that the inferred TFAs are not only numerically stable but also biologically relevant to muscle differentiation process. In particular, several inferred TFAs like those of MyoD, myogenin and YY1 are well supported by biological experiments.ConclusionA novel computational approach, mNCA, has been developed to integrate motif information and gene expression data for regulatory network reconstruction. Specifically, motif analysis is used to obtain initial network topology, and stability analysis is developed and applied with mNCA to extract stable TFAs. Experimental results on muscle regeneration microarray data have demonstrated that mNCA is a practical and reliable computational method for regulatory network inference and pathway discovery.
| S-EPMC2259422 | biostudies-literature
Project description:An ability to rationally design complex networks from the bottom up can offer valuable quantitative model systems for use in gaining a deeper appreciation for the principles governing the self-organization and functional characteristics of complex systems. We report herein the de novo design, graph prediction, experimental analysis, and characterization of simple self-organized, nonlinear molecular networks. Our approach makes use of the sequence-dependent auto- and cross-catalytic functional characteristics of template-directed peptide fragment condensation reactions in neutral aqueous solutions. Starting with an array of 81 sequence similar 32-residue coiled-coil peptides, we estimated the relative stability difference between all plausible A(2)B-type coiled-coil ensembles and used this information to predict the auto- and cross-catalysis pathways and the resulting plausible network motif and connectivities. Similar to most complex systems, the generated graph displays clustered nodes with an overall hierarchical architecture. To test the validity of the design principles used, nine nodes composing a main segment of the graph were experimentally analyzed for their capacity in establishing the predicted network connectivity. The resulting self-organized chemical network is shown to display 25 directed edges in good agreement with the graph analysis estimations. Moreover, we show that by varying the system parameters (presence or absence of certain substrates or templates), its operating network motif can be altered, even to the extremes of turning pathways on or off. We suggest that this approach can be expanded for the construction of large-scale networks, offering a means to study and to understand better the emergent, collective behaviors of networks.
| S-EPMC503713 | biostudies-literature
Project description:The prevalence of graph-based data has spurred the rapid development of graph neural networks (GNNs) and related machine learning algorithms. Yet, despite the many datasets naturally modeled as directed graphs, including citation, website, and traffic networks, the vast majority of this research focuses on undirected graphs. In this paper, we propose MagNet, a GNN for directed graphs based on a complex Hermitian matrix known as the magnetic Laplacian. This matrix encodes undirected geometric structure in the magnitude of its entries and directional information in their phase. A "charge" parameter attunes spectral information to variation among directed cycles. We apply our network to a variety of directed graph node classification and link prediction tasks showing that MagNet performs well on all tasks and that its performance exceeds all other methods on a majority of such tasks. The underlying principles of MagNet are such that it can be adapted to other GNN architectures.
| S-EPMC9425115 | biostudies-literature
Project description:Atrial flutter (AFL) is a common atrial arrhythmia typically characterized by electrical activity propagating around specific anatomical regions. It is usually treated with catheter ablation. However, the identification of rotational activities is not straightforward, and requires an intense effort during the first phase of the electrophysiological (EP) study, i.e., the mapping phase, in which an anatomical 3D model is built and electrograms (EGMs) are recorded. In this study, we modeled the electrical propagation pattern of AFL (measured during mapping) using network theory (NT), a well-known field of research from the computer science domain. The main advantage of NT is the large number of available algorithms that can efficiently analyze the network. Using directed network mapping, we employed a cycle-finding algorithm to detect all cycles in the network, resembling the main propagation pattern of AFL. The method was tested on two subjects in sinus rhythm, six in an experimental model of in-silico simulations, and 10 subjects diagnosed with AFL who underwent a catheter ablation. The algorithm correctly detected the electrical propagation of both sinus rhythm cases and in-silico simulations. Regarding the AFL cases, arrhythmia mechanisms were either totally or partially identified in most of the cases (8 out of 10), i.e., cycles around the mitral valve, tricuspid valve and figure-of-eight reentries. The other two cases presented a poor mapping quality or a major complexity related to previous ablations, large areas of fibrotic tissue, etc. Directed network mapping represents an innovative tool that showed promising results in identifying AFL mechanisms in an automatic fashion. Further investigations are needed to assess the reliability of the method in different clinical scenarios.
| S-EPMC8577834 | biostudies-literature
Project description:Using chromatin immunoprecipitation combined with microarrays we have identified targets of No tail (Ntl), a zebrafish Brachyury ortholog that plays a central role in mesoderm formation. We show that Ntl regulates a downstream network of other transcription factors and identify an in vivo Ntl binding site that resembles the consensus T-box binding site (TBS) previously identified by in vitro studies. We show floating head (flh) is a direct transcriptional target of Ntl in the notochord and that TBS in the flh upstream region are required for Ntl directed expression. Using this genome-scale data we have assembled a Gene Regulatory Network that describes mesoderm formation and patterning in the early zebrafish embryo and which predicts a role for Ntl in somite segmentation. Keywords: ChIP-chip Genomic array design. Microarrays were designed as described below and manufactured by Agilent Technologies (www.agilent.com). We have previously described the ChIP-chip technique in zebrafish and the design of promoter microarrays which cover a 2kb region around the transcription start sites (TSSs) of 11,512 zebrafish genes based on Zv4 (Wardle et al, 2006. Genome Biology 7:R71). In order to capture additional regulatory information further from the basal promoter region we also designed an expanded set of 60mer probes that cover 9kb upstream and 3kb downstream of the TSS of these genes using the same parameters as for the 2kb microarrays. We also incorporated several sets of control probes, both positive and negative. On each array there are 753 negative probes designed against zebrafish gene desert regions and Arabidopsis thaliana genes. In addition we included duplicates of promoter probes for several genes that we anticipated may be bound by factors of interest for this and other studies (wnt11, flh, vent, msgn1, myod, fgf8, pcdh8) and these probes are arrayed on both slides slide. We also included in this design 502 intensity control probes. Finally there are 2,256 controls added by Agilent. The final design contained 378,002 probes, including 365,537 experimental probes divided between nine microarray slides: slides 1-8 contained 40,616 experimental probes and slide 9 contained 40, 609. Further information on design and manufacture of the microarrays can be found on the Agilent Technologies website (www.agilent.com).
