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Sample records for multiple gene networks

  1. Optimal Control of Gene Regulatory Networks with Effectiveness of Multiple Drugs: A Boolean Network Approach

    PubMed Central

    Kobayashi, Koichi; Hiraishi, Kunihiko

    2013-01-01

    Developing control theory of gene regulatory networks is one of the significant topics in the field of systems biology, and it is expected to apply the obtained results to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of gene regulatory networks, and gene expression is expressed by a binary value (0 or 1). In the control problem, we assume that the concentration level of a part of genes is arbitrarily determined as the control input. However, there are cases that no gene satisfying this assumption exists, and it is important to consider structural control via external stimuli. Furthermore, these controls are realized by multiple drugs, and it is also important to consider multiple effects such as duration of effect and side effects. In this paper, we propose a BN model with two types of the control inputs and an optimal control method with duration of drug effectiveness. First, a BN model and duration of drug effectiveness are discussed. Next, the optimal control problem is formulated and is reduced to an integer linear programming problem. Finally, numerical simulations are shown. PMID:24058904

  2. 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

  3. NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

    PubMed

    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.

  4. 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

  5. Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases

    PubMed Central

    Nakka, Priyanka; Raphael, Benjamin J.; Ramachandran, Sohini

    2016-01-01

    Genome-wide association (GWA) studies typically lack power to detect genotypes significantly associated with complex diseases, where different causal mutations of small effect may be present across cases. A common, tractable approach for identifying genomic elements associated with complex traits is to evaluate combinations of variants in known pathways or gene sets with shared biological function. Such gene-set analyses require the computation of gene-level P-values or gene scores; these gene scores are also useful when generating hypotheses for experimental validation. However, commonly used methods for generating GWA gene scores are computationally inefficient, biased by gene length, imprecise, or have low true positive rate (TPR) at low false positive rates (FPR), leading to erroneous hypotheses for functional validation. Here we introduce a new method, PEGASUS, for analytically calculating gene scores. PEGASUS produces gene scores with as much as 10 orders of magnitude higher numerical precision than competing methods. In simulation, PEGASUS outperforms existing methods, achieving up to 30% higher TPR when the FPR is fixed at 1%. We use gene scores from PEGASUS as input to HotNet2 to identify networks of interacting genes associated with multiple complex diseases and traits; this is the first application of HotNet2 to common variation. In ulcerative colitis and waist–hip ratio, we discover networks that include genes previously associated with these phenotypes, as well as novel candidate genes. In contrast, existing methods fail to identify these networks. We also identify networks for attention-deficit/hyperactivity disorder, in which GWA studies have yet to identify any significant SNPs. PMID:27489002

  6. Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases.

    PubMed

    Nakka, Priyanka; Raphael, Benjamin J; Ramachandran, Sohini

    2016-10-01

    Genome-wide association (GWA) studies typically lack power to detect genotypes significantly associated with complex diseases, where different causal mutations of small effect may be present across cases. A common, tractable approach for identifying genomic elements associated with complex traits is to evaluate combinations of variants in known pathways or gene sets with shared biological function. Such gene-set analyses require the computation of gene-level P-values or gene scores; these gene scores are also useful when generating hypotheses for experimental validation. However, commonly used methods for generating GWA gene scores are computationally inefficient, biased by gene length, imprecise, or have low true positive rate (TPR) at low false positive rates (FPR), leading to erroneous hypotheses for functional validation. Here we introduce a new method, PEGASUS, for analytically calculating gene scores. PEGASUS produces gene scores with as much as 10 orders of magnitude higher numerical precision than competing methods. In simulation, PEGASUS outperforms existing methods, achieving up to 30% higher TPR when the FPR is fixed at 1%. We use gene scores from PEGASUS as input to HotNet2 to identify networks of interacting genes associated with multiple complex diseases and traits; this is the first application of HotNet2 to common variation. In ulcerative colitis and waist-hip ratio, we discover networks that include genes previously associated with these phenotypes, as well as novel candidate genes. In contrast, existing methods fail to identify these networks. We also identify networks for attention-deficit/hyperactivity disorder, in which GWA studies have yet to identify any significant SNPs.

  7. 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

  8. Model Reduction Using Multiple Time Scales in Stochastic Gene Regulatory Networks

    DTIC Science & Technology

    2006-08-28

    illustrate the efficiency of our method in two gene network examples, which describe two substantially different biological processes -- cellular heat shock response and expression of the pap gene in Escherichia coli bacteria .

  9. A group LASSO-based method for robustly inferring gene regulatory networks from multiple time-course datasets

    PubMed Central

    2014-01-01

    Background As an abstract mapping of the gene regulations in the cell, gene regulatory network is important to both biological research study and practical applications. The reverse engineering of gene regulatory networks from microarray gene expression data is a challenging research problem in systems biology. With the development of biological technologies, multiple time-course gene expression datasets might be collected for a specific gene network under different circumstances. The inference of a gene regulatory network can be improved by integrating these multiple datasets. It is also known that gene expression data may be contaminated with large errors or outliers, which may affect the inference results. Results A novel method, Huber group LASSO, is proposed to infer the same underlying network topology from multiple time-course gene expression datasets as well as to take the robustness to large error or outliers into account. To solve the optimization problem involved in the proposed method, an efficient algorithm which combines the ideas of auxiliary function minimization and block descent is developed. A stability selection method is adapted to our method to find a network topology consisting of edges with scores. The proposed method is applied to both simulation datasets and real experimental datasets. It shows that Huber group LASSO outperforms the group LASSO in terms of both areas under receiver operating characteristic curves and areas under the precision-recall curves. Conclusions The convergence analysis of the algorithm theoretically shows that the sequence generated from the algorithm converges to the optimal solution of the problem. The simulation and real data examples demonstrate the effectiveness of the Huber group LASSO in integrating multiple time-course gene expression datasets and improving the resistance to large errors or outliers. PMID:25350697

  10. Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems

    PubMed Central

    Zainudin, Suhaila; Arif, Shereena M.

    2017-01-01

    Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5. PMID:28250767

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

    PubMed

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

    2016-02-11

    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.

  12. Single-nucleotide polymorphism-gene intermixed networking reveals co-linkers connected to multiple gene expression phenotypes

    PubMed Central

    Gong, Bin-Sheng; Zhang, Qing-Pu; Zhang, Guang-Mei; Zhang, Shao-Jun; Zhang, Wei; Lv, Hong-Chao; Zhang, Fan; Lv, Sa-Li; Li, Chuan-Xing; Rao, Shao-Qi; Li, Xia

    2007-01-01

    Gene expression profiles and single-nucleotide polymorphism (SNP) profiles are modern data for genetic analysis. It is possible to use the two types of information to analyze the relationships among genes by some genetical genomics approaches. In this study, gene expression profiles were used as expression traits. And relationships among the genes, which were co-linked to a common SNP(s), were identified by integrating the two types of information. Further research on the co-expressions among the co-linked genes was carried out after the gene-SNP relationships were established using the Haseman-Elston sib-pair regression. The results showed that the co-expressions among the co-linked genes were significantly higher if the number of connections between the genes and a SNP(s) was more than six. Then, the genes were interconnected via one or more SNP co-linkers to construct a gene-SNP intermixed network. The genes sharing more SNPs tended to have a stronger correlation. Finally, a gene-gene network was constructed with their intensities of relationships (the number of SNP co-linkers shared) as the weights for the edges. PMID:18466544

  13. Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis

    PubMed Central

    Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Tabatabaei, Seyyed Mohammad; Namaki, Saeed

    2016-01-01

    Background The involvement of multiple genes and missing heritability, which are dominant in complex diseases such as multiple sclerosis (MS), entail using network biology to better elucidate their molecular basis and genetic factors. We therefore aimed to integrate interactome (protein–protein interaction (PPI)) and transcriptomes data to construct and analyze PPI networks for MS disease. Methods Gene expression profiles in paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from MS patients, sampled in relapse or remission and controls, were analyzed. Differentially expressed genes which determined only in CSF (MS vs. control) and PBMCs (relapse vs. remission) separately integrated with PPI data to construct the Query-Query PPI (QQPPI) networks. The networks were further analyzed to investigate more central genes, functional modules and complexes involved in MS progression. Results The networks were analyzed and high centrality genes were identified. Exploration of functional modules and complexes showed that the majority of high centrality genes incorporated in biological pathways driving MS pathogenesis. Proteasome and spliceosome were also noticeable in enriched pathways in PBMCs (relapse vs. remission) which were identified by both modularity and clique analyses. Finally, STK4, RB1, CDKN1A, CDK1, RAC1, EZH2, SDCBP genes in CSF (MS vs. control) and CDC37, MAP3K3, MYC genes in PBMCs (relapse vs. remission) were identified as potential candidate genes for MS, which were the more central genes involved in biological pathways. Discussion This study showed that network-based analysis could explicate the complex interplay between biological processes underlying MS. Furthermore, an experimental validation of candidate genes can lead to identification of potential therapeutic targets. PMID:28028462

  14. Bottom-up GGM algorithm for constructing multiple layered hierarchical gene regulatory networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. A bottom-up graphic Gaus...

  15. 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

  16. 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.

  17. 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

  18. Alteration of Multiple Leukocyte Gene Expression Networks is Linked with Magnetic Resonance Markers of Prognosis After Acute ST-Elevation Myocardial Infarction

    PubMed Central

    Teren, A.; Kirsten, H.; Beutner, F.; Scholz, M.; Holdt, L. M.; Teupser, D.; Gutberlet, M.; Thiery, J.; Schuler, G.; Eitel, I.

    2017-01-01

    Prognostic relevant pathways of leukocyte involvement in human myocardial ischemic-reperfusion injury are largely unknown. We enrolled 136 patients with ST-elevation myocardial infarction (STEMI) after primary angioplasty within 12 h after onset of symptoms. Following reperfusion, whole blood was collected within a median time interval of 20 h (interquartile range: 15–25 h) for genome-wide gene expression analysis. Subsequent CMR scans were performed using a standard protocol to determine infarct size (IS), area at risk (AAR), myocardial salvage index (MSI) and the extent of late microvascular obstruction (lateMO). We found 398 genes associated with lateMO and two genes with IS. Neither AAR, nor MSI showed significant correlations with gene expression. Genes correlating with lateMO were strongly related to several canonical pathways, including positive regulation of T-cell activation (p = 3.44 × 10−5), and regulation of inflammatory response (p = 1.86 × 10−3). Network analysis of multiple gene expression alterations associated with larger lateMO identified the following functional consequences: facilitated utilisation and decreased concentration of free fatty acid, repressed cell differentiation, enhanced phagocyte movement, increased cell death, vascular disease and compensatory vasculogenesis. In conclusion, the extent of lateMO after acute, reperfused STEMI correlated with altered activation of multiple genes related to fatty acid utilisation, lymphocyte differentiation, phagocyte mobilisation, cell survival, and vascular dysfunction. PMID:28155873

  19. Multiple horizontal gene transfer events and domain fusions have created novel regulatory and metabolic networks in the oomycete genome.

    PubMed

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

    2009-07-02

    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

  20. 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

  1. 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.

  2. 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.

  3. Predicting Gene-Disease Associations Using Multiple Species Data

    DTIC Science & Technology

    2011-10-20

    regulatory networks from only positive and unlabeled data. BMC Bioinformatics, 11, 2010. [4] Ernesto Estrada and Desmond J. Higham. Network properties revealed...David Warde-Farley, Chris Grouios, and Quaid Morris . GeneMANIA: a real-time multiple association network integration algorithm for predicting gene

  4. Clarifying the molecular mechanism associated with carfilzomib resistance in human multiple myeloma using microarray gene expression profile and genetic interaction network.

    PubMed

    Zheng, Zhihong; Liu, Tingbo; Zheng, Jing; Hu, Jianda

    2017-01-01

    Carfilzomib is a Food and Drug Administration-approved selective proteasome inhibitor for patients with multiple myeloma (MM). However, recent studies indicate that MM cells still develop resistance to carfilzomib, and the molecular mechanisms associated with carfilzomib resistance have not been studied in detail. In this study, to better understand its potential resistant effect and its underlying mechanisms in MM, microarray gene expression profile associated with carfilzomib-resistant KMS-11 and its parental cell line was downloaded from Gene Expression Omnibus database. Raw fluorescent signals were normalized and differently expressed genes were identified using Significance Analysis of Microarrays method. Genetic interaction network was expanded using String, a biomolecular interaction network JAVA platform. Meanwhile, molecular function, biological process and signaling pathway enrichment analysis were performed based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Totally, 27 upregulated and 36 downregulated genes were identified and a genetic interaction network associated with the resistant effect was expanded basing on String, which consisted of 100 nodes and 249 edges. In addition, signaling pathway enrichment analysis indicated that cytokine-cytokine receptor interaction, autophagy, ErbB signaling pathway, microRNAs in cancer and fatty acid metabolism pathways were aberrant in carfilzomib-resistant KMS-11 cells. Thus, in this study, we demonstrated that carfilzomib potentially conferred drug resistance to KMS-11 cells by cytokine-cytokine receptor interaction, autophagy, ErbB signaling pathway, microRNAs in cancer and fatty acid metabolism pathways, which may provide some potential molecular therapeutic targets for drug combination therapy against carfilzomib resistance.

  5. Clarifying the molecular mechanism associated with carfilzomib resistance in human multiple myeloma using microarray gene expression profile and genetic interaction network

    PubMed Central

    Zheng, Zhihong; Liu, Tingbo; Zheng, Jing; Hu, Jianda

    2017-01-01

    Carfilzomib is a Food and Drug Administration-approved selective proteasome inhibitor for patients with multiple myeloma (MM). However, recent studies indicate that MM cells still develop resistance to carfilzomib, and the molecular mechanisms associated with carfilzomib resistance have not been studied in detail. In this study, to better understand its potential resistant effect and its underlying mechanisms in MM, microarray gene expression profile associated with carfilzomib-resistant KMS-11 and its parental cell line was downloaded from Gene Expression Omnibus database. Raw fluorescent signals were normalized and differently expressed genes were identified using Significance Analysis of Microarrays method. Genetic interaction network was expanded using String, a biomolecular interaction network JAVA platform. Meanwhile, molecular function, biological process and signaling pathway enrichment analysis were performed based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Totally, 27 upregulated and 36 downregulated genes were identified and a genetic interaction network associated with the resistant effect was expanded basing on String, which consisted of 100 nodes and 249 edges. In addition, signaling pathway enrichment analysis indicated that cytokine–cytokine receptor interaction, autophagy, ErbB signaling pathway, microRNAs in cancer and fatty acid metabolism pathways were aberrant in carfilzomib-resistant KMS-11 cells. Thus, in this study, we demonstrated that carfilzomib potentially conferred drug resistance to KMS-11 cells by cytokine–cytokine receptor interaction, autophagy, ErbB signaling pathway, microRNAs in cancer and fatty acid metabolism pathways, which may provide some potential molecular therapeutic targets for drug combination therapy against carfilzomib resistance. PMID:28280367

  6. Functional Module Analysis for Gene Coexpression Networks with Network Integration

    PubMed Central

    Zhang, Shuqin; Zhao, Hongyu

    2015-01-01

    Network has been a general tool for studying the complex interactions between different genes, proteins and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with 3 complete subgraphs, and 11 modules with 2 complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally. PMID:26451826

  7. Introduction: Cancer Gene Networks.

    PubMed

    Clarke, Robert

    2017-01-01

    Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary

  8. Inference of Low and High-Grade Glioma Gene Regulatory Networks Delineates the Role of Rnd3 in Establishing Multiple Hallmarks of Cancer.

