Active Subnetworks Research Articles

Identification of highly synchronized subnetworks from gene expression data

BackgroundThere has been a growing interest in identifying context-specific active protein-protein interaction (PPI) subnetworks through integration of PPI and time course gene expression data. However the interaction dynamics during the biological process under study has not been sufficiently considered previously.MethodsHere we propose a topology-phase locking (TopoPL) based scoring metric for identifying active PPI subnetworks from time series expression data. First the temporal coordination in gene expression changes is evaluated through phase locking analysis; The results are subsequently integrated with PPI to define an activity score for each PPI subnetwork, based on individual member expression, as well topological characteristics of the PPI network and of the expression temporal coordination network; Lastly, the subnetworks with the top scores in the whole PPI network are identified through simulated annealing search.ResultsApplication of TopoPL to simulated data and to the yeast cell cycle data showed that it can more sensitively identify biologically meaningful subnetworks than the method that only utilizes the static PPI topology, or the additive scoring method. Using TopoPL we identified a core subnetwork with 49 genes important to yeast cell cycle. Interestingly, this core contains a protein complex known to be related to arrangement of ribosome subunits that exhibit extremely high gene expression synchronization.ConclusionsInclusion of interaction dynamics is important to the identification of relevant gene networks.

Feedback-induced stationary localized patterns in networks of diffusively coupled bistable elements

Effects of feedbacks on self-organization phenomena in networks of diffusively coupled bistable elements are investigated. For regular trees, an approximate analytical theory for localized stationary patterns under application of global feedbacks is constructed. Using it, properties of such patterns in different parts of the parameter space are discussed. Numerical investigations are performed for large random Erdös-Rényi and scale-free networks. In both kinds of systems, localized stationary activation patterns have been observed. The active nodes in such a pattern form a subnetwork, whose size decreases as the feedback intensity is increased. For strong feedbacks, active subnetworks are organized as trees. Additionally, local feedbacks affecting only the nodes with high degrees (i.e., hubs) or the periphery nodes are considered.

Active subnetwork recovery with a mechanism-dependent scoring function; with application to angiogenesis and organogenesis studies

BackgroundThe learning active subnetworks problem involves finding subnetworks of a bio-molecular network that are active in a particular condition. Many approaches integrate observation data (e.g., gene expression) with the network topology to find candidate subnetworks. Increasingly, pathway databases contain additional annotation information that can be mined to improve prediction accuracy, e.g., interaction mechanism (e.g., transcription, microRNA, cleavage) annotations. We introduce a mechanism-based approach to active subnetwork recovery which exploits such annotations. We suggest that neighboring interactions in a network tend to be co-activated in a way that depends on the “correlation” of their mechanism annotations. e.g., neighboring phosphorylation and de-phosphorylation interactions may be more likely to be co-activated than neighboring phosphorylation and covalent bonding interactions.ResultsOur method iteratively learns the mechanism correlations and finds the most likely active subnetwork. We use a probabilistic graphical model with a Markov Random Field component which creates dependencies between the states (active or non-active) of neighboring interactions, that incorporates a mechanism-based component to the function. We apply a heuristic-based EM-based algorithm suitable for the problem. We validated our method’s performance using simulated data in networks downloaded from GeneGO against the same approach without the mechanism-based component, and two other existing methods. We validated our methods performance in correctly recovering (1) the true interaction states, and (2) global network properties of the original network against these other methods. We applied our method to networks generated from time-course gene expression studies in angiogenesis and lung organogenesis and validated the findings from a biological perspective against current literature.ConclusionsThe advantage of our mechanism-based approach is best seen in networks composed of connected regions with a large number of interactions annotated with a subset of mechanisms, e.g., a regulatory region of transcription interactions, or a cleavage cascade region. When applied to real datasets, our method recovered novel and biologically meaningful putative interactions, e.g., interactions from an integrin signaling pathway using the angiogenesis dataset, and a group of regulatory microRNA interactions in an organogenesis network.

