Science.gov

Sample records for multiple gene networks

  1. Consensus gene regulatory networks: combining multiple microarray gene expression datasets

    NASA Astrophysics Data System (ADS)

    Peeling, Emma; Tucker, Allan

    2007-09-01

    In this paper we present a method for modelling gene regulatory networks by forming a consensus Bayesian network model from multiple microarray gene expression datasets. Our method is based on combining Bayesian network graph topologies and does not require any special pre-processing of the datasets, such as re-normalisation. We evaluate our method on a synthetic regulatory network and part of the yeast heat-shock response regulatory network using publicly available yeast microarray datasets. Results are promising; the consensus networks formed provide a broader view of the potential underlying network, obtaining an increased true positive rate over networks constructed from a single data source.

  2. Construction of a gene-gene interaction network with a combined score across multiple approaches.

    PubMed

    Zhang, A M; Song, H; Shen, Y H; Liu, Y

    2015-01-01

    Recent progress in computational methods for inves-tigating physical and functional gene interactions has provided new insights into the complexity of biological processes. An essential part of these methods is presented visually in the form of gene interaction networks that can be valuable in exploring the mechanisms of disease. Here, a combined network based on gene pairs with an extra layer of re-liability was constructed after converting and combining the gene pair scores using a novel algorithm across multiple approaches. Four groups of kidney cancer data sets from ArrayExpress were downloaded and analyzed to identify differentially expressed genes using a rank prod-ucts analysis tool. Gene co-expression network, protein-protein interac-tion, co-occurrence network and a combined network were constructed using empirical Bayesian meta-analysis approach, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, an odds ratio formula of the cBioPortal for Cancer Genomics and a novel rank algorithm with combined score, respectively. The topological features of these networks were then compared to evaluate their performances. The results indicated that the gene pairs and their relationship rank-ings were not uniform. The values of topological parameters, such as clustering coefficient and the fitting coefficient R(2) of interaction net-work constructed using our ranked based combination score, were much greater than the other networks. The combined network had a classic small world property which transferred information quickly and displayed great resilience to the dysfunction of low-degree hubs with high-clustering and short average path length. It also followed distinct-ly a scale-free network with a higher reliability. PMID:26125911

  3. Network tuned multiple rank aggregation and applications to gene ranking

    PubMed Central

    2015-01-01

    With the development of various high throughput technologies and analysis methods, researchers can study different aspects of a biological phenomenon simultaneously or one aspect repeatedly with different experimental techniques and analysis methods. The output from each study is a rank list of components of interest. Aggregation of the rank lists of components, such as proteins, genes and single nucleotide variants (SNV), produced by these experiments has been proven to be helpful in both filtering the noise and bringing forth a more complete understanding of the biological problems. Current available rank aggregation methods do not consider the network information that has been observed to provide vital contributions in many data integration studies. We developed network tuned rank aggregation methods incorporating network information and demonstrated its superior performance over aggregation methods without network information. The methods are tested on predicting the Gene Ontology function of yeast proteins. We validate the methods using combinations of three gene expression data sets and three protein interaction networks as well as an integrated network by combining the three networks. Results show that the aggregated rank lists are more meaningful if protein interaction network is incorporated. Among the methods compared, CGI_RRA and CGI_Endeavour, which integrate rank lists with networks using CGI [1] followed by rank aggregation using either robust rank aggregation (RRA) [2] or Endeavour [3] perform the best. Finally, we use the methods to locate target genes of transcription factors. PMID:25708095

  4. Inference of gene interaction networks using conserved subsequential patterns from multiple time course gene expression datasets

    PubMed Central

    2015-01-01

    Motivation Deciphering gene interaction networks (GINs) from time-course gene expression (TCGx) data is highly valuable to understand gene behaviors (e.g., activation, inhibition, time-lagged causality) at the system level. Existing methods usually use a global or local proximity measure to infer GINs from a single dataset. As the noise contained in a single data set is hardly self-resolved, the results are sometimes not reliable. Also, these proximity measurements cannot handle the co-existence of the various in vivo positive, negative and time-lagged gene interactions. Methods and results We propose to infer reliable GINs from multiple TCGx datasets using a novel conserved subsequential pattern of gene expression. A subsequential pattern is a maximal subset of genes sharing positive, negative or time-lagged correlations of one expression template on their own subsets of time points. Based on these patterns, a GIN can be built from each of the datasets. It is assumed that reliable gene interactions would be detected repeatedly. We thus use conserved gene pairs from the individual GINs of the multiple TCGx datasets to construct a reliable GIN for a species. We apply our method on six TCGx datasets related to yeast cell cycle, and validate the reliable GINs using protein interaction networks, biopathways and transcription factor-gene regulations. We also compare the reliable GINs with those GINs reconstructed by a global proximity measure Pearson correlation coefficient method from single datasets. It has been demonstrated that our reliable GINs achieve much better prediction performance especially with much higher precision. The functional enrichment analysis also suggests that gene sets in a reliable GIN are more functionally significant. Our method is especially useful to decipher GINs from multiple TCGx datasets related to less studied organisms where little knowledge is available except gene expression data. PMID:26681650

  5. Walking on multiple disease-gene networks to prioritize candidate genes.

    PubMed

    Jiang, Rui

    2015-06-01

    Uncovering causal genes for human inherited diseases, as the primary step toward understanding the pathogenesis of these diseases, requires a combined analysis of genetic and genomic data. Although bioinformatics methods have been designed to prioritize candidate genes resulting from genetic linkage analysis or association studies, the coverage of both diseases and genes in existing methods is quite limited, thereby preventing the scan of causal genes for a significant proportion of diseases at the whole-genome level. To overcome this limitation, we propose a method named pgWalk to prioritize candidate genes by integrating multiple phenomic and genomic data. We derive three types of phenotype similarities among 7719 diseases and nine types of functional similarities among 20327 genes. Based on a pair of phenotype and gene similarities, we construct a disease-gene network and then simulate the process that a random walker wanders on such a heterogeneous network to quantify the strength of association between a candidate gene and a query disease. A weighted version of the Fisher's method with dependent correction is adopted to integrate 27 scores obtained in this way, and a final q-value is calibrated for prioritizing candidate genes. A series of validation experiments are conducted to demonstrate the superior performance of this approach. We further show the effectiveness of this method in exome sequencing studies of autism and epileptic encephalopathies. An online service and the standalone software of pgWalk can be found at http://bioinfo.au.tsinghua.edu.cn/jianglab/pgwalk. PMID:25681405

  6. Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis

    PubMed Central

    Creanza, Teresa Maria; Liguori, Maria; Liuni, Sabino; Nuzziello, Nicoletta; Ancona, Nicola

    2016-01-01

    Differential gene expression analyses to investigate multiple sclerosis (MS) molecular pathogenesis cannot detect genes harboring genetic and/or epigenetic modifications that change the gene functions without affecting their expression. Differential co-expression network approaches may capture changes in functional interactions resulting from these alterations. We re-analyzed 595 mRNA arrays from publicly available datasets by studying changes in gene co-expression networks in MS and in response to interferon (IFN)-β treatment. Interestingly, MS networks show a reduced connectivity relative to the healthy condition, and the treatment activates the transcription of genes and increases their connectivity in MS patients. Importantly, the analysis of changes in gene connectivity in MS patients provides new evidence of association for genes already implicated in MS by single-nucleotide polymorphism studies and that do not show differential expression. This is the case of amiloride-sensitive cation channel 1 neuronal (ACCN1) that shows a reduced number of interacting partners in MS networks, and it is known for its role in synaptic transmission and central nervous system (CNS) development. Furthermore, our study confirms a deregulation of the vitamin D system: among the transcription factors that potentially regulate the deregulated genes, we find TCF3 and SP1 that are both involved in vitamin D3-induced p27Kip1 expression. Unveiling differential network properties allows us to gain systems-level insights into disease mechanisms and may suggest putative targets for the treatment. PMID:27314336

  7. Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis.

    PubMed

    Creanza, Teresa Maria; Liguori, Maria; Liuni, Sabino; Nuzziello, Nicoletta; Ancona, Nicola

    2016-01-01

    Differential gene expression analyses to investigate multiple sclerosis (MS) molecular pathogenesis cannot detect genes harboring genetic and/or epigenetic modifications that change the gene functions without affecting their expression. Differential co-expression network approaches may capture changes in functional interactions resulting from these alterations. We re-analyzed 595 mRNA arrays from publicly available datasets by studying changes in gene co-expression networks in MS and in response to interferon (IFN)-β treatment. Interestingly, MS networks show a reduced connectivity relative to the healthy condition, and the treatment activates the transcription of genes and increases their connectivity in MS patients. Importantly, the analysis of changes in gene connectivity in MS patients provides new evidence of association for genes already implicated in MS by single-nucleotide polymorphism studies and that do not show differential expression. This is the case of amiloride-sensitive cation channel 1 neuronal (ACCN1) that shows a reduced number of interacting partners in MS networks, and it is known for its role in synaptic transmission and central nervous system (CNS) development. Furthermore, our study confirms a deregulation of the vitamin D system: among the transcription factors that potentially regulate the deregulated genes, we find TCF3 and SP1 that are both involved in vitamin D3-induced p27Kip1 expression. Unveiling differential network properties allows us to gain systems-level insights into disease mechanisms and may suggest putative targets for the treatment. PMID:27314336

  8. NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms

    PubMed Central

    Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

    2014-01-01

    One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available

  9. Analyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles

    PubMed Central

    2015-01-01

    Background Despite the large increase of transcriptomic studies that look for gene signatures on diseases, there is still a need for integrative approaches that obtain separation of multiple pathological states providing robust selection of gene markers for each disease subtype and information about the possible links or relations between those genes. Results We present a network-oriented and data-driven bioinformatic approach that searches for association of genes and diseases based on the analysis of genome-wide expression data derived from microarrays or RNA-Seq studies. The approach aims to (i) identify gene sets associated to different pathological states analysed together; (ii) identify a minimum subset within these genes that unequivocally differentiates and classifies the compared disease subtypes; (iii) provide a measurement of the discriminant power of these genes and (iv) identify links between the genes that characterise each of the disease subtypes. This bioinformatic approach is implemented in an R package, named geNetClassifier, available as an open access tool in Bioconductor. To illustrate the performance of the tool, we applied it to two independent datasets: 250 samples from patients with four major leukemia subtypes analysed using expression arrays; another leukemia dataset analysed with RNA-Seq that includes a subtype also present in the previous set. The results show the selection of key deregulated genes recently reported in the literature and assigned to the leukemia subtypes studied. We also show, using these independent datasets, the selection of similar genes in a network built for the same disease subtype. Conclusions The construction of gene networks related to specific disease subtypes that include parameters such as gene-to-gene association, gene disease specificity and gene discriminant power can be very useful to draw gene-disease maps and to unravel the molecular features that characterize specific pathological states. The

  10. Regulatory network analysis of transcription factors, microRNAs, target genes and host genes in human multiple myeloma.

    PubMed

    Huang, Zhuoyan; Xu, Zhiwen; Kunhao Wang, Kunhao Wang; Wang, Ning; Wang, Shang

    2015-11-01

    In recent years, molecular biologists have achieved great advance in micro RNA (miRNA) and gene investigation about the pathogenesis of multiple myeloma (MM). Existing research data of the transcription factors (TFs) and miRNAs is disperse and unorganized, which prevents researchers from investigating the mechanism and analyze regulatory pathways of MM systematically. In our research, regulatory interactions among miRNAs, TFs, host genes and target genes were imported to construct regulatory networks at three levels, including the abnormally expressed network and the related network as well as the global network. The abnormally expressed network was primary investigated cause it was an experimentally validated topological network, and it systematically explained the regulatory mechanism of MM. Its outstanding significance lies in that if we correct each abnormally expressed gene and miRNA to normal expression level by transcriptional control adjustment, thus the whole genetic expression network will return to normal state, and MM may not relapse. Additionally, analyses and comparisons to upstream as well as downstream of abnormally expressed miRNAs and genes in three networks highlighted some important regulators and key signaling pathways. For example, STAT3 and hsa-miR-125b, PIAS3 and hsa-miR-21 respectively formed self adaptation feedback regulations. The current research proposed a novel perspective to systematically explained the regulatory mechanism of MM and may contribute to further research and therapy of carcinomas. PMID:26687742

  11. A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks.

    PubMed

    Gregoretti, Francesco; Belcastro, Vincenzo; di Bernardo, Diego; Oliva, Gennaro

    2010-01-01

    The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes--as is the case in biological networks--due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications. PMID:20422008

  12. Gene-Disease Interaction Retrieval from Multiple Sources: A Network Based Method

    PubMed Central

    Huang, Lan; Wang, Yan

    2016-01-01

    The number of gene-related databases has been growing largely along with the research on genes of bioinformatics. Those databases are filled with various gene functions, pathways, interactions, and so forth, while much biomedical knowledge about human diseases is stored as text in all kinds of literatures. Researchers have developed many methods to extract structured biomedical knowledge. Some study and improve text mining algorithms to achieve efficiency in order to cover as many data sources as possible, while some build open source database to accept individual submissions in order to achieve accuracy. This paper combines both efforts and biomedical ontologies to build an interaction network of multiple biomedical ontologies, which guarantees its robustness as well as its wide coverage of biomedical publications. Upon the network, we accomplish an algorithm which discovers paths between concept pairs and shows potential relations. PMID:27478829

  13. GENE EXPRESSION NETWORKS

    EPA Science Inventory

    "Gene expression network" is the term used to describe the interplay, simple or complex, between two or more gene products in performing a specific cellular function. Although the delineation of such networks is complicated by the existence of multiple and subtle types of intera...

  14. Gene regulatory network inference using fused LASSO on multiple data sets

    PubMed Central

    Omranian, Nooshin; Eloundou-Mbebi, Jeanne M. O.; Mueller-Roeber, Bernd; Nikoloski, Zoran

    2016-01-01

    Devising computational methods to accurately reconstruct gene regulatory networks given gene expression data is key to systems biology applications. Here we propose a method for reconstructing gene regulatory networks by simultaneous consideration of data sets from different perturbation experiments and corresponding controls. The method imposes three biologically meaningful constraints: (1) expression levels of each gene should be explained by the expression levels of a small number of transcription factor coding genes, (2) networks inferred from different data sets should be similar with respect to the type and number of regulatory interactions, and (3) relationships between genes which exhibit similar differential behavior over the considered perturbations should be favored. We demonstrate that these constraints can be transformed in a fused LASSO formulation for the proposed method. The comparative analysis on transcriptomics time-series data from prokaryotic species, Escherichia coli and Mycobacterium tuberculosis, as well as a eukaryotic species, mouse, demonstrated that the proposed method has the advantages of the most recent approaches for regulatory network inference, while obtaining better performance and assigning higher scores to the true regulatory links. The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions. PMID:26864687

  15. Insertional mutagenesis identifies multiple networks of cooperating genes driving intestinal tumorigenesis.

    PubMed

    March, H Nikki; Rust, Alistair G; Wright, Nicholas A; ten Hoeve, Jelle; de Ridder, Jeroen; Eldridge, Matthew; van der Weyden, Louise; Berns, Anton; Gadiot, Jules; Uren, Anthony; Kemp, Richard; Arends, Mark J; Wessels, Lodewyk F A; Winton, Douglas J; Adams, David J

    2011-12-01

    The evolution of colorectal cancer suggests the involvement of many genes. To identify new drivers of intestinal cancer, we performed insertional mutagenesis using the Sleeping Beauty transposon system in mice carrying germline or somatic Apc mutations. By analyzing common insertion sites (CISs) isolated from 446 tumors, we identified many hundreds of candidate cancer drivers. Comparison to human data sets suggested that 234 CIS-targeted genes are also dysregulated in human colorectal cancers. In addition, we found 183 CIS-containing genes that are candidate Wnt targets and showed that 20 CISs-containing genes are newly discovered modifiers of canonical Wnt signaling. We also identified mutations associated with a subset of tumors containing an expanded number of Paneth cells, a hallmark of deregulated Wnt signaling, and genes associated with more severe dysplasia included those encoding members of the FGF signaling cascade. Some 70 genes had co-occurrence of CIS pairs, clustering into 38 sub-networks that may regulate tumor development. PMID:22057237

  16. ChIP-Array 2: integrating multiple omics data to construct gene regulatory networks

    PubMed Central

    Wang, Panwen; Qin, Jing; Qin, Yiming; Zhu, Yun; Wang, Lily Yan; Li, Mulin Jun; Zhang, Michael Q.; Wang, Junwen

    2015-01-01

    Transcription factors (TFs) play an important role in gene regulation. The interconnections among TFs, chromatin interactions, epigenetic marks and cis-regulatory elements form a complex gene transcription apparatus. Our previous work, ChIP-Array, combined TF binding and transcriptome data to construct gene regulatory networks (GRNs). Here we present an enhanced version, ChIP-Array 2, to integrate additional types of omics data including long-range chromatin interaction, open chromatin region and histone modification data to dissect more comprehensive GRNs involving diverse regulatory components. Moreover, we substantially extended our motif database for human, mouse, rat, fruit fly, worm, yeast and Arabidopsis, and curated large amount of omics data for users to select as input or backend support. With ChIP-Array 2, we compiled a library containing regulatory networks of 18 TFs/chromatin modifiers in mouse embryonic stem cell (mESC). The web server and the mESC library are publicly free and accessible athttp://jjwanglab.org/chip-array. PMID:25916854

  17. Multiple-Ring Digital Communication Network

    NASA Technical Reports Server (NTRS)

    Kirkham, Harold

    1992-01-01

    Optical-fiber digital communication network to support data-acquisition and control functions of electric-power-distribution networks. Optical-fiber links of communication network follow power-distribution routes. Since fiber crosses open power switches, communication network includes multiple interconnected loops with occasional spurs. At each intersection node is needed. Nodes of communication network include power-distribution substations and power-controlling units. In addition to serving data acquisition and control functions, each node acts as repeater, passing on messages to next node(s). Multiple-ring communication network operates on new AbNET protocol and features fiber-optic communication.

  18. Network-Based Meta-Analyses of Associations of Multiple Gene Expression Profiles with Bone Mineral Density Variations in Women

    PubMed Central

    Niu, Tianhua; Zhou, Yu; Zhang, Lan; Zeng, Yong; Zhu, Wei; Wang, Yu-ping; Deng, Hong-wen

    2016-01-01

    Background Existing microarray studies of bone mineral density (BMD) have been critical for understanding the pathophysiology of osteoporosis, and have identified a number of candidate genes. However, these studies were limited by their relatively small sample sizes and were usually analyzed individually. Here, we propose a novel network-based meta-analysis approach that combines data across six microarray studies to identify functional modules from human protein-protein interaction (PPI) data, and highlight several differentially expressed genes (DEGs) and a functional module that may play an important role in BMD regulation in women. Methods Expression profiling studies were identified by searching PubMed, Gene Expression Omnibus (GEO) and ArrayExpress. Two meta-analysis methods were applied across different gene expression profiling studies. The first, a nonparametric Fisher’s method, combined p-values from individual experiments to identify genes with large effect sizes. The second method combined effect sizes from individual datasets into a meta-effect size to gain a higher precision of effect size estimation across all datasets. Genes with Q test’s p-values < 0.05 or I2 values > 50% were assessed by a random effects model and the remainder by a fixed effects model. Using Fisher’s combined p-values, functional modules were identified through an integrated analysis of microarray data in the context of large protein–protein interaction (PPI) networks. Two previously published meta-analysis studies of genome-wide association (GWA) datasets were used to determine whether these module genes were genetically associated with BMD. Pathway enrichment analysis was performed with a hypergeometric test. Results Six gene expression datasets were identified, which included a total of 249 (129 high BMD and 120 low BMD) female subjects. Using a network-based meta-analysis, a consensus module containing 58 genes (nodes) and 83 edges was detected. Pathway enrichment

  19. Identifying Gene Interaction Networks

    PubMed Central

    Bebek, Gurkan

    2016-01-01

    In this chapter, we introduce interaction networks by describing how they are generated, where they are stored, and how they are shared. We focus on publicly available interaction networks and describe a simple way of utilizing these resources. As a case study, we used Cytoscape, an open source and easy-to-use network visualization and analysis tool to first gather and visualize a small network. We have analyzed this network’s topological features and have looked at functional enrichment of the network nodes by integrating the gene ontology database. The methods described are applicable to larger networks that can be collected from various resources. PMID:22307715

  20. The gap gene network

    PubMed Central

    2010-01-01

    Gap genes are involved in segment determination during the early development of the fruit fly Drosophila melanogaster as well as in other insects. This review attempts to synthesize the current knowledge of the gap gene network through a comprehensive survey of the experimental literature. I focus on genetic and molecular evidence, which provides us with an almost-complete picture of the regulatory interactions responsible for trunk gap gene expression. I discuss the regulatory mechanisms involved, and highlight the remaining ambiguities and gaps in the evidence. This is followed by a brief discussion of molecular regulatory mechanisms for transcriptional regulation, as well as precision and size-regulation provided by the system. Finally, I discuss evidence on the evolution of gap gene expression from species other than Drosophila. My survey concludes that studies of the gap gene system continue to reveal interesting and important new insights into the role of gene regulatory networks in development and evolution. PMID:20927566

  1. Gene expression networks.

    PubMed

    Thomas, Reuben; Portier, Christopher J

    2013-01-01

    With the advent of microarrays and next-generation biotechnologies, the use of gene expression data has become ubiquitous in biological research. One potential drawback of these data is that they are very rich in features or genes though cost considerations allow for the use of only relatively small sample sizes. A useful way of getting at biologically meaningful interpretations of the environmental or toxicological condition of interest would be to make inferences at the level of a priori defined biochemical pathways or networks of interacting genes or proteins that are known to perform certain biological functions. This chapter describes approaches taken in the literature to make such inferences at the biochemical pathway level. In addition this chapter describes approaches to create hypotheses on genes playing important roles in response to a treatment, using organism level gene coexpression or protein-protein interaction networks. Also, approaches to reverse engineer gene networks or methods that seek to identify novel interactions between genes are described. Given the relatively small sample numbers typically available, these reverse engineering approaches are generally useful in inferring interactions only among a relatively small or an order 10 number of genes. Finally, given the vast amounts of publicly available gene expression data from different sources, this chapter summarizes the important sources of these data and characteristics of these sources or databases. In line with the overall aims of this book of providing practical knowledge to a researcher interested in analyzing gene expression data from a network perspective, the chapter provides convenient publicly accessible tools for performing analyses described, and in addition describe three motivating examples taken from the published literature that illustrate some of the relevant analyses. PMID:23086841

  2. Multiple Horizontal Gene Transfer Events and Domain Fusions Have Created Novel Regulatory and Metabolic Networks in the Oomycete Genome

    PubMed Central

    Morris, Paul Francis; Schlosser, Laura Rose; Onasch, Katherine Diane; Wittenschlaeger, Tom; Austin, Ryan; Provart, Nicholas

    2009-01-01

    Complex enzymes with multiple catalytic activities are hypothesized to have evolved from more primitive precursors. Global analysis of the Phytophthora sojae genome using conservative criteria for evaluation of complex proteins identified 273 novel multifunctional proteins that were also conserved in P. ramorum. Each of these proteins contains combinations of protein motifs that are not present in bacterial, plant, animal, or fungal genomes. A subset of these proteins were also identified in the two diatom genomes, but the majority of these proteins have formed after the split between diatoms and oomycetes. Documentation of multiple cases of domain fusions that are common to both oomycetes and diatom genomes lends additional support for the hypothesis that oomycetes and diatoms are monophyletic. Bifunctional proteins that catalyze two steps in a metabolic pathway can be used to infer the interaction of orthologous proteins that exist as separate entities in other genomes. We postulated that the novel multifunctional proteins of oomycetes could function as potential Rosetta Stones to identify interacting proteins of conserved metabolic and regulatory networks in other eukaryotic genomes. However ortholog analysis of each domain within our set of 273 multifunctional proteins against 39 sequenced bacterial and eukaryotic genomes, identified only 18 candidate Rosetta Stone proteins. Thus the majority of multifunctional proteins are not Rosetta Stones, but they may nonetheless be useful in identifying novel metabolic and regulatory networks in oomycetes. Phylogenetic analysis of all the enzymes in three pathways with one or more novel multifunctional proteins was conducted to determine the probable origins of individual enzymes. These analyses revealed multiple examples of horizontal transfer from both bacterial genomes and the photosynthetic endosymbiont in the ancestral genome of Stramenopiles. The complexity of the phylogenetic origins of these metabolic pathways and

  3. The nuclear receptor gene nhr-25 plays multiple roles in the C. elegans heterochronic gene network to control the larva-to-adult transition

    PubMed Central

    Hada, Kazumasa; Asahina, Masako; Hasegawa, Hiroshi; Kanaho, Yasunori; Slack, Frank J.; Niwa, Ryusuke

    2010-01-01

    Developmental timing in the nematode Caenorhabditis elegans is controlled by heterochronic genes, mutations in which cause changes in the relative timing of developmental events. One of the heterochronic genes, let-7, encodes a microRNA that is highly evolutionarily conserved, suggesting that similar genetic pathways control developmental timing across phyla. Here we report that the nuclear receptor nhr-25, which belongs to the evolutionarily conserved fushi tarazu-factor 1/nuclear receptor NR5A subfamily, interacts with heterochronic genes that regulate the larva-to-adult transition in C. elegans. We identified nhr-25 as a regulator of apl-1, a homolog of the Alzheimer’s amyloid precursor protein-like gene that is downstream of let-7 family microRNAs. NHR-25 controls not only apl-1 expression but also regulates developmental progression in the larva-to-adult transition. NHR-25 negatively regulates the expression of the adult-specific collagen gene col-19 in lateral epidermal seam cells. In contrast, NHR-25 positively regulates the larva-to-adult transition for other timed events in seam cells, such as cell fusion, cell division and alae formation. The genetic relationships between nhr-25 and other heterochronic genes are strikingly varied among several adult developmental events. We propose that nhr-25 has multiple roles in both promoting and inhibiting the C. elegans heterochronic gene pathway controlling adult differentiation programs. PMID:20678979

  4. Protocol for multiple node network

    NASA Technical Reports Server (NTRS)

    Kirkham, Harold (Inventor)

    1995-01-01

    The invention is a multiple interconnected network of intelligent message-repeating remote nodes which employs an antibody recognition message termination process performed by all remote nodes and a remote node polling process performed by other nodes which are master units controlling remote nodes in respective zones of the network assigned to respective master nodes. Each remote node repeats only those messages originated in the local zone, to provide isolation among the master nodes.

  5. Protocol for multiple node network

    NASA Technical Reports Server (NTRS)

    Kirkham, Harold (Inventor)

    1994-01-01

    The invention is a multiple interconnected network of intelligent message-repeating remote nodes which employs an antibody recognition message termination process performed by all remote nodes and a remote node polling process performed by other nodes which are master units controlling remote nodes in respective zones of the network assigned to respective master nodes. Each remote node repeats only those messages originated in the local zone, to provide isolation among the master nodes.

  6. Computation in gene networks

    NASA Astrophysics Data System (ADS)

    Ben-Hur, Asa; Siegelmann, Hava T.

    2004-03-01

    Genetic regulatory networks have the complex task of controlling all aspects of life. Using a model of gene expression by piecewise linear differential equations we show that this process can be considered as a process of computation. This is demonstrated by showing that this model can simulate memory bounded Turing machines. The simulation is robust with respect to perturbations of the system, an important property for both analog computers and biological systems. Robustness is achieved using a condition that ensures that the model equations, that are generally chaotic, follow a predictable dynamics.

  7. The Roles of Genes in the Neuronal Migration and Neurite Outgrowth Network in Developmental Dyslexia: Single- and Multiple-Risk Genetic Variants.

    PubMed

    Shao, Shanshan; Kong, Rui; Zou, Li; Zhong, Rong; Lou, Jiao; Zhou, Jie; Guo, Shengnan; Wang, Jia; Zhang, Xiaohui; Zhang, Jiajia; Song, Ranran

    2016-08-01

    Abnormal regulation of neural migration and neurite growth is thought to be an important feature of developmental dyslexia (DD). We investigated 16 genetic variants, selected by bioinformatics analyses, in six key genes in the neuronal migration and neurite outgrowth network in a Chinese population. We first observed that KIAA0319L rs28366021, KIAA0319 rs4504469, and DOCK4 rs2074130 were significantly associated with DD risk after false discovery rate (FDR) adjustment for multiple comparisons (odds ratio (OR) = 0.672, 95 % confidence interval (CI) = 0.505-0.894, P = 0.006; OR = 1.608, 95 % CI = 1.174-2.203, P = 0.003; OR = 1.681, 95 % CI = 1.203-2.348, P = 0.002). The following classification and regression tree (CART) analysis revealed a prediction value of gene-gene interactions among DOCK4 rs2074130, KIAA0319 rs4504469, DCDC2 rs2274305, and KIAA0319L rs28366021 variants. Compared with the lowest risk carriers of the combination of rs2074130 CC, rs4504469 CC, and rs2274305 GG genotype, individuals carrying the combined genotypes of rs2074130 CC, rs4504469 CT or TT, and rs28366021 GG had a significantly increased risk for DD (OR = 2.492, 95 % CI = 1.447-4.290, P = 0.001); individuals with the combination of rs2074130 CT or TT and rs28366021 GG genotype exhibited the highest risk for DD (OR = 2.770, 95 % CI = 2.265-6.276, P = 0.000). A significant dose effect was observed among these four variants (P for trend = 0.000). In summary, this study supports the importance of single- and multiple-risk variants in this network in DD susceptibility in China. PMID:26184631

  8. Integrating heterogeneous gene expression data for gene regulatory network modelling.

    PubMed

    Sîrbu, Alina; Ruskin, Heather J; Crane, Martin

    2012-06-01

    Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors' knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets. PMID:21948152

  9. Hysteresis in a synthetic mammalian gene network.

    PubMed

    Kramer, Beat P; Fussenegger, Martin

    2005-07-01

    Bistable and hysteretic switches, enabling cells to adopt multiple internal expression states in response to a single external input signal, have a pivotal impact on biological systems, ranging from cell-fate decisions to cell-cycle control. We have designed a synthetic hysteretic mammalian transcription network. A positive feedback loop, consisting of a transgene and transactivator (TA) cotranscribed by TA's cognate promoter, is repressed by constitutive expression of a macrolide-dependent transcriptional silencer, whose activity is modulated by the macrolide antibiotic erythromycin. The antibiotic concentration, at which a quasi-discontinuous switch of transgene expression occurs, depends on the history of the synthetic transcription circuitry. If the network components are imbalanced, a graded rather than a quasi-discontinuous signal integration takes place. These findings are consistent with a mathematical model. Synthetic gene networks, which are able to emulate natural gene expression behavior, may foster progress in future gene therapy and tissue engineering initiatives. PMID:15972812

  10. Multiple-Access Quantum-Classical Networks

    NASA Astrophysics Data System (ADS)

    Razavi, Mohsen

    2011-10-01

    A multi-user network that supports both classical and quantum communication is proposed. By relying on optical code-division multiple access techniques, this system offers simultaneous key exchange between multiple pairs of network users. A lower bound on the secure key generation rate will be derived for decoy-state quantum key distribution protocols.

  11. Stability analysis of genetic regulatory networks with multiple time delays.

    PubMed

    Wu, Fang-Xiang

    2007-01-01

    A genetic regulatory network is a dynamic system to describe interactions among genes (mRNA) and its products (proteins). From the statistic thermodynamics and biochemical reaction principle, a genetic regulatory network can be described by a group of nonlinear differential equations with time delays. Stability is one of interesting properties for genetic regulatory network. Previous studies have investigated stability of genetic regulatory networks with a single time delay. In this paper, we investigate properties of genetic regulatory networks with multiple time delays in the notion of delay-independent stability. We present necessary and sufficient condition for the local delay-independent stability of genetic regulatory network with multiple time delays which are independent or commensurate. PMID:18002223

  12. Building Developmental Gene Regulatory Networks

    PubMed Central

    Li, Enhu; Davidson, Eric H.

    2009-01-01

    Animal development is an elaborate process programmed by genomic regulatory instructions. Regulatory genes encode transcription factors and signal molecules, and their expression is under the control of cis-regulatory modules that define the logic of transcriptional responses to the inputs of other regulatory genes. The functional linkages amongst regulatory genes constitute the gene regulatory networks (GRNs) that govern cell specification and patterning in development. Constructing such networks requires identification of the regulatory genes involved and characterization of their temporal and spatial expression patterns. Interactions (activation/repression) among transcription factors or signals can be investigated by large-scale perturbation analysis, in which the function of each gene is specifically blocked. Resultant expression changes are then integrated to identify direct linkages, and to reveal the structure of the GRN. Predicted GRN linkages can be tested and verified by cis-regulatory analysis. The explanatory power of the GRN was shown in the lineage specification of sea urchin endomesoderm. Acquiring such networks is essential for a systematic and mechanistic understanding of the developmental process. PMID:19530131

  13. Measuring multiple evolution mechanisms of complex networks

    PubMed Central

    Zhang, Qian-Ming; Xu, Xiao-Ke; Zhu, Yu-Xiao; Zhou, Tao

    2015-01-01

    Numerous concise models such as preferential attachment have been put forward to reveal the evolution mechanisms of real-world networks, which show that real-world networks are usually jointly driven by a hybrid mechanism of multiplex features instead of a single pure mechanism. To get an accurate simulation for real networks, some researchers proposed a few hybrid models by mixing multiple evolution mechanisms. Nevertheless, how a hybrid mechanism of multiplex features jointly influence the network evolution is not very clear. In this study, we introduce two methods (link prediction and likelihood analysis) to measure multiple evolution mechanisms of complex networks. Through tremendous experiments on artificial networks, which can be controlled to follow multiple mechanisms with different weights, we find the method based on likelihood analysis performs much better and gives very accurate estimations. At last, we apply this method to some real-world networks which are from different domains (including technology networks and social networks) and different countries (e.g., USA and China), to see how popularity and clustering co-evolve. We find most of them are affected by both popularity and clustering, but with quite different weights. PMID:26065382

  14. Gene networks controlling petal organogenesis.

    PubMed

    Huang, Tengbo; Irish, Vivian F

    2016-01-01

    One of the biggest unanswered questions in developmental biology is how growth is controlled. Petals are an excellent organ system for investigating growth control in plants: petals are dispensable, have a simple structure, and are largely refractory to environmental perturbations that can alter their size and shape. In recent studies, a number of genes controlling petal growth have been identified. The overall picture of how such genes function in petal organogenesis is beginning to be elucidated. This review will focus on studies using petals as a model system to explore the underlying gene networks that control organ initiation, growth, and final organ morphology. PMID:26428062

  15. Buffering in cyclic gene networks

    NASA Astrophysics Data System (ADS)

    Glyzin, S. D.; Kolesov, A. Yu.; Rozov, N. Kh.

    2016-06-01

    We consider cyclic chains of unidirectionally coupled delay differential-difference equations that are mathematical models of artificial oscillating gene networks. We establish that the buffering phenomenon is realized in these system for an appropriate choice of the parameters: any given finite number of stable periodic motions of a special type, the so-called traveling waves, coexist.

  16. The Gene Network Underlying Hypodontia.

    PubMed

    Yin, W; Bian, Z

    2015-07-01

    Mammalian tooth development is a precise and complicated procedure. Several signaling pathways, such as nuclear factor (NF)-κB and WNT, are key regulators of tooth development. Any disturbance of these signaling pathways can potentially affect or block normal tooth development, and presently, there are more than 150 syndromes and 80 genes known to be related to tooth agenesis. Clarifying the interaction and crosstalk among these genes will provide important information regarding the mechanisms underlying missing teeth. In the current review, we summarize recently published findings on genes related to isolated and syndromic tooth agenesis; most of these genes function as positive regulators of cell proliferation or negative regulators of cell differentiation and apoptosis. Furthermore, we explore the corresponding networks involving these genes in addition to their implications for the clinical management of tooth agenesis. We conclude that this requires further study to improve patients' quality of life in the future. PMID:25910507

  17. Plant Evolution: Evolving Antagonistic Gene Regulatory Networks.

    PubMed

    Cooper, Endymion D

    2016-06-20

    Developing a structurally complex phenotype requires a complex regulatory network. A new study shows how gene duplication provides a potential source of antagonistic interactions, an important component of gene regulatory networks. PMID:27326708

  18. Diversity Performance Analysis on Multiple HAP Networks.

    PubMed

    Dong, Feihong; Li, Min; Gong, Xiangwu; Li, Hongjun; Gao, Fengyue

    2015-01-01

    One of the main design challenges in wireless sensor networks (WSNs) is achieving a high-data-rate transmission for individual sensor devices. The high altitude platform (HAP) is an important communication relay platform for WSNs and next-generation wireless networks. Multiple-input multiple-output (MIMO) techniques provide the diversity and multiplexing gain, which can improve the network performance effectively. In this paper, a virtual MIMO (V-MIMO) model is proposed by networking multiple HAPs with the concept of multiple assets in view (MAV). In a shadowed Rician fading channel, the diversity performance is investigated. The probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-noise ratio (SNR) are derived. In addition, the average symbol error rate (ASER) with BPSK and QPSK is given for the V-MIMO model. The system capacity is studied for both perfect channel state information (CSI) and unknown CSI individually. The ergodic capacity with various SNR and Rician factors for different network configurations is also analyzed. The simulation results validate the effectiveness of the performance analysis. It is shown that the performance of the HAPs network in WSNs can be significantly improved by utilizing the MAV to achieve overlapping coverage, with the help of the V-MIMO techniques. PMID:26134102

  19. Diversity Performance Analysis on Multiple HAP Networks

    PubMed Central

    Dong, Feihong; Li, Min; Gong, Xiangwu; Li, Hongjun; Gao, Fengyue

    2015-01-01

    One of the main design challenges in wireless sensor networks (WSNs) is achieving a high-data-rate transmission for individual sensor devices. The high altitude platform (HAP) is an important communication relay platform for WSNs and next-generation wireless networks. Multiple-input multiple-output (MIMO) techniques provide the diversity and multiplexing gain, which can improve the network performance effectively. In this paper, a virtual MIMO (V-MIMO) model is proposed by networking multiple HAPs with the concept of multiple assets in view (MAV). In a shadowed Rician fading channel, the diversity performance is investigated. The probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-noise ratio (SNR) are derived. In addition, the average symbol error rate (ASER) with BPSK and QPSK is given for the V-MIMO model. The system capacity is studied for both perfect channel state information (CSI) and unknown CSI individually. The ergodic capacity with various SNR and Rician factors for different network configurations is also analyzed. The simulation results validate the effectiveness of the performance analysis. It is shown that the performance of the HAPs network in WSNs can be significantly improved by utilizing the MAV to achieve overlapping coverage, with the help of the V-MIMO techniques. PMID:26134102

  20. Estrogen Receptor α Controls a Gene Network in Luminal-Like Breast Cancer Cells Comprising Multiple Transcription Factors and MicroRNAs

    PubMed Central

    Cicatiello, Luigi; Mutarelli, Margherita; Grober, Oli M.V.; Paris, Ornella; Ferraro, Lorenzo; Ravo, Maria; Tarallo, Roberta; Luo, Shujun; Schroth, Gary P.; Seifert, Martin; Zinser, Christian; Luisa Chiusano, Maria; Traini, Alessandra; De Bortoli, Michele; Weisz, Alessandro

    2010-01-01

    Luminal-like breast tumor cells express estrogen receptor α (ERα), a member of the nuclear receptor family of ligand-activated transcription factors that controls their proliferation, survival, and functional status. To identify the molecular determinants of this hormone-responsive tumor phenotype, a comprehensive genome-wide analysis was performed in estrogen stimulated MCF-7 and ZR-75.1 cells by integrating time-course mRNA expression profiling with global mapping of genomic ERα binding sites by chromatin immunoprecipitation coupled to massively parallel sequencing, microRNA expression profiling, and in silico analysis of transcription units and receptor binding regions identified. All 1270 genes that were found to respond to 17β-estradiol in both cell lines cluster in 33 highly concordant groups, each of which showed defined kinetics of RNA changes. This hormone-responsive gene set includes several direct targets of ERα and is organized in a gene regulation cascade, stemming from ligand-activated receptor and reaching a large number of downstream targets via AP-2γ, B-cell activating transcription factor, E2F1 and 2, E74-like factor 3, GTF2IRD1, hairy and enhancer of split homologue-1, MYB, SMAD3, RARα, and RXRα transcription factors. MicroRNAs are also integral components of this gene regulation network because miR-107, miR-424, miR-570, miR-618, and miR-760 are regulated by 17β-estradiol along with other microRNAs that can target a significant number of transcripts belonging to one or more estrogen-responsive gene clusters. PMID:20348243

  1. Trainable Gene Regulation Networks with Applications to Drosophila Pattern Formation

    NASA Technical Reports Server (NTRS)

    Mjolsness, Eric

    2000-01-01

    This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila melanogaster. For details the reader is referred to the papers introduced below. It will then introduce a new gene regulation network model which can describe promoter-level substructure in gene regulation. As described in chapter 2, gene regulation may be thought of as a combination of cis-acting regulation by the extended promoter of a gene (including all regulatory sequences) by way of the transcription complex, and of trans-acting regulation by the transcription factor products of other genes. If we simplify the cis-action by using a phenomenological model which can be tuned to data, such as a unit or other small portion of an artificial neural network, then the full transacting interaction between multiple genes during development can be modelled as a larger network which can again be tuned or trained to data. The larger network will in general need to have recurrent (feedback) connections since at least some real gene regulation networks do. This is the basic modeling approach taken, which describes how a set of recurrent neural networks can be used as a modeling language for multiple developmental processes including gene regulation within a single cell, cell-cell communication, and cell division. Such network models have been called "gene circuits", "gene regulation networks", or "genetic regulatory networks", sometimes without distinguishing the models from the actual modeled systems.

  2. Gene networks and liar paradoxes

    PubMed Central

    Isalan, Mark

    2009-01-01

    Network motifs are small patterns of connections, found over-represented in gene regulatory networks. An example is the negative feedback loop (e.g. factor A represses itself). This opposes its own state so that when ‘on’ it tends towards ‘off’ – and vice versa. Here, we argue that such self-opposition, if considered dimensionlessly, is analogous to the liar paradox: ‘This statement is false’. When ‘true’ it implies ‘false’ – and vice versa. Such logical constructs have provided philosophical consternation for over 2000 years. Extending the analogy, other network topologies give strikingly varying outputs over different dimensions. For example, the motif ‘A activates B and A. B inhibits A’ can give switches or oscillators with time only, or can lead to Turing-type patterns with both space and time (spots, stripes or waves). It is argued here that the dimensionless form reduces to a variant of ‘The following statement is true. The preceding statement is false’. Thus, merely having a static topological description of a gene network can lead to a liar paradox. Network diagrams are only snapshots of dynamic biological processes and apparent paradoxes can reveal important biological mechanisms that are far from paradoxical when considered explicitly in time and space. PMID:19722183

  3. Gene networks and liar paradoxes.

    PubMed

    Isalan, Mark

    2009-10-01

    Network motifs are small patterns of connections, found over-represented in gene regulatory networks. An example is the negative feedback loop (e.g. factor A represses itself). This opposes its own state so that when 'on' it tends towards 'off' - and vice versa. Here, we argue that such self-opposition, if considered dimensionlessly, is analogous to the liar paradox: 'This statement is false'. When 'true' it implies 'false' - and vice versa. Such logical constructs have provided philosophical consternation for over 2000 years. Extending the analogy, other network topologies give strikingly varying outputs over different dimensions. For example, the motif 'A activates B and A. B inhibits A' can give switches or oscillators with time only, or can lead to Turing-type patterns with both space and time (spots, stripes or waves). It is argued here that the dimensionless form reduces to a variant of 'The following statement is true. The preceding statement is false'. Thus, merely having a static topological description of a gene network can lead to a liar paradox. Network diagrams are only snapshots of dynamic biological processes and apparent paradoxes can reveal important biological mechanisms that are far from paradoxical when considered explicitly in time and space. PMID:19722183

  4. Predicting Protein Function via Semantic Integration of Multiple Networks.

    PubMed

    Yu, Guoxian; Fu, Guangyuan; Wang, Jun; Zhu, Hailong

    2016-01-01

    Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically i ntegrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at https://sites.google.com/site/guoxian85/simnet. PMID:26800544

  5. Preferential attachment in multiple trade networks

    NASA Astrophysics Data System (ADS)

    Foschi, Rachele; Riccaboni, Massimo; Schiavo, Stefano

    2014-08-01

    In this paper we develop a model for the evolution of multiple networks which is able to replicate the concentrated and sparse nature of world trade data. Our model is an extension of the preferential attachment growth model to the case of multiple networks. Countries trade a variety of goods of different complexity. Every country progressively evolves from trading less sophisticated to high-tech goods. The probabilities of capturing more trade opportunities at a given level of complexity and of starting to trade more complex goods are both proportional to the number of existing trade links. We provide a set of theoretical predictions and simulative results. A calibration exercise shows that our model replicates the same concentration level of world trade as well as the sparsity pattern of the trade matrix. We also discuss a set of numerical solutions to deal with large multiple networks.

  6. Arabidopsis gene co-expression network and its functional modules

    PubMed Central

    Mao, Linyong; Van Hemert, John L; Dash, Sudhansu; Dickerson, Julie A

    2009-01-01

    Background Biological networks characterize the interactions of biomolecules at a systems-level. One important property of biological networks is the modular structure, in which nodes are densely connected with each other, but between which there are only sparse connections. In this report, we attempted to find the relationship between the network topology and formation of modular structure by comparing gene co-expression networks with random networks. The organization of gene functional modules was also investigated. Results We constructed a genome-wide Arabidopsis gene co-expression network (AGCN) by using 1094 microarrays. We then analyzed the topological properties of AGCN and partitioned the network into modules by using an efficient graph clustering algorithm. In the AGCN, 382 hub genes formed a clique, and they were densely connected only to a small subset of the network. At the module level, the network clustering results provide a systems-level understanding of the gene modules that coordinate multiple biological processes to carry out specific biological functions. For instance, the photosynthesis module in AGCN involves a very large number (> 1000) of genes which participate in various biological processes including photosynthesis, electron transport, pigment metabolism, chloroplast organization and biogenesis, cofactor metabolism, protein biosynthesis, and vitamin metabolism. The cell cycle module orchestrated the coordinated expression of hundreds of genes involved in cell cycle, DNA metabolism, and cytoskeleton organization and biogenesis. We also compared the AGCN constructed in this study with a graphical Gaussian model (GGM) based Arabidopsis gene network. The photosynthesis, protein biosynthesis, and cell cycle modules identified from the GGM network had much smaller module sizes compared with the modules found in the AGCN, respectively. Conclusion This study reveals new insight into the topological properties of biological networks. The

  7. Adaptive Models for Gene Networks

    PubMed Central

    Shin, Yong-Jun; Sayed, Ali H.; Shen, Xiling

    2012-01-01

    Biological systems are often treated as time-invariant by computational models that use fixed parameter values. In this study, we demonstrate that the behavior of the p53-MDM2 gene network in individual cells can be tracked using adaptive filtering algorithms and the resulting time-variant models can approximate experimental measurements more accurately than time-invariant models. Adaptive models with time-variant parameters can help reduce modeling complexity and can more realistically represent biological systems. PMID:22359614

  8. A multiple-criteria network optimization

    NASA Astrophysics Data System (ADS)

    Tramelli, Anna; De Natale, Giuseppe; Troise, Claudia; Orazi, Massimo

    2013-04-01

    Optimizing a seismic network for locating earthquake is a crucial issue in seismology. Precise earthquake location is indeed the key factor for most sophisticated seismological analyses and for monitoring activities. Seismic monitoring of tectonic and volcanic areas involves the use of seismic networks which should have high efficiency in detecting and locating microearthquakes. Network testing and optimization should therefore be a basic procedure to plan, install and improve a monitoring seismic network. In this paper, we firstly review the most appropriate methods for network testing, using two different approaches based, respectively, on linearized and Bayesian methods. Furthermore, we developed and tested a complete optimization technique which allows to take into account different parameters (i.e. cost, transmission lines redundancy, etc.) in addition to the location quality. The new method is based on the direct comparison of all the possible networks resulting from the permutation of N stations in M sites (with M?N). The location performance of a network can be defined using different criteria; in our method we use either the determinant of the covariance matrix, which is generally used in literature, and the condition number of the coefficient matrix, a different approach which is new in this field. We show that our new procedure is very efficient for network optimization with respect to multiple criteria, and overcomes several problems of the actual procedures. The method is thus applied to the Campi Flegrei volcanic area, to optimize and improve its seismic monitoring network.

  9. Multiple network alignment on quantum computers

    NASA Astrophysics Data System (ADS)

    Daskin, Anmer; Grama, Ananth; Kais, Sabre

    2014-12-01

    Comparative analyses of graph-structured datasets underly diverse problems. Examples of these problems include identification of conserved functional components (biochemical interactions) across species, structural similarity of large biomolecules, and recurring patterns of interactions in social networks. A large class of such analyses methods quantify the topological similarity of nodes across networks. The resulting correspondence of nodes across networks, also called node alignment, can be used to identify invariant subgraphs across the input graphs. Given graphs as input, alignment algorithms use topological information to assign a similarity score to each -tuple of nodes, with elements (nodes) drawn from each of the input graphs. Nodes are considered similar if their neighbors are also similar. An alternate, equivalent view of these network alignment algorithms is to consider the Kronecker product of the input graphs and to identify high-ranked nodes in the Kronecker product graph. Conventional methods such as PageRank and HITS (Hypertext-Induced Topic Selection) can be used for this purpose. These methods typically require computation of the principal eigenvector of a suitably modified Kronecker product matrix of the input graphs. We adopt this alternate view of the problem to address the problem of multiple network alignment. Using the phase estimation algorithm, we show that the multiple network alignment problem can be efficiently solved on quantum computers. We characterize the accuracy and performance of our method and show that it can deliver exponential speedups over conventional (non-quantum) methods.

  10. Multiple network alignment on quantum computers

    NASA Astrophysics Data System (ADS)

    Daskin, Anmer; Grama, Ananth; Kais, Sabre

    2014-09-01

    Comparative analyses of graph structured datasets underly diverse problems. Examples of these problems include identification of conserved functional components (biochemical interactions) across species, structural similarity of large biomolecules, and recurring patterns of interactions in social networks. A large class of such analyses methods quantify the topological similarity of nodes across networks. The resulting correspondence of nodes across networks, also called node alignment, can be used to identify invariant subgraphs across the input graphs. Given $k$ graphs as input, alignment algorithms use topological information to assign a similarity score to each $k$-tuple of nodes, with elements (nodes) drawn from each of the input graphs. Nodes are considered similar if their neighbors are also similar. An alternate, equivalent view of these network alignment algorithms is to consider the Kronecker product of the input graphs, and to identify high-ranked nodes in the Kronecker product graph. Conventional methods such as PageRank and HITS (Hypertext Induced Topic Selection) can be used for this purpose. These methods typically require computation of the principal eigenvector of a suitably modified Kronecker product matrix of the input graphs. We adopt this alternate view of the problem to address the problem of multiple network alignment. Using the phase estimation algorithm, we show that the multiple network alignment problem can be efficiently solved on quantum computers. We characterize the accuracy and performance of our method, and show that it can deliver exponential speedups over conventional (non-quantum) methods.

  11. Multiple communication networks for a radiological PACS

    NASA Astrophysics Data System (ADS)

    Wong, Albert W. K.; Stewart, Brent K.; Lou, Shyhliang A.; Chan, Kelby K.; Huang, H. K.

    1991-07-01

    The authors have implemented a communication network connecting multiple buildings for their picture archiving and communication system (PACS) in the Radiology Department at UCLA. The network consists of three types of local area networks (LANs) and a 1.0-km fiber-optic link connecting the outpatient and inpatient facilities. Images from radiologic imaging devices (4 CT scanners, 5 MR scanners, 4 CR units and 5 film digitizers) are transmitted to the acquisition computers via the Ethernet LAN. The fiber distributed data interface (FDDI) LAN then provides data communication among the cluster controllers, the acquisition computers, and the database servers. A 1-gigabit UltraNet LAN is used to route images from the cluster controllers to remote display workstations. All inter-building connections are through fiber-optic cables. Among these multiple networks, Ethernet offers multi-access to the multimodal PACS in image acquisition, FDDI controls a fast data flow so that all acquired images have a shorter residence time on local disks, and UltraNet provides high-speed transfer of images from the cluster controllers to the display workstations. The three-tiered functionality of Ethernet, FDDI, and UltraNet eliminates network traffic bottlenecks and hence provides high performance in image communication. The delay time of a 2K X 2K X 8-bit CR image (4 MBytes) from acquisition to display is less than 5 minutes. In addition, the standard Ethernet serves as a backup to guarantee network connectivity of the entire PACS.

  12. Analysis of bHLH coding genes using gene co-expression network approach.

    PubMed

    Srivastava, Swati; Sanchita; Singh, Garima; Singh, Noopur; Srivastava, Gaurava; Sharma, Ashok

    2016-07-01

    Network analysis provides a powerful framework for the interpretation of data. It uses novel reference network-based metrices for module evolution. These could be used to identify module of highly connected genes showing variation in co-expression network. In this study, a co-expression network-based approach was used for analyzing the genes from microarray data. Our approach consists of a simple but robust rank-based network construction. The publicly available gene expression data of Solanum tuberosum under cold and heat stresses were considered to create and analyze a gene co-expression network. The analysis provide highly co-expressed module of bHLH coding genes based on correlation values. Our approach was to analyze the variation of genes expression, according to the time period of stress through co-expression network approach. As the result, the seed genes were identified showing multiple connections with other genes in the same cluster. Seed genes were found to be vary in different time periods of stress. These analyzed seed genes may be utilized further as marker genes for developing the stress tolerant plant species. PMID:27178572

  13. The transfer and transformation of collective network information in gene-matched networks

    PubMed Central

    Kitsukawa, Takashi; Yagi, Takeshi

    2015-01-01

    Networks, such as the human society network, social and professional networks, and biological system networks, contain vast amounts of information. Information signals in networks are distributed over nodes and transmitted through intricately wired links, making the transfer and transformation of such information difficult to follow. Here we introduce a novel method for describing network information and its transfer using a model network, the Gene-matched network (GMN), in which nodes (neurons) possess attributes (genes). In the GMN, nodes are connected according to their expression of common genes. Because neurons have multiple genes, the GMN is cluster-rich. We show that, in the GMN, information transfer and transformation were controlled systematically, according to the activity level of the network. Furthermore, information transfer and transformation could be traced numerically with a vector using genes expressed in the activated neurons, the active-gene array, which was used to assess the relative activity among overlapping neuronal groups. Interestingly, this coding style closely resembles the cell-assembly neural coding theory. The method introduced here could be applied to many real-world networks, since many systems, including human society and various biological systems, can be represented as a network of this type. PMID:26450411

  14. The transfer and transformation of collective network information in gene-matched networks.

    PubMed

    Kitsukawa, Takashi; Yagi, Takeshi

    2015-01-01

    Networks, such as the human society network, social and professional networks, and biological system networks, contain vast amounts of information. Information signals in networks are distributed over nodes and transmitted through intricately wired links, making the transfer and transformation of such information difficult to follow. Here we introduce a novel method for describing network information and its transfer using a model network, the Gene-matched network (GMN), in which nodes (neurons) possess attributes (genes). In the GMN, nodes are connected according to their expression of common genes. Because neurons have multiple genes, the GMN is cluster-rich. We show that, in the GMN, information transfer and transformation were controlled systematically, according to the activity level of the network. Furthermore, information transfer and transformation could be traced numerically with a vector using genes expressed in the activated neurons, the active-gene array, which was used to assess the relative activity among overlapping neuronal groups. Interestingly, this coding style closely resembles the cell-assembly neural coding theory. The method introduced here could be applied to many real-world networks, since many systems, including human society and various biological systems, can be represented as a network of this type. PMID:26450411

  15. Multiple Stochastic Point Processes in Gene Expression

    NASA Astrophysics Data System (ADS)

    Murugan, Rajamanickam

    2008-04-01

    We generalize the idea of multiple-stochasticity in chemical reaction systems to gene expression. Using Chemical Langevin Equation approach we investigate how this multiple-stochasticity can influence the overall molecular number fluctuations. We show that the main sources of this multiple-stochasticity in gene expression could be the randomness in transcription and translation initiation times which in turn originates from the underlying bio-macromolecular recognition processes such as the site-specific DNA-protein interactions and therefore can be internally regulated by the supra-molecular structural factors such as the condensation/super-coiling of DNA. Our theory predicts that (1) in case of gene expression system, the variances ( φ) introduced by the randomness in transcription and translation initiation-times approximately scales with the degree of condensation ( s) of DNA or mRNA as φ ∝ s -6. From the theoretical analysis of the Fano factor as well as coefficient of variation associated with the protein number fluctuations we predict that (2) unlike the singly-stochastic case where the Fano factor has been shown to be a monotonous function of translation rate, in case of multiple-stochastic gene expression the Fano factor is a turn over function with a definite minimum. This in turn suggests that the multiple-stochastic processes can also be well tuned to behave like a singly-stochastic point processes by adjusting the rate parameters.