    PubMed

    Clarke, Kim; Daubon, Thomas; Turan, Nil; Soulet, Fabienne; Mohd Zahari, Maihafizah; Ryan, Katie R; Durant, Sarah; He, Shan; Herbert, John; Ankers, John; Heath, John K; Bjerkvig, Rolf; Bicknell, Roy; Hotchin, Neil A; Bikfalvi, Andreas; Falciani, Francesco

    2015-07-01

    Gliomas are a highly heterogeneous group of brain tumours that are refractory to treatment, highly invasive and pro-angiogenic. Glioblastoma patients have an average survival time of less than 15 months. Understanding the molecular basis of different grades of glioma, from well differentiated, low-grade tumours to high-grade tumours, is a key step in defining new therapeutic targets. Here we use a data-driven approach to learn the structure of gene regulatory networks from observational data and use the resulting models to formulate hypothesis on the molecular determinants of glioma stage. Remarkably, integration of available knowledge with functional genomics datasets representing clinical and pre-clinical studies reveals important properties within the regulatory circuits controlling low and high-grade glioma. Our analyses first show that low and high-grade gliomas are characterised by a switch in activity of two subsets of Rho GTPases. The first one is involved in maintaining normal glial cell function, while the second is linked to the establishment of multiple hallmarks of cancer. Next, the development and application of a novel data integration methodology reveals novel functions of RND3 in controlling glioma cell migration, invasion, proliferation, angiogenesis and clinical outcome.

  9. apex: phylogenetics with multiple genes.

    PubMed

    Jombart, Thibaut; Archer, Frederick; Schliep, Klaus; Kamvar, Zhian; Harris, Rebecca; Paradis, Emmanuel; Goudet, Jérome; Lapp, Hilmar

    2017-01-01

    Genetic sequences of multiple genes are becoming increasingly common for a wide range of organisms including viruses, bacteria and eukaryotes. While such data may sometimes be treated as a single locus, in practice, a number of biological and statistical phenomena can lead to phylogenetic incongruence. In such cases, different loci should, at least as a preliminary step, be examined and analysed separately. The r software has become a popular platform for phylogenetics, with several packages implementing distance-based, parsimony and likelihood-based phylogenetic reconstruction, and an even greater number of packages implementing phylogenetic comparative methods. Unfortunately, basic data structures and tools for analysing multiple genes have so far been lacking, thereby limiting potential for investigating phylogenetic incongruence. In this study, we introduce the new r package apex to fill this gap. apex implements new object classes, which extend existing standards for storing DNA and amino acid sequences, and provides a number of convenient tools for handling, visualizing and analysing these data. In this study, we introduce the main features of the package and illustrate its functionalities through the analysis of a simple data set.

  10. Network analysis reveals crosstalk between autophagy genes and disease genes

    PubMed Central

    Wang, Ji-Ye; Yao, Wei-Xuan; Wang, Yun; Fan, Yi-lei; Wu, Jian-Bing

    2017-01-01

    Autophagy is a protective and life-sustaining process in which cytoplasmic components are packaged into double-membrane vesicles and targeted to lysosomes for degradation. Accumulating evidence supports that autophagy is associated with several pathological conditions. However, research on the functional cross-links between autophagy and disease genes remains in its early stages. In this study, we constructed a disease-autophagy network (DAN) by integrating known disease genes, known autophagy genes and protein-protein interactions (PPI). Dissecting the topological properties of the DAN suggested that nodes that both autophagy and disease genes (inter-genes), are topologically important in the DAN structure. Next, a core network from the DAN was extracted to analyze the functional links between disease and autophagy genes. The genes in the core network were significantly enriched in multiple disease-related pathways, suggesting that autophagy genes may function in various disease processes. Of 17 disease classes, 11 significantly overlapped with autophagy genes, including cancer diseases, metabolic diseases and hematological diseases, a finding that is supported by the literatures. We also found that autophagy genes have a bridging role in the connections between pairs of disease classes. Altogether, our study provides a better understanding of the molecular mechanisms underlying human diseases and the autophagy process. PMID:28295050

  11. Reverse engineering transcriptional gene networks.

    PubMed

    Belcastro, Vincenzo; di Bernardo, Diego

    2014-01-01

    The aim of this chapter is a step-by-step guide on how to infer gene networks from gene expression profiles. The definition of a gene network is given in Subheading 1, where the different types of networks are discussed. The chapter then guides the readers through a data-gathering process in order to build a compendium of gene expression profiles from a public repository. Gene expression profiles are then discretized and a statistical relationship between genes, called mutual information (MI), is computed. Gene pairs with insignificant MI scores are then discarded by applying one of the described pruning steps. The retained relationships are then used to build up a Boolean adjacency matrix used as input for a clustering algorithm to divide the network into modules (or communities). The gene network can then be used as a hypothesis generator for discovering gene function and analyzing gene signatures. Some case studies are presented, and an online web-tool called Netview is described.

  12. Phenotypic switching in gene regulatory networks.

    PubMed

    Thomas, Philipp; Popović, Nikola; Grima, Ramon

    2014-05-13

    Noise in gene expression can lead to reversible phenotypic switching. Several experimental studies have shown that the abundance distributions of proteins in a population of isogenic cells may display multiple distinct maxima. Each of these maxima may be associated with a subpopulation of a particular phenotype, the quantification of which is important for understanding cellular decision-making. Here, we devise a methodology which allows us to quantify multimodal gene expression distributions and single-cell power spectra in gene regulatory networks. Extending the commonly used linear noise approximation, we rigorously show that, in the limit of slow promoter dynamics, these distributions can be systematically approximated as a mixture of Gaussian components in a wide class of networks. The resulting closed-form approximation provides a practical tool for studying complex nonlinear gene regulatory networks that have thus far been amenable only to stochastic simulation. We demonstrate the applicability of our approach in a number of genetic networks, uncovering previously unidentified dynamical characteristics associated with phenotypic switching. Specifically, we elucidate how the interplay of transcriptional and translational regulation can be exploited to control the multimodality of gene expression distributions in two-promoter networks. We demonstrate how phenotypic switching leads to birhythmical expression in a genetic oscillator, and to hysteresis in phenotypic induction, thus highlighting the ability of regulatory networks to retain memory.

  13. Distributed multiple path routing in complex networks

    NASA Astrophysics Data System (ADS)

    Chen, Guang; Wang, San-Xiu; Wu, Ling-Wei; Mei, Pan; Yang, Xu-Hua; Wen, Guang-Hui

    2016-12-01

    Routing in complex transmission networks is an important problem that has garnered extensive research interest in the recent years. In this paper, we propose a novel routing strategy called the distributed multiple path (DMP) routing strategy. For each of the O-D node pairs in a given network, the DMP routing strategy computes and stores multiple short-length paths that overlap less with each other in advance. And during the transmission stage, it rapidly selects an actual routing path which provides low transmission cost from the pre-computed paths for each transmission task, according to the real-time network transmission status information. Computer simulation results obtained for the lattice, ER random, and scale-free networks indicate that the strategy can significantly improve the anti-congestion ability of transmission networks, as well as provide favorable routing robustness against partial network failures.

  14. Discovering Study-Specific Gene Regulatory Networks

    PubMed Central

    Bo, Valeria; Curtis, Tanya; Lysenko, Artem; Saqi, Mansoor; Swift, Stephen; Tucker, Allan

    2014-01-01

    Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets. PMID:25191999

  15. 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

  16. Differential network analysis from cross-platform gene expression data

    PubMed Central

    Zhang, Xiao-Fei; Ou-Yang, Le; Zhao, Xing-Ming; Yan, Hong

    2016-01-01

    Understanding how the structure of gene dependency network changes between two patient-specific groups is an important task for genomic research. Although many computational approaches have been proposed to undertake this task, most of them estimate correlation networks from group-specific gene expression data independently without considering the common structure shared between different groups. In addition, with the development of high-throughput technologies, we can collect gene expression profiles of same patients from multiple platforms. Therefore, inferring differential networks by considering cross-platform gene expression profiles will improve the reliability of network inference. We introduce a two dimensional joint graphical lasso (TDJGL) model to simultaneously estimate group-specific gene dependency networks from gene expression profiles collected from different platforms and infer differential networks. TDJGL can borrow strength across different patient groups and data platforms to improve the accuracy of estimated networks. Simulation studies demonstrate that TDJGL provides more accurate estimates of gene networks and differential networks than previous competing approaches. We apply TDJGL to the PI3K/AKT/mTOR pathway in ovarian tumors to build differential networks associated with platinum resistance. The hub genes of our inferred differential networks are significantly enriched with known platinum resistance-related genes and include potential platinum resistance-related genes. PMID:27677586

  17. Multiple crossbar network: Integrated supercomputing framework

    SciTech Connect

    Hoebelheinrich, R. )

    1989-01-01

    At Los Alamos National Laboratory, site of one of the world's most powerful scientific supercomputing facilities, a prototype network for an environment that links supercomputers and workstations is being developed. Driven by a need to provide graphics data at movie rates across a network from a Cray supercomputer to a Sun scientific workstation, the network is called the Multiple Crossbar Network (MCN). It is intended to be coarsely grained, loosely coupled, general-purpose interconnection network that will vastly increase the speed at which supercomputers communicate with each other in large networks. The components of the network are described, as well as work done in collaboration with vendors who are interested in providing commercial products. 9 refs.

  18. Metamorphic labral axis patterning in the beetle Tribolium castaneum requires multiple upstream, but few downstream, genes in the appendage patterning network

    PubMed Central

    Smith, Frank W.; Angelini, David R.; Gaudio, Matthew; Jockusch, Elizabeth L.

    2014-01-01

    The arthropod labrum is an anterior appendage-like structure that forms the dorsal side of the preoral cavity. Conflicting interpretations of fossil, nervous system and developmental data have led to a proliferation of scenarios for labral evolution. The best supported hypothesis is that the labrum is a novel structure that shares development with appendages as a result of co-option. Here, we use RNA interference in the red flour beetle Tribolium castaneum to compare metamorphic patterning of the labrum to previously published data on ventral appendage patterning. As expected under the co-option hypothesis, depletion of several genes resulted in similar defects in the labrum and ventral appendages. These include proximal deletions and proximal-to-distal transformations resulting from depletion of the leg gap genes homothorax and extradenticle, large-scale deletions resulting from depletion of the leg gap gene Distal-less, and smaller distal deletions resulting from knockdown of the EGF ligand Keren. However, depletion of dachshund and many of the genes that function downstream of the leg gap genes in the ventral appendages had either subtle or no effects on labral axis patterning. This pattern of partial similarity suggests that upstream genes act through different downstream targets in the labrum. We also discovered that many appendage axis patterning genes have roles in patterning the epipharyngeal sensillum array, suggesting that they have become integrated into a novel regulatory network. These genes include Notch, Delta, and decapentaplegic, and the transcription factors abrupt, bric à brac, homothorax, extradenticle and the paralogs apterous a and apterous b. PMID:24617987

  19. Metamorphic labral axis patterning in the beetle Tribolium castaneum requires multiple upstream, but few downstream, genes in the appendage patterning network.

    PubMed

    Smith, Frank W; Angelini, David R; Gaudio, Matthew S; Jockusch, Elizabeth L

    2014-03-01

    The arthropod labrum is an anterior appendage-like structure that forms the dorsal side of the preoral cavity. Conflicting interpretations of fossil, nervous system, and developmental data have led to a proliferation of scenarios for labral evolution. The best supported hypothesis is that the labrum is a novel structure that shares development with appendages as a result of co-option. Here, we use RNA interference in the red flour beetle Tribolium castaneum to compare metamorphic patterning of the labrum to previously published data on ventral appendage patterning. As expected under the co-option hypothesis, depletion of several genes resulted in similar defects in the labrum and ventral appendages. These include proximal deletions and proximal-to-distal transformations resulting from depletion of the leg gap genes homothorax and extradenticle, large-scale deletions resulting from depletion of the leg gap gene Distal-less, and smaller distal deletions resulting from knockdown of the EGF ligand Keren. However, depletion of dachshund and many of the genes that function downstream of the leg gap genes in the ventral appendages had either subtle or no effects on labral axis patterning. This pattern of partial similarity suggests that upstream genes act through different downstream targets in the labrum. We also discovered that many appendage axis patterning genes have roles in patterning the epipharyngeal sensillum array, suggesting that they have become integrated into a novel regulatory network. These genes include Notch, Delta, and decapentaplegic, and the transcription factors abrupt, bric à brac, homothorax, extradenticle and the paralogs apterous a and apterous b.

  20. 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

  1. 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 integrate 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.

  2. 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.

  3. 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.

  4. Mining Gene Expression Data of Multiple Sclerosis

    PubMed Central

    Zhu, Zhenli; Huang, Zhengliang; Li, Ke

    2014-01-01

    Objectives Microarray produces a large amount of gene expression data, containing various biological implications. The challenge is to detect a panel of discriminative genes associated with disease. This study proposed a robust classification model for gene selection using gene expression data, and performed an analysis to identify disease-related genes using multiple sclerosis as an example. Materials and methods Gene expression profiles based on the transcriptome of peripheral blood mononuclear cells from a total of 44 samples from 26 multiple sclerosis patients and 18 individuals with other neurological diseases (control) were analyzed. Feature selection algorithms including Support Vector Machine based on Recursive Feature Elimination, Receiver Operating Characteristic Curve, and Boruta algorithms were jointly performed to select candidate genes associating with multiple sclerosis. Multiple classification models categorized samples into two different groups based on the identified genes. Models’ performance was evaluated using cross-validation methods, and an optimal classifier for gene selection was determined. Results An overlapping feature set was identified consisting of 8 genes that were differentially expressed between the two phenotype groups. The genes were significantly associated with the pathways of apoptosis and cytokine-cytokine receptor interaction. TNFSF10 was significantly associated with multiple sclerosis. A Support Vector Machine model was established based on the featured genes and gave a practical accuracy of ∼86%. This binary classification model also outperformed the other models in terms of Sensitivity, Specificity and F1 score. Conclusions The combined analytical framework integrating feature ranking algorithms and Support Vector Machine model could be used for selecting genes for other diseases. PMID:24932510

  5. 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.

  6. 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.

  7. 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.

  8. The labeled systems of multiple neural networks.

    PubMed

    Nemissi, M; Seridi, H; Akdag, H

    2008-08-01

    This paper proposes an implementation scheme of K-class classification problem using systems of multiple neural networks. Usually, a multi-class problem is decomposed into simple sub-problems solved independently using similar single neural networks. For the reason that these sub-problems are not equivalent in their complexity, we propose a system that includes reinforced networks destined to solve complicated parts of the entire problem. Our approach is inspired from principles of the multi-classifiers systems and the labeled classification, which aims to improve performances of the networks trained by the Back-Propagation algorithm. We propose two implementation schemes based on both OAO (one-against-all) and OAA (one-against-one). The proposed models are evaluated using iris and human thigh databases.

  9. 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

  10. 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.

  11. 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.

  12. Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network.

    PubMed

    Jiang, Xue; Zhang, Han; Quan, Xiongwen

    2016-01-01

    Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets.

  13. Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network

    PubMed Central

    Quan, Xiongwen

    2016-01-01

    Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets. PMID:28042568

  14. Effects of coupling strength and space on the dynamics of coupled toggle switches in stochastic gene networks with multiple-delayed reactions

    NASA Astrophysics Data System (ADS)

    Ribeiro, Andre S.