Discovery and analysis of consistent active sub-networks in cancers

Gene expression profiles can show significant changes when genetically diseased cells are compared with non-diseased cells. Biological networks are often used to identify active subnetworks (ASNs) of the diseases from the expression profiles to understand the reason behind the observed changes. Current methodologies for discovering ASNs mostly use undirected PPI networks and node centric approaches. This can limit their ability to find the meaningful ASNs when using integrated networks having comprehensive information than the traditional protein-protein interaction networks. Using appropriate scoring functions to assess both genes and their interactions may allow the discovery of better ASNs.In this paper, we present CASNet, which aims to identify better ASNs using (i) integrated interaction networks (mixed graphs), (ii) directions of regulations of genes, and (iii) combined node and edge scores. We simplify and extend previous methodologies to incorporate edge evaluations and lessen their sensitivity to significance thresholds. We formulate our objective functions using mixed integer programming (MIP) and show that optimal solutions may be obtained.We compare the ASNs obtained by CASNet and similar other approaches to show that CASNet can often discover more meaningful and stable regulatory ASNs. Our analysis of a breast cancer dataset finds that the positive feedback loops across 7 genes, AR, ESR1, MYC, E2F2, PGR, BCL2 and CCND1 are conserved across the basal/triple negative subtypes in multiple datasets that could potentially explain the aggressive nature of this cancer subtype. Furthermore, comparison of the basal subtype of breast cancer and the mesenchymal subtype of glioblastoma ASNs shows that an ASN in the vicinity of IL6 is conserved across the two subtypes. This result suggests that subtypes of different cancers can show molecular similarities indicating that the therapeutic approaches in different types of cancers may be shared.

Subnetwork selection in deep cortical layers is mediated by beta-oscillation dependent firing

GENERAL COMMENTARY article Front. Syst. Neurosci., 24 June 2013 | https://doi.org/10.3389/fnsys.2013.00025

Dynamics of Regulatory Networks in the Developing Mouse Retina

Understanding gene regulation is crucial to dissect the molecular basis of human development and disease. Previous studies on transcription regulatory networks often focused on their static properties. Here we used retinal development as a model system to investigate the dynamics of regulatory networks that are comprised of transcription factors, microRNAs and other protein-coding genes. We found that the active sub-networks are topologically different at early and late stages of retinal development. At early stages, the active sub-networks tend to be highly connected, while at late stages, the active sub-networks are more organized in modular structures. Interestingly, network motif usage at early and late stages is also distinct. For example, network motifs containing reciprocal feedback regulatory relationships between two regulators are overrepresented in early developmental stages. Additionally, our analysis of regulatory network dynamics revealed a natural turning point at which the regulatory network undergoes drastic topological changes. Taken together, this work demonstrates that adding a dynamic dimension to network analysis can provide new insights into retinal development, and we suggest the same approach would likely be useful for the analysis of other developing tissues.

A Self-Organized Model for Cell-Differentiation Based on Variations of Molecular Decay Rates

Systemic properties of living cells are the result of molecular dynamics governed by so-called genetic regulatory networks (GRN). These networks capture all possible features of cells and are responsible for the immense levels of adaptation characteristic to living systems. At any point in time only small subsets of these networks are active. Any active subset of the GRN leads to the expression of particular sets of molecules (expression modes). The subsets of active networks change over time, leading to the observed complex dynamics of expression patterns. Understanding of these dynamics becomes increasingly important in systems biology and medicine. While the importance of transcription rates and catalytic interactions has been widely recognized in modeling genetic regulatory systems, the understanding of the role of degradation of biochemical agents (mRNA, protein) in regulatory dynamics remains limited. Recent experimental data suggests that there exists a functional relation between mRNA and protein decay rates and expression modes. In this paper we propose a model for the dynamics of successions of sequences of active subnetworks of the GRN. The model is able to reproduce key characteristics of molecular dynamics, including homeostasis, multi-stability, periodic dynamics, alternating activity, differentiability, and self-organized critical dynamics. Moreover the model allows to naturally understand the mechanism behind the relation between decay rates and expression modes. The model explains recent experimental observations that decay-rates (or turnovers) vary between differentiated tissue-classes at a general systemic level and highlights the role of intracellular decay rate control mechanisms in cell differentiation.