  16. Functional Gene Networks: R/Bioc package to generate and analyse gene networks derived from functional enrichment and clustering

    PubMed Central

    Aibar, Sara; Fontanillo, Celia; Droste, Conrad; De Las Rivas, Javier

    2015-01-01

    Summary: Functional Gene Networks (FGNet) is an R/Bioconductor package that generates gene networks derived from the results of functional enrichment analysis (FEA) and annotation clustering. The sets of genes enriched with specific biological terms (obtained from a FEA platform) are transformed into a network by establishing links between genes based on common functional annotations and common clusters. The network provides a new view of FEA results revealing gene modules with similar functions and genes that are related to multiple functions. In addition to building the functional network, FGNet analyses the similarity between the groups of genes and provides a distance heatmap and a bipartite network of functionally overlapping genes. The application includes an interface to directly perform FEA queries using different external tools: DAVID, GeneTerm Linker, TopGO or GAGE; and a graphical interface to facilitate the use. Availability and implementation: FGNet is available in Bioconductor, including a tutorial. URL: http://bioconductor.org/packages/release/bioc/html/FGNet.html Contact: jrivas@usal.es Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25600944

  17. In silico gene prioritization by integrating multiple data sources.

    PubMed

    Chen, Yixuan; Wang, Wenhui; Zhou, Yingyao; Shields, Robert; Chanda, Sumit K; Elston, Robert C; Li, Jing

    2011-01-01

    Identifying disease genes is crucial to the understanding of disease pathogenesis, and to the improvement of disease diagnosis and treatment. In recent years, many researchers have proposed approaches to prioritize candidate genes by considering the relationship of candidate genes and existing known disease genes, reflected in other data sources. In this paper, we propose an expandable framework for gene prioritization that can integrate multiple heterogeneous data sources by taking advantage of a unified graphic representation. Gene-gene relationships and gene-disease relationships are then defined based on the overall topology of each network using a diffusion kernel measure. These relationship measures are in turn normalized to derive an overall measure across all networks, which is utilized to rank all candidate genes. Based on the informativeness of available data sources with respect to each specific disease, we also propose an adaptive threshold score to select a small subset of candidate genes for further validation studies. We performed large scale cross-validation analysis on 110 disease families using three data sources. Results have shown that our approach consistently outperforms other two state of the art programs. A case study using Parkinson disease (PD) has identified four candidate genes (UBB, SEPT5, GPR37 and TH) that ranked higher than our adaptive threshold, all of which are involved in the PD pathway. In particular, a very recent study has observed a deletion of TH in a patient with PD, which supports the importance of the TH gene in PD pathogenesis. A web tool has been implemented to assist scientists in their genetic studies. PMID:21731658

  18. Evolving Robust Gene Regulatory Networks

    PubMed Central

    Noman, Nasimul; Monjo, Taku; Moscato, Pablo; Iba, Hitoshi

    2015-01-01

    Design and implementation of robust network modules is essential for construction of complex biological systems through hierarchical assembly of ‘parts’ and ‘devices’. The robustness of gene regulatory networks (GRNs) is ascribed chiefly to the underlying topology. The automatic designing capability of GRN topology that can exhibit robust behavior can dramatically change the current practice in synthetic biology. A recent study shows that Darwinian evolution can gradually develop higher topological robustness. Subsequently, this work presents an evolutionary algorithm that simulates natural evolution in silico, for identifying network topologies that are robust to perturbations. We present a Monte Carlo based method for quantifying topological robustness and designed a fitness approximation approach for efficient calculation of topological robustness which is computationally very intensive. The proposed framework was verified using two classic GRN behaviors: oscillation and bistability, although the framework is generalized for evolving other types of responses. The algorithm identified robust GRN architectures which were verified using different analysis and comparison. Analysis of the results also shed light on the relationship among robustness, cooperativity and complexity. This study also shows that nature has already evolved very robust architectures for its crucial systems; hence simulation of this natural process can be very valuable for designing robust biological systems. PMID:25616055

  19. Tools and Models for Integrating Multiple Cellular Networks

    SciTech Connect

    Gerstein, Mark

    2015-11-06

    In this grant, we have systematically investigated the integrated networks, which are responsible for the coordination of activity between metabolic pathways in prokaryotes. We have developed several computational tools to analyze the topology of the integrated networks consisting of metabolic, regulatory, and physical interaction networks. The tools are all open-source, and they are available to download from Github, and can be incorporated in the Knowledgebase. Here, we summarize our work as follow. Understanding the topology of the integrated networks is the first step toward understanding its dynamics and evolution. For Aim 1 of this grant, we have developed a novel algorithm to determine and measure the hierarchical structure of transcriptional regulatory networks [1]. The hierarchy captures the direction of information flow in the network. The algorithm is generally applicable to regulatory networks in prokaryotes, yeast and higher organisms. Integrated datasets are extremely beneficial in understanding the biology of a system in a compact manner due to the conflation of multiple layers of information. Therefore for Aim 2 of this grant, we have developed several tools and carried out analysis for integrating system-wide genomic information. To make use of the structural data, we have developed DynaSIN for protein-protein interactions networks with various dynamical interfaces [2]. We then examined the association between network topology with phenotypic effects such as gene essentiality. In particular, we have organized E. coli and S. cerevisiae transcriptional regulatory networks into hierarchies. We then correlated gene phenotypic effects by tinkering with different layers to elucidate which layers were more tolerant to perturbations [3]. In the context of evolution, we also developed a workflow to guide the comparison between different types of biological networks across various species using the concept of rewiring [4], and Furthermore, we have developed

  20. GINI: From ISH Images to Gene Interaction Networks

    PubMed Central

    Puniyani, Kriti; Xing, Eric P.

    2013-01-01

    Accurate inference of molecular and functional interactions among genes, especially in multicellular organisms such as Drosophila, often requires statistical analysis of correlations not only between the magnitudes of gene expressions, but also between their temporal-spatial patterns. The ISH (in-situ-hybridization)-based gene expression micro-imaging technology offers an effective approach to perform large-scale spatial-temporal profiling of whole-body mRNA abundance. However, analytical tools for discovering gene interactions from such data remain an open challenge due to various reasons, including difficulties in extracting canonical representations of gene activities from images, and in inference of statistically meaningful networks from such representations. In this paper, we present GINI, a machine learning system for inferring gene interaction networks from Drosophila embryonic ISH images. GINI builds on a computer-vision-inspired vector-space representation of the spatial pattern of gene expression in ISH images, enabled by our recently developed system; and a new multi-instance-kernel algorithm that learns a sparse Markov network model, in which, every gene (i.e., node) in the network is represented by a vector-valued spatial pattern rather than a scalar-valued gene intensity as in conventional approaches such as a Gaussian graphical model. By capturing the notion of spatial similarity of gene expression, and at the same time properly taking into account the presence of multiple images per gene via multi-instance kernels, GINI is well-positioned to infer statistically sound, and biologically meaningful gene interaction networks from image data. Using both synthetic data and a small manually curated data set, we demonstrate the effectiveness of our approach in network building. Furthermore, we report results on a large publicly available collection of Drosophila embryonic ISH images from the Berkeley Drosophila Genome Project, where GINI makes novel and

  1. Stabilizing gene regulatory networks through feedforward loops

    NASA Astrophysics Data System (ADS)

    Kadelka, C.; Murrugarra, D.; Laubenbacher, R.

    2013-06-01

    The global dynamics of gene regulatory networks are known to show robustness to perturbations in the form of intrinsic and extrinsic noise, as well as mutations of individual genes. One molecular mechanism underlying this robustness has been identified as the action of so-called microRNAs that operate via feedforward loops. We present results of a computational study, using the modeling framework of stochastic Boolean networks, which explores the role that such network motifs play in stabilizing global dynamics. The paper introduces a new measure for the stability of stochastic networks. The results show that certain types of feedforward loops do indeed buffer the network against stochastic effects.

  2. Multicolor labeling in developmental gene regulatory network analysis.

    PubMed

    Sethi, Aditya J; Angerer, Robert C; Angerer, Lynne M

    2014-01-01

    The sea urchin embryo is an important model system for developmental gene regulatory network (GRN) analysis. This chapter describes the use of multicolor fluorescent in situ hybridization (FISH) as well as a combination of FISH and immunohistochemistry in sea urchin embryonic GRN studies. The methods presented here can be applied to a variety of experimental settings where accurate spatial resolution of multiple gene products is required for constructing a developmental GRN. PMID:24567220

  3. Modeling of hysteresis in gene regulatory networks.

    PubMed

    Hu, J; Qin, K R; Xiang, C; Lee, T H

    2012-08-01

    Hysteresis, observed in many gene regulatory networks, has a pivotal impact on biological systems, which enhances the robustness of cell functions. In this paper, a general model is proposed to describe the hysteretic gene regulatory network by combining the hysteresis component and the transient dynamics. The Bouc-Wen hysteresis model is modified to describe the hysteresis component in the mammalian gene regulatory networks. Rigorous mathematical analysis on the dynamical properties of the model is presented to ensure the bounded-input-bounded-output (BIBO) stability and demonstrates that the original Bouc-Wen model can only generate a clockwise hysteresis loop while the modified model can describe both clockwise and counter clockwise hysteresis loops. Simulation studies have shown that the hysteresis loops from our model are consistent with the experimental observations in three mammalian gene regulatory networks and two E.coli gene regulatory networks, which demonstrate the ability and accuracy of the mathematical model to emulate natural gene expression behavior with hysteresis. A comparison study has also been conducted to show that this model fits the experiment data significantly better than previous ones in the literature. The successful modeling of the hysteresis in all the five hysteretic gene regulatory networks suggests that the new model has the potential to be a unified framework for modeling hysteresis in gene regulatory networks and provide better understanding of the general mechanism that drives the hysteretic function. PMID:22588784

  4. Analysis of Cascading Failure in Gene Networks

    PubMed Central

    Sun, Longxiao; Wang, Shudong; Li, Kaikai; Meng, Dazhi

    2012-01-01

    It is an important subject to research the functional mechanism of cancer-related genes make in formation and development of cancers. The modern methodology of data analysis plays a very important role for deducing the relationship between cancers and cancer-related genes and analyzing functional mechanism of genome. In this research, we construct mutual information networks using gene expression profiles of glioblast and renal in normal condition and cancer conditions. We investigate the relationship between structure and robustness in gene networks of the two tissues using a cascading failure model based on betweenness centrality. Define some important parameters such as the percentage of failure nodes of the network, the average size-ratio of cascading failure, and the cumulative probability of size-ratio of cascading failure to measure the robustness of the networks. By comparing control group and experiment groups, we find that the networks of experiment groups are more robust than that of control group. The gene that can cause large scale failure is called structural key gene. Some of them have been confirmed to be closely related to the formation and development of glioma and renal cancer respectively. Most of them are predicted to play important roles during the formation of glioma and renal cancer, maybe the oncogenes, suppressor genes, and other cancer candidate genes in the glioma and renal cancer cells. However, these studies provide little information about the detailed roles of identified cancer genes. PMID:23248647

  5. Multiple network interface core apparatus and method

    SciTech Connect

    Underwood, Keith D.; Hemmert, Karl Scott

    2011-04-26

    A network interface controller and network interface control method comprising providing a single integrated circuit as a network interface controller and employing a plurality of network interface cores on the single integrated circuit.

  6. [Susceptibility gene in multiple system atrophy (MSA)].

    PubMed

    Tsuji, Shoji

    2014-01-01

    To elucidate molecular bases of multiple system atrophy (MSA), we first focused on recently identified MSA multiplex families. Though linkage analyses followed by whole genome resequencing, we have identified a causative gene, COQ2, for MSA. We then conducted comprehensive nucleotide sequence analysis of COQ2 of sporadic MSA cases and controls, and found that functionally deleterious COQ2 variants confer a strong risk for developing MSA. COQ2 encodes an enzyme in the biosynthetic pathway of coenzyme Q10. Decreased synthesis of coenzyme Q10 is considered to be involved in the pathogenesis of MSA through decreased electron transport in mitochondria and increased vulnerability to oxidative stress. PMID:25672683

  7. Genes2FANs: connecting genes through functional association networks

    PubMed Central

    2012-01-01

    Background Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent. Results Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user’s PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories. Conclusions Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in

  8. Gene Regulation Networks for Modeling Drosophila Development

    NASA Technical Reports Server (NTRS)

    Mjolsness, E.

    1999-01-01

    This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila Melanogaster.

  9. Wisdom of crowds for robust gene network inference

    PubMed Central

    Marbach, Daniel; Costello, James C.; Küffner, Robert; Vega, Nicci; Prill, Robert J.; Camacho, Diogo M.; Allison, Kyle R.; Kellis, Manolis; Collins, James J.; Stolovitzky, Gustavo

    2012-01-01

    Reconstructing gene regulatory networks from high-throughput data is a long-standing problem. Through the DREAM project (Dialogue on Reverse Engineering Assessment and Methods), we performed a comprehensive blind assessment of over thirty network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae, and in silico microarray data. We characterize performance, data requirements, and inherent biases of different inference approaches offering guidelines for both algorithm application and development. We observe that no single inference method performs optimally across all datasets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse datasets. Thereby, we construct high-confidence networks for E. coli and S. aureus, each comprising ~1700 transcriptional interactions at an estimated precision of 50%. We experimentally test 53 novel interactions in E. coli, of which 23 were supported (43%). Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks. PMID:22796662

  10. Supervised classification for gene network reconstruction.

    PubMed

    Soinov, L A

    2003-12-01

    One of the central problems of functional genomics is revealing gene expression networks - the relationships between genes that reflect observations of how the expression level of each gene affects those of others. Microarray data are currently a major source of information about the interplay of biochemical network participants in living cells. Various mathematical techniques, such as differential equations, Bayesian and Boolean models and several statistical methods, have been applied to expression data in attempts to extract the underlying knowledge. Unsupervised clustering methods are often considered as the necessary first step in visualization and analysis of the expression data. As for supervised classification, the problem mainly addressed so far has been how to find discriminative genes separating various samples or experimental conditions. Numerous methods have been applied to identify genes that help to predict treatment outcome or to confirm a diagnosis, as well as to identify primary elements of gene regulatory circuits. However, less attention has been devoted to using supervised learning to uncover relationships between genes and/or their products. To start filling this gap a machine-learning approach for gene networks reconstruction is described here. This approach is based on building classifiers--functions, which determine the state of a gene's transcription machinery through expression levels of other genes. The method can be applied to various cases where relationships between gene expression levels could be expected. PMID:14641098

  11. Multiple quantitative trait analysis using bayesian networks.

    PubMed

    Scutari, Marco; Howell, Phil; Balding, David J; Mackay, Ian

    2014-09-01

    Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness. PMID:25236454

  12. Autonomous Boolean modeling of gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Socolar, Joshua; Sun, Mengyang; Cheng, Xianrui

    2014-03-01

    In cases where the dynamical properties of gene regulatory networks are important, a faithful model must include three key features: a network topology; a functional response of each element to its inputs; and timing information about the transmission of signals across network links. Autonomous Boolean network (ABN) models are efficient representations of these elements and are amenable to analysis. We present an ABN model of the gene regulatory network governing cell fate specification in the early sea urchin embryo, which must generate three bands of distinct tissue types after several cell divisions, beginning from an initial condition with only two distinct cell types. Analysis of the spatial patterning problem and the dynamics of a network constructed from available experimental results reveals that a simple mechanism is at work in this case. Supported by NSF Grant DMS-10-68602

  13. Modularity and evolutionary constraints in a baculovirus gene regulatory network

    PubMed Central

    2013-01-01

    Background The structure of regulatory networks remains an open question in our understanding of complex biological systems. Interactions during complete viral life cycles present unique opportunities to understand how host-parasite network take shape and behave. The Anticarsia gemmatalis multiple nucleopolyhedrovirus (AgMNPV) is a large double-stranded DNA virus, whose genome may encode for 152 open reading frames (ORFs). Here we present the analysis of the ordered cascade of the AgMNPV gene expression. Results We observed an earlier onset of the expression than previously reported for other baculoviruses, especially for genes involved in DNA replication. Most ORFs were expressed at higher levels in a more permissive host cell line. Genes with more than one copy in the genome had distinct expression profiles, which could indicate the acquisition of new functionalities. The transcription gene regulatory network (GRN) for 149 ORFs had a modular topology comprising five communities of highly interconnected nodes that separated key genes that are functionally related on different communities, possibly maximizing redundancy and GRN robustness by compartmentalization of important functions. Core conserved functions showed expression synchronicity, distinct GRN features and significantly less genetic diversity, consistent with evolutionary constraints imposed in key elements of biological systems. This reduced genetic diversity also had a positive correlation with the importance of the gene in our estimated GRN, supporting a relationship between phylogenetic data of baculovirus genes and network features inferred from expression data. We also observed that gene arrangement in overlapping transcripts was conserved among related baculoviruses, suggesting a principle of genome organization. Conclusions Albeit with a reduced number of nodes (149), the AgMNPV GRN had a topology and key characteristics similar to those observed in complex cellular organisms, which indicates

  14. Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways

    PubMed Central

    Guo, Nancy Lan; Wan, Ying-Wooi

    2014-01-01

    Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson’s correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson’s correlation networks when evaluated with MSigDB database. PMID:25392692

  15. Mutated Genes in Schizophrenia Map to Brain Networks

    MedlinePlus

    ... 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks in the prefrontal cortex area of the brain. ... of spontaneous mutations in genes that form a network in the front region of the brain. The ...

  16. Inference of Gene Regulatory Network Based on Local Bayesian Networks

    PubMed Central

    Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Chen, Luonan

    2016-01-01

    The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce

  17. Inference of Gene Regulatory Network Based on Local Bayesian Networks.

    PubMed

    Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Wei, Ze-Gang; Chen, Luonan

    2016-08-01

    The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce

  18. An algorithm for constructing parsimonious hybridization networks with multiple phylogenetic trees.

    PubMed

    Wu, Yufeng

    2013-10-01

    A phylogenetic network is a model for reticulate evolution. A hybridization network is one type of phylogenetic network for a set of discordant gene trees and "displays" each gene tree. A central computational problem on hybridization networks is: given a set of gene trees, reconstruct the minimum (i.e., most parsimonious) hybridization network that displays each given gene tree. This problem is known to be NP-hard, and existing approaches for this problem are either heuristics or making simplifying assumptions (e.g., work with only two input trees or assume some topological properties). In this article, we develop an exact algorithm (called PIRNC) for inferring the minimum hybridization networks from multiple gene trees. The PIRNC algorithm does not rely on structural assumptions (e.g., the so-called galled networks). To the best of our knowledge, PIRNC is the first exact algorithm implemented for this formulation. When the number of reticulation events is relatively small (say, four or fewer), PIRNC runs reasonably efficient even for moderately large datasets. For building more complex networks, we also develop a heuristic version of PIRNC called PIRNCH. Simulation shows that PIRNCH usually produces networks with fewer reticulation events than those by an existing method. PIRNC and PIRNCH have been implemented as part of the software package called PIRN and is available online. PMID:24093230

  19. Genes and gene networks implicated in aggression related behaviour.

    PubMed

    Malki, Karim; Pain, Oliver; Du Rietz, Ebba; Tosto, Maria Grazia; Paya-Cano, Jose; Sandnabba, Kenneth N; de Boer, Sietse; Schalkwyk, Leonard C; Sluyter, Frans

    2014-10-01

    Aggressive behaviour is a major cause of mortality and morbidity. Despite of moderate heritability estimates, progress in identifying the genetic factors underlying aggressive behaviour has been limited. There are currently three genetic mouse models of high and low aggression created using selective breeding. This is the first study to offer a global transcriptomic characterization of the prefrontal cortex across all three genetic mouse models of aggression. A systems biology approach has been applied to transcriptomic data across the three pairs of selected inbred mouse strains (Turku Aggressive (TA) and Turku Non-Aggressive (TNA), Short Attack Latency (SAL) and Long Attack Latency (LAL) mice and North Carolina Aggressive (NC900) and North Carolina Non-Aggressive (NC100)), providing novel insight into the neurobiological mechanisms and genetics underlying aggression. First, weighted gene co-expression network analysis (WGCNA) was performed to identify modules of highly correlated genes associated with aggression. Probe sets belonging to gene modules uncovered by WGCNA were carried forward for network analysis using ingenuity pathway analysis (IPA). The RankProd non-parametric algorithm was then used to statistically evaluate expression differences across the genes belonging to modules significantly associated with aggression. IPA uncovered two pathways, involving NF-kB and MAPKs. The secondary RankProd analysis yielded 14 differentially expressed genes, some of which have previously been implicated in pathways associated with aggressive behaviour, such as Adrbk2. The results highlighted plausible candidate genes and gene networks implicated in aggression-related behaviour. PMID:25142712

  20. Mutational Robustness of Gene Regulatory Networks

    PubMed Central

    van Dijk, Aalt D. J.; van Mourik, Simon; van Ham, Roeland C. H. J.

    2012-01-01

    Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor – target gene interactions) but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive). In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence. PMID:22295094

  1. Performance analysis of electronic code division multiple access based virtual private networks over passive optical networks

    NASA Astrophysics Data System (ADS)

    Nadarajah, Nishaanthan; Nirmalathas, Ampalavanapillai

    2008-03-01

    A solution for implementing multiple secure virtual private networks over a passive optical network using electronic code division multiple access is proposed and experimentally demonstrated. The multiple virtual private networking capability is experimentally demonstrated with 40 Mb/s data multiplexed with a 640 Mb/s electronic code that is unique to each of the virtual private networks in the passive optical network, and the transmission of the electronically coded data is carried out using Fabry-Perot laser diodes. A theoretical scalability analysis for electronic code division multiple access based virtual private networks over a passive optical network is also carried out to identify the performance limits of the scheme. Several sources of noise such as optical beat interference and multiple access interference that are present in the receiver are considered with different operating system parameters such as transmitted optical power, spectral width of the broadband optical source, and processing gain to study the scalability of the network.

  2. From gene expressions to genetic networks

    NASA Astrophysics Data System (ADS)

    Cieplak, Marek

    2009-03-01

    A method based on the principle of entropy maximization is used to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles [1]. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher order correlations. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabollic oscillations identifies a gene interaction network that reflects the intracellular communication pathways. These pathways adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems. The time-dependent behavior of the genetic network is found to involve only a few fundamental modes [2,3]. [4pt] REFERENCES:[0pt] [1] T. R. Lezon, J. R. Banavar, M. Cieplak, A. Maritan, and N. Fedoroff, Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns, Proc. Natl. Acad. Sci. (USA) 103, 19033-19038 (2006) [0pt] [2] N. S. Holter, M. Mitra, A. Maritan, M. Cieplak, J. R. Banavar, and N. V. Fedoroff, Fundamental patterns underlying gene expression profiles: simplicity from complexity, Proc. Natl. Acad. Sci. USA 97, 8409-8414 (2000) [0pt] [3] N. S. Holter, A. Maritan, M. Cieplak, N. V. Fedoroff, and J. R. Banavar, Dynamic modeling of gene expression data, Proc. Natl. Acad. Sci. USA 98, 1693-1698 (2001)

  3. Network enrichment analysis: extension of gene-set enrichment analysis to gene networks

    PubMed Central

    2012-01-01

    Background Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis. Results We developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study. Conclusions The results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps. PMID:22966941

  4. Lists2Networks: Integrated analysis of gene/protein lists

    PubMed Central

    2010-01-01

    Background Systems biologists are faced with the difficultly of analyzing results from large-scale studies that profile the activity of many genes, RNAs and proteins, applied in different experiments, under different conditions, and reported in different publications. To address this challenge it is desirable to compare the results from different related studies such as mRNA expression microarrays, genome-wide ChIP-X, RNAi screens, proteomics and phosphoproteomics experiments in a coherent global framework. In addition, linking high-content multilayered experimental results with prior biological knowledge can be useful for identifying functional themes and form novel hypotheses. Results We present Lists2Networks, a web-based system that allows users to upload lists of mammalian genes/proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system. Conclusions Lists2Networks is a user friendly web-based software system expected to significantly ease the computational analysis process for experimental systems biologists employing high-throughput experiments at multiple layers of regulation. The system is freely available at http://www.lists2networks.org. PMID:20152038

  5. Gene interaction network analysis suggests differences between high and low doses of acetaminophen

    SciTech Connect

    Toyoshiba, Hiroyoshi . E-mail: toyoshiba.hiroyoshi@nies.go.jp; Sone, Hideko; Yamanaka, Takeharu; Parham, Frederick M.; Irwin, Richard D.; Boorman, Gary A.; Portier, Christopher J.

    2006-09-15

    Bayesian networks for quantifying linkages between genes were applied to detect differences in gene expression interaction networks between multiple doses of acetaminophen at multiple time points. Seventeen (17) genes were selected from the gene expression profiles from livers of rats orally exposed to 50, 150 and 1500 mg/kg acetaminophen (APAP) at 6, 24 and 48 h after exposure using a variety of statistical and bioinformatics approaches. The selected genes are related to three biological categories: apoptosis, oxidative stress and other. Gene interaction networks between all 17 genes were identified for the nine dose-time observation points by the TAO-Gen algorithm. Using k-means clustering analysis, the estimated nine networks could be clustered into two consensus networks, the first consisting of the low and middle dose groups, and the second consisting of the high dose. The analysis suggests that the networks could be segregated by doses and were consistent in structure over time of observation within grouped doses. The consensus networks were quantified to calculate the probability distribution for the strength of the linkage between genes connected in the networks. The quantifying analysis showed that, at lower doses, the genes related to the oxidative stress signaling pathway did not interact with the apoptosis-related genes. In contrast, the high-dose network demonstrated significant interactions between the oxidative stress genes and the apoptosis genes and also demonstrated a different network between genes in the oxidative stress pathway. The approaches shown here could provide predictive information to understand high- versus low-dose mechanisms of toxicity.

  6. Predicate calculus for an architecture of multiple neural networks

    NASA Astrophysics Data System (ADS)

    Consoli, Robert H.

    1990-08-01

    Future projects with neural networks will require multiple individual network components. Current efforts along these lines are ad hoc. This paper relates the neural network to a classical device and derives a multi-part architecture from that model. Further it provides a Predicate Calculus variant for describing the location and nature of the trainings and suggests Resolution Refutation as a method for determining the performance of the system as well as the location of needed trainings for specific proofs. 2. THE NEURAL NETWORK AND A CLASSICAL DEVICE Recently investigators have been making reports about architectures of multiple neural networksL234. These efforts are appearing at an early stage in neural network investigations they are characterized by architectures suggested directly by the problem space. Touretzky and Hinton suggest an architecture for processing logical statements1 the design of this architecture arises from the syntax of a restricted class of logical expressions and exhibits syntactic limitations. In similar fashion a multiple neural netword arises out of a control problem2 from the sequence learning problem3 and from the domain of machine learning. 4 But a general theory of multiple neural devices is missing. More general attempts to relate single or multiple neural networks to classical computing devices are not common although an attempt is made to relate single neural devices to a Turing machines and Sun et a!. develop a multiple neural architecture that performs pattern classification.

  7. Discovery of core biotic stress responsive genes in Arabidopsis by weighted gene co-expression network analysis.

    PubMed

    Amrine, Katherine C H; Blanco-Ulate, Barbara; Cantu, Dario

    2015-01-01

    Intricate signal networks and transcriptional regulators translate the recognition of pathogens into defense responses. In this study, we carried out a gene co-expression analysis of all currently publicly available microarray data, which were generated in experiments that studied the interaction of the model plant Arabidopsis thaliana with microbial pathogens. This work was conducted to identify (i) modules of functionally related co-expressed genes that are differentially expressed in response to multiple biotic stresses, and (ii) hub genes that may function as core regulators of disease responses. Using Weighted Gene Co-expression Network Analysis (WGCNA) we constructed an undirected network leveraging a rich curated expression dataset comprising 272 microarrays that involved microbial infections of Arabidopsis plants with a wide array of fungal and bacterial pathogens with biotrophic, hemibiotrophic, and necrotrophic lifestyles. WGCNA produced a network with scale-free and small-world properties composed of 205 distinct clusters of co-expressed genes. Modules of functionally related co-expressed genes that are differentially regulated in response to multiple pathogens were identified by integrating differential gene expression testing with functional enrichment analyses of gene ontology terms, known disease associated genes, transcriptional regulators, and cis-regulatory elements. The significance of functional enrichments was validated by comparisons with randomly generated networks. Network topology was then analyzed to identify intra- and inter-modular gene hubs. Based on high connectivity, and centrality in meta-modules that are clearly enriched in defense responses, we propose a list of 66 target genes for reverse genetic experiments to further dissect the Arabidopsis immune system. Our results show that statistical-based data trimming prior to network analysis allows the integration of expression datasets generated by different groups, under different

  8. Discovery of Core Biotic Stress Responsive Genes in Arabidopsis by Weighted Gene Co-Expression Network Analysis

    PubMed Central

    Amrine, Katherine C. H.; Blanco-Ulate, Barbara; Cantu, Dario

    2015-01-01

    Intricate signal networks and transcriptional regulators translate the recognition of pathogens into defense responses. In this study, we carried out a gene co-expression analysis of all currently publicly available microarray data, which were generated in experiments that studied the interaction of the model plant Arabidopsis thaliana with microbial pathogens. This work was conducted to identify (i) modules of functionally related co-expressed genes that are differentially expressed in response to multiple biotic stresses, and (ii) hub genes that may function as core regulators of disease responses. Using Weighted Gene Co-expression Network Analysis (WGCNA) we constructed an undirected network leveraging a rich curated expression dataset comprising 272 microarrays that involved microbial infections of Arabidopsis plants with a wide array of fungal and bacterial pathogens with biotrophic, hemibiotrophic, and necrotrophic lifestyles. WGCNA produced a network with scale-free and small-world properties composed of 205 distinct clusters of co-expressed genes. Modules of functionally related co-expressed genes that are differentially regulated in response to multiple pathogens were identified by integrating differential gene expression testing with functional enrichment analyses of gene ontology terms, known disease associated genes, transcriptional regulators, and cis-regulatory elements. The significance of functional enrichments was validated by comparisons with randomly generated networks. Network topology was then analyzed to identify intra- and inter-modular gene hubs. Based on high connectivity, and centrality in meta-modules that are clearly enriched in defense responses, we propose a list of 66 target genes for reverse genetic experiments to further dissect the Arabidopsis immune system. Our results show that statistical-based data trimming prior to network analysis allows the integration of expression datasets generated by different groups, under different

  9. Generation of oscillating gene regulatory network motifs

    NASA Astrophysics Data System (ADS)

    van Dorp, M.; Lannoo, B.; Carlon, E.

    2013-07-01

    Using an improved version of an evolutionary algorithm originally proposed by François and Hakim [Proc. Natl. Acad. Sci. USAPNASA60027-842410.1073/pnas.0304532101 101, 580 (2004)], we generated small gene regulatory networks in which the concentration of a target protein oscillates in time. These networks may serve as candidates for oscillatory modules to be found in larger regulatory networks and protein interaction networks. The algorithm was run for 105 times to produce a large set of oscillating modules, which were systematically classified and analyzed. The robustness of the oscillations against variations of the kinetic rates was also determined, to filter out the least robust cases. Furthermore, we show that the set of evolved networks can serve as a database of models whose behavior can be compared to experimentally observed oscillations. The algorithm found three smallest (core) oscillators in which nonlinearities and number of components are minimal. Two of those are two-gene modules: the mixed feedback loop, already discussed in the literature, and an autorepressed gene coupled with a heterodimer. The third one is a single gene module which is competitively regulated by a monomer and a dimer. The evolutionary algorithm also generated larger oscillating networks, which are in part extensions of the three core modules and in part genuinely new modules. The latter includes oscillators which do not rely on feedback induced by transcription factors, but are purely of post-transcriptional type. Analysis of post-transcriptional mechanisms of oscillation may provide useful information for circadian clock research, as recent experiments showed that circadian rhythms are maintained even in the absence of transcription.

  10. Optical Multiple Access Network (OMAN) for advanced processing satellite applications

    NASA Technical Reports Server (NTRS)

    Mendez, Antonio J.; Gagliardi, Robert M.; Park, Eugene; Ivancic, William D.; Sherman, Bradley D.

    1991-01-01

    An OMAN breadboard for exploring advanced processing satellite circuit switch applications is introduced. Network architecture, hardware trade offs, and multiple user interference issues are presented. The breadboard test set up and experimental results are discussed.

  11. CyLineUp: A Cytoscape app for visualizing data in network small multiples

    PubMed Central

    Ligterink, Wilco; Hilhorst, Henk W.M.; de Ridder, Dick; Nijveen, Harm

    2016-01-01

    CyLineUp is a Cytoscape 3 app for the projection of high-throughput measurement data from multiple experiments/samples on a network or pathway map using “small multiples”. This visualization method allows for easy comparison of different experiments in the context of the network or pathway. The user can import various kinds of measurement data and select any appropriate Cytoscape network or WikiPathways pathway map. CyLineUp creates small multiples by replicating the loaded network as many times as there are experiments/samples (e.g. time points, stress conditions, tissues, etc.). The measurement data for each experiment are then mapped onto the nodes (genes, proteins etc.) of the corresponding network using a color gradient. Each step of creating the visualization can be customized to the user’s needs. The results can be exported as a high quality vector image. PMID:27347378

  12. Gene regulatory networks and the underlying biology of developmental toxicity

    EPA Science Inventory

    Embryonic cells are specified by large-scale networks of functionally linked regulatory genes. Knowledge of the relevant gene regulatory networks is essential for understanding phenotypic heterogeneity that emerges from disruption of molecular functions, cellular processes or sig...

  13. A gene regulatory network armature for T-lymphocyte specification

    SciTech Connect

    Fung, Elizabeth-sharon

    2008-01-01

    Choice of a T-lymphoid fate by hematopoietic progenitor cells depends on sustained Notch-Delta signaling combined with tightly-regulated activities of multiple transcription factors. To dissect the regulatory network connections that mediate this process, we have used high-resolution analysis of regulatory gene expression trajectories from the beginning to the end of specification; tests of the short-term Notchdependence of these gene expression changes; and perturbation analyses of the effects of overexpression of two essential transcription factors, namely PU.l and GATA-3. Quantitative expression measurements of >50 transcription factor and marker genes have been used to derive the principal components of regulatory change through which T-cell precursors progress from primitive multipotency to T-lineage commitment. Distinct parts of the path reveal separate contributions of Notch signaling, GATA-3 activity, and downregulation of PU.l. Using BioTapestry, the results have been assembled into a draft gene regulatory network for the specification of T-cell precursors and the choice of T as opposed to myeloid dendritic or mast-cell fates. This network also accommodates effects of E proteins and mutual repression circuits of Gfil against Egr-2 and of TCF-l against PU.l as proposed elsewhere, but requires additional functions that remain unidentified. Distinctive features of this network structure include the intense dose-dependence of GATA-3 effects; the gene-specific modulation of PU.l activity based on Notch activity; the lack of direct opposition between PU.l and GATA-3; and the need for a distinct, late-acting repressive function or functions to extinguish stem and progenitor-derived regulatory gene expression.

  14. Multiple image sensor data fusion through artificial neural networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    With multisensor data fusion technology, the data from multiple sensors are fused in order to make a more accurate estimation of the environment through measurement, processing and analysis. Artificial neural networks are the computational models that mimic biological neural networks. With high per...

  15. Hybrid stochastic simplifications for multiscale gene networks

    PubMed Central

    Crudu, Alina; Debussche, Arnaud; Radulescu, Ovidiu

    2009-01-01

    Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion [1-3] which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered) approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach. PMID:19735554

  16. Methods for monitoring multiple gene expression

    DOEpatents

    Berka, Randy; Bachkirova, Elena; Rey, Michael

    2008-06-01

    The present invention relates to methods for monitoring differential expression of a plurality of genes in a first filamentous fungal cell relative to expression of the same genes in one or more second filamentous fungal cells using microarrays containing Trichoderma reesei ESTs or SSH clones, or a combination thereof. The present invention also relates to computer readable media and substrates containing such array features for monitoring expression of a plurality of genes in filamentous fungal cells.

  17. Methods for monitoring multiple gene expression

    DOEpatents

    Berka, Randy; Bachkirova, Elena; Rey, Michael

    2012-05-01

    The present invention relates to methods for monitoring differential expression of a plurality of genes in a first filamentous fungal cell relative to expression of the same genes in one or more second filamentous fungal cells using microarrays containing Trichoderma reesei ESTs or SSH clones, or a combination thereof. The present invention also relates to computer readable media and substrates containing such array features for monitoring expression of a plurality of genes in filamentous fungal cells.

  18. Methods for monitoring multiple gene expression

    DOEpatents

    Berka, Randy; Bachkirova, Elena; Rey, Michael

    2013-10-01

    The present invention relates to methods for monitoring differential expression of a plurality of genes in a first filamentous fungal cell relative to expression of the same genes in one or more second filamentous fungal cells using microarrays containing Trichoderma reesei ESTs or SSH clones, or a combination thereof. The present invention also relates to computer readable media and substrates containing such array features for monitoring expression of a plurality of genes in filamentous fungal cells.

  19. Network management convergence of multiple subnetworks in GSM infrastructure

    NASA Astrophysics Data System (ADS)

    Zong, Baiqing; Su, Jiang; Zhong, Tao; Dong, Hui; Zhong, Shuangli; Zhou, Weiming; Chen, Wansheng

    2001-10-01

    Network convergence, including service, timing and management convergence, is a trend of future telecommunication networks. Because network convergence can provide carriers with cost reduction, highly integrated applications as well as greater flexibility and functionality, new technologies and standards have driven this convergence tide. However, network management convergence, managing disparate networks with a unified platform, has been a challenging task in sophisticated telecommunication network environments. Administrators are faced with the task of managing various devices with several different applications, without an effective tool set to provide visibility across the network. In general, multiple transmission networks such as SDH, PON, HDSL and digital microwave are adopted in GSM infrastructure to transport mobile traffic between BTS and BSC. Traditional method of managing these devices is that GSM network (MSC, BSC and BTS), SDH, PON, HDSL and digital microwave are managed independently. In this paper, a converged network management platform, named OMConvergence, is proposed and demonstrated. The platform aims at managing the whole GSM network covering SDH, PON, HDSL or digital microwave sub-networks within it. The OMConvergence comprises of remote access methods of OAM message, as well as processing of multiple network management protocol such as ECC (Embedded Control Channel), Q3 and simple network management protocol (SNMP). The management and maintenance message of various devices physically converges to E1 timeslots at the sides of BTS, and then convert to unified IP packages before it is terminated at the side of BSC or network administration center. In addition, extended applications of OMConvergence in image monitoring of BTS surroundings are also demonstrated.

  20. Construction of gene regulatory networks using biclustering and bayesian networks

    PubMed Central

    2011-01-01

    Background Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling. Results In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method. Conclusions Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods. PMID:22018164

  1. Engineering stability in gene networks by autoregulation

    NASA Astrophysics Data System (ADS)

    Becskei, Attila; Serrano, Luis

    2000-06-01

    The genetic and biochemical networks which underlie such things as homeostasis in metabolism and the developmental programs of living cells, must withstand considerable variations and random perturbations of biochemical parameters. These occur as transient changes in, for example, transcription, translation, and RNA and protein degradation. The intensity and duration of these perturbations differ between cells in a population. The unique state of cells, and thus the diversity in a population, is owing to the different environmental stimuli the individual cells experience and the inherent stochastic nature of biochemical processes (for example, refs 5 and 6). It has been proposed, but not demonstrated, that autoregulatory, negative feedback loops in gene circuits provide stability, thereby limiting the range over which the concentrations of network components fluctuate. Here we have designed and constructed simple gene circuits consisting of a regulator and transcriptional repressor modules in Escherichia coli and we show the gain of stability produced by negative feedback.

  2. Engineering stability in gene networks by autoregulation.

    PubMed

    Becskei, A; Serrano, L

    2000-06-01

    The genetic and biochemical networks which underlie such things as homeostasis in metabolism and the developmental programs of living cells, must withstand considerable variations and random perturbations of biochemical parameters. These occur as transient changes in, for example, transcription, translation, and RNA and protein degradation. The intensity and duration of these perturbations differ between cells in a population. The unique state of cells, and thus the diversity in a population, is owing to the different environmental stimuli the individual cells experience and the inherent stochastic nature of biochemical processes (for example, refs 5 and 6). It has been proposed, but not demonstrated, that autoregulatory, negative feedback loops in gene circuits provide stability, thereby limiting the range over which the concentrations of network components fluctuate. Here we have designed and constructed simple gene circuits consisting of a regulator and transcriptional repressor modules in Escherichia coli and we show the gain of stability produced by negative feedback. PMID:10850721

  3. Pathway network inference from gene expression data

    PubMed Central

    2014-01-01

    Background The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. Results We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example. Conclusions PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data. PMID:25032889

  4. Gene Regulatory Networks Elucidating Huanglongbing Disease Mechanisms

    PubMed Central

    Martinelli, Federico; Reagan, Russell L.; Uratsu, Sandra L.; Phu, My L.; Albrecht, Ute; Zhao, Weixiang; Davis, Cristina E.; Bowman, Kim D.; Dandekar, Abhaya M.

    2013-01-01

    Next-generation sequencing was exploited to gain deeper insight into the response to infection by Candidatus liberibacter asiaticus (CaLas), especially the immune disregulation and metabolic dysfunction caused by source-sink disruption. Previous fruit transcriptome data were compared with additional RNA-Seq data in three tissues: immature fruit, and young and mature leaves. Four categories of orchard trees were studied: symptomatic, asymptomatic, apparently healthy, and healthy. Principal component analysis found distinct expression patterns between immature and mature fruits and leaf samples for all four categories of trees. A predicted protein – protein interaction network identified HLB-regulated genes for sugar transporters playing key roles in the overall plant responses. Gene set and pathway enrichment analyses highlight the role of sucrose and starch metabolism in disease symptom development in all tissues. HLB-regulated genes (glucose-phosphate-transporter, invertase, starch-related genes) would likely determine the source-sink relationship disruption. In infected leaves, transcriptomic changes were observed for light reactions genes (downregulation), sucrose metabolism (upregulation), and starch biosynthesis (upregulation). In parallel, symptomatic fruits over-expressed genes involved in photosynthesis, sucrose and raffinose metabolism, and downregulated starch biosynthesis. We visualized gene networks between tissues inducing a source-sink shift. CaLas alters the hormone crosstalk, resulting in weak and ineffective tissue-specific plant immune responses necessary for bacterial clearance. Accordingly, expression of WRKYs (including WRKY70) was higher in fruits than in leaves. Systemic acquired responses were inadequately activated in young leaves, generally considered the sites where most new infections occur. PMID:24086326

  5. Circuit-wide Transcriptional Profiling Reveals Brain Region-Specific Gene Networks Regulating Depression Susceptibility.

    PubMed

    Bagot, Rosemary C; Cates, Hannah M; Purushothaman, Immanuel; Lorsch, Zachary S; Walker, Deena M; Wang, Junshi; Huang, Xiaojie; Schlüter, Oliver M; Maze, Ian; Peña, Catherine J; Heller, Elizabeth A; Issler, Orna; Wang, Minghui; Song, Won-Min; Stein, Jason L; Liu, Xiaochuan; Doyle, Marie A; Scobie, Kimberly N; Sun, Hao Sheng; Neve, Rachael L; Geschwind, Daniel; Dong, Yan; Shen, Li; Zhang, Bin; Nestler, Eric J

    2016-06-01

    Depression is a complex, heterogeneous disorder and a leading contributor to the global burden of disease. Most previous research has focused on individual brain regions and genes contributing to depression. However, emerging evidence in humans and animal models suggests that dysregulated circuit function and gene expression across multiple brain regions drive depressive phenotypes. Here, we performed RNA sequencing on four brain regions from control animals and those susceptible or resilient to chronic social defeat stress at multiple time points. We employed an integrative network biology approach to identify transcriptional networks and key driver genes that regulate susceptibility to depressive-like symptoms. Further, we validated in vivo several key drivers and their associated transcriptional networks that regulate depression susceptibility and confirmed their functional significance at the levels of gene transcription, synaptic regulation, and behavior. Our study reveals novel transcriptional networks that control stress susceptibility and offers fundamentally new leads for antidepressant drug discovery. PMID:27181059

  6. NP-MuScL: unsupervised global prediction of interaction networks from multiple data sources.

    PubMed

    Puniyani, Kriti; Xing, Eric P

    2013-11-01

    Inference of gene interaction networks from expression data usually focuses on either supervised or unsupervised edge prediction from a single data source. However, in many real world applications, multiple data sources, such as microarray and ISH (in situ hybridization) measurements of mRNA abundances, are available to offer multiview information about the same set of genes. We propose ISH to estimate a gene interaction network that is consistent with such multiple data sources, which are expected to reflect the same underlying relationships between the genes. NP-MuScL casts the network estimation problem as estimating the structure of a sparse undirected graphical model. We use the semiparametric Gaussian copula to model the distribution of the different data sources, with the different copulas sharing the same precision (i.e., inverse covariance) matrix, and we present an efficient algorithm to estimate such a model in the high-dimensional scenario. Results are reported on synthetic data, where NP-MuScL outperforms baseline algorithms significantly, even in the presence of noisy data sources. Experiments are also run on two real-world scenarios: two yeast microarray datasets and three Drosophila embryonic gene expression datasets, where NP-MuScL predicts a higher number of known gene interactions than existing techniques. PMID:24134391

  7. NP-MuScL: Unsupervised Global Prediction of Interaction Networks from Multiple Data Sources

    PubMed Central

    Puniyani, Kriti

    2013-01-01

    Abstract Inference of gene interaction networks from expression data usually focuses on either supervised or unsupervised edge prediction from a single data source. However, in many real world applications, multiple data sources, such as microarray and ISH (in situ hybridization) measurements of mRNA abundances, are available to offer multiview information about the same set of genes. We propose ISH to estimate a gene interaction network that is consistent with such multiple data sources, which are expected to reflect the same underlying relationships between the genes. NP-MuScL casts the network estimation problem as estimating the structure of a sparse undirected graphical model. We use the semiparametric Gaussian copula to model the distribution of the different data sources, with the different copulas sharing the same precision (i.e., inverse covariance) matrix, and we present an efficient algorithm to estimate such a model in the high-dimensional scenario. Results are reported on synthetic data, where NP-MuScL outperforms baseline algorithms significantly, even in the presence of noisy data sources. Experiments are also run on two real-world scenarios: two yeast microarray datasets and three Drosophila embryonic gene expression datasets, where NP-MuScL predicts a higher number of known gene interactions than existing techniques. PMID:24134391

  8. Wisdom of crowds for robust gene network inference.

    PubMed

    Marbach, Daniel; Costello, James C; Küffner, Robert; Vega, Nicole M; Prill, Robert J; Camacho, Diogo M; Allison, Kyle R; Kellis, Manolis; Collins, James J; Stolovitzky, Gustavo

    2012-08-01

    Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks. PMID:22796662

  9. Intersecting transcription networks constrain gene regulatory evolution.

    PubMed

    Sorrells, Trevor R; Booth, Lauren N; Tuch, Brian B; Johnson, Alexander D

    2015-07-16

    Epistasis-the non-additive interactions between different genetic loci-constrains evolutionary pathways, blocking some and permitting others. For biological networks such as transcription circuits, the nature of these constraints and their consequences are largely unknown. Here we describe the evolutionary pathways of a transcription network that controls the response to mating pheromone in yeast. A component of this network, the transcription regulator Ste12, has evolved two different modes of binding to a set of its target genes. In one group of species, Ste12 binds to specific DNA binding sites, while in another lineage it occupies DNA indirectly, relying on a second transcription regulator to recognize DNA. We show, through the construction of various possible evolutionary intermediates, that evolution of the direct mode of DNA binding was not directly accessible to the ancestor. Instead, it was contingent on a lineage-specific change to an overlapping transcription network with a different function, the specification of cell type. These results show that analysing and predicting the evolution of cis-regulatory regions requires an understanding of their positions in overlapping networks, as this placement constrains the available evolutionary pathways. PMID:26153861

  10. Intersecting transcription networks constrain gene regulatory evolution

    PubMed Central

    Sorrells, Trevor R; Booth, Lauren N; Tuch, Brian B; Johnson, Alexander D

    2015-01-01

    Epistasis—the non-additive interactions between different genetic loci—constrains evolutionary pathways, blocking some and permitting others1–8. For biological networks such as transcription circuits, the nature of these constraints and their consequences are largely unknown. Here we describe the evolutionary pathways of a transcription network that controls the response to mating pheromone in yeasts9. A component of this network, the transcription regulator Ste12, has evolved two different modes of binding to a set of its target genes. In one group of species, Ste12 binds to specific DNA binding sites, while in another lineage it occupies DNA indirectly, relying on a second transcription regulator to recognize DNA. We show, through the construction of various possible evolutionary intermediates, that evolution of the direct mode of DNA binding was not directly accessible to the ancestor. Instead, it was contingent on a lineage-specific change to an overlapping transcription network with a different function, the specification of cell type. These results show that analyzing and predicting the evolution of cis-regulatory regions requires an understanding of their positions in overlapping networks, as this placement constrains the available evolutionary pathways. PMID:26153861

  11. Histone Gene Multiplicity and Position Effect Variegation in DROSOPHILA MELANOGASTER

    PubMed Central

    Moore, Gerald D.; Sinclair, Donald A.; Grigliatti, Thomas A.

    1983-01-01

    The histone genes of wild-type Drosophila melanogaster are reiterated 100–150 times per haploid genome and are located in the segment of chromosome 2 that corresponds to polytene bands 39D2-3 to E1-2. The influence of altered histone gene multiplicity on chromatin structure has been assayed by measuring modification of the gene inactivation associated with position effect variegation in genotypes bearing deletions of the 39D-E segment. The proportion of cells in which a variegating gene is active is increased in genotypes that are heterozygous for a deficiency that removes the histone gene complex. Deletions that remove segments adjacent to the histone gene complex have no effect on the expression of variegating genes. Suppression of position effect variegation associated with reduction of histone gene multiplicity applies to both X-linked and autosomal variegating genes. Position effects exerted by both autosomal and sex-chromosome heterochromatin were suppressible by deletions of the histone gene complex. The suppression was independent of the presence of the Y chromosome. A deficiency that deletes only the distal portion of the histone gene complex also has the ability to suppress position effect variegation. Duplication of the histone gene complex did not enhance position effect variegation. Deletion or duplication of the histone gene complex in the maternal genome had no effect on the extent of variegation in progeny whose histone gene multiplicity was normal. These results are discussed with respect to current knowledge of the organization of the histone gene complex and control of its expression. PMID:17246163

  12. Screening for Multiple Genes Influencing Dyslexia.

    ERIC Educational Resources Information Center

    Smith, Shelley D.; And Others

    1991-01-01

    Examines the "sib pair" method of linkage analysis designed to locate genes influencing dyslexia, which has several advantages over the "LOD" score method. Notes that the sib pair analysis was able to detect the same linkages as the LOD method, plus a possible third region. Confirms that the sib pair method is an effective means of screening. (RS)

  13. A fast and high performance multiple data integration algorithm for identifying human disease genes

    PubMed Central

    2015-01-01

    Background Integrating multiple data sources is indispensable in improving disease gene identification. It is not only due to the fact that disease genes associated with similar genetic diseases tend to lie close with each other in various biological networks, but also due to the fact that gene-disease associations are complex. Although various algorithms have been proposed to identify disease genes, their prediction performances and the computational time still should be further improved. Results In this study, we propose a fast and high performance multiple data integration algorithm for identifying human disease genes. A posterior probability of each candidate gene associated with individual diseases is calculated by using a Bayesian analysis method and a binary logistic regression model. Two prior probability estimation strategies and two feature vector construction methods are developed to test the performance of the proposed algorithm. Conclusions The proposed algorithm is not only generated predictions with high AUC scores, but also runs very fast. When only a single PPI network is employed, the AUC score is 0.769 by using F2 as feature vectors. The average running time for each leave-one-out experiment is only around 1.5 seconds. When three biological networks are integrated, the AUC score using F3 as feature vectors increases to 0.830, and the average running time for each leave-one-out experiment takes only about 12.54 seconds. It is better than many existing algorithms. PMID:26399620

  14. Paper-based synthetic gene networks.

    PubMed

    Pardee, Keith; Green, Alexander A; Ferrante, Tom; Cameron, D Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J

    2014-11-01

    Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides an alternate, versatile venue for synthetic biologists to operate and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze dried onto paper, enabling the inexpensive, sterile, and abiotic distribution of synthetic-biology-based technologies for the clinic, global health, industry, research, and education. For field use, we create circuits with colorimetric outputs for detection by eye and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small-molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors. PMID:25417167

  15. Paper-based Synthetic Gene Networks

    PubMed Central

    Pardee, Keith; Green, Alexander A.; Ferrante, Tom; Cameron, D. Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J.

    2014-01-01

    Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides a new venue for synthetic biologists to operate, and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze-dried onto paper, enabling the inexpensive, sterile and abiotic distribution of synthetic biology-based technologies for the clinic, global health, industry, research and education. For field use, we create circuits with colorimetric outputs for detection by eye, and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors. PMID:25417167

  16. Expression of DNA methylation genes in secondary progressive multiple sclerosis.

    PubMed

    Fagone, Paolo; Mangano, Katia; Di Marco, Roberto; Touil-Boukoffa, Chafia; Chikovan, Tinatin; Signorelli, Santo; Lombardo, Giuseppe A G; Patti, Francesco; Mammana, Santa; Nicoletti, Ferdinando

    2016-01-15

    Multiple sclerosis (MS) is an immunoinflammatory disease of the central nervous system that seems to be influenced by DNA methylation. We sought to explore the expression pattern of genes involved in the control of DNA methylation in Secondary Progressive (SP) MS patients' PBMCs. We have found that SP MS is characterized by a significant upregulation of two genes belonging to the MBD family genes, MBD2 and MBD4, and by a downregulation of TDG and TET3. PMID:26711572

  17. Identification of Hub Genes Related to the Recovery Phase of Irradiation Injury by Microarray and Integrated Gene Network Analysis

    PubMed Central

    Zhang, Jing; Yang, Yue; Wang, Yin; Zhang, Jinyuan; Wang, Zejian; Yin, Ming; Shen, Xudong

    2011-01-01

    Background Irradiation commonly causes long-term bone marrow injury charactertized by defective HSC self-renewal and a decrease in HSC reserve. However, the effect of high-dose IR on global gene expression during bone marrow recovery remains unknown. Methodology Microarray analysis was used to identify differentially expressed genes that are likely to be critical for bone marrow recovery. Multiple bioinformatics analyses were conducted to identify key hub genes, pathways and biological processes. Principal Findings 1) We identified 1302 differentially expressed genes in murine bone marrow at 3, 7, 11 and 21 days after irradiation. Eleven of these genes are known to be HSC self-renewal associated genes, including Adipoq, Ccl3, Ccnd1, Ccnd2, Cdkn1a, Cxcl12, Junb, Pten, Tal1, Thy1 and Tnf; 2) These 1302 differentially expressed genes function in multiple biological processes of immunity, including hematopoiesis and response to stimuli, and cellular processes including cell proliferation, differentiation, adhesion and signaling; 3) Dynamic Gene Network analysis identified a subgroup of 25 core genes that participate in immune response, regulation of transcription and nucleosome assembly; 4) A comparison of our data with known irradiation-related genes extracted from literature showed 42 genes that matched the results of our microarray analysis, thus demonstrated consistency between studies; 5) Protein-protein interaction network and pathway analyses indicated several essential protein-protein interactions and signaling pathways, including focal adhesion and several immune-related signaling pathways. Conclusions Comparisons to other gene array datasets indicate that global gene expression profiles of irradiation damaged bone marrow show significant differences between injury and recovery phases. Our data suggest that immune response (including hematopoiesis) can be considered as a critical biological process in bone marrow recovery. Several critical hub genes that are

  18. Identification of the key genes connected with plasma cells of multiple myeloma using expression profiles

    PubMed Central

    Zhang, Kefeng; Xu, Zhongyang; Sun, Zhaoyun

    2015-01-01

    Objective To uncover the potential regulatory mechanisms of the relevant genes that contribute to the prognosis and prevention of multiple myeloma (MM). Methods Microarray data (GSE13591) were downloaded, including five plasma cell samples from normal donors and 133 plasma cell samples from MM patients. Differentially expressed genes (DEGs) were identified by Student’s t-test. Functional enrichment analysis was performed for DEGs using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Transcription factors and tumor-associated genes were also explored by mapping genes in the TRANSFAC, the tumor suppressor gene (TSGene), and tumor-associated gene (TAG) databases. A protein–protein interaction (PPI) network and PPI subnetworks were constructed by Cytoscape software using the Search Tool for the Retrieval of Interacting Genes (STRING) database. Results A total of 63 DEGs (42 downregulated, 21 upregulated) were identified. Functional enrichment analysis showed that HLA-DRB1 and VCAM1 might be involved in the positive regulation of immune system processes, and HLA-DRB1 might be related to the intestinal immune network for IgA production pathway. The genes CEBPD, JUND, and ATF3 were identified as transcription factors. The top ten nodal genes in the PPI network were revealed including HLA-DRB1, VCAM1, and TFRC. In addition, genes in the PPI subnetwork, such as HLA-DRB1 and VCAM1, were enriched in the cell adhesion molecules pathway, whereas CD4 and TFRC were both enriched in the hematopoietic cell pathway. Conclusion Several crucial genes correlated to MM were identified, including CD4, HLA-DRB1, TFRC, and VCAM1, which might exert their roles in MM progression via immune-mediated pathways. There might be certain regulatory correlations between HLA-DRB1, CD4, and TFRC. PMID:26229487

  19. TEMPI: probabilistic modeling time-evolving differential PPI networks with multiPle information

    PubMed Central

    Kim, Yongsoo; Jang, Jin-Hyeok; Choi, Seungjin; Hwang, Daehee

    2014-01-01

    Motivation: Time-evolving differential protein–protein interaction (PPI) networks are essential to understand serial activation of differentially regulated (up- or downregulated) cellular processes (DRPs) and their interplays over time. Despite developments in the network inference, current methods are still limited in identifying temporal transition of structures of PPI networks, DRPs associated with the structural transition and the interplays among the DRPs over time. Results: Here, we present a probabilistic model for estimating Time-Evolving differential PPI networks with MultiPle Information (TEMPI). This model describes probabilistic relationships among network structures, time-course gene expression data and Gene Ontology biological processes (GOBPs). By maximizing the likelihood of the probabilistic model, TEMPI estimates jointly the time-evolving differential PPI networks (TDNs) describing temporal transition of PPI network structures together with serial activation of DRPs associated with transiting networks. This joint estimation enables us to interpret the TDNs in terms of temporal transition of the DRPs. To demonstrate the utility of TEMPI, we applied it to two time-course datasets. TEMPI identified the TDNs that correctly delineated temporal transition of DRPs and time-dependent associations between the DRPs. These TDNs provide hypotheses for mechanisms underlying serial activation of key DRPs and their temporal associations. Availability and implementation: Source code and sample data files are available at http://sbm.postech.ac.kr/tempi/sources.zip. Contact: seungjin@postech.ac.kr or dhwang@dgist.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25161233

  20. Next-Generation Synthetic Gene Networks

    PubMed Central

    Lu, Timothy K.; Khalil, Ahmad S.; Collins, James J.