    2007-06-01

    Genetic toggle switches (TSs) are one of the best studied small gene regulatory networks (GRNs), due to their simplicity and relevant role. They have been interpreted as decision circuits in cell differentiation, a process long hypothesized to be bistable [1], or as cellular memory units [2]. In these contexts, they must be reliable. Once a “decision” is made, the system must remain stable. One way to gain stability is by duplicating the genes of a TS and coupling the two TSs. Using a recent modeling strategy of GRNs, driven by a delayed stochastic simulation algorithm (delayed SSA) that allows modeling transcription and translation as multidelayed reactions, we analyze the stability of systems of coupled TSs. For this, we introduce the coupling strength (C) , a parameter to characterize the GRN structure, against which we compare the GRN stability (S) . We first show that time delays in transcription, associated to the promoter region release, ensure bistability of a TS, given no cooperative binding or self-activation reactions. Next, we couple two TSs and measure their toggling frequencies as C varies. Three dynamical regimes are observed: (i) for weak coupling, high frequency synchronized oscillations, (ii) for average coupling, low frequency synchronized oscillations, and (iii) for strong coupling the system becomes stable after a transient, in one of two steady states. The system stability, S , goes through a first order phase transition as C increases, in the average coupling regime. After, we study the effects of spatial separation in two compartments on the dynamics of two coupled TSs, where spatial separation is modeled as normally distributed random time delayed reactions. The phase transition of S , as C increases, occurs for lower values of C than when the two TSs are in the same compartment. Finally, we couple weakly and homogeneously several TSs within a single compartment and observe that as the number of coupled TSs increases, the system goes

  15. Synchronization in networks with multiple interaction layers

    PubMed Central

    del Genio, Charo I.; Gómez-Gardeñes, Jesús; Bonamassa, Ivan; Boccaletti, Stefano

    2016-01-01

    The structure of many real-world systems is best captured by networks consisting of several interaction layers. Understanding how a multilayered structure of connections affects the synchronization properties of dynamical systems evolving on top of it is a highly relevant endeavor in mathematics and physics and has potential applications in several socially relevant topics, such as power grid engineering and neural dynamics. We propose a general framework to assess the stability of the synchronized state in networks with multiple interaction layers, deriving a necessary condition that generalizes the master stability function approach. We validate our method by applying it to a network of Rössler oscillators with a double layer of interactions and show that highly rich phenomenology emerges from this. This includes cases where the stability of synchronization can be induced even if both layers would have individually induced unstable synchrony, an effect genuinely arising from the true multilayer structure of the interactions among the units in the network. PMID:28138540

  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. Harnessing gene expression networks to prioritize candidate epileptic encephalopathy genes.

    PubMed

    Oliver, Karen L; Lukic, Vesna; Thorne, Natalie P; Berkovic, Samuel F; Scheffer, Ingrid E; Bahlo, Melanie

    2014-01-01

    We apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. We utilized developing brain and adult brain gene expression data from the Allen Human Brain Atlas (AHBA) and compared this to data from Celsius: a large, heterogeneous gene expression data warehouse. We show replicable prioritization results using these three independent gene expression resources, two of which are brain-specific, with small sample size, and the third derived from a heterogeneous collection of tissues with large sample size. Of the nineteen genes that we predicted with the highest likelihood to be true Epileptic Encephalopathy genes, two (GNAO1 and GRIN2B) have recently been independently reported and confirmed. We compare our results to those produced by an established in silico prioritization approach called Endeavour, and finally present gene expression networks for the known and candidate Epileptic Encephalopathy genes. This highlights sub-networks of gene expression, particularly in the network derived from the adult AHBA gene expression dataset. These networks give clues to the likely biological interactions between Epileptic Encephalopathy genes, potentially highlighting underlying mechanisms and avenues for therapeutic targets.

  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. Systems Approaches to Identifying Gene Regulatory Networks in Plants

    PubMed Central

    Long, Terri A.; Brady, Siobhan M.; Benfey, Philip N.

    2009-01-01

    Complex gene regulatory networks are composed of genes, noncoding RNAs, proteins, metabolites, and signaling components. The availability of genome-wide mutagenesis libraries; large-scale transcriptome, proteome, and metabalome data sets; and new high-throughput methods that uncover protein interactions underscores the need for mathematical modeling techniques that better enable scientists to synthesize these large amounts of information and to understand the properties of these biological systems. Systems biology approaches can allow researchers to move beyond a reductionist approach and to both integrate and comprehend the interactions of multiple components within these systems. Descriptive and mathematical models for gene regulatory networks can reveal emergent properties of these plant systems. This review highlights methods that researchers are using to obtain large-scale data sets, and examples of gene regulatory networks modeled with these data. Emergent properties revealed by the use of these network models and perspectives on the future of systems biology are discussed. PMID:18616425

  20. Advanced Multiple In-Multiple Out (MIMO) Antenna Communications for Airborne Networks

    DTIC Science & Technology

    2015-03-01

    ADVANCED MULTIPLE IN-MULTIPLE OUT (MIMO) ANTENNA COMMUNICATIONS FOR AIRBORNE NETWORKS SYRACUSE UNIVERSITY MARCH 2015 FINAL TECHNICAL REPORT...TECHNICAL REPORT 3. DATES COVERED (From - To) OCT 2011 – SEP 2014 4. TITLE AND SUBTITLE ADVANCED MULTIPLE IN-MULTIPLE OUT (MIMO) ANTENNA ...MIMO system with over the air transmission. 15. SUBJECT TERMS Multiple In-Multiple Out (MIMO Antenna Communications, Airborne Networks, D-BLAST

  1. 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

  2. Multiple network interface core apparatus and method

    DOEpatents

    Underwood, Keith D [Albuquerque, NM; Hemmert, Karl Scott [Albuquerque, NM

    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.

  3. microRNA and gene networks in human laryngeal cancer

    PubMed Central

    ZHANG, FENGYU; XU, ZHIWEN; WANG, KUNHAO; SUN, LINLIN; LIU, GENGHE; HAN, BAIXU

    2015-01-01

    Genes and microRNAs (miRNAs) are considered to be key biological factors in human carcinogenesis. To date, considerable data have been obtained regarding genes and miRNAs in cancer; however, the regulatory mechanisms associated with the genes and miRNAs in cancer have yet to be fully elucidated. The aim of the present study was to use the key genes and miRNAs associated with laryngeal cancer (LC) to construct three regulatory networks (differentially expressed, LC-related and global). A network topology of the development of LC, involving 10 differentially expressed miRNAs and 55 differentially expressed genes, was obtained. These genes exhibited multiple identities, including target genes of miRNA, transcription factors (TFs) and host genes. The key regulatory interactions were determined by comparing the similarities and differences among the three networks. The nodes and pathways in LC, as well as the association between each pair of factors within the networks, such as TFs and miRNA, miRNA and target genes and miRNA and its host gene, were discussed. The mechanisms of LC involved certain key pathways featuring self-adaptation regulation and nodes without direct predecessors or successors. The findings of the present study have further elucidated the pathogenesis of LC and are likely to be beneficial for future research into LC. PMID:26668624

  4. Plasticity of Sensorimotor Networks: Multiple Overlapping Mechanisms.

    PubMed

    Buch, Ethan R; Liew, Sook-Lei; Cohen, Leonardo G

    2016-03-16

    Redundancy is an important feature of the motor system, as abundant degrees of freedom are prominent at every level of organization across the central and peripheral nervous systems, and musculoskeletal system. This basic feature results in a system that is both flexible and robust, and which can be sustainably adapted through plasticity mechanisms in response to intrinsic organismal changes and dynamic environments. While much early work of motor system organization has focused on synaptic-based plasticity processes that are driven via experience, recent investigations of neuron-glia interactions, epigenetic mechanisms and large-scale network dynamics have revealed a plethora of plasticity mechanisms that support motor system organization across multiple, overlapping spatial and temporal scales. Furthermore, an important role of these mechanisms is the regulation of intrinsic variability. Here, we review several of these mechanisms and discuss their potential role in neurorehabilitation.

  5. Fused Regression for Multi-source Gene Regulatory Network Inference

    PubMed Central

    Lam, Kari Y.; Westrick, Zachary M.; Müller, Christian L.; Christiaen, Lionel; Bonneau, Richard

    2016-01-01

    Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method’s utility in learning from data collected on different experimental platforms. PMID:27923054

  6. The incorporation of epigenetics in artificial gene regulatory networks.

    PubMed

    Turner, Alexander P; Lones, Michael A; Fuente, Luis A; Stepney, Susan; Caves, Leo S D; Tyrrell, Andy M

    2013-05-01

    Artificial gene regulatory networks are computational models that draw inspiration from biological networks of gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world, such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper describes a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. Our results demonstrate that AERNs are more adept at controlling multiple opposing trajectories when applied to a chaos control task within a conservative dynamical system, suggesting that AERNs are an interesting area for further investigation.

  7. 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.

  8. 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

  9. [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.

  10. 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

  11. Analysis of cascading failure in gene networks.

    PubMed

    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.

  12. Accurate multiple network alignment through context-sensitive random walk

    PubMed Central

    2015-01-01

    Background Comparative network analysis can provide an effective means of analyzing large-scale biological networks and gaining novel insights into their structure and organization. Global network alignment aims to predict the best overall mapping between a given set of biological networks, thereby identifying important similarities as well as differences among the networks. It has been shown that network alignment methods can be used to detect pathways or network modules that are conserved across different networks. Until now, a number of network alignment algorithms have been proposed based on different formulations and approaches, many of them focusing on pairwise alignment. Results In this work, we propose a novel multiple network alignment algorithm based on a context-sensitive random walk model. The random walker employed in the proposed algorithm switches between two different modes, namely, an individual walk on a single network and a simultaneous walk on two networks. The switching decision is made in a context-sensitive manner by examining the current neighborhood, which is effective for quantitatively estimating the degree of correspondence between nodes that belong to different networks, in a manner that sensibly integrates node similarity and topological similarity. The resulting node correspondence scores are then used to predict the maximum expected accuracy (MEA) alignment of the given networks. Conclusions Performance evaluation based on synthetic networks as well as real protein-protein interaction networks shows that the proposed algorithm can construct more accurate multiple network alignments compared to other leading methods. PMID:25707987

  13. Network Topology Reveals Key Cardiovascular Disease Genes

    PubMed Central

    Stojković, Neda; Radak, Djordje; Pržulj, Nataša

    2013-01-01

    The structure of protein-protein interaction (PPI) networks has already been successfully used as a source of new biological information. Even though cardiovascular diseases (CVDs) are a major global cause of death, many CVD genes still await discovery. We explore ways to utilize the structure of the human PPI network to find important genes for CVDs that should be targeted by drugs. The hope is to use the properties of such important genes to predict new ones, which would in turn improve a choice of therapy. We propose a methodology that examines the PPI network wiring around genes involved in CVDs. We use the methodology to identify a subset of CVD-related genes that are statistically significantly enriched in drug targets and “driver genes.” We seek such genes, since driver genes have been proposed to drive onset and progression of a disease. Our identified subset of CVD genes has a large overlap with the Core Diseasome, which has been postulated to be the key to disease formation and hence should be the primary object of therapeutic intervention. This indicates that our methodology identifies “key” genes responsible for CVDs. Thus, we use it to predict new CVD genes and we validate over 70% of our predictions in the literature. Finally, we show that our predicted genes are functionally similar to currently known CVD drug targets, which confirms a potential utility of our methodology towards improving therapy for CVDs. PMID:23977067

  14. 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

  15. 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.

  16. 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

  17. Identifying genes of gene regulatory networks using formal concept analysis.

    PubMed

    Gebert, Jutta; Motameny, Susanne; Faigle, Ulrich; Forst, Christian V; Schrader, Rainer

    2008-03-01

    In order to understand the behavior of a gene regulatory network, it is essential to know the genes that belong to it. Identifying the correct members (e.g., in order to build a model) is a difficult task even for small subnetworks. Usually only few members of a network are known and one needs to guess the missing members based on experience or informed speculation. It is beneficial if one can additionally rely on experimental data to support this guess. In this work we present a new method based on formal concept analysis to detect unknown members of a gene regulatory network from gene expression time series data. We show that formal concept analysis is able to find a list of candidate genes for inclusion into a partially known basic network. This list can then be reduced by a statistical analysis so that the resulting genes interact strongly with the basic network and therefore should be included when modeling the network. The method has been applied to the DNA repair system of Mycobacterium tuberculosis. In this application, our method produces comparable results to an already existing method of component selection while it is applicable to a broader range of problems.

  18. Combinatorial explosion in model gene networks

    NASA Astrophysics Data System (ADS)

    Edwards, R.; Glass, L.

    2000-09-01

    The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such

  19. 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

  20. Modeling gene regulatory network motifs using statecharts

    PubMed Central

    2012-01-01

    Background Gene regulatory networks are widely used by biologists to describe the interactions among genes, proteins and other components at the intra-cellular level. Recently, a great effort has been devoted to give gene regulatory networks a formal semantics based on existing computational frameworks. For this purpose, we consider Statecharts, which are a modular, hierarchical and executable formal model widely used to represent software systems. We use Statecharts for modeling small and recurring patterns of interactions in gene regulatory networks, called motifs. Results We present an improved method for modeling gene regulatory network motifs using Statecharts and we describe the successful modeling of several motifs, including those which could not be modeled or whose models could not be distinguished using the method of a previous proposal. We model motifs in an easy and intuitive way by taking advantage of the visual features of Statecharts. Our modeling approach is able to simulate some interesting temporal properties of gene regulatory network motifs: the delay in the activation and the deactivation of the "output" gene in the coherent type-1 feedforward loop, the pulse in the incoherent type-1 feedforward loop, the bistability nature of double positive and double negative feedback loops, the oscillatory behavior of the negative feedback loop, and the "lock-in" effect of positive autoregulation. Conclusions We present a Statecharts-based approach for the modeling of gene regulatory network motifs in biological systems. The basic motifs used to build more complex networks (that is, simple regulation, reciprocal regulation, feedback loop, feedforward loop, and autoregulation) can be faithfully described and their temporal dynamics can be analyzed. PMID:22536967

  1. Modeling gene regulatory networks: A network simplification algorithm

    NASA Astrophysics Data System (ADS)

    Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.

    2016-12-01

    Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.

  2. Mutated Genes in Schizophrenia Map to Brain Networks

    MedlinePlus

    ... Matters NIH Research Matters August 12, 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks in the prefrontal ... Vasculitis Therapy as Effective as Standard Care Mutated Genes in Schizophrenia Map to Brain Networks Connect with Us Subscribe to ...

  3. NCATE PDS Standards and Multiple Site Networks.

    ERIC Educational Resources Information Center

    Harris, Mary M.; Tunks, Jeanne; Hamilton, Michelle

    2002-01-01

    In 2001, the National Council for Accreditation of Teacher Education published "Standards for Professional Development Schools," which differ from the 1997 draft standards in their explicit attention to large networks of professional development schools (PDS). Exemplary practices from four large PDS networks demonstrate the applicability of the…

  4. Inferring gene regression networks with model trees

    PubMed Central

    2010-01-01

    Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate

  5. 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.

  6. Multiple-Access Techniques for Broadband Networks

    DTIC Science & Technology

    1991-12-20

    satellite-to- earth -station links of corn- some of the transmitted signals either separately (subop- munieation networks. When bursty data or voice traffic...frequency-sclective and lite-to- earth -station links in communication networks such multipath fading. These properties of SSMA are of inter- as the...Muy, nd. Cokle Pat, through the Nati• l Sciemce Foundation’s Enering Rmo&l Ca0 PraSm: NSF CDI NMOIOI MULTI-SENSOR CORRELATION AND QUANTIZATION IN

  7. Cascading Failures Due to Multiple Causes in Interdependent Networks

    NASA Astrophysics Data System (ADS)

    Kornbluth, Yosef; Buldyrev, Sergey

    2014-03-01

    In recent years, several models of network failure have been introduced. Some of these models are based on overload, in which increased traffic destroys nodes, while others are based on partial isolation, in which a node needs several functional neighbors to survive. In these systems, failure of a small fraction of nodes can cause a cascade of failures which may completely destroy the network. The majority of these models are studied in single networks. However, many real-world systems are comprised of multiple interdependent networks. Recent studies based on the concept of mutual percolation show that these systems are much more vulnerable than a single network. We numerically and analytically investigate how multiple causes of failure simultaneously acting in a system of interdependent networks affect their vulnerability.

  8. Gene network biological validity based on gene-gene interaction relevance.

    PubMed

    Gómez-Vela, Francisco; Díaz-Díaz, Norberto

    2014-01-01

    In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.

  9. 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...

  10. Multiple-Valued Immune Network with Apoptosis System

    NASA Astrophysics Data System (ADS)

    Yamaguchi, Takayuki; Tang, Zheng

    In this paper, we describe a new model of immune network based on biological immune response network. We propose an immunity like multiple-valued network with apoptosis mechanism. The model is based on the interaction between B cells and T cells and the biological apoptosis mechanism in human body. With the mechanism, a naturally immune system can be reproduced. The model is also applied to pattern recognition. It gets possible with a conventional model to restricting categories increase of memory patterns.