Dissecting ensemble networks in ES cell populations reveals micro-heterogeneity underlying pluripotency.

Analysis of transcription at the level of single cells in prokaryotes and eukaryotes has revealed the existence of heterogeneities in the expression of individual genes within genetically homogeneous populations. This variation is an emerging hallmark of populations of Embryonic Stem (ES) cells and has been ascribed to the stochasticity associated with the biochemical events that mediate gene expression. It has been suggested that these heterogeneities play a role in the maintenance of pluripotency. However, for the most part, studies have focused on individual genes in large cell populations. Here we use an existing dataset on the expression of eight genes involved in pluripotency in eighty-three ES cells to create Gene Regulatory Networks (GRNs) at the single cell level. We observe widespread heterogeneities in the expression of the eight genes, but analysis of correlations within individual cells reveals three distinct classes centered on the expression of Nanog, a marker of pluripotency, and Fgf5, a gene associated with differentiation: high levels of Nanog and low levels of Fgf5, low levels of Nanog and high levels of Fgf5, and low levels of both. Each of these classes is associated with a collection of active sub-networks, with differing degrees of connectivity between their elements, which define a cellular state: self-renewal, primed for differentiation or transition between the two. Though every cell should be governed by the same network, the active sub-networks may emerge due to considerations such as variation in (i) the expression level of active transcription factors (e.g. through post-translational modification or ligand/co-factor availability) or (ii) access to the target gene locus (e.g. via changes in chromatin status or epigenetic modifications). We conclude that heterogeneities in gene expression should not be interpreted as representing different states of a single unique network, but as a reflection of the activity of different sub-networks in sub-populations of cells.

THE SMALL-WORLD PROPERTY IN NETWORKS GROWING BY ACTIVE EDGES

In the last three years, we have witnessed an increasing number of complex network models based on a 'fractal' approach, in which parts of the network are repeatedly replaced by a given pattern. Our focus is on models that can be defined by repeatedly adding a pattern network to selected edges, called active edges. We prove that when a pattern network has at least two active edges, then the resulting network has an average distance at most logarithmic in the number of nodes. This suggests that real-world networks based on a similar growth mechanism are likely to have small average distance. We provide an estimate of the clustering coefficient and verify its accuracy using simulations. Using numerous examples of simple patterns, our simulations show various ways to generate small-world networks. Finally, we discuss extensions to our framework encompassing probabilistic patterns and active subnetworks.

Functional networks for Salvia miltiorrhiza and Panax notoginseng in combination explored with text mining and bioinformatical approach

Salvia miltiorrhiza (SM) and Panax notoginseng (PN) in combination (SMPN) have been widely used primarily in Traditional Chinese Medicine (TCM), for the treatment of coronary heart disease, and its pharmacological activity should be complicated because of its multiple components. Here, we combine text mining with bioinformatics to predict functional networks for the combination. 53 genes related with SMPN were found with text mining. Protein-protein interaction information for these genes from databases and Literature data was searched. Eight highly-connected regions were detected by IPCA algorithm to infer significant complexes or pathways in this network. Over-represented Gene Ontology categories of highly-connected regions by biological network gene ontology tool involved in small GTPase mediated signal transduction, apoptosis, regulation of immune effector process, phosphorylation about enzyme linked receptor protein signaling pathway, positive regulation of biological process. Integrate expression data from six microarray experiments about coronary heart disease into the SMPN network, and use the jActiveModules tool to find active subnetworks in differential expression conditions. The most relevant functions and pathways extracted from these subnetworks were related to proliferation and apoptosis of endothelial cell, apoptosis of arterial smooth muscle cell, apoptosis and regulation of immune system process within macrophages during foam cell formation, cardiocyte apoptosis. Analysis of the subnetwork composition indicated that there were in each subnetworks, and in the most subnetworks were dominant, the nodes came from SM network more than from PN network. It was suggested that, therapeutic efficacies of SMPN should be results of interaction between SM and PN in the multiple pathways and biological processes, and SM maybe play a principal role and PN serve as adjuvant one to assist the effects during the treatment of coronary heart disease. Key words: Panax notoginseng, salvia miltiorrhiza, herbal combination, bioinformatics, text mining, pharamcological activity.