    2009-01-01

    Synthetic biology is focused on the rational construction of biological systems based on engineering principles. During the field’s first decade of development, significant progress has been made in designing biological parts and assembling them into genetic circuits to achieve basic functionalities. These circuits have been used to construct proof-of-principle systems with promising results in industrial and medical applications. However, advances in synthetic biology have been limited by a lack of interoperable parts, techniques for dynamically probing biological systems, and frameworks for the reliable construction and operation of complex, higher-order networks. Here, we highlight challenges and goals for next-generation synthetic gene networks, in the context of potential applications in medicine, biotechnology, bioremediation, and bioenergy. PMID:20010597

  1. Pathway-Dependent Effectiveness of Network Algorithms for Gene Prioritization

    PubMed Central

    Shim, Jung Eun; Hwang, Sohyun; Lee, Insuk

    2015-01-01

    A network-based approach has proven useful for the identification of novel genes associated with complex phenotypes, including human diseases. Because network-based gene prioritization algorithms are based on propagating information of known phenotype-associated genes through networks, the pathway structure of each phenotype might significantly affect the effectiveness of algorithms. We systematically compared two popular network algorithms with distinct mechanisms – direct neighborhood which propagates information to only direct network neighbors, and network diffusion which diffuses information throughout the entire network – in prioritization of genes for worm and human phenotypes. Previous studies reported that network diffusion generally outperforms direct neighborhood for human diseases. Although prioritization power is generally measured for all ranked genes, only the top candidates are significant for subsequent functional analysis. We found that high prioritizing power of a network algorithm for all genes cannot guarantee successful prioritization of top ranked candidates for a given phenotype. Indeed, the majority of the phenotypes that were more efficiently prioritized by network diffusion showed higher prioritizing power for top candidates by direct neighborhood. We also found that connectivity among pathway genes for each phenotype largely determines which network algorithm is more effective, suggesting that the network algorithm used for each phenotype should be chosen with consideration of pathway gene connectivity. PMID:26091506

  2. Discovering Implicit Entity Relation with the Gene-Citation-Gene Network

    PubMed Central

    Song, Min; Han, Nam-Gi; Kim, Yong-Hwan; Ding, Ying; Chambers, Tamy

    2013-01-01

    In this paper, we apply the entitymetrics model to our constructed Gene-Citation-Gene (GCG) network. Based on the premise there is a hidden, but plausible, relationship between an entity in one article and an entity in its citing article, we constructed a GCG network of gene pairs implicitly connected through citation. We compare the performance of this GCG network to a gene-gene (GG) network constructed over the same corpus but which uses gene pairs explicitly connected through traditional co-occurrence. Using 331,411 MEDLINE abstracts collected from 18,323 seed articles and their references, we identify 25 gene pairs. A comparison of these pairs with interactions found in BioGRID reveal that 96% of the gene pairs in the GCG network have known interactions. We measure network performance using degree, weighted degree, closeness, betweenness centrality and PageRank. Combining all measures, we find the GCG network has more gene pairs, but a lower matching rate than the GG network. However, combining top ranked genes in both networks produces a matching rate of 35.53%. By visualizing both the GG and GCG networks, we find that cancer is the most dominant disease associated with the genes in both networks. Overall, the study indicates that the GCG network can be useful for detecting gene interaction in an implicit manner. PMID:24358368

  3. Discovering implicit entity relation with the gene-citation-gene network.

    PubMed

    Song, Min; Han, Nam-Gi; Kim, Yong-Hwan; Ding, Ying; Chambers, Tamy

    2013-01-01

    In this paper, we apply the entitymetrics model to our constructed Gene-Citation-Gene (GCG) network. Based on the premise there is a hidden, but plausible, relationship between an entity in one article and an entity in its citing article, we constructed a GCG network of gene pairs implicitly connected through citation. We compare the performance of this GCG network to a gene-gene (GG) network constructed over the same corpus but which uses gene pairs explicitly connected through traditional co-occurrence. Using 331,411 MEDLINE abstracts collected from 18,323 seed articles and their references, we identify 25 gene pairs. A comparison of these pairs with interactions found in BioGRID reveal that 96% of the gene pairs in the GCG network have known interactions. We measure network performance using degree, weighted degree, closeness, betweenness centrality and PageRank. Combining all measures, we find the GCG network has more gene pairs, but a lower matching rate than the GG network. However, combining top ranked genes in both networks produces a matching rate of 35.53%. By visualizing both the GG and GCG networks, we find that cancer is the most dominant disease associated with the genes in both networks. Overall, the study indicates that the GCG network can be useful for detecting gene interaction in an implicit manner. PMID:24358368

  4. The Role of Multiple Transcription Factors In Archaeal Gene Expression

    SciTech Connect

    Charles J. Daniels

    2008-09-23

    Since the inception of this research program, the project has focused on two central questions: What is the relationship between the 'eukaryal-like' transcription machinery of archaeal cells and its counterparts in eukaryal cells? And, how does the archaeal cell control gene expression using its mosaic of eukaryal core transcription machinery and its bacterial-like transcription regulatory proteins? During the grant period we have addressed these questions using a variety of in vivo approaches and have sought to specifically define the roles of the multiple TATA binding protein (TBP) and TFIIB-like (TFB) proteins in controlling gene expression in Haloferax volcanii. H. volcanii was initially chosen as a model for the Archaea based on the availability of suitable genetic tools; however, later studies showed that all haloarchaea possessed multiple tbp and tfb genes, which led to the proposal that multiple TBP and TFB proteins may function in a manner similar to alternative sigma factors in bacterial cells. In vivo transcription and promoter analysis established a clear relationship between the promoter requirements of haloarchaeal genes and those of the eukaryal RNA polymerase II promoter. Studies on heat shock gene promoters, and the demonstration that specific tfb genes were induced by heat shock, provided the first indication that TFB proteins may direct expression of specific gene families. The construction of strains lacking tbp or tfb genes, coupled with the finding that many of these genes are differentially expressed under varying growth conditions, provided further support for this model. Genetic tools were also developed that led to the construction of insertion and deletion mutants, and a novel gene expression scheme was designed that allowed the controlled expression of these genes in vivo. More recent studies have used a whole genome array to examine the expression of these genes and we have established a linkage between the expression of specific tfb

  5. Modular composition of gene transcription networks.

    PubMed

    Gyorgy, Andras; Del Vecchio, Domitilla

    2014-03-01

    Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations) and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal, scaling, and mixing retroactivity on the performance of recurrent network motifs, including negative autoregulation, combinatorial regulation, two-gene clocks, the toggle switch, and the single-input motif. We further provide a quantitative metric that determines how robust the dynamic behavior of a module is to interconnection with other modules. This metric can be employed both to evaluate the extent of modularity of natural networks and to establish concrete design guidelines to minimize retroactivity between modules in synthetic systems. PMID:24626132

  6. Modular Composition of Gene Transcription Networks

    PubMed Central

    Gyorgy, Andras; Del Vecchio, Domitilla

    2014-01-01

    Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations) and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal, scaling, and mixing retroactivity on the performance of recurrent network motifs, including negative autoregulation, combinatorial regulation, two-gene clocks, the toggle switch, and the single-input motif. We further provide a quantitative metric that determines how robust the dynamic behavior of a module is to interconnection with other modules. This metric can be employed both to evaluate the extent of modularity of natural networks and to establish concrete design guidelines to minimize retroactivity between modules in synthetic systems. PMID:24626132

  7. Biomarker Gene Signature Discovery Integrating Network Knowledge

    PubMed Central

    Cun, Yupeng; Fröhlich, Holger

    2012-01-01

    Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step towards a better personalized medicine. During the last decade various methods, mainly coming from the machine learning or statistical domain, have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Here we review the current state of research in this field by giving an overview about so-far proposed approaches. PMID:24832044

  8. Stochastic Gene Expression in Networks of Post-transcriptional Regulators

    NASA Astrophysics Data System (ADS)

    Baker, Charles; Jia, Tao; Pendar, Hodjat; Kulkarni, Rahul

    2012-02-01

    Post-transcriptional regulators, such as small RNAs and microRNAs, are critical elements of diverse cellular pathways. It has been postulated that, in several important cases, the role of these regulators is to to modulate the noise in gene expression for the regulated target. Correspondingly, general stochastic models have been developed, and results obtained, for the case in which a single sRNA regulates a single mRNA target. We generalize these results to networks containing a single mRNA regulated by multiple sRNAs and to networks containing multiple mRNAs regulated by a single sRNA. For these systems, we obtain exact expressions relating the mean levels of the sRNAs to the mean levels of the mRNAs. Additionally, we consider the convergence of the original model to an approximate model which considers sRNA concentrations to be high; for the latter model we derive an analytic form for the generating function of the protein distribution. Finally, we discuss potential experimental protocols which, in combination with the derived results, can be used to infer the underlying gene expression parameters.

  9. A novel interacting multiple model based network intrusion detection scheme

    NASA Astrophysics Data System (ADS)

    Xin, Ruichi; Venkatasubramanian, Vijay; Leung, Henry

    2006-04-01

    In today's information age, information and network security are of primary importance to any organization. Network intrusion is a serious threat to security of computers and data networks. In internet protocol (IP) based network, intrusions originate in different kinds of packets/messages contained in the open system interconnection (OSI) layer 3 or higher layers. Network intrusion detection and prevention systems observe the layer 3 packets (or layer 4 to 7 messages) to screen for intrusions and security threats. Signature based methods use a pre-existing database that document intrusion patterns as perceived in the layer 3 to 7 protocol traffics and match the incoming traffic for potential intrusion attacks. Alternately, network traffic data can be modeled and any huge anomaly from the established traffic pattern can be detected as network intrusion. The latter method, also known as anomaly based detection is gaining popularity for its versatility in learning new patterns and discovering new attacks. It is apparent that for a reliable performance, an accurate model of the network data needs to be established. In this paper, we illustrate using collected data that network traffic is seldom stationary. We propose the use of multiple models to accurately represent the traffic data. The improvement in reliability of the proposed model is verified by measuring the detection and false alarm rates on several datasets.

  10. Reverse engineering of gene regulatory networks.

    PubMed

    Cho, K H; Choo, S M; Jung, S H; Kim, J R; Choi, H S; Kim, J

    2007-05-01

    Systems biology is a multi-disciplinary approach to the study of the interactions of various cellular mechanisms and cellular components. Owing to the development of new technologies that simultaneously measure the expression of genetic information, systems biological studies involving gene interactions are increasingly prominent. In this regard, reconstructing gene regulatory networks (GRNs) forms the basis for the dynamical analysis of gene interactions and related effects on cellular control pathways. Various approaches of inferring GRNs from gene expression profiles and biological information, including machine learning approaches, have been reviewed, with a brief introduction of DNA microarray experiments as typical tools for measuring levels of messenger ribonucleic acid (mRNA) expression. In particular, the inference methods are classified according to the required input information, and the main idea of each method is elucidated by comparing its advantages and disadvantages with respect to the other methods. In addition, recent developments in this field are introduced and discussions on the challenges and opportunities for future research are provided. PMID:17591174

  11. Protein networks identify novel symbiogenetic genes resulting from plastid endosymbiosis.

    PubMed

    Méheust, Raphaël; Zelzion, Ehud; Bhattacharya, Debashish; Lopez, Philippe; Bapteste, Eric

    2016-03-29

    The integration of foreign genetic information is central to the evolution of eukaryotes, as has been demonstrated for the origin of the Calvin cycle and of the heme and carotenoid biosynthesis pathways in algae and plants. For photosynthetic lineages, this coordination involved three genomes of divergent phylogenetic origins (the nucleus, plastid, and mitochondrion). Major hurdles overcome by the ancestor of these lineages were harnessing the oxygen-evolving organelle, optimizing the use of light, and stabilizing the partnership between the plastid endosymbiont and host through retargeting of proteins to the nascent organelle. Here we used protein similarity networks that can disentangle reticulate gene histories to explore how these significant challenges were met. We discovered a previously hidden component of algal and plant nuclear genomes that originated from the plastid endosymbiont: symbiogenetic genes (S genes). These composite proteins, exclusive to photosynthetic eukaryotes, encode a cyanobacterium-derived domain fused to one of cyanobacterial or another prokaryotic origin and have emerged multiple, independent times during evolution. Transcriptome data demonstrate the existence and expression of S genes across a wide swath of algae and plants, and functional data indicate their involvement in tolerance to oxidative stress, phototropism, and adaptation to nitrogen limitation. Our research demonstrates the "recycling" of genetic information by photosynthetic eukaryotes to generate novel composite genes, many of which function in plastid maintenance. PMID:26976593

  12. Protein networks identify novel symbiogenetic genes resulting from plastid endosymbiosis

    PubMed Central

    Méheust, Raphaël; Zelzion, Ehud; Bhattacharya, Debashish; Lopez, Philippe; Bapteste, Eric

    2016-01-01

    The integration of foreign genetic information is central to the evolution of eukaryotes, as has been demonstrated for the origin of the Calvin cycle and of the heme and carotenoid biosynthesis pathways in algae and plants. For photosynthetic lineages, this coordination involved three genomes of divergent phylogenetic origins (the nucleus, plastid, and mitochondrion). Major hurdles overcome by the ancestor of these lineages were harnessing the oxygen-evolving organelle, optimizing the use of light, and stabilizing the partnership between the plastid endosymbiont and host through retargeting of proteins to the nascent organelle. Here we used protein similarity networks that can disentangle reticulate gene histories to explore how these significant challenges were met. We discovered a previously hidden component of algal and plant nuclear genomes that originated from the plastid endosymbiont: symbiogenetic genes (S genes). These composite proteins, exclusive to photosynthetic eukaryotes, encode a cyanobacterium-derived domain fused to one of cyanobacterial or another prokaryotic origin and have emerged multiple, independent times during evolution. Transcriptome data demonstrate the existence and expression of S genes across a wide swath of algae and plants, and functional data indicate their involvement in tolerance to oxidative stress, phototropism, and adaptation to nitrogen limitation. Our research demonstrates the “recycling” of genetic information by photosynthetic eukaryotes to generate novel composite genes, many of which function in plastid maintenance. PMID:26976593

  13. Multiple-membership multiple-classification models for social network and group dependences

    PubMed Central

    Tranmer, Mark; Steel, David; Browne, William J

    2014-01-01

    The social network literature on network dependences has largely ignored other sources of dependence, such as the school that a student attends, or the area in which an individual lives. The multilevel modelling literature on school and area dependences has, in turn, largely ignored social networks. To bridge this divide, a multiple-membership multiple-classification modelling approach for jointly investigating social network and group dependences is presented. This allows social network and group dependences on individual responses to be investigated and compared. The approach is used to analyse a subsample of the Adolescent Health Study data set from the USA, where the response variable of interest is individual level educational attainment, and the three individual level covariates are sex, ethnic group and age. Individual, network, school and area dependences are accounted for in the analysis. The network dependences can be accounted for by including the network as a classification in the model, using various network configurations, such as ego-nets and cliques. The results suggest that ignoring the network affects the estimates of variation for the classifications that are included in the random part of the model (school, area and individual), as well as having some influence on the point estimates and standard errors of the estimates of regression coefficients for covariates in the fixed part of the model. From a substantive perspective, this approach provides a flexible and practical way of investigating variation in an individual level response due to social network dependences, and estimating the share of variation of an individual response for network, school and area classifications. PMID:25598585

  14. Gene network inference by fusing data from diverse distributions

    PubMed Central

    Žitnik, Marinka; Zupan, Blaž

    2015-01-01

    Motivation: Markov networks are undirected graphical models that are widely used to infer relations between genes from experimental data. Their state-of-the-art inference procedures assume the data arise from a Gaussian distribution. High-throughput omics data, such as that from next generation sequencing, often violates this assumption. Furthermore, when collected data arise from multiple related but otherwise nonidentical distributions, their underlying networks are likely to have common features. New principled statistical approaches are needed that can deal with different data distributions and jointly consider collections of datasets. Results: We present FuseNet, a Markov network formulation that infers networks from a collection of nonidentically distributed datasets. Our approach is computationally efficient and general: given any number of distributions from an exponential family, FuseNet represents model parameters through shared latent factors that define neighborhoods of network nodes. In a simulation study, we demonstrate good predictive performance of FuseNet in comparison to several popular graphical models. We show its effectiveness in an application to breast cancer RNA-sequencing and somatic mutation data, a novel application of graphical models. Fusion of datasets offers substantial gains relative to inference of separate networks for each dataset. Our results demonstrate that network inference methods for non-Gaussian data can help in accurate modeling of the data generated by emergent high-throughput technologies. Availability and implementation: Source code is at https://github.com/marinkaz/fusenet. Contact: blaz.zupan@fri.uni-lj.si Supplementary information: Supplementary information is available at Bioinformatics online. PMID:26072487

  15. Business Computer Network--A "Gateway" to Multiple Databanks.

    ERIC Educational Resources Information Center

    O'Leary, Mick

    1985-01-01

    Business Computer Network (BCN) employs automatic calling and logon, multiple database access, disk search capture, and search assistance interfaces to provide single access to 15 online services. Telecommunications software (SuperScout) used to reach BCN and participating online services offers storage and message options and is accompanied by…

  16. Identifying node role in social network based on multiple indicators.

    PubMed

    Huang, Shaobin; Lv, Tianyang; Zhang, Xizhe; Yang, Yange; Zheng, Weimin; Wen, Chao

    2014-01-01

    It is a classic topic of social network analysis to evaluate the importance of nodes and identify the node that takes on the role of core or bridge in a network. Because a single indicator is not sufficient to analyze multiple characteristics of a node, it is a natural solution to apply multiple indicators that should be selected carefully. An intuitive idea is to select some indicators with weak correlations to efficiently assess different characteristics of a node. However, this paper shows that it is much better to select the indicators with strong correlations. Because indicator correlation is based on the statistical analysis of a large number of nodes, the particularity of an important node will be outlined if its indicator relationship doesn't comply with the statistical correlation. Therefore, the paper selects the multiple indicators including degree, ego-betweenness centrality and eigenvector centrality to evaluate the importance and the role of a node. The importance of a node is equal to the normalized sum of its three indicators. A candidate for core or bridge is selected from the great degree nodes or the nodes with great ego-betweenness centrality respectively. Then, the role of a candidate is determined according to the difference between its indicators' relationship with the statistical correlation of the overall network. Based on 18 real networks and 3 kinds of model networks, the experimental results show that the proposed methods perform quite well in evaluating the importance of nodes and in identifying the node role. PMID:25089823

  17. Identifying Node Role in Social Network Based on Multiple Indicators

    PubMed Central

    Huang, Shaobin; Lv, Tianyang; Zhang, Xizhe; Yang, Yange; Zheng, Weimin; Wen, Chao

    2014-01-01

    It is a classic topic of social network analysis to evaluate the importance of nodes and identify the node that takes on the role of core or bridge in a network. Because a single indicator is not sufficient to analyze multiple characteristics of a node, it is a natural solution to apply multiple indicators that should be selected carefully. An intuitive idea is to select some indicators with weak correlations to efficiently assess different characteristics of a node. However, this paper shows that it is much better to select the indicators with strong correlations. Because indicator correlation is based on the statistical analysis of a large number of nodes, the particularity of an important node will be outlined if its indicator relationship doesn't comply with the statistical correlation. Therefore, the paper selects the multiple indicators including degree, ego-betweenness centrality and eigenvector centrality to evaluate the importance and the role of a node. The importance of a node is equal to the normalized sum of its three indicators. A candidate for core or bridge is selected from the great degree nodes or the nodes with great ego-betweenness centrality respectively. Then, the role of a candidate is determined according to the difference between its indicators' relationship with the statistical correlation of the overall network. Based on 18 real networks and 3 kinds of model networks, the experimental results show that the proposed methods perform quite well in evaluating the importance of nodes and in identifying the node role. PMID:25089823

  18. Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks

    PubMed Central

    Sîrbu, Alina; Crane, Martin; Ruskin, Heather J.

    2015-01-01

    Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions). Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

  19. Multiple Gene Repression in Cyanobacteria Using CRISPRi.

    PubMed

    Yao, Lun; Cengic, Ivana; Anfelt, Josefine; Hudson, Elton P

    2016-03-18

    We describe the application of clustered regularly interspaced short palindromic repeats interference (CRISPRi) for gene repression in the model cyanobacterium Synechcocystis sp. PCC 6803. The nuclease-deficient Cas9 from the type-II CRISPR/Cas of Streptrococcus pyogenes was used to repress green fluorescent protein (GFP) to negligible levels. CRISPRi was also used to repress formation of carbon storage compounds polyhydroxybutryate (PHB) and glycogen during nitrogen starvation. As an example of the potential of CRISPRi for basic and applied cyanobacteria research, we simultaneously knocked down 4 putative aldehyde reductases and dehydrogenases at 50-95% repression. This work also demonstrates that tightly repressed promoters allow for inducible and reversible CRISPRi in cyanobacteria. PMID:26689101

  20. Loops and multiple edges in modularity maximization of networks

    NASA Astrophysics Data System (ADS)

    Cafieri, Sonia; Hansen, Pierre; Liberti, Leo

    2010-04-01

    The modularity maximization model proposed by Newman and Girvan for the identification of communities in networks works for general graphs possibly with loops and multiple edges. However, the applications usually correspond to simple graphs. These graphs are compared to a null model where the degree distribution is maintained but edges are placed at random. Therefore, in this null model there will be loops and possibly multiple edges. Sharp bounds on the expected number of loops, and their impact on the modularity, are derived. Then, building upon the work of Massen and Doye, but using algebra rather than simulation, we propose modified null models associated with graphs without loops but with multiple edges, graphs with loops but without multiple edges and graphs without loops nor multiple edges. We validate our models by using the exact algorithm for clique partitioning of Grötschel and Wakabayashi.

  1. Multiple component networks support working memory in prefrontal cortex.

    PubMed

    Markowitz, David A; Curtis, Clayton E; Pesaran, Bijan

    2015-09-01

    Lateral prefrontal cortex (PFC) is regarded as the hub of the brain's working memory (WM) system, but it remains unclear whether WM is supported by a single distributed network or multiple specialized network components in this region. To investigate this problem, we recorded from neurons in PFC while monkeys made delayed eye movements guided by memory or vision. We show that neuronal responses during these tasks map to three anatomically specific modes of persistent activity. The first two modes encode early and late forms of information storage, whereas the third mode encodes response preparation. Neurons that reflect these modes are concentrated at different anatomical locations in PFC and exhibit distinct patterns of coordinated firing rates and spike timing during WM, consistent with distinct networks. These findings support multiple component models of WM and consequently predict distinct failures that could contribute to neurologic dysfunction. PMID:26283366

  2. Inferring genetic networks from DNA microarray data by multiple regression analysis.

    PubMed

    Kato, M; Tsunoda, T; Takagi, T

    2000-01-01

    Inferring gene regulatory networks by differential equations from the time series data of a DNA microarray is one of the most challenging tasks in the post-genomic era. However, there have been no studies actually inferring gene regulatory networks by differential equations from genome-level data. The reason for this is that the number of parameters in the equations exceeds the number of measured time points. We here succeeded in executing the inference, not by directly determining parameters but by applying multiple regression analysis to our equations. We derived our differential equations and steady state equations from the rate equations of transcriptional reactions in an organism. Verification with a number of genes related to respiration indicated the validity and effectiveness of our method. Moreover, the steady state equations were more appropriate than the differential equations for the microarray data used. PMID:11700593

  3. Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks

    PubMed Central

    Blatti, Charles; Sinha, Saurabh

    2016-01-01

    Motivation: Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or ‘properties’ such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene–gene or gene–property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks. Results: We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals. Availability and Implementation: DRaWR was implemented as

  4. Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes

    PubMed Central

    Childs, Kevin L.; Davidson, Rebecca M.; Buell, C. Robin

    2011-01-01

    With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa) gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional annotation of those

  5. NetVenn: an integrated network analysis web platform for gene lists

    PubMed Central

    Wang, Yi; Thilmony, Roger; Gu, Yong Q.

    2014-01-01

    Many lists containing biological identifiers, such as gene lists, have been generated in various genomics projects. Identifying the overlap among gene lists can enable us to understand the similarities and differences between the data sets. Here, we present an interactome network-based web application platform named NetVenn for comparing and mining the relationships among gene lists. NetVenn contains interactome network data publically available for several species and supports a user upload of customized interactome network data. It has an efficient and interactive graphic tool that provides a Venn diagram view for comparing two to four lists in the context of an interactome network. NetVenn also provides a comprehensive annotation of genes in the gene lists by using enriched terms from multiple functional databases. In addition, it allows for mapping the gene expression data, providing information of transcription status of genes in the network. The power graph analysis tool is integrated in NetVenn for simplified visualization of gene relationships in the network. NetVenn is freely available at http://probes.pw.usda.gov/NetVenn or http://wheat.pw.usda.gov/NetVenn. PMID:24771340

  6. Gene-sharing networks reveal organizing principles of transcriptomes in Arabidopsis and other multicellular organisms.

    PubMed

    Li, Song; Pandey, Sona; Gookin, Timothy E; Zhao, Zhixin; Wilson, Liza; Assmann, Sarah M

    2012-04-01

    Understanding tissue-related gene expression patterns can provide important insights into gene, tissue, and organ function. Transcriptome analyses often have focused on housekeeping or tissue-specific genes or on gene coexpression. However, by analyzing thousands of single-gene expression distributions in multiple tissues of Arabidopsis thaliana, rice (Oryza sativa), human (Homo sapiens), and mouse (Mus musculus), we found that these organisms primarily operate by gene sharing, a phenomenon where, in each organism, most genes exhibit a high expression level in a few key tissues. We designed an analytical pipeline to characterize this phenomenon and then derived Arabidopsis and human gene-sharing networks, in which tissues are connected solely based on the extent of shared preferentially expressed genes. The results show that tissues or cell types from the same organ system tend to group together to form network modules. Tissues that are in consecutive developmental stages or have common physiological functions are connected in these networks, revealing the importance of shared preferentially expressed genes in conferring specialized functions of each tissue type. The networks provide predictive power for each tissue type regarding gene functions of both known and heretofore unknown genes, as shown by the identification of four new genes with functions in guard cell and abscisic acid response. We provide a Web interface that enables, based on the extent of gene sharing, both prediction of tissue-related functions for any Arabidopsis gene of interest and predictions concerning the relatedness of tissues. Common gene-sharing patterns observed in the four model organisms suggest that gene sharing evolved as a fundamental organizing principle of gene expression in diverse multicellular eukaryotes. PMID:22517316

  7. Phosphorylation network rewiring by gene duplication

    PubMed Central

    Freschi, Luca; Courcelles, Mathieu; Thibault, Pierre; Michnick, Stephen W; Landry, Christian R

    2011-01-01

    Elucidating how complex regulatory networks have assembled during evolution requires a detailed understanding of the evolutionary dynamics that follow gene duplication events, including changes in post-translational modifications. We compared the phosphorylation profiles of paralogous proteins in the budding yeast Saccharomyces cerevisiae to that of a species that diverged from the budding yeast before the duplication of those genes. We found that 100 million years of post-duplication divergence are sufficient for the majority of phosphorylation sites to be lost or gained in one paralog or the other, with a strong bias toward losses. However, some losses may be partly compensated for by the evolution of other phosphosites, as paralogous proteins tend to preserve similar numbers of phosphosites over time. We also found that up to 50% of kinase–substrate relationships may have been rewired during this period. Our results suggest that after gene duplication, proteins tend to subfunctionalize at the level of post-translational regulation and that even when phosphosites are preserved, there is a turnover of the kinases that phosphorylate them. PMID:21734643

  8. How to identify essential genes from molecular networks?

    PubMed Central

    del Rio, Gabriel; Koschützki, Dirk; Coello, Gerardo

    2009-01-01

    Background The prediction of essential genes from molecular networks is a way to test the understanding of essentiality in the context of what is known about the network. However, the current knowledge on molecular network structures is incomplete yet, and consequently the strategies aimed to predict essential genes are prone to uncertain predictions. We propose that simultaneously evaluating different network structures and different algorithms representing gene essentiality (centrality measures) may identify essential genes in networks in a reliable fashion. Results By simultaneously analyzing 16 different centrality measures on 18 different reconstructed metabolic networks for Saccharomyces cerevisiae, we show that no single centrality measure identifies essential genes from these networks in a statistically significant way; however, the combination of at least 2 centrality measures achieves a reliable prediction of most but not all of the essential genes. No improvement is achieved in the prediction of essential genes when 3 or 4 centrality measures were combined. Conclusion The method reported here describes a reliable procedure to predict essential genes from molecular networks. Our results show that essential genes may be predicted only by combining centrality measures, revealing the complex nature of the function of essential genes. PMID:19822021

  9. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data.

    PubMed

    Xia, Jianguo; Gill, Erin E; Hancock, Robert E W

    2015-06-01

    Meta-analysis of gene expression data sets is increasingly performed to help identify robust molecular signatures and to gain insights into underlying biological processes. The complicated nature of such analyses requires both advanced statistics and innovative visualization strategies to support efficient data comparison, interpretation and hypothesis generation. NetworkAnalyst (http://www.networkanalyst.ca) is a comprehensive web-based tool designed to allow bench researchers to perform various common and complex meta-analyses of gene expression data via an intuitive web interface. By coupling well-established statistical procedures with state-of-the-art data visualization techniques, NetworkAnalyst allows researchers to easily navigate large complex gene expression data sets to determine important features, patterns, functions and connections, thus leading to the generation of new biological hypotheses. This protocol provides a step-wise description of how to effectively use NetworkAnalyst to perform network analysis and visualization from gene lists; to perform meta-analysis on gene expression data while taking into account multiple metadata parameters; and, finally, to perform a meta-analysis of multiple gene expression data sets. NetworkAnalyst is designed to be accessible to biologists rather than to specialist bioinformaticians. The complete protocol can be executed in ∼1.5 h. Compared with other similar web-based tools, NetworkAnalyst offers a unique visual analytics experience that enables data analysis within the context of protein-protein interaction networks, heatmaps or chord diagrams. All of these analysis methods provide the user with supporting statistical and functional evidence. PMID:25950236

  10. Antipsychotic Induced Gene Regulation in Multiple Brain Regions

    PubMed Central

    Girgenti, Matthew James; Nisenbaum, Laura K.; Bymaster, Franklin; Terwilliger, Rosemarie; Duman, Ronald S; Newton, Samuel Sathyanesan

    2010-01-01

    The molecular mechanism of action of antipsychotic drugs is not well understood. Their complex receptor affinity profiles indicate that their action could extend beyond dopamine receptor blockade. Single gene expression studies and high-throughput gene profiling have shown the induction of genes from several molecular classes and functional categories. Using a focused microarray approach we investigated gene regulation in rat striatum, frontal cortex and hippocampus after chronic administration of haloperidol or olanzapine. Regulated genes were validated by in-situ hybridization, realtime PCR and immunohistochemistry. Only limited overlap was observed in genes regulated by haloperidol and olanzapine. Both drugs elicited maximal gene regulation in the striatum and least in the hippocampus. Striatal gene induction by haloperidol was predominantly in neurotransmitter signaling, G-protein coupled receptors and transcription factors. Olanzapine prominently induced retinoic acid and trophic factor signaling genes in the frontal cortex. The data also revealed the induction of several genes that could be targeted in future drug development efforts. The study uncovered the induction of several novel genes, including somatostatin receptors and metabotropic glutamate receptors. The results demonstrating the regulation of multiple receptors and transcription factors suggests that both typical and atypical antipsychotics could possess a complex molecular mechanism of action. PMID:20070867

  11. A k-mode synchronization methodology for multiple satellite networks

    NASA Astrophysics Data System (ADS)

    Sharifi, M. Hossein; Arozullah, Mohammed

    The authors describe a k-mode burst synchronization methodology that can improve the synchronization performance of the digital communication networks with bursty dynamic users. The method is suitable for the applications such as centralized and distributed multiple satellite networking, where the system supports a large number of low-orbit user satellites. In the mobile networking environment usually there is no network synchronization and the users are highly dynamic. Therefore, more stringent analysis of the system synchronization performance is required. The methodology defined provides flexibility of selecting the k-synchronization stage, which provides a more stable synchronization. The major features of this synchronization method are: a) the synchronizer avoids returning to bit-by-bit comparison mode from higher modes for small errors; b) since there are many modes with different synchronization levels, the synchronizer provides a more stable synchronization; and c) the synchronizer is more stable in environments with burst noise or jamming.

  12. Centrality Analysis Methods for Biological Networks and Their Application to Gene Regulatory Networks

    PubMed Central

    Koschützki, Dirk; Schreiber, Falk

    2008-01-01

    The structural analysis of biological networks includes the ranking of the vertices based on the connection structure of a network. To support this analysis we discuss centrality measures which indicate the importance of vertices, and demonstrate their applicability on a gene regulatory network. We show that common centrality measures result in different valuations of the vertices and that novel measures tailored to specific biological investigations are useful for the analysis of biological networks, in particular gene regulatory networks. PMID:19787083

  13. Reconstruction of large-scale gene regulatory networks using Bayesian model averaging.

    PubMed

    Kim, Haseong; Gelenbe, Erol

    2012-09-01

    Gene regulatory networks provide the systematic view of molecular interactions in a complex living system. However, constructing large-scale gene regulatory networks is one of the most challenging problems in systems biology. Also large burst sets of biological data require a proper integration technique for reliable gene regulatory network construction. Here we present a new reverse engineering approach based on Bayesian model averaging which attempts to combine all the appropriate models describing interactions among genes. This Bayesian approach with a prior based on the Gibbs distribution provides an efficient means to integrate multiple sources of biological data. In a simulation study with maximum of 2000 genes, our method shows better sensitivity than previous elastic-net and Gaussian graphical models, with a fixed specificity of 0.99. The study also shows that the proposed method outperforms the other standard methods for a DREAM dataset generated by nonlinear stochastic models. In brain tumor data analysis, three large-scale networks consisting of 4422 genes were built using the gene expression of non-tumor, low and high grade tumor mRNA expression samples, along with DNA-protein binding affinity information. We found that genes having a large variation of degree distribution among the three tumor networks are the ones that see most involved in regulatory and developmental processes, which possibly gives a novel insight concerning conventional differentially expressed gene analysis. PMID:22987132

  14. The Inferred Cardiogenic Gene Regulatory Network in the Mammalian Heart

    PubMed Central

    Li, Xing; Thiagarajan, Raghuram; Nelson, Timothy J.; Tomita-Mitchell, Aoy; Beard, Daniel A.

    2014-01-01

    Cardiac development is a complex, multiscale process encompassing cell fate adoption, differentiation and morphogenesis. To elucidate pathways underlying this process, a recently developed algorithm to reverse engineer gene regulatory networks was applied to time-course microarray data obtained from the developing mouse heart. Approximately 200 genes of interest were input into the algorithm to generate putative network topologies that are capable of explaining the experimental data via model simulation. To cull specious network interactions, thousands of putative networks are merged and filtered to generate scale-free, hierarchical networks that are statistically significant and biologically relevant. The networks are validated with known gene interactions and used to predict regulatory pathways important for the developing mammalian heart. Area under the precision-recall curve and receiver operator characteristic curve are 9% and 58%, respectively. Of the top 10 ranked predicted interactions, 4 have already been validated. The algorithm is further tested using a network enriched with known interactions and another depleted of them. The inferred networks contained more interactions for the enriched network versus the depleted network. In all test cases, maximum performance of the algorithm was achieved when the purely data-driven method of network inference was combined with a data-independent, functional-based association method. Lastly, the network generated from the list of approximately 200 genes of interest was expanded using gene-profile uniqueness metrics to include approximately 900 additional known mouse genes and to form the most likely cardiogenic gene regulatory network. The resultant network supports known regulatory interactions and contains several novel cardiogenic regulatory interactions. The method outlined herein provides an informative approach to network inference and leads to clear testable hypotheses related to gene regulation. PMID:24971943

  15. Aberrant Gene Promoter Methylation Associated with Sporadic Multiple Colorectal Cancer

    PubMed Central

    Gonzalo, Victoria; Lozano, Juan José; Muñoz, Jenifer; Balaguer, Francesc; Pellisé, Maria; de Miguel, Cristina Rodríguez; Andreu, Montserrat; Jover, Rodrigo; Llor, Xavier; Giráldez, M. Dolores; Ocaña, Teresa; Serradesanferm, Anna; Alonso-Espinaco, Virginia; Jimeno, Mireya; Cuatrecasas, Miriam; Sendino, Oriol; Castellví-Bel, Sergi; Castells, Antoni

    2010-01-01

    Background Colorectal cancer (CRC) multiplicity has been mainly related to polyposis and non-polyposis hereditary syndromes. In sporadic CRC, aberrant gene promoter methylation has been shown to play a key role in carcinogenesis, although little is known about its involvement in multiplicity. To assess the effect of methylation in tumor multiplicity in sporadic CRC, hypermethylation of key tumor suppressor genes was evaluated in patients with both multiple and solitary tumors, as a proof-of-concept of an underlying epigenetic defect. Methodology/Principal Findings We examined a total of 47 synchronous/metachronous primary CRC from 41 patients, and 41 gender, age (5-year intervals) and tumor location-paired patients with solitary tumors. Exclusion criteria were polyposis syndromes, Lynch syndrome and inflammatory bowel disease. DNA methylation at the promoter region of the MGMT, CDKN2A, SFRP1, TMEFF2, HS3ST2 (3OST2), RASSF1A and GATA4 genes was evaluated by quantitative methylation specific PCR in both tumor and corresponding normal appearing colorectal mucosa samples. Overall, patients with multiple lesions exhibited a higher degree of methylation in tumor samples than those with solitary tumors regarding all evaluated genes. After adjusting for age and gender, binomial logistic regression analysis identified methylation of MGMT2 (OR, 1.48; 95% CI, 1.10 to 1.97; p = 0.008) and RASSF1A (OR, 2.04; 95% CI, 1.01 to 4.13; p = 0.047) as variables independently associated with tumor multiplicity, being the risk related to methylation of any of these two genes 4.57 (95% CI, 1.53 to 13.61; p = 0.006). Moreover, in six patients in whom both tumors were available, we found a correlation in the methylation levels of MGMT2 (r = 0.64, p = 0.17), SFRP1 (r = 0.83, 0.06), HPP1 (r = 0.64, p = 0.17), 3OST2 (r = 0.83, p = 0.06) and GATA4 (r = 0.6, p = 0.24). Methylation in normal appearing colorectal mucosa from patients with multiple

  16. Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty

    PubMed Central

    Kraus, Johann M.; Groß, Alexander; Palm, Günther; Kühl, Michael; Kestler, Hans A.

    2015-01-01

    Gene interactions in cells can be represented by gene regulatory networks. A Boolean network models gene interactions according to rules where gene expression is represented by binary values (on / off or {1, 0}). In reality, however, the gene’s state can have multiple values due to biological properties. Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene. Here we present a new Boolean network paradigm to allow intermediate values on the interval [0, 1]. As in the Boolean network, fixed points or attractors of such a model correspond to biological phenotypes or states. We use our new extension of the Boolean network paradigm to model gene expression in first and second heart field lineages which are cardiac progenitor cell populations involved in early vertebrate heart development. By this we are able to predict additional biological phenotypes that the Boolean model alone is not able to identify without utilizing additional biological knowledge. The additional phenotypes predicted by the model were confirmed by published biological experiments. Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes. PMID:26207376

  17. Expression of multiple gamma-glutamyltransferase genes in man.

    PubMed Central

    Courtay, C; Heisterkamp, N; Siest, G; Groffen, J

    1994-01-01

    In clinical and pharmacological laboratories, the assay for gamma-glutamyltransferase (GGT) activity is an important diagnostic test, but one with high biological variability. Although the human genome contains multiple GGT genomic sequences, the diagnostic tests generally assume that only a single GGT gene is active. In the current study, segments encompassing parts of seven different potential human GGT genes have been molecularly cloned. Based on sequence determination of exons within these distinct genomic clones, oligonucleotide primers were designed which would prime and PCR-amplify putative mRNA of all seven potential GGT genes, if expressed. Gene-specific oligonucleotide probes were then utilized to assay the transcriptional status of the seven possible GGT genes in a wide variety of human RNAs. Our results show that a single GGT gene exhibits ubiquitous expression in all RNAs tested, including those from fetal and adult liver. A surprisingly large number of four additional GGT genes is expressed in man. Interestingly, these novel GGT genes are expressed in a tissue-restricted manner, which suggests that their corresponding gene products exhibit distinct functions in these specific tissues. Images Figure 3 Figure 4 PMID:7906515

  18. A Comprehensive Evaluation of Disease Phenotype Networks for Gene Prioritization

    PubMed Central

    Li, Jianhua; Lin, Xiaoyan; Teng, Yueyang; Qi, Shouliang; Xiao, Dayu; Zhang, Jianying; Kang, Yan

    2016-01-01

    Identification of disease-causing genes is a fundamental challenge for human health studies. The phenotypic similarity among diseases may reflect the interactions at the molecular level, and phenotype comparison can be used to predict disease candidate genes. Online Mendelian Inheritance in Man (OMIM) is a database of human genetic diseases and related genes that has become an authoritative source of disease phenotypes. However, disease phenotypes have been described by free text; thus, standardization of phenotypic descriptions is needed before diseases can be compared. Several disease phenotype networks have been established in OMIM using different standardization methods. Two of these networks are important for phenotypic similarity analysis: the first and most commonly used network (mimMiner) is standardized by medical subject heading, and the other network (resnikHPO) is the first to be standardized by human phenotype ontology. This paper comprehensively evaluates for the first time the accuracy of these two networks in gene prioritization based on protein–protein interactions using large-scale, leave-one-out cross-validation experiments. The results show that both networks can effectively prioritize disease-causing genes, and the approach that relates two diseases using a logistic function improves prioritization performance. Tanimoto, one of four methods for normalizing resnikHPO, generates a symmetric network and it performs similarly to mimMiner. Furthermore, an integration of these two networks outperforms either network alone in gene prioritization, indicating that these two disease networks are complementary. PMID:27415759

  19. High speed polling protocol for multiple node network

    NASA Technical Reports Server (NTRS)

    Kirkham, Harold (Inventor)

    1995-01-01

    The invention is a multiple interconnected network of intelligent message-repeating remote nodes which employs a remote node polling process performed by a master node by transmitting a polling message generically addressed to all remote nodes associated with the master node. Each remote node responds upon receipt of the generically addressed polling message by transmitting a poll-answering informational message and by relaying the polling message to other adjacent remote nodes.

  20. Graphical Features of Functional Genes in Human Protein Interaction Network.

    PubMed

    Wang, Pei; Chen, Yao; Lü, Jinhu; Wang, Qingyun; Yu, Xinghuo

    2016-06-01

    With the completion of the human genome project, it is feasible to investigate large-scale human protein interaction network (HPIN) with complex networks theory. Proteins are encoded by genes. Essential, viable, disease, conserved, housekeeping (HK) and tissue-enriched (TE) genes are functional genes, which are organized and functioned via interaction networks. Based on up-to-date data from various databases or literature, two large-scale HPINs and six subnetworks are constructed. We illustrate that the HPINs and most of the subnetworks are sparse, small-world, scale-free, disassortative and with hierarchical modularity. Among the six subnetworks, essential, disease and HK subnetworks are more densely connected than the others. Statistical analysis on the topological structures of the HPIN reveals that the lethal, the conserved, the HK and the TE genes are with hallmark graphical features. Receiver operating characteristic (ROC) curves indicate that the essential genes can be distinguished from the viable ones with accuracy as high as almost 70%. Closeness, semi-local and eigenvector centralities can distinguish the HK genes from the TE ones with accuracy around 82%. Furthermore, the Venn diagram, cluster dendgrams and classifications of disease genes reveal that some classes of disease genes are with hallmark graphical features, especially for cancer genes, HK disease genes and TE disease genes. The findings facilitate the identification of some functional genes via topological structures. The investigations shed some light on the characteristics of the compete interactome, which have potential implications in networked medicine and biological network control. PMID:26841412

  1. Synchronisation and scaling properties of chaotic networks with multiple delays

    NASA Astrophysics Data System (ADS)

    D'Huys, Otti; Zeeb, Steffen; Jüngling, Thomas; Heiligenthal, Sven; Yanchuk, Serhiy; Kinzel, Wolfgang

    2013-07-01

    We study chaotic systems with multiple time delays that range over several orders of magnitude. We show that the spectrum of Lyapunov exponents (LEs) in such systems possesses a hierarchical structure, with different parts scaling with the different delays. This leads to different types of chaos, depending on the scaling of the maximal LE. Our results are relevant, in particular, for the synchronisation properties of hierarchical networks (networks of networks) where the nodes of subnetworks are coupled with shorter delays and couplings between different subnetworks are realised with longer delay times. Units within a subnetwork can synchronise if the maximal exponent scales with the shorter delay, long-range synchronisation between different subnetworks is only possible if the maximal exponent scales with the longer delay. The results are illustrated analytically for Bernoulli maps and numerically for tent maps and semiconductor lasers.

  2. The Effects of Gene Recruitment on the Evolvability and Robustness of Pattern-Forming Gene Networks

    NASA Astrophysics Data System (ADS)

    Spirov, Alexander V.; Holloway, David M.

    Gene recruitment or co-option is defined as the placement of a new gene under a foreign regulatory system. Such re-arrangement of pre-existing regulatory networks can lead to an increase in genomic complexity. This reorganization is recognized as a major driving force in evolution. We simulated the evolution of gene networks by means of the Genetic Algorithms (GA) technique. We used standard GA methods of point mutation and multi-point crossover, as well as our own operators for introducing or withdrawing new genes on the network. The starting point for our computer evolutionary experiments was a 4-gene dynamic model representing the real genetic network controlling segmentation in the fruit fly Drosophila. Model output was fit to experimentally observed gene expression patterns in the early fly embryo. We compared this to output for networks with more and less genes, and with variation in maternal regulatory input. We found that the mutation operator, together with the gene introduction procedure, was sufficient for recruiting new genes into pre-existing networks. Reinforcement of the evolutionary search by crossover operators facilitates this recruitment, but is not necessary. Gene recruitment causes outgrowth of an evolving network, resulting in redundancy, in the sense that the number of genes goes up, as well as the regulatory interactions on the original genes. The recruited genes can have uniform or patterned expressions, many of which recapitulate gene patterns seen in flies, including genes which are not explicitly put in our model. Recruitment of new genes can affect the evolvability of networks (in general, their ability to produce the variation to facilitate adaptive evolution). We see this in particular with a 2-gene subnetwork. To study robustness, we have subjected the networks to experimental levels of variability in maternal regulatory patterns. The majority of networks are not robust to these perturbations. However, a significant subset of the

  3. Exhaustive Search for Fuzzy Gene Networks from Microarray Data

    SciTech Connect

    Sokhansanj, B A; Fitch, J P; Quong, J N; Quong, A A

    2003-07-07

    Recent technological advances in high-throughput data collection allow for the study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are required to interpret large and complex data sets. Rationally designed system perturbations (e.g. gene knock-outs, metabolite removal, etc) can be used to iteratively refine hypothetical models, leading to a modeling-experiment cycle for high-throughput biological system analysis. We use fuzzy logic gene network models because they have greater resolution than Boolean logic models and do not require the precise parameter measurement needed for chemical kinetics-based modeling. The fuzzy gene network approach is tested by exhaustive search for network models describing cyclin gene interactions in yeast cell cycle microarray data, with preliminary success in recovering interactions predicted by previous biological knowledge and other analysis techniques. Our goal is to further develop this method in combination with experiments we are performing on bacterial regulatory networks.

  4. C. elegans Metabolic Gene Regulatory Networks Govern the Cellular Economy

    PubMed Central

    Watson, Emma; Walhout, Albertha J.M.

    2014-01-01

    Diet greatly impacts metabolism in health and disease. In response to the presence or absence of specific nutrients, metabolic gene regulatory networks sense the metabolic state of the cell and regulate metabolic flux accordingly, for instance by the transcriptional control of metabolic enzymes. Here we discuss recent insights regarding metazoan metabolic regulatory networks using the nematode Caenorhabditis elegans as a model, including the modular organization of metabolic gene regulatory networks, the prominent impact of diet on the transcriptome and metabolome, specialized roles of nuclear hormone receptors in responding to dietary conditions, regulation of metabolic genes and metabolic regulators by microRNAs, and feedback between metabolic genes and their regulators. PMID:24731597

  5. Functional-Network-Based Gene Set Analysis Using Gene-Ontology

    PubMed Central

    Chang, Billy; Kustra, Rafal; Tian, Weidong

    2013-01-01

    To account for the functional non-equivalence among a set of genes within a biological pathway when performing gene set analysis, we introduce GOGANPA, a network-based gene set analysis method, which up-weights genes with functions relevant to the gene set of interest. The genes are weighted according to its degree within a genome-scale functional network constructed using the functional annotations available from the gene ontology database. By benchmarking GOGANPA using a well-studied P53 data set and three breast cancer data sets, we will demonstrate the power and reproducibility of our proposed method over traditional unweighted approaches and a competing network-based approach that involves a complex integrated network. GOGANPA’s sole reliance on gene ontology further allows GOGANPA to be widely applicable to the analysis of any gene-ontology-annotated genome. PMID:23418449

  6. A multiple-responsive self-healing supramolecular polymer gel network based on multiple orthogonal interactions.

    PubMed

    Zhan, Jiayi; Zhang, Mingming; Zhou, Mi; Liu, Bin; Chen, Dong; Liu, Yuanyuan; Chen, Qianqian; Qiu, Huayu; Yin, Shouchun

    2014-08-01

    Supramolecular polymer networks have attracted considerable attention not only due to their topological importance but also because they can show some fantastic properties such as stimuli-responsiveness and self-healing. Although various supramolecular networks are constructed by supramolecular chemists based on different non-covalent interactions, supramolecular polymer networks based on multiple orthogonal interactions are still rare. Here, a supramolecular polymer network is presented on the basis of the host-guest interactions between dibenzo-24-crown-8 (DB24C8) and dibenzylammonium salts (DBAS), the metal-ligand coordination interactions between terpyridine and Zn(OTf)2 , and between 1,2,3-triazole and PdCl2 (PhCN)2 . The topology of the networks can be easily tuned from monomer to main-chain supramolecular polymer and then to the supramolecular networks. This process is well studied by various characterization methods such as (1) H NMR, UV-vis, DOSY, viscosity, and rheological measurements. More importantly, a supramolecular gel is obtained at high concentrations of the supramolecular networks, which demonstrates both stimuli-responsiveness and self-healing properties. PMID:24943122

  7. Identifying disease candidate genes via large-scale gene network analysis.

    PubMed

    Kim, Haseong; Park, Taesung; Gelenbe, Erol

    2014-01-01

    Gene Regulatory Networks (GRN) provide systematic views of complex living systems, offering reliable and large-scale GRNs to identify disease candidate genes. A reverse engineering technique, Bayesian Model Averaging-based Networks (BMAnet), which ensembles all appropriate linear models to tackle uncertainty in model selection that integrates heterogeneous biological data sets is introduced. Using network evaluation metrics, we compare the networks that are thus identified. The metric 'Random walk with restart (Rwr)' is utilised to search for disease genes. In a simulation our method shows better performance than elastic-net and Gaussian graphical models, but topological quantities vary among the three methods. Using real-data, brain tumour gene expression samples consisting of non-tumour, grade III and grade IV are analysed to estimate networks with a total of 4422 genes. Based on these networks, 169 brain tumour-related candidate genes were identified and some were found to relate to 'wound', 'apoptosis', and 'cell death' processes. PMID:25796737

  8. Linkage analysis of candidate myelin genes in familial multiple sclerosis.

    PubMed

    Seboun, E; Oksenberg, J R; Rombos, A; Usuku, K; Goodkin, D E; Lincoln, R R; Wong, M; Pham-Dinh, D; Boesplug-Tanguy, O; Carsique, R; Fitoussi, R; Gartioux, C; Reyes, C; Ribierre, F; Faure, S; Fizames, C; Gyapay, G; Weissenbach, J; Dautigny, A; Rimmler, J B; Garcia, M E; Pericak-Vance, M A; Haines, J L; Hauser, S L

    1999-09-01

    Multiple sclerosis (MS) is an autoimmune demyelinating disease of the central nervous system. A complex genetic etiology is thought to underlie susceptibility to this disease. The present study was designed to analyze whether differences in genes that encode myelin proteins influence susceptibility to MS. We performed linkage analysis of MS to markers in chromosomal regions that include the genes encoding myelin basic protein (MBP), proteolipid protein (PLP), myelin-associated glycoprotein (MAG), oligodendrocyte myelin glycoprotein (OMGP), and myelin oligodendrocyte glycoprotein (MOG) in a well-characterized population of 65 multiplex MS families consisting of 399 total individuals, 169 affected with MS and 102 affected sibpairs. Physical mapping data permitted placement of MAG and PLP genes on the Genethon genetic map; all other genes were mapped on the Genethon genetic map by linkage analysis. For each gene, at least one marker within the gene and/or two tightly linked flanking markers were analyzed. Marker data analysis employed a combination of genetic trait model-dependent (parametric) and model-independent linkage methods. Results indicate that MAG, MBP, OMGP, and PLP genes do not have a significant genetic effect on susceptibility to MS in this population. As MOG resides within the MHC, a potential role of the MOG gene could not be excluded. PMID:10541588

  9. Cell cycle gene expression networks discovered using systems biology: Significance in carcinogenesis.

    PubMed

    Scott, Robert E; Ghule, Prachi N; Stein, Janet L; Stein, Gary S

    2015-10-01

    The early stages of carcinogenesis are linked to defects in the cell cycle. A series of cell cycle checkpoints are involved in this process. The G1/S checkpoint that serves to integrate the control of cell proliferation and differentiation is linked to carcinogenesis and the mitotic spindle checkpoint is associated with the development of chromosomal instability. This paper presents the outcome of systems biology studies designed to evaluate if networks of covariate cell cycle gene transcripts exist in proliferative mammalian tissues including mice, rats, and humans. The GeneNetwork website that contains numerous gene expression datasets from different species, sexes, and tissues represents the foundational resource for these studies (www.genenetwork.org). In addition, WebGestalt, a gene ontology tool, facilitated the identification of expression networks of genes that co-vary with key cell cycle targets, especially Cdc20 and Plk1 (www.bioinfo.vanderbilt.edu/webgestalt). Cell cycle expression networks of such covariate mRNAs exist in multiple proliferative tissues including liver, lung, pituitary, adipose, and lymphoid tissues among others but not in brain or retina that have low proliferative potential. Sixty-three covariate cell cycle gene transcripts (mRNAs) compose the average cell cycle network with P = e(-13) to e(-36) . Cell cycle expression networks show species, sex and tissue variability, and they are enriched in mRNA transcripts associated with mitosis, many of which are associated with chromosomal instability. PMID:25808367

  10. Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer

    PubMed Central

    Zhang, Yun-Xia

    2016-01-01

    Purpose. The objective of our study was to predicate candidate genes in cervical cancer (CC) using a network-based strategy and to understand the pathogenic process of CC. Methods. A pathogenic network of CC was extracted based on known pathogenic genes (seed genes) and differentially expressed genes (DEGs) between CC and normal controls. Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE. Each gene in the pathogenic network was assigned a weight value, and then candidate genes were obtained based on the weight distribution. Eventually, pathway enrichment analysis for candidate genes was performed. Results. In this work, a total of 330 DEGs were identified between CC and normal controls. From the pathogenic network, 2 intensely connected clusters were extracted, and a total of 52 candidate genes were detected under the weight values greater than 0.10. Among these candidate genes, VIM had the highest weight value. Moreover, candidate genes MMP1, CDC45, and CAT were, respectively, enriched in pathway in cancer, cell cycle, and methane metabolism. Conclusion. Candidate pathogenic genes including MMP1, CDC45, CAT, and VIM might be involved in the pathogenesis of CC. We believe that our results can provide theoretical guidelines for future clinical application. PMID:27034707

  11. Implicit methods for qualitative modeling of gene regulatory networks.

    PubMed

    Garg, Abhishek; Mohanram, Kartik; De Micheli, Giovanni; Xenarios, Ioannis

    2012-01-01

    Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from the single-gene single-protein experimental approach to studying the behavior of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behavior in terms of network structures such as signaling pathways and gene regulatory networks. In these networks, different biological entities such as genes, proteins, and metabolites interact with each other, giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model increasingly large networks comprising thousands of nodes. These challenges have renewed interest in developing new computational techniques for modeling complex biological systems. This chapter presents a modeling framework based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulation in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady-state behavior, stochasticity, and gene perturbation experiments. PMID:21938638

  12. 3D Actin Network Centerline Extraction with Multiple Active Contours

    PubMed Central

    Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

    2013-01-01

    Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and actin cables. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we propose a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D Total Internal Reflection Fluorescence Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy. Quantitative evaluation of the method using synthetic images shows that for images with SNR above 5.0, the average vertex error measured by the distance between our result and ground truth is 1 voxel, and the average Hausdorff distance is below 10 voxels. PMID:24316442

  13. 3D Filament Network Segmentation with Multiple Active Contours

    NASA Astrophysics Data System (ADS)

    Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

    2014-03-01

    Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and microtubules. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we developed a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D TIRF Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy.

  14. Motor network efficiency and disability in multiple sclerosis

    PubMed Central

    Yaldizli, Özgür; Sethi, Varun; Muhlert, Nils; Liu, Zheng; Samson, Rebecca S.; Altmann, Daniel R.; Ron, Maria A.; Wheeler-Kingshott, Claudia A.M.; Miller, David H.; Chard, Declan T.

    2015-01-01

    Objective: To develop a composite MRI-based measure of motor network integrity, and determine if it explains disability better than conventional MRI measures in patients with multiple sclerosis (MS). Methods: Tract density imaging and constrained spherical deconvolution tractography were used to identify motor network connections in 22 controls. Fractional anisotropy (FA), magnetization transfer ratio (MTR), and normalized volume were computed in each tract in 71 people with relapse onset MS. Principal component analysis was used to distill the FA, MTR, and tract volume data into a single metric for each tract, which in turn was used to compute a composite measure of motor network efficiency (composite NE) using graph theory. Associations were investigated between the Expanded Disability Status Scale (EDSS) and the following MRI measures: composite motor NE, NE calculated using FA alone, FA averaged in the combined motor network tracts, brain T2 lesion volume, brain parenchymal fraction, normal-appearing white matter MTR, and cervical cord cross-sectional area. Results: In univariable analysis, composite motor NE explained 58% of the variation in EDSS in the whole MS group, more than twice that of the other MRI measures investigated. In a multivariable regression model, only composite NE and disease duration were independently associated with EDSS. Conclusions: A composite MRI measure of motor NE was able to predict disability substantially better than conventional non-network-based MRI measures. PMID:26320199

  15. An Arabidopsis gene network based on the graphical Gaussian model

    PubMed Central

    Ma, Shisong; Gong, Qingqiu; Bohnert, Hans J.