  11. Automatic Parameter Learning for Multiple Local Network Alignment

    PubMed Central

    Novak, Antal; Do, Chuong B.; Srinivasan, Balaji S.; Batzoglou, Serafim

    2009-01-01

    Abstract We developed Græmlin 2.0, a new multiple network aligner with (1) a new multi-stage approach to local network alignment; (2) a novel scoring function that can use arbitrary features of a multiple network alignment, such as protein deletions, protein duplications, protein mutations, and interaction losses; (3) a parameter learning algorithm that uses a training set of known network alignments to learn parameters for our scoring function and thereby adapt it to any set of networks; and (4) an algorithm that uses our scoring function to find approximate multiple network alignments in linear time. We tested Græmlin 2.0's accuracy on protein interaction networks from IntAct, DIP, and the Stanford Network Database. We show that, on each of these datasets, Græmlin 2.0 has higher sensitivity and specificity than existing network aligners. Græmlin 2.0 is available under the GNU public license at http://graemlin.stanford.edu. PMID:19645599

  12. Screening of differentially expressed genes between multiple trauma patients with and without sepsis.

    PubMed

    Ji, S C; Pan, Y T; Lu, Q Y; Sun, Z Y; Liu, Y Z

    2014-03-17

    The purpose of this study was to identify critical genes associated with septic multiple trauma by comparing peripheral whole blood samples from multiple trauma patients with and without sepsis. A microarray data set was downloaded from the Gene Expression Omnibus (GEO) database. This data set included 70 samples, 36 from multiple trauma patients with sepsis and 34 from multiple trauma patients without sepsis (as a control set). The data were preprocessed, and differentially expressed genes (DEGs) were then screened for using packages of the R language. Functional analysis of DEGs was performed with DAVID. Interaction networks were then established for the most up- and down-regulated genes using HitPredict. Pathway-enrichment analysis was conducted for genes in the networks using WebGestalt. Fifty-eight DEGs were identified. The expression levels of PLAU (down-regulated) and MMP8 (up-regulated) presented the largest fold-changes, and interaction networks were established for these genes. Further analysis revealed that PLAT (plasminogen activator, tissue) and SERPINF2 (serpin peptidase inhibitor, clade F, member 2), which interact with PLAU, play important roles in the pathway of the component and coagulation cascade. We hypothesize that PLAU is a major regulator of the component and coagulation cascade, and down-regulation of PLAU results in dysfunction of the pathway, causing sepsis.

  13. Computational gene network study on antibiotic resistance genes of Acinetobacter baumannii.

    PubMed

    Anitha, P; Anbarasu, Anand; Ramaiah, Sudha

    2014-05-01

    Multi Drug Resistance (MDR) in Acinetobacter baumannii is one of the major threats for emerging nosocomial infections in hospital environment. Multidrug-resistance in A. baumannii may be due to the implementation of multi-combination resistance mechanisms such as β-lactamase synthesis, Penicillin-Binding Proteins (PBPs) changes, alteration in porin proteins and in efflux pumps against various existing classes of antibiotics. Multiple antibiotic resistance genes are involved in MDR. These resistance genes are transferred through plasmids, which are responsible for the dissemination of antibiotic resistance among Acinetobacter spp. In addition, these resistance genes may also have a tendency to interact with each other or with their gene products. Therefore, it becomes necessary to understand the impact of these interactions in antibiotic resistance mechanism. Hence, our study focuses on protein and gene network analysis on various resistance genes, to elucidate the role of the interacting proteins and to study their functional contribution towards antibiotic resistance. From the search tool for the retrieval of interacting gene/protein (STRING), a total of 168 functional partners for 15 resistance genes were extracted based on the confidence scoring system. The network study was then followed up with functional clustering of associated partners using molecular complex detection (MCODE). Later, we selected eight efficient clusters based on score. Interestingly, the associated protein we identified from the network possessed greater functional similarity with known resistance genes. This network-based approach on resistance genes of A. baumannii could help in identifying new genes/proteins and provide clues on their association in antibiotic resistance.

  14. Summing up the noise in gene networks.

    PubMed

    Paulsson, Johan

    2004-01-29

    Random fluctuations in genetic networks are inevitable as chemical reactions are probabilistic and many genes, RNAs and proteins are present in low numbers per cell. Such 'noise' affects all life processes and has recently been measured using green fluorescent protein (GFP). Two studies show that negative feedback suppresses noise, and three others identify the sources of noise in gene expression. Here I critically analyse these studies and present a simple equation that unifies and extends both the mathematical and biological perspectives.

  15. 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.

  16. Modeling transcriptional networks in Drosophila development at multiple scales.

    PubMed

    Wunderlich, Zeba; DePace, Angela H

    2011-12-01

    Quantitative models of developmental processes can provide insights at multiple scales. Ultimately, models may be particularly informative for key questions about network level behavior during development such as how does the system respond to environmental perturbation, or operate reliably in different genetic backgrounds? The transcriptional networks that pattern the Drosophila embryo have been the subject of numerous quantitative experimental studies coupled to modeling frameworks in recent years. In this review, we describe three studies that consider these networks at different levels of molecular detail and therefore result in different types of insights. We also discuss other developmental transcriptional networks operating in Drosophila, with the goal of highlighting what additional insights they may provide.

  17. GenePANDA—a novel network-based gene prioritizing tool for complex diseases

    PubMed Central

    Yin, Tianshu; Chen, Shu; Wu, Xiaohui; Tian, Weidong

    2017-01-01

    Here we describe GenePANDA, a novel network-based tool for prioritizing candidate disease genes. GenePANDA assesses whether a gene is likely a candidate disease gene based on its relative distance to known disease genes in a functional association network. A unique feature of GenePANDA is the introduction of adjusted network distance derived by normalizing the raw network distance between two genes with their respective mean raw network distance to all other genes in the network. The use of adjusted network distance significantly improves GenePANDA’s performance on prioritizing complex disease genes. GenePANDA achieves superior performance over five previously published algorithms for prioritizing disease genes. Finally, GenePANDA can assist in prioritizing functionally important SNPs identified by GWAS. PMID:28252032

  18. 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

  19. 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.

  20. Methods for monitoring multiple gene expression

    SciTech Connect

    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.

  1. Methods for monitoring multiple gene expression

    SciTech Connect

    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.

  2. 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.

  3. Inferring transcription factor collaborations in gene regulatory networks

    PubMed Central

    2014-01-01

    Background Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the expression of target genes is a challenging task, especially when multiple TFs collaboratively participate in the transcriptional regulation. Results We model the underlying regulatory interactions in terms of the directions (activation or repression) and their logical roles (necessary and/or sufficient) with a modified association rule mining approach, called mTRIM. The experiment on Yeast discovered 670 regulatory interactions, in which multiple TFs express their functions on common target genes collaboratively. The evaluation on yeast genetic interactions, TF knockouts and a synthetic dataset shows that our algorithm is significantly better than the existing ones. Conclusions mTRIM is a novel method to infer TF collaborations in transcriptional regulation networks. mTRIM is available at http://www.msu.edu/~jinchen/mTRIM. PMID:24565025

  4. 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...

  5. 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.

  6. Integration of molecular network data reconstructs Gene Ontology

    PubMed Central

    Gligorijević, Vladimir; Janjić, Vuk; Pržulj, Nataša

    2014-01-01

    Motivation: Recently, a shift was made from using Gene Ontology (GO) to evaluate molecular network data to using these data to construct and evaluate GO. Dutkowski et al. provide the first evidence that a large part of GO can be reconstructed solely from topologies of molecular networks. Motivated by this work, we develop a novel data integration framework that integrates multiple types of molecular network data to reconstruct and update GO. We ask how much of GO can be recovered by integrating various molecular interaction data. Results: We introduce a computational framework for integration of various biological networks using penalized non-negative matrix tri-factorization (PNMTF). It takes all network data in a matrix form and performs simultaneous clustering of genes and GO terms, inducing new relations between genes and GO terms (annotations) and between GO terms themselves. To improve the accuracy of our predicted relations, we extend the integration methodology to include additional topological information represented as the similarity in wiring around non-interacting genes. Surprisingly, by integrating topologies of bakers’ yeasts protein–protein interaction, genetic interaction (GI) and co-expression networks, our method reports as related 96% of GO terms that are directly related in GO. The inclusion of the wiring similarity of non-interacting genes contributes 6% to this large GO term association capture. Furthermore, we use our method to infer new relationships between GO terms solely from the topologies of these networks and validate 44% of our predictions in the literature. In addition, our integration method reproduces 48% of cellular component, 41% of molecular function and 41% of biological process GO terms, outperforming the previous method in the former two domains of GO. Finally, we predict new GO annotations of yeast genes and validate our predictions through GIs profiling. Availability and implementation: Supplementary Tables of new GO

  7. MINER: exploratory analysis of gene interaction networks by machine learning from expression data

    PubMed Central

    2009-01-01

    Background The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. Results We have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Conclusion Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing. PMID:19958480

  8. Evolution of the mammalian embryonic pluripotency gene regulatory network

    PubMed Central

    Fernandez-Tresguerres, Beatriz; Cañon, Susana; Rayon, Teresa; Pernaute, Barbara; Crespo, Miguel; Torroja, Carlos; Manzanares, Miguel

    2010-01-01

    Embryonic pluripotency in the mouse is established and maintained by a gene-regulatory network under the control of a core set of transcription factors that include octamer-binding protein 4 (Oct4; official name POU domain, class 5, transcription factor 1, Pou5f1), sex-determining region Y (SRY)-box containing gene 2 (Sox2), and homeobox protein Nanog. Although this network is largely conserved in eutherian mammals, very little information is available regarding its evolutionary conservation in other vertebrates. We have compared the embryonic pluripotency networks in mouse and chick by means of expression analysis in the pregastrulation chicken embryo, genomic comparisons, and functional assays of pluripotency-related regulatory elements in ES cells and blastocysts. We find that multiple components of the network are either novel to mammals or have acquired novel expression domains in early developmental stages of the mouse. We also find that the downstream action of the mouse core pluripotency factors is mediated largely by genomic sequence elements nonconserved with chick. In the case of Sox2 and Fgf4, we find that elements driving expression in embryonic pluripotent cells have evolved by a small number of nucleotide changes that create novel binding sites for core factors. Our results show that the network in charge of embryonic pluripotency is an evolutionary novelty of mammals that is related to the comparatively extended period during which mammalian embryonic cells need to be maintained in an undetermined state before engaging in early differentiation events. PMID:21048080

  9. Multiple-predators-based capture process on complex networks

    NASA Astrophysics Data System (ADS)

    Ramiz Sharafat, Rajput; Pu, Cunlai; Li, Jie; Chen, Rongbin; Xu, Zhongqi

    2017-03-01

    The predator/prey (capture) problem is a prototype of many network-related applications. We study the capture process on complex networks by considering multiple predators from multiple sources. In our model, some lions start from multiple sources simultaneously to capture the lamb by biased random walks, which are controlled with a free parameter $\\alpha$. We derive the distribution of the lamb's lifetime and the expected lifetime $\\left\\langle T\\right\\rangle $. Through simulation, we find that the expected lifetime drops substantially with the increasing number of lions. We also study how the underlying topological structure affects the capture process, and obtain that locating on small-degree nodes is better than large-degree nodes to prolong the lifetime of the lamb. Moreover, dense or homogeneous network structures are against the survival of the lamb.

  10. 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

  11. Efficient quantum transmission in multiple-source networks.

    PubMed

    Luo, Ming-Xing; Xu, Gang; Chen, Xiu-Bo; Yang, Yi-Xian; Wang, Xiaojun

    2014-04-02

    A difficult problem in quantum network communications is how to efficiently transmit quantum information over large-scale networks with common channels. We propose a solution by developing a quantum encoding approach. Different quantum states are encoded into a coherent superposition state using quantum linear optics. The transmission congestion in the common channel may be avoided by transmitting the superposition state. For further decoding and continued transmission, special phase transformations are applied to incoming quantum states using phase shifters such that decoders can distinguish outgoing quantum states. These phase shifters may be precisely controlled using classical chaos synchronization via additional classical channels. Based on this design and the reduction of multiple-source network under the assumption of restricted maximum-flow, the optimal scheme is proposed for specially quantized multiple-source network. In comparison with previous schemes, our scheme can greatly increase the transmission efficiency.

  12. Multiple-node basin stability in complex dynamical networks

    NASA Astrophysics Data System (ADS)

    Mitra, Chiranjit; Choudhary, Anshul; Sinha, Sudeshna; Kurths, Jürgen; Donner, Reik V.

    2017-03-01

    Dynamical entities interacting with each other on complex networks often exhibit multistability. The stability of a desired steady regime (e.g., a synchronized state) to large perturbations is critical in the operation of many real-world networked dynamical systems such as ecosystems, power grids, the human brain, etc. This necessitates the development of appropriate quantifiers of stability of multiple stable states of such systems. Motivated by the concept of basin stability (BS) [P. J. Menck et al., Nat. Phys. 9, 89 (2013), 10.1038/nphys2516], we propose here the general framework of multiple-node basin stability for gauging the global stability and robustness of networked dynamical systems in response to nonlocal perturbations simultaneously affecting multiple nodes of a system. The framework of multiple-node BS provides an estimate of the critical number of nodes that, when simultaneously perturbed, significantly reduce the capacity of the system to return to the desired stable state. Further, this methodology can be applied to estimate the minimum number of nodes of the network to be controlled or safeguarded from external perturbations to ensure proper operation of the system. Multiple-node BS can also be utilized for probing the influence of spatially localized perturbations or targeted attacks to specific parts of a network. We demonstrate the potential of multiple-node BS in assessing the stability of the synchronized state in a deterministic scale-free network of Rössler oscillators and a conceptual model of the power grid of the United Kingdom with second-order Kuramoto-type nodal dynamics.

  13. Ethanol modulation of gene networks: implications for alcoholism.

    PubMed

    Farris, Sean P; Miles, Michael F

    2012-01-01

    Alcoholism is a complex disease caused by a confluence of environmental and genetic factors influencing multiple brain pathways to produce a variety of behavioral sequelae, including addiction. Genetic factors contribute to over 50% of the risk for alcoholism and recent evidence points to a large number of genes with small effect sizes as the likely molecular basis for this disease. Recent progress in genomics (microarrays or RNA-Seq) and genetics has led to the identification of a large number of potential candidate genes influencing ethanol behaviors or alcoholism itself. To organize this complex information, investigators have begun to focus on the contribution of gene networks, rather than individual genes, for various ethanol-induced behaviors in animal models or behavioral endophenotypes comprising alcoholism. This chapter reviews some of the methods used for constructing gene networks from genomic data and some of the recent progress made in applying such approaches to the study of the neurobiology of ethanol. We show that rapid technology development in gathering genomic data, together with sophisticated experimental design and a growing collection of analysis tools are producing novel insights for understanding the molecular basis of alcoholism and that such approaches promise new opportunities for therapeutic development.

  14. 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.

  15. 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

  16. Pathway-based network analysis of myeloma tumors: monoclonal gammopathy of unknown significance, smoldering multiple myeloma, and multiple myeloma.

    PubMed

    Dong, L; Chen, C Y; Ning, B; Xu, D L; Gao, J H; Wang, L L; Yan, S Y; Cheng, S

    2015-08-14

    Although many studies have been carried out on monoclonal gammopathy of unknown significances (MGUS), smoldering multiple myeloma (SMM), and multiple myeloma (MM), their classification and underlying pathogenesis are far from elucidated. To discover the relationships among MGUS, SMM, and MM at the transcriptome level, differentially expressed genes in MGUS, SMM, and MM were identified by the rank product method, and then co-expression networks were constructed by integrating the data. Finally, a pathway-network was constructed based on Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and the relationships between the pathways were identified. The results indicated that there were 55, 78, and 138 pathways involved in the myeloma tumor developmental stages of MGUS, SMM, and MM, respectively. The biological processes identified therein were found to have a close relationship with the immune system. Processes and pathways related to the abnormal activity of DNA and RNA were also present in SMM and MM. Six common pathways were found in the whole process of myeloma tumor development. Nine pathways were shown to participate in the progression of MGUS to SMM, and prostate cancer was the sole pathway that was involved only in MGUS and MM. Pathway-network analysis might provide a new indicator for the developmental stage diagnosis of myeloma tumors.