Finding active subnetworks in genetic diseases by using integrated biological networks

Finding active subnetworks in genetic diseases by using integrated biological networks

Contextual analysis of RNAi-based functional screens using interaction networks

Considerable attention has been directed in recent years toward the development of methods for the contextual analysis of expression data using interaction networks. Of particular interest has been the identification of active subnetworks by detecting regions enriched with differential expression. In contrast, however, very little effort has been made toward the application of comparable methods to other types of high-throughput data. Here, we propose a new method based on co-clustering that is specifically designed for the exploratory analysis of large-scale, RNAi-based functional screens. We demonstrate our approach by applying it to a genome-scale dataset aimed at identifying host factors of the human pathogen, hepatitis C virus (HCV). In addition to recovering known cellular modules relevant to HCV infection, the results enabled us to identify new candidates and formulate biological hypotheses regarding possible roles and mechanisms for a number of them. For example, our analysis indicated that HCV, similar to other enveloped viruses, exploits elements within the endosomal pathway in order to acquire a membrane and facilitate assembly and release. This echoed a number of recent studies which showed that the ESCRT-III complex is essential to productive infection. gonzalez@bio.ifi.lmu.de Supplementary data are available at Bioinformatics online.

Drafting the topological network of protein‐protein interactions by in‐depth data mining and inference

The biological knowledge had often been represented by network, especially the protein‐protein interaction (PPI) network since the advent of systems biology. While current experimental methods for mapping PPI networks remained costly and often inefficient, the development of new computational approaches for building the networks was very imperative. Here, an in silico method for efficiently mapping protein interaction networks was presented in dependence on the integration of in‐depth data mining for proteomic information (including sequences, motifs, structures and interactions) with the smart predictive tricks. The sketch of PPI network was generated from an initial target protein (as a start node) by progressively determining the peripheral interacting partners that acted as new start nodes respectively in the following cycle. Analyses of protein functions and discovery of active pathways or subnetworks with definitely physiological implications were also addressed in detail according to an optimum search algorism. The integrative computational approach could draft the topological networks of PPI in a high‐throughput and low‐cost fashion before actual experimental procedures.This work was supported by a grant from National Natural Science Foundation of China (NSFC, no. 20803098)

A Scalable Approach for Discovering Conserved Active Subnetworks across Species

Overlaying differential changes in gene expression on protein interaction networks has proven to be a useful approach to interpreting the cell's dynamic response to a changing environment. Despite successes in finding active subnetworks in the context of a single species, the idea of overlaying lists of differentially expressed genes on networks has not yet been extended to support the analysis of multiple species' interaction networks. To address this problem, we designed a scalable, cross-species network search algorithm, neXus (Network - cross(X)-species - Search), that discovers conserved, active subnetworks based on parallel differential expression studies in multiple species. Our approach leverages functional linkage networks, which provide more comprehensive coverage of functional relationships than physical interaction networks by combining heterogeneous types of genomic data. We applied our cross-species approach to identify conserved modules that are differentially active in stem cells relative to differentiated cells based on parallel gene expression studies and functional linkage networks from mouse and human. We find hundreds of conserved active subnetworks enriched for stem cell-associated functions such as cell cycle, DNA repair, and chromatin modification processes. Using a variation of this approach, we also find a number of species-specific networks, which likely reflect mechanisms of stem cell function that have diverged between mouse and human. We assess the statistical significance of the subnetworks by comparing them with subnetworks discovered on random permutations of the differential expression data. We also describe several case examples that illustrate the utility of comparative analysis of active subnetworks.