    2007-01-01

    We describe a gene network for the Arabidopsis thaliana transcriptome based on a modified graphical Gaussian model (GGM). Through partial correlation (pcor), GGM infers coregulation patterns between gene pairs conditional on the behavior of other genes. Regularized GGM calculated pcor between gene pairs among ∼2000 input genes at a time. Regularized GGM coupled with iterative random samplings of genes was expanded into a network that covered the Arabidopsis genome (22,266 genes). This resulted in a network of 18,625 interactions (edges) among 6760 genes (nodes) with high confidence and connections representing ∼0.01% of all possible edges. When queried for selected genes, locally coherent subnetworks mainly related to metabolic functions, and stress responses emerged. Examples of networks for biochemical pathways, cell wall metabolism, and cold responses are presented. GGM displayed known coregulation pathways as subnetworks and added novel components to known edges. Finally, the network reconciled individual subnetworks in a topology joined at the whole-genome level and provided a general framework that can instruct future studies on plant metabolism and stress responses. The network model is included. PMID:17921353

  16. Hub-Centered Gene Network Reconstruction Using Automatic Relevance Determination

    PubMed Central

    Böck, Matthias; Ogishima, Soichi; Tanaka, Hiroshi; Kramer, Stefan; Kaderali, Lars

    2012-01-01

    Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity of the task, due to the combinatorial explosion of different network topologies for increasing network size. To handle this problem, constraints are frequently used, for example on the node degree, number of edges, or constraints on regulation functions between network components. We propose to exploit topological considerations in the inference of gene regulatory networks. Such systems are often controlled by a small number of hub genes, while most other genes have only limited influence on the network's dynamic. We model gene regulation using a Bayesian network with discrete, Boolean nodes. A hierarchical prior is employed to identify hub genes. The first layer of the prior is used to regularize weights on edges emanating from one specific node. A second prior on hyperparameters controls the magnitude of the former regularization for different nodes. The net effect is that central nodes tend to form in reconstructed networks. Network reconstruction is then performed by maximization of or sampling from the posterior distribution. We evaluate our approach on simulated and real experimental data, indicating that we can reconstruct main regulatory interactions from the data. We furthermore compare our approach to other state-of-the art methods, showing superior performance in identifying hubs. Using a large publicly available dataset of over 800 cell cycle regulated genes, we are able to identify several main hub genes. Our method may thus provide a valuable tool to identify interesting candidate genes for further study. Furthermore, the approach presented may stimulate further developments in regularization methods for network reconstruction from data. PMID:22570688

  17. Using Effective Subnetworks to Predict Selected Properties of Gene Networks

    PubMed Central

    Gunaratne, Gemunu H.; Gunaratne, Preethi H.; Seemann, Lars; Török, Andrei

    2010-01-01

    Background Difficulties associated with implementing gene therapy are caused by the complexity of the underlying regulatory networks. The forms of interactions between the hundreds of genes, proteins, and metabolites in these networks are not known very accurately. An alternative approach is to limit consideration to genes on the network. Steady state measurements of these influence networks can be obtained from DNA microarray experiments. However, since they contain a large number of nodes, the computation of influence networks requires a prohibitively large set of microarray experiments. Furthermore, error estimates of the network make verifiable predictions impossible. Methodology/Principal Findings Here, we propose an alternative approach. Rather than attempting to derive an accurate model of the network, we ask what questions can be addressed using lower dimensional, highly simplified models. More importantly, is it possible to use such robust features in applications? We first identify a small group of genes that can be used to affect changes in other nodes of the network. The reduced effective empirical subnetwork (EES) can be computed using steady state measurements on a small number of genetically perturbed systems. We show that the EES can be used to make predictions on expression profiles of other mutants, and to compute how to implement pre-specified changes in the steady state of the underlying biological process. These assertions are verified in a synthetic influence network. We also use previously published experimental data to compute the EES associated with an oxygen deprivation network of E.coli, and use it to predict gene expression levels on a double mutant. The predictions are significantly different from the experimental results for less than of genes. Conclusions/Significance The constraints imposed by gene expression levels of mutants can be used to address a selected set of questions about a gene network. PMID:20949025

  18. Reveal genes functionally associated with ACADS by a network study.

    PubMed

    Chen, Yulong; Su, Zhiguang

    2015-09-15

    Establishing a systematic network is aimed at finding essential human gene-gene/gene-disease pathway by means of network inter-connecting patterns and functional annotation analysis. In the present study, we have analyzed functional gene interactions of short-chain acyl-coenzyme A dehydrogenase gene (ACADS). ACADS plays a vital role in free fatty acid β-oxidation and regulates energy homeostasis. Modules of highly inter-connected genes in disease-specific ACADS network are derived by integrating gene function and protein interaction data. Among the 8 genes in ACADS web retrieved from both STRING and GeneMANIA, ACADS is effectively conjoined with 4 genes including HAHDA, HADHB, ECHS1 and ACAT1. The functional analysis is done via ontological briefing and candidate disease identification. We observed that the highly efficient-interlinked genes connected with ACADS are HAHDA, HADHB, ECHS1 and ACAT1. Interestingly, the ontological aspect of genes in the ACADS network reveals that ACADS, HAHDA and HADHB play equally vital roles in fatty acid metabolism. The gene ACAT1 together with ACADS indulges in ketone metabolism. Our computational gene web analysis also predicts potential candidate disease recognition, thus indicating the involvement of ACADS, HAHDA, HADHB, ECHS1 and ACAT1 not only with lipid metabolism but also with infant death syndrome, skeletal myopathy, acute hepatic encephalopathy, Reye-like syndrome, episodic ketosis, and metabolic acidosis. The current study presents a comprehensible layout of ACADS network, its functional strategies and candidate disease approach associated with ACADS network. PMID:26045367

  19. Network analysis of EtOH-related candidate genes.

    PubMed

    Guo, An-Yuan; Sun, Jingchun; Jia, Peilin; Zhao, Zhongming

    2010-05-01

    Recently, we collected many large-scale datasets for alcohol dependence and EtOH response in five organisms and deposited them in our EtOH-related gene resource database (ERGR, http://bioinfo.mc.vanderbilt.edu/ERGR/). Based on multidimensional evidence among these datasets, we prioritized 57 EtOH-related candidate genes. To explore their biological roles, and the molecular mechanisms of EtOH response and alcohol dependence, we examined the features of these genes by the Gene Ontology (GO) term-enrichment test and network/pathway analysis. Our analysis revealed that these candidate genes were highly enriched in alcohol dependence/alcoholism and highly expressed in brain or liver tissues. All the significantly enriched GO terms were related to neurotransmitter systems or EtOH metabolic processes. Using the Ingenuity Pathway Analysis system, we found that these genes were involved in networks of neurological disease, cardiovascular disease, inflammatory response, and small molecular metabolism. Many key genes in signaling pathways were in the central position of these networks. Furthermore, our protein-protein interaction (PPI) network analysis suggested some novel candidate genes which also had evidence in the ERGR database. This study demonstrated that our candidate gene selection is effective and our network/pathway analysis is useful for uncovering the molecular mechanisms of EtOH response and alcohol dependence. This approach can be applied to study the features of candidate genes of other complex traits/phenotypes. PMID:20491071

  20. Expression profile of telomere-associated genes in multiple myeloma

    PubMed Central

    de la Guardia, Rafael Díaz; Catalina, Purificación; Panero, Julieta; Elosua, Carolina; Pulgarin, Andrés; López, María Belén; Ayllón, Verónica; Ligero, Gertrudis; Slavutsky, Irma; Leone, Paola E

    2012-01-01

    To further contribute to the understanding of multiple myeloma, we have focused our research interests on the mechanisms by which tumour plasma cells have a higher survival rate than normal plasma cells. In this article, we study the expression profile of genes involved in the regulation and protection of telomere length, telomerase activity and apoptosis in samples from patients with monoclonal gammopathy of undetermined significance, smouldering multiple myeloma, multiple myeloma (MM) and plasma cell leukaemia (PCL), as well as several human myeloma cell lines (HMCLs). Using conventional cytogenetic and fluorescence in situ hybridization studies, we identified a high number of telomeric associations (TAs). Moreover, telomere length measurements by terminal restriction fragment (TRF) assay showed a shorter mean TRF peak value, with a consistent correlation with the number of TAs. Using gene expression arrays and quantitative PCR we identified the hTERT gene together with 16 other genes directly involved in telomere length maintenance: HSPA9, KRAS, RB1, members of the Small nucleolar ribonucleoproteins family, A/B subfamily of ubiquitously expressed heterogeneous nuclear ribonucleoproteins, and 14-3-3 family. The expression levels of these genes were even higher than those in human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs), which have unlimited proliferation capacity. In conclusion, the gene signature suggests that MM tumour cells are able to maintain stable short telomere lengths without exceeding the short critical length, allowing cell divisions to continue. We propose that this could be a mechanism contributing to MM tumour cells expansion in the bone marrow (BM). PMID:22947336

  1. Comparison of Multiple Gene Assembly Methods for Metabolic Engineering

    NASA Astrophysics Data System (ADS)

    Lu, Chenfeng; Mansoorabadi, Karen; Jeffries, Thomas

    A universal, rapid DNA assembly method for efficient multigene plasmid construction is important for biological research and for optimizing gene expression in industrial microbes. Three different approaches to achieve this goal were evaluated. These included creating long complementary extensions using a uracil-DNA glycosylase technique, overlap extension polymerase chain reaction, and a SfiI-based ligation method. SfiI ligation was the only successful approach for assembling large DNA fragments that contained repeated homologous regions. In addition, the SfiI method has been improved over a similar, previous published technique so that it is more flexible and does not require polymerase chain reaction to incorporate adaptors. In the present study, Saccharomyces cerevisiae genes TAL1, TKL1, and PYK1 under control of the 6-phosphogluconate dehydrogenase promoter were successfully ligated together using multiple unique SfiI restriction sites. The desired construct was obtained 65% of the time during vector construction using four-piece ligations. The SfiI method consists of three steps: first a SfiI linker vector is constructed, whose multiple cloning site is flanked by two three-base linkers matching the neighboring SfiI linkers on SfiI digestion; second, the linkers are attached to the desired genes by cloning them into SfiI linker vectors; third, the genes flanked by the three-base linkers, are released by SfiI digestion. In the final step, genes of interest are joined together in a simple one-step ligation.

  2. Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes

    PubMed Central

    Lieberman, Tami D.; Michel, Jean-Baptiste; Aingaran, Mythili; Potter-Bynoe, Gail; Roux, Damien; Davis, Michael R.; Skurnik, David; Leiby, Nicholas; LiPuma, John J.; Goldberg, Joanna B.; McAdam, Alexander J.; Priebe, Gregory P.; Kishony, Roy

    2011-01-01

    Bacterial pathogens evolve during the infection of their human hosts1-8, but separating adaptive and neutral mutations remains challenging9-11. Here, we identify bacterial genes under adaptive evolution by tracking recurrent patterns of mutations in the same pathogenic strain during the infection of multiple patients. We conducted a retrospective study of a Burkholderia dolosa outbreak among people with cystic fibrosis, sequencing the genomes of 112 isolates collected from 14 individuals over 16 years. We find that 17 bacterial genes acquired non-synonymous mutations in multiple individuals, which indicates parallel adaptive evolution. Mutations in these genes illuminate the genetic basis of important pathogenic phenotypes, including antibiotic resistance and bacterial membrane composition, and implicate oxygen-dependent gene regulation as paramount in lung infections. Several genes have not been previously implicated in pathogenesis, suggesting new therapeutic targets. The identification of parallel molecular evolution suggests key selection forces acting on pathogens within humans and can help predict and prepare for their future evolutionary course. PMID:22081229

  3. Multiple Routes to Subfunctionalization and Gene Duplicate Specialization

    PubMed Central

    Proulx, Stephen R.

    2012-01-01

    Gene duplication is arguably the most significant source of new functional genetic material. A better understanding of the processes that lead to the stable incorporation of gene duplications into the genome is important both because it relates to interspecific differences in genome composition and because it can shed light on why some classes of gene are more prone to duplication than others. Typically, models of gene duplication consider the periods before duplication, during the spread and fixation of a new duplicate, and following duplication as distinct phases without a common underlying selective environment. I consider a scenario where a gene that is initially expressed in multiple contexts can undergo mutations that alter its expression profile or its functional coding sequence. The selective regime that acts on the functional output of the allele copies carried by an individual is constant. If there is a potential selective benefit to having different coding sequences expressed in each context, then, regardless of the constraints on functional variation at the single-locus gene, the waiting time until a gene duplication is incorporated goes down as population size increases. PMID:22143920

  4. Assessing mechanical vulnerability in water distribution networks under multiple failures

    NASA Astrophysics Data System (ADS)

    Berardi, Luigi; Ugarelli, Rita; Røstum, Jon; Giustolisi, Orazio

    2014-03-01

    Understanding mechanical vulnerability of water distribution networks (WDN) is of direct relevance for water utilities since it entails two different purposes. On the one hand, it might support the identification of severe failure scenarios due to external causes (e.g., natural or intentional events) which result into the most critical consequences on WDN supply capacity. On the other hand, it aims at figure out the WDN portions which are more prone to be affected by asset disruptions. The complexity of such analysis stems from the number of possible scenarios with single and multiple simultaneous shutdowns of asset elements leading to modifications of network topology and insufficient water supply to customers. In this work, the search for the most disruptive combinations of multiple asset failure events is formulated and solved as a multiobjective optimization problem. The higher vulnerability failure scenarios are detected as those causing the lower supplied demand due to the lower number of simultaneous failures. The automatic detection of WDN topology, subsequent to the detachments of failed elements, is combined with pressure-driven analysis. The methodology is demonstrated on a real water distribution network. Results show that, besides the failures causing the detachment of reservoirs, tanks, or pumps, there are other different topological modifications which may cause severe WDN service disruptions. Such information is of direct relevance to support planning asset enhancement works and improve the preparedness to extreme events.

  5. Evolutionary and Topological Properties of Genes and Community Structures in Human Gene Regulatory Networks.

    PubMed

    Szedlak, Anthony; Smith, Nicholas; Liu, Li; Paternostro, Giovanni; Piermarocchi, Carlo

    2016-06-01

    The diverse, specialized genes present in today's lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins' binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity. A deep understanding of the relationship between the evolutionary history of single genes and the topological properties of the underlying GRN is integral to evolutionary genetics. Here, we show that the topological properties of an acute myeloid leukemia GRN and a general human GRN are strongly coupled with its genes' evolutionary properties. Slowly evolving ("cold"), old genes tend to interact with each other, as do rapidly evolving ("hot"), young genes. This naturally causes genes to segregate into community structures with relatively homogeneous evolutionary histories. We argue that gene duplication placed old, cold genes and communities at the center of the networks, and young, hot genes and communities at the periphery. We demonstrate this with single-node centrality measures and two new measures of efficiency, the set efficiency and the interset efficiency. We conclude that these methods for studying the relationships between a GRN's community structures and its genes' evolutionary properties provide new perspectives for understanding evolutionary genetics. PMID:27359334

  6. Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes.

    PubMed

    Himmelstein, Daniel S; Baranzini, Sergio E

    2015-07-01

    The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks--graphs with multiple node and edge types--for accomplishing both tasks. First we constructed a network with 18 node types--genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections--and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as influential mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data

  7. Inferring slowly-changing dynamic gene-regulatory networks

    PubMed Central

    2015-01-01

    Dynamic gene-regulatory networks are complex since the interaction patterns between their components mean that it is impossible to study parts of the network in separation. This holistic character of gene-regulatory networks poses a real challenge to any type of modelling. Graphical models are a class of models that connect the network with a conditional independence relationships between random variables. By interpreting these random variables as gene activities and the conditional independence relationships as functional non-relatedness, graphical models have been used to describe gene-regulatory networks. Whereas the literature has been focused on static networks, most time-course experiments are designed in order to tease out temporal changes in the underlying network. It is typically reasonable to assume that changes in genomic networks are few, because biological systems tend to be stable. We introduce a new model for estimating slow changes in dynamic gene-regulatory networks, which is suitable for high-dimensional data, e.g. time-course microarray data. Our aim is to estimate a dynamically changing genomic network based on temporal activity measurements of the genes in the network. Our method is based on the penalized likelihood with ℓ1-norm, that penalizes conditional dependencies between genes as well as differences between conditional independence elements across time points. We also present a heuristic search strategy to find optimal tuning parameters. We re-write the penalized maximum likelihood problem into a standard convex optimization problem subject to linear equality constraints. We show that our method performs well in simulation studies. Finally, we apply the proposed model to a time-course T-cell dataset. PMID:25917062

  8. [Strategies for regulating multiple genes in microbial cell factories].

    PubMed

    Jiang, Tianyi; Li, Lixiang; Ma, Cuiqing; Xu, Ping

    2010-10-01

    Microbial metabolic engineering and synthetic biology are important disciplines of microbial technology nowadays. Microbial cells are fast growing, easy to be cultivated in large scale, clear in genetic background and convenient in genetic modification. They play an important role in many domains. Microbial cell factory means an artificial microbial metabolic system that can be used in chemical production. The construction of a microbial cell factory needs transferring of multiple genes or a whole metabolic pathway, which may cause some problems such as metabolism imbalance and accumulation of mesostates. This review focuses on the regulation strategies of different levels involving simultaneous engagement of multiple genes. Future perspectives on the development of this domain were also discussed. PMID:21218630

  9. Time-Delayed Models of Gene Regulatory Networks

    PubMed Central

    Parmar, K.; Blyuss, K. B.; Kyrychko, Y. N.; Hogan, S. J.

    2015-01-01

    We discuss different mathematical models of gene regulatory networks as relevant to the onset and development of cancer. After discussion of alternative modelling approaches, we use a paradigmatic two-gene network to focus on the role played by time delays in the dynamics of gene regulatory networks. We contrast the dynamics of the reduced model arising in the limit of fast mRNA dynamics with that of the full model. The review concludes with the discussion of some open problems. PMID:26576197

  10. Optimized routing strategy for complex network with multiple priorities

    NASA Astrophysics Data System (ADS)

    Li, Shi-Bao; Sun, Zong-Xing; Liu, Jian-Hang; Chen, Hai-Hua

    2016-08-01

    Different loads in the network require distinct QoS standard, while present routing strategies for complex networks ignored this fact. To solve this problem, we designed a routing strategy RS-MP with multiple priorities by which packets are classified into privileged-packets and common-packets. In RS-MP, privileged-packets route by the Shortest Path Algorithm, and do not need to queue up. Common-packets’ routes are determined by a new factor BJ max of the network. The BJ max stands for the largest betweenness centrality. By minimizing BJ max, the throughout capacity of the network can be maximized. The simulation results show that RS-MP can guarantee privileged-packets with the shortest path length and smallest delay, and maximized throughout capacity for common packets in the no-congestion state. Project supported by the Fundamental Research Funds for the Central University, China (Grant Nos. 24720152047A and 15CX05025A), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2014FM017), the Science and Technology Development Plan of Huangdao District, Qingdao, China (Grant No. 2014-1-45).

  11. Global map of physical interactions among differentially expressed genes in multiple sclerosis relapses and remissions.

    PubMed

    Tuller, Tamir; Atar, Shimshi; Ruppin, Eytan; Gurevich, Michael; Achiron, Anat

    2011-09-15

    Multiple sclerosis (MS) is a central nervous system autoimmune inflammatory T-cell-mediated disease with a relapsing-remitting course in the majority of patients. In this study, we performed a high-resolution systems biology analysis of gene expression and physical interactions in MS relapse and remission. To this end, we integrated 164 large-scale measurements of gene expression in peripheral blood mononuclear cells of MS patients in relapse or remission and healthy subjects, with large-scale information about the physical interactions between these genes obtained from public databases. These data were analyzed with a variety of computational methods. We find that there is a clear and significant global network-level signal that is related to the changes in gene expression of MS patients in comparison to healthy subjects. However, despite the clear differences in the clinical symptoms of MS patients in relapse versus remission, the network level signal is weaker when comparing patients in these two stages of the disease. This result suggests that most of the genes have relatively similar expression levels in the two stages of the disease. In accordance with previous studies, we found that the pathways related to regulation of cell death, chemotaxis and inflammatory response are differentially expressed in the disease in comparison to healthy subjects, while pathways related to cell adhesion, cell migration and cell-cell signaling are activated in relapse in comparison to remission. However, the current study includes a detailed report of the exact set of genes involved in these pathways and the interactions between them. For example, we found that the genes TP53 and IL1 are 'network-hub' that interacts with many of the differentially expressed genes in MS patients versus healthy subjects, and the epidermal growth factor receptor is a 'network-hub' in the case of MS patients with relapse versus remission. The statistical approaches employed in this study enabled us

  12. Railway network design with multiple project stages and time sequencing

    NASA Astrophysics Data System (ADS)

    Kuby, Michael; Xu, Zhongyi; Xie, Xiaodong

    This paper presents a spatial decision support system for network design problems in which different kinds of projects can be built in stages over time. It was developed by the World Bank and China's Ministry of Railways to plan investment strategies for China's overburdened railway system. We first present a mixed-integer program for the single-period network design problem with project choices such as single or multiple tracks and/or electrification with economies of scale. Then, because such projects can be built all at once or in stages, we developed a heuristic backwards time sequencing procedure with a cost adjustment factor to solve the ``project staging'' problem. Other innovations include a preloading routine; coordinated modeling of arcs, paths, and corridors; and a custom-built GIS.

  13. Phenotype accessibility and noise in random threshold gene regulatory networks.

    PubMed

    Pinho, Ricardo; Garcia, Victor; Feldman, Marcus W

    2014-01-01

    Evolution requires phenotypic variation in a population of organisms for selection to function. Gene regulatory processes involved in organismal development affect the phenotypic diversity of organisms. Since only a fraction of all possible phenotypes are predicted to be accessed by the end of development, organisms may evolve strategies to use environmental cues and noise-like fluctuations to produce additional phenotypic diversity, and hence to enhance the speed of adaptation. We used a generic model of organismal development --gene regulatory networks-- to investigate how different levels of noise on gene expression states (i.e. phenotypes) may affect access to new, unique phenotypes, thereby affecting phenotypic diversity. We studied additional strategies that organisms might adopt to attain larger phenotypic diversity: either by augmenting their genome or the number of gene expression states. This was done for different types of gene regulatory networks that allow for distinct levels of regulatory influence on gene expression or are more likely to give rise to stable phenotypes. We found that if gene expression is binary, increasing noise levels generally decreases phenotype accessibility for all network types studied. If more gene expression states are considered, noise can moderately enhance the speed of discovery if three or four gene expression states are allowed, and if there are enough distinct regulatory networks in the population. These results were independent of the network types analyzed, and were robust to different implementations of noise. Hence, for noise to increase the number of accessible phenotypes in gene regulatory networks, very specific conditions need to be satisfied. If the number of distinct regulatory networks involved in organismal development is large enough, and the acquisition of more genes or fine tuning of their expression states proves costly to the organism, noise can be useful in allowing access to more unique phenotypes

  14. Phenotype Accessibility and Noise in Random Threshold Gene Regulatory Networks

    PubMed Central

    Feldman, Marcus W.

    2015-01-01

    Evolution requires phenotypic variation in a population of organisms for selection to function. Gene regulatory processes involved in organismal development affect the phenotypic diversity of organisms. Since only a fraction of all possible phenotypes are predicted to be accessed by the end of development, organisms may evolve strategies to use environmental cues and noise-like fluctuations to produce additional phenotypic diversity, and hence to enhance the speed of adaptation. We used a generic model of organismal development --gene regulatory networks-- to investigate how different levels of noise on gene expression states (i.e. phenotypes) may affect access to new, unique phenotypes, thereby affecting phenotypic diversity. We studied additional strategies that organisms might adopt to attain larger phenotypic diversity: either by augmenting their genome or the number of gene expression states. This was done for different types of gene regulatory networks that allow for distinct levels of regulatory influence on gene expression or are more likely to give rise to stable phenotypes. We found that if gene expression is binary, increasing noise levels generally decreases phenotype accessibility for all network types studied. If more gene expression states are considered, noise can moderately enhance the speed of discovery if three or four gene expression states are allowed, and if there are enough distinct regulatory networks in the population. These results were independent of the network types analyzed, and were robust to different implementations of noise. Hence, for noise to increase the number of accessible phenotypes in gene regulatory networks, very specific conditions need to be satisfied. If the number of distinct regulatory networks involved in organismal development is large enough, and the acquisition of more genes or fine tuning of their expression states proves costly to the organism, noise can be useful in allowing access to more unique phenotypes

  15. Gene co-expression networks shed light into diseases of brain iron accumulation

    PubMed Central

    Bettencourt, Conceição; Forabosco, Paola; Wiethoff, Sarah; Heidari, Moones; Johnstone, Daniel M.; Botía, Juan A.; Collingwood, Joanna F.; Hardy, John; Milward, Elizabeth A.; Ryten, Mina; Houlden, Henry

    2016-01-01

    Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention. PMID:26707700

  16. Gene co-expression networks shed light into diseases of brain iron accumulation.

    PubMed

    Bettencourt, Conceição; Forabosco, Paola; Wiethoff, Sarah; Heidari, Moones; Johnstone, Daniel M; Botía, Juan A; Collingwood, Joanna F; Hardy, John; Milward, Elizabeth A; Ryten, Mina; Houlden, Henry

    2016-03-01

    Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention. PMID:26707700

  17. The propagation of perturbations in rewired bacterial gene networks

    PubMed Central

    Baumstark, Rebecca; Hänzelmann, Sonja; Tsuru, Saburo; Schaerli, Yolanda; Francesconi, Mirko; Mancuso, Francesco M.; Castelo, Robert; Isalan, Mark

    2015-01-01

    What happens to gene expression when you add new links to a gene regulatory network? To answer this question, we profile 85 network rewirings in E. coli. Here we report that concerted patterns of differential expression propagate from reconnected hub genes. The rewirings link promoter regions to different transcription factor and σ-factor genes, resulting in perturbations that span four orders of magnitude, changing up to ∼70% of the transcriptome. Importantly, factor connectivity and promoter activity both associate with perturbation size. Perturbations from related rewirings have more similar transcription profiles and a statistical analysis reveals ∼20 underlying states of the system, associating particular gene groups with rewiring constructs. We examine two large clusters (ribosomal and flagellar genes) in detail. These represent alternative global outcomes from different rewirings because of antagonism between these major cell states. This data set of systematically related perturbations enables reverse engineering and discovery of underlying network interactions. PMID:26670742

  18. Regulatory gene networks and the properties of the developmental process

    NASA Technical Reports Server (NTRS)

    Davidson, Eric H.; McClay, David R.; Hood, Leroy

    2003-01-01

    Genomic instructions for development are encoded in arrays of regulatory DNA. These specify large networks of interactions among genes producing transcription factors and signaling components. The architecture of such networks both explains and predicts developmental phenomenology. Although network analysis is yet in its early stages, some fundamental commonalities are already emerging. Two such are the use of multigenic feedback loops to ensure the progressivity of developmental regulatory states and the prevalence of repressive regulatory interactions in spatial control processes. Gene regulatory networks make it possible to explain the process of development in causal terms and eventually will enable the redesign of developmental regulatory circuitry to achieve different outcomes.

  19. Revealing shared and distinct gene network organization in Arabidopsis immune responses by integrative analysis.

    PubMed

    Dong, Xiaobao; Jiang, Zhenhong; Peng, You-Liang; Zhang, Ziding

    2015-03-01

    Pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) are two main plant immune responses to counter pathogen invasion. Genome-wide gene network organizing principles leading to quantitative differences between PTI and ETI have remained elusive. We combined an advanced machine learning method and modular network analysis to systematically characterize the organizing principles of Arabidopsis (Arabidopsis thaliana) PTI and ETI at three network resolutions. At the single network node/edge level, we ranked genes and gene interactions based on their ability to distinguish immune response from normal growth and successfully identified many immune-related genes associated with PTI and ETI. Topological analysis revealed that the top-ranked gene interactions tend to link network modules. At the subnetwork level, we identified a subnetwork shared by PTI and ETI encompassing 1,159 genes and 1,289 interactions. This subnetwork is enriched in interactions linking network modules and is also a hotspot of attack by pathogen effectors. The subnetwork likely represents a core component in the coordination of multiple biological processes to favor defense over development. Finally, we constructed modular network models for PTI and ETI to explain the quantitative differences in the global network architecture. Our results indicate that the defense modules in ETI are organized into relatively independent structures, explaining the robustness of ETI to genetic mutations and effector attacks. Taken together, the multiscale comparisons of PTI and ETI provide a systems biology perspective on plant immunity and emphasize coordination among network modules to establish a robust immune response. PMID:25614062

  20. Revealing Shared and Distinct Gene Network Organization in Arabidopsis Immune Responses by Integrative Analysis1

    PubMed Central

    Dong, Xiaobao; Jiang, Zhenhong; Peng, You-Liang; Zhang, Ziding

    2015-01-01

    Pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) are two main plant immune responses to counter pathogen invasion. Genome-wide gene network organizing principles leading to quantitative differences between PTI and ETI have remained elusive. We combined an advanced machine learning method and modular network analysis to systematically characterize the organizing principles of Arabidopsis (Arabidopsis thaliana) PTI and ETI at three network resolutions. At the single network node/edge level, we ranked genes and gene interactions based on their ability to distinguish immune response from normal growth and successfully identified many immune-related genes associated with PTI and ETI. Topological analysis revealed that the top-ranked gene interactions tend to link network modules. At the subnetwork level, we identified a subnetwork shared by PTI and ETI encompassing 1,159 genes and 1,289 interactions. This subnetwork is enriched in interactions linking network modules and is also a hotspot of attack by pathogen effectors. The subnetwork likely represents a core component in the coordination of multiple biological processes to favor defense over development. Finally, we constructed modular network models for PTI and ETI to explain the quantitative differences in the global network architecture. Our results indicate that the defense modules in ETI are organized into relatively independent structures, explaining the robustness of ETI to genetic mutations and effector attacks. Taken together, the multiscale comparisons of PTI and ETI provide a systems biology perspective on plant immunity and emphasize coordination among network modules to establish a robust immune response. PMID:25614062

  1. Next generation communications satellites: multiple access and network studies

    NASA Technical Reports Server (NTRS)

    Meadows, H. E.; Schwartz, M.; Stern, T. E.; Ganguly, S.; Kraimeche, B.; Matsuo, K.; Gopal, I.

    1982-01-01

    Efficient resource allocation and network design for satellite systems serving heterogeneous user populations with large numbers of small direct-to-user Earth stations are discussed. Focus is on TDMA systems involving a high degree of frequency reuse by means of satellite-switched multiple beams (SSMB) with varying degrees of onboard processing. Algorithms for the efficient utilization of the satellite resources were developed. The effect of skewed traffic, overlapping beams and batched arrivals in packet-switched SSMB systems, integration of stream and bursty traffic, and optimal circuit scheduling in SSMB systems: performance bounds and computational complexity are discussed.

  2. Communication: Separable potential energy surfaces from multiplicative artificial neural networks

    SciTech Connect

    Koch, Werner Zhang, Dong H.

    2014-07-14

    We present a potential energy surface fitting scheme based on multiplicative artificial neural networks. It has the sum of products form required for efficient computation of the dynamics of multidimensional quantum systems with the multi configuration time dependent Hartree method. Moreover, it results in analytic potential energy matrix elements when combined with quantum dynamics methods using Gaussian basis functions, eliminating the need for a local harmonic approximation. Scaling behavior with respect to the complexity of the potential as well as the requested accuracy is discussed.

  3. Parallel Recruitment of Multiple Genes into C4 Photosynthesis

    PubMed Central

    Christin, Pascal-Antoine; Boxall, Susanna F.; Gregory, Richard; Edwards, Erika J.; Hartwell, James; Osborne, Colin P.

    2013-01-01

    During the diversification of living organisms, novel adaptive traits usually evolve through the co-option of preexisting genes. However, most enzymes are encoded by gene families, whose members vary in their expression and catalytic properties. Each may therefore differ in its suitability for recruitment into a novel function. In this work, we test for the presence of such a gene recruitment bias using the example of C4 photosynthesis, a complex trait that evolved recurrently in flowering plants as a response to atmospheric CO2 depletion. We combined the analysis of complete nuclear genomes and high-throughput transcriptome data for three grass species that evolved the C4 trait independently. For five of the seven enzymes analyzed, the same gene lineage was recruited across the independent C4 origins, despite the existence of multiple copies. The analysis of a closely related C3 grass confirmed that C4 expression patterns were not present in the C3 ancestors but were acquired during the evolutionary transition to C4 photosynthesis. The significant bias in gene recruitment indicates that some genes are more suitable for a novel function, probably because the mutations they accumulated brought them closer to the characteristics required for the new function. PMID:24179135

  4. How multiple social networks affect user awareness: The information diffusion process in multiplex networks

    NASA Astrophysics Data System (ADS)

    Li, Weihua; Tang, Shaoting; Fang, Wenyi; Guo, Quantong; Zhang, Xiao; Zheng, Zhiming

    2015-10-01

    The information diffusion process in single complex networks has been extensively studied, especially for modeling the spreading activities in online social networks. However, individuals usually use multiple social networks at the same time, and can share the information they have learned from one social network to another. This phenomenon gives rise to a new diffusion process on multiplex networks with more than one network layer. In this paper we account for this multiplex network spreading by proposing a model of information diffusion in two-layer multiplex networks. We develop a theoretical framework using bond percolation and cascading failure to describe the intralayer and interlayer diffusion. This allows us to obtain analytical solutions for the fraction of informed individuals as a function of transmissibility T and the interlayer transmission rate θ . Simulation results show that interaction between layers can greatly enhance the information diffusion process. And explosive diffusion can occur even if the transmissibility of the focal layer is under the critical threshold, due to interlayer transmission.

  5. Global multiple protein-protein interaction network alignment by combining pairwise network alignments

    PubMed Central

    2015-01-01

    Background A wealth of protein interaction data has become available in recent years, creating an urgent need for powerful analysis techniques. In this context, the problem of finding biologically meaningful correspondences between different protein-protein interaction networks (PPIN) is of particular interest. The PPIN of a species can be compared with that of other species through the process of PPIN alignment. Such an alignment can provide insight into basic problems like species evolution and network component function determination, as well as translational problems such as target identification and elucidation of mechanisms of disease spread. Furthermore, multiple PPINs can be aligned simultaneously, expanding the analytical implications of the result. While there are several pairwise network alignment algorithms, few methods are capable of multiple network alignment. Results We propose SMAL, a MNA algorithm based on the philosophy of scaffold-based alignment. SMAL is capable of converting results from any global pairwise alignment algorithms into a MNA in linear time. Using this method, we have built multiple network alignments based on combining pairwise alignments from a number of publicly available (pairwise) network aligners. We tested SMAL using PPINs of eight species derived from the IntAct repository and employed a number of measures to evaluate performance. Additionally, as part of our experimental investigations, we compared the effectiveness of SMAL while aligning up to eight input PPINs, and examined the effect of scaffold network choice on the alignments. Conclusions A key advantage of SMAL lies in its ability to create MNAs through the use of pairwise network aligners for which native MNA implementations do not exist. Experiments indicate that the performance of SMAL was comparable to that of the native MNA implementation of established methods such as IsoRankN and SMETANA. However, in terms of computational time, SMAL was significantly faster

  6. PoplarGene: poplar gene network and resource for mining functional information for genes from woody plants

    PubMed Central

    Liu, Qi; Ding, Changjun; Chu, Yanguang; Chen, Jiafei; Zhang, Weixi; Zhang, Bingyu; Huang, Qinjun; Su, Xiaohua

    2016-01-01

    Poplar is not only an important resource for the production of paper, timber and other wood-based products, but it has also emerged as an ideal model system for studying woody plants. To better understand the biological processes underlying various traits in poplar, e.g., wood development, a comprehensive functional gene interaction network is highly needed. Here, we constructed a genome-wide functional gene network for poplar (covering ~70% of the 41,335 poplar genes) and created the network web service PoplarGene, offering comprehensive functional interactions and extensive poplar gene functional annotations. PoplarGene incorporates two network-based gene prioritization algorithms, neighborhood-based prioritization and context-based prioritization, which can be used to perform gene prioritization in a complementary manner. Furthermore, the co-functional information in PoplarGene can be applied to other woody plant proteomes with high efficiency via orthology transfer. In addition to poplar gene sequences, the webserver also accepts Arabidopsis reference gene as input to guide the search for novel candidate functional genes in PoplarGene. We believe that PoplarGene (http://bioinformatics.caf.ac.cn/PoplarGene and http://124.127.201.25/PoplarGene) will greatly benefit the research community, facilitating studies of poplar and other woody plants. PMID:27515999

  7. PoplarGene: poplar gene network and resource for mining functional information for genes from woody plants.

    PubMed

    Liu, Qi; Ding, Changjun; Chu, Yanguang; Chen, Jiafei; Zhang, Weixi; Zhang, Bingyu; Huang, Qinjun; Su, Xiaohua

    2016-01-01

    Poplar is not only an important resource for the production of paper, timber and other wood-based products, but it has also emerged as an ideal model system for studying woody plants. To better understand the biological processes underlying various traits in poplar, e.g., wood development, a comprehensive functional gene interaction network is highly needed. Here, we constructed a genome-wide functional gene network for poplar (covering ~70% of the 41,335 poplar genes) and created the network web service PoplarGene, offering comprehensive functional interactions and extensive poplar gene functional annotations. PoplarGene incorporates two network-based gene prioritization algorithms, neighborhood-based prioritization and context-based prioritization, which can be used to perform gene prioritization in a complementary manner. Furthermore, the co-functional information in PoplarGene can be applied to other woody plant proteomes with high efficiency via orthology transfer. In addition to poplar gene sequences, the webserver also accepts Arabidopsis reference gene as input to guide the search for novel candidate functional genes in PoplarGene. We believe that PoplarGene (http://bioinformatics.caf.ac.cn/PoplarGene and http://124.127.201.25/PoplarGene) will greatly benefit the research community, facilitating studies of poplar and other woody plants. PMID:27515999

  8. In silico network topology-based prediction of gene essentiality

    NASA Astrophysics Data System (ADS)

    da Silva, João Paulo Müller; Acencio, Marcio Luis; Mombach, José Carlos Merino; Vieira, Renata; da Silva, José Camargo; Lemke, Ney; Sinigaglia, Marialva

    2008-02-01

    The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance.

  9. Evaluation of Gene Association Methods for Coexpression Network Construction and Biological Knowledge Discovery

    PubMed Central

    Kumari, Sapna; Nie, Jeff; Chen, Huann-Sheng; Ma, Hao; Stewart, Ron; Li, Xiang; Lu, Meng-Zhu; Taylor, William M.; Wei, Hairong

    2012-01-01

    Background Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. Methods and Results In this study, we compared eight gene association methods – Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson – and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. Conclusions We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction. PMID:23226279

  10. SHC2 gene copy number in multiple system atrophy (MSA)

    PubMed Central

    Ferguson, Marcus C.; Garland, Emily M.; Hedges, Lora; Womack-Nunley, Bethany; Hamid, Rizwan; Phillips, John A.; Shibao, Cyndya A.; Raj, Satish R.; Biaggioni, Italo; Robertson, David

    2013-01-01

    Purpose Multiple system atrophy (MSA) is a sporadic, late onset, rapidly-progressing neurodegenerative disorder, which is characterized by autonomic failure, together with parkinsonian, cerebellar, and pyramidal motor symptoms. The pathologic hallmark is the glial cytoplasmic inclusion with alpha-synuclein aggregates. MSA is thus an alpha synucleinopathy. Recently, Sasaki et al. reported that heterozygosity for copy number loss of Src homology 2 domain containing-transforming protein 2 (SHC2) genes (heterozygous SHC2 gene deletions) occurred in DNAs from many Japanese individuals with MSA. Because background copy number variation (CNV) can be distinct in different human populations, we assessed SHC2 allele copy number in DNAs from a US cohort of individuals with MSA, to determine the contribution of SHC2 gene copy number variation in an American cohort followed at a US referral center for MSA. Our cohort included 105 carefully phenotyped individuals with MSA. Methods We studied 105 well characterized patients with MSA and 5 control subjects with reduced SHC2 gene copy number. We used two TaqMan Gene Copy Number Assays, to determine the copy number of two segments of the SHC2 gene that are separated by 27 Kb. Results Assay results of DNAs from all of our 105 subjects with MSA showed two copies of both segments of their SHC2 genes. Conclusion Our results indicate that SHC2 gene deletions underlie few, if any, cases of well characterized MSA in the US population. This is in contrast to the Japanese experience reported by Sasaki et al., likely reflecting heterogeneity of the disease in different genetic backgrounds. PMID:24170347

  11. Evolutionary and Topological Properties of Genes and Community Structures in Human Gene Regulatory Networks

    PubMed Central

    Szedlak, Anthony; Smith, Nicholas; Liu, Li; Paternostro, Giovanni; Piermarocchi, Carlo

    2016-01-01

    The diverse, specialized genes present in today’s lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins’ binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity. A deep understanding of the relationship between the evolutionary history of single genes and the topological properties of the underlying GRN is integral to evolutionary genetics. Here, we show that the topological properties of an acute myeloid leukemia GRN and a general human GRN are strongly coupled with its genes’ evolutionary properties. Slowly evolving (“cold”), old genes tend to interact with each other, as do rapidly evolving (“hot”), young genes. This naturally causes genes to segregate into community structures with relatively homogeneous evolutionary histories. We argue that gene duplication placed old, cold genes and communities at the center of the networks, and young, hot genes and communities at the periphery. We demonstrate this with single-node centrality measures and two new measures of efficiency, the set efficiency and the interset efficiency. We conclude that these methods for studying the relationships between a GRN’s community structures and its genes’ evolutionary properties provide new perspectives for understanding evolutionary genetics. PMID:27359334

  12. Asymmetric Regulation of Peripheral Genes by Two Transcriptional Regulatory Networks

    PubMed Central

    Li, Jing-Ru; Suzuki, Takahiro; Nishimura, Hajime; Kishima, Mami; Maeda, Shiori; Suzuki, Harukazu

    2016-01-01

    Transcriptional regulatory network (TRN) reconstitution and deconstruction occur simultaneously during reprogramming; however, it remains unclear how the starting and targeting TRNs regulate the induction and suppression of peripheral genes. Here we analyzed the regulation using direct cell reprogramming from human dermal fibroblasts to monocytes as the platform. We simultaneously deconstructed fibroblastic TRN and reconstituted monocytic TRN; monocytic and fibroblastic gene expression were analyzed in comparison with that of fibroblastic TRN deconstruction only or monocytic TRN reconstitution only. Global gene expression analysis showed cross-regulation of TRNs. Detailed analysis revealed that knocking down fibroblastic TRN positively affected half of the upregulated monocytic genes, indicating that intrinsic fibroblastic TRN interfered with the expression of induced genes. In contrast, reconstitution of monocytic TRN showed neutral effects on the majority of fibroblastic gene downregulation. This study provides an explicit example that demonstrates how two networks together regulate gene expression during cell reprogramming processes and contributes to the elaborate exploration of TRNs. PMID:27483142

  13. Comparative genomics of mammalian hibernators using gene networks.

    PubMed

    Villanueva-Cañas, José Luis; Faherty, Sheena L; Yoder, Anne D; Albà, M Mar

    2014-09-01

    In recent years, the study of the molecular processes involved in mammalian hibernation has shifted from investigating a few carefully selected candidate genes to large-scale analysis of differential gene expression. The availability of high-throughput data provides an unprecedented opportunity to ask whether phylogenetically distant species show similar mechanisms of genetic control, and how these relate to particular genes and pathways involved in the hibernation phenotype. In order to address these questions, we compare 11 datasets of differentially expressed (DE) genes from two ground squirrel species, one bat species, and the American black bear, as well as a list of genes extracted from the literature that previously have been correlated with the drastic physiological changes associated with hibernation. We identify several genes that are DE in different species, indicating either ancestral adaptations or evolutionary convergence. When we use a network approach to expand the original datasets of DE genes to large gene networks using available interactome data, a higher agreement between datasets is achieved. This indicates that the same key pathways are important for activating and maintaining the hibernation phenotype. Functional-term-enrichment analysis identifies several important metabolic and mitochondrial processes that are critical for hibernation, such as fatty acid beta-oxidation and mitochondrial transport. We do not detect any enrichment of positive selection signatures in the coding sequences of genes from the networks of hibernation-associated genes, supporting the hypothesis that the genetic processes shaping the hibernation phenotype are driven primarily by changes in gene regulation. PMID:24881044

  14. Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

    PubMed Central

    Himmelstein, Daniel S.; Baranzini, Sergio E.

    2015-01-01

    The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks—graphs with multiple node and edge types—for accomplishing both tasks. First we constructed a network with 18 node types—genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections—and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as influential mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data

  15. Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes

    PubMed Central

    Franke, Lude; Bakel, Harm van; Fokkens, Like; de Jong, Edwin D.; Egmont-Petersen, Michael; Wijmenga, Cisca

    2006-01-01

    Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray coexpressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which

  16. Correlated gene expression supports synchronous activity in brain networks

    PubMed Central

    Richiardi, Jonas; Altmann, Andre; Milazzo, Anna-Clare; Chang, Catie; Chakravarty, M. Mallar; Banaschewski, Tobias; Barker, Gareth J.; Bokde, Arun L.W.; Bromberg, Uli; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Lemaître, Hervé; Mann, Karl F.; Martinot, Jean-Luc; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Rietschel, Marcella; Robbins, Trevor W.; Smolka, Michael N.; Spanagel, Rainer; Ströhle, Andreas; Schumann, Gunter; Hawrylycz, Mike; Poline, Jean-Baptiste; Greicius, Michael D.

    2016-01-01

    During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function. PMID:26068849

  17. Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data

    PubMed Central

    Faria, José P.; Overbeek, Ross; Taylor, Ronald C.; Conrad, Neal; Vonstein, Veronika; Goelzer, Anne; Fromion, Vincent; Rocha, Miguel; Rocha, Isabel; Henry, Christopher S.

    2016-01-01

    We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of Bacillus subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs, and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches, and small regulatory RNAs. Overall, regulatory information is included in the model for ∼2500 of the ∼4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how ARs for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how ARs can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental

  18. Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data.

    PubMed

    Faria, José P; Overbeek, Ross; Taylor, Ronald C; Conrad, Neal; Vonstein, Veronika; Goelzer, Anne; Fromion, Vincent; Rocha, Miguel; Rocha, Isabel; Henry, Christopher S

    2016-01-01

    We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of Bacillus subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs, and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches, and small regulatory RNAs. Overall, regulatory information is included in the model for ∼2500 of the ∼4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same "ON" and "OFF" gene expression profiles across multiple samples of experimental data. We show how ARs for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how ARs can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions

  19. Visualization of multiple alignments, phylogenies and gene family evolution.

    PubMed

    Procter, James B; Thompson, Julie; Letunic, Ivica; Creevey, Chris; Jossinet, Fabrice; Barton, Geoffrey J

    2010-03-01

    Software for visualizing sequence alignments and trees are essential tools for life scientists. In this review, we describe the major features and capabilities of a selection of stand-alone and web-based applications useful when investigating the function and evolution of a gene family. These range from simple viewers, to systems that provide sophisticated editing and analysis functions. We conclude with a discussion of the challenges that these tools now face due to the flood of next generation sequence data and the increasingly complex network of bioinformatics information sources. PMID:20195253

  20. Adaptive Horizontal Gene Transfers between Multiple Cheese-Associated Fungi.

    PubMed

    Ropars, Jeanne; Rodríguez de la Vega, Ricardo C; López-Villavicencio, Manuela; Gouzy, Jérôme; Sallet, Erika; Dumas, Émilie; Lacoste, Sandrine; Debuchy, Robert; Dupont, Joëlle; Branca, Antoine; Giraud, Tatiana

    2015-10-01

    Domestication is an excellent model for studies of adaptation because it involves recent and strong selection on a few, identified traits [1-5]. Few studies have focused on the domestication of fungi, with notable exceptions [6-11], despite their importance to bioindustry [12] and to a general understanding of adaptation in eukaryotes [5]. Penicillium fungi are ubiquitous molds among which two distantly related species have been independently selected for cheese making-P. roqueforti for blue cheeses like Roquefort and P. camemberti for soft cheeses like Camembert. The selected traits include morphology, aromatic profile, lipolytic and proteolytic activities, and ability to grow at low temperatures, in a matrix containing bacterial and fungal competitors [13-15]. By comparing the genomes of ten Penicillium species, we show that adaptation to cheese was associated with multiple recent horizontal transfers of large genomic regions carrying crucial metabolic genes. We identified seven horizontally transferred regions (HTRs) spanning more than 10 kb each, flanked by specific transposable elements, and displaying nearly 100% identity between distant Penicillium species. Two HTRs carried genes with functions involved in the utilization of cheese nutrients or competition and were found nearly identical in multiple strains and species of cheese-associated Penicillium fungi, indicating recent selective sweeps; they were experimentally associated with faster growth and greater competitiveness on cheese and contained genes highly expressed in the early stage of cheese maturation. These findings have industrial and food safety implications and improve our understanding of the processes of adaptation to rapid environmental changes. PMID:26412136

  1. Susceptibility Genes for Multiple Sclerosis Identified in a Gene-Based Genome-Wide Association Study

    PubMed Central

    Lin, Xiang; Deng, Fei-Yan; Lu, Xin

    2015-01-01

    Background and Purpose Multiple sclerosis (MS) is a demyelinating and inflammatory disease of the central nervous system. The aim of this study was to identify more genes associated with MS. Methods Based on the publicly available data of the single-nucleotide polymorphism-based genome-wide association study (GWAS) from the database of Genotypes and Phenotypes, we conducted a powerful gene-based GWAS in an initial sample with 931 family trios, and a replication study sample with 978 cases and 883 controls. For interesting genes, gene expression in MS-related cells between MS cases and controls was examined by using publicly available datasets. Results A total of 58 genes was identified, including 20 "novel" genes significantly associated with MS (p<1.40×10-4). In the replication study, 44 of the 58 identified genes had been genotyped and 35 replicated the association. In the gene-expression study, 21 of the 58 identified genes exhibited differential expressions in MS-related cells. Thus, 15 novel genes were supported by replicated association and/or differential expression. In particular, four of the novel genes, those encoding myelin oligodendrocyte glycoprotein (MOG), coiled-coil alpha-helical rod protein 1 (CCHCR1), human leukocyte antigen complex group 22 (HCG22), and major histocompatibility complex, class II, DM alpha (HLA-DMA), were supported by the evidence of both. Conclusions The results of this study emphasize the high power of gene-based GWAS in detecting the susceptibility genes of MS. The novel genes identified herein may provide new insights into the molecular genetic mechanisms underlying MS. PMID:26320842

  2. Gene-based and semantic structure of the Gene Ontology as a complex network

    NASA Astrophysics Data System (ADS)

    Coronnello, Claudia; Tumminello, Michele; Miccichè, Salvatore

    2016-09-01

    The last decade has seen the advent and consolidation of ontology based tools for the identification and biological interpretation of classes of genes, such as the Gene Ontology. The Gene Ontology (GO) is constantly evolving over time. The information accumulated time-by-time and included in the GO is encoded in the definition of terms and in the setting up of semantic relations amongst terms. Here we investigate the Gene Ontology from a complex network perspective. We consider the semantic network of terms naturally associated with the semantic relationships provided by the Gene Ontology consortium. Moreover, the GO is a natural example of bipartite network of terms and genes. Here we are interested in studying the properties of the projected network of terms, i.e. a gene-based weighted network of GO terms, in which a link between any two terms is set if at least one gene is annotated in both terms. One aim of the present paper is to compare the structural properties of the semantic and the gene-based network. The relative importance of terms is very similar in the two networks, but the community structure changes. We show that in some cases GO terms that appear to be distinct from a semantic point of view are instead connected, and appear in the same community when considering their gene content. The identification of such gene-based communities of terms might therefore be the basis of a simple protocol aiming at improving the semantic structure of GO. Information about terms that share large gene content might also be important from a biomedical point of view, as it might reveal how genes over-expressed in a certain term also affect other biological processes, molecular functions and cellular components not directly linked according to GO semantics.

  3. Network-Based Inference Framework for Identifying Cancer Genes from Gene Expression Data

    PubMed Central

    Yang, Bo; Zhang, Junying; Yin, Yaling; Zhang, Yuanyuan

    2013-01-01

    Great efforts have been devoted to alleviate uncertainty of detected cancer genes as accurate identification of oncogenes is of tremendous significance and helps unravel the biological behavior of tumors. In this paper, we present a differential network-based framework to detect biologically meaningful cancer-related genes. Firstly, a gene regulatory network construction algorithm is proposed, in which a boosting regression based on likelihood score and informative prior is employed for improving accuracy of identification. Secondly, with the algorithm, two gene regulatory networks are constructed from case and control samples independently. Thirdly, by subtracting the two networks, a differential-network model is obtained and then used to rank differentially expressed hub genes for identification of cancer biomarkers. Compared with two existing gene-based methods (t-test and lasso), the method has a significant improvement in accuracy both on synthetic datasets and two real breast cancer datasets. Furthermore, identified six genes (TSPYL5, CD55, CCNE2, DCK, BBC3, and MUC1) susceptible to breast cancer were verified through the literature mining, GO analysis, and pathway functional enrichment analysis. Among these oncogenes, TSPYL5 and CCNE2 have been already known as prognostic biomarkers in breast cancer, CD55 has been suspected of playing an important role in breast cancer prognosis from literature evidence, and other three genes are newly discovered breast cancer biomarkers. More generally, the differential-network schema can be extended to other complex diseases for detection of disease associated-genes. PMID:24073403

  4. Network analysis of microRNAs, transcription factors, target genes and host genes in nasopharyngeal carcinoma

    PubMed Central

    WANG, HAO; XU, ZHIWEN; MA, MENGYAO; WANG, NING; WANG, KUNHAO

    2016-01-01

    Numerous studies on the morbidity of nasopharyngeal carcinoma (NPC) have identified several genes, microRNAs (miRNAs or miRs) and transcription factors (TFs) that influence the pathogenesis of NPC. However, summarizing all the regulatory networks involved in NPC is challenging. In the present study, the genes, miRNAs and TFs involved in NPC were considered as the nodes of the so-called regulatory network, and the associations between them were investigated. To clearly represent these associations, three regulatory networks were built seperately, namely, the differentially expressed network, the associated network and the global network. The differentially expressed network is the most important one of these three networks, since its nodes are differentially expressed genes whose mutations may lead to the development of NPC. Therefore, by modifying the aberrant expression of those genes that are differentially expressed in this network, their dysregulation may be corrected and the tumorigenesis of NPC may thus be prevented. Analysis of the aforementioned three networks highlighted the importance of certain pathways, such as self-adaptation pathways, in the development of NPC. For example, cyclin D1 (CCND1) was observed to regulate Homo sapiens-miR-20a, which in turn targeted CCND1. The present study conducted a systematic analysis of the pathogenesis of NPC through the three aforementioned regulatory networks, and provided a theoretical model for biologists. Future studies are required to evaluate the influence of the highlighted pathways in NPC. PMID:27313701

  5. Gene expression in self-repressing system with multiple gene copies.

    PubMed

    Miekisz, Jacek; Szymańska, Paulina

    2013-02-01

    We analyze a simple model of a self-repressing system with multiple gene copies. Protein molecules may bound to DNA promoters and block their own transcription. We derive analytical expressions for the variance of the number of protein molecules in the stationary state in the self-consistent mean-field approximation. We show that the Fano factor (the variance divided by the mean value) is bigger for the one-gene case than for two gene copies and the difference decreases to zero as frequencies of binding and unbinding increase to infinity. PMID:23354928

  6. Noisy attractors and ergodic sets in models of gene regulatory networks.

    PubMed

    Ribeiro, Andre S; Kauffman, Stuart A

    2007-08-21

    We investigate the hypothesis that cell types are attractors. This hypothesis was criticized with the fact that real gene networks are noisy systems and, thus, do not have attractors [Kadanoff, L., Coppersmith, S., Aldana, M., 2002. Boolean Dynamics with Random Couplings. http://www.citebase.org/abstract?id=oai:arXiv.org:nlin/0204062]. Given the concept of "ergodic set" as a set of states from which the system, once entering, does not leave when subject to internal noise, first, using the Boolean network model, we show that if all nodes of states on attractors are subject to internal state change with a probability p due to noise, multiple ergodic sets are very unlikely. Thereafter, we show that if a fraction of those nodes are "locked" (not subject to state fluctuations caused by internal noise), multiple ergodic sets emerge. Finally, we present an example of a gene network, modelled with a realistic model of transcription and translation and gene-gene interaction, driven by a stochastic simulation algorithm with multiple time-delayed reactions, which has internal noise and that we also subject to external perturbations. We show that, in this case, two distinct ergodic sets exist and are stable within a wide range of parameters variations and, to some extent, to external perturbations. PMID:17543998

  7. Topological origin of global attractors in gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Zhang, YunJun; Ouyang, Qi; Geng, Zhi

    2015-02-01

    Fixed-point attractors with global stability manifest themselves in a number of gene regulatory networks. This property indicates the stability of regulatory networks against small state perturbations and is closely related to other complex dynamics. In this paper, we aim to reveal the core modules in regulatory networks that determine their global attractors and the relationship between these core modules and other motifs. This work has been done via three steps. Firstly, inspired by the signal transmission in the regulation process, we extract the model of chain-like network from regulation networks. We propose a module of "ideal transmission chain (ITC)", which is proved sufficient and necessary (under certain condition) to form a global fixed-point in the context of chain-like network. Secondly, by examining two well-studied regulatory networks (i.e., the cell-cycle regulatory networks of Budding yeast and Fission yeast), we identify the ideal modules in true regulation networks and demonstrate that the modules have a superior contribution to network stability (quantified by the relative size of the biggest attraction basin). Thirdly, in these two regulation networks, we find that the double negative feedback loops, which are the key motifs of forming bistability in regulation, are connected to these core modules with high network stability. These results have shed new light on the connection between the topological feature and the dynamic property of regulatory networks.

  8. Investigating the Effects of Imputation Methods for Modelling Gene Networks Using a Dynamic Bayesian Network from Gene Expression Data

    PubMed Central

    CHAI, Lian En; LAW, Chow Kuan; MOHAMAD, Mohd Saberi; CHONG, Chuii Khim; CHOON, Yee Wen; DERIS, Safaai; ILLIAS, Rosli Md

    2014-01-01

    Background: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN). Methods: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error. Results: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset). Conclusion: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes. PMID:24876803

  9. Functional Analysis of Sirtuin Genes in Multiple Plasmodium falciparum Strains

    PubMed Central

    Merrick, Catherine J.; Jiang, Rays H. Y.; Skillman, Kristen M.; Samarakoon, Upeka; Moore, Rachel M.; Dzikowski, Ron; Ferdig, Michael T.; Duraisingh, Manoj T.

    2015-01-01

    Plasmodium falciparum, the causative agent of severe human malaria, employs antigenic variation to avoid host immunity. Antigenic variation is achieved by transcriptional switching amongst polymorphic var genes, enforced by epigenetic modification of chromatin. The histone-modifying ‘sirtuin’ enzymes PfSir2a and PfSir2b have been implicated in this process. Disparate patterns of var expression have been reported in patient isolates as well as in cultured strains. We examined var expression in three commonly used laboratory strains (3D7, NF54 and FCR-3) in parallel. NF54 parasites express significantly lower levels of var genes compared to 3D7, despite the fact that 3D7 was originally a clone of the NF54 strain. To investigate whether this was linked to the expression of sirtuins, genetic disruption of both sirtuins was attempted in all three strains. No dramatic changes in var gene expression occurred in NF54 or FCR-3 following PfSir2b disruption, contrasting with previous observations in 3D7. In 3D7, complementation of the PfSir2a genetic disruption resulted in a significant decrease in previously-elevated var gene expression levels, but with the continued expression of multiple var genes. Finally, rearranged chromosomes were observed in the 3D7 PfSir2a knockout line. Our results focus on the potential for parasite genetic background to contribute to sirtuin function in regulating virulence gene expression and suggest a potential role for sirtuins in maintaining genome integrity. PMID:25780929

  10. Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction

    PubMed Central

    2013-01-01

    Background Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. Results This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. Conclusions Our newly developed method for HMC takes into account network information in the learning phase: When

  11. A complex network analysis of hypertension-related genes

    NASA Astrophysics Data System (ADS)

    Wang, Huan; Xu, Chuan-Yun; Hu, Jing-Bo; Cao, Ke-Fei

    2014-01-01

    In this paper, a network of hypertension-related genes is constructed by analyzing the correlations of gene expression data among the Dahl salt-sensitive rat and two consomic rat strains. The numerical calculations show that this sparse and assortative network has small-world and scale-free properties. Further, 16 key hub genes (Col4a1, Lcn2, Cdk4, etc.) are determined by introducing an integrated centrality and have been confirmed by biological/medical research to play important roles in hypertension.

  12. Gene network and pathway generation and analysis: Editorial

    SciTech Connect

    Zhao, Zhongming; Sanfilippo, Antonio P.; Huang, Kun

    2011-02-18

    The past decade has witnessed an exponential growth of biological data including genomic sequences, gene annotations, expression and regulation, and protein-protein interactions. A key aim in the post-genome era is to systematically catalogue gene networks and pathways in a dynamic living cell and apply them to study diseases and phenotypes. To promote the research in systems biology and its application to disease studies, we organized a workshop focusing on the reconstruction and analysis of gene networks and pathways in any organisms from high-throughput data collected through techniques such as microarray analysis and RNA-Seq.