  17. 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…

  18. 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.

  19. Improving gene regulatory network inference using network topology information.

    PubMed

    Nair, Ajay; Chetty, Madhu; Wangikar, Pramod P

    2015-09-01

    Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum-number-of-parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well known but literature on the detailed analysis of the widely used maxP technique is lacking. The optimal search methods require large computational time. We report the theoretical analysis and experimental results of the strengths and limitations of the maxP technique. Further, using an optimal search method, we combine the strengths of the maxP technique and the known GRN topology to propose two novel algorithms. These algorithms are implemented in a Bayesian network framework and tested on biological, realistic, and in silico networks of different sizes and topologies. They overcome the limitations of the maxP technique and show superior computational speed when compared to the current optimal search algorithms.

  20. 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

  1. 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.

  2. 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

  3. Cooperative MIMO technology in multiple hops wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Yuan, Lina; Chen, Huajun; Gong, Jing

    2017-03-01

    The limited lifetime is one of the important factors restricted wireless sensor networks (WSNs), when possible, wireless nodes often operate with small batteries, while battery replacement is a very difficult and expensive. So the nodes must work long hours in the case of no battery replacement. Therefore, in WSNs, minimizing energy consumption is an important design consideration, at the same time, the transmission strategies of energy efficiency must be used for data forwarding. This paper, using cooperative multiple input multiple output(MIMO) technology combined with multiple hops technology, has put forward a new transmission model, i.e., the MIMO-MISO(multi-input multi-output)/MIMO-MIMO model. Simulation results demonstrate the proposed MIMO-MISO/MIMO-MIMO to minimize energy consumption of each node every node for multi-hop WSNs, to save a great deal of energy for a larger transmission distance, which makes the life of the entire network be extended.

  4. Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

    PubMed Central

    Lähdesmäki, Harri; Hautaniemi, Sampsa; Shmulevich, Ilya; Yli-Harja, Olli

    2006-01-01

    A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes. PMID:17415411

  5. Threshold-limited spreading in social networks with multiple initiators

    NASA Astrophysics Data System (ADS)

    Singh, P.; Sreenivasan, S.; Szymanski, B. K.; Korniss, G.

    2013-07-01

    A classical model for social-influence-driven opinion change is the threshold model. Here we study cascades of opinion change driven by threshold model dynamics in the case where multiple initiators trigger the cascade, and where all nodes possess the same adoption threshold ϕ. Specifically, using empirical and stylized models of social networks, we study cascade size as a function of the initiator fraction p. We find that even for arbitrarily high value of ϕ, there exists a critical initiator fraction pc(ϕ) beyond which the cascade becomes global. Network structure, in particular clustering, plays a significant role in this scenario. Similarly to the case of single-node or single-clique initiators studied previously, we observe that community structure within the network facilitates opinion spread to a larger extent than a homogeneous random network. Finally, we study the efficacy of different initiator selection strategies on the size of the cascade and the cascade window.

  6. Threshold-limited spreading in social networks with multiple initiators

    PubMed Central

    Singh, P.; Sreenivasan, S.; Szymanski, B. K.; Korniss, G.

    2013-01-01

    A classical model for social-influence-driven opinion change is the threshold model. Here we study cascades of opinion change driven by threshold model dynamics in the case where multiple initiators trigger the cascade, and where all nodes possess the same adoption threshold ϕ. Specifically, using empirical and stylized models of social networks, we study cascade size as a function of the initiator fraction p. We find that even for arbitrarily high value of ϕ, there exists a critical initiator fraction pc(ϕ) beyond which the cascade becomes global. Network structure, in particular clustering, plays a significant role in this scenario. Similarly to the case of single-node or single-clique initiators studied previously, we observe that community structure within the network facilitates opinion spread to a larger extent than a homogeneous random network. Finally, we study the efficacy of different initiator selection strategies on the size of the cascade and the cascade window. PMID:23900230

  7. 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

  8. Dissecting the gene network of dietary restriction to identify evolutionarily conserved pathways and new functional genes.

    PubMed

    Wuttke, Daniel; Connor, Richard; Vora, Chintan; Craig, Thomas; Li, Yang; Wood, Shona; Vasieva, Olga; Shmookler Reis, Robert; Tang, Fusheng; de Magalhães, João Pedro

    2012-01-01

    Dietary restriction (DR), limiting nutrient intake from diet without causing malnutrition, delays the aging process and extends lifespan in multiple organisms. The conserved life-extending effect of DR suggests the involvement of fundamental mechanisms, although these remain a subject of debate. To help decipher the life-extending mechanisms of DR, we first compiled a list of genes that if genetically altered disrupt or prevent the life-extending effects of DR. We called these DR-essential genes and identified more than 100 in model organisms such as yeast, worms, flies, and mice. In order for other researchers to benefit from this first curated list of genes essential for DR, we established an online database called GenDR (http://genomics.senescence.info/diet/). To dissect the interactions of DR-essential genes and discover the underlying lifespan-extending mechanisms, we then used a variety of network and systems biology approaches to analyze the gene network of DR. We show that DR-essential genes are more conserved at the molecular level and have more molecular interactions than expected by chance. Furthermore, we employed a guilt-by-association method to predict novel DR-essential genes. In budding yeast, we predicted nine genes related to vacuolar functions; we show experimentally that mutations deleting eight of those genes prevent the life-extending effects of DR. Three of these mutants (OPT2, FRE6, and RCR2) had extended lifespan under ad libitum, indicating that the lack of further longevity under DR is not caused by a general compromise of fitness. These results demonstrate how network analyses of DR using GenDR can be used to make phenotypically relevant predictions. Moreover, gene-regulatory circuits reveal that the DR-induced transcriptional signature in yeast involves nutrient-sensing, stress responses and meiotic transcription factors. Finally, comparing the influence of gene expression changes during DR on the interactomes of multiple organisms led

  9. 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

  10. 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

  11. Paper-based synthetic gene networks.

    PubMed

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

    2014-11-06

    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.

  12. Estimating Gene Regulatory Networks with pandaR.

    PubMed

    Schlauch, Daniel; Paulson, Joseph N; Young, Albert; Glass, Kimberly; Quackenbush, John

    2017-03-11

    PANDA (Passing Attributes betweenNetworks forData Assimilation) is a gene regulatory network inference method that begins with amodel of transcription factor-target gene interactions and usesmessage passing to update the network model given available transcriptomic and protein-protein interaction data. PANDA is used to estimate networks for each experimental group and the network models are then compared between groups to explore transcriptional processes that distinguish the groups. We present pandaR (bioconductor.org/packages/pandaR), a Bioconductor package that implements PANDA and provides a framework for exploratory data analysis on gene regulatory networks.

  13. 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

  14. 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.

  15. 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.

  16. 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

  17. Gene identification and analysis: an application of neural network-based information fusion

    SciTech Connect

    Matis, S.; Xu, Y.; Shah, M.B.; Mural, R.J.; Einstein, J.R.; Uberbacher, E.C.

    1996-10-01

    Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a gene localization and modeling system called GRAIL. GRAIL is a multiple sensor-neural network based system. It localizes genes in anonymous DNA sequence by recognizing gene features related to protein-coding slice sites, and then combines the recognized features using a neural network system. Localized coding regions are then optimally parsed into a gene mode. RNA polymerase II promoters can also be predicted. Through years of extensive testing, GRAIL consistently localizes about 90 percent of coding portions of test genes with a false positive rate of about 10 percent. A number of genes for major genetic diseases have been located through the use of GRAIL, and over 1000 research laboratories worldwide use GRAIL on regular bases for localization of genes on their newly sequenced DNA.

  18. 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

  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. 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

  1. 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.

  2. Multiple-View Object Recognition in Smart Camera Networks

    NASA Astrophysics Data System (ADS)

    Yang, Allen Y.; Maji, Subhransu; Christoudias, C. Mario; Darrell, Trevor; Malik, Jitendra; Sastry, S. Shankar

    We study object recognition in low-power, low-bandwidth smart camera networks. The ability to perform robust object recognition is crucial for applications such as visual surveillance to track and identify objects of interest, and overcome visual nuisances such as occlusion and pose variations between multiple camera views. To accommodate limited bandwidth between the cameras and the base-station computer, the method utilizes the available computational power on the smart sensors to locally extract SIFT-type image features to represent individual camera views. We show that between a network of cameras, high-dimensional SIFT histograms exhibit a joint sparse pattern corresponding to a set of shared features in 3-D. Such joint sparse patterns can be explicitly exploited to encode the distributed signal via random projections. At the network station, multiple decoding schemes are studied to simultaneously recover the multiple-view object features based on a distributed compressive sensing theory. The system has been implemented on the Berkeley CITRIC smart camera platform. The efficacy of the algorithm is validated through extensive simulation and experiment.

  3. 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.

  4. A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks.

    PubMed

    Petrovskaya, Olga V; Petrovskiy, Evgeny D; Lavrik, Inna N; Ivanisenko, Vladimir A

    2016-12-22

    Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

  5. A synthetic small molecule for rapid induction of multiple pluripotency genes in mouse embryonic fibroblasts

    NASA Astrophysics Data System (ADS)

    Pandian, Ganesh N.; Nakano, Yusuke; Sato, Shinsuke; Morinaga, Hironobu; Bando, Toshikazu; Nagase, Hiroki; Sugiyama, Hiroshi

    2012-07-01

    Cellular reprogramming involves profound alterations in genome-wide gene expression that is precisely controlled by a hypothetical epigenetic code. Small molecules have been shown to artificially induce epigenetic modifications in a sequence independent manner. Recently, we showed that specific DNA binding hairpin pyrrole-imidazole polyamides (PIPs) could be conjugated with chromatin modifying histone deacetylase inhibitors like SAHA to epigenetically activate certain pluripotent genes in mouse fibroblasts. In our steadfast progress to improve the efficiency of SAHA-PIPs, we identified a novel compound termed, δ that could dramatically induce the endogenous expression of Oct-3/4 and Nanog. Genome-wide gene analysis suggests that in just 24 h and at nM concentration, δ induced multiple pluripotency-associated genes including Rex1 and Cdh1 by more than ten-fold. δ treated MEFs also rapidly overcame the rate-limiting step of epithelial transition in cellular reprogramming by switching ``'' the complex transcriptional gene network.

  6. 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

  7. Customized Regulation of Diverse Stress Response Genes by the Multiple Antibiotic Resistance Activator MarA

    PubMed Central

    2017-01-01

    Stress response networks frequently have a single upstream regulator that controls many downstream genes. However, the downstream targets are often diverse, therefore it remains unclear how their expression is specialized when under the command of a common regulator. To address this, we focused on a stress response network where the multiple antibiotic resistance activator MarA from Escherichia coli regulates diverse targets ranging from small RNAs to efflux pumps. Using single-cell experiments and computational modeling, we showed that each downstream gene studied has distinct activation, noise, and information transmission properties. Critically, our results demonstrate that understanding biological context is essential; we found examples where strong activation only occurs outside physiologically relevant ranges of MarA and others where noise is high at wild type MarA levels and decreases as MarA reaches its physiological limit. These results demonstrate how a single regulatory protein can maintain specificity while orchestrating the response of many downstream genes. PMID:28060821

  8. Customized Regulation of Diverse Stress Response Genes by the Multiple Antibiotic Resistance Activator MarA.

    PubMed

    Rossi, Nicholas A; Dunlop, Mary J

    2017-01-01

    Stress response networks frequently have a single upstream regulator that controls many downstream genes. However, the downstream targets are often diverse, therefore it remains unclear how their expression is specialized when under the command of a common regulator. To address this, we focused on a stress response network where the multiple antibiotic resistance activator MarA from Escherichia coli regulates diverse targets ranging from small RNAs to efflux pumps. Using single-cell experiments and computational modeling, we showed that each downstream gene studied has distinct activation, noise, and information transmission properties. Critically, our results demonstrate that understanding biological context is essential; we found examples where strong activation only occurs outside physiologically relevant ranges of MarA and others where noise is high at wild type MarA levels and decreases as MarA reaches its physiological limit. These results demonstrate how a single regulatory protein can maintain specificity while orchestrating the response of many downstream genes.

  9. Estimating genome-wide gene networks using nonparametric Bayesian network models on massively parallel computers.

    PubMed

    Tamada, Yoshinori; Imoto, Seiya; Araki, Hiromitsu; Nagasaki, Masao; Print, Cristin; Charnock-Jones, D Stephen; Miyano, Satoru

    2011-01-01

    We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.

  10. GRAIL: A multi-agent neural network system for gene identification

    SciTech Connect

    Xu, Y.; Mural, R.J.; Einstein, J.R.; Shah, M.B.; Uberbacher, E.C.

    1996-10-01

    Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. The authors describe a gene localization and modeling system, called GRAIL. GRAIL is a multiple sensor-neural network-based system. It localizes genes in anonymous DNA sequence by recognizing features related to protein-coding regions and the boundaries of coding regions, and then combines the recognized features using a neural network system. Localized coding regions are then optimally parsed into a gene model. Through years of extensive testing, GRAIL consistently localizes about 90% of coding portions of test genes with a false positive rate of about 10%. A number of genes for major genetic diseases have been located through the use of GRAIL, and over 1,000 research laboratories worldwide use GRAIL on regular bases for localization of genes on their newly sequenced DNA.

  11. 3D actin network centerline extraction with multiple active contours.

    PubMed

    Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

    2014-02-01

    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.

  12. Network rewiring is an important mechanism of gene essentiality change

    PubMed Central

    Kim, Jinho; Kim, Inhae; Han, Seong Kyu; Bowie, James U.; Kim, Sanguk

    2012-01-01

    Gene essentiality changes are crucial for organismal evolution. However, it is unclear how essentiality of orthologs varies across species. We investigated the underlying mechanism of gene essentiality changes between yeast and mouse based on the framework of network evolution and comparative genomic analysis. We found that yeast nonessential genes become essential in mouse when their network connections rapidly increase through engagement in protein complexes. The increased interactions allowed the previously nonessential genes to become members of vital pathways. By accounting for changes in gene essentiality, we firmly reestablished the centrality-lethality rule, which proposed the relationship of essential genes and network hubs. Furthermore, we discovered that the number of connections associated with essential and non-essential genes depends on whether they were essential in ancestral species. Our study describes for the first time how network evolution occurs to change gene essentiality. PMID:23198090

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

    PubMed

    Grieb, Melanie; Burkovski, Andre; Sträng, J Eric; 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.