ExprEssence - Revealing the essence of differential experimental data in the context of an interaction/regulation net-work

BackgroundExperimentalists are overwhelmed by high-throughput data and there is an urgent need to condense information into simple hypotheses. For example, large amounts of microarray and deep sequencing data are becoming available, describing a variety of experimental conditions such as gene knockout and knockdown, the effect of interventions, and the differences between tissues and cell lines.ResultsTo address this challenge, we developed a method, implemented as a Cytoscape plugin called ExprEssence. As input we take a network of interaction, stimulation and/or inhibition links between genes/proteins, and differential data, such as gene expression data, tracking an intervention or development in time. We condense the network, highlighting those links across which the largest changes can be observed. Highlighting is based on a simple formula inspired by the law of mass action. We can interactively modify the threshold for highlighting and instantaneously visualize results. We applied ExprEssence to three scenarios describing kidney podocyte biology, pluripotency and ageing: 1) We identify putative processes involved in podocyte (de-)differentiation and validate one prediction experimentally. 2) We predict and validate the expression level of a transcription factor involved in pluripotency. 3) Finally, we generate plausible hypotheses on the role of apoptosis, cell cycle deregulation and DNA repair in ageing data obtained from the hippocampus.ConclusionReducing the size of gene/protein networks to the few links affected by large changes allows to screen for putative mechanistic relationships among the genes/proteins that are involved in adaptation to different experimental conditions, yielding important hypotheses, insights and suggestions for new experiments. We note that we do not focus on the identification of 'active subnetworks'. Instead we focus on the identification of single links (which may or may not form subnetworks), and these single links are much easier to validate experimentally than submodules. ExprEssence is available at http://sourceforge.net/projects/expressence/.

Dynamic network rewiring determines temporal regulatory functions in Drosophilamelanogaster development processes

The identification of network motifs has been widely considered as a significant step towards uncovering the design principles of biomolecular regulatory networks. To date, time-invariant networks have been considered. However, such approaches cannot be used to reveal time-specific biological traits due to the dynamic nature of biological systems, and hence may not be applicable to development, where temporal regulation of gene expression is an indispensable characteristic. We propose a concept of a "temporal sequence of network motifs", a sequence of network motifs in active sub-networks constructed over time, and investigate significant network motifs in the active temporal sub-networks of Drosophila melanogaster. Based on this concept, we find a temporal sequence of network motifs which changes according to developmental stages and thereby cannot be identified from the whole static network. Moreover, we show that the temporal sequence of network motifs corresponding to each developmental stage can be used to describe pivotal developmental events.

Identification of responsive gene modules by network-based gene clustering and extending: application to inflammation and angiogenesis

BackgroundCell responses to environmental stimuli are usually organized as relatively separate responsive gene modules at the molecular level. Identification of responsive gene modules rather than individual differentially expressed (DE) genes will provide important information about the underlying molecular mechanisms. Most of current methods formulate module identification as an optimization problem: find the active sub-networks in the genome-wide gene network by maximizing the objective function considering the gene differential expression and/or the gene-gene co-expression information. Here we presented a new formulation of this task: a group of closely-connected and co-expressed DE genes in the gene network are regarded as the signatures of the underlying responsive gene modules; the modules can be identified by finding the signatures and then recovering the "missing parts" by adding the intermediate genes that connect the DE genes in the gene network.ResultsClustEx, a two-step method based on the new formulation, was developed and applied to identify the responsive gene modules of human umbilical vein endothelial cells (HUVECs) in inflammation and angiogenesis models by integrating the time-course microarray data and genome-wide PPI data. It shows better performance than several available module identification tools by testing on the reference responsive gene sets. Gene set analysis of KEGG pathways, GO terms and microRNAs (miRNAs) target gene sets further supports the ClustEx predictions.ConclusionTaking the closely-connected and co-expressed DE genes in the condition-specific gene network as the signatures of the underlying responsive gene modules provides a new strategy to solve the module identification problem. The identified responsive gene modules of HUVECs and the corresponding enriched pathways/miRNAs provide useful resources for understanding the inflammatory and angiogenic responses of vascular systems.