  13. Gene regulatory networks modelling using a dynamic evolutionary hybrid

    PubMed Central

    2010-01-01

    Background Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type. Results The recurrent, self-organizing structure and evolutionary training of our network yield an optimized pool of regulatory relations, while its fuzzy nature avoids noise-related problems. Furthermore, we are able to assign scores for each regulation, highlighting the confidence in the retrieved relations. The approach was tested by applying it to several benchmark datasets of yeast, managing to acquire biologically validated relations among genes. Conclusions The results demonstrate the effectiveness of the ENFRN in retrieving biologically valid regulatory relations and providing meaningful insights for better understanding the dynamics of gene regulatory networks. The algorithms and methods described in this paper have been implemented in a Matlab toolbox and are available from: http://bioserver-1.bioacademy.gr/DataRepository/Project_ENFRN_GRN/. PMID:20298548

  14. Development of a synthetic gene network to modulate gene expression by mechanical forces

    PubMed Central

    Kis, Zoltán; Rodin, Tania; Zafar, Asma; Lai, Zhangxing; Freke, Grace; Fleck, Oliver; Del Rio Hernandez, Armando; Towhidi, Leila; Pedrigi, Ryan M.; Homma, Takayuki; Krams, Rob

    2016-01-01

    The majority of (mammalian) cells in our body are sensitive to mechanical forces, but little work has been done to develop assays to monitor mechanosensor activity. Furthermore, it is currently impossible to use mechanosensor activity to drive gene expression. To address these needs, we developed the first mammalian mechanosensitive synthetic gene network to monitor endothelial cell shear stress levels and directly modulate expression of an atheroprotective transcription factor by shear stress. The technique is highly modular, easily scalable and allows graded control of gene expression by mechanical stimuli in hard-to-transfect mammalian cells. We call this new approach mechanosyngenetics. To insert the gene network into a high proportion of cells, a hybrid transfection procedure was developed that involves electroporation, plasmids replication in mammalian cells, mammalian antibiotic selection, a second electroporation and gene network activation. This procedure takes 1 week and yielded over 60% of cells with a functional gene network. To test gene network functionality, we developed a flow setup that exposes cells to linearly increasing shear stress along the length of the flow channel floor. Activation of the gene network varied logarithmically as a function of shear stress magnitude. PMID:27404994

  15. Development of a synthetic gene network to modulate gene expression by mechanical forces.

    PubMed

    Kis, Zoltán; Rodin, Tania; Zafar, Asma; Lai, Zhangxing; Freke, Grace; Fleck, Oliver; Del Rio Hernandez, Armando; Towhidi, Leila; Pedrigi, Ryan M; Homma, Takayuki; Krams, Rob

    2016-01-01

    The majority of (mammalian) cells in our body are sensitive to mechanical forces, but little work has been done to develop assays to monitor mechanosensor activity. Furthermore, it is currently impossible to use mechanosensor activity to drive gene expression. To address these needs, we developed the first mammalian mechanosensitive synthetic gene network to monitor endothelial cell shear stress levels and directly modulate expression of an atheroprotective transcription factor by shear stress. The technique is highly modular, easily scalable and allows graded control of gene expression by mechanical stimuli in hard-to-transfect mammalian cells. We call this new approach mechanosyngenetics. To insert the gene network into a high proportion of cells, a hybrid transfection procedure was developed that involves electroporation, plasmids replication in mammalian cells, mammalian antibiotic selection, a second electroporation and gene network activation. This procedure takes 1 week and yielded over 60% of cells with a functional gene network. To test gene network functionality, we developed a flow setup that exposes cells to linearly increasing shear stress along the length of the flow channel floor. Activation of the gene network varied logarithmically as a function of shear stress magnitude. PMID:27404994

  16. Bayesian Nonlinear Model Selection for Gene Regulatory Networks

    PubMed Central

    Ni, Yang; Stingo, Francesco C.; Baladandayuthapani, Veerabhadran

    2015-01-01

    Summary Gene regulatory networks represent the regulatory relationships between genes and their products and are important for exploring and defining the underlying biological processes of cellular systems. We develop a novel framework to recover the structure of nonlinear gene regulatory networks using semiparametric spline-based directed acyclic graphical models. Our use of splines allows the model to have both flexibility in capturing nonlinear dependencies as well as control of overfitting via shrinkage, using mixed model representations of penalized splines. We propose a novel discrete mixture prior on the smoothing parameter of the splines that allows for simultaneous selection of both linear and nonlinear functional relationships as well as inducing sparsity in the edge selection. Using simulation studies, we demonstrate the superior performance of our methods in comparison with several existing approaches in terms of network reconstruction and functional selection. We apply our methods to a gene expression dataset in glioblastoma multiforme, which reveals several interesting and biologically relevant nonlinear relationships. PMID:25854759

  17. CDX2 regulates multiple trophoblast genes in bovine trophectoderm CT-1 cells.

    PubMed

    Schiffmacher, Andrew T; Keefer, Carol L

    2013-10-01

    The bovine trophectoderm (TE) undergoes a dramatic morphogenetic transition prior to uterine endometrial attachment. Many studies have documented trophoblast-specific gene expression profiles at various pre-attachment stages, yet genetic interactions within the transitioning TE gene regulatory network are not well characterized. During bovine embryogenesis, transcription factors OCT4 and CDX2 are co-expressed during early trophoblast elongation. In this study, the bovine trophectoderm-derived CT-1 cell line was utilized as a genetic model to examine the roles of CDX2 and OCT4 within the bovine trophoblast gene regulatory network. An RT-PCR screen for TE-lineage transcription factors identified expression of CDX2, ERRB, ID2, SOX15, ELF5, HAND1, and ASCL2. CT-1 cells also express a nuclear-localized, 360 amino acid OCT4 ortholog of the pluripotency-specific human OCT4A. To delineate the roles of CDX2 and OCT4 within the CT-1 gene network, CDX2 and OCT4 levels were manipulated via overexpression and siRNA-mediated knockdown. An increase in CDX2 negatively regulated OCT4 expression, but increased expression of IFNT, HAND1, ASCL2, SOX15, and ELF5. A reduction of CDX2 levels exhibited a reciprocal effect, resulting in decreased expression of IFNT, HAND1, ASCL2, and SOX15. Both overexpression and knockdown of CDX2 increased ETS2 transcription. In contrast to CDX2, manipulation of OCT4 levels only revealed a positive autoregulatory mechanism and upregulation of ASCL2. Together, these results suggest that CDX2 is a core regulator of multiple trophoblast genes within CT-1 cells. PMID:23836438

  18. Multiple-server Flexible Blind Quantum Computation in Networks

    NASA Astrophysics Data System (ADS)

    Kong, Xiaoqin; Li, Qin; Wu, Chunhui; Yu, Fang; He, Jinjun; Sun, Zhiyuan

    2016-06-01

    Blind quantum computation (BQC) can allow a client with limited quantum power to delegate his quantum computation to a powerful server and still keep his own data private. In this paper, we present a multiple-server flexible BQC protocol, where a client who only needs the ability of accessing qua ntum channels can delegate the computational task to a number of servers. Especially, the client's quantum computation also can be achieved even when one or more delegated quantum servers break down in networks. In other words, when connections to certain quantum servers are lost, clients can adjust flexibly and delegate their quantum computation to other servers. Obviously it is trivial that the computation will be unsuccessful if all servers are interrupted.

  19. Multiple-server Flexible Blind Quantum Computation in Networks

    NASA Astrophysics Data System (ADS)

    Kong, Xiaoqin; Li, Qin; Wu, Chunhui; Yu, Fang; He, Jinjun; Sun, Zhiyuan

    2016-02-01

    Blind quantum computation (BQC) can allow a client with limited quantum power to delegate his quantum computation to a powerful server and still keep his own data private. In this paper, we present a multiple-server flexible BQC protocol, where a client who only needs the ability of accessing qua ntum channels can delegate the computational task to a number of servers. Especially, the client's quantum computation also can be achieved even when one or more delegated quantum servers break down in networks. In other words, when connections to certain quantum servers are lost, clients can adjust flexibly and delegate their quantum computation to other servers. Obviously it is trivial that the computation will be unsuccessful if all servers are interrupted.

  20. Modeling DNA sequence-based cis-regulatory gene networks.

    PubMed

    Bolouri, Hamid; Davidson, Eric H

    2002-06-01

    Gene network analysis requires computationally based models which represent the functional architecture of regulatory interactions, and which provide directly testable predictions. The type of model that is useful is constrained by the particular features of developmentally active cis-regulatory systems. These systems function by processing diverse regulatory inputs, generating novel regulatory outputs. A computational model which explicitly accommodates this basic concept was developed earlier for the cis-regulatory system of the endo16 gene of the sea urchin. This model represents the genetically mandated logic functions that the system executes, but also shows how time-varying kinetic inputs are processed in different circumstances into particular kinetic outputs. The same basic design features can be utilized to construct models that connect the large number of cis-regulatory elements constituting developmental gene networks. The ultimate aim of the network models discussed here is to represent the regulatory relationships among the genomic control systems of the genes in the network, and to state their functional meaning. The target site sequences of the cis-regulatory elements of these genes constitute the physical basis of the network architecture. Useful models for developmental regulatory networks must represent the genetic logic by which the system operates, but must also be capable of explaining the real time dynamics of cis-regulatory response as kinetic input and output data become available. Most importantly, however, such models must display in a direct and transparent manner fundamental network design features such as intra- and intercellular feedback circuitry; the sources of parallel inputs into each cis-regulatory element; gene battery organization; and use of repressive spatial inputs in specification and boundary formation. Successful network models lead to direct tests of key architectural features by targeted cis-regulatory analysis. PMID

  1. Efficient experimental design for uncertainty reduction in gene regulatory networks

    PubMed Central

    2015-01-01

    Background An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first. Results The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks. Conclusions Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/. PMID:26423515

  2. microRNA and gene networks in human pancreatic cancer.

    PubMed

    Zhu, Minghui; Xu, Zhiwen; Wang, Kunhao; Wang, Ning; Li, Yang

    2013-10-01

    To date, scientists have obtained a substantial amount of knowledge with regard to genes and microRNAs (miRNAs) in pancreatic cancer (PC). However, deciphering the regulatory mechanism of these genes and miRNAs remains difficult. In the present study, three regulatory networks consisting of a differentially-expressed network, a related network and a global network, were constructed in order to identify the mechanisms and certain key miRNA and gene pathways in PC. The interactions between transcription factors (TFs) and miRNAs, miRNAs and target genes and an miRNA and its host gene were investigated. The present study compared and analyzed the similarities and differences between the three networks in order to distinguish the key pathways. Certain pathways involving the differentially-expressed genes and miRNAs demonstrated specific features. TP53 and hsa-miR-125b were observed to form a self-adaptation association. A further 16 significant differentially-expressed miRNAs were obtained and it was observed that an miRNA and its host gene exhibit specific features in PC, for example, hsa-miR-196a-1 and its host gene, HOXB7, form a self-adaptation association. The differentially-expressed network partially illuminated the mechanism of PC. The present study provides comprehensive data that is associated with PC and may aid future studies in obtaining pertinent data results with regards to PC. In the future, an improved understanding of PC may be obtained through an increased knowledge of the occurrence, mechanism, improvement, metastasis and treatment of the disease. PMID:24137477

  3. Regulatory network of microRNAs, target genes, transcription factors and host genes in endometrial cancer.

    PubMed

    Xue, Lu-Chen; Xu, Zhi-Wen; Wang, Kun-Hao; Wang, Ning; Zhang, Xiao-Xu; Wang, Shang

    2015-01-01

    Genes and microRNAs (miRNAs) have important roles in human oncology. However, most of the biological factors are reported in disperse form which makes it hard to discover the pathology. In this study, genes and miRNAs involved in human endometrial cancer(EC) were collected and formed into regulatory networks following their interactive relations, including miRNAs targeting genes, transcription factors (TFs) regulating miRNAs and miRNAs included in their host genes. Networks are constructed hierarchically at three levels: differentially expressed, related and global. Among the three, the differentially expressed network is the most important and fundamental network that contains the key genes and miRNAs in EC. The target genes, TFs and miRNAs are differentially expressed in EC so that any mutation in them may impact on EC development. Some key pathways in networks were highlighted to analyze how they interactively influence other factors and carcinogenesis. Upstream and downstream pathways of the differentially expressed genes and miRNAs were compared and analyzed. The purpose of this study was to partially reveal the deep regulatory mechanisms in EC using a new method that combines comprehensive genes and miRNAs together with their relationships. It may contribute to cancer prevention and gene therapy of EC. PMID:25684474

  4. How Gene Networks Can Uncover Novel CVD Players

    PubMed Central

    Parnell, Laurence D; Casas-Agustench, Patricia; Iyer, Lakshmanan K; Ordovas, Jose M

    2014-01-01

    Cardiovascular diseases (CVD) are complex, involving numerous biological entities from genes and small molecules to organ function. Placing these entities in networks where the functional relationships among the constituents are drawn can aid in our understanding of disease onset, progression and prevention. While networks, or interactomes, are often classified by a general term, say lipids or inflammation, it is a more encompassing class of network that is more informative in showing connections among the active entities and allowing better hypotheses of novel CVD players to be formulated. A range of networks will be presented whereby the potential to bring new objects into the CVD milieu will be exemplified. PMID:24683432

  5. How Gene Networks Can Uncover Novel CVD Players.

    PubMed

    Parnell, Laurence D; Casas-Agustench, Patricia; Iyer, Lakshmanan K; Ordovas, Jose M

    2014-01-01

    Cardiovascular diseases (CVD) are complex, involving numerous biological entities from genes and small molecules to organ function. Placing these entities in networks where the functional relationships among the constituents are drawn can aid in our understanding of disease onset, progression and prevention. While networks, or interactomes, are often classified by a general term, say lipids or inflammation, it is a more encompassing class of network that is more informative in showing connections among the active entities and allowing better hypotheses of novel CVD players to be formulated. A range of networks will be presented whereby the potential to bring new objects into the CVD milieu will be exemplified. PMID:24683432

  6. A Synthesis Method of Gene Networks Having Cyclic Expression Pattern Sequences by Network Learning

    NASA Astrophysics Data System (ADS)

    Mori, Yoshihiro; Kuroe, Yasuaki

    Recently, synthesis of gene networks having desired functions has become of interest to many researchers because it is a complementary approach to understanding gene networks, and it could be the first step in controlling living cells. There exist several periodic phenomena in cells, e.g. circadian rhythm. These phenomena are considered to be generated by gene networks. We have already proposed synthesis method of gene networks based on gene expression. The method is applicable to synthesizing gene networks possessing the desired cyclic expression pattern sequences. It ensures that realized expression pattern sequences are periodic, however, it does not ensure that their corresponding solution trajectories are periodic, which might bring that their oscillations are not persistent. In this paper, in order to resolve the problem we propose a synthesis method of gene networks possessing the desired cyclic expression pattern sequences together with their corresponding solution trajectories being periodic. In the proposed method the persistent oscillations of the solution trajectories are realized by specifying passing points of them.

  7. Estrogen Signaling Multiple Pathways to Impact Gene Transcription

    PubMed Central

    Marino, Maria; Galluzzo, Paola; Ascenzi, Paolo

    2006-01-01

    Steroid hormones exert profound effects on cell growth, development, differentiation, and homeostasis. Their effects are mediated through specific intracellular steroid receptors that act via multiple mechanisms. Among others, the action mechanism starting upon 17β-estradiol (E2) binds to its receptors (ER) is considered a paradigmatic example of how steroid hormones function. Ligand-activated ER dimerizes and translocates in the nucleus where it recognizes specific hormone response elements located in or near promoter DNA regions of target genes. Behind the classical genomic mechanism shared with other steroid hormones, E2 also modulates gene expression by a second indirect mechanism that involves the interaction of ER with other transcription factors which, in turn, bind their cognate DNA elements. In this case, ER modulates the activities of transcription factors such as the activator protein (AP)-1, nuclear factor-κB (NF-κB) and stimulating protein-1 (Sp-1), by stabilizing DNA-protein complexes and/or recruiting co-activators. In addition, E2 binding to ER may also exert rapid actions that start with the activation of a variety of signal transduction pathways (e.g. ERK/MAPK, p38/MAPK, PI3K/AKT, PLC/PKC). The debate about the contribution of different ER-mediated signaling pathways to coordinate the expression of specific sets of genes is still open. This review will focus on the recent knowledge about the mechanism by which ERs regulate the expression of target genes and the emerging field of integration of membrane and nuclear receptor signaling, giving examples of the ways by which the genomic and non-genomic actions of ERs on target genes converge. PMID:18369406

  8. Bayesian state space models for dynamic genetic network construction across multiple tissues.

    PubMed

    Liang, Yulan; Kelemen, Arpad

    2016-08-01

    Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes. PMID:27343475

  9. Diversified Control Paths: A Significant Way Disease Genes Perturb the Human Regulatory Network

    PubMed Central

    Wang, Bingbo; Gao, Lin; Zhang, Qingfang; Li, Aimin; Deng, Yue; Guo, Xingli

    2015-01-01

    Background The complexity of biological systems motivates us to use the underlying networks to provide deep understanding of disease etiology and the human diseases are viewed as perturbations of dynamic properties of networks. Control theory that deals with dynamic systems has been successfully used to capture systems-level knowledge in large amount of quantitative biological interactions. But from the perspective of system control, the ways by which multiple genetic factors jointly perturb a disease phenotype still remain. Results In this work, we combine tools from control theory and network science to address the diversified control paths in complex networks. Then the ways by which the disease genes perturb biological systems are identified and quantified by the control paths in a human regulatory network. Furthermore, as an application, prioritization of candidate genes is presented by use of control path analysis and gene ontology annotation for definition of similarities. We use leave-one-out cross-validation to evaluate the ability of finding the gene-disease relationship. Results have shown compatible performance with previous sophisticated works, especially in directed systems. Conclusions Our results inspire a deeper understanding of molecular mechanisms that drive pathological processes. Diversified control paths offer a basis for integrated intervention techniques which will ultimately lead to the development of novel therapeutic strategies. PMID:26284649

  10. Gene-network inference by message passing

    NASA Astrophysics Data System (ADS)

    Braunstein, A.; Pagnani, A.; Weigt, M.; Zecchina, R.

    2008-01-01

    The inference of gene-regulatory processes from gene-expression data belongs to the major challenges of computational systems biology. Here we address the problem from a statistical-physics perspective and develop a message-passing algorithm which is able to infer sparse, directed and combinatorial regulatory mechanisms. Using the replica technique, the algorithmic performance can be characterized analytically for artificially generated data. The algorithm is applied to genome-wide expression data of baker's yeast under various environmental conditions. We find clear cases of combinatorial control, and enrichment in common functional annotations of regulated genes and their regulators.

  11. Adaptive Horizontal Gene Transfers between Multiple Cheese-Associated Fungi

    PubMed Central

    Ropars, Jeanne; Rodríguez de la Vega, Ricardo C.; López-Villavicencio, Manuela; Gouzy, Jérôme; Sallet, Erika; Dumas, Émilie; Lacoste, Sandrine; Debuchy, Robert; Dupont, Joëlle; Branca, Antoine; Giraud, Tatiana

    2015-01-01

    Summary Domestication is an excellent model for studies of adaptation because it involves recent and strong selection on a few, identified traits [1–5]. Few studies have focused on the domestication of fungi, with notable exceptions [6–11], despite their importance to bioindustry [12] and to a general understanding of adaptation in eukaryotes [5]. Penicillium fungi are ubiquitous molds among which two distantly related species have been independently selected for cheese making—P. roqueforti for blue cheeses like Roquefort and P. camemberti for soft cheeses like Camembert. The selected traits include morphology, aromatic profile, lipolytic and proteolytic activities, and ability to grow at low temperatures, in a matrix containing bacterial and fungal competitors [13–15]. By comparing the genomes of ten Penicillium species, we show that adaptation to cheese was associated with multiple recent horizontal transfers of large genomic regions carrying crucial metabolic genes. We identified seven horizontally transferred regions (HTRs) spanning more than 10 kb each, flanked by specific transposable elements, and displaying nearly 100% identity between distant Penicillium species. Two HTRs carried genes with functions involved in the utilization of cheese nutrients or competition and were found nearly identical in multiple strains and species of cheese-associated Penicillium fungi, indicating recent selective sweeps; they were experimentally associated with faster growth and greater competitiveness on cheese and contained genes highly expressed in the early stage of cheese maturation. These findings have industrial and food safety implications and improve our understanding of the processes of adaptation to rapid environmental changes. PMID:26412136

  12. Inferring the Gene Network Underlying the Branching of Tomato Inflorescence

    PubMed Central

    Astola, Laura; Stigter, Hans; van Dijk, Aalt D. J.; van Daelen, Raymond; Molenaar, Jaap

    2014-01-01

    The architecture of tomato inflorescence strongly affects flower production and subsequent crop yield. To understand the genetic activities involved, insight into the underlying network of genes that initiate and control the sympodial growth in the tomato is essential. In this paper, we show how the structure of this network can be derived from available data of the expressions of the involved genes. Our approach starts from employing biological expert knowledge to select the most probable gene candidates behind branching behavior. To find how these genes interact, we develop a stepwise procedure for computational inference of the network structure. Our data consists of expression levels from primary shoot meristems, measured at different developmental stages on three different genotypes of tomato. With the network inferred by our algorithm, we can explain the dynamics corresponding to all three genotypes simultaneously, despite their apparent dissimilarities. We also correctly predict the chronological order of expression peaks for the main hubs in the network. Based on the inferred network, using optimal experimental design criteria, we are able to suggest an informative set of experiments for further investigation of the mechanisms underlying branching behavior. PMID:24699171

  13. Synthetic Gene Networks: De novo constructs -- in numero descriptions

    NASA Astrophysics Data System (ADS)

    Hasty, Jeff

    2007-03-01

    Uncovering the structure and function of gene regulatory networks has become one of the central challenges of the post-genomic era. Theoretical models of protein-DNA feedback loops and gene regulatory networks have long been proposed, and recently, certain qualitative features of such models have been experimentally corroborated. This talk will focus on model and experimental results that demonstrate how a naturally occurring gene network can be used as a ``parts list'' for synthetic network design. The model formulation leads to computational and analytical approaches relevant to nonlinear dynamics and statistical physics, and the utility of such a formulation will be demonstrated through the consideration of specific design criteria for several novel genetic devices. Fluctuations originating from small molecule-number effects will be discussed in the context of model predictions, and the experimental validation of these stochastic effects underscores the importance of internal noise in gene expression. The underlying methodology highlights the utility of engineering-based methods in the design of synthetic gene regulatory networks.

  14. Multiscale Embedded Gene Co-expression Network Analysis

    PubMed Central

    Song, Won-Min; Zhang, Bin

    2015-01-01

    Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma. PMID:26618778

  15. Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain

    PubMed Central

    Krienen, Fenna M.; Yeo, B. T. Thomas; Ge, Tian; Buckner, Randy L.; Sherwood, Chet C.

    2016-01-01

    The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute’s human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections. PMID:26739559

  16. Multiple coupled landscapes and non-adiabatic dynamics with applications to self-activating genes.

    PubMed

    Chen, Cong; Zhang, Kun; Feng, Haidong; Sasai, Masaki; Wang, Jin

    2015-11-21

    Many physical, chemical and biochemical systems (e.g. electronic dynamics and gene regulatory networks) are governed by continuous stochastic processes (e.g. electron dynamics on a particular electronic energy surface and protein (gene product) synthesis) coupled with discrete processes (e.g. hopping among different electronic energy surfaces and on and off switching of genes). One can also think of the underlying dynamics as the continuous motion on a particular landscape and discrete hoppings among different landscapes. The main difference of such systems from the intra-landscape dynamics alone is the emergence of the timescale involved in transitions among different landscapes in addition to the timescale involved in a particular landscape. The adiabatic limit when inter-landscape hoppings are fast compared to continuous intra-landscape dynamics has been studied both analytically and numerically, but the analytical treatment of the non-adiabatic regime where the inter-landscape hoppings are slow or comparable to continuous intra-landscape dynamics remains challenging. In this study, we show that there exists mathematical mapping of the dynamics on 2(N) discretely coupled N continuous dimensional landscapes onto one single landscape in 2N dimensional extended continuous space. On this 2N dimensional landscape, eddy current emerges as a sign of non-equilibrium non-adiabatic dynamics and plays an important role in system evolution. Many interesting physical effects such as the enhancement of fluctuations, irreversibility, dissipation and optimal kinetics emerge due to non-adiabaticity manifested by the eddy current illustrated for an N = 1 self-activator. We further generalize our theory to the N-gene network with multiple binding sites and multiple synthesis rates for discretely coupled non-equilibrium stochastic physical and biological systems. PMID:26455835

  17. Modeling Gene Networks in Saccharomyces cerevisiae Based on Gene Expression Profiles

    PubMed Central

    Zhang, Yulin; Lv, Kebo; Wang, Shudong; Su, Jionglong; Meng, Dazhi

    2015-01-01

    Detailed and innovative analysis of gene regulatory network structures may reveal novel insights to biological mechanisms. Here we study how gene regulatory network in Saccharomyces cerevisiae can differ under aerobic and anaerobic conditions. To achieve this, we discretized the gene expression profiles and calculated the self-entropy of down- and upregulation of gene expression as well as joint entropy. Based on these quantities the uncertainty coefficient was calculated for each gene triplet, following which, separate gene logic networks were constructed for the aerobic and anaerobic conditions. Four structural parameters such as average degree, average clustering coefficient, average shortest path, and average betweenness were used to compare the structure of the corresponding aerobic and anaerobic logic networks. Five genes were identified to be putative key components of the two energy metabolisms. Furthermore, community analysis using the Newman fast algorithm revealed two significant communities for the aerobic but only one for the anaerobic network. David Gene Functional Classification suggests that, under aerobic conditions, one such community reflects the cell cycle and cell replication, while the other one is linked to the mitochondrial respiratory chain function. PMID:26839582

  18. BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks.

    PubMed

    Richiardi, Jonas; Altmann, Andre; Milazzo, Anna-Clare; Chang, Catie; Chakravarty, M Mallar; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Lemaître, Hervé; Mann, Karl F; Martinot, Jean-Luc; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Rietschel, Marcella; Robbins, Trevor W; Smolka, Michael N; Spanagel, Rainer; Ströhle, Andreas; Schumann, Gunter; Hawrylycz, Mike; Poline, Jean-Baptiste; Greicius, Michael D

    2015-06-12

    During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function. PMID:26068849

  19. Multiple Differential Networks Strategy Reveals Carboplatin and Melphalan-Induced Dynamic Module Changes in Retinoblastoma.

    PubMed

    Chen, Cui; Ma, Feng-Wei; Du, Cui-Yun; Wang, Ping

    2016-01-01

    BACKGROUND Retinoblastoma (RB) is the most common malignant tumor of the eye in childhood. The objective of this paper was to investigate carboplatin (CAR)- and melphalan (MEL)-induced dynamic module changes in RB based on multiple (M) differential networks, and to generate systems-level insights into RB progression. MATERIAL AND METHODS To achieve this goal, we constructed M-differential co-expression networks (DCNs), assigned a weight to each edge, and identified seed genes in M DCNs by ranking genes based on their topological features. Starting with seed genes, a module search was performed to explore candidate modules in CAR and MEL condition. M-DMs were detected according to significance evaluations of M-modules, which originated from refinement of candidate modules. Further, we revealed dynamic changes in M-DM activity and connectivity on the basis of significance of Module Connectivity Dynamic Score (MCDS). RESULTS In the present study, M=2, a total of 21 seed genes were obtained. By assessing module search, refinement, and evaluation, we gained 18 2-DMs. Moreover, 3 significant 2-DMs (Module 1, Module 2, and Module 3) with dynamic changes across CAR and MEL condition were determined, and we denoted them as dynamic modules. Module 1 had 27 nodes of which 6 were seed genes and 56 edges. Module 2 was composed of 28 nodes and 54 edges. A total of 28 nodes interacted with 45 edges presented in Module 3. CONCLUSIONS We have identified 3 dynamic modules with changes induced by CAR and MEL in RB, which might give insights in revealing molecular mechanism for RB therapy. PMID:27144687

  20. A pathway-based network analysis of hypertension-related genes

    NASA Astrophysics Data System (ADS)

    Wang, Huan; Hu, Jing-Bo; Xu, Chuan-Yun; Zhang, De-Hai; Yan, Qian; Xu, Ming; Cao, Ke-Fei; Zhang, Xu-Sheng

    2016-02-01

    Complex network approach has become an effective way to describe interrelationships among large amounts of biological data, which is especially useful in finding core functions and global behavior of biological systems. Hypertension is a complex disease caused by many reasons including genetic, physiological, psychological and even social factors. In this paper, based on the information of biological pathways, we construct a network model of hypertension-related genes of the salt-sensitive rat to explore the interrelationship between genes. Statistical and topological characteristics show that the network has the small-world but not scale-free property, and exhibits a modular structure, revealing compact and complex connections among these genes. By the threshold of integrated centrality larger than 0.71, seven key hub genes are found: Jun, Rps6kb1, Cycs, Creb312, Cdk4, Actg1 and RT1-Da. These genes should play an important role in hypertension, suggesting that the treatment of hypertension should focus on the combination of drugs on multiple genes.

  1. Time-course gene profiling and networks in demethylated retinoblastoma cell line

    PubMed Central

    Malusa, Federico; Taranta, Monia; Zaki, Nazar; Cinti, Caterina; Capobianco, Enrico

    2015-01-01

    Retinoblastoma, a very aggressive cancer of the developing retina, initiatiates by the biallelic loss of RB1 gene, and progresses very quickly following RB1 inactivation. While its genome is stable, multiple pathways are deregulated, also epigenetically. After reviewing the main findings in relation with recently validated markers, we propose an integrative bioinformatics approach to include in the previous group new markers obtained from the analysis of a single cell line subject to epigenetic treatment. In particular, differentially expressed genes are identified from time course microarray experiments on the WERI-RB1 cell line treated with 5-Aza-2′-deoxycytidine (decitabine; DAC). By inducing demethylation of CpG island in promoter genes that are involved in biological processes, for instance apoptosis, we performed the following main integrative analysis steps: i) Gene expression profiling at 48h, 72h and 96h after DAC treatment; ii) Time differential gene co-expression networks and iii) Context-driven marker association (transcriptional factor regulated protein networks, master regulatory paths). The observed DAC-driven temporal profiles and regulatory connectivity patterns are obtained by the application of computational tools, with support from curated literature. It is worth emphasizing the capacity of networks to reconcile multi-type evidences, thus generating testable hypotheses made available by systems scale predictive inference power. Despite our small experimental setting, we propose through such integrations valuable impacts of epigenetic treatment in terms of gene expression measurements, and then validate evidenced apoptotic effects. PMID:26143641

  2. Gene-Sharing Networks Reveal Organizing Principles of Transcriptomes in Arabidopsis and Other Multicellular Organisms[W

    PubMed Central

    Li, Song; Pandey, Sona; Gookin, Timothy E.; Zhao, Zhixin; Wilson, Liza; Assmann, Sarah M.

    2012-01-01

    Understanding tissue-related gene expression patterns can provide important insights into gene, tissue, and organ function. Transcriptome analyses often have focused on housekeeping or tissue-specific genes or on gene coexpression. However, by analyzing thousands of single-gene expression distributions in multiple tissues of Arabidopsis thaliana, rice (Oryza sativa), human (Homo sapiens), and mouse (Mus musculus), we found that these organisms primarily operate by gene sharing, a phenomenon where, in each organism, most genes exhibit a high expression level in a few key tissues. We designed an analytical pipeline to characterize this phenomenon and then derived Arabidopsis and human gene-sharing networks, in which tissues are connected solely based on the extent of shared preferentially expressed genes. The results show that tissues or cell types from the same organ system tend to group together to form network modules. Tissues that are in consecutive developmental stages or have common physiological functions are connected in these networks, revealing the importance of shared preferentially expressed genes in conferring specialized functions of each tissue type. The networks provide predictive power for each tissue type regarding gene functions of both known and heretofore unknown genes, as shown by the identification of four new genes with functions in guard cell and abscisic acid response. We provide a Web interface that enables, based on the extent of gene sharing, both prediction of tissue-related functions for any Arabidopsis gene of interest and predictions concerning the relatedness of tissues. Common gene-sharing patterns observed in the four model organisms suggest that gene sharing evolved as a fundamental organizing principle of gene expression in diverse multicellular eukaryotes. PMID:22517316

  3. Gene expression profiles of autophagy-related genes in multiple sclerosis.

    PubMed

    Igci, Mehri; Baysan, Mehmet; Yigiter, Remzi; Ulasli, Mustafa; Geyik, Sirma; Bayraktar, Recep; Bozgeyik, İbrahim; Bozgeyik, Esra; Bayram, Ali; Cakmak, Ecir Ali

    2016-08-15

    Multiple sclerosis (MS) is an imflammatory disease of central nervous system caused by genetic and environmental factors that remain largely unknown. Autophagy is the process of degradation and recycling of damaged cytoplasmic organelles, macromolecular aggregates, and long-lived proteins. Malfunction of autophagy contributes to the pathogenesis of neurological diseases, and autophagy genes may modulate the T cell survival. We aimed to examine the expression levels of autophagy-related genes. The blood samples of 95 unrelated patients (aged 17-65years, 37 male, 58 female) diagnosed as MS and 95 healthy controls were used to extract the RNA samples. After conversion to single stranded cDNA using polyT priming: the targeted genes were pre-amplified, and 96×78 (samples×primers) qRT-PCR reactions were performed for each primer pair on each sample on a 96.96 array of Fluidigm BioMark™. Compared to age- and sex-matched controls, gene expression levels of ATG16L2, ATG9A, BCL2, FAS, GAA, HGS, PIK3R1, RAB24, RGS19, ULK1, FOXO1, HTT were significantly altered (false discovery rate<0.05). Thus, altered expression levels of several autophagy related genes may affect protein levels, which in turn would influence the activity of autophagy, or most probably, those genes might be acting independent of autophagy and contributing to MS pathogenesis as risk factors. The indeterminate genetic causes leading to alterations in gene expressions require further analysis. PMID:27125224

  4. Reference genes for quantitative gene expression studies in multiple avian species.

    PubMed

    Olias, Philipp; Adam, Iris; Meyer, Anne; Scharff, Constance; Gruber, Achim D

    2014-01-01

    Quantitative real-time PCR (qPCR) rapidly and reliably quantifies gene expression levels across different experimental conditions. Selection of suitable reference genes is essential for meaningful normalization and thus correct interpretation of data. In recent years, an increasing number of avian species other than the chicken has been investigated molecularly, highlighting the need for an experimentally validated pan-avian primer set for reference genes. Here we report testing a set for 14 candidate reference genes (18S, ABL, GAPDH, GUSB, HMBS, HPRT, PGK1, RPL13, RPL19, RPS7, SDHA, TFRC, VIM, YWHAZ) on different tissues of the mallard (Anas platyrhynchos), domestic chicken (Gallus gallus domesticus), common crane (Grus grus), white-tailed eagle (Haliaeetus albicilla), domestic turkey (Meleagris gallopavo f. domestica), cockatiel (Nymphicus hollandicus), Humboldt penguin (Sphenicus humboldti), ostrich (Struthio camelus) and zebra finch (Taeniopygia guttata), spanning a broad range of the phylogenetic tree of birds. Primer pairs for six to 11 genes were successfully established for each of the nine species. As a proof of principle, we analyzed expression levels of 10 candidate reference genes as well as FOXP2 and the immediate early genes, EGR1 and CFOS, known to be rapidly induced by singing in the avian basal ganglia. We extracted RNA from microbiopsies of the striatal song nucleus Area X of adult male zebra finches after they had sang or remained silent. Using three different statistical algorithms, we identified five genes (18S, PGK1, RPS7, TFRC, YWHAZ) that were stably expressed within each group and also between the singing and silent conditions, establishing them as suitable reference genes. In conclusion, the newly developed pan-avian primer set allows accurate normalization and quantification of gene expression levels in multiple avian species. PMID:24926893

  5. Reference Genes for Quantitative Gene Expression Studies in Multiple Avian Species

    PubMed Central

    Meyer, Anne; Scharff, Constance; Gruber, Achim D.

    2014-01-01

    Quantitative real-time PCR (qPCR) rapidly and reliably quantifies gene expression levels across different experimental conditions. Selection of suitable reference genes is essential for meaningful normalization and thus correct interpretation of data. In recent years, an increasing number of avian species other than the chicken has been investigated molecularly, highlighting the need for an experimentally validated pan-avian primer set for reference genes. Here we report testing a set for 14 candidate reference genes (18S, ABL, GAPDH, GUSB, HMBS, HPRT, PGK1, RPL13, RPL19, RPS7, SDHA, TFRC, VIM, YWHAZ) on different tissues of the mallard (Anas platyrhynchos), domestic chicken (Gallus gallus domesticus), common crane (Grus grus), white-tailed eagle (Haliaeetus albicilla), domestic turkey (Meleagris gallopavo f. domestica), cockatiel (Nymphicus hollandicus), Humboldt penguin (Sphenicus humboldti), ostrich (Struthio camelus) and zebra finch (Taeniopygia guttata), spanning a broad range of the phylogenetic tree of birds. Primer pairs for six to 11 genes were successfully established for each of the nine species. As a proof of principle, we analyzed expression levels of 10 candidate reference genes as well as FOXP2 and the immediate early genes, EGR1 and CFOS, known to be rapidly induced by singing in the avian basal ganglia. We extracted RNA from microbiopsies of the striatal song nucleus Area X of adult male zebra finches after they had sang or remained silent. Using three different statistical algorithms, we identified five genes (18S, PGK1, RPS7, TFRC, YWHAZ) that were stably expressed within each group and also between the singing and silent conditions, establishing them as suitable reference genes. In conclusion, the newly developed pan-avian primer set allows accurate normalization and quantification of gene expression levels in multiple avian species. PMID:24926893

  6. Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network

    PubMed Central

    Qin, Tingting; Matmati, Nabil; Tsoi, Lam C.; Mohanty, Bidyut K.; Gao, Nan; Tang, Jijun; Lawson, Andrew B.; Hannun, Yusuf A.; Zheng, W. Jim

    2014-01-01

    To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes’ Ontology Fingerprints—a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms’ corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. PMID:25063300

  7. Visualizing Gene - Interactions within the Rice and Maize Network

    NASA Astrophysics Data System (ADS)

    Sampong, A.; Feltus, A.; Smith, M.

    2014-12-01

    The purpose of this research was to design a simpler visualization tool for comparing or viewing gene interaction graphs in systems biology. This visualization tool makes it possible and easier for a researcher to visualize the biological metadata of a plant and interact with the graph on a webpage. Currently available visualization software like Cytoscape and Walrus are difficult to interact with and do not scale effectively for large data sets, limiting the ability to visualize interactions within a biological system. The visualization tool developed is useful for viewing and interpreting the dataset of a gene interaction network. The graph layout drawn by this visualization tool is an improvement from the previous method of comparing lines of genes in two separate data files to, now having the ability to visually see the layout of the gene networks and how the two systems are related. The graph layout presented by the visualization tool draws a graph of the sample rice and maize gene networks, linking the common genes found in both plants and highlighting the functions served by common genes from each plant. The success of this visualization tool will enable Dr. Feltus to continue his investigations and draw conclusions on the biological evolution of the sorghum plant as well. REU Funded by NSF ACI Award 1359223 Vetria L. Byrd, PI

  8. NETGEM: Network Embedded Temporal GEnerative Model for gene expression data

    PubMed Central

    2011-01-01

    Background Temporal analysis of gene expression data has been limited to identifying genes whose expression varies with time and/or correlation between genes that have similar temporal profiles. Often, the methods do not consider the underlying network constraints that connect the genes. It is becoming increasingly evident that interactions change substantially with time. Thus far, there is no systematic method to relate the temporal changes in gene expression to the dynamics of interactions between them. Information on interaction dynamics would open up possibilities for discovering new mechanisms of regulation by providing valuable insight into identifying time-sensitive interactions as well as permit studies on the effect of a genetic perturbation. Results We present NETGEM, a tractable model rooted in Markov dynamics, for analyzing the dynamics of the interactions between proteins based on the dynamics of the expression changes of the genes that encode them. The model treats the interaction strengths as random variables which are modulated by suitable priors. This approach is necessitated by the extremely small sample size of the datasets, relative to the number of interactions. The model is amenable to a linear time algorithm for efficient inference. Using temporal gene expression data, NETGEM was successful in identifying (i) temporal interactions and determining their strength, (ii) functional categories of the actively interacting partners and (iii) dynamics of interactions in perturbed networks. Conclusions NETGEM represents an optimal trade-off between model complexity and data requirement. It was able to deduce actively interacting genes and functional categories from temporal gene expression data. It permits inference by incorporating the information available in perturbed networks. Given that the inputs to NETGEM are only the network and the temporal variation of the nodes, this algorithm promises to have widespread applications, beyond biological

  9. Genetics of gene expression responses to temperature stress in a sea urchin gene network.

    PubMed

    Runcie, Daniel E; Garfield, David A; Babbitt, Courtney C; Wygoda, Jennifer A; Mukherjee, Sayan; Wray, Gregory A

    2012-09-01

    Stress responses play an important role in shaping species distributions and robustness to climate change. We investigated how stress responses alter the contribution of additive genetic variation to gene expression during development of the purple sea urchin, Strongylocentrotus purpuratus, under increased temperatures that model realistic climate change scenarios. We first measured gene expression responses in the embryos by RNA-seq to characterize molecular signatures of mild, chronic temperature stress in an unbiased manner. We found that an increase from 12 to 18 °C caused widespread alterations in gene expression including in genes involved in protein folding, RNA processing and development. To understand the quantitative genetic architecture of this response, we then focused on a well-characterized gene network involved in endomesoderm and ectoderm specification. Using a breeding design with wild-caught individuals, we measured genetic and gene-environment interaction effects on 72 genes within this network. We found genetic or maternal effects in 33 of these genes and that the genetic effects were correlated in the network. Fourteen network genes also responded to higher temperatures, but we found no significant genotype-environment interactions in any of the genes. This absence may be owing to an effective buffering of the temperature perturbations within the network. In support of this hypothesis, perturbations to regulatory genes did not affect the expression of the genes that they regulate. Together, these results provide novel insights into the relationship between environmental change and developmental evolution and suggest that climate change may not expose large amounts of cryptic genetic variation to selection in this species. PMID:22856327

  10. Using gene expression programming to infer gene regulatory networks from time-series data.

    PubMed

    Zhang, Yongqing; Pu, Yifei; Zhang, Haisen; Su, Yabo; Zhang, Lifang; Zhou, Jiliu

    2013-12-01

    Gene regulatory networks inference is currently a topic under heavy research in the systems biology field. In this paper, gene regulatory networks are inferred via evolutionary model based on time-series microarray data. A non-linear differential equation model is adopted. Gene expression programming (GEP) is applied to identify the structure of the model and least mean square (LMS) is used to optimize the parameters in ordinary differential equations (ODEs). The proposed work has been first verified by synthetic data with noise-free and noisy time-series data, respectively, and then its effectiveness is confirmed by three real time-series expression datasets. Finally, a gene regulatory network was constructed with 12 Yeast genes. Experimental results demonstrate that our model can improve the prediction accuracy of microarray time-series data effectively. PMID:24140883

  11. Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

    PubMed Central

    Tsai, Yu-Shuen; Aguan, Kripamoy; Pal, Nikhil R.; Chung, I-Fang

    2011-01-01

    Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing

  12. Functional Analysis of Prognostic Gene Expression Network Genes in Metastatic Breast Cancer Models

    PubMed Central

    Geiger, Thomas R.; Ha, Ngoc-Han; Faraji, Farhoud; Michael, Helen T.; Rodriguez, Loren; Walker, Renard C.; Green, Jeffery E.; Simpson, R. Mark; Hunter, Kent W.

    2014-01-01

    Identification of conserved co-expression networks is a useful tool for clustering groups of genes enriched for common molecular or cellular functions [1]. The relative importance of genes within networks can frequently be inferred by the degree of connectivity, with those displaying high connectivity being significantly more likely to be associated with specific molecular functions [2]. Previously we utilized cross-species network analysis to identify two network modules that were significantly associated with distant metastasis free survival in breast cancer. Here, we validate one of the highly connected genes as a metastasis associated gene. Tpx2, the most highly connected gene within a proliferation network specifically prognostic for estrogen receptor positive (ER+) breast cancers, enhances metastatic disease, but in a tumor autonomous, proliferation-independent manner. Histologic analysis suggests instead that variation of TPX2 levels within disseminated tumor cells may influence the transition between dormant to actively proliferating cells in the secondary site. These results support the co-expression network approach for identification of new metastasis-associated genes to provide new information regarding the etiology of breast cancer progression and metastatic disease. PMID:25368990

  13. Identification of driving network of cellular differentiation from single sample time course gene expression data

    NASA Astrophysics Data System (ADS)

    Chen, Ye; Wolanyk, Nathaniel; Ilker, Tunc; Gao, Shouguo; Wang, Xujing

    Methods developed based on bifurcation theory have demonstrated their potential in driving network identification for complex human diseases, including the work by Chen, et al. Recently bifurcation theory has been successfully applied to model cellular differentiation. However, there one often faces a technical challenge in driving network prediction: time course cellular differentiation study often only contains one sample at each time point, while driving network prediction typically require multiple samples at each time point to infer the variation and interaction structures of candidate genes for the driving network. In this study, we investigate several methods to identify both the critical time point and the driving network through examination of how each time point affects the autocorrelation and phase locking. We apply these methods to a high-throughput sequencing (RNA-Seq) dataset of 42 subsets of thymocytes and mature peripheral T cells at multiple time points during their differentiation (GSE48138 from GEO). We compare the predicted driving genes with known transcription regulators of cellular differentiation. We will discuss the advantages and limitations of our proposed methods, as well as potential further improvements of our methods.

  14. Transcriptional control in the segmentation gene network of Drosophila.

    PubMed

    Schroeder, Mark D; Pearce, Michael; Fak, John; Fan, HongQing; Unnerstall, Ulrich; Emberly, Eldon; Rajewsky, Nikolaus; Siggia, Eric D; Gaul, Ulrike

    2004-09-01

    The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross-) regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules) with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a uniform set of

  15. Transcriptional Control in the Segmentation Gene Network of Drosophila

    PubMed Central

    Fan, HongQing; Unnerstall, Ulrich; Emberly, Eldon; Rajewsky, Nikolaus; Siggia, Eric D

    2004-01-01

    The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross-) regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules) with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a uniform set of

  16. Unique expression patterns of multiple key genes associated with the evolution of mammalian flight

    PubMed Central

    Wang, Zhe; Dai, Mengyao; Wang, Yao; Cooper, Kimberly L.; Zhu, Tengteng; Dong, Dong; Zhang, Junpeng; Zhang, Shuyi

    2014-01-01

    Bats are the only mammals capable of true flight. Critical adaptations for flight include a pair of dramatically elongated hands with broad wing membranes. To study the molecular mechanisms of bat wing evolution, we perform genomewide mRNA sequencing and in situ hybridization for embryonic bat limbs. We identify seven key genes that display unique expression patterns in embryonic bat wings and feet, compared with mouse fore- and hindlimbs. The expression of all 5′HoxD genes (Hoxd9–13) and Tbx3, six known crucial transcription factors for limb and digit development, is extremely high and prolonged in the elongating wing area. The expression of Fam5c, a tumour suppressor, in bat limbs is bat-specific and significantly high in all short digit regions (the thumb and foot digits). These results suggest multiple genetic changes occurred independently during the evolution of bat wings to elongate the hand digits, promote membrane growth and keep other digits short. Our findings also indicate that the evolution of limb morphology depends on the complex integration of multiple gene regulatory networks and biological processes that control digit formation and identity, chondrogenesis, and interdigital regression or retention. PMID:24695426

  17. EXAMINE: a computational approach to reconstructing gene regulatory networks.

    PubMed

    Deng, Xutao; Geng, Huimin; Ali, Hesham

    2005-08-01

    Reverse-engineering of gene networks using linear models often results in an underdetermined system because of excessive unknown parameters. In addition, the practical utility of linear models has remained unclear. We address these problems by developing an improved method, EXpression Array MINing Engine (EXAMINE), to infer gene regulatory networks from time-series gene expression data sets. EXAMINE takes advantage of sparse graph theory to overcome the excessive-parameter problem with an adaptive-connectivity model and fitting algorithm. EXAMINE also guarantees that the most parsimonious network structure will be found with its incremental adaptive fitting process. Compared to previous linear models, where a fully connected model is used, EXAMINE reduces the number of parameters by O(N), thereby increasing the chance of recovering the underlying regulatory network. The fitting algorithm increments the connectivity during the fitting process until a satisfactory fit is obtained. We performed a systematic study to explore the data mining ability of linear models. A guideline for using linear models is provided: If the system is small (3-20 elements), more than 90% of the regulation pathways can be determined correctly. For a large-scale system, either clustering is needed or it is necessary to integrate information in addition to expression profile. Coupled with the clustering method, we applied EXAMINE to rat central nervous system development (CNS) data with 112 genes. We were able to efficiently generate regulatory networks with statistically significant pathways that have been predicted previously. PMID:15951103

  18. Compartmentalized gene regulatory network of the pathogenic fungus Fusarium graminearum.

    PubMed

    Guo, Li; Zhao, Guoyi; Xu, Jin-Rong; Kistler, H Corby; Gao, Lixin; Ma, Li-Jun

    2016-07-01

    Head blight caused by Fusarium graminearum threatens world-wide wheat production, resulting in both yield loss and mycotoxin contamination. We reconstructed the global F. graminearum gene regulatory network (GRN) from a large collection of transcriptomic data using Bayesian network inference, a machine-learning algorithm. This GRN reveals connectivity between key regulators and their target genes. Focusing on key regulators, this network contains eight distinct but interwoven modules. Enriched for unique functions, such as cell cycle, DNA replication, transcription, translation and stress responses, each module exhibits distinct expression profiles. Evolutionarily, the F. graminearum genome can be divided into core regions shared with closely related species and variable regions harboring genes that are unique to F. graminearum and perform species-specific functions. Interestingly, the inferred top regulators regulate genes that are significantly enriched from the same genomic regions (P < 0.05), revealing a compartmentalized network structure that may reflect network rewiring related to specific adaptation of this plant pathogen. This first-ever reconstructed filamentous fungal GRN primes our understanding of pathogenicity at the systems biology level and provides enticing prospects for novel disease control strategies involving the targeting of master regulators in pathogens. The program can be used to construct GRNs of other plant pathogens. PMID:26990214

  19. Vitamin D-gene interactions in multiple sclerosis.

    PubMed

    Berlanga-Taylor, Antonio J; Disanto, Giulio; Ebers, George C; Ramagopalan, Sreeram V

    2011-12-15

    Vitamin D has been studied for over a century and its functions related to calcium homeostasis are well established. Over the last 30 years or so it has become increasingly clear that it has a wider role in physiology and, importantly, also in disease. Vitamin D deficiency has been linked to multiple sclerosis (MS); however the molecular mechanisms of this association were poorly understood. Recent technological advances have provided major insights as to how vitamin D may exert its role, particularly through the actions of the vitamin D receptor (VDR). In this review we aim to highlight the importance of the interaction between vitamin D and MS associated genes which provide a biological basis for the association between vitamin D and MS risk. PMID:22000399

  20. Multiple lineage specific expansions within the guanylyl cyclase gene family

    PubMed Central

    Fitzpatrick, David A; O'Halloran, Damien M; Burnell, Ann M

    2006-01-01

    the GC gene family during metazoan evolution. Our phylogenetic analyses reveal that the rGC and sGC multi-domain proteins evolved early in eumetazoan evolution. Subsequent gene duplications, tissue specific expression patterns and lineage specific expansions resulted in the evolution of new networks of interaction and new biological functions associated with the maintenance of organismal complexity and homeostasis. PMID:16549024

  1. Gene Regulatory Networks Activated during Chronic Tuberculosis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Chronic tuberculosis represents a burden for most of world’s population. Several genes were found to be up-regulated at the late stage of chronic tuberculosis when DNA microarray protocol was used to analyze murine tuberculosis. Rv0348 is a potential transcriptional regulator that is highly expresse...

  2. Network Analysis of Human Genes Influencing Susceptibility to Mycobacterial Infections

    PubMed Central

    Lipner, Ettie M.; Garcia, Benjamin J.; Strong, Michael

    2016-01-01

    Tuberculosis and nontuberculous mycobacterial infections constitute a high burden of pulmonary disease in humans, resulting in over 1.5 million deaths per year. Building on the premise that genetic factors influence the instance, progression, and defense of infectious disease, we undertook a systems biology approach to investigate relationships among genetic factors that may play a role in increased susceptibility or control of mycobacterial infections. We combined literature and database mining with network analysis and pathway enrichment analysis to examine genes, pathways, and networks, involved in the human response to Mycobacterium tuberculosis and nontuberculous mycobacterial infections. This approach allowed us to examine functional relationships among reported genes, and to identify novel genes and enriched pathways that may play a role in mycobacterial susceptibility or control. Our findings suggest that the primary pathways and genes influencing mycobacterial infection control involve an interplay between innate and adaptive immune proteins and pathways. Signaling pathways involved in autoimmune disease were significantly enriched as revealed in our networks. Mycobacterial disease susceptibility networks were also examined within the context of gene-chemical relationships, in order to identify putative drugs and nutrients with potential beneficial immunomodulatory or anti-mycobacterial effects. PMID:26751573

  3. Listening to the noise: random fluctuations reveal gene network parameters

    SciTech Connect

    Munsky, Brian; Khammash, Mustafa

    2009-01-01

    The cellular environment is abuzz with noise. The origin of this noise is attributed to the inherent random motion of reacting molecules that take part in gene expression and post expression interactions. In this noisy environment, clonal populations of cells exhibit cell-to-cell variability that frequently manifests as significant phenotypic differences within the cellular population. The stochastic fluctuations in cellular constituents induced by noise can be measured and their statistics quantified. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. This establishes a potentially powerful approach for the identification of gene networks and offers a new window into the workings of these networks.

  4. Beyond antioxidant genes in the ancient NRF2 regulatory network

    PubMed Central

    Lacher, Sarah E.; Lee, Joslynn S.; Wang, Xuting; Campbell, Michelle R.; Bell, Douglas A.; Slattery, Matthew

    2016-01-01

    NRF2, a basic leucine zipper transcription factor encoded by the gene NFE2L2, is a master regulator of the transcriptional response to oxidative stress. NRF2 is structurally and functionally conserved from insects to humans, and it heterodimerizes with the small MAF transcription factors to bind a consensus DNA sequence (the antioxidant response element, or ARE) and regulate gene expression. We have used genome-wide chromatin immunoprecipitation (ChIP-seq) and gene expression data to identify direct NRF2 target genes in Drosophila and humans. These data have allowed us to construct the deeply conserved ancient NRF2 regulatory network – target genes that are conserved from Drosophila to human. The ancient network consists of canonical antioxidant genes, as well as genes related to proteasomal pathways, metabolism, and a number of less expected genes. We have also used enhancer reporter assays and electrophoretic mobility shift assays to confirm NRF2-mediated regulation of ARE (antioxidant response element) activity at a number of these novel target genes. Interestingly, the ancient network also highlights a prominent negative feedback loop; this, combined with the finding that and NRF2-mediated regulatory output is tightly linked to the quality of the ARE it is targeting, suggests that precise regulation of nuclear NRF2 concentration is necessary to achieve proper quantitative regulation of distinct gene sets. Together, these findings highlight the importance of balance in the NRF2-ARE pathway, and indicate that NRF2-mediated regulation of xenobiotic metabolism, glucose metabolism, and proteostasis have been central to this pathway since its inception. PMID:26163000

  5. In silico evolution of gene cooption in pattern-forming gene networks.

    PubMed

    Spirov, Alexander V; Sabirov, Marat A; Holloway, David M

    2012-01-01

    Gene recruitment or cooption occurs when a gene, which may be part of an existing gene regulatory network (GRN), comes under the control of a new regulatory system. Such re-arrangement of pre-existing networks is likely more common for increasing genomic complexity than the creation of new genes. Using evolutionary computations (EC), we investigate how cooption affects the evolvability, outgrowth and robustness of GRNs. We use a data-driven model of insect segmentation, for the fruit fly Drosophila, and evaluate fitness by robustness to maternal variability-a major constraint in biological development. We compare two mechanisms of gene cooption: a simpler one with gene Introduction and Withdrawal operators; and one in which GRN elements can be altered by transposon infection. Starting from a minimal 2-gene network, insufficient for fitting the Drosophila gene expression patterns, we find a general trend of coopting available genes into the GRN, in order to better fit the data. With the transposon mechanism, we find co-evolutionary oscillations between genes and their transposons. These oscillations may offer a new technique in EC for overcoming premature convergence. Finally, we comment on how a differential equations (in contrast to Boolean) approach is necessary for addressing realistic continuous variation in biochemical parameters. PMID:23365523

  6. On Multiple-Input Multiple-Output OFDM with Index Modulation for Next Generation Wireless Networks

    NASA Astrophysics Data System (ADS)

    Basar, Ertugrul

    2016-08-01

    Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) is a novel multicarrier transmission technique which has been proposed recently as an alternative to classical MIMO-OFDM. In this scheme, OFDM with index modulation (OFDM-IM) concept is combined with MIMO transmission to take advantage of the benefits of these two techniques. In this paper, we shed light on the implementation and error performance analysis of the MIMO-OFDM-IM scheme for next generation 5G wireless networks. Maximum likelihood (ML), near-ML, simple minimum mean square error (MMSE) and ordered successive interference cancellation (OSIC) based MMSE detectors of MIMO-OFDM-IM are proposed and their theoretical performance is investigated. It has been shown via extensive computer simulations that MIMO-OFDM-IM scheme provides an interesting trade-off between error performance and spectral efficiency as well as it achieves considerably better error performance than classical MIMO-OFDM using different type detectors and under realistic conditions.