  14. On control of singleton attractors in multiple Boolean networks: integer programming-based method

    PubMed Central

    2014-01-01

    Background Boolean network (BN) is a mathematical model for genetic network and control of genetic networks has become an important issue owing to their potential application in the field of drug discovery and treatment of intractable diseases. Early researches have focused primarily on the analysis of attractor control for a randomly generated BN. However, one may also consider how anti-cancer drugs act in both normal and cancer cells. Thus, the development of controls for multiple BNs is an important and interesting challenge. Results In this article, we formulate three novel problems about attractor control for two BNs (i.e., normal cell and cancer cell). The first is about finding a control that can significantly damage cancer cells but has a limited damage to normal cells. The second is about finding a control for normal cells with a guaranteed damaging effect on cancer cells. Finally, we formulate a definition for finding a control for cancer cells with limited damaging effect on normal cells. We propose integer programming-based methods for solving these problems in a unified manner, and we conduct computational experiments to illustrate the efficiency and the effectiveness of our method for our multiple-BN control problems. Conclusions We present three novel control problems for multiple BNs that are realistic control models for gene regulation networks and adopt an integer programming approach to address these problems. Experimental results indicate that our proposed method is useful and effective for moderate size BNs. PMID:24565276

  15. Pan- and core- network analysis of co-expression genes in a model plant

    PubMed Central

    He, Fei; Maslov, Sergei

    2016-01-01

    Genome-wide gene expression experiments have been performed using the model plant Arabidopsis during the last decade. Some studies involved construction of coexpression networks, a popular technique used to identify groups of co-regulated genes, to infer unknown gene functions. One approach is to construct a single coexpression network by combining multiple expression datasets generated in different labs. We advocate a complementary approach in which we construct a large collection of 134 coexpression networks based on expression datasets reported in individual publications. To this end we reanalyzed public expression data. To describe this collection of networks we introduced concepts of ‘pan-network’ and ‘core-network’ representing union and intersection between a sizeable fractions of individual networks, respectively. We showed that these two types of networks are different both in terms of their topology and biological function of interacting genes. For example, the modules of the pan-network are enriched in regulatory and signaling functions, while the modules of the core-network tend to include components of large macromolecular complexes such as ribosomes and photosynthetic machinery. Our analysis is aimed to help the plant research community to better explore the information contained within the existing vast collection of gene expression data in Arabidopsis. PMID:27982071

  16. COXPRESdb: a database of coexpressed gene networks in mammals.

    PubMed

    Obayashi, Takeshi; Hayashi, Shinpei; Shibaoka, Masayuki; Saeki, Motoshi; Ohta, Hiroyuki; Kinoshita, Kengo

    2008-01-01

    A database of coexpressed gene sets can provide valuable information for a wide variety of experimental designs, such as targeting of genes for functional identification, gene regulation and/or protein-protein interactions. Coexpressed gene databases derived from publicly available GeneChip data are widely used in Arabidopsis research, but platforms that examine coexpression for higher mammals are rather limited. Therefore, we have constructed a new database, COXPRESdb (coexpressed gene database) (http://coxpresdb.hgc.jp), for coexpressed gene lists and networks in human and mouse. Coexpression data could be calculated for 19 777 and 21 036 genes in human and mouse, respectively, by using the GeneChip data in NCBI GEO. COXPRESdb enables analysis of the four types of coexpression networks: (i) highly coexpressed genes for every gene, (ii) genes with the same GO annotation, (iii) genes expressed in the same tissue and (iv) user-defined gene sets. When the networks became too big for the static picture on the web in GO networks or in tissue networks, we used Google Maps API to visualize them interactively. COXPRESdb also provides a view to compare the human and mouse coexpression patterns to estimate the conservation between the two species.

  17. 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.

  18. GENIES: gene network inference engine based on supervised analysis.

    PubMed

    Kotera, Masaaki; Yamanishi, Yoshihiro; Moriya, Yuki; Kanehisa, Minoru; Goto, Susumu

    2012-07-01

    Gene network inference engine based on supervised analysis (GENIES) is a web server to predict unknown part of gene network from various types of genome-wide data in the framework of supervised network inference. The originality of GENIES lies in the construction of a predictive model using partially known network information and in the integration of heterogeneous data with kernel methods. The GENIES server accepts any 'profiles' of genes or proteins (e.g. gene expression profiles, protein subcellular localization profiles and phylogenetic profiles) or pre-calculated gene-gene similarity matrices (or 'kernels') in the tab-delimited file format. As a training data set to learn a predictive model, the users can choose either known molecular network information in the KEGG PATHWAY database or their own gene network data. The user can also select an algorithm of supervised network inference, choose various parameters in the method, and control the weights of heterogeneous data integration. The server provides the list of newly predicted gene pairs, maps the predicted gene pairs onto the associated pathway diagrams in KEGG PATHWAY and indicates candidate genes for missing enzymes in organism-specific metabolic pathways. GENIES (http://www.genome.jp/tools/genies/) is publicly available as one of the genome analysis tools in GenomeNet.

  19. 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

  20. 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).

  1. 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.

  2. Three gene expression vector sets for concurrently expressing multiple genes in Saccharomyces cerevisiae.

    PubMed

    Ishii, Jun; Kondo, Takashi; Makino, Harumi; Ogura, Akira; Matsuda, Fumio; Kondo, Akihiko

    2014-05-01

    Yeast has the potential to be used in bulk-scale fermentative production of fuels and chemicals due to its tolerance for low pH and robustness for autolysis. However, expression of multiple external genes in one host yeast strain is considerably labor-intensive due to the lack of polycistronic transcription. To promote the metabolic engineering of yeast, we generated systematic and convenient genetic engineering tools to express multiple genes in Saccharomyces cerevisiae. We constructed a series of multi-copy and integration vector sets for concurrently expressing two or three genes in S. cerevisiae by embedding three classical promoters. The comparative expression capabilities of the constructed vectors were monitored with green fluorescent protein, and the concurrent expression of genes was monitored with three different fluorescent proteins. Our multiple gene expression tool will be helpful to the advanced construction of genetically engineered yeast strains in a variety of research fields other than metabolic engineering.

  3. 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

  4. Gene Networks Underlying Chronic Sleep Deprivation in Drosophila

    DTIC Science & Technology

    2014-06-15

    SECURITY CLASSIFICATION OF: Studies of the gene network affected by sleep deprivation and stress in the fruit fly Drosophila have revealed the...transduction pathways are affected. Subseuqent tests of mutants in these pathways demonstrated a strong effect on sleep maintenance. Further...15-Apr-2009 14-Apr-2013 Approved for Public Release; Distribution Unlimited Gene Networks Underlying Chronic Sleep Deprivation in Drosophila The

  5. 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.

  6. 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.

  7. RiceFREND: a platform for retrieving coexpressed gene networks in rice.

    PubMed

    Sato, Yutaka; Namiki, Nobukazu; Takehisa, Hinako; Kamatsuki, Kaori; Minami, Hiroshi; Ikawa, Hiroshi; Ohyanagi, Hajime; Sugimoto, Kazuhiko; Itoh, Jun-Ichi; Antonio, Baltazar A; Nagamura, Yoshiaki

    2013-01-01

    Similarity of gene expression across a wide range of biological conditions can be efficiently used in characterization of gene function. We have constructed a rice gene coexpression database, RiceFREND (http://ricefrend.dna.affrc.go.jp/), to identify gene modules with similar expression profiles and provide a platform for more accurate prediction of gene functions. Coexpression analysis of 27 201 genes was performed against 815 microarray data derived from expression profiling of various organs and tissues at different developmental stages, mature organs throughout the growth from transplanting until harvesting in the field and plant hormone treatment conditions, using a single microarray platform. The database is provided with two search options, namely, 'single guide gene search' and 'multiple guide gene search' to efficiently retrieve information on coexpressed genes. A user-friendly web interface facilitates visualization and interpretation of gene coexpression networks in HyperTree, Cytoscape Web and Graphviz formats. In addition, analysis tools for identification of enriched Gene Ontology terms and cis-elements provide clue for better prediction of biological functions associated with the coexpressed genes. These features allow users to clarify gene functions and gene regulatory networks that could lead to a more thorough understanding of many complex agronomic traits.

  8. 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.

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

    PubMed

    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.

  10. Gene Coexpression Network Topology of Cardiac Development, Hypertrophy, and Failure

    PubMed Central

    Dewey, Frederick E.; Perez, Marco V.; Wheeler, Matthew T.; Watt, Clifton; Spin, Joshua; Langfelder, Peter; Horvath, Stephen; Hannenhalli, Sridhar; Cappola, Thomas P.; Ashley, Euan A.

    2011-01-01

    Background Network analysis techniques allow a more accurate reflection of underlying systems biology to be realized than traditional unidimensional molecular biology approaches. Here, using gene coexpression network analysis, we define the gene expression network topology of cardiac hypertrophy and failure and the extent of recapitulation of fetal gene expression programs in failing and hypertrophied adult myocardium. Methods and Results We assembled all myocardial transcript data in the Gene Expression Omnibus (n = 1617). Since hierarchical analysis revealed species had primacy over disease clustering, we focused this analysis on the most complete (murine) dataset (n = 478). Using gene coexpression network analysis, we derived functional modules, regulatory mediators and higher order topological relationships between genes and identified 50 gene co-expression modules in developing myocardium that were not present in normal adult tissue. We found that known gene expression markers of myocardial adaptation were members of upregulated modules but not hub genes. We identified ZIC2 as a novel transcription factor associated with coexpression modules common to developing and failing myocardium. Of 50 fetal gene co-expression modules, three (6%) were reproduced in hypertrophied myocardium and seven (14%) were reproduced in failing myocardium. One fetal module was common to both failing and hypertrophied myocardium. Conclusions Network modeling allows systems analysis of cardiovascular development and disease. While we did not find evidence for a global coordinated program of fetal gene expression in adult myocardial adaptation, our analysis revealed specific gene expression modules active during both development and disease and specific candidates for their regulation. PMID:21127201

  11. 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

  12. A study of multiple access schemes in satellite control network

    NASA Astrophysics Data System (ADS)

    Mo, Zijian; Wang, Zhonghai; Xiang, Xingyu; Wang, Gang; Chen, Genshe; Nguyen, Tien; Pham, Khanh; Blasch, Erik

    2016-05-01

    Satellite Control Networks (SCN) have provided launch control for space lift vehicles; tracking, telemetry and commanding (TTC) for on-orbit satellites; and, test support for space experiments since the 1960s. Currently, SCNs encounter a new challenge: how to maintain the high reliability of services when sharing the spectrum with emerging commercial services. To achieve this goal, the capability of multiple satellites reception is deserved as an update/modernization of SCN in the future. In this paper, we conducts an investigation of multiple access techniques in SCN scenario, e.g., frequency division multiple access (FDMA) and coded division multiple access (CDMA). First, we introduce two upgrade options of SCN based on FDMA and CDMA techniques. Correspondingly, we also provide their performance analysis, especially the system improvement in spectrum efficiency and interference mitigation. Finally, to determine the optimum upgrade option, this work uses CRISP, i.e., Cost, Risk, Installation, Supportability and Performance, as the baseline approach for a comprehensive trade study of these two options. Extensive numerical and simulation results are presented to illustrate the theoretical development.

  13. 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.

  14. Gene expression profiles in Finnish twins with multiple sclerosis

    PubMed Central

    Särkijärvi, Silja; Kuusisto, Hanna; Paalavuo, Raija; Levula, Mari; Airla, Nina; Lehtimäki, Terho; Kaprio, Jaakko; Koskenvuo, Markku; Elovaara, Irina

    2006-01-01

    Background Since genetic alterations influencing susceptibility to multiple sclerosis (MS), the most common autoimmune demyelinating disease of the central nervous system (CNS), are as yet poorly understood, the purpose of this study was to identify genes responsible for MS by studying monozygotic (MZ) twin pairs discordant for MS. Methods In order to identify genes involved in MS development, the gene expression profiles in blood mononuclear cells obtained from eight MZ twin pairs discordant for MS were analyzed by cDNA microarray technology detecting the expression of 8 300 genes. The twins were collected from the Finnish Twin Cohort Study and both affected subjects and their healthy siblings underwent neurological evaluation and cerebral and spinal magnetic resonance imaging. Gene expressions were confirmed by relative quantitative reverse transcription PCR. Results It appeared that 25 genes were at least two-fold up-regulated and 15 genes down-regulated in 25% (2/8) of twins with MS when compared to their healthy siblings. Moreover, 6/25 genes were up-regulated in 40% of MS twins and one gene, interferon alpha-inducible protein (clone IFI-6-16) (G1P3), in 50% of them. The six most constantly expressed genes are (1) G1P3, (2) POU domain, class 3, transcription factor 1, (3) myxovirus resistance 2, (4) lysosomal-associated multispanning membrane protein-5, (5) hemoglobin alpha 2 and (6) hemoglobin beta. Conclusion Over two-fold up-regulation of these six genes in almost half of MZ twins with MS suggests their role in MS pathogenesis. Studies using MZ MS twins obtained from genetically homogeneous population offer a unique opportunity to explore the genetic nature of MS. PMID:16504146

  15. Caenorhabditis elegans metabolic gene regulatory networks govern the cellular economy.

    PubMed

    Watson, Emma; Walhout, Albertha J M

    2014-10-01

    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 (NHRs) in responding to dietary conditions, regulation of metabolic genes and metabolic regulators by miRNAs, and feedback between metabolic genes and their regulators.

  16. Multiple Aspects of Gene Dysregulation in Huntington’s Disease

    PubMed Central

    Moumné, Lara; Betuing, Sandrine; Caboche, Jocelyne

    2013-01-01

    Huntington’s Disease (HD) is a genetic neurodegenerative disease caused by a CAG expansion in the gene encoding Huntingtin (Htt). It is characterized by chorea, cognitive, and psychiatric disorders. The most affected brain region is the striatum, and the clinical symptoms are directly correlated to the rate of striatal degeneration. The wild-type Htt is a ubiquitous protein and its deletion is lethal. Mutated (expanded) Htt produces excitotoxicity, mitochondrial dysfunctions, axonal transport deficit, altered proteasome activity, and gene dysregulation. Transcriptional dysregulation occurs at early neuropathological stages in HD patients. Multiple genes are dysregulated, with overlaps of altered transcripts between mouse models of HD and patient brains. Nuclear localization of Exp-Htt interferes with transcription factors, co-activators, and proteins of the transcriptional machinery. Another key mechanism described so far, is an alteration of cytoplasmic retention of the transcriptional repressor REST, which is normally associated with wild-type Htt. As such, Exp-Htt causes alteration of transcription of multiple genes involved in neuronal survival, plasticity, signaling, and mitochondrial biogenesis and respiration. Besides these transcriptional dysregulations, Exp-Htt affects the chromatin structure through altered post-translational modifications (PTM) of histones and methylation of DNA. Multiple alterations of histone PTM are described, including acetylation, methylation, ubiquitylation, polyamination, and phosphorylation. Exp-Htt also affects the expression and regulation of non-coding microRNAs (miRNAs). First multiple neural miRNAs are controlled by REST, and dysregulated in HD, with concomitant de-repression of downstream mRNA targets. Second, Exp-Htt protein or RNA may also play a major role in the processing of miRNAs and hence pathogenesis. These pleiotropic effects of Exp-Htt on gene expression may represent seminal deleterious effects in the

  17. NetDiff - Bayesian model selection for differential gene regulatory network inference.

    PubMed

    Thorne, Thomas

    2016-12-16

    Differential networks allow us to better understand the changes in cellular processes that are exhibited in conditions of interest, identifying variations in gene regulation or protein interaction between, for example, cases and controls, or in response to external stimuli. Here we present a novel methodology for the inference of differential gene regulatory networks from gene expression microarray data. Specifically we apply a Bayesian model selection approach to compare models of conserved and varying network structure, and use Gaussian graphical models to represent the network structures. We apply a variational inference approach to the learning of Gaussian graphical models of gene regulatory networks, that enables us to perform Bayesian model selection that is significantly more computationally efficient than Markov Chain Monte Carlo approaches. Our method is demonstrated to be more robust than independent analysis of data from multiple conditions when applied to synthetic network data, generating fewer false positive predictions of differential edges. We demonstrate the utility of our approach on real world gene expression microarray data by applying it to existing data from amyotrophic lateral sclerosis cases with and without mutations in C9orf72, and controls, where we are able to identify differential network interactions for further investigation.