2009-02-19 | E-GEOD-12331 | biostudies-arrayexpress
Project description:Tasks that demand externalized attention reliably suppress default network activity while activating the dorsal attention network. These networks have an intrinsic competitive relationship; activation of one suppresses activity of the other. Consequently, many assume that default network activity is suppressed during goal-directed cognition. We challenge this assumption in an fMRI study of planning. Recent studies link default network activity with internally focused cognition, such as imagining personal future events, suggesting a role in autobiographical planning. However, it is unclear how goal-directed cognition with an internal focus is mediated by these opposing networks. A third anatomically interposed 'frontoparietal control network' might mediate planning across domains, flexibly coupling with either the default or dorsal attention network in support of internally versus externally focused goal-directed cognition, respectively. We tested this hypothesis by analyzing brain activity during autobiographical versus visuospatial planning. Autobiographical planning engaged the default network, whereas visuospatial planning engaged the dorsal attention network, consistent with the anti-correlated domains of internalized and externalized cognition. Critically, both planning tasks engaged the frontoparietal control network. Task-related activation of these three networks was anatomically consistent with independently defined resting-state functional connectivity MRI maps. Task-related functional connectivity analyses demonstrate that the default network can be involved in goal-directed cognition when its activity is coupled with the frontoparietal control network. Additionally, the frontoparietal control network may flexibly couple with the default and dorsal attention networks according to task domain, serving as a cortical mediator linking the two networks in support of goal-directed cognitive processes.
| S-EPMC2914129 | biostudies-literature
Project description:Tools pose a challenge to the need to select actions appropriate for task goals and environmental constraints. For many tools (e.g., calculator), actions for "using" and "grasping-to-move" conflict with each other and may compete during selection. To date, little is known about the mechanisms that enable selection between possible tool actions or their neural substrates. The study of patients with chronic left hemisphere stroke, many of whom are deficient in tool-use action (apraxic), provides an opportunity to elucidate these issues. Here, 31 such patients pantomimed or recognized tool use actions for "conflict" and "non-conflict" tools. Voxel-based lesion-symptom mapping (VLSM), lesion subtraction, and tractographic overlap analyses were used to determine brain regions necessary for selecting among tool-directed actions. Lesions to posterior middle temporal gyrus (pMTG) and anterior intraparietal sulcus (aIPS) tended to impair production of use actions similarly for both conflict and non-conflict tools. By contrast, lesions to the supramarginal gyrus (SMG), inferior frontal gyrus (IFG)/anterior insula, and superior longitudinal fasciculus (SLF) specifically impaired production of use actions for conflict tools. Patients' errors on conflict tools suggested inappropriate selection of grasping actions and difficulty selecting single actions. Use/grasp conflict had no effect on action recognition. We suggest that the SMG/SLF/IFG pathway implements biased competition between possible tool actions, while aIPS and pMTG compute the structure-based and skilled use actions, respectively, that constitute input to this competitive process. This is the first study to demonstrate a reliable link between a characteristic of single tools (i.e., their association with different use and grasp actions) and action selection difficulties. Additionally, the data allow us to posit an SMG-involved subtype of apraxia characterized by an inability to resolve action competition.
| S-EPMC4385438 | biostudies-literature
Project description:Accurately identifying gene regulatory network is an important task in understanding in vivo biological activities. The inference of such networks is often accomplished through the use of gene expression data. Many methods have been developed to evaluate gene expression dependencies between transcription factor and its target genes, and some methods also eliminate transitive interactions. The regulatory (or edge) direction is undetermined if the target gene is also a transcription factor. Some methods predict the regulatory directions in the gene regulatory networks by locating the eQTL single nucleotide polymorphism, or by observing the gene expression changes when knocking out/down the candidate transcript factors; regrettably, these additional data are usually unavailable, especially for the samples deriving from human tissues.In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.
| S-EPMC5001240 | biostudies-literature
Project description:Directed network motifs are the building blocks of complex networks, such as human brain networks, and capture deep connectivity information that is not contained in standard network measures. In this paper we present the first application of directed network motifs in vivo to human brain networks, utilizing recently developed directed progression networks which are built upon rates of cortical thickness changes between brain regions. This is in contrast to previous studies which have relied on simulations and in vitro analysis of non-human brains. We show that frequencies of specific directed network motifs can be used to distinguish between patients with Alzheimer's disease (AD) and normal control (NC) subjects. Especially interesting from a clinical standpoint, these motif frequencies can also distinguish between subjects with mild cognitive impairment who remained stable over three years (MCI) and those who converted to AD (CONV). Furthermore, we find that the entropy of the distribution of directed network motifs increased from MCI to CONV to AD, implying that the distribution of pathology is more structured in MCI but becomes less so as it progresses to CONV and further to AD. Thus, directed network motifs frequencies and distributional properties provide new insights into the progression of Alzheimer's disease as well as new imaging markers for distinguishing between normal controls, stable mild cognitive impairment, MCI converters and Alzheimer's disease.
| S-EPMC4400037 | biostudies-literature