  7. Inferring Gene Regulatory Networks Using Conditional Regulation Pattern to Guide Candidate Genes

    PubMed Central

    Xiao, Fei; Gao, Lin; Ye, Yusen; Hu, Yuxuan; He, Ruijie

    2016-01-01

    Combining path consistency (PC) algorithms with conditional mutual information (CMI) are widely used in reconstruction of gene regulatory networks. CMI has many advantages over Pearson correlation coefficient in measuring non-linear dependence to infer gene regulatory networks. It can also discriminate the direct regulations from indirect ones. However, it is still a challenge to select the conditional genes in an optimal way, which affects the performance and computation complexity of the PC algorithm. In this study, we develop a novel conditional mutual information-based algorithm, namely RPNI (Regulation Pattern based Network Inference), to infer gene regulatory networks. For conditional gene selection, we define the co-regulation pattern, indirect-regulation pattern and mixture-regulation pattern as three candidate patterns to guide the selection of candidate genes. To demonstrate the potential of our algorithm, we apply it to gene expression data from DREAM challenge. Experimental results show that RPNI outperforms existing conditional mutual information-based methods in both accuracy and time complexity for different sizes of gene samples. Furthermore, the robustness of our algorithm is demonstrated by noisy interference analysis using different types of noise. PMID:27171286

  8. Structured association analysis leads to insight into Saccharomyces cerevisiae gene regulation by finding multiple contributing eQTL hotspots associated with functional gene modules

    PubMed Central

    2013-01-01

    Background Association analysis using genome-wide expression quantitative trait locus (eQTL) data investigates the effect that genetic variation has on cellular pathways and leads to the discovery of candidate regulators. Traditional analysis of eQTL data via pairwise statistical significance tests or linear regression does not leverage the availability of the structural information of the transcriptome, such as presence of gene networks that reveal correlation and potentially regulatory relationships among the study genes. We employ a new eQTL mapping algorithm, GFlasso, which we have previously developed for sparse structured regression, to reanalyze a genome-wide yeast dataset. GFlasso fully takes into account the dependencies among expression traits to suppress false positives and to enhance the signal/noise ratio. Thus, GFlasso leverages the gene-interaction network to discover the pleiotropic effects of genetic loci that perturb the expression level of multiple (rather than individual) genes, which enables us to gain more power in detecting previously neglected signals that are marginally weak but pleiotropically significant. Results While eQTL hotspots in yeast have been reported previously as genomic regions controlling multiple genes, our analysis reveals additional novel eQTL hotspots and, more interestingly, uncovers groups of multiple contributing eQTL hotspots that affect the expression level of functional gene modules. To our knowledge, our study is the first to report this type of gene regulation stemming from multiple eQTL hotspots. Additionally, we report the results from in-depth bioinformatics analysis for three groups of these eQTL hotspots: ribosome biogenesis, telomere silencing, and retrotransposon biology. We suggest candidate regulators for the functional gene modules that map to each group of hotspots. Not only do we find that many of these candidate regulators contain mutations in the promoter and coding regions of the genes, in the case

  9. Epidermal differentiation gene regulatory networks controlled by MAF and MAFB.

    PubMed

    Labott, Andrew T; Lopez-Pajares, Vanessa

    2016-06-01

    Numerous regulatory factors in epidermal differentiation and their role in regulating different cell states have been identified in recent years. However, the genetic interactions between these regulators over the dynamic course of differentiation have not been studied. In this Extra-View article, we review recent work by Lopez-Pajares et al. that explores a new regulatory network in epidermal differentiation. They analyze the changing transcriptome throughout epidermal regeneration to identify 3 separate gene sets enriched in the progenitor, early and late differentiation states. Using expression module mapping, MAF along with MAFB, are identified as transcription factors essential for epidermal differentiation. Through double knock-down of MAF:MAFB using siRNA and CRISPR/Cas9-mediated knockout, epidermal differentiation was shown to be impaired both in-vitro and in-vivo, confirming MAF:MAFB's role to activate genes that drive differentiation. Lopez-Pajares and collaborators integrated 42 published regulator gene sets and the MAF:MAFB gene set into the dynamic differentiation gene expression landscape and found that lncRNAs TINCR and ANCR act as upstream regulators of MAF:MAFB. Furthermore, ChIP-seq analysis of MAF:MAFB identified key transcription factor genes linked to epidermal differentiation as downstream effectors. Combined, these findings illustrate a dynamically regulated network with MAF:MAFB as a crucial link for progenitor gene repression and differentiation gene activation. PMID:27097296

  10. miRTargetLink—miRNAs, Genes and Interaction Networks

    PubMed Central

    Hamberg, Maarten; Backes, Christina; Fehlmann, Tobias; Hart, Martin; Meder, Benjamin; Meese, Eckart; Keller, Andreas

    2016-01-01

    Information on miRNA targeting genes is growing rapidly. For high-throughput experiments, but also for targeted analyses of few genes or miRNAs, easy analysis with concise representation of results facilitates the work of life scientists. We developed miRTargetLink, a tool for automating respective analysis procedures that are frequently applied. Input of the web-based solution is either a single gene or single miRNA, but also sets of genes or miRNAs, can be entered. Validated and predicted targets are extracted from databases and an interaction network is presented. Users can select whether predicted targets, experimentally validated targets with strong or weak evidence, or combinations of those are considered. Central genes or miRNAs are highlighted and users can navigate through the network interactively. To discover the most relevant biochemical processes influenced by the target network, gene set analysis and miRNA set analysis are integrated. As a showcase for miRTargetLink, we analyze targets of five cardiac miRNAs. miRTargetLink is freely available without restrictions at www.ccb.uni-saarland.de/mirtargetlink. PMID:27089332

  11. Anti-oxidative stress response genes: bioinformatic analysis of their expression and relevance in multiple cancers

    PubMed Central

    Melino, Gerry; Knight, Richard A.

    2013-01-01

    Cells mount a transcriptional anti-oxidative stress (AOS) response program to scavenge reactive oxygen species (ROS) that arise from chemical, physical, and metabolic challenges. This protective program has been shown to reduce carcinogenesis triggered by chemical and physical insults. However, it is also hijacked by established cancers to thrive and proliferate within the hostile tumor microenvironment and to gain resistance against chemo- and radiotherapies. Therefore, targeting the AOS response proteins that are exploited by cancer cells is an attractive therapeutic strategy. In order to identify the AOS genes that are suspected to support cancer progression and resistance, we analyzed the expression patterns of 285 genes annotated for being involved in oxidative stress in 994 tumors and 353 normal tissues. Thereby we identified a signature of 116 genes that are highly overexpressed in multiple cancers while being only minimally expressed in normal tissues. To establish which of these genes are more likely to functionally drive cancer resistance and progression, we further identified those whose overexpression correlates with negative patient outcome in breast and lung carcinoma. Gene-set enrichment, gene ontology, network, and pathway analyses revealed that members of the thioredoxin and glutathione pathways are prominent components of this oncogenic signature and that activation of these pathways is common feature of many cancer entities. Interestingly, a large fraction of these AOS genes are downstream targets of the transcription factors NRF2, NF-kappaB, and FOXM1, and rely on NADPH for their enzymatic activities highlighting promising drug targets. We discuss these findings and propose therapeutic strategies that may be applied to overcome cancer resistance. PMID:24342878

  12. Random matrix analysis of localization properties of gene coexpression network

    NASA Astrophysics Data System (ADS)

    Jalan, Sarika; Solymosi, Norbert; Vattay, Gábor; Li, Baowen

    2010-04-01

    We analyze gene coexpression network under the random matrix theory framework. The nearest-neighbor spacing distribution of the adjacency matrix of this network follows Gaussian orthogonal statistics of random matrix theory (RMT). Spectral rigidity test follows random matrix prediction for a certain range and deviates afterwards. Eigenvector analysis of the network using inverse participation ratio suggests that the statistics of bulk of the eigenvalues of network is consistent with those of the real symmetric random matrix, whereas few eigenvalues are localized. Based on these IPR calculations, we can divide eigenvalues in three sets: (a) The nondegenerate part that follows RMT. (b) The nondegenerate part, at both ends and at intermediate eigenvalues, which deviates from RMT and expected to contain information about important nodes in the network. (c) The degenerate part with zero eigenvalue, which fluctuates around RMT-predicted value. We identify nodes corresponding to the dominant modes of the corresponding eigenvectors and analyze their structural properties.

  13. Identification of Causal Genes, Networks, and Transcriptional Regulators of REM Sleep and Wake

    PubMed Central

    Millstein, Joshua; Winrow, Christopher J.; Kasarskis, Andrew; Owens, Joseph R.; Zhou, Lili; Summa, Keith C.; Fitzpatrick, Karrie; Zhang, Bin; Vitaterna, Martha H.; Schadt, Eric E.; Renger, John J.; Turek, Fred W.

    2011-01-01

    Study Objective: Sleep-wake traits are well-known to be under substantial genetic control, but the specific genes and gene networks underlying primary sleep-wake traits have largely eluded identification using conventional approaches, especially in mammals. Thus, the aim of this study was to use systems genetics and statistical approaches to uncover the genetic networks underlying 2 primary sleep traits in the mouse: 24-h duration of REM sleep and wake. Design: Genome-wide RNA expression data from 3 tissues (anterior cortex, hypothalamus, thalamus/midbrain) were used in conjunction with high-density genotyping to identify candidate causal genes and networks mediating the effects of 2 QTL regulating the 24-h duration of REM sleep and one regulating the 24-h duration of wake. Setting: Basic sleep research laboratory. Patients or Participants: Male [C57BL/6J × (BALB/cByJ × C57BL/6J*) F1] N2 mice (n = 283). Interventions: None. Measurements and Results: The genetic variation of a mouse N2 mapping cross was leveraged against sleep-state phenotypic variation as well as quantitative gene expression measurement in key brain regions using integrative genomics approaches to uncover multiple causal sleep-state regulatory genes, including several surprising novel candidates, which interact as components of networks that modulate REM sleep and wake. In particular, it was discovered that a core network module, consisting of 20 genes, involved in the regulation of REM sleep duration is conserved across the cortex, hypothalamus, and thalamus. A novel application of a formal causal inference test was also used to identify those genes directly regulating sleep via control of expression. Conclusion: Systems genetics approaches reveal novel candidate genes, complex networks and specific transcriptional regulators of REM sleep and wake duration in mammals. Citation: Millstein J; Winrow CJ; Kasarskis A; Owens JR; Zhou L; Summa KC; Fitzpatrick K; Zhang B; Vitaterna MH; Schadt EE

  14. EGN: a wizard for construction of gene and genome similarity networks

    PubMed Central

    2013-01-01

    Background Increasingly, similarity networks are being used for evolutionary analyses of molecular datasets. These networks are very useful, in particular for the analysis of gene sharing, lateral gene transfer and for the detection of distant homologs. Currently, such analyses require some computer programming skills due to the limited availability of user-friendly freely distributed software. Consequently, although appealing, the construction and analyses of these networks remain less familiar to biologists than do phylogenetic approaches. Results In order to ease the use of similarity networks in the community of evolutionary biologists, we introduce a software program, EGN, that runs under Linux or MacOSX. EGN automates the reconstruction of gene and genome networks from nucleic and proteic sequences. EGN also implements statistics describing genetic diversity in these samples, for various user-defined thresholds of similarities. In the interest of studying the complexity of evolutionary processes affecting microbial evolution, we applied EGN to a dataset of 571,044 proteic sequences from the three domains of life and from mobile elements. We observed that, in Borrelia, plasmids play a different role than in most other eubacteria. Rather than being genetic couriers involved in lateral gene transfer, Borrelia’s plasmids and their genes act as private genetic goods, that contribute to the creation of genetic diversity within their parasitic hosts. Conclusion EGN can be used for constructing, analyzing, and mining molecular datasets in evolutionary studies. The program can help increase our knowledge of the processes through which genes from distinct sources and/or from multiple genomes co-evolve in lineages of cellular organisms. PMID:23841456

  15. Identification of Functional Modules by Integration of Multiple Data Sources Using a Bayesian Network Classifier

    PubMed Central

    Wang, Jinlian; Zuo, Yiming; Liu, Lun; Man, Yangao; Tadesse, Mahlet G.; Ressom, Habtom W

    2014-01-01

    Background Prediction of functional modules is indispensable for detecting protein deregulation in human complex diseases such as cancer. Bayesian network (BN) is one of the most commonly used models to integrate heterogeneous data from multiple sources such as protein domain, interactome, functional annotation, genome-wide gene expression, and the literature. Methods and Results In this paper, we present a BN classifier that is customized to: 1) increase the ability to integrate diverse information from different sources, 2) effectively predict protein-protein interactions, 3) infer aberrant networks with scale-free and small world properties, and 4) group molecules into functional modules or pathways based on the primary function and biological features. Application of this model on discovering protein biomarkers of hepatocelluar carcinoma (HCC) leads to the identification of functional modules that provide insights into the mechanism of the development and progression of HCC. These functional modules include cell cycle deregulation, increased angiogenesis (e.g., vascular endothelial growth factor, blood vessel morphogenesis), oxidative metabolic alterations, and aberrant activation of signaling pathways involved in cellular proliferation, survival, and differentiation. Conclusion The discoveries and conclusions derived from our customized BN classifier are consistent with previously published results. The proposed approach for determining BN structure facilitates the integration of heterogeneous data from multiple sources to elucidate the mechanisms of complex diseases. PMID:24736851

  16. Local and global responses in complex gene regulation networks

    NASA Astrophysics Data System (ADS)

    Tsuchiya, Masa; Selvarajoo, Kumar; Piras, Vincent; Tomita, Masaru; Giuliani, Alessandro

    2009-04-01

    An exacerbated sensitivity to apparently minor stimuli and a general resilience of the entire system stay together side-by-side in biological systems. This apparent paradox can be explained by the consideration of biological systems as very strongly interconnected network systems. Some nodes of these networks, thanks to their peculiar location in the network architecture, are responsible for the sensitivity aspects, while the large degree of interconnection is at the basis of the resilience properties of the system. One relevant feature of the high degree of connectivity of gene regulation networks is the emergence of collective ordered phenomena influencing the entire genome and not only a specific portion of transcripts. The great majority of existing gene regulation models give the impression of purely local ‘hard-wired’ mechanisms disregarding the emergence of global ordered behavior encompassing thousands of genes while the general, genome wide, aspects are less known. Here we address, on a data analysis perspective, the discrimination between local and global scale regulations, this goal was achieved by means of the examination of two biological systems: innate immune response in macrophages and oscillating growth dynamics in yeast. Our aim was to reconcile the ‘hard-wired’ local view of gene regulation with a global continuous and scalable one borrowed from statistical physics. This reconciliation is based on the network paradigm in which the local ‘hard-wired’ activities correspond to the activation of specific crucial nodes in the regulation network, while the scalable continuous responses can be equated to the collective oscillations of the network after a perturbation.

  17. An algebra-based method for inferring gene regulatory networks

    PubMed Central

    2014-01-01

    Background The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. Results This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also

  18. Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes

    PubMed Central

    2012-01-01

    Background To understand the roles they play in complex diseases, genes need to be investigated in the networks they are involved in. Integration of gene expression and network data is a promising approach to prioritize disease-associated genes. Some methods have been developed in this field, but the problem is still far from being solved. Results In this paper, we developed a method, Networked Gene Prioritizer (NGP), to prioritize cancer-associated genes. Applications on several breast cancer and lung cancer datasets demonstrated that NGP performs better than the existing methods. It provides stable top ranking genes between independent datasets. The top-ranked genes by NGP are enriched in the cancer-associated pathways. The top-ranked genes by NGP-PLK1, MCM2, MCM3, MCM7, MCM10 and SKP2 might coordinate to promote cell cycle related processes in cancer but not normal cells. Conclusions In this paper, we have developed a method named NGP, to prioritize cancer-associated genes. Our results demonstrated that NGP performs better than the existing methods. PMID:22838965

  19. Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks

    PubMed Central

    Yamanaka, Ryota; Kitano, Hiroaki

    2013-01-01

    Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks. PMID:24278007

  20. Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks.

    PubMed

    Wu, Siqi; Joseph, Antony; Hammonds, Ann S; Celniker, Susan E; Yu, Bin; Frise, Erwin

    2016-04-19

    Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set ofDrosophilaearly embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation ofDrosophilaexpression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. The performance of PP with theDrosophiladata suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data. PMID:27071099

  1. A gene network engineering platform for lactic acid bacteria

    PubMed Central

    Kong, Wentao; Kapuganti, Venkata S.; Lu, Ting

    2016-01-01

    Recent developments in synthetic biology have positioned lactic acid bacteria (LAB) as a major class of cellular chassis for applications. To achieve the full potential of LAB, one fundamental prerequisite is the capacity for rapid engineering of complex gene networks, such as natural biosynthetic pathways and multicomponent synthetic circuits, into which cellular functions are encoded. Here, we present a synthetic biology platform for rapid construction and optimization of large-scale gene networks in LAB. The platform involves a copy-controlled shuttle for hosting target networks and two associated strategies that enable efficient genetic editing and phenotypic validation. By using a nisin biosynthesis pathway and its variants as examples, we demonstrated multiplex, continuous editing of small DNA parts, such as ribosome-binding sites, as well as efficient manipulation of large building blocks such as genes and operons. To showcase the platform, we applied it to expand the phenotypic diversity of the nisin pathway by quickly generating a library of 63 pathway variants. We further demonstrated its utility by altering the regulatory topology of the nisin pathway for constitutive bacteriocin biosynthesis. This work demonstrates the feasibility of rapid and advanced engineering of gene networks in LAB, fostering their applications in biomedicine and other areas. PMID:26503255

  2. Compartmentalized gene regulatory network of the pathogenic fungus Fusarium graminearum

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Head blight caused by Fusarium graminearum (Fg) is a major limiting factor of wheat production with both yield loss and mycotoxin contamination. Here we report a model for global Fg gene regulatory networks (GRNs) inferred from a large collection of transcriptomic data using a machine-learning appro...

  3. A gene network engineering platform for lactic acid bacteria.

    PubMed

    Kong, Wentao; Kapuganti, Venkata S; Lu, Ting

    2016-02-29

    Recent developments in synthetic biology have positioned lactic acid bacteria (LAB) as a major class of cellular chassis for applications. To achieve the full potential of LAB, one fundamental prerequisite is the capacity for rapid engineering of complex gene networks, such as natural biosynthetic pathways and multicomponent synthetic circuits, into which cellular functions are encoded. Here, we present a synthetic biology platform for rapid construction and optimization of large-scale gene networks in LAB. The platform involves a copy-controlled shuttle for hosting target networks and two associated strategies that enable efficient genetic editing and phenotypic validation. By using a nisin biosynthesis pathway and its variants as examples, we demonstrated multiplex, continuous editing of small DNA parts, such as ribosome-binding sites, as well as efficient manipulation of large building blocks such as genes and operons. To showcase the platform, we applied it to expand the phenotypic diversity of the nisin pathway by quickly generating a library of 63 pathway variants. We further demonstrated its utility by altering the regulatory topology of the nisin pathway for constitutive bacteriocin biosynthesis. This work demonstrates the feasibility of rapid and advanced engineering of gene networks in LAB, fostering their applications in biomedicine and other areas. PMID:26503255

  4. Network analysis of genes and their association with diseases.

    PubMed

    Kontou, Panagiota I; Pavlopoulou, Athanasia; Dimou, Niki L; Pavlopoulos, Georgios A; Bagos, Pantelis G

    2016-09-15

    A plethora of network-based approaches within the Systems Biology universe have been applied, to date, to investigate the underlying molecular mechanisms of various human diseases. In the present study, we perform a bipartite, topological and clustering graph analysis in order to gain a better understanding of the relationships between human genetic diseases and the relationships between the genes that are implicated in them. For this purpose, disease-disease and gene-gene networks were constructed from combined gene-disease association networks. The latter, were created by collecting and integrating data from three diverse resources, each one with different content covering from rare monogenic disorders to common complex diseases. This data pluralism enabled us to uncover important associations between diseases with unrelated phenotypic manifestations but with common genetic origin. For our analysis, the topological attributes and the functional implications of the individual networks were taken into account and are shortly discussed. We believe that some observations of this study could advance our understanding regarding the etiology of a disease with distinct pathological manifestations, and simultaneously provide the springboard for the development of preventive and therapeutic strategies and its underlying genetic mechanisms. PMID:27265032

  5. Using SPEEDES to simulate the blue gene interconnect network

    NASA Technical Reports Server (NTRS)

    Springer, P.; Upchurch, E.

    2003-01-01

    JPL and the Center for Advanced Computer Architecture (CACR) is conducting application and simulation analyses of BG/L in order to establish a range of effectiveness for the Blue Gene/L MPP architecture in performing important classes of computations and to determine the design sensitivity of the global interconnect network in support of real world ASCI application execution.

  6. Neural Networks Art: Solving Problems with Multiple Solutions and New Teaching Algorithm

    PubMed Central

    Dmitrienko, V. D; Zakovorotnyi, A. Yu.; Leonov, S. Yu.; Khavina, I. P

    2014-01-01

    A new discrete neural networks adaptive resonance theory (ART), which allows solving problems with multiple solutions, is developed. New algorithms neural networks teaching ART to prevent degradation and reproduction classes at training noisy input data is developed. Proposed learning algorithms discrete ART networks, allowing obtaining different classification methods of input. PMID:25246988

  7. Research on model of combining multiple neural networks by fuzzy integral-MNNF

    NASA Astrophysics Data System (ADS)

    Fu, Yue; Chai, Bianfang

    2013-03-01

    The method of multiple neural network Fusion using Fuzzy Integral (MNNF) presented by this paper is to improve the detection performance of data mining-based intrusion detection system. The basic idea of MNNF is to mine on distinct feature training dataset by neural networks separately, and detect TCP/IP data by different neural networks, and then nonlinearly combine the results from multiple neural networks by fuzzy integral. The experiment results show that this technique is superior to single neural networks for intrusion detection in terms of classification accuracy. Compared with other combination methods such as Majority, Average, Borda count, fuzzy integral is better than one of them.

  8. Effects of Gene Dose, Chromatin, and Network Topology on Expression in Drosophila melanogaster.

    PubMed

    Lee, Hangnoh; Cho, Dong-Yeon; Whitworth, Cale; Eisman, Robert; Phelps, Melissa; Roote, John; Kaufman, Thomas; Cook, Kevin; Russell, Steven; Przytycka, Teresa; Oliver, Brian

    2016-09-01

    Deletions, commonly referred to as deficiencies by Drosophila geneticists, are valuable tools for mapping genes and for genetic pathway discovery via dose-dependent suppressor and enhancer screens. More recently, it has become clear that deviations from normal gene dosage are associated with multiple disorders in a range of species including humans. While we are beginning to understand some of the transcriptional effects brought about by gene dosage changes and the chromosome rearrangement breakpoints associated with them, much of this work relies on isolated examples. We have systematically examined deficiencies of the left arm of chromosome 2 and characterize gene-by-gene dosage responses that vary from collapsed expression through modest partial dosage compensation to full or even over compensation. We found negligible long-range effects of creating novel chromosome domains at deletion breakpoints, suggesting that cases of gene regulation due to altered nuclear architecture are rare. These rare cases include trans de-repression when deficiencies delete chromatin characterized as repressive in other studies. Generally, effects of breakpoints on expression are promoter proximal (~100bp) or in the gene body. Effects of deficiencies genome-wide are in genes with regulatory relationships to genes within the deleted segments, highlighting the subtle expression network defects in these sensitized genetic backgrounds. PMID:27599372

  9. Oncogenes and tumor suppressor genes: comparative genomics and network perspectives

    PubMed Central

    2015-01-01

    Background Defective tumor suppressor genes (TSGs) and hyperactive oncogenes (OCGs) heavily contribute to cell proliferation and apoptosis during cancer development through genetic variations such as somatic mutations and deletions. Moreover, they usually do not perform their cellular functions individually but rather execute jointly. Therefore, a comprehensive comparison of their mutation patterns and network properties may provide a deeper understanding of their roles in the cancer development and provide some clues for identification of novel targets. Results In this study, we performed a comprehensive survey of TSGs and OCGs from the perspectives of somatic mutations and network properties. For comparative purposes, we choose five gene sets: TSGs, OCGs, cancer drug target genes, essential genes, and other genes. Based on the data from Pan-Cancer project, we found that TSGs had the highest mutation frequency in most tumor types and the OCGs second. The essential genes had the lowest mutation frequency in all tumor types. For the network properties in the human protein-protein interaction (PPI) network, we found that, relative to target proteins, essential proteins, and other proteins, the TSG proteins and OCG proteins both tended to have higher degrees, higher betweenness, lower clustering coefficients, and shorter shortest-path distances. Moreover, the TSG proteins and OCG proteins tended to have direct interactions with cancer drug target proteins. To further explore their relationship, we generated a TSG-OCG network and found that TSGs and OCGs connected strongly with each other. The integration of the mutation frequency with the TSG-OCG network offered a network view of TSGs, OCGs, and their interactions, which may provide new insights into how the TSGs and OCGs jointly contribute to the cancer development. Conclusions Our study first discovered that the OCGs and TSGs had different mutation patterns, but had similar and stronger protein

  10. Obtain osteoarthritis related molecular signature genes through regulation network.

    PubMed

    Li, Yawei; Wang, Bing; Lv, Guohua; Xiong, Guangzhong; Liu, Wei Dong; Li, Lei

    2012-01-01

    Osteoarthritis (OA), also known as degenerative joint disease or osteoarthrosis, is the most common form of arthritis. OA occurs when cartilage in the joints wears down over time. We used the GSE1919 series to identify potential genes that correlated to OA. The aim of our study was to obtain a molecular signature of OA through the regulation network based on differentially expressed genes. From the result of regulation network construction in OA, a number of transcription factors (TFs) and pathways closely related to OA were linked by our method. Peroxisome proliferator-activated receptor γ also arises as hub nodes in our transcriptome network and certain TFs containing CEBPD, EGR2 and ETS2 were shown to be related to OA by a previous study. PMID:21946934

  11. Gap Gene Regulatory Dynamics Evolve along a Genotype Network

    PubMed Central

    Crombach, Anton; Wotton, Karl R.; Jiménez-Guri, Eva; Jaeger, Johannes

    2016-01-01

    Developmental gene networks implement the dynamic regulatory mechanisms that pattern and shape the organism. Over evolutionary time, the wiring of these networks changes, yet the patterning outcome is often preserved, a phenomenon known as “system drift.” System drift is illustrated by the gap gene network—involved in segmental patterning—in dipteran insects. In the classic model organism Drosophila melanogaster and the nonmodel scuttle fly Megaselia abdita, early activation and placement of gap gene expression domains show significant quantitative differences, yet the final patterning output of the system is essentially identical in both species. In this detailed modeling analysis of system drift, we use gene circuits which are fit to quantitative gap gene expression data in M. abdita and compare them with an equivalent set of models from D. melanogaster. The results of this comparative analysis show precisely how compensatory regulatory mechanisms achieve equivalent final patterns in both species. We discuss the larger implications of the work in terms of “genotype networks” and the ways in which the structure of regulatory networks can influence patterns of evolutionary change (evolvability). PMID:26796549

  12. Ensemble Inference and Inferability of Gene Regulatory Networks

    PubMed Central

    Ud-Dean, S. M. Minhaz; Gunawan, Rudiyanto

    2014-01-01

    The inference of gene regulatory network (GRN) from gene expression data is an unsolved problem of great importance. This inference has been stated, though not proven, to be underdetermined implying that there could be many equivalent (indistinguishable) solutions. Motivated by this fundamental limitation, we have developed new framework and algorithm, called TRaCE, for the ensemble inference of GRNs. The ensemble corresponds to the inherent uncertainty associated with discriminating direct and indirect gene regulations from steady-state data of gene knock-out (KO) experiments. We applied TRaCE to analyze the inferability of random GRNs and the GRNs of E. coli and yeast from single- and double-gene KO experiments. The results showed that, with the exception of networks with very few edges, GRNs are typically not inferable even when the data are ideal (unbiased and noise-free). Finally, we compared the performance of TRaCE with top performing methods of DREAM4 in silico network inference challenge. PMID:25093509

  13. Robustness and Accuracy in Sea Urchin Developmental Gene Regulatory Networks

    PubMed Central

    Ben-Tabou de-Leon, Smadar

    2016-01-01

    Developmental gene regulatory networks robustly control the timely activation of regulatory and differentiation genes. The structure of these networks underlies their capacity to buffer intrinsic and extrinsic noise and maintain embryonic morphology. Here I illustrate how the use of specific architectures by the sea urchin developmental regulatory networks enables the robust control of cell fate decisions. The Wnt-βcatenin signaling pathway patterns the primary embryonic axis while the BMP signaling pathway patterns the secondary embryonic axis in the sea urchin embryo and across bilateria. Interestingly, in the sea urchin in both cases, the signaling pathway that defines the axis controls directly the expression of a set of downstream regulatory genes. I propose that this direct activation of a set of regulatory genes enables a uniform regulatory response and a clear cut cell fate decision in the endoderm and in the dorsal ectoderm. The specification of the mesodermal pigment cell lineage is activated by Delta signaling that initiates a triple positive feedback loop that locks down the pigment specification state. I propose that the use of compound positive feedback circuitry provides the endodermal cells enough time to turn off mesodermal genes and ensures correct mesoderm vs. endoderm fate decision. Thus, I argue that understanding the control properties of repeatedly used regulatory architectures illuminates their role in embryogenesis and provides possible explanations to their resistance to evolutionary change. PMID:26913048

  14. Remoting alternatives for a multiple phased-array antenna network

    NASA Astrophysics Data System (ADS)

    Shi, Zan; Foshee, James J.

    2001-10-01

    Significant improvements in technology have made phased array antennas an attractive alternative to the traditional dish antenna for use on wide body airplanes. These improvements have resulted in reduced size, reduced cost, reduced losses in the transmit and receive channels (simplifying the design), a significant extension in the bandwidth capability, and an increase in the functional capability. Flush mounting (thus reduced drag) and rapid beam switching are among the evolving desirable features of phased array antennas. Beam scanning of phased array antennas is limited to +/-45 degrees at best and therefore multiple phased array antennas would need to be used to insure instantaneous communications with any ground station (stations located at different geographical locations on the ground) and with other airborne stations. The exact number of phased array antennas and the specific installation location of each antenna on the wide body airplane would need to be determined by the specific communication requirements, but it is conceivable as many as five phased array antennas may need to be used to provide the required coverage. Control and switching of these antennas would need to be accomplished at a centralized location on the airplane and since these antennas would be at different locations on the airplane an efficient scheme of remoting would need to be used. To save in cost and keep the phased array antennas as small as possible the design of the phased array antennas would need to be kept simple. A dish antenna and a blade antenna (small size) could also be used to augment the system. Generating the RF signals at the central location and then using RF cables or waveguide to get the signal to any given antenna could result in significant RF losses. This paper will evaluate a number of remoting alternatives to keep the system design simple, reduce system cost, and utilize the functional capability of networking multiple phased array antennas on a wide body

  15. Biology-Driven Gene-Gene Interaction Analysis of Age-Related Cataract in the eMERGE Network.

    PubMed

    Hall, Molly A; Verma, Shefali S; Wallace, John; Lucas, Anastasia; Berg, Richard L; Connolly, John; Crawford, Dana C; Crosslin, David R; de Andrade, Mariza; Doheny, Kimberly F; Haines, Jonathan L; Harley, John B; Jarvik, Gail P; Kitchner, Terrie; Kuivaniemi, Helena; Larson, Eric B; Carrell, David S; Tromp, Gerard; Vrabec, Tamara R; Pendergrass, Sarah A; McCarty, Catherine A; Ritchie, Marylyn D

    2015-07-01

    Bioinformatics approaches to examine gene-gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge-driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty-three SNP-SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)-rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell-to-cell adhesion signaling, cell-cell junction organization, and cell-cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP-SNP models, which included signal transduction and PI3K-Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology-driven method, applicable for any genome-wide association study dataset. PMID:25982363

  16. Biology-Driven Gene-Gene Interaction Analysis of Age-Related Cataract in the eMERGE Network

    PubMed Central

    Hall, Molly A; Verma, Shefali S; Wallace, John; Lucas, Anastasia; Berg, Richard L; Connolly, John; Crawford, Dana C; Crosslin, David R; de Andrade, Mariza; Doheny, Kimberly F; Haines, Jonathan L; Harley, John B; Jarvik, Gail P; Kitchner, Terrie; Kuivaniemi, Helena; Larson, Eric B; Carrell, David S; Tromp, Gerard; Vrabec, Tamara R; Pendergrass, Sarah A; McCarty, Catherine A; Ritchie, Marylyn D

    2015-01-01

    Bioinformatics approaches to examine gene-gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge-driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty-three SNP-SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)–rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell-to-cell adhesion signaling, cell-cell junction organization, and cell-cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP-SNP models, which included signal transduction and PI3K-Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology-driven method, applicable for any genome-wide association study dataset. PMID:25982363

  17. Propagation of genetic variation in gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Plahte, Erik; Gjuvsland, Arne B.; Omholt, Stig W.

    2013-08-01

    A future quantitative genetics theory should link genetic variation to phenotypic variation in a causally cohesive way based on how genes actually work and interact. We provide a theoretical framework for predicting and understanding the manifestation of genetic variation in haploid and diploid regulatory networks with arbitrary feedback structures and intra-locus and inter-locus functional dependencies. Using results from network and graph theory, we define propagation functions describing how genetic variation in a locus is propagated through the network, and show how their derivatives are related to the network’s feedback structure. Similarly, feedback functions describe the effect of genotypic variation of a locus on itself, either directly or mediated by the network. A simple sign rule relates the sign of the derivative of the feedback function of any locus to the feedback loops involving that particular locus. We show that the sign of the phenotypically manifested interaction between alleles at a diploid locus is equal to the sign of the dominant feedback loop involving that particular locus, in accordance with recent results for a single locus system. Our results provide tools by which one can use observable equilibrium concentrations of gene products to disclose structural properties of the network architecture. Our work is a step towards a theory capable of explaining the pleiotropy and epistasis features of genetic variation in complex regulatory networks as functions of regulatory anatomy and functional location of the genetic variation.

  18. Mapping functional transcription factor networks from gene expression data

    PubMed Central

    Haynes, Brian C.; Maier, Ezekiel J.; Kramer, Michael H.; Wang, Patricia I.; Brown, Holly; Brent, Michael R.

    2013-01-01

    A critical step in understanding how a genome functions is determining which transcription factors (TFs) regulate each gene. Accordingly, extensive effort has been devoted to mapping TF networks. In Saccharomyces cerevisiae, protein–DNA interactions have been identified for most TFs by ChIP-chip, and expression profiling has been done on strains deleted for most TFs. These studies revealed that there is little overlap between the genes whose promoters are bound by a TF and those whose expression changes when the TF is deleted, leaving us without a definitive TF network for any eukaryote and without an efficient method for mapping functional TF networks. This paper describes NetProphet, a novel algorithm that improves the efficiency of network mapping from gene expression data. NetProphet exploits a fundamental observation about the nature of TF networks: The response to disrupting or overexpressing a TF is strongest on its direct targets and dissipates rapidly as it propagates through the network. Using S. cerevisiae data, we show that NetProphet can predict thousands of direct, functional regulatory interactions, using only gene expression data. The targets that NetProphet predicts for a TF are at least as likely to have sites matching the TF's binding specificity as the targets implicated by ChIP. Unlike most ChIP targets, the NetProphet targets also show evidence of functional regulation. This suggests a surprising conclusion: The best way to begin mapping direct, functional TF-promoter interactions may not be by measuring binding. We also show that NetProphet yields new insights into the functions of several yeast TFs, including a well-studied TF, Cbf1, and a completely unstudied TF, Eds1. PMID:23636944

  19. Floral morphogenesis: stochastic explorations of a gene network epigenetic landscape.

    PubMed

    Alvarez-Buylla, Elena R; Chaos, Alvaro; Aldana, Maximino; Benítez, Mariana; Cortes-Poza, Yuriria; Espinosa-Soto, Carlos; Hartasánchez, Diego A; Lotto, R Beau; Malkin, David; Escalera Santos, Gerardo J; Padilla-Longoria, Pablo

    2008-01-01

    In contrast to the classical view of development as a preprogrammed and deterministic process, recent studies have demonstrated that stochastic perturbations of highly non-linear systems may underlie the emergence and stability of biological patterns. Herein, we address the question of whether noise contributes to the generation of the stereotypical temporal pattern in gene expression during flower development. We modeled the regulatory network of organ identity genes in the Arabidopsis thaliana flower as a stochastic system. This network has previously been shown to converge to ten fixed-point attractors, each with gene expression arrays that characterize inflorescence cells and primordial cells of sepals, petals, stamens, and carpels. The network used is binary, and the logical rules that govern its dynamics are grounded in experimental evidence. We introduced different levels of uncertainty in the updating rules of the network. Interestingly, for a level of noise of around 0.5-10%, the system exhibited a sequence of transitions among attractors that mimics the sequence of gene activation configurations observed in real flowers. We also implemented the gene regulatory network as a continuous system using the Glass model of differential equations, that can be considered as a first approximation of kinetic-reaction equations, but which are not necessarily equivalent to the Boolean model. Interestingly, the Glass dynamics recover a temporal sequence of attractors, that is qualitatively similar, although not identical, to that obtained using the Boolean model. Thus, time ordering in the emergence of cell-fate patterns is not an artifact of synchronous updating in the Boolean model. Therefore, our model provides a novel explanation for the emergence and robustness of the ubiquitous temporal pattern of floral organ specification. It also constitutes a new approach to understanding morphogenesis, providing predictions on the population dynamics of cells with different

  20. [A generalized chemical-kinetic method for modeling gene networks].

    PubMed

    Likhoshvaĭ, V A; Matushkin, Iu G; Ratushnyĭ, A V; Anan'ko, E A; Ignat'eva, E V; Podkolodnaia, O A

    2001-01-01

    Development of methods for mathematical simulation of biological systems and building specific simulations is an important trend of bioinformatics development. Here we describe the method of generalized chemokinetic simulation generating flexible and adequate simulations of various biological systems. Adequate simulations of complex nonlinear gene networks--control system of cholesterol by synthesis in the cell and erythrocyte differentiation and maturation--are given as the examples. The simulations were expressed in terms of unit processes--biochemical reactions. Optimal sets of parameters were determined and the systems were numerically simulated under various conditions. The simulations allow us to study possible functional conditions of these gene networks, calculate consequences of mutations, and define optimal strategies for their correction including therapeutic ones. Graphical user interface for these simulations is available at http://wwwmgs.bionet.nsc.ru/systems/MGL/GeneNet/. PMID:11771132

  1. Ensemble-Based Network Aggregation Improves the Accuracy of Gene Network Reconstruction

    PubMed Central

    Xiao, Guanghua; Xie, Yang

    2014-01-01

    Reverse engineering approaches to constructing gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings, such as the discovery of novel drug targets. However, the reliability of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improving the accuracy of network topologies constructed from mRNA expression data. To evaluate the performances of different approaches, we created dozens of simulated networks from combinations of gene-set sizes and sample sizes and also tested our methods on three Escherichia coli datasets. We demonstrate that the ensemble-based network aggregation approach can be used to effectively integrate GRNs constructed from different studies – producing more accurate networks. We also apply this approach to building a network from epithelial mesenchymal transition (EMT) signature microarray data and identify hub genes that might be potential drug targets. The R code used to perform all of the analyses is available in an R package entitled “ENA”, accessible on CRAN (http://cran.r-project.org/web/packages/ENA/). PMID:25390635

  2. Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network

    PubMed Central

    Hwang, Sohyun; Rhee, Seung Y; Marcotte, Edward M; Lee, Insuk

    2012-01-01

    AraNet is a functional gene network for the reference plant Arabidopsis and has been constructed in order to identify new genes associated with plant traits. It is highly predictive for diverse biological pathways and can be used to prioritize genes for functional screens. Moreover, AraNet provides a web-based tool with which plant biologists can efficiently discover novel functions of Arabidopsis genes (http://www.functionalnet.org/aranet/). This protocol explains how to conduct network-based prediction of gene functions using AraNet and how to interpret the prediction results. Functional discovery in plant biology is facilitated by combining candidate prioritization by AraNet with focused experimental tests. PMID:21886106

  3. Characterization of Genes for Beef Marbling Based on Applying Gene Coexpression Network

    PubMed Central

    Lim, Dajeong; Kim, Nam-Kuk; Lee, Seung-Hwan; Park, Hye-Sun; Cho, Yong-Min; Chai, Han-Ha; Kim, Heebal

    2014-01-01

    Marbling is an important trait in characterization beef quality and a major factor for determining the price of beef in the Korean beef market. In particular, marbling is a complex trait and needs a system-level approach for identifying candidate genes related to the trait. To find the candidate gene associated with marbling, we used a weighted gene coexpression network analysis from the expression value of bovine genes. Hub genes were identified; they were topologically centered with large degree and BC values in the global network. We performed gene expression analysis to detect candidate genes in M. longissimus with divergent marbling phenotype (marbling scores 2 to 7) using qRT-PCR. The results demonstrate that transmembrane protein 60 (TMEM60) and dihydropyrimidine dehydrogenase (DPYD) are associated with increasing marbling fat. We suggest that the network-based approach in livestock may be an important method for analyzing the complex effects of candidate genes associated with complex traits like marbling or tenderness. PMID:24624372

  4. Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks

    PubMed Central

    Singhal, Amit; Kumar, Pavanish; de Libero, Gennaro; Poidinger, Michael; Monterola, Christopher

    2015-01-01

    Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data) accompanying this manuscript. PMID:26393364

  5. Measuring semantic similarities by combining gene ontology annotations and gene co-function networks

    SciTech Connect

    Peng, Jiajie; Uygun, Sahra; Kim, Taehyong; Wang, Yadong; Rhee, Seung Y.; Chen, Jin

    2015-02-14

    Background: Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms. Results: We introduce a novel approach called NETSIM (network-based similarity measure) that incorporates information from gene co-function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstrate that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families. Conclusions: Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome-specific information. NETSIM incorporates both GO annotations and gene co-function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon-specific manner become measurable when GO annotations are limited.

  6. Measuring semantic similarities by combining gene ontology annotations and gene co-function networks

    DOE PAGESBeta

    Peng, Jiajie; Uygun, Sahra; Kim, Taehyong; Wang, Yadong; Rhee, Seung Y.; Chen, Jin

    2015-02-14

    Background: Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms. Results: We introduce a novel approach called NETSIM (network-based similarity measure) that incorporates information from gene co-function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstratemore » that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families. Conclusions: Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome-specific information. NETSIM incorporates both GO annotations and gene co-function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon-specific manner become measurable when GO annotations are limited.« less

  7. New Method for Joint Network Analysis Reveals Common and Different Coexpression Patterns among Genes and Proteins in Breast Cancer

    PubMed Central

    2016-01-01

    We focus on characterizing common and different coexpression patterns among RNAs and proteins in breast cancer tumors. To address this problem, we introduce Joint Random Forest (JRF), a novel nonparametric algorithm to simultaneously estimate multiple coexpression networks by effectively borrowing information across protein and gene expression data. The performance of JRF was evaluated through extensive simulation studies using different network topologies and data distribution functions. Advantages of JRF over other algorithms that estimate class-specific networks separately were observed across all simulation settings. JRF also outperformed a competing method based on Gaussian graphic models. We then applied JRF to simultaneously construct gene and protein coexpression networks based on protein and RNAseq data from CPTAC-TCGA breast cancer study. We identified interesting common and differential coexpression patterns among genes and proteins. This information can help to cast light on the potential disease mechanisms of breast cancer. PMID:26733076

  8. Variations of the perforin gene in patients with multiple sclerosis.

    PubMed

    Cappellano, G; Orilieri, E; Comi, C; Chiocchetti, A; Bocca, S; Boggio, E; Bernardone, I S; Cometa, A; Clementi, R; Barizzone, N; D'Alfonso, S; Corrado, L; Galimberti, D; Scarpini, E; Guerini, F R; Caputo, D; Paolicelli, D; Trojano, M; Figà-Talamanca, L; Salvetti, M; Perla, F; Leone, M; Monaco, F; Dianzani, U

    2008-07-01

    Perforin is involved in cell-mediated cytotoxicity and mutations of its gene (PRF1) cause familial hemophagocytic lymphohistiocytosis (FLH2). PRF1 sequencing in 190 patients with multiple sclerosis and 268 controls detected two FLH2-associated variations (A91V, N252S) in both groups and six novel mutations (C999T, G1065A, G1428A, A1620G, G719A, C1069T) in patients. All together, carriers of these variations were more frequent in patients than in controls (phenotype frequency: 17 vs 9%, P=0.0166; odds ratio (OR)=2.06, 95% confidence interval (CI): 1.13-3.77). Although A91V was the most frequent variation and displayed a trend of association with multiple sclerosis (MS) in the first population of patients and controls (frequency of the 91V allele: 0.076 vs 0.043, P=0.044), we used it as a marker to confirm PRF1 involvement in MS and assessed its frequency in a second population of 966 patients and 1520 controls. Frequency of the 91V allele was significantly higher in patients than in controls also in the second population (0.075 vs 0.058%, P=0.019). In the combined cohorts of 1156 patients and 1788 controls, presence of the 91V allele in single or double dose conferred an OR=1.38 (95% CI=1.10-1.74). These data suggest that A91V and possibly other perforin variations indicate susceptibility to MS. PMID:18496551

  9. TERT gene harbors multiple variants associated with pancreatic cancer susceptibility.

    PubMed

    Campa, Daniele; Rizzato, Cosmeri; Stolzenberg-Solomon, Rachael; Pacetti, Paola; Vodicka, Pavel; Cleary, Sean P; Capurso, Gabriele; Bueno-de-Mesquita, H B As; Werner, Jens; Gazouli, Maria; Butterbach, Katja; Ivanauskas, Audrius; Giese, Nathalia; Petersen, Gloria M; Fogar, Paola; Wang, Zhaoming; Bassi, Claudio; Ryska, Miroslav; Theodoropoulos, George E; Kooperberg, Charles; Li, Donghui; Greenhalf, William; Pasquali, Claudio; Hackert, Thilo; Fuchs, Charles S; Mohelnikova-Duchonova, Beatrice; Sperti, Cosimo; Funel, Niccola; Dieffenbach, Aida Karina; Wareham, Nicholas J; Buring, Julie; Holcátová, Ivana; Costello, Eithne; Zambon, Carlo-Federico; Kupcinskas, Juozas; Risch, Harvey A; Kraft, Peter; Bracci, Paige M; Pezzilli, Raffaele; Olson, Sara H; Sesso, Howard D; Hartge, Patricia; Strobel, Oliver; Małecka-Panas, Ewa; Visvanathan, Kala; Arslan, Alan A; Pedrazzoli, Sergio; Souček, Pavel; Gioffreda, Domenica; Key, Timothy J; Talar-Wojnarowska, Renata; Scarpa, Aldo; Mambrini, Andrea; Jacobs, Eric J; Jamroziak, Krzysztof; Klein, Alison; Tavano, Francesca; Bambi, Franco; Landi, Stefano; Austin, Melissa A; Vodickova, Ludmila; Brenner, Hermann; Chanock, Stephen J; Delle Fave, Gianfranco; Piepoli, Ada; Cantore, Maurizio; Zheng, Wei; Wolpin, Brian M; Amundadottir, Laufey T; Canzian, Federico

    2015-11-01

    A small number of common susceptibility loci have been identified for pancreatic cancer, one of which is marked by rs401681 in the TERT-CLPTM1L gene region on chromosome 5p15.33. Because this region is characterized by low linkage disequilibrium, we sought to identify whether additional single nucleotide polymorphisms (SNPs) could be related to pancreatic cancer risk, independently of rs401681. We performed an in-depth analysis of genetic variability of the telomerase reverse transcriptase (TERT) and the telomerase RNA component (TERC) genes, in 5,550 subjects with pancreatic cancer and 7,585 controls from the PANcreatic Disease ReseArch (PANDoRA) and the PanScan consortia. We identified a significant association between a variant in TERT and pancreatic cancer risk (rs2853677, odds ratio = 0.85; 95% confidence interval = 0.80-0.90, p = 8.3 × 10(-8)). Additional analysis adjusting rs2853677 for rs401681 indicated that the two SNPs are independently associated with pancreatic cancer risk, as suggested by the low linkage disequilibrium between them (r(2) = 0.07, D' = 0.28). Three additional SNPs in TERT reached statistical significance after correction for multiple testing: rs2736100 (p = 3.0 × 10(-5) ), rs4583925 (p = 4.0 × 10(-5) ) and rs2735948 (p = 5.0 × 10(-5) ). In conclusion, we confirmed that the TERT locus is associated with pancreatic cancer risk, possibly through several independent variants. PMID:25940397

  10. A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes

    PubMed Central

    Larremore, Daniel B.; Clauset, Aaron; Buckee, Caroline O.

    2013-01-01

    The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences. PMID:24130474

  11. Multiple sugar: phosphotransferase system permeases participate in catabolite modification of gene expression in Streptococcus mutans.

    PubMed

    Zeng, Lin; Burne, Robert A

    2008-10-01

    Streptococcus mutans is particularly well adapted for high-affinity, high-capacity catabolism of multiple carbohydrate sources. S. mutansenzyme II (EII(Lev)), a fructose/mannose permease encoded by the levDEFG genes, and fruA, which encodes a hydrolase that releases fructose from fructan polymers, are transcriptionally regulated by the LevQRST four-component signal transduction system. Here, we demonstrate that: (i) levDEFGX are co-transcribed and the levE/F intergenic region is required for optimal expression of levFGX; (ii) D-mannose is a potent inducer of the levD and fruA operons; (iii) CcpA regulates levD expression in a carbohydrate-specific manner; (iv) deletion of the genes for the fructose/mannose-EII enzymes of S. mutans (manL, fruI and levD) enhances levD expression; (v) repression of the LevQRST regulon by EII enzymes depends on the presence of their substrates and requires LevR, but not LevQST; and (vi) CcpA inhibits expression of the manL and fruI genes to indirectly control the LevQRST regulon. Further, the manL, ccpA, fruI/fruCD and levD gene products differentially exert control over the cellobiose and lactose operons. Collectively, the results reveal the existence of a global regulatory network in S. mutans that governs the utilization of non-preferred carbohydrates in response to the availability and source of multiple preferred carbohydrates. PMID:18699864

  12. Prioritization of Susceptibility Genes for Ectopic Pregnancy by Gene Network Analysis

    PubMed Central

    Liu, Ji-Long; Zhao, Miao

    2016-01-01

    Ectopic pregnancy is a very dangerous complication of pregnancy, affecting 1%–2% of all reported pregnancies. Due to ethical constraints on human biopsies and the lack of suitable animal models, there has been little success in identifying functionally important genes in the pathogenesis of ectopic pregnancy. In the present study, we developed a random walk–based computational method named TM-rank to prioritize ectopic pregnancy–related genes based on text mining data and gene network information. Using a defined threshold value, we identified five top-ranked genes: VEGFA (vascular endothelial growth factor A), IL8 (interleukin 8), IL6 (interleukin 6), ESR1 (estrogen receptor 1) and EGFR (epidermal growth factor receptor). These genes are promising candidate genes that can serve as useful diagnostic biomarkers and therapeutic targets. Our approach represents a novel strategy for prioritizing disease susceptibility genes. PMID:26840308

  13. Prioritization of Susceptibility Genes for Ectopic Pregnancy by Gene Network Analysis.

    PubMed

    Liu, Ji-Long; Zhao, Miao

    2016-01-01

    Ectopic pregnancy is a very dangerous complication of pregnancy, affecting 1%-2% of all reported pregnancies. Due to ethical constraints on human biopsies and the lack of suitable animal models, there has been little success in identifying functionally important genes in the pathogenesis of ectopic pregnancy. In the present study, we developed a random walk-based computational method named TM-rank to prioritize ectopic pregnancy-related genes based on text mining data and gene network information. Using a defined threshold value, we identified five top-ranked genes: VEGFA (vascular endothelial growth factor A), IL8 (interleukin 8), IL6 (interleukin 6), ESR1 (estrogen receptor 1) and EGFR (epidermal growth factor receptor). These genes are promising candidate genes that can serve as useful diagnostic biomarkers and therapeutic targets. Our approach represents a novel strategy for prioritizing disease susceptibility genes. PMID:26840308

  14. Functional Connectivity in Multiple Cortical Networks Is Associated with Performance Across Cognitive Domains in Older Adults

    PubMed Central

    Shaw, Emily E.; Schultz, Aaron P.; Sperling, Reisa A.

    2015-01-01

    Abstract Intrinsic functional connectivity MRI has become a widely used tool for measuring integrity in large-scale cortical networks. This study examined multiple cortical networks using Template-Based Rotation (TBR), a method that applies a priori network and nuisance component templates defined from an independent dataset to test datasets of interest. A priori templates were applied to a test dataset of 276 older adults (ages 65–90) from the Harvard Aging Brain Study to examine the relationship between multiple large-scale cortical networks and cognition. Factor scores derived from neuropsychological tests represented processing speed, executive function, and episodic memory. Resting-state BOLD data were acquired in two 6-min acquisitions on a 3-Tesla scanner and processed with TBR to extract individual-level metrics of network connectivity in multiple cortical networks. All results controlled for data quality metrics, including motion. Connectivity in multiple large-scale cortical networks was positively related to all cognitive domains, with a composite measure of general connectivity positively associated with general cognitive performance. Controlling for the correlations between networks, the frontoparietal control network (FPCN) and executive function demonstrated the only significant association, suggesting specificity in this relationship. Further analyses found that the FPCN mediated the relationships of the other networks with cognition, suggesting that this network may play a central role in understanding individual variation in cognition during aging. PMID:25827242

  15. Functional Connectivity in Multiple Cortical Networks Is Associated with Performance Across Cognitive Domains in Older Adults.

    PubMed

    Shaw, Emily E; Schultz, Aaron P; Sperling, Reisa A; Hedden, Trey

    2015-10-01

    Intrinsic functional connectivity MRI has become a widely used tool for measuring integrity in large-scale cortical networks. This study examined multiple cortical networks using Template-Based Rotation (TBR), a method that applies a priori network and nuisance component templates defined from an independent dataset to test datasets of interest. A priori templates were applied to a test dataset of 276 older adults (ages 65-90) from the Harvard Aging Brain Study to examine the relationship between multiple large-scale cortical networks and cognition. Factor scores derived from neuropsychological tests represented processing speed, executive function, and episodic memory. Resting-state BOLD data were acquired in two 6-min acquisitions on a 3-Tesla scanner and processed with TBR to extract individual-level metrics of network connectivity in multiple cortical networks. All results controlled for data quality metrics, including motion. Connectivity in multiple large-scale cortical networks was positively related to all cognitive domains, with a composite measure of general connectivity positively associated with general cognitive performance. Controlling for the correlations between networks, the frontoparietal control network (FPCN) and executive function demonstrated the only significant association, suggesting specificity in this relationship. Further analyses found that the FPCN mediated the relationships of the other networks with cognition, suggesting that this network may play a central role in understanding individual variation in cognition during aging. PMID:25827242

  16. Toxic Diatom Aldehydes Affect Defence Gene Networks in Sea Urchins.

    PubMed

    Varrella, Stefano; Romano, Giovanna; Costantini, Susan; Ruocco, Nadia; Ianora, Adrianna; Bentley, Matt G; Costantini, Maria

    2016-01-01

    Marine organisms possess a series of cellular strategies to counteract the negative effects of toxic compounds, including the massive reorganization of gene expression networks. Here we report the modulated dose-dependent response of activated genes by diatom polyunsaturated aldehydes (PUAs) in the sea urchin Paracentrotus lividus. PUAs are secondary metabolites deriving from the oxidation of fatty acids, inducing deleterious effects on the reproduction and development of planktonic and benthic organisms that feed on these unicellular algae and with anti-cancer activity. Our previous results showed that PUAs target several genes, implicated in different functional processes in this sea urchin. Using interactomic Ingenuity Pathway Analysis we now show that the genes targeted by PUAs are correlated with four HUB genes, NF-κB, p53, δ-2-catenin and HIF1A, which have not been previously reported for P. lividus. We propose a working model describing hypothetical pathways potentially involved in toxic aldehyde stress response in sea urchins. This represents the first report on gene networks affected by PUAs, opening new perspectives in understanding the cellular mechanisms underlying the response of benthic organisms to diatom exposure. PMID:26914213

  17. Toxic Diatom Aldehydes Affect Defence Gene Networks in Sea Urchins

    PubMed Central

    Varrella, Stefano; Ruocco, Nadia; Ianora, Adrianna; Bentley, Matt G.; Costantini, Maria

    2016-01-01

    Marine organisms possess a series of cellular strategies to counteract the negative effects of toxic compounds, including the massive reorganization of gene expression networks. Here we report the modulated dose-dependent response of activated genes by diatom polyunsaturated aldehydes (PUAs) in the sea urchin Paracentrotus lividus. PUAs are secondary metabolites deriving from the oxidation of fatty acids, inducing deleterious effects on the reproduction and development of planktonic and benthic organisms that feed on these unicellular algae and with anti-cancer activity. Our previous results showed that PUAs target several genes, implicated in different functional processes in this sea urchin. Using interactomic Ingenuity Pathway Analysis we now show that the genes targeted by PUAs are correlated with four HUB genes, NF-κB, p53, δ-2-catenin and HIF1A, which have not been previously reported for P. lividus. We propose a working model describing hypothetical pathways potentially involved in toxic aldehyde stress response in sea urchins. This represents the first report on gene networks affected by PUAs, opening new perspectives in understanding the cellular mechanisms underlying the response of benthic organisms to diatom exposure. PMID:26914213

  18. How difficult is inference of mammalian causal gene regulatory networks?

    PubMed

    Djordjevic, Djordje; Yang, Andrian; Zadoorian, Armella; Rungrugeecharoen, Kevin; Ho, Joshua W K

    2014-01-01

    Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on > 2,000 pieces of experimental genetic perturbation evidence from manually reading > 150 primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for

  19. PDCD10 Gene Mutations in Multiple Cerebral Cavernous Malformations

    PubMed Central

    Cigoli, Maria Sole; Avemaria, Francesca; De Benedetti, Stefano; Gesu, Giovanni P.; Accorsi, Lucio Giordano; Parmigiani, Stefano; Corona, Maria Franca; Capra, Valeria; Mosca, Andrea; Giovannini, Simona; Notturno, Francesca; Ciccocioppo, Fausta; Volpi, Lilia; Estienne, Margherita; De Michele, Giuseppe; Antenora, Antonella; Bilo, Leda; Tavoni, Antonietta; Zamponi, Nelia; Alfei, Enrico; Baranello, Giovanni; Riva, Daria; Penco, Silvana

    2014-01-01

    Cerebral cavernous malformations (CCMs) are vascular abnormalities that may cause seizures, intracerebral haemorrhages, and focal neurological deficits. Familial form shows an autosomal dominant pattern of inheritance with incomplete penetrance and variable clinical expression. Three genes have been identified causing familial CCM: KRIT1/CCM1, MGC4607/CCM2, and PDCD10/CCM3. Aim of this study is to report additional PDCD10/CCM3 families poorly described so far which account for 10-15% of hereditary cerebral cavernous malformations. Our group investigated 87 consecutive Italian affected individuals (i.e. positive Magnetic Resonance Imaging) with multiple/familial CCM through direct sequencing and Multiplex Ligation-Dependent Probe Amplification (MLPA) analysis. We identified mutations in over 97.7% of cases, and PDCD10/CCM3 accounts for 13.1%. PDCD10/CCM3 molecular screening revealed four already known mutations and four novel ones. The mutated patients show an earlier onset of clinical manifestations as compared to CCM1/CCM2 mutated patients. The study of further families carrying mutations in PDCD10/CCM3 may help define a possible correlation between genotype and phenotype; an accurate clinical follow up of the subjects would help define more precisely whether mutations in PDCD10/CCM3 lead to a characteristic phenotype. PMID:25354366

  20. Contributions to gene activation by multiple functions of Bicoid.

    PubMed Central

    Ma, X; Yuan, D; Scarborough, T; Ma, J

    1999-01-01

    Bicoid is a Drosophila morphogenetic protein required for the development of anterior structures in the embryo. To gain a better understanding of how Bicoid works as a transcriptional activator, we systematically analysed various functions of Bicoid required for gene activation. We provide evidence suggesting that Bicoid is an intrinsically weak activator. First, our biochemical experiments demonstrate that the Bicoid-DNA complexes are very unstable, suggesting a weak DNA-binding function of Bicoid. This idea is further supported by our experiments demonstrating that the same number of LexA-Bicoid fusion molecules can activate transcription more effectively from LexA sites than from Bicoid sites. Secondly, we demonstrate that transcriptional activation by the weak activator Bicoid is readily influenced by the local enhancer environment. These influences are decreased when the Bicoid function is enforced by attaching to it either a known dimerization domain or the strong activation domain VP16. VP16 can also compensate for the loss of some Bicoid sites in an enhancer element. Our experiments demonstrate that the outcome of transcriptional activation by Bicoid is determined by multiple weak functions that are interconnected, a finding that can further help us to understand how this morphogenetic protein achieves its molecular functions. PMID:10024522

  1. Translational cross talk in gene networks.

    PubMed

    Mather, William H; Hasty, Jeff; Tsimring, Lev S; Williams, Ruth J

    2013-06-01

    It has been shown experimentally that competition for limited translational resources by upstream mRNAs can lead to an anticorrelation between protein counts. Here, we investigate a stochastic model for this phenomenon, in which gene transcripts of different types compete for a finite pool of ribosomes. Throughout, we utilize concepts from the theory of multiclass queues to describe a qualitative shift in protein count statistics as the system transitions from being underloaded (ribosomes exceed transcripts in number) to being overloaded (transcripts exceed ribosomes in number). The exact analytical solution of a simplified stochastic model, in which the numbers of competing mRNAs and ribosomes are fixed, exhibits weak positive correlations between steady-state protein counts when total transcript count slightly exceeds ribosome count, whereas the solution can exhibit strong negative correlations when total transcript count significantly exceeds ribosome count. Extending this analysis, we find approximate but reasonably accurate solutions for a more realistic model, in which abundances of mRNAs and ribosomes are allowed to fluctuate randomly. Here, ribosomal fluctuations contribute positively and mRNA fluctuations contribute negatively to correlations, and when mRNA fluctuations dominate ribosomal fluctuations, a strong anticorrelation extremum reliably occurs near the transition from the underloaded to the overloaded regime. PMID:23746529

  2. Vitamin D and gene networks in human osteoblasts

    PubMed Central

    van de Peppel, Jeroen; van Leeuwen, Johannes P. T. M.

    2014-01-01

    Bone formation is indirectly influenced by 1,25-dihydroxyvitamin D3 (1,25D3) through the stimulation of calcium uptake in the intestine and re-absorption in the kidneys. Direct effects on osteoblasts and bone formation have also been established. The vitamin D receptor (VDR) is expressed in osteoblasts and 1,25D3 modifies gene expression of various osteoblast differentiation and mineralization-related genes, such as alkaline phosphatase (ALPL), osteocalcin (BGLAP), and osteopontin (SPP1). 1,25D3 is known to stimulate mineralization of human osteoblasts in vitro, and recently it was shown that 1,25D3 induces mineralization via effects in the period preceding mineralization during the pre-mineralization period. For a full understanding of the action of 1,25D3 in osteoblasts it is important to get an integrated network view of the 1,25D3-regulated genes during osteoblast differentiation and mineralization. The current data will be presented and discussed alluding to future studies to fully delineate the 1,25D3 action in osteoblast. Describing and understanding the vitamin D regulatory networks and identifying the dominant players in these networks may help develop novel (personalized) vitamin D-based treatments. The following topics will be discussed in this overview: (1) Bone metabolism and osteoblasts, (2) Vitamin D, bone metabolism and osteoblast function, (3) Vitamin D induced transcriptional networks in the context of osteoblast differentiation and bone formation. PMID:24782782

  3. Motif for controllable toggle switch in gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Zhao, Chen; Bin, Ao; Ye, Weiming; Fan, Ying; Di, Zengru

    2015-02-01

    Toggle switch as a common phenomenon in gene regulatory networks has been recognized important for biological functions. Despite much effort dedicated to understanding the toggle switch and designing synthetic biology circuit to achieve the biological function, we still lack a comprehensive understanding of the intrinsic dynamics behind such phenomenon and the minimum structure that is imperative for producing toggle switch. In this paper, we discover a minimum structure, a motif that enables a controllable toggle switch. In particular, the motif consists of a transformative double negative feedback loop (DNFL) that is regulated by an additional driver node. By enumerating all possible regulatory configurations from the driver node, we identify two types of motifs associated with the toggle switch that is captured by the existence of bistable states. The toggle switch is controllable in the sense that the gap between the bistable states is adjustable as determined by the regulatory strength from the driver nodes. We test the effect of the motifs in self-oscillating gene regulatory network (SON) with respect to the interplay between the motifs and the other genes, and find that the switching dynamics of the whole network can be successfully controlled insofar as the network contains a single motif. Our findings are important to uncover the underlying nonlinear dynamics of controllable toggle switch and can have implications in devising biology circuit in the field of synthetic biology.

  4. Identification of 2R-ohnologue gene families displaying the same mutation-load skew in multiple cancers

    PubMed Central

    Tinti, Michele; Dissanayake, Kumara; Synowsky, Silvia; Albergante, Luca; MacKintosh, Carol

    2014-01-01

    The complexity of signalling pathways was boosted at the origin of the vertebrates, when two rounds of whole genome duplication (2R-WGD) occurred. Those genes and proteins that have survived from the 2R-WGD—termed 2R-ohnologues—belong to families of two to four members, and are enriched in signalling components relevant to cancer. Here, we find that while only approximately 30% of human transcript-coding genes are 2R-ohnologues, they carry 42–60% of the gene mutations in 30 different cancer types. Across a subset of cancer datasets, including melanoma, breast, lung adenocarcinoma, liver and medulloblastoma, we identified 673 2R-ohnologue families in which one gene carries mutations at multiple positions, while sister genes in the same family are relatively mutation free. Strikingly, in 315 of the 322 2R-ohnologue families displaying such a skew in multiple cancers, the same gene carries the heaviest mutation load in each cancer, and usually the second-ranked gene is also the same in each cancer. Our findings inspire the hypothesis that in certain cancers, heterogeneous combinations of genetic changes impair parts of the 2R-WGD signalling networks and force information flow through a limited set of oncogenic pathways in which specific non-mutated 2R-ohnologues serve as effectors. The non-mutated 2R-ohnologues are therefore potential therapeutic targets. These include proteins linked to growth factor signalling, neurotransmission and ion channels. PMID:24806839

  5. Multiple tipping points and optimal repairing in interacting networks.

    PubMed

    Majdandzic, Antonio; Braunstein, Lidia A; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Stanley, H Eugene; Havlin, Shlomo

    2016-01-01

    Systems composed of many interacting dynamical networks-such as the human body with its biological networks or the global economic network consisting of regional clusters-often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two 'forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model. PMID:26926803

  6. Multiple tipping points and optimal repairing in interacting networks

    NASA Astrophysics Data System (ADS)

    Majdandzic, Antonio; Braunstein, Lidia A.; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Eugene Stanley, H.; Havlin, Shlomo

    2016-03-01

    Systems composed of many interacting dynamical networks--such as the human body with its biological networks or the global economic network consisting of regional clusters--often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two `forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model.

  7. The DACH/EYA/SIX gene network and its role in tumor initiation and progression.

    PubMed

    Liu, Yu; Han, Na; Zhou, Si; Zhou, Rong; Yuan, Xun; Xu, Hanxiao; Zhang, Cuntai; Yin, Tiejun; Wu, Kongming

    2016-03-01

    The functional abnormality of developmental genes is a common phenomenon in cancer initiation and progression. The retinal determination gene network (RDGN) is a key signal in Drosophila eye specification, and this conservative pathway is also required for the development of multiple organs in mammalian species. Recent studies demonstrated that aberrant expressions of RDGN components in vertebrates, mainly Dach, Six, and Eya, represent a novel tumor signal. RDGN regulates proliferation, apoptosis, tumor growth and metastasis through interactions with multiple signaling pathways in a co-ordinated fashion; Dach acts as a tumor suppressor, whereas Six and Eya function as oncogenes. Clinical analyses demonstrated that the expression levels of RDGN correlate with tumor stage, metastasis and survival, suggesting that combinational detection of this pathway might be used as a promising biomarker for the stratification of therapy and for the prediction of the prognosis of cancer patients. PMID:26096807

  8. Establishing the Architecture of Plant Gene Regulatory Networks.

    PubMed

    Yang, F; Ouma, W Z; Li, W; Doseff, A I; Grotewold, E

    2016-01-01

    Gene regulatory grids (GRGs) encompass the space of all the possible transcription factor (TF)-target gene interactions that regulate gene expression, with gene regulatory networks (GRNs) representing a temporal and spatial manifestation of a portion of the GRG, essential for the specification of gene expression. Thus, understanding GRG architecture provides a valuable tool to explain how genes are expressed in an organism, an important aspect of synthetic biology and essential toward the development of the "in silico" cell. Progress has been made in some unicellular model systems (eg, yeast), but significant challenges remain in more complex multicellular organisms such as plants. Key to understanding the organization of GRGs is therefore identifying the genes that TFs bind to, and control. The application of sensitive and high-throughput methods to investigate genome-wide TF-target gene interactions is providing a wealth of information that can be linked to important agronomic traits. We describe here the methods and resources that have been developed to investigate the architecture of plant GRGs and GRNs. We also provide information regarding where to obtain clones or other resources necessary for synthetic biology or metabolic engineering. PMID:27480690

  9. Learning a Markov Logic network for supervised gene regulatory network inference

    PubMed Central

    2013-01-01

    Background Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. Results We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate “regulates”, starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black

  10. Distributed power allocation for sink-centric clusters in multiple sink wireless sensor networks.

    PubMed

    Cao, Lei; Xu, Chen; Shao, Wei; Zhang, Guoan; Zhou, Hui; Sun, Qiang; Guo, Yuehua

    2010-01-01

    Due to the battery resource constraints, saving energy is a critical issue in wireless sensor networks, particularly in large sensor networks. One possible solution is to deploy multiple sink nodes simultaneously. Another possible solution is to employ an adaptive clustering hierarchy routing scheme. In this paper, we propose a multiple sink cluster wireless sensor networks scheme which combines the two solutions, and propose an efficient transmission power control scheme for a sink-centric cluster routing protocol in multiple sink wireless sensor networks, denoted as MSCWSNs-PC. It is a distributed, scalable, self-organizing, adaptive system, and the sensor nodes do not require knowledge of the global network and their location. All sinks effectively work out a representative view of a monitored region, after which power control is employed to optimize network topology. The simulations demonstrate the advantages of our new protocol. PMID:22294911

  11. Inefficiency in Networks with Multiple Sources and Sinks

    NASA Astrophysics Data System (ADS)

    Youn, Hyejin; Gastner, Michael T.; Jeong, Hawoong

    We study the problem of optimizing traffic in decentralized transportation networks, where the cost of a link depends on its congestion. If users of a transportation network are permitted to choose their own routes, they generally try to minimize their personal travel time. In the absence of centralized coordination, such a behavior can be inefficient for society and even for each individual user. This inefficiency can be quantified by the “price of anarchy”, the ratio of the suboptimal total cost to the socially optimal cost. Here we study the price of anarchy in multi-commodity networks, (i.e., networks where traffic simultaneously flows between different origins and destinations).

  12. Evolutionary Signatures amongst Disease Genes Permit Novel Methods for Gene Prioritization and Construction of Informative Gene-Based Networks

    PubMed Central

    Priedigkeit, Nolan; Wolfe, Nicholas; Clark, Nathan L.

    2015-01-01

    Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC), is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting “disease map” network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks. PMID:25679399

  13. Multiple abnormalities due to a nonsense mutation in the Alx4 gene.

    PubMed

    Chen, B; Chen, Lu; Zhou, Y; Mi, T; Chen, D Y; Chen, Li; Yin, J; Xue, Z F

    2013-01-01

    Patterning of the limb anterior-posterior axes depends on several signals that derive from the three signaling centers of the limb bud. These signals interact to constitute a complex and ordered network that critically contributes to the development of limb buds. Preaxial polydactyly in mouse is predominantly caused by ectopic expression of the zone of polarizing activity or Sonic hedgehog in the anterior region of the limb bud. In this study, we describe an N-ethyl-N-nitrosourea-induced polydactylous mouse (Alx4m1Yzcm) with an extra digit on the anterior aspect of one or two hinddigits. The mutation was mapped to chromosome 2, between markers D2Mit45 and D2Mit184. The Alx4 gene was identified as a potential candidate gene in this location. Sequence analysis of the Alx4 gene for polydactylous heterozygotes revealed an A/T transversion mutation that resulted in substitution of a lysine codon with a stop (nonsense) codon at position 145. Alx4m1Yzcm homozygous mice exhibited multiple abnormalities, including extensive preaxial polydactyly of all four limbs (up to seven digits) and the formation of omphalocele. PMID:23979902

  14. Comparative analysis of housekeeping and tissue-selective genes in human based on network topologies and biological properties.

    PubMed

    Yang, Lei; Wang, Shiyuan; Zhou, Meng; Chen, Xiaowen; Zuo, Yongchun; Sun, Dianjun; Lv, Yingli

    2016-06-01

    Housekeeping genes are genes that are turned on most of the time in almost every tissue to maintain cellular functions. Tissue-selective genes are predominantly expressed in one or a few biologically relevant tissue types. Benefitting from the massive gene expression microarray data obtained over the past decades, the properties of housekeeping and tissue-selective genes can now be investigated on a large-scale manner. In this study, we analyzed the topological properties of housekeeping and tissue-selective genes in the protein-protein interaction (PPI) network. Furthermore, we compared the biological properties and amino acid usage between these two gene groups. The results indicated that there were significant differences in topological properties between housekeeping and tissue-selective genes in the PPI network, and housekeeping genes had higher centrality properties and may play important roles in the complex biological network environment. We also found that there were significant differences in multiple biological properties and many amino acid compositions. The functional genes enrichment and subcellular localizations analysis was also performed to investigate the characterization of housekeeping and tissue-selective genes. The results indicated that the two gene groups showed significant different enrichment in drug targets, disease genes and toxin targets, and located in different subcellular localizations. At last, the discriminations between the properties of two gene groups were measured by the F-score, and expression stage had the most discriminative index in all properties. These findings may elucidate the biological mechanisms for understanding housekeeping and tissue-selective genes and may contribute to better annotate housekeeping and tissue-selective genes in other organisms. PMID:26897376

  15. Joint Modeling of Multiple Social Networks to Elucidate Primate Social Dynamics: I. Maximum Entropy Principle and Network-Based Interactions

    PubMed Central

    Chan, Stephanie; Fushing, Hsieh; Beisner, Brianne A.; McCowan, Brenda

    2013-01-01

    In a complex behavioral system, such as an animal society, the dynamics of the system as a whole represent the synergistic interaction among multiple aspects of the society. We constructed multiple single-behavior social networks for the purpose of approximating from multiple aspects a single complex behavioral system of interest: rhesus macaque society. Instead of analyzing these networks individually, we describe a new method for jointly analyzing them in order to gain comprehensive understanding about the system dynamics as a whole. This method of jointly modeling multiple networks becomes valuable analytical tool for studying the complex nature of the interaction among multiple aspects of any system. Here we develop a bottom-up, iterative modeling approach based upon the maximum entropy principle. This principle is applied to a multi-dimensional link-based distributional framework, which is derived by jointly transforming the multiple directed behavioral social network data, for extracting patterns of synergistic inter-behavioral relationships. Using a rhesus macaque group as a model system, we jointly modeled and analyzed four different social behavioral networks at two different time points (one stable and one unstable) from a rhesus macaque group housed at the California National Primate Research Center (CNPRC). We report and discuss the inter-behavioral dynamics uncovered by our joint modeling approach with respect to social stability. PMID:23468833

  16. Modifier Genes and the Plasticity of Genetic Networks in Mice

    PubMed Central

    Hamilton, Bruce A.; Yu, Benjamin D.

    2012-01-01

    Modifier genes are an integral part of the genetic landscape in both humans and experimental organisms, but have been less well explored in mammals than other systems. A growing number of modifier genes in mouse models of disease nonetheless illustrate the potential for novel findings, while new technical advances promise many more to come. Modifier genes in mouse models include induced mutations and spontaneous or wild-derived variations captured in inbred strains. Identification of modifiers among wild-derived variants in particular should detect disease modifiers that have been shaped by selection and might therefore be compatible with high fitness and function. Here we review selected examples and argue that modifier genes derived from natural variation may provide a bias for nodes in genetic networks that have greater intrinsic plasticity and whose therapeutic manipulation may therefore be more resilient to side effects than conventional targets. PMID:22511884

  17. An Arabidopsis Gene Regulatory Network for Secondary Cell Wall Synthesis

    PubMed Central

    Taylor-Teeples, M; Lin, L; de Lucas, M; Turco, G; Toal, TW; Gaudinier, A; Young, NF; Trabucco, GM; Veling, MT; Lamothe, R; Handakumbura, PP; Xiong, G; Wang, C; Corwin, J; Tsoukalas, A; Zhang, L; Ware, D; Pauly, M; Kliebenstein, DJ; Dehesh, K; Tagkopoulos, I; Breton, G; Pruneda-Paz, JL; Ahnert, SE; Kay, SA; Hazen, SP; Brady, SM

    2014-01-01

    Summary The plant cell wall is an important factor for determining cell shape, function and response to the environment. Secondary cell walls, such as those found in xylem, are composed of cellulose, hemicelluloses and lignin and account for the bulk of plant biomass. The coordination between transcriptional regulation of synthesis for each polymer is complex and vital to cell function. A regulatory hierarchy of developmental switches has been proposed, although the full complement of regulators remains unknown. Here, we present a protein-DNA network between Arabidopsis transcription factors and secondary cell wall metabolic genes with gene expression regulated by a series of feed-forward loops. This model allowed us to develop and validate new hypotheses about secondary wall gene regulation under abiotic stress. Distinct stresses are able to perturb targeted genes to potentially promote functional adaptation. These interactions will serve as a foundation for understanding the regulation of a complex, integral plant component. PMID:25533953

  18. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

    PubMed Central

    Parikshak, Neelroop N.; Gandal, Michael J.; Geschwind, Daniel H.

    2015-01-01

    Genetic and genomic approaches have implicated hundreds of genetic loci in neurodevelopmental disorders and neurodegeneration, but mechanistic understanding continues to lag behind the pace of gene discovery. Understanding the role of specific genetic variants in the brain involves dissecting a functional hierarchy that encompasses molecular pathways, diverse cell types, neural circuits and, ultimately, cognition and behaviour. With a focus on transcriptomics, this Review discusses how high-throughput molecular, integrative and network approaches inform disease biology by placing human genetics in a molecular systems and neurobiological context. We provide a framework for interpreting network biology studies and leveraging big genomics data sets in neurobiology. PMID:26149713

  19. EpiRegNet: constructing epigenetic regulatory network from high throughput gene expression data for humans.

    PubMed

    Wang, Lily Yan; Wang, Panwen; Li, Mulin Jun; Qin, Jing; Wang, Xiaowo; Zhang, Michael Q; Wang, Junwen

    2011-12-01

    The advances of high throughput profiling methods, such as microarray gene profiling and RNA-seq, have enabled researchers to identify thousands of differentially expressed genes under a certain perturbation. Much work has been done to understand the genetic factors that contribute to the expression changes by searching the over-represented regulatory motifs in the promoter regions of these genes. However, the changes could also be caused by epigenetic regulation, especially histone modifications, and no web server has been constructed to study the epigenetic factors responsible for gene expression changes. Here, we pre-sent a web tool for this purpose. Provided with different categories of genes (e.g., up-regulated, down-regulated or unchanged genes), the server will find epigenetic factors responsible for the difference among the categories and construct an epigenetic regulatory network. Furthermore, it will perform co-localization analyses between these epigenetic factors and transcription factors, which were collected from large scale experimental ChIP-seq or computational predicted data. In addition, for users who want to analyze dynamic change of a histone modification mark under different cell conditions, the server will find direct and indirect target genes of this mark by integrative analysis of experimental data and computational prediction, and present a regulatory network around this mark. Both networks can be visualized by a user friendly interface and the data are downloadable in batch. The server currently supports 12 cell types in human, including ESC and CD4+ T cells, and will expand as more public data are available. It also allows user to create a self-defined cell type, upload and analyze multiple ChIP-seq data. It is freely available to academic users at http://jjwanglab.org/EpiRegNet. PMID:22139581

  20. Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia.

    PubMed

    Wilson, Jennifer L; Dalin, Simona; Gosline, Sara; Hemann, Michael; Fraenkel, Ernest; Lauffenburger, Douglas A

    2016-07-11

    Data integration stands to improve interpretation of RNAi screens which, as a result of off-target effects, typically yield numerous gene hits of which only a few validate. These off-target effects can result from seed matches to unintended gene targets (reagent-based) or cellular pathways, which can compensate for gene perturbations (biology-based). We focus on the biology-based effects and use network modeling tools to discover pathways de novo around RNAi hits. By looking at hits in a functional context, we can uncover novel biology not identified from any individual 'omics measurement. We leverage multiple 'omic measurements using the Simultaneous Analysis of Multiple Networks (SAMNet) computational framework to model a genome scale shRNA screen investigating Acute Lymphoblastic Leukemia (ALL) progression in vivo. Our network model is enriched for cellular processes associated with hematopoietic differentiation and homeostasis even though none of the individual 'omic sets showed this enrichment. The model identifies genes associated with the TGF-beta pathway and predicts a role in ALL progression for many genes without this functional annotation. We further experimentally validate the hidden genes - Wwp1, a ubiquitin ligase, and Hgs, a multi-vesicular body associated protein - for their role in ALL progression. Our ALL pathway model includes genes with roles in multiple types of leukemia and roles in hematological development. We identify a tumor suppressor role for Wwp1 in ALL progression. This work demonstrates that network integration approaches can compensate for off-target effects, and that these methods can uncover novel biology retroactively on existing screening data. We anticipate that this framework will be valuable to multiple functional genomic technologies - siRNA, shRNA, and CRISPR - generally, and will improve the utility of functional genomic studies. PMID:27315426

  1. Reconstruction of Gene Networks of Iron Response in Shewanella oneidensis

    SciTech Connect

    Yang, Yunfeng; Harris, Daniel P; Luo, Feng; Joachimiak, Marcin; Wu, Liyou; Dehal, Paramvir; Jacobsen, Janet; Yang, Zamin Koo; Gao, Haichun; Arkin, Adam; Palumbo, Anthony Vito; Zhou, Jizhong

    2009-01-01

    It is of great interest to study the iron response of the -proteobacterium Shewanella oneidensis since it possesses a high content of iron and is capable of utilizing iron for anaerobic respiration. We report here that the iron response in S. oneidensis is a rapid process. To gain more insights into the bacterial response to iron, temporal gene expression profiles were examined for iron depletion and repletion, resulting in identification of iron-responsive biological pathways in a gene co-expression network. Iron acquisition systems, including genes unique to S. oneidensis, were rapidly and strongly induced by iron depletion, and repressed by iron repletion. Some were required for iron depletion, as exemplified by the mutational analysis of the putative siderophore biosynthesis protein SO3032. Unexpectedly, a number of genes related to anaerobic energy metabolism were repressed by iron depletion and induced by repletion, which might be due to the iron storage potential of their protein products. Other iron-responsive biological pathways include protein degradation, aerobic energy metabolism and protein synthesis. Furthermore, sequence motifs enriched in gene clusters as well as their corresponding DNA-binding proteins (Fur, CRP and RpoH) were identified, resulting in a regulatory network of iron response in S. oneidensis. Together, this work provides an overview of iron response and reveals novel features in S. oneidensis, including Shewanella-specific iron acquisition systems, and suggests the intimate relationship between anaerobic energy metabolism and iron response.

  2. Multiple tipping points and optimal repairing in interacting networks

    PubMed Central

    Majdandzic, Antonio; Braunstein, Lidia A.; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Eugene Stanley, H.; Havlin, Shlomo

    2016-01-01

    Systems composed of many interacting dynamical networks—such as the human body with its biological networks or the global economic network consisting of regional clusters—often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two ‘forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model. PMID:26926803

  3. Multiple regimes of robust patterns between network structure and biodiversity.

    PubMed

    Jover, Luis F; Flores, Cesar O; Cortez, Michael H; Weitz, Joshua S

    2015-01-01

    Ecological networks such as plant-pollinator and host-parasite networks have structured interactions that define who interacts with whom. The structure of interactions also shapes ecological and evolutionary dynamics. Yet, there is significant ongoing debate as to whether certain structures, e.g., nestedness, contribute positively, negatively or not at all to biodiversity. We contend that examining variation in life history traits is key to disentangling the potential relationship between network structure and biodiversity. Here, we do so by analyzing a dynamic model of virus-bacteria interactions across a spectrum of network structures. Consistent with prior studies, we find plausible parameter domains exhibiting strong, positive relationships between nestedness and biodiversity. Yet, the same model can exhibit negative relationships between nestedness and biodiversity when examined in a distinct, plausible region of parameter space. We discuss steps towards identifying when network structure could, on its own, drive the resilience, sustainability, and even conservation of ecological communities. PMID:26632996

  4. Multiple regimes of robust patterns between network structure and biodiversity

    NASA Astrophysics Data System (ADS)

    Jover, Luis F.; Flores, Cesar O.; Cortez, Michael H.; Weitz, Joshua S.

    2015-12-01

    Ecological networks such as plant-pollinator and host-parasite networks have structured interactions that define who interacts with whom. The structure of interactions also shapes ecological and evolutionary dynamics. Yet, there is significant ongoing debate as to whether certain structures, e.g., nestedness, contribute positively, negatively or not at all to biodiversity. We contend that examining variation in life history traits is key to disentangling the potential relationship between network structure and biodiversity. Here, we do so by analyzing a dynamic model of virus-bacteria interactions across a spectrum of network structures. Consistent with prior studies, we find plausible parameter domains exhibiting strong, positive relationships between nestedness and biodiversity. Yet, the same model can exhibit negative relationships between nestedness and biodiversity when examined in a distinct, plausible region of parameter space. We discuss steps towards identifying when network structure could, on its own, drive the resilience, sustainability, and even conservation of ecological communities.

  5. Multiple regimes of robust patterns between network structure and biodiversity

    PubMed Central

    Jover, Luis F.; Flores, Cesar O.; Cortez, Michael H.; Weitz, Joshua S.

    2015-01-01

    Ecological networks such as plant-pollinator and host-parasite networks have structured interactions that define who interacts with whom. The structure of interactions also shapes ecological and evolutionary dynamics. Yet, there is significant ongoing debate as to whether certain structures, e.g., nestedness, contribute positively, negatively or not at all to biodiversity. We contend that examining variation in life history traits is key to disentangling the potential relationship between network structure and biodiversity. Here, we do so by analyzing a dynamic model of virus-bacteria interactions across a spectrum of network structures. Consistent with prior studies, we find plausible parameter domains exhibiting strong, positive relationships between nestedness and biodiversity. Yet, the same model can exhibit negative relationships between nestedness and biodiversity when examined in a distinct, plausible region of parameter space. We discuss steps towards identifying when network structure could, on its own, drive the resilience, sustainability, and even conservation of ecological communities. PMID:26632996

  6. Annotation of gene function in citrus using gene expression information and co-expression networks

    PubMed Central

    2014-01-01

    Background The genus Citrus encompasses major cultivated plants such as sweet orange, mandarin, lemon and grapefruit, among the world’s most economically important fruit crops. With increasing volumes of transcriptomics data available for these species, Gene Co-expression Network (GCN) analysis is a viable option for predicting gene function at a genome-wide scale. GCN analysis is based on a “guilt-by-association” principle whereby genes encoding proteins involved in similar and/or related biological processes may exhibit similar expression patterns across diverse sets of experimental conditions. While bioinformatics resources such as GCN analysis are widely available for efficient gene function prediction in model plant species including Arabidopsis, soybean and rice, in citrus these tools are not yet developed. Results We have constructed a comprehensive GCN for citrus inferred from 297 publicly available Affymetrix Genechip Citrus Genome microarray datasets, providing gene co-expression relationships at a genome-wide scale (33,000 transcripts). The comprehensive citrus GCN consists of a global GCN (condition-independent) and four condition-dependent GCNs that survey the sweet orange species only, all citrus fruit tissues, all citrus leaf tissues, or stress-exposed plants. All of these GCNs are clustered using genome-wide, gene-centric (guide) and graph clustering algorithms for flexibility of gene function prediction. For each putative cluster, gene ontology (GO) enrichment and gene expression specificity analyses were performed to enhance gene function, expression and regulation pattern prediction. The guide-gene approach was used to infer novel roles of genes involved in disease susceptibility and vitamin C metabolism, and graph-clustering approaches were used to investigate isoprenoid/phenylpropanoid metabolism in citrus peel, and citric acid catabolism via the GABA shunt in citrus fruit. Conclusions Integration of citrus gene co-expression networks

  7. Cell Cycle Gene Networks Are Associated with Melanoma Prognosis

    PubMed Central

    Watkins, Wendy; Araki, Hiromitsu; Tamada, Yoshinori; Muthukaruppan, Anita; Ranjard, Louis; Derkac, Eliane; Imoto, Seiya; Miyano, Satoru; Crampin, Edmund J.; Print, Cristin G.

    2012-01-01

    Background Our understanding of the molecular pathways that underlie melanoma remains incomplete. Although several published microarray studies of clinical melanomas have provided valuable information, we found only limited concordance between these studies. Therefore, we took an in vitro functional genomics approach to understand melanoma molecular pathways. Methodology/Principal Findings Affymetrix microarray data were generated from A375 melanoma cells treated in vitro with siRNAs against 45 transcription factors and signaling molecules. Analysis of this data using unsupervised hierarchical clustering and Bayesian gene networks identified proliferation-association RNA clusters, which were co-ordinately expressed across the A375 cells and also across melanomas from patients. The abundance in metastatic melanomas of these cellular proliferation clusters and their putative upstream regulators was significantly associated with patient prognosis. An 8-gene classifier derived from gene network hub genes correctly classified the prognosis of 23/26 metastatic melanoma patients in a cross-validation study. Unlike the RNA clusters associated with cellular proliferation described above, co-ordinately expressed RNA clusters associated with immune response were clearly identified across melanoma tumours from patients but not across the siRNA-treated A375 cells, in which immune responses are not active. Three uncharacterised genes, which the gene networks predicted to be upstream of apoptosis- or cellular proliferation-associated RNAs, were found to significantly alter apoptosis and cell number when over-expressed in vitro. Conclusions/Significance This analysis identified co-expression of RNAs that encode functionally-related proteins, in particular, proliferation-associated RNA clusters that are linked to melanoma patient prognosis. Our analysis suggests that A375 cells in vitro may be valid models in which to study the gene expression modules that underlie some melanoma

  8. Integrating ChIP-sequencing and digital gene expression profiling to identify BRD7 downstream genes and construct their regulating network.

    PubMed

    Xu, Ke; Xiong, Wei; Zhou, Ming; Wang, Heran; Yang, Jing; Li, Xiayu; Chen, Pan; Liao, Qianjin; Deng, Hao; Li, Xiaoling; Li, Guiyuan; Zeng, Zhaoyang

    2016-01-01

    BRD7 is a single bromodomain-containing protein that functions as a subunit of the SWI/SNF chromatin-remodeling complex to regulate transcription. It also interacts with the well-known tumor suppressor protein p53 to trans-activate genes involved in cell cycle arrest. In this paper, we report an integrative analysis of genome-wide chromatin occupancy of BRD7 by chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) and digital gene expression (DGE) profiling by RNA-sequencing upon the overexpression of BRD7 in human cells. We localized 156 BRD7-binding peaks representing 184 genes by ChIP-sequencing, and most of these peaks were co-localized with histone modification sites. Four novel motifs were significantly represented in these BRD7-enriched regions. Ingenuity pathway analysis revealed that 22 of these BRD7 target genes were involved in a network regulating cell death and survival. DGE profiling identified 560 up-regulated genes and 1088 down-regulated genes regulated by BRD7. Using Gene Ontology and pathway analysis, we found significant enrichment of the cell cycle and apoptosis pathway genes. For the integrative analysis of the ChIP-seq and DEG data, we constructed a regulating network of BRD7 downstream genes, and this network suggests multiple feedback regulations of the pathways. Furthermore, we validated BIRC2, BIRC3, TXN2, and NOTCH1 genes as direct, functional BRD7 targets, which were involved in the cell cycle and apoptosis pathways. These results provide a genome-wide view of chromatin occupancy and the gene regulation network of the BRD7 signaling pathway. PMID:26407966

  9. miRNA-target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer.

    PubMed

    Chekouo, Thierry; Stingo, Francesco C; Doecke, James D; Do, Kim-Anh

    2015-06-01

    The availability of cross-platform, large-scale genomic data has enabled the investigation of complex biological relationships for many cancers. Identification of reliable cancer-related biomarkers requires the characterization of multiple interactions across complex genetic networks. MicroRNAs are small non-coding RNAs that regulate gene expression; however, the direct relationship between a microRNA and its target gene is difficult to measure. We propose a novel Bayesian model to identify microRNAs and their target genes that are associated with survival time by incorporating the microRNA regulatory network through prior distributions. We assume that biomarkers involved in regulatory networks are likely associated with survival time. We employ non-local prior distributions and a stochastic search method for the selection of biomarkers associated with the survival outcome. We use KEGG pathway information to incorporate correlated gene effects within regulatory networks. Using simulation studies, we assess the performance of our method, and apply it to experimental data of kidney renal cell carcinoma (KIRC) obtained from The Cancer Genome Atlas. Our novel method validates previously identified cancer biomarkers and identifies biomarkers specific to KIRC progression that were not previously discovered. Using the KIRC data, we confirm that biomarkers involved in regulatory networks are more likely to be associated with survival time, showing connections in one regulatory network for five out of six such genes we identified. PMID:25639276

  10. miRNA-Target Gene Regulatory Networks: A Bayesian Integrative Approach to Biomarker Selection with Application to Kidney Cancer

    PubMed Central

    Chekouo, Thierry; Stingo, Francesco C.; Doecke, James D.; Do, Kim-Anh

    2015-01-01

    Summary The availability of cross-platform, large-scale genomic data has enabled the investigation of complex biological relationships for many cancers. Identification of reliable cancer-related biomarkers requires the characterization of multiple interactions across complex genetic networks. MicroRNAs are small non-coding RNAs that regulate gene expression; however, the direct relationship between a microRNA and its target gene is difficult to measure. We propose a novel Bayesian model to identify microRNAs and their target genes that are associated with survival time by incorporating the microRNA regulatory network through prior distributions. We assume that biomarkers involved in regulatory networks are likely associated with survival time. We employ non-local prior distributions and a stochastic search method for the selection of biomarkers associated with the survival outcome. We use KEGG pathway information to incorporate correlated gene effects within regulatory networks. Using simulation studies, we assess the performance of our method, and apply it to experimental data of kidney renal cell carcinoma (KIRC) obtained from The Cancer Genome Atlas. Our novel method validates previously identified cancer biomarkers and identifies biomarkers specific to KIRC progression that were not previously discovered. Using the KIRC data, we confirm that biomarkers involved in regulatory networks are more likely to be associated with survival time, showing connections in one regulatory network for five out of six such genes we identified. PMID:25639276

  11. Charting gene regulatory networks: strategies, challenges and perspectives

    PubMed Central

    2004-01-01

    One of the foremost challenges in the post-genomic era will be to chart the gene regulatory networks of cells, including aspects such as genome annotation, identification of cis-regulatory elements and transcription factors, information on protein–DNA and protein–protein interactions, and data mining and integration. Some of these broad sets of data have already been assembled for building networks of gene regulation. Even though these datasets are still far from comprehensive, and the approach faces many important and difficult challenges, some strategies have begun to make connections between disparate regulatory events and to foster new hypotheses. In this article we review several different genomics and proteomics technologies, and present bioinformatics methods for exploring these data in order to make novel discoveries. PMID:15080794

  12. Coherent organization in gene regulation: a study on six networks

    NASA Astrophysics Data System (ADS)

    Aral, Neşe; Kabakçıoğlu, Alkan

    2016-04-01

    Structural and dynamical fingerprints of evolutionary optimization in biological networks are still unclear. Here we analyze the dynamics of genetic regulatory networks responsible for the regulation of cell cycle and cell differentiation in three organisms or cell types each, and show that they follow a version of Hebb's rule which we have termed coherence. More precisely, we find that simultaneously expressed genes with a common target are less likely to act antagonistically at the attractors of the regulatory dynamics. We then investigate the dependence of coherence on structural parameters, such as the mean number of inputs per node and the activatory/repressory interaction ratio, as well as on dynamically determined quantities, such as the basin size and the number of expressed genes.

  13. Coherent organization in gene regulation: a study on six networks.

    PubMed

    Aral, Neşe; Kabakçıoğlu, Alkan

    2016-01-01

    Structural and dynamical fingerprints of evolutionary optimization in biological networks are still unclear. Here we analyze the dynamics of genetic regulatory networks responsible for the regulation of cell cycle and cell differentiation in three organisms or cell types each, and show that they follow a version of Hebb's rule which we have termed coherence. More precisely, we find that simultaneously expressed genes with a common target are less likely to act antagonistically at the attractors of the regulatory dynamics. We then investigate the dependence of coherence on structural parameters, such as the mean number of inputs per node and the activatory/repressory interaction ratio, as well as on dynamically determined quantities, such as the basin size and the number of expressed genes. PMID:27171925

  14. Improving Steiner trees of a network under multiple constraints

    SciTech Connect

    Krumke, S.O.; Noltemeier, H.; Marathe, M.V.; Ravi, R.; Ravi, S.S.

    1996-07-01

    The authors consider the problem of decreasing the edge weights of a given network so that the modified network has a Steiner tree in which two performance measures are simultaneously optimized. They formulate these problems, referred to as bicriteria network improvement problems, by specifying a budget on the total modification cost, a constraint on one of the performance measures and using the other performance measure as a minimization objective. Network improvement problems are known to be NP-hard even when only one performance measure is considered. The authors present the first polynomial time approximation algorithms for bicriteria network improvement problems. The approximation algorithms are for two pairs of performance measures, namely (diameter, total cost) and (degree, total cost). These algorithms produce solutions which are within a logarithmic factor of the optimum value of the minimization objective while violating the constraints only by a logarithmic factor. The techniques also yield approximation schemes when the given network has bounded treewidth. Many of the approximation results can be extended to more general network design problems.

  15. Spectrum of the elimination of loops and multiple arrows in coupled cell networks

    NASA Astrophysics Data System (ADS)

    Dias, A. P. S.; Moreira, C. S.

    2012-11-01

    A uniform lift of a given network is a network with no loops and no multiple arrows that admits the first network as quotient. Given a regular network (in which all cells have the same type and receive the same number of inputs and all arrows have the same type) with loops or multiple arrows, we prove that it is always possible to construct a uniform lift whose adjacency matrix has only two possible eigenvalues, namely, 0 and -1, in addition to all eigenvalues of the initial network adjacency matrix. Moreover, this uniform lift has the minimal number of cells over all uniform lifts. We also prove that if a non-vanishing eigenvalue of the initial adjacency matrix is fixed then it is always possible to construct a uniform lift that preserves the number of eigenvalues with the same real part of that eigenvalue. Finally, for the eigenvalue zero we show that such a construction is not always possible proving that there are networks with multiple arrows whose uniform lifts all have the eigenvalue 0, in addition to all eigenvalues of the initial network adjacency matrix. Using the concept of ODE-equivalence, we prove then that it is always possible to study a degenerate bifurcation arising in a system whose regular network has multiple arrows as a bifurcation of a bigger system associated with a regular uniform network.

  16. A Crystallin Gene Network in the Mouse Retina

    PubMed Central

    Templeton, Justin P.; Wang, XiangDi; Freeman, Natalie E.; Ma, Zhiwei; Lu, Anna; Hejtmancik, Fielding; Geisert, Eldon E.

    2013-01-01

    The present study was designed to examine the regulation of crystallin genes and protein in the mouse retina using the BXD recombinant inbred (RI) strains. Illumina Sentrix BeadChip Arrays (MouseWG-6v2) were used to analyze mRNA levels in 75 BXD RI strains along with the parental strains (C57Bl/6J and DBA/2J), and the reciprocal crosses in the Hamilton Eye Institute (HEI) Retina Dataset (www.genenetwork.org). Protein levels were investigated using immunoblots to quantify levels of proteins and indirect immunohistochemistry to define the distribution of protein. Algorithms in the Genomatix program were used to identify transcription factor binding sites common to the regulatory sequences in the 5′ regions of co-regulated set of crystallin and other genes as compared to a set of control genes. As subset of genes, including many encoding lens crystallins is part of a tightly co-regulated network that is active in the retina. Expression of this crystallin network appears to be binary in nature, being expressed either at relatively low levels or being highly upregulated. Relative to a control set of genes, the 5′ regulatory sequences of the crystallin network genes show an increased frequency of a set of common transcription factor-binding sites, the most common being those of the Maf family. Chromatin immunoprecipitation of human lens epithelial cells (HLEC) and rat retinal ganglion cells (RGC) confirmed the functionality of these sites, showing that MafA binds the predicted sites of CRYGA and CRYGD in HLE and CRYAB, CRYGA, CRYBA1, and CRYBB3 in RGC cells. In the retina there is a highly correlated group of genes containing many members of the α- β- and γ-crystallin families. These genes can be dramatically upregulated in the retina. One transcription factor that appears to be involved in this coordinated expression is the MAF family transcription of factors associated with both lens and extralenticular expression of crystallin genes. PMID:23978599

  17. Phase transitions in the evolution of gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Skanata, Antun; Kussell, Edo

    The role of gene regulatory networks is to respond to environmental conditions and optimize growth of the cell. A typical example is found in bacteria, where metabolic genes are activated in response to nutrient availability, and are subsequently turned off to conserve energy when their specific substrates are depleted. However, in fluctuating environmental conditions, regulatory networks could experience strong evolutionary pressures not only to turn the right genes on and off, but also to respond optimally under a wide spectrum of fluctuation timescales. The outcome of evolution is predicted by the long-term growth rate, which differentiates between optimal strategies. Here we present an analytic computation of the long-term growth rate in randomly fluctuating environments, by using mean-field and higher order expansion in the environmental history. We find that optimal strategies correspond to distinct regions in the phase space of fluctuations, separated by first and second order phase transitions. The statistics of environmental randomness are shown to dictate the possible evolutionary modes, which either change the structure of the regulatory network abruptly, or gradually modify and tune the interactions between its components.

  18. Drug target prioritization by perturbed gene expression and network information

    PubMed Central

    Isik, Zerrin; Baldow, Christoph; Cannistraci, Carlo Vittorio; Schroeder, Michael

    2015-01-01

    Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs’ targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects. PMID:26615774

  19. Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

    PubMed Central

    Huttenhower, Curtis; Flamholz, Avi I; Landis, Jessica N; Sahi, Sauhard; Myers, Chad L; Olszewski, Kellen L; Hibbs, Matthew A; Siemers, Nathan O; Troyanskaya, Olga G; Coller, Hilary A

    2007-01-01

    Background The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes). Results We developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets

  20. Multiple fuzzy neural network system for outcome prediction and classification of 220 lymphoma patients on the basis of molecular profiling.

    PubMed

    Ando, Tatsuya; Suguro, Miyuki; Kobayashi, Takeshi; Seto, Masao; Honda, Hiroyuki

    2003-10-01

    A fuzzy neural network (FNN) using gene expression profile data can select combinations of genes from thousands of genes, and is applicable to predict outcome for cancer patients after chemotherapy. However, wide clinical heterogeneity reduces the accuracy of prediction. To overcome this problem, we have proposed an FNN system based on majoritarian decision using multiple noninferior models. We used transcriptional profiling data, which were obtained from "Lymphochip" DNA microarrays (http://llmpp.nih.gov/DLBCL), reported by Rosenwald (N Engl J Med 2002; 346: 1937-47). When the data were analyzed by our FNN system, accuracy (73.4%) of outcome prediction using only 1 FNN model with 4 genes was higher than that (68.5%) of the Cox model using 17 genes. Higher accuracy (91%) was obtained when an FNN system with 9 noninferior models, consisting of 35 independent genes, was used. The genes selected by the system included genes that are informative in the prognosis of Diffuse large B-cell lymphoma (DLBCL), such as genes showing an expression pattern similar to that of CD10 and BCL-6 or similar to that of IRF-4 and BCL-4. We classified 220 DLBCL patients into 5 groups using the prediction results of 9 FNN models. These groups may correspond to DLBCL subtypes. In group A containing half of the 220 patients, patients with poor outcome were found to satisfy 2 rules, i.e., high expression of MAX dimerization with high expression of unknown A (LC_26146), or high expression of MAX dimerization with low expression of unknown B (LC_33144). The present paper is the first to describe the multiple noninferior FNN modeling system. This system is a powerful tool for predicting outcome and classifying patients, and is applicable to other heterogeneous diseases. PMID:14556665

  1. Integrated protein function prediction by mining function associations, sequences, and protein–protein and gene–gene interaction networks

    PubMed Central

    Cao, Renzhi; Cheng, Jianlin

    2016-01-01

    Motivations Protein function prediction is an important and challenging problem in bioinformatics and computational biology. Functionally relevant biological information such as protein sequences, gene expression, and protein–protein interactions has been used mostly separately for protein function prediction. One of the major challenges is how to effectively integrate multiple sources of both traditional and new information such as spatial gene–gene interaction networks generated from chromosomal conformation data together to improve protein function prediction. Results In this work, we developed three different probabilistic scores (MIS, SEQ, and NET score) to combine protein sequence, function associations, and protein–protein interaction and spatial gene–gene interaction networks for protein function prediction. The MIS score is mainly generated from homologous proteins found by PSI-BLAST search, and also association rules between Gene Ontology terms, which are learned by mining the Swiss-Prot database. The SEQ score is generated from protein sequences. The NET score is generated from protein–protein interaction and spatial gene–gene interaction networks. These three scores were combined in a new Statistical Multiple Integrative Scoring System (SMISS) to predict protein function. We tested SMISS on the data set of 2011 Critical Assessment of Function Annotation (CAFA). The method performed substantially better than three base-line methods and an advanced method based on protein profile–sequence comparison, profile–profile comparison, and domain co-occurrence networks according to the maximum F-measure. PMID:26370280

  2. Multiple neural network approaches to clinical expert systems

    NASA Astrophysics Data System (ADS)

    Stubbs, Derek F.

    1990-08-01

    We briefly review the concept of computer aided medical diagnosis and more extensively review the the existing literature on neural network applications in the field. Neural networks can function as simple expert systems for diagnosis or prognosis. Using a public database we develop a neural network for the diagnosis of a major presenting symptom while discussing the development process and possible approaches. MEDICAL EXPERTS SYSTEMS COMPUTER AIDED DIAGNOSIS Biomedicine is an incredibly diverse and multidisciplinary field and it is not surprising that neural networks with their many applications are finding more and more applications in the highly non-linear field of biomedicine. I want to concentrate on neural networks as medical expert systems for clinical diagnosis or prognosis. Expert Systems started out as a set of computerized " ifthen" rules. Everything was reduced to boolean logic and the promised land of computer experts was said to be in sight. It never came. Why? First the computer code explodes as the number of " ifs" increases. All the " ifs" have to interact. Second experts are not very good at reducing expertise to language. It turns out that experts recognize patterns and have non-verbal left-brain intuition decision processes. Third learning by example rather than learning by rule is the way natural brains works and making computers work by rule-learning is hideously labor intensive. Neural networks can learn from example. They learn the results

  3. Semi-Supervised Multi-View Learning for Gene Network Reconstruction

    PubMed Central

    Ceci, Michelangelo; Pio, Gianvito; Kuzmanovski, Vladimir; Džeroski, Sašo

    2015-01-01

    The task of gene regulatory network reconstruction from high-throughput data is receiving increasing attention in recent years. As a consequence, many inference methods for solving this task have been proposed in the literature. It has been recently observed, however, that no single inference method performs optimally across all datasets. It has also been shown that the integration of predictions from multiple inference methods is more robust and shows high performance across diverse datasets. Inspired by this research, in this paper, we propose a machine learning solution which learns to combine predictions from multiple inference methods. While this approach adds additional complexity to the inference process, we expect it would also carry substantial benefits. These would come from the automatic adaptation to patterns on the outputs of individual inference methods, so that it is possible to identify regulatory interactions more reliably when these patterns occur. This article demonstrates the benefits (in terms of accuracy of the reconstructed networks) of the proposed method, which exploits an iterative, semi-supervised ensemble-based algorithm. The algorithm learns to combine the interactions predicted by many different inference methods in the multi-view learning setting. The empirical evaluation of the proposed algorithm on a prokaryotic model organism (E. coli) and on a eukaryotic model organism (S. cerevisiae) clearly shows improved performance over the state of the art methods. The results indicate that gene regulatory network reconstruction for the real datasets is more difficult for S. cerevisiae than for E. coli. The software, all the datasets used in the experiments and all the results are available for download at the following link: http://figshare.com/articles/Semi_supervised_Multi_View_Learning_for_Gene_Network_Reconstruction/1604827. PMID:26641091

  4. Semi-Supervised Multi-View Learning for Gene Network Reconstruction.

    PubMed

    Ceci, Michelangelo; Pio, Gianvito; Kuzmanovski, Vladimir; Džeroski, Sašo

    2015-01-01

    The task of gene regulatory network reconstruction from high-throughput data is receiving increasing attention in recent years. As a consequence, many inference methods for solving this task have been proposed in the literature. It has been recently observed, however, that no single inference method performs optimally across all datasets. It has also been shown that the integration of predictions from multiple inference methods is more robust and shows high performance across diverse datasets. Inspired by this research, in this paper, we propose a machine learning solution which learns to combine predictions from multiple inference methods. While this approach adds additional complexity to the inference process, we expect it would also carry substantial benefits. These would come from the automatic adaptation to patterns on the outputs of individual inference methods, so that it is possible to identify regulatory interactions more reliably when these patterns occur. This article demonstrates the benefits (in terms of accuracy of the reconstructed networks) of the proposed method, which exploits an iterative, semi-supervised ensemble-based algorithm. The algorithm learns to combine the interactions predicted by many different inference methods in the multi-view learning setting. The empirical evaluation of the proposed algorithm on a prokaryotic model organism (E. coli) and on a eukaryotic model organism (S. cerevisiae) clearly shows improved performance over the state of the art methods. The results indicate that gene regulatory network reconstruction for the real datasets is more difficult for S. cerevisiae than for E. coli. The software, all the datasets used in the experiments and all the results are available for download at the following link: http://figshare.com/articles/Semi_supervised_Multi_View_Learning_for_Gene_Network_Reconstruction/1604827. PMID:26641091

  5. Identification of microRNA-regulated gene networks by expression analysis of target genes.

    PubMed

    Gennarino, Vincenzo Alessandro; D'Angelo, Giovanni; Dharmalingam, Gopuraja; Fernandez, Serena; Russolillo, Giorgio; Sanges, Remo; Mutarelli, Margherita; Belcastro, Vincenzo; Ballabio, Andrea; Verde, Pasquale; Sardiello, Marco; Banfi, Sandro

    2012-06-01

    MicroRNAs (miRNAs) and transcription factors control eukaryotic cell proliferation, differentiation, and metabolism through their specific gene regulatory networks. However, differently from transcription factors, our understanding of the processes regulated by miRNAs is currently limited. Here, we introduce gene network analysis as a new means for gaining insight into miRNA biology. A systematic analysis of all human miRNAs based on Co-expression Meta-analysis of miRNA Targets (CoMeTa) assigns high-resolution biological functions to miRNAs and provides a comprehensive, genome-scale analysis of human miRNA regulatory networks. Moreover, gene cotargeting analyses show that miRNAs synergistically regulate cohorts of genes that participate in similar processes. We experimentally validate the CoMeTa procedure through focusing on three poorly characterized miRNAs, miR-519d/190/340, which CoMeTa predicts to be associated with the TGFβ pathway. Using lung adenocarcinoma A549 cells as a model system, we show that miR-519d and miR-190 inhibit, while miR-340 enhances TGFβ signaling and its effects on cell proliferation, morphology, and scattering. Based on these findings, we formalize and propose co-expression analysis as a general paradigm for second-generation procedures to recognize bona fide targets and infer biological roles and network communities of miRNAs. PMID:22345618

  6. Identification of microRNA-regulated gene networks by expression analysis of target genes

    PubMed Central

    Gennarino, Vincenzo Alessandro; D'Angelo, Giovanni; Dharmalingam, Gopuraja; Fernandez, Serena; Russolillo, Giorgio; Sanges, Remo; Mutarelli, Margherita; Belcastro, Vincenzo; Ballabio, Andrea; Verde, Pasquale; Sardiello, Marco; Banfi, Sandro

    2012-01-01

    MicroRNAs (miRNAs) and transcription factors control eukaryotic cell proliferation, differentiation, and metabolism through their specific gene regulatory networks. However, differently from transcription factors, our understanding of the processes regulated by miRNAs is currently limited. Here, we introduce gene network analysis as a new means for gaining insight into miRNA biology. A systematic analysis of all human miRNAs based on Co-expression Meta-analysis of miRNA Targets (CoMeTa) assigns high-resolution biological functions to miRNAs and provides a comprehensive, genome-scale analysis of human miRNA regulatory networks. Moreover, gene cotargeting analyses show that miRNAs synergistically regulate cohorts of genes that participate in similar processes. We experimentally validate the CoMeTa procedure through focusing on three poorly characterized miRNAs, miR-519d/190/340, which CoMeTa predicts to be associated with the TGFβ pathway. Using lung adenocarcinoma A549 cells as a model system, we show that miR-519d and miR-190 inhibit, while miR-340 enhances TGFβ signaling and its effects on cell proliferation, morphology, and scattering. Based on these findings, we formalize and propose co-expression analysis as a general paradigm for second-generation procedures to recognize bona fide targets and infer biological roles and network communities of miRNAs. PMID:22345618

  7. Functional divergence and convergence between the transcript network and gene network in lung adenocarcinoma

    PubMed Central

    Hsu, Min-Kung; Pan, Chia-Lin; Chen, Feng-Chi

    2016-01-01

    Introduction Alternative RNA splicing is a critical regulatory mechanism during tumorigenesis. However, previous oncological studies mainly focused on the splicing of individual genes. Whether and how transcript isoforms are coordinated to affect cellular functions remain underexplored. Also of great interest is how the splicing regulome cooperates with the transcription regulome to facilitate tumorigenesis. The answers to these questions are of fundamental importance to cancer biology. Results Here, we report a comparative study between the transcript-based network (TN) and the gene-based network (GN) derived from the transcriptomes of paired tumor–normal tissues from 77 lung adenocarcinoma patients. We demonstrate that the two networks differ significantly from each other in terms of patient clustering and the number and functions of network modules. Interestingly, the majority (89.5%) of multi-transcript genes have their transcript isoforms distributed in at least two TN modules, suggesting regulatory and functional divergences between transcript isoforms. Furthermore, TN and GN modules share onlŷ50%–60% of their biological functions. TN thus appears to constitute a regulatory layer separate from GN. Nevertheless, our results indicate that functional convergence and divergence both occur between TN and GN, implying complex interactions between the two regulatory layers. Finally, we report that the expression profiles of module members in both TN and GN shift dramatically yet concordantly during tumorigenesis. The mechanisms underlying this coordinated shifting remain unclear yet are worth further explorations. Conclusion We show that in lung adenocarcinoma, transcript isoforms per se are coordinately regulated to conduct biological functions not conveyed by the network of genes. However, the two networks may interact closely with each other by sharing the same or related biological functions. Unraveling the effects and mechanisms of such interactions will

  8. Multiple Hub Network Choice in the Liberalized European Market

    NASA Technical Reports Server (NTRS)

    Berechman, Joseph; deWit, Jaap

    1997-01-01

    . In the meantime, open skies agreements have been concluded between the USA and most of the EU member states to facilitate strategic alliances between airlines of the states involved. As a result of this on-going liberalization the model of the single 'national' carrier using the national home base as its single hub for the designated third, fourth and sixth freedom operations will stepwise disappear. Within the EU the concept of the national carrier has already been replaced by that of the community carrier. State ownership in more and more European carriers is reduced. On the longer run mergers or even bankruptcy will further undermine the "single national carrier - single national hub" model in Europe. In the meantime, strategic alliances between national carriers in Europe will already reduce the airlines' loyalty to a single airport. Profit maximization and accountability to share holders will supersede the loyalty of these newly emerging alliances, probably looking for the opportunities of a multiple hub network to adequately cover the whole European market. As a consequence, some European airports might see a substantial decline in arriving, departing and transfer traffic, thus in revenues and financial solvency, as well as in their connection to other inter-continental and intra-European destinations. At the same time, other airports might realize a significant increase in traffic as they will be sought after by the profit maximizing airlines as their major gateway hubs. Which will be the losing airports and which will be the winning ones? Can airports anticipate the actions of airlines in deregulated markets and utilize policies which will improve their relative position? If so, what should be these anticipatory policies? These questions become the more urgent, since an increasing number of major European airports will be privatized in the near future. Although increasing airport congestion in Europe will also be reflected in a growing demand pressure for

  9. Detection of driver pathways using mutated gene network in cancer.

    PubMed

    Li, Feng; Gao, Lin; Ma, Xiaoke; Yang, Xiaofei

    2016-06-21

    Distinguishing driver pathways has been extensively studied because they are critical for understanding the development and molecular mechanisms of cancers. Most existing methods for driver pathways are based on high coverage as well as high mutual exclusivity, with the underlying assumption that mutations are exclusive. However, in many cases, mutated driver genes in the same pathways are not strictly mutually exclusive. Based on this observation, we propose an index for quantifying mutual exclusivity between gene pairs. Then, we construct a mutated gene network for detecting driver pathways by integrating the proposed index and coverage. The detection of driver pathways on the mutated gene network consists of two steps: raw pathways are obtained using a CPM method, and the final driver pathways are selected using a strict testing strategy. We apply this method to glioblastoma and breast cancers and find that our method is more accurate than state-of-the-art methods in terms of enrichment of KEGG pathways. Furthermore, the detected driver pathways intersect with well-known pathways with moderate exclusivity, which cannot be discovered using the existing algorithms. In conclusion, the proposed method provides an effective way to investigate driver pathways in cancers. PMID:27118146

  10. Gene flow networks among American Aedes aegypti populations

    PubMed Central

    Gonçalves da Silva, Anders; Cunha, Ivana C L; Santos, Walter S; Luz, Sérgio L B; Ribolla, Paulo E M; Abad-Franch, Fernando

    2012-01-01

    The mosquito Aedes aegypti, the dengue virus vector, has spread throughout the tropics in historical times. While this suggests man-mediated dispersal, estimating contemporary connectivity among populations has remained elusive. Here, we use a large mtDNA dataset and a Bayesian coalescent framework to test a set of hypotheses about gene flow among American Ae. aegypti populations. We assessed gene flow patterns at the continental and subregional (Amazon basin) scales. For the Americas, our data favor a stepping-stone model in which gene flow is higher among adjacent populations but in which, at the same time, North American and southeastern Brazilian populations are directly connected, likely via sea trade. Within Amazonia, the model with highest support suggests extensive gene flow among major cities; Manaus, located at the center of the subregional transport network, emerges as a potentially important connecting hub. Our results suggest substantial connectivity across Ae. aegypti populations in the Americas. As long-distance active dispersal has not been observed in this species, our data support man-mediated dispersal as a major determinant of the genetic structure of American Ae. aegypti populations. The inferred topology of interpopulation connectivity can inform network models of Ae. aegypti and dengue spread. PMID:23144654

  11. Cross-Tissue Regulatory Gene Networks in Coronary Artery Disease.

    PubMed

    Talukdar, Husain A; Foroughi Asl, Hassan; Jain, Rajeev K; Ermel, Raili; Ruusalepp, Arno; Franzén, Oscar; Kidd, Brian A; Readhead, Ben; Giannarelli, Chiara; Kovacic, Jason C; Ivert, Torbjörn; Dudley, Joel T; Civelek, Mete; Lusis, Aldons J; Schadt, Eric E; Skogsberg, Josefin; Michoel, Tom; Björkegren, Johan L M

    2016-03-23

    Inferring molecular networks can reveal how genetic perturbations interact with environmental factors to cause common complex diseases. We analyzed genetic and gene expression data from seven tissues relevant to coronary artery disease (CAD) and identified regulatory gene networks (RGNs) and their key drivers. By integrating data from genome-wide association studies, we identified 30 CAD-causal RGNs interconnected in vascular and metabolic tissues, and we validated them with corresponding data from the Hybrid Mouse Diversity Panel. As proof of concept, by targeting the key drivers AIP, DRAP1, POLR2I, and PQBP1 in a cross-species-validated, arterial-wall RGN involving RNA-processing genes, we re-identified this RGN in THP-1 foam cells and independent data from CAD macrophages and carotid lesions. This characterization of the molecular landscape in CAD will help better define the regulation of CAD candidate genes identified by genome-wide association studies and is a first step toward achieving the goals of precision medicine. PMID:27135365

  12. Network Security via Biometric Recognition of Patterns of Gene Expression

    NASA Technical Reports Server (NTRS)

    Shaw, Harry C.

    2016-01-01

    Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time expression and assay of gene expression products.

  13. Network Security via Biometric Recognition of Patterns of Gene Expression

    NASA Technical Reports Server (NTRS)

    Shaw, Harry C.

    2016-01-01

    Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT (Information Technology) organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time assays of gene expression products.

  14. Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation

    PubMed Central

    Goode, Debbie K.; Obier, Nadine; Vijayabaskar, M.S.; Lie-A-Ling, Michael; Lilly, Andrew J.; Hannah, Rebecca; Lichtinger, Monika; Batta, Kiran; Florkowska, Magdalena; Patel, Rahima; Challinor, Mairi; Wallace, Kirstie; Gilmour, Jane; Assi, Salam A.; Cauchy, Pierre; Hoogenkamp, Maarten; Westhead, David R.; Lacaud, Georges; Kouskoff, Valerie; Göttgens, Berthold; Bonifer, Constanze

    2016-01-01

    Summary Metazoan development involves the successive activation and silencing of specific gene expression programs and is driven by tissue-specific transcription factors programming the chromatin landscape. To understand how this process executes an entire developmental pathway, we generated global gene expression, chromatin accessibility, histone modification, and transcription factor binding data from purified embryonic stem cell-derived cells representing six sequential stages of hematopoietic specification and differentiation. Our data reveal the nature of regulatory elements driving differential gene expression and inform how transcription factor binding impacts on promoter activity. We present a dynamic core regulatory network model for hematopoietic specification and demonstrate its utility for the design of reprogramming experiments. Functional studies motivated by our genome-wide data uncovered a stage-specific role for TEAD/YAP factors in mammalian hematopoietic specification. Our study presents a powerful resource for studying hematopoiesis and demonstrates how such data advance our understanding of mammalian development. PMID:26923725

  15. Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation.

    PubMed

    Goode, Debbie K; Obier, Nadine; Vijayabaskar, M S; Lie-A-Ling, Michael; Lilly, Andrew J; Hannah, Rebecca; Lichtinger, Monika; Batta, Kiran; Florkowska, Magdalena; Patel, Rahima; Challinor, Mairi; Wallace, Kirstie; Gilmour, Jane; Assi, Salam A; Cauchy, Pierre; Hoogenkamp, Maarten; Westhead, David R; Lacaud, Georges; Kouskoff, Valerie; Göttgens, Berthold; Bonifer, Constanze

    2016-03-01

    Metazoan development involves the successive activation and silencing of specific gene expression programs and is driven by tissue-specific transcription factors programming the chromatin landscape. To understand how this process executes an entire developmental pathway, we generated global gene expression, chromatin accessibility, histone modification, and transcription factor binding data from purified embryonic stem cell-derived cells representing six sequential stages of hematopoietic specification and differentiation. Our data reveal the nature of regulatory elements driving differential gene expression and inform how transcription factor binding impacts on promoter activity. We present a dynamic core regulatory network model for hematopoietic specification and demonstrate its utility for the design of reprogramming experiments. Functional studies motivated by our genome-wide data uncovered a stage-specific role for TEAD/YAP factors in mammalian hematopoietic specification. Our study presents a powerful resource for studying hematopoiesis and demonstrates how such data advance our understanding of mammalian development. PMID:26923725

  16. Multiple Sink Positioning and Routing to Maximize the Lifetime of Sensor Networks

    NASA Astrophysics Data System (ADS)

    Kim, Haeyong; Kwon, Taekyoung; Mah, Pyeongsoo

    In wireless sensor networks, the sensor nodes collect data, which are routed to a sink node. Most of the existing proposals address the routing problem to maximize network lifetime in the case of a single sink node. In this paper, we extend this problem into the case of multiple sink nodes. To maximize network lifetime, we consider the two problems: (i) how to position multiple sink nodes in the area, and (ii) how to route traffic flows from sensor nodes to sink nodes. In this paper, the solutions to these problems are formulated into a Mixed Integer Linear Programming (MILP) model. However, it is computationally difficult to solve the MILP formulation as the size of sensor network grows because MILP is NP-hard. Thus, we propose a heuristic algorithm, which produces a solution in polynomial time. From our experiments, we found out that the proposed heuristic algorithm provides a near-optimal solution for maximizing network lifetime in dense sensor networks.

  17. Node Handprinting: A Scalable and Accurate Algorithm for Aligning Multiple Biological Networks.

    PubMed

    Radu, Alex; Charleston, Michael

    2015-07-01

    Due to recent advancements in high-throughput sequencing technologies, progressively more protein-protein interactions have been identified for a growing number of species. Subsequently, the protein-protein interaction networks for these species have been further refined. The increase in the quality and availability of these networks has in turn brought a demand for efficient methods to analyze such networks. The pairwise alignment of these networks has been moderately investigated, with numerous algorithms available, but there is very little progress in the field of multiple network alignment. Multiple alignment of networks from different organisms is ideal at finding abnormally conserved or disparate subnetworks. We present a fast and accurate algorithmic approach, Node Handprinting (NH), based on our previous work with Node Fingerprinting, which enables quick and accurate alignment of multiple networks. We also propose two new metrics for the analysis of multiple alignments, as the current metrics are not as sophisticated as their pairwise alignment counterparts. To assess the performance of NH, we use previously aligned datasets as well as protein interaction networks generated from the public database BioGRID. Our results indicate that NH compares favorably with current methodologies and is the only algorithm capable of performing the more complex alignments. PMID:25695597

  18. Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits: From RNA Integrity to Network Topology

    PubMed Central

    O'Brien, M.A.; Costin, B.N.; Miles, M.F.

    2014-01-01

    Postgenomic studies of the function of genes and their role in disease have now become an area of intense study since efforts to define the raw sequence material of the genome have largely been completed. The use of whole-genome approaches such as microarray expression profiling and, more recently, RNA-sequence analysis of transcript abundance has allowed an unprecedented look at the workings of the genome. However, the accurate derivation of such high-throughput data and their analysis in terms of biological function has been critical to truly leveraging the postgenomic revolution. This chapter will describe an approach that focuses on the use of gene networks to both organize and interpret genomic expression data. Such networks, derived from statistical analysis of large genomic datasets and the application of multiple bioinformatics data resources, poten-tially allow the identification of key control elements for networks associated with human disease, and thus may lead to derivation of novel therapeutic approaches. However, as discussed in this chapter, the leveraging of such networks cannot occur without a thorough understanding of the technical and statistical factors influencing the derivation of genomic expression data. Thus, while the catch phrase may be “it's the network … stupid,” the understanding of factors extending from RNA isolation to genomic profiling technique, multivariate statistics, and bioinformatics are all critical to defining fully useful gene networks for study of complex biology. PMID:23195313

  19. Dynamical analysis of uncertain neural networks with multiple time delays

    NASA Astrophysics Data System (ADS)

    Arik, Sabri

    2016-02-01

    This paper investigates the robust stability problem for dynamical neural networks in the presence of time delays and norm-bounded parameter uncertainties with respect to the class of non-decreasing, non-linear activation functions. By employing the Lyapunov stability and homeomorphism mapping theorems together, a new delay-independent sufficient condition is obtained for the existence, uniqueness and global asymptotic stability of the equilibrium point for the delayed uncertain neural networks. The condition obtained for robust stability establishes a matrix-norm relationship between the network parameters of the neural system, which can be easily verified by using properties of the class of the positive definite matrices. Some constructive numerical examples are presented to show the applicability of the obtained result and its advantages over the previously published corresponding literature results.

  20. Evolution of a Core Gene Network for Skeletogenesis in Chordates

    PubMed Central

    Hecht, Jochen; Panopoulou, Georgia; Podsiadlowski, Lars; Poustka, Albert J.; Dieterich, Christoph; Ehrich, Siegfried; Suvorova, Julia; Mundlos, Stefan; Seitz, Volkhard

    2008-01-01

    The skeleton is one of the most important features for the reconstruction of vertebrate phylogeny but few data are available to understand its molecular origin. In mammals the Runt genes are central regulators of skeletogenesis. Runx2 was shown to be essential for osteoblast differentiation, tooth development, and bone formation. Both Runx2 and Runx3 are essential for chondrocyte maturation. Furthermore, Runx2 directly regulates Indian hedgehog expression, a master coordinator of skeletal development. To clarify the correlation of Runt gene evolution and the emergence of cartilage and bone in vertebrates, we cloned the Runt genes from hagfish as representative of jawless fish (MgRunxA, MgRunxB) and from dogfish as representative of jawed cartilaginous fish (ScRunx1–3). According to our phylogenetic reconstruction the stem species of chordates harboured a single Runt gene and thereafter Runt locus duplications occurred during early vertebrate evolution. All newly isolated Runt genes were expressed in cartilage according to quantitative PCR. In situ hybridisation confirmed high MgRunxA expression in hard cartilage of hagfish. In dogfish ScRunx2 and ScRunx3 were expressed in embryonal cartilage whereas all three Runt genes were detected in teeth and placoid scales. In cephalochordates (lancelets) Runt, Hedgehog and SoxE were strongly expressed in the gill bars and expression of Runt and Hedgehog was found in endo- as well as ectodermal cells. Furthermore we demonstrate that the lancelet Runt protein binds to Runt binding sites in the lancelet Hedgehog promoter and regulates its activity. Together, these results suggest that Runt and Hedgehog were part of a core gene network for cartilage formation, which was already active in the gill bars of the common ancestor of cephalochordates and vertebrates and diversified after Runt duplications had occurred during vertebrate evolution. The similarities in expression patterns of Runt genes support the view that teeth and

  1. Moving multiple sinks through wireless sensor networks for lifetime maximization.

    SciTech Connect

    Petrioli, Chiara; Carosi, Alessio; Basagni, Stefano; Phillips, Cynthia Ann

    2008-01-01

    Unattended sensor networks typically watch for some phenomena such as volcanic events, forest fires, pollution, or movements in animal populations. Sensors report to a collection point periodically or when they observe reportable events. When sensors are too far from the collection point to communicate directly, other sensors relay messages for them. If the collection point location is static, sensor nodes that are closer to the collection point relay far more messages than those on the periphery. Assuming all sensor nodes have roughly the same capabilities, those with high relay burden experience battery failure much faster than the rest of the network. However, since their death disconnects the live nodes from the collection point, the whole network is then dead. We consider the problem of moving a set of collectors (sinks) through a wireless sensor network to balance the energy used for relaying messages, maximizing the lifetime of the network. We show how to compute an upper bound on the lifetime for any instance using linear and integer programming. We present a centralized heuristic that produces sink movement schedules that produce network lifetimes within 1.4% of the upper bound for realistic settings. We also present a distributed heuristic that produces lifetimes at most 25:3% below the upper bound. More specifically, we formulate a linear program (LP) that is a relaxation of the scheduling problem. The variables are naturally continuous, but the LP relaxes some constraints. The LP has an exponential number of constraints, but we can satisfy them all by enforcing only a polynomial number using a separation algorithm. This separation algorithm is a p-median facility location problem, which we can solve efficiently in practice for huge instances using integer programming technology. This LP selects a set of good sensor configurations. Given the solution to the LP, we can find a feasible schedule by selecting a subset of these configurations, ordering them

  2. Monoallelic Expression of Multiple Genes in the CNS

    PubMed Central

    Wang, Jinhui; Valo, Zuzana; Smith, David; Singer-Sam, Judith

    2007-01-01

    The inheritance pattern of a number of major genetic disorders suggests the possible involvement of genes that are expressed from one allele and silent on the other, but such genes are difficult to detect. Since DNA methylation in regulatory regions is often a mark of gene silencing, we modified existing microarray-based assays to detect both methylated and unmethylated DNA sequences in the same sample, a variation we term the MAUD assay. We probed a 65 Mb region of mouse Chr 7 for gene-associated sequences that show two distinct DNA methylation patterns in the mouse CNS. Selected genes were then tested for allele-specific expression in clonal neural stem cell lines derived from reciprocal F1 (C57BL/6×JF1) hybrid mice. In addition, using a separate approach, we directly analyzed allele-specific expression of a group of genes interspersed within clusters of OlfR genes, since the latter are subject to allelic exclusion. Altogether, of the 500 known genes in the chromosomal region surveyed, five show monoallelic expression, four identified by the MAUD assay (Agc1, p (pink-eyed dilution), P4ha3 and Thrsp), and one by its proximity to OlfR genes (Trim12). Thrsp (thyroid hormone responsive SPOT14 homolog) is expressed in hippocampus, but the human protein homolog, S14, has also been implicated in aggressive breast cancer. Monoallelic expression of the five genes is not coordinated at a chromosome-wide level, but rather regulated at individual loci. Taken together, our results suggest that at least 1% of previously untested genes are subject to allelic exclusion, and demonstrate a dual approach to expedite their identification. PMID:18074017

  3. Monoallelic expression of multiple genes in the CNS.

    PubMed

    Wang, Jinhui; Valo, Zuzana; Smith, David; Singer-Sam, Judith

    2007-01-01

    The inheritance pattern of a number of major genetic disorders suggests the possible involvement of genes that are expressed from one allele and silent on the other, but such genes are difficult to detect. Since DNA methylation in regulatory regions is often a mark of gene silencing, we modified existing microarray-based assays to detect both methylated and unmethylated DNA sequences in the same sample, a variation we term the MAUD assay. We probed a 65 Mb region of mouse Chr 7 for gene-associated sequences that show two distinct DNA methylation patterns in the mouse CNS. Selected genes were then tested for allele-specific expression in clonal neural stem cell lines derived from reciprocal F(1) (C57BL/6xJF1) hybrid mice. In addition, using a separate approach, we directly analyzed allele-specific expression of a group of genes interspersed within clusters of OlfR genes, since the latter are subject to allelic exclusion. Altogether, of the 500 known genes in the chromosomal region surveyed, five show monoallelic expression, four identified by the MAUD assay (Agc1, p (pink-eyed dilution), P4ha3 and Thrsp), and one by its proximity to OlfR genes (Trim12). Thrsp (thyroid hormone responsive SPOT14 homolog) is expressed in hippocampus, but the human protein homolog, S14, has also been implicated in aggressive breast cancer. Monoallelic expression of the five genes is not coordinated at a chromosome-wide level, but rather regulated at individual loci. Taken together, our results suggest that at least 1% of previously untested genes are subject to allelic exclusion, and demonstrate a dual approach to expedite their identification. PMID:18074017

  4. Locus heterogeneity disease genes encode proteins with high interconnectivity in the human protein interaction network

    PubMed Central

    Keith, Benjamin P.; Robertson, David L.; Hentges, Kathryn E.

    2014-01-01

    Mutations in genes potentially lead to a number of genetic diseases with differing severity. These disease genes have been the focus of research in recent years showing that the disease gene population as a whole is not homogeneous, and can be categorized according to their interactions. Locus heterogeneity describes a single disorder caused by mutations in different genes each acting individually to cause the same disease. Using datasets of experimentally derived human disease genes and protein interactions, we created a protein interaction network to investigate the relationships between the products of genes associated with a disease displaying locus heterogeneity, and use network parameters to suggest properties that distinguish these disease genes from the overall disease gene population. Through the manual curation of known causative genes of 100 diseases displaying locus heterogeneity and 397 single-gene Mendelian disorders, we use network parameters to show that our locus heterogeneity network displays distinct properties from the global disease network and a Mendelian network. Using the global human proteome, through random simulation of the network we show that heterogeneous genes display significant interconnectivity. Further topological analysis of this network revealed clustering of locus heterogeneity genes that cause identical disorders, indicating that these disease genes are involved in similar biological processes. We then use this information to suggest additional genes that may contribute to diseases with locus heterogeneity. PMID:25538735

  5. Overexpressing the Multiple-Stress Responsive Gene At1g74450 Reduces Plant Height and Male Fertility in Arabidopsis thaliana

    PubMed Central

    Visscher, Anne M.; Belfield, Eric J.; Vlad, Daniela; Irani, Niloufer; Moore, Ian; Harberd, Nicholas P.

    2015-01-01

    A subset of genes in Arabidopsis thaliana is known to be up-regulated in response to a wide range of different environmental stress factors. However, not all of these genes are characterized as yet with respect to their functions. In this study, we used transgenic knockout, overexpression and reporter gene approaches to try to elucidate the biological roles of five unknown multiple-stress responsive genes in Arabidopsis. The selected genes have the following locus identifiers: At1g18740, At1g74450, At4g27652, At4g29780 and At5g12010. Firstly, T-DNA insertion knockout lines were identified for each locus and screened for altered phenotypes. None of the lines were found to be visually different from wildtype Col-0. Secondly, 35S-driven overexpression lines were generated for each open reading frame. Analysis of these transgenic lines showed altered phenotypes for lines overexpressing the At1g74450 ORF. Plants overexpressing the multiple-stress responsive gene At1g74450 are stunted in height and have reduced male fertility. Alexander staining of anthers from flowers at developmental stage 12–13 showed either an absence or a reduction in viable pollen compared to wildtype Col-0 and At1g74450 knockout lines. Interestingly, the effects of stress on crop productivity are most severe at developmental stages such as male gametophyte development. However, the molecular factors and regulatory networks underlying environmental stress-induced male gametophytic alterations are still largely unknown. Our results indicate that the At1g74450 gene provides a potential link between multiple environmental stresses, plant height and pollen development. In addition, ruthenium red staining analysis showed that At1g74450 may affect the composition of the inner seed coat mucilage layer. Finally, C-terminal GFP fusion proteins for At1g74450 were shown to localise to the cytosol. PMID:26485022

  6. Pitx2 modulates a Tbx5-dependent gene regulatory network to maintain atrial rhythm.

    PubMed

    Nadadur, Rangarajan D; Broman, Michael T; Boukens, Bastiaan; Mazurek, Stefan R; Yang, Xinan; van den Boogaard, Malou; Bekeny, Jenna; Gadek, Margaret; Ward, Tarsha; Zhang, Min; Qiao, Yun; Martin, James F; Seidman, Christine E; Seidman, Jon; Christoffels, Vincent; Efimov, Igor R; McNally, Elizabeth M; Weber, Christopher R; Moskowitz, Ivan P

    2016-08-31

    Cardiac rhythm is extremely robust, generating 2 billion contraction cycles during the average human life span. Transcriptional control of cardiac rhythm is poorly understood. We found that removal of the transcription factor gene Tbx5 from the adult mouse caused primary spontaneous and sustained atrial fibrillation (AF). Atrial cardiomyocytes from the Tbx5-mutant mice exhibited action potential abnormalities, including spontaneous depolarizations, which were rescued by chelating free calcium. We identified a multitiered transcriptional network that linked seven previously defined AF risk loci: TBX5 directly activated PITX2, and TBX5 and PITX2 antagonistically regulated membrane effector genes Scn5a, Gja1, Ryr2, Dsp, and Atp2a2 In addition, reduced Tbx5 dose by adult-specific haploinsufficiency caused decreased target gene expression, myocardial automaticity, and AF inducibility, which were all rescued by Pitx2 haploinsufficiency in mice. These results defined a transcriptional architecture for atrial rhythm control organized as an incoherent feed-forward loop, driven by TBX5 and modulated by PITX2. TBX5/PITX2 interplay provides tight control of atrial rhythm effector gene expression, and perturbation of the co-regulated network caused AF susceptibility. This work provides a model for the molecular mechanisms underpinning the genetic implication of multiple AF genome-wide association studies loci and will contribute to future efforts to stratify patients for AF risk by genotype. PMID:27582060

  7. Sesterterpene ophiobolin biosynthesis involving multiple gene clusters in Aspergillus ustus

    PubMed Central

    Chai, Hangzhen; Yin, Ru; Liu, Yongfeng; Meng, Huiying; Zhou, Xianqiang; Zhou, Guolin; Bi, Xupeng; Yang, Xue; Zhu, Tonghan; Zhu, Weiming; Deng, Zixin; Hong, Kui

    2016-01-01

    Terpenoids are the most diverse and abundant natural products among which sesterterpenes account for less than 2%, with very few reports on their biosynthesis. Ophiobolins are tricyclic 5–8–5 ring sesterterpenes with potential pharmaceutical application. Aspergillus ustus 094102 from mangrove rizhosphere produces ophiobolin and other terpenes. We obtained five gene cluster knockout mutants, with altered ophiobolin yield using genome sequencing and in silico analysis, combined with in vivo genetic manipulation. Involvement of the five gene clusters in ophiobolin synthesis was confirmed by investigation of the five key terpene synthesis relevant enzymes in each gene cluster, either by gene deletion and complementation or in vitro verification of protein function. The results demonstrate that ophiobolin skeleton biosynthesis involves five gene clusters, which are responsible for C15, C20, C25, and C30 terpenoid biosynthesis. PMID:27273151

  8. Sesterterpene ophiobolin biosynthesis involving multiple gene clusters in Aspergillus ustus.

    PubMed

    Chai, Hangzhen; Yin, Ru; Liu, Yongfeng; Meng, Huiying; Zhou, Xianqiang; Zhou, Guolin; Bi, Xupeng; Yang, Xue; Zhu, Tonghan; Zhu, Weiming; Deng, Zixin; Hong, Kui

    2016-01-01

    Terpenoids are the most diverse and abundant natural products among which sesterterpenes account for less than 2%, with very few reports on their biosynthesis. Ophiobolins are tricyclic 5-8-5 ring sesterterpenes with potential pharmaceutical application. Aspergillus ustus 094102 from mangrove rizhosphere produces ophiobolin and other terpenes. We obtained five gene cluster knockout mutants, with altered ophiobolin yield using genome sequencing and in silico analysis, combined with in vivo genetic manipulation. Involvement of the five gene clusters in ophiobolin synthesis was confirmed by investigation of the five key terpene synthesis relevant enzymes in each gene cluster, either by gene deletion and complementation or in vitro verification of protein function. The results demonstrate that ophiobolin skeleton biosynthesis involves five gene clusters, which are responsible for C15, C20, C25, and C30 terpenoid biosynthesis. PMID:27273151

  9. A multistage gene normalization system integrating multiple effective methods.

    PubMed

    Li, Lishuang; Liu, Shanshan; Li, Lihua; Fan, Wenting; Huang, Degen; Zhou, Huiwei

    2013-01-01

    Gene/protein recognition and normalization is an important preliminary step for many biological text mining tasks. In this paper, we present a multistage gene normalization system which consists of four major subtasks: pre-processing, dictionary matching, ambiguity resolution and filtering. For the first subtask, we apply the gene mention tagger developed in our earlier work, which achieves an F-score of 88.42% on the BioCreative II GM testing set. In the stage of dictionary matching, the exact matching and approximate matching between gene names and the EntrezGene lexicon have been combined. For the ambiguity resolution subtask, we propose a semantic similarity disambiguation method based on Munkres' Assignment Algorithm. At the last step, a filter based on Wikipedia has been built to remove the false positives. Experimental results show that the presented system can achieve an F-score of 90.1%, outperforming most of the state-of-the-art systems. PMID:24349160

  10. A Multistage Gene Normalization System Integrating Multiple Effective Methods

    PubMed Central

    Li, Lishuang; Liu, Shanshan; Li, Lihua; Fan, Wenting; Huang, Degen; Zhou, Huiwei

    2013-01-01

    Gene/protein recognition and normalization is an important preliminary step for many biological text mining tasks. In this paper, we present a multistage gene normalization system which consists of four major subtasks: pre-processing, dictionary matching, ambiguity resolution and filtering. For the first subtask, we apply the gene mention tagger developed in our earlier work, which achieves an F-score of 88.42% on the BioCreative II GM testing set. In the stage of dictionary matching, the exact matching and approximate matching between gene names and the EntrezGene lexicon have been combined. For the ambiguity resolution subtask, we propose a semantic similarity disambiguation method based on Munkres' Assignment Algorithm. At the last step, a filter based on Wikipedia has been built to remove the false positives. Experimental results show that the presented system can achieve an F-score of 90.1%, outperforming most of the state-of-the-art systems. PMID:24349160

  11. Bioinformatics analyses of differentially expressed genes associated with bisphosphonate-related osteonecrosis of the jaw in patients with multiple myeloma

    PubMed Central

    Sun, Jingnan; Wen, Xue; Jin, Fengyan; Li, Yuying; Hu, Jifan; Sun, Yunpeng

    2015-01-01

    Purpose This study aimed to explore the molecular mechanisms associated with bisphosphonate (BP)-related osteonecrosis of the jaw (ONJ) in patients with multiple myeloma (MM). Methods The gene expression profile GSE7116 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) from eleven patients with ONJ resulting from MM treated with BPs (ONJBPs) and ten MM patients without ONJ treated with BPs (MMBPs) were analyzed. Gene ontology (GO) and pathway enrichment analyses of DEGs were performed, followed by functional annotation and protein–protein interaction network construction. Finally, sub-network modules were constructed and analyzed. Results A total of 166 up- and 473 down-regulated DEGs were identified. The up-regulated DEGs were enriched in pathways related to cancer, and the down-regulated DEGs were enriched in pathways related to the immune system. Moreover, the GO terms enriched by the up-regulated DEGs were associated with misfolded proteins, and the down-regulated DEGs were associated with immune responses. After functional annotation, 16 transcription factors were identified, including X-box binding protein 1 (XBP1). In protein–protein interaction network analysis, tumor necrosis factor (TNF) and interleukin 1, beta (IL1B) had higher connectivity degrees. Among the constructed sub-network modules, module 1 was the best one, and DEAD (Asp-Glu-Ala-Asp) box helicase 5 (DDX5) was a hub gene. The DEGs in module 1 were mainly enriched in GO terms related to RNA splicing. Conclusion DEGs of ONJ were mainly enriched in pathways related to the immune system and RNA splicing. DEGs such as TNF, ILB1, DDX5, and XBP1 may be the potential targets of ONJ treatment. PMID:26445550

  12. RegnANN: Reverse Engineering Gene Networks Using Artificial Neural Networks

    PubMed Central

    Grimaldi, Marco; Visintainer, Roberto; Jurman, Giuseppe

    2011-01-01

    RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems. PMID:22216103

  13. Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence

    PubMed Central

    Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

    2016-01-01

    We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case. PMID:27298752

  14. Wireless sensor networks for monitoring physiological signals of multiple patients.

    PubMed

    Dilmaghani, R S; Bobarshad, H; Ghavami, M; Choobkar, S; Wolfe, C

    2011-08-01

    This paper presents the design of a novel wireless sensor network structure to monitor patients with chronic diseases in their own homes through a remote monitoring system of physiological signals. Currently, most of the monitoring systems send patients' data to a hospital with the aid of personal computers (PC) located in the patients' home. Here, we present a new design which eliminates the need for a PC. The proposed remote monitoring system is a wireless sensor network with the nodes of the network installed in the patients' homes. These nodes are then connected to a central node located at a hospital through an Internet connection. The nodes of the proposed wireless sensor network are created by using a combination of ECG sensors, MSP430 microcontrollers, a CC2500 low-power wireless radio, and a network protocol called the SimpliciTI protocol. ECG signals are first sampled by a small portable device which each patient carries. The captured signals are then wirelessly transmitted to an access point located within the patients' home. This connectivity is based on wireless data transmission at 2.4-GHz frequency. The access point is also a small box attached to the Internet through a home asynchronous digital subscriber line router. Afterwards, the data are sent to the hospital via the Internet in real time for analysis and/or storage. The benefits of this remote monitoring are wide ranging: the patients can continue their normal lives, they do not need a PC all of the time, their risk of infection is reduced, costs significantly decrease for the hospital, and clinicians can check data in a short time. PMID:23851949

  15. Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes.

    PubMed

    Li, Bing; Yang, Ying; Ma, Liping; Ju, Feng; Guo, Feng; Tiedje, James M; Zhang, Tong

    2015-11-01

    A metagenomic approach and network analysis was used to investigate the wide-spectrum profiles of antibiotic resistance genes (ARGs) and their co-occurrence patterns in 50 samples from 10 typical environments. In total, 260 ARG subtypes belonging to 18 ARG types were detected with an abundance range of 5.4 × 10(-6)-2.2 × 10(-1) copy of ARG per copy of 16S-rRNA gene. The trend of the total ARG abundances in environments matched well with the levels of anthropogenic impacts on these environments. From the less impacted environments to the seriously impacted environments, the total ARG abundances increased up to three orders of magnitude, that is, from 3.2 × 10(-3) to 3.1 × 10(0) copy of ARG per copy of 16S-rRNA gene. The abundant ARGs were associated with aminoglycoside, bacitracin, β-lactam, chloramphenicol, macrolide-lincosamide-streptogramin, quinolone, sulphonamide and tetracycline, in agreement with the antibiotics extensively used in human medicine or veterinary medicine/promoters. The widespread occurrences and abundance variation trend of vancomycin resistance genes in different environments might imply the spread of vancomycin resistance genes because of the selective pressure resulting from vancomycin use. The simultaneous enrichment of 12 ARG types in adult chicken faeces suggests the coselection of multiple ARGs in this production system. Non-metric multidimensional scaling analysis revealed that samples belonging to the same environment generally possessed similar ARG compositions. Based on the co-occurrence pattern revealed by network analysis, tetM and aminoglycoside resistance protein, the hubs of the ARG network, are proposed to be indicators to quantitatively estimate the abundance of 23 other co-occurring ARG subtypes by power functions. PMID:25918831

  16. Complex Dynamic Behavior in Simple Gene Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Santillán Zerón, Moisés

    2007-02-01

    Knowing the complete genome of a given species is just a piece of the puzzle. To fully unveil the systems behavior of an organism, an organ, or even a single cell, we need to understand the underlying gene regulatory dynamics. Given the complexity of the whole system, the ultimate goal is unattainable for the moment. But perhaps, by analyzing the most simple genetic systems, we may be able to develop the mathematical techniques and procedures required to tackle more complex genetic networks in the near future. In the present work, the techniques for developing mathematical models of simple bacterial gene networks, like the tryptophan and lactose operons are introduced. Despite all of the underlying assumptions, such models can provide valuable information regarding gene regulation dynamics. Here, we pay special attention to robustness as an emergent property. These notes are organized as follows. In the first section, the long historical relation between mathematics, physics, and biology is briefly reviewed. Recently, the multidisciplinary work in biology has received great attention in the form of systems biology. The main concepts of this novel science are discussed in the second section. A very slim introduction to the essential concepts of molecular biology is given in the third section. In the fourth section, a brief introduction to chemical kinetics is presented. Finally, in the fifth section, a mathematical model for the lactose operon is developed and analyzed..

  17. Identification of crucial genes in intracranial aneurysm based on weighted gene coexpression network analysis.

    PubMed

    Zheng, X; Xue, C; Luo, G; Hu, Y; Luo, W; Sun, X

    2015-05-01

    The rupture of intracranial aneurysm (IA) is the leading cause for devastating subarachnoid hemorrhage. This study aimed to investigate genes related to IA and potential diagnosis targets. Two data sets (GSE15629 and GSE54083) were downloaded from Gene Expression Omnibus database. GSE15629 contained eight RI (ruptured IA), six UI (unruptured IA) and five control IA samples. GSE54083 included 8 RI, 5 UI and 10 superficial temporal artery samples. In total, 452 differentially expressed genes (DEGs) between RI and control, and 570 DEGs between UI and control, were identified. Protein-protein interaction networks for two kinds of DEGs related to RI and UI were constructed, respectively. Module networks were searched for DEGs related to RI or UI based on WGCNA (weighted gene coexpression network analysis). In the significant modules, FOS, CCL2, COL4A2 and CXCL5 were screened as crucial nodes with high degrees. Among them, FOS and CCL2 were enriched in immune response and COL4A2 was involved in the ECM (extracellular matrix) pathway, whereas CXCL5 was related to cytokine-cytokine receptor pathway. Taken together, FOS, CCL2, COL4A2 and CXCL5 might participate in the pathogenesis of RI or UI, and could serve as potential diagnosis targets. PMID:25721208

  18. Predicting Gene Structures from Multiple RT-PCR Tests

    NASA Astrophysics Data System (ADS)

    Kováč, Jakub; Vinař, Tomáš; Brejová, Broňa

    It has been demonstrated that the use of additional information such as ESTs and protein homology can significantly improve accuracy of gene prediction. However, many sources of external information are still being omitted from consideration. Here, we investigate the use of product lengths from RT-PCR experiments in gene finding. We present hardness results and practical algorithms for several variants of the problem and apply our methods to a real RT-PCR data set in the Drosophila genome. We conclude that the use of RT-PCR data can improve the sensitivity of gene prediction and locate novel splicing variants.

  19. Multiple cardiac arrhythmia recognition using adaptive wavelet network.

    PubMed

    Lin, Chia-Hung; Chen, Pei-Jarn; Chen, Yung-Fu; Lee, You-Yun; Chen, Tainsong

    2005-01-01

    This paper proposes a method for electrocardiogram (ECG) heartbeat pattern recognition using adaptive wavelet network (AWN). The ECG beat recognition can be divided into a sequence of stages, starting from feature extraction and conversion of QRS complexes, and then identifying cardiac arrhythmias based on the detected features. The discrimination method of ECG beats is a two-subnetwork architecture, consisting of a wavelet layer and a probabilistic neural network (PNN). Morlet wavelets are used to extract the features from each heartbeat, and then PNN is used to analyze the meaningful features and perform discrimination tasks. The AWN is suitable for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The experimental results obtained by testing the data of the MIT-BIH arrhythmia database demonstrate the efficiency of the proposed method. PMID:17281539

  20. Quantitative assessment of gene expression network module-validation methods.

    PubMed

    Li, Bing; Zhang, Yingying; Yu, Yanan; Wang, Pengqian; Wang, Yongcheng; Wang, Zhong; Wang, Yongyan

    2015-01-01

    Validation of pluripotent modules in diverse networks holds enormous potential for systems biology and network pharmacology. An arising challenge is how to assess the accuracy of discovering all potential modules from multi-omic networks and validating their architectural characteristics based on innovative computational methods beyond function enrichment and biological validation. To display the framework progress in this domain, we systematically divided the existing Computational Validation Approaches based on Modular Architecture (CVAMA) into topology-based approaches (TBA) and statistics-based approaches (SBA). We compared the available module validation methods based on 11 gene expression datasets, and partially consistent results in the form of homogeneous models were obtained with each individual approach, whereas discrepant contradictory results were found between TBA and SBA. The TBA of the Zsummary value had a higher Validation Success Ratio (VSR) (51%) and a higher Fluctuation Ratio (FR) (80.92%), whereas the SBA of the approximately unbiased (AU) p-value had a lower VSR (12.3%) and a lower FR (45.84%). The Gray area simulated study revealed a consistent result for these two models and indicated a lower Variation Ratio (VR) (8.10%) of TBA at 6 simulated levels. Despite facing many novel challenges and evidence limitations, CVAMA may offer novel insights into modular networks. PMID:26470848

  1. Atlantic cod (Gadus morhua) hemoglobin genes: multiplicity and polymorphism

    PubMed Central

    Borza, Tudor; Stone, Cynthia; Gamperl, A Kurt; Bowman, Sharen

    2009-01-01

    Background Hemoglobin (Hb) polymorphism, assessed by protein gel electrophoresis, has been used almost exclusively to characterize the genetic structure of Atlantic cod (Gadus morhua) populations and to establish correlations with phenotypic traits such as Hb oxygen binding capacity, temperature tolerance and growth characteristics. The genetic system used to explain the results of gel electrophoresis entails the presence of one polymorphic locus with two major alleles (HbI-1; HbI-2). However, vertebrates have more than one gene encoding Hbs and recent studies have reported that more than one Hb gene is present in Atlantic cod. These observations prompted us to re-evaluate the number of Hb genes expressed in Atlantic cod, and to perform an in depth search for polymorphisms that might produce relevant phenotypes for breeding programs. Results Analysis of Expressed Sequence Tags (ESTs) led to the identification of nine distinct Hb transcripts; four corresponding to the α Hb gene family and five to the β Hb gene family. To gain insights about the Hb genes encoding these transcripts, genomic sequence data was generated from heterozygous (HbI-1/2) parents and fifteen progeny; five of each HbI type, i.e., HbI-1/1, HbI-1/2 and HbI-2/2. β Hb genes displayed more polymorphism than α Hb genes. Two major allele types (β1A and β1B) that differ by two linked non-synonymous substitutions (Met55Val and Lys62Ala) were found in the β1 Hb gene, and the distribution of these β1A and β1B alleles among individuals was congruent with that of the HbI-1 and HbI-2 alleles determined by protein gel electrophoresis. RT-PCR and Q-PCR analysis of the nine Hb genes indicates that all genes are expressed in adult fish, but their level of expression varies greatly; higher expression of almost all Hb genes was found in individuals displaying the HbI-2/2 electrophoretic type. Conclusion This study indicates that more Hb genes are present and expressed in adult Atlantic cod than previously

  2. Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

    PubMed Central

    Liu, Zhi-Ping

    2015-01-01

    Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented. PMID:25937810

  3. Data and programs in support of network analysis of genes and their association with diseases.

    PubMed

    Kontou, Panagiota I; Pavlopoulou, Athanasia; Dimou, Niki L; Pavlopoulos, Georgios A; Bagos, Pantelis G

    2016-09-01

    The network-based approaches that were employed in order to depict the relationships between human genetic diseases and their associated genes are described. Towards this direction, monopartite disease-disease and gene-gene networks were constructed from bipartite gene-disease association networks. The latter were created by collecting and integrating data from three diverse resources, each one with different content, covering from rare monogenic disorders to common complex diseases. Moreover, topological and clustering graph analyses were performed. The methodology and the programs presented in this article are related to the research article entitled "Network analysis of genes and their association with diseases" [1]. PMID:27508260

  4. Multiple deep convolutional neural networks averaging for face alignment

    NASA Astrophysics Data System (ADS)

    Zhang, Shaohua; Yang, Hua; Yin, Zhouping

    2015-05-01

    Face alignment is critical for face recognition, and the deep learning-based method shows promise for solving such issues, given that competitive results are achieved on benchmarks with additional benefits, such as dispensing with handcrafted features and initial shape. However, most existing deep learning-based approaches are complicated and quite time-consuming during training. We propose a compact face alignment method for fast training without decreasing its accuracy. Rectified linear unit is employed, which allows all networks approximately five times faster convergence than a tanh neuron. An eight learnable layer deep convolutional neural network (DCNN) based on local response normalization and a padding convolutional layer (PCL) is designed to provide reliable initial values during prediction. A model combination scheme is presented to further reduce errors, while showing that only two network architectures and hyperparameter selection procedures are required in our approach. A three-level cascaded system is ultimately built based on the DCNNs and model combination mode. Extensive experiments validate the effectiveness of our method and demonstrate comparable accuracy with state-of-the-art methods on BioID, labeled face parts in the wild, and Helen datasets.

  5. Genetic manipulation to analyze pheromone responses: knockouts of multiple receptor genes.

    PubMed

    Ishii, Tomohiro

    2013-01-01

    Gene targeting in the mouse is an essential technique to study gene function in vivo. Multigene families encoding vomeronasal receptor (VR) type 1 and type 2 consist of ~300 intact genes, which are clustered at multiple loci in the mouse genome. To understand the function of VRs and neurons expressing a particular VR in vivo, individual endogenous receptor genes can be manipulated by conventional gene targeting to create loss-of-function mutations or to visualize neurons and their axons expressing the VR. Multiple receptor genes in a cluster can also be deleted simultaneously by chromosome engineering, allowing analysis of function of a particular VR subfamily. Here, we describe protocols for conventional gene targeting and chromosome engineering for deleting a large genomic region in mouse embryonic stem (ES) cells. PMID:24014359

  6. Symbolic dynamics and computation in model gene networks.

    PubMed

    Edwards, R.; Siegelmann, H. T.; Aziza, K.; Glass, L.

    2001-03-01

    We analyze a class of ordinary differential equations representing a simplified model of a genetic network. In this network, the model genes control the production rates of other genes by a logical function. The dynamics in these equations are represented by a directed graph on an n-dimensional hypercube (n-cube) in which each edge is directed in a unique orientation. The vertices of the n-cube correspond to orthants of state space, and the edges correspond to boundaries between adjacent orthants. The dynamics in these equations can be represented symbolically. Starting from a point on the boundary between neighboring orthants, the equation is integrated until the boundary is crossed for a second time. Each different cycle, corresponding to a different sequence of orthants that are traversed during the integration of the equation always starting on a boundary and ending the first time that same boundary is reached, generates a different letter of the alphabet. A word consists of a sequence of letters corresponding to a possible sequence of orthants that arise from integration of the equation starting and ending on the same boundary. The union of the words defines the language. Letters and words correspond to analytically computable Poincare maps of the equation. This formalism allows us to define bifurcations of chaotic dynamics of the differential equation that correspond to changes in the associated language. Qualitative knowledge about the dynamics found by integrating the equation can be used to help solve the inverse problem of determining the underlying network generating the dynamics. This work places the study of dynamics in genetic networks in a context comprising both nonlinear dynamics and the theory of computation. (c) 2001 American Institute of Physics. PMID:12779450

  7. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function.

    PubMed

    Warde-Farley, David; Donaldson, Sylva L; Comes, Ovi; Zuberi, Khalid; Badrawi, Rashad; Chao, Pauline; Franz, Max; Grouios, Chris; Kazi, Farzana; Lopes, Christian Tannus; Maitland, Anson; Mostafavi, Sara; Montojo, Jason; Shao, Quentin; Wright, George; Bader, Gary D; Morris, Quaid

    2010-07-01

    GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae) and hundreds of data sets have been collected from GEO, BioGRID, Pathway Commons and I2D, as well as organism-specific functional genomics data sets. Users can select arbitrary subsets of the data sets associated with an organism to perform their analyses and can upload their own data sets to analyze. The GeneMANIA algorithm performs as well or better than other gene function prediction methods on yeast and mouse benchmarks. The high accuracy of the GeneMANIA prediction algorithm, an intuitive user interface and large database make GeneMANIA a useful tool for any biologist. PMID:20576703

  8. A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks

    PubMed Central

    2013-01-01

    Background The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level. Results The genome-wide GenoMesh literature mining algorithm was developed by sequentially generating a gene-article matrix, a normalized gene-MeSH term matrix, and a gene-gene matrix. The gene-gene matrix relies on the calculation of pairwise gene dissimilarities based on gene-MeSH relationships. An optimized dissimilarity score was identified from six well-studied functions based on a receiver operating characteristic (ROC) analysis. Based on the studies with well-studied Escherichia coli and less-studied Brucella spp., GenoMesh was found to accurately identify gene functions using weighted MeSH terms, predict gene-gene interactions not reported in the literature, and cluster all the genes studied from an organism using the MeSH-based gene-gene matrix. A web-based GenoMesh literature mining program is also available at: http://genomesh.hegroup.org. GenoMesh also predicts gene interactions and networks among genes associated with specific MeSH terms or user-selected gene lists. Conclusions The GenoMesh algorithm and web program provide the first genome

  9. Quantitative and logic modelling of gene and molecular networks

    PubMed Central

    Le Novère, Nicolas

    2015-01-01

    Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms. PMID:25645874

  10. Evolutionary Design of Gene Networks: Forced Evolution by Genomic Parasites

    PubMed Central

    Spirov, A. V.; Zagriychuk, E. A.; Holloway, D. M.

    2014-01-01

    The co-evolution of species with their genomic parasites (transposons) is thought to be one of the primary ways of rewiring gene regulatory networks (GRNs). We develop a framework for conducting evolutionary computations (EC) using the transposon mechanism. We find that the selective pressure of transposons can speed evolutionary searches for solutions and lead to outgrowth of GRNs (through co-option of new genes to acquire insensitivity to the attacking transposons). We test the approach by finding GRNs which can solve a fundamental problem in developmental biology: how GRNs in early embryo development can robustly read maternal signaling gradients, despite continued attacks on the genome by transposons. We observed co-evolutionary oscillations in the abundance of particular GRNs and their transposons, reminiscent of predator-prey or host-parasite dynamics. PMID:25558118

  11. A search engine to identify pathway genes from expression data on multiple organisms

    PubMed Central

    Chen, Chunnuan; Weirauch, Matthew T; Powell, Corey C; Zambon, Alexander C; Stuart, Joshua M

    2007-01-01

    Background The completion of several genome projects showed that most genes have not yet been characterized, especially in multicellular organisms. Although most genes have unknown functions, a large collection of data is available describing their transcriptional activities under many different experimental conditions. In many cases, the coregulatation of a set of genes across a set of conditions can be used to infer roles for genes of unknown function. Results We developed a search engine, the Multiple-Species Gene Recommender (MSGR), which scans gene expression datasets from multiple organisms to identify genes that participate in a genetic pathway. The MSGR takes a query consisting of a list of genes that function together in a genetic pathway from one of six organisms: Homo sapiens, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Arabidopsis thaliana, and Helicobacter pylori. Using a probabilistic method to merge searches, the MSGR identifies genes that are significantly coregulated with the query genes in one or more of those organisms. The MSGR achieves its highest accuracy for many human pathways when searches are combined across species. We describe specific examples in which new genes were identified to be involved in a neuromuscular signaling pathway and a cell-adhesion pathway. Conclusion The search engine can scan large collections of gene expression data for new genes that are significantly coregulated with a pathway of interest. By integrating searches across organisms, the MSGR can identify pathway members whose coregulation is either ancient or newly evolved. PMID:17477880

  12. Multiscale Modeling of Gene-Behavior Associations in an Artificial Neural Network Model of Cognitive Development.

    PubMed

    Thomas, Michael S C; Forrester, Neil A; Ronald, Angelica

    2016-01-01

    In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description-four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity (to different behaviors, to function vs. structure), to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene-environment interactions. We argue that gene

  13. Discovering gene re-ranking efficiency and conserved gene-gene relationships derived from gene co-expression network analysis on breast cancer data

    PubMed Central

    Bourdakou, Marilena M.; Athanasiadis, Emmanouil I.; Spyrou, George M.

    2016-01-01

    Systemic approaches are essential in the discovery of disease-specific genes, offering a different perspective and new tools on the analysis of several types of molecular relationships, such as gene co-expression or protein-protein interactions. However, due to lack of experimental information, this analysis is not fully applicable. The aim of this study is to reveal the multi-potent contribution of statistical network inference methods in highlighting significant genes and interactions. We have investigated the ability of statistical co-expression networks to highlight and prioritize genes for breast cancer subtypes and stages in terms of: (i) classification efficiency, (ii) gene network pattern conservation, (iii) indication of involved molecular mechanisms and (iv) systems level momentum to drug repurposing pipelines. We have found that statistical network inference methods are advantageous in gene prioritization, are capable to contribute to meaningful network signature discovery, give insights regarding the disease-related mechanisms and boost drug discovery pipelines from a systems point of view. PMID:26892392

  14. Network-based prediction and knowledge mining of disease genes

    PubMed Central

    2015-01-01

    Background In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of relationships between biological molecules emerges. The growing popularity of network analysis and data mining has allowed researchers to recognize indirect connections between these molecules. Due to the interdependent nature of network entities, evaluating proteins in this context can reveal relationships that may not otherwise be evident. Methods We examined the human protein interaction network as it relates to human illness using the Disease Ontology. After calculating several topological metrics, we trained an alternating decision tree (ADTree) classifier to identify disease-associated proteins. Using a bootstrapping method, we created a tree to highlight conserved characteristics shared by many of these proteins. Subsequently, we reviewed a set of non-disease-associated proteins that were misclassified by the algorithm with high confidence and searched for evidence of a disease relationship. Results Our classifier was able to predict disease-related genes with 79% area under the receiver operating characteristic (ROC) curve (AUC), which indicates the tradeoff between sensitivity and specificity and is a good predictor of how a classifier will perform on future data sets. We found that a combination of several network characteristics including degree centrality, disease neighbor ratio, eccentricity, and neighborhood connectivity help to distinguish between disease- and non-disease-related proteins. Furthermore, the ADTree allowed us to understand which combinations of strongly predictive attributes contributed most to protein-disease classification. In our post-processing evaluation, we found several examples of potential novel disease-related proteins and corresponding literature evidence. In addition, we showed that first- and second

  15. Identification of a gene regulatory network associated with prion replication

    PubMed Central

    Marbiah, Masue M; Harvey, Anna; West, Billy T; Louzolo, Anais; Banerjee, Priya; Alden, Jack; Grigoriadis, Anita; Hummerich, Holger; Kan, Ho-Man; Cai, Ying; Bloom, George S; Jat, Parmjit; Collinge, John; Klöhn, Peter-Christian

    2014-01-01

    Prions consist of aggregates of abnormal conformers of the cellular prion protein (PrPC). They propagate by recruiting host-encoded PrPC although the critical interacting proteins and the reasons for the differences in susceptibility of distinct cell lines and populations are unknown. We derived a lineage of cell lines with markedly differing susceptibilities, unexplained by PrPC expression differences, to identify such factors. Transcriptome analysis of prion-resistant revertants, isolated from highly susceptible cells, revealed a gene expression signature associated with susceptibility and modulated by differentiation. Several of these genes encode proteins with a role in extracellular matrix (ECM) remodelling, a compartment in which disease-related PrP is deposited. Silencing nine of these genes significantly increased susceptibility. Silencing of Papss2 led to undersulphated heparan sulphate and increased PrPC deposition at the ECM, concomitantly with increased prion propagation. Moreover, inhibition of fibronectin 1 binding to integrin α8 by RGD peptide inhibited metalloproteinases (MMP)-2/9 whilst increasing prion propagation. In summary, we have identified a gene regulatory network associated with prion propagation at the ECM and governed by the cellular differentiation state. PMID:24843046

  16. Relative stability of network states in Boolean network models of gene regulation in development.

    PubMed

    Zhou, Joseph Xu; Samal, Areejit; d'Hérouël, Aymeric Fouquier; Price, Nathan D; Huang, Sui

    2016-01-01

    Progress in cell type reprogramming has revived the interest in Waddington's concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington's landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols. PMID:26965665

  17. A multiple-input OTA circuit for neural networks

    NASA Astrophysics Data System (ADS)

    Reed, Russel D.; Geiger, Randall L.

    1989-05-01

    An operational transconductance amplifier (OTA) circuit suitable for modeling neurons in VLSI implementations of artificial neural networks (NN) is described. It generates an output voltage which is a sigmoidal-like function of the linear sum of a number of weighted inputs. The weight of each input is individually controlled by a bias voltage which can be varied continuously and dynamically. Large numbers of these cells can be fashioned in regular arrays. It appears to be efficient because each weighted connection is implemented with only two MOSFET transistors.

  18. H19 controls reactivation of the imprinted gene network during muscle regeneration.

    PubMed

    Martinet, Clémence; Monnier, Paul; Louault, Yann; Benard, Matthieu; Gabory, Anne; Dandolo, Luisa

    2016-03-15

    The H19 locus controls fetal growth by regulating expression of several genes from the imprinted gene network (IGN). H19 is fully repressed after birth, except in skeletal muscle. Using loss-of-function H19(Δ3) mice, we investigated the function of H19 in adult muscle. Mutant muscles display hypertrophy and hyperplasia, with increased Igf2 and decreased myostatin (Mstn) expression. Many imprinted genes are expressed in muscle stem cells or satellite cells. Unexpectedly, the number of satellite cells was reduced by 50% in H19(Δ3) muscle fibers. This reduction occurred after postnatal day 21, suggesting a link with their entry into quiescence. We investigated the biological function of these mutant satellite cells in vivo using a regeneration assay induced by multiple injections of cardiotoxin. Surprisingly, despite their reduced number, the self-renewal capacity of these cells is fully retained in the absence of H19. In addition, we observed a better regeneration potential of the mutant muscles, with enhanced expression of several IGN genes and genes from the IGF pathway. PMID:26980793

  19. Transcriptome analysis of genes and gene networks involved in aggressive behavior in mouse and zebrafish.

    PubMed

    Malki, Karim; Du Rietz, Ebba; Crusio, Wim E; Pain, Oliver; Paya-Cano, Jose; Karadaghi, Rezhaw L; Sluyter, Frans; de Boer, Sietse F; Sandnabba, Kenneth; Schalkwyk, Leonard C; Asherson, Philip; Tosto, Maria Grazia

    2016-09-01

    Despite moderate heritability estimates, the molecular architecture of aggressive behavior remains poorly characterized. This study compared gene expression profiles from a genetic mouse model of aggression with zebrafish, an animal model traditionally used to study aggression. A meta-analytic, cross-species approach was used to identify genomic variants associated with aggressive behavior. The Rankprod algorithm was used to evaluated mRNA differences from prefrontal cortex tissues of three sets of mouse lines (N = 18) selectively bred for low and high aggressive behavior (SAL/LAL, TA/TNA, and NC900/NC100). The same approach was used to evaluate mRNA differences in zebrafish (N = 12) exposed to aggressive or non-aggressive social encounters. Results were compared to uncover genes consistently implicated in aggression across both studies. Seventy-six genes were differentially expressed (PFP < 0.05) in aggressive compared to non-aggressive mice. Seventy genes were differentially expressed in zebrafish exposed to a fight encounter compared to isolated zebrafish. Seven genes (Fos, Dusp1, Hdac4, Ier2, Bdnf, Btg2, and Nr4a1) were differentially expressed across both species 5 of which belonging to a gene-network centred on the c-Fos gene hub. Network analysis revealed an association with the MAPK signaling cascade. In human studies HDAC4 haploinsufficiency is a key genetic mechanism associated with brachydactyly mental retardation syndrome (BDMR), which is associated with aggressive behaviors. Moreover, the HDAC4 receptor is a drug target for valproic acid, which is being employed as an effective pharmacological treatment for aggressive behavior in geriatric, psychiatric, and brain-injury patients. © 2016 Wiley Periodicals, Inc. PMID:27090961

  20. MicroRNA and gene networks in human diffuse large B-cell lymphoma.

    PubMed

    Wang, Kunhao; Xu, Zhiwen; Wang, Ning; Xu, Ting; Zhu, Minghui

    2014-11-01

    Molecular biologists have collected considerable data regarding the involvement of genes and microRNAs (miRNAs) in cancer. However the underlying mechanisms of cancer with regard to genes and miRNAs remain unclear. The aim of the present study was to evaluate diffuse large B-cell lymphoma (DLBCL) and construct regulatory networks of genes and miRNAs to gradually reveal the underlying mechanisms of DLBCL development. The first differential expression network that is presented is an experimentally validated network of miRNAs and genes. This network presents known biological regulatory associations among miRNAs and genes in the human body. The second network is a DLBCL differential expression network. Differentially expressed gene and miRNA data regarding DLBCL were collected and, based on the first network and the differentially expressed data, the second network was inferred, which demonstrates the irregular regulatory associations that may lead to the occurrence of DLBCL. The third network is a DLBCL-associated network. This network is comprised of non-differentially expressed genes and miRNAs that contribute to numerous DLBCL processes. The similarities and differences among the three networks were extracted and compared to distinguish key regulatory associations; furthermore, important signaling pathways in DLBCL were identified. The present study partially clarified the pathogenesis of DLBCL and provided an improved understanding of the underlying molecular mechanisms, as well as a potential treatment for DLBCL. PMID:25289101

  1. Genes under weaker stabilizing selection increase network evolvability and rapid regulatory adaptation to an environmental shift.

    PubMed

    Laarits, T; Bordalo, P; Lemos, B

    2016-08-01

    Regulatory networks play a central role in the modulation of gene expression, the control of cellular differentiation, and the emergence of complex phenotypes. Regulatory networks could constrain or facilitate evolutionary adaptation in gene expression levels. Here, we model the adaptation of regulatory networks and gene expression levels to a shift in the environment that alters the optimal expression level of a single gene. Our analyses show signatures of natural selection on regulatory networks that both constrain and facilitate rapid evolution of gene expression level towards new optima. The analyses are interpreted from the standpoint of neutral expectations and illustrate the challenge to making inferences about network adaptation. Furthermore, we examine the consequence of variable stabilizing selection across genes on the strength and direction of interactions in regulatory networks and in their subsequent adaptation. We observe that directional selection on a highly constrained gene previously under strong stabilizing selection was more efficient when the gene was embedded within a network of partners under relaxed stabilizing selection pressure. The observation leads to the expectation that evolutionarily resilient regulatory networks will contain optimal ratios of genes whose expression is under weak and strong stabilizing selection. Altogether, our results suggest that the variable strengths of stabilizing selection across genes within regulatory networks might itself contribute to the long-term adaptation of complex phenotypes. PMID:27213992

  2. Realization of tristability in a multiplicatively coupled dual-loop genetic network

    PubMed Central

    Huang, Bo; Xia, Yun; Liu, Feng; Wang, Wei

    2016-01-01

    Multistability is a crucial recurring theme in cell signaling. Multistability is attributed to the presence of positive feedback loops, but the general condition and essential mechanism for realizing multistability remain unclear. Here, we build a generic circuit model comprising two transcription factors and a microRNA, representing a kind of core architecture in gene regulatory networks. The circuit can be decomposed into two positive feedback loops (PFLs) or one PFL and one negative feedback loop (NFL), which are multiplicatively coupled. Bifurcation analyses of the model reveal that the circuit can achieve tristability through four kinds of bifurcation scenarios when parameter values are varied in a wide range. We formulate the general requirement for tristability in terms of logarithmic gain of the circuit. The parameter ranges for tristability and possible transition routes among steady states are determined by the combination of gain features of individual feedback l