  18. NetDiff – Bayesian model selection for differential gene regulatory network inference

    PubMed Central

    Thorne, Thomas

    2016-01-01

    Differential networks allow us to better understand the changes in cellular processes that are exhibited in conditions of interest, identifying variations in gene regulation or protein interaction between, for example, cases and controls, or in response to external stimuli. Here we present a novel methodology for the inference of differential gene regulatory networks from gene expression microarray data. Specifically we apply a Bayesian model selection approach to compare models of conserved and varying network structure, and use Gaussian graphical models to represent the network structures. We apply a variational inference approach to the learning of Gaussian graphical models of gene regulatory networks, that enables us to perform Bayesian model selection that is significantly more computationally efficient than Markov Chain Monte Carlo approaches. Our method is demonstrated to be more robust than independent analysis of data from multiple conditions when applied to synthetic network data, generating fewer false positive predictions of differential edges. We demonstrate the utility of our approach on real world gene expression microarray data by applying it to existing data from amyotrophic lateral sclerosis cases with and without mutations in C9orf72, and controls, where we are able to identify differential network interactions for further investigation. PMID:27982083

  19. Efficient reverse-engineering of a developmental gene regulatory network.

    PubMed

    Crombach, Anton; Wotton, Karl R; Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

    2012-01-01

    Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to

  20. Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

    PubMed Central

    Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

    2012-01-01

    Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to

  1. On the robustness of complex heterogeneous gene expression networks.

    PubMed

    Gómez-Gardeñes, Jesús; Moreno, Yamir; Floría, Luis M

    2005-04-01

    We analyze a continuous gene expression model on the underlying topology of a complex heterogeneous network. Numerical simulations aimed at studying the chaotic and periodic dynamics of the model are performed. The results clearly indicate that there is a region in which the dynamical and structural complexity of the system avoid chaotic attractors. However, contrary to what has been reported for Random Boolean Networks, the chaotic phase cannot be completely suppressed, which has important bearings on network robustness and gene expression modeling.

  2. Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks

    PubMed Central

    Emmert-Streib, Frank; Dehmer, Matthias; Haibe-Kains, Benjamin

    2014-01-01

    In recent years gene regulatory networks (GRNs) have attracted a lot of interest and many methods have been introduced for their statistical inference from gene expression data. However, despite their popularity, GRNs are widely misunderstood. For this reason, we provide in this paper a general discussion and perspective of gene regulatory networks. Specifically, we discuss their meaning, the consistency among different network inference methods, ensemble methods, the assessment of GRNs, the estimated number of existing GRNs and their usage in different application domains. Furthermore, we discuss open questions and necessary steps in order to utilize gene regulatory networks in a clinical context and for personalized medicine. PMID:25364745

  3. Computational inference of gene regulatory networks: Approaches, limitations and opportunities.

    PubMed

    Banf, Michael; Rhee, Seung Y

    2017-01-01

    Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

  4. Identification of multiple genetic loci that regulate adenovirus gene therapy.

    PubMed

    Zhang, H-G; Hsu, H-C; Yang, P-A; Yang, X; Wu, Q; Liu, Z; Yi, N; Mountz, J D

    2004-01-01

    A key aspect of the immune response to adenovirus (Ad) gene therapy is the generation of a cytotoxic T-cell (CTL) response. To better understand the genetic network underlying these events, 20 strains of C57BL/6 x DBA/2 (BXD) recombinant inbred (RI) mice were administered with AdLacZ and analyzed at days 7, 21, 30, and 50 for liver beta-galactosidase (LacZ) expression and CTL response. Sera levels of interferon gamma (IFN-gamma), tumor necrosis factor-alpha (TNF-alpha), and interleukin-6 (IL-6) were analyzed at different times after AdLacZ. There was a distinct strain-dependent expression of LacZ, which was strongly correlated with the CTL response. Among the five BXD RI strains that exhibited significantly prolonged LacZ expression, four also exhibited a marked defect in the production of Ad-specific CTL. There was a strong correlation between the sera levels of IFN-gamma, TNF-alpha, and IL-6, but cytokine responses were not significantly correlated with LacZ expression or the CTL response. Quantitative trait loci regulating LacZ on day 30 were found on chromosome (Chr) 19 (33 cM) and Chr 15 (42.8 cM). Cytotoxicity mapped to Chr 7 (41.0 and 57.4-65.2 cM), Chr 15 (61.7 cM), and Chr X (27.8 cM). IFN-gamma production mapped to Chr 18 (22, 27, and 32 cM) and Chr 11 (64.0 cM). TNF-alpha and IL-6 production mapped to Chr 6 (91.5 cM) Chr 9 (42.0 cM) and Chr 8 (52 and 73.0 cM). These results indicate that different strains of mice exhibit different pathways for effective clearance of AdLacZ depending on genetic polymorphisms and interactions at multiple genetic loci.

  5. Approaches for recognizing disease genes based on network.

    PubMed

    Zou, Quan; Li, Jinjin; Wang, Chunyu; Zeng, Xiangxiang

    2014-01-01

    Diseases are closely related to genes, thus indicating that genetic abnormalities may lead to certain diseases. The recognition of disease genes has long been a goal in biology, which may contribute to the improvement of health care and understanding gene functions, pathways, and interactions. However, few large-scale gene-gene association datasets, disease-disease association datasets, and gene-disease association datasets are available. A number of machine learning methods have been used to recognize disease genes based on networks. This paper states the relationship between disease and gene, summarizes the approaches used to recognize disease genes based on network, analyzes the core problems and challenges of the methods, and outlooks future research direction.

  6. Dose response relationship in anti-stress gene regulatory networks.

    PubMed

    Zhang, Qiang; Andersen, Melvin E

    2007-03-02

    To maintain a stable intracellular environment, cells utilize complex and specialized defense systems against a variety of external perturbations, such as electrophilic stress, heat shock, and hypoxia, etc. Irrespective of the type of stress, many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops. In general, the degree of deviation of the controlled variables, such as electrophiles, misfolded proteins, and O2, is first detected by specialized sensor molecules, then the signal is transduced to specific transcription factors. Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species (controlled variables, transcription factors, and gene products) in these gene regulatory networks. Our work indicated that the shape of dose response curves (linear, superlinear, or sublinear) depends on changes in the specific values of local response coefficients (gains) distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear, and depending on

  7. The Max-Min High-Order Dynamic Bayesian Network for Learning Gene Regulatory Networks with Time-Delayed Regulations.

    PubMed

    Li, Yifeng; Chen, Haifen; Zheng, Jie; Ngom, Alioune

    2016-01-01

    Accurately reconstructing gene regulatory network (GRN) from gene expression data is a challenging task in systems biology. Although some progresses have been made, the performance of GRN reconstruction still has much room for improvement. Because many regulatory events are asynchronous, learning gene interactions with multiple time delays is an effective way to improve the accuracy of GRN reconstruction. Here, we propose a new approach, called Max-Min high-order dynamic Bayesian network (MMHO-DBN) by extending the Max-Min hill-climbing Bayesian network technique originally devised for learning a Bayesian network's structure from static data. Our MMHO-DBN can explicitly model the time lags between regulators and targets in an efficient manner. It first uses constraint-based ideas to limit the space of potential structures, and then applies search-and-score ideas to search for an optimal HO-DBN structure. The performance of MMHO-DBN to GRN reconstruction was evaluated using both synthetic and real gene expression time-series data. Results show that MMHO-DBN is more accurate than current time-delayed GRN learning methods, and has an intermediate computing performance. Furthermore, it is able to learn long time-delayed relationships between genes. We applied sensitivity analysis on our model to study the performance variation along different parameter settings. The result provides hints on the setting of parameters of MMHO-DBN.

  8. Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification.

    PubMed

    Tian, Xue W; Lim, Joon S

    2015-01-01

    Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.

  9. Evolvability and hierarchy in rewired bacterial gene networks

    PubMed Central

    Isalan, Mark; Lemerle, Caroline; Michalodimitrakis, Konstantinos; Beltrao, Pedro; Horn, Carsten; Raineri, Emanuele; Garriga-Canut, Mireia; Serrano, Luis

    2009-01-01

    Sequencing DNA from several organisms has revealed that duplication and drift of existing genes have primarily molded the contents of a given genome. Though the effect of knocking out or over-expressing a particular gene has been studied in many organisms, no study has systematically explored the effect of adding new links in a biological network. To explore network evolvability, we constructed 598 recombinations of promoters (including regulatory regions) with different transcription or σ-factor genes in Escherichia coli, added over a wild-type genetic background. Here we show that ~95% of new networks are tolerated by the bacteria, that very few alter growth, and that expression level correlates with factor position in the wild-type network hierarchy. Most importantly, we find that certain networks consistently survive over the wild-type under various selection pressures. Therefore new links in the network are rarely a barrier for evolution and can even confer a fitness advantage. PMID:18421347

  10. Genes and Environment in Multiple Sclerosis project: A platform to investigate multiple sclerosis risk.

    PubMed

    Xia, Zongqi; White, Charles C; Owen, Emily K; Von Korff, Alina; Clarkson, Sarah R; McCabe, Cristin A; Cimpean, Maria; Winn, Phoebe A; Hoesing, Ashley; Steele, Sonya U; Cortese, Irene C M; Chitnis, Tanuja; Weiner, Howard L; Reich, Daniel S; Chibnik, Lori B; De Jager, Philip L

    2016-02-01

    The Genes and Environment in Multiple Sclerosis project establishes a platform to investigate the events leading to multiple sclerosis (MS) in at-risk individuals. It has recruited 2,632 first-degree relatives from across the USA. Using an integrated genetic and environmental risk score, we identified subjects with twice the MS risk when compared to the average family member, and we report an initial incidence rate in these subjects that is 30 times greater than that of sporadic MS. We discuss the feasibility of large-scale studies of asymptomatic at-risk subjects that leverage modern tools of subject recruitment to execute collaborative projects.

  11. INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Synchronization in Complex Networks with Multiple Connections

    NASA Astrophysics Data System (ADS)

    Wu, Qing-Chu; Fu, Xin-Chu; Sun, Wei-Gang

    2010-01-01

    In this paper a class of networks with multiple connections are discussed. The multiple connections include two different types of links between nodes in complex networks. For this new model, we give a simple generating procedure. Furthermore, we investigate dynamical synchronization behavior in a delayed two-layer network, giving corresponding theoretical analysis and numerical examples.

  12. Multiple de novo mutations in the MECP2 gene.

    PubMed

    Bunyan, David J; Robinson, David O

    2008-09-01

    Rett syndrome is an X-linked dominant disorder that usually arises following a single de novo mutation in the MECP2 gene. Point mutation testing and gene dosage analysis of a cohort of British Rett syndrome patients in our laboratory revealed four females who each had two different de novo causative mutations, presumed to be in cis because the patients showed no deviation from the classical Rett syndrome phenotype. Two of these cases had a point mutation and a small intraexonic deletion, a third had a whole exon deletion and a separate small intraexonic deletion, and a fourth case had a small intraexonic deletion and a large duplication. These findings highlight the necessity to perform both point mutation analysis and exon dosage analysis in such cases, particularly because of the possibility of undetected parental mosaicism and the implications for prenatal diagnosis in future pregnancies. These cases also suggest that the MECP2 gene may be particularly prone to multiple mutation events.

  13. 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

  14. 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

  15. Regulatory network analysis of microRNAs and genes in imatinib-resistant chronic myeloid leukemia.

    PubMed

    Soltani, Ismael; Gharbi, Hanen; Hassine, Islem Ben; Bouguerra, Ghada; Douzi, Kais; Teber, Mouheb; Abbes, Salem; Menif, Samia

    2016-09-16

    Targeted therapy in the form of selective breakpoint cluster region-abelson (BCR/ABL) tyrosine kinase inhibitor (imatinib mesylate) has successfully been introduced in the treatment of the chronic myeloid leukemia (CML). However, acquired resistance against imatinib mesylate (IM) has been reported in nearly half of patients and has been recognized as major issue in clinical practice. Multiple resistance genes and microRNAs (miRNAs) are thought to be involved in the IM resistance process. These resistance genes and miRNAs tend to interact with each other through a regulatory network. Therefore, it is crucial to study the impact of these interactions in the IM resistance process. The present study focused on miRNA and gene network analysis in order to elucidate the role of interacting elements and to understand their functional contribution in therapeutic failure. Unlike previous studies which were centered only on genes or miRNAs, the prime focus of the present study was on relationships. To this end, three regulatory networks including differentially expressed, related, and global networks were constructed and analyzed in search of similarities and differences. Regulatory associations between miRNAs and their target genes, transcription factors and miRNAs, as well as miRNAs and their host genes were also macroscopically investigated. Certain key pathways in the three networks, especially in the differentially expressed network, were featured. The differentially expressed network emerged as a fault map of IM-resistant CML. Theoretically, the IM resistance process could be prevented by correcting the included errors. The present network-based approach to study resistance miRNAs and genes might help in understanding the molecular mechanisms of IM resistance in CML as well as in the improvement of CML therapy.

  16. Reverse engineering of gene regulatory networks: a comparative study.

    PubMed

    Hache, Hendrik; Lehrach, Hans; Herwig, Ralf

    2009-01-01

    Reverse engineering of gene regulatory networks has been an intensively studied topic in bioinformatics since it constitutes an intermediate step from explorative to causative gene expression analysis. Many methods have been proposed through recent years leading to a wide range of mathematical approaches. In practice, different mathematical approaches will generate different resulting network structures, thus, it is very important for users to assess the performance of these algorithms. We have conducted a comparative study with six different reverse engineering methods, including relevance networks, neural networks, and Bayesian networks. Our approach consists of the generation of defined benchmark data, the analysis of these data with the different methods, and the assessment of algorithmic performances by statistical analyses. Performance was judged by network size and noise levels. The results of the comparative study highlight the neural network approach as best performing method among those under study.

  17. 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

  18. Variable neighborhood search for reverse engineering of gene regulatory networks.

    PubMed

    Nicholson, Charles; Goodwin, Leslie; Clark, Corey

    2017-01-01

    A new search heuristic, Divided Neighborhood Exploration Search, designed to be used with inference algorithms such as Bayesian networks to improve on the reverse engineering of gene regulatory networks is presented. The approach systematically moves through the search space to find topologies representative of gene regulatory networks that are more likely to explain microarray data. In empirical testing it is demonstrated that the novel method is superior to the widely employed greedy search techniques in both the quality of the inferred networks and computational time.

  19. 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.

  20. 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.

  1. Copy number variations exploration of multiple genes in Graves’ disease

    PubMed Central

    Song, Rong-hua; Shao, Xiao-qing; Li, Ling; Wang, Wen; Zhang, Jin-an

    2017-01-01

    Abstract Background: Few previous published papers reported copy number variations of genes could affect the predisposition of Graves’ disease (GD). Herein, the aim of this study was to explore the association between copy number variations (CNV) profile and GD. Methods: The preliminary copy number microarray used to screen copy number variant genes was performed in 6 GD patients. Five CNV candidate genes (CFH, CFHR1, KIAA0125, UGT2B15, and UGT2B17) were then validated in an independent set of samples (50 GD patients and 50 matched healthy ones) by the Accucopy assay method. The CNV of the other 2 genes TRY6 and CCL3L1 was investigated in 144 GD patients and 144 healthy volunteers by the definitive genotyping technique using the Taqman quantitative polymerase-chain-reaction (Taqman qPCR). TRY6 gene-associated single nucleotide polymorphism (SNP), rs13230029, was genotyped by the PCR-ligase detection reaction (LDR) in 675 GD patients and 898 healthy controls. Results: There were no correlation of the gene copy number (GCN) of CFH, CFHR1, KIAA0125, UGT2B15, and UGT2B17 with GD. In comparison with that of controls, the GCN distribution of TRY6 and CCL3L1 in GD patients did not show significantly differ (P > 0.05). Furthermore, TRY6-related polymorphism (rs13230029) showed no difference between GD patients and controls. No correlation was found between CNV or SNP genotype and clinical phenotypes. Generally, there were no link of the copy numbers of several genes, including CFH, CFHR1, KIAA0125, UGT2B15, UGT2B17, TRY6, and CCL3L1 to GD. Conclusion: Our results clearly indicated that the copy number variations of multiple genes, namely CFH, CFHR1, KIAA0125, UGT2B15, UGT2B17, TRY6, and CCL3L1, were not associated with the development of GD. PMID:28121931

  2. A Maize Gene Regulatory Network for Phenolic Metabolism.

    PubMed

    Yang, Fan; Li, Wei; Jiang, Nan; Yu, Haidong; Morohashi, Kengo; Ouma, Wilberforce Zachary; Morales-Mantilla, Daniel E; Gomez-Cano, Fabio Andres; Mukundi, Eric; Prada-Salcedo, Luis Daniel; Velazquez, Roberto Alers; Valentin, Jasmin; Mejía-Guerra, Maria Katherine; Gray, John; Doseff, Andrea I; Grotewold, Erich

    2017-03-06

    The translation of the genotype into phenotype, represented for example by the expression of genes encoding enzymes required for the biosynthesis of phytochemicals that are important for interaction of plants with the environment, is largely carried out by transcription factors (TFs) that recognize specific cis-regulatory elements in the genes that they control. TFs and their target genes are organized in gene regulatory networks (GRNs), and thus uncovering GRN architecture presents an important biological challenge necessary to explain gene regulation. Linking TFs to the genes they control, central to understanding GRNs, can be carried out using gene- or TF-centered approaches. In this study, we employed a gene-centered approach utilizing the yeast one-hybrid assay to generate a network of protein-DNA interactions that participate in the transcriptional control of genes involved in the biosynthesis of maize phenolic compounds including general phenylpropanoids, lignins, and flavonoids. We identified 1100 protein-DNA interactions involving 54 phenolic gene promoters and 568 TFs. A set of 11 TFs recognized 10 or more promoters, suggesting a role in coordinating pathway gene expression. The integration of the gene-centered network with information derived from TF-centered approaches provides a foundation for a phenolics GRN characterized by interlaced feed-forward loops that link developmental regulators with biosynthetic genes.

  3. Multiple-to-Multiple Relationships between MicroRNAs and Target Genes in Gastric Cancer

    PubMed Central

    Hashimoto, Yutaka; Akiyama, Yoshimitsu; Yuasa, Yasuhito

    2013-01-01

    MicroRNAs (miRNAs) act as transcriptional regulators and play pivotal roles in carcinogenesis. According to miRNA target databases, one miRNA may regulate many genes as its targets, while one gene may be targeted by many miRNAs. These findings indicate that relationships between miRNAs and their targets may not be one-to-one. However, many reports have described only a one-to-one, one-to-multiple or multiple-to-one relationship between miRNA and its target gene in human cancers. Thus, it is necessary to determine whether or not a combination of some miRNAs would regulate multiple targets and be involved in carcinogenesis. To find some groups of miRNAs that may synergistically regulate their targets in human gastric cancer (GC), we re-analyzed our previous miRNA expression array data and found that 50 miRNAs were up-regulated on treatment with 5-aza-2'-deoxycytidine in a GC cell line. The “TargetScan” miRNA target database predicted that some of these miRNAs have common target genes. We also referred to the GEO database for expression of these common target genes in human GCs, which might be related to gastric carcinogenesis. In this study, we analyzed two miRNA combinations, miR-224 and -452, and miR-181c and -340. Over-expression of both miRNA combinations dramatically down-regulated their target genes, DPYSL2 and KRAS, and KRAS and MECP2, respectively. These miRNA combinations synergistically decreased cell proliferation upon transfection. Furthermore, we revealed that these miRNAs were down-regulated through promoter hypermethylation in GC cells. Thus, it is likely that the relationships between miRNAs and their targets are not one-to-one but multiple-to-multiple in GCs, and that these complex relationships may be related to gastric carcinogenesis. PMID:23667495

  4. Analysis of Gene Sets Based on the Underlying Regulatory Network

    PubMed Central

    Michailidis, George

    2009-01-01

    Abstract Networks are often used to represent the interactions among genes and proteins. These interactions are known to play an important role in vital cell functions and should be included in the analysis of genes that are differentially expressed. Methods of gene set analysis take advantage of external biological information and analyze a priori defined sets of genes. These methods can potentially preserve the correlation among genes; however, they do not directly incorporate the information about the gene network. In this paper, we propose a latent variable model that directly incorporates the network information. We then use the theory of mixed linear models to present a general inference framework for the problem of testing the significance of subnetworks. Several possible test procedures are introduced and a network based method for testing the changes in expression levels of genes as well as the structure of the network is presented. The performance of the proposed method is compared with methods of gene set analysis using both simulation studies, as well as real data on genes related to the galactose utilization pathway in yeast. PMID:19254181

  5. 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

  6. 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.

  7. A combination of gene expression ranking and co-expression network analysis increases discovery rate in large-scale mutant screens for novel Arabidopsis thaliana abiotic stress genes.

    PubMed

    Ransbotyn, Vanessa; Yeger-Lotem, Esti; Basha, Omer; Acuna, Tania; Verduyn, Christoph; Gordon, Michal; Chalifa-Caspi, Vered; Hannah, Matthew A; Barak, Simon

    2015-05-01

    As challenges to food security increase, the demand for lead genes for improving crop production is growing. However, genetic screens of plant mutants typically yield very low frequencies of desired phenotypes. Here, we present a powerful computational approach for selecting candidate genes for screening insertion mutants. We combined ranking of Arabidopsis thaliana regulatory genes according to their expression in response to multiple abiotic stresses (Multiple Stress [MST] score), with stress-responsive RNA co-expression network analysis to select candidate multiple stress regulatory (MSTR) genes. Screening of 62 T-DNA insertion mutants defective in candidate MSTR genes, for abiotic stress germination phenotypes yielded a remarkable hit rate of up to 62%; this gene discovery rate is 48-fold greater than that of other large-scale insertional mutant screens. Moreover, the MST score of these genes could be used to prioritize them for screening. To evaluate the contribution of the co-expression analysis, we screened 64 additional mutant lines of MST-scored genes that did not appear in the RNA co-expression network. The screening of these MST-scored genes yielded a gene discovery rate of 36%, which is much higher than that of classic mutant screens but not as high as when picking candidate genes from the co-expression network. The MSTR co-expression network that we created, AraSTressRegNet is publicly available at http://netbio.bgu.ac.il/arnet. This systems biology-based screening approach combining gene ranking and network analysis could be generally applicable to enhancing identification of genes regulating additional processes in plants and other organisms provided that suitable transcriptome data are available.

  8. Multi-Commodity Network Flow for Tracking Multiple People.

    PubMed

    Ben Shitrit, Horesh; Berclaz, Jérôme; Fleuret, Francois; Fua, Pascal

    2014-08-01

    In this paper, we show that tracking multiple people whose paths may intersect can be formulated as a multi-commodity network flow problem. Our proposed framework is designed to exploit image appearance cues to prevent identity switches. Our method is effective even when such cues are only available at distant time intervals. This is unlike many current approaches that depend on appearance being exploitable from frame-to-frame. Furthermore, our algorithm lends itself to a real-time implementation. We validate our approach on three publicly available datasets that contain long and complex sequences, the APIDIS basketball dataset, the ISSIA soccer dataset, and the PETS'09 pedestrian dataset. We also demonstrate its performance on a newer basketball dataset that features complete world championship basketball matches. In all cases, our approach preserves identity better than state-of-the-art tracking algorithms.

  9. Multi-Commodity Network Flow for Tracking Multiple People.

    PubMed

    Ben Shitrit, Horesh; Berclaz, Jérôme; Fleuret, François; Fua, Pascal

    2013-10-17

    n this paper, we show that tracking multiple people whose paths may intersect can be formulated as a multi-commodity network flow problem. Our proposed framework is designed to exploit image appearance cues to prevent identity switches. Our method is effective even when such cues are only available at distant time intervals. This is unlike many current approaches that depend on appearance being exploitable from frame to frame. Furthermore, our algorithm lends itself to a real-time implementation. We validate our approach on three publicly available datasets that contain long and complex sequences, the APIDIS basketball dataset, the ISSIA soccer dataset and the PETS’09 pedestrian dataset. We also demonstrate its performance on a newer basketball dataset that features complete world championship basketball matches. In all cases, our approach preserves identity better than state-of-the-art tracking algorithms.

  10. Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease

    PubMed Central

    Hartman, John L.; Stisher, Chandler; Outlaw, Darryl A.; Guo, Jingyu; Shah, Najaf A.; Tian, Dehua; Santos, Sean M.; Rodgers, John W.; White, Richard A.

    2015-01-01

    The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease. PMID:25668739

  11. Identifying gene regulatory network rewiring using latent differential graphical models

    PubMed Central

    Tian, Dechao; Gu, Quanquan; Ma, Jian

    2016-01-01

    Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a specific cell/tissue type or in a tissue-naive way, or do not specifically focus on network rewiring between different tissues. Here, we describe a new method called Latent Differential Graphical Model (LDGM). The motivation of our method is to estimate the differential network between two tissue types directly without inferring the network for individual tissues, which has the advantage of utilizing much smaller sample size to achieve reliable differential network estimation. Our simulation results demonstrated that LDGM consistently outperforms other Gaussian graphical model based methods. We further evaluated LDGM by applying to the brain and blood gene expression data from the GTEx consortium. We also applied LDGM to identify network rewiring between cancer subtypes using the TCGA breast cancer samples. Our results suggest that LDGM is an effective method to infer differential network using high-throughput gene expression data to identify GRN dynamics among different cellular conditions. PMID:27378774

  12. Identification of Genes Discriminating Multiple Sclerosis Patients from Controls by Adapting a Pathway Analysis Method

    PubMed Central

    Zhang, Lei; Wang, Linlin; Tian, Pu

    2016-01-01

    The focus of analyzing data from microarray experiments has shifted from the identification of associated individual genes to that of associated biological pathways or gene sets. In bioinformatics, a feature selection algorithm is usually used to cope with the high dimensionality of microarray data. In addition to those algorithms that use the biological information contained within a gene set as a priori to facilitate the process of feature selection, various gene set analysis methods can be applied directly or modified readily for the purpose of feature selection. Significance analysis of microarray to gene-set reduction analysis (SAM-GSR) algorithm, a novel direction of gene set analysis, is one of such methods. Here, we explore the feature selection property of SAM-GSR and provide a modification to better achieve the goal of feature selection. In a multiple sclerosis (MS) microarray data application, both SAM-GSR and our modification of SAM-GSR perform well. Our results show that SAM-GSR can carry out feature selection indeed, and modified SAM-GSR outperforms SAM-GSR. Given pathway information is far from completeness, a statistical method capable of constructing biologically meaningful gene networks is of interest. Consequently, both SAM-GSR algorithms will be continuously revaluated in our future work, and thus better characterized. PMID:27846233

  13. 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

  14. Gene duplication models for directed networks with limits on growth

    NASA Astrophysics Data System (ADS)

    Enemark, Jakob; Sneppen, Kim

    2007-11-01

    Background: Duplication of genes is important for evolution of molecular networks. Many authors have therefore considered gene duplication as a driving force in shaping the topology of molecular networks. In particular it has been noted that growth via duplication would act as an implicit means of preferential attachment, and thereby provide the observed broad degree distributions of molecular networks. Results: We extend current models of gene duplication and rewiring by including directions and the fact that molecular networks are not a result of unidirectional growth. We introduce upstream sites and downstream shapes to quantify potential links during duplication and rewiring. We find that this in itself generates the observed scaling of transcription factors for genome sites in prokaryotes. The dynamical model can generate a scale-free degree distribution, p(k)\\propto 1/k^{\\gamma } , with exponent γ = 1 in the non-growing case, and with γ>1 when the network is growing. Conclusions: We find that duplication of genes followed by substantial recombination of upstream regions could generate features of genetic regulatory networks. Our steady state degree distribution is however too broad to be consistent with data, thereby suggesting that selective pruning acts as a main additional constraint on duplicated genes. Our analysis shows that gene duplication can only be a main cause for the observed broad degree distributions if there are also substantial recombinations between upstream regions of genes.

  15. Gene regulatory network inference using out of equilibrium statistical mechanics

    PubMed Central

    Benecke, Arndt

    2008-01-01

    Spatiotemporal control of gene expression is fundamental to multicellular life. Despite prodigious efforts, the encoding of gene expression regulation in eukaryotes is not understood. Gene expression analyses nourish the hope to reverse engineer effector-target gene networks using inference techniques. Inference from noisy and circumstantial data relies on using robust models with few parameters for the underlying mechanisms. However, a systematic path to gene regulatory network reverse engineering from functional genomics data is still impeded by fundamental problems. Recently, Johannes Berg from the Theoretical Physics Institute of Cologne University has made two remarkable contributions that significantly advance the gene regulatory network inference problem. Berg, who uses gene expression data from yeast, has demonstrated a nonequilibrium regime for mRNA concentration dynamics and was able to map the gene regulatory process upon simple stochastic systems driven out of equilibrium. The impact of his demonstration is twofold, affecting both the understanding of the operational constraints under which transcription occurs and the capacity to extract relevant information from highly time-resolved expression data. Berg has used his observation to predict target genes of selected transcription factors, and thereby, in principle, demonstrated applicability of his out of equilibrium statistical mechanics approach to the gene network inference problem. PMID:19404429

  16. Multiple time scale behaviors and network dynamics in liquid methanol.

    PubMed

    Sharma, Ruchi; Chakravarty, Charusita; Milotti, Edoardo

    2008-07-31

    Canonical ensemble molecular dynamics simulations of liquid methanol, modeled using a rigid-body, pair-additive potential, are used to compute static distributions and temporal correlations of tagged molecule potential energies as a means of characterizing the liquid state dynamics. The static distribution of tagged molecule potential energies shows a clear multimodal structure with three distinct peaks, similar to those observed previously in water and liquid silica. The multimodality is shown to originate from electrostatic effects, but not from local, hydrogen bond interactions. An interesting outcome of this study is the remarkable similarity in the tagged potential energy power spectra of methanol, water, and silica, despite the differences in the underlying interactions and the dimensionality of the network. All three liquids show a distinct multiple time scale (MTS) regime with a 1/ f (alpha) dependence with a clear positive correlation between the scaling exponent alpha and the diffusivity. The low-frequency limit of the MTS regime is determined by the frequency of crossover to white noise behavior which occurs at approximately 0.1 cm (-1) in the case of methanol under standard temperature and pressure conditions. The power spectral regime above 200 cm (-1) in all three systems is dominated by resonances due to localized vibrations, such as librations. The correlation between alpha and the diffusivity in all three liquids appears to be related to the strength of the coupling between the localized motions and the larger length/time scale network reorganizations. Thus, the time scales associated with network reorganization dynamics appear to be qualitatively similar in these systems, despite the fact that water and silica both display diffusional anomalies but methanol does not.

  17. 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

    2015-10-05

    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.

  18. Anti-inflammatory genes associated with multiple sclerosis: a gene expression study.

    PubMed

    Perga, S; Montarolo, F; Martire, S; Berchialla, P; Malucchi, S; Bertolotto, A

    2015-02-15

    Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system caused by a complex interaction between multiple genes and environmental factors. HLA region is the strongest susceptibility locus, but recent huge genome-wide association studies identified new susceptibility genes. Among these, BACH2, PTGER4, RGS1 and ZFP36L1 were highlighted. Here, a gene expression analysis revealed that three of them, namely BACH2, PTGER4 and ZFP36L1, are down-regulated in MS patients' blood cells compared to healthy subjects. Interestingly, all these genes are involved in the immune system regulation with predominant anti-inflammatory role and their reduction could predispose to MS development.

  19. Gene transcriptional networks integrate microenvironmental signals in human breast cancer.

    PubMed

    Xu, Ren; Mao, Jian-Hua

    2011-04-01

    A significant amount of evidence shows that microenvironmental signals generated from extracellular matrix (ECM) molecules, soluble factors, and cell-cell adhesion complexes cooperate at the extra- and intracellular level. This synergetic action of microenvironmental cues is crucial for normal mammary gland development and breast malignancy. To explore how the microenvironmental genes coordinate in human breast cancer at the genome level, we have performed gene co-expression network analysis in three independent microarray datasets and identified two microenvironment networks in human breast cancer tissues. Network I represents crosstalk and cooperation of ECM microenvironment and soluble factors during breast malignancy. The correlated expression of cytokines, chemokines, and cell adhesion proteins in Network II implicates the coordinated action of these molecules in modulating the immune response in breast cancer tissues. These results suggest that microenvironmental cues are integrated with gene transcriptional networks to promote breast cancer development.

  20. Inference of the Xenopus tropicalis embryonic