Discovering Active Subnetwork in Protein Interaction Network*

Traditional analysis of gene expression data focused on identifying differentially expressed and co-expressed genes,which didn't take known interactions into consideration.In recent years,many methods have been developed to identify active subnetwork by integrating protein interaction networks with gene expression profiles.Current approaches failed to take full account of both difference and correlation in gene expression that may lead to false positive results.A new algorithm is proposed for identifying active subnetwork by considering both difference and correlation of gene expression profile.The algorithm is employed in the process of gene expression profiles of human immunodeficiency virus infection.The results showed that the algorithm can identify the active subnetwork that has extremely high biologically functional connectivity with human immunodeficiency virus,and effectively avoid the bias introduced by considering differences of gene expression profiles only,i.e.,genes less differentially expressed are also included due to high correlations in gene expression.

Precisely timed signal transmission in neocortical networks with reliable intermediate-range projections

The mammalian neocortex has a remarkable ability to precisely reproduce behavioral sequences or to reliably retrieve stored information. In contrast, spiking activity in behaving animals shows a considerable trial-to-trial variability and temporal irregularity. The signal propagation and processing underlying these conflicting observations is based on fundamental neurophysiological processes like synaptic transmission, signal integration within single cells, and spike formation. Each of these steps in the neuronal signaling chain has been studied separately to a great extend, but it has been difficult to judge how they interact and sum up in active sub-networks of neocortical cells. In the present study, we experimentally assessed the precision and reliability of small neocortical networks consisting of trans-columnar, intermediate-range projections (200–1000 μm) on a millisecond time-scale. Employing photo-uncaging of glutamate in acute slices, we activated a number of distant presynaptic cells in a spatio-temporally precisely controlled manner, while monitoring the resulting membrane potential fluctuations of a postsynaptic cell. We found that signal integration in this part of the network is highly reliable and temporally precise. As numerical simulations showed, the residual membrane potential variability can be attributed to amplitude variability in synaptic transmission and may significantly contribute to trial-to-trial output variability of a rate signal. However, it does not impair the temporal accuracy of signal integration. We conclude that signals from intermediate-range projections onto neocortical neurons are propagated and integrated in a highly reliable and precise manner, and may serve as a substrate for temporally precise signal transmission in neocortical networks.

Visualizing Genome Expression and Regulatory Network Dynamics in Genomic and Metabolic Context

AbstractDNA microarrays are used to measure the expression levels of thousands of genes simultaneously. In a time series experiment, the gene expressions are measured as a function of time. We present an application for integrated visualization of genome expression and network dynamics in both regulatory networks and metabolic pathways. Integration of these two levels of cellular processes is necessary, since it provides the link between the measurements at the transcriptional level (gene expression levels approximated from microarray data) and the phenotype (the observable characteristics of an organism) at the functional and behavioral level. The integration requires visualization approaches besides traditional clustering and statistical analysis methods. Our application can (i) visualize the data from time series experiments in the context of a regulatory network and KEGG metabolic pathways; (ii) identify and visualize active regulatory subnetworks from the gene expression data; (iii) perform a statistical test to identify and subsequently visualize pathways that are affected by differentially expressed genes. We present a case study, which demonstrates that our approach and application both facilitates and speeds up data analysis tremendously in comparison to a more traditional approach that involves many manual, laborious, and error‐prone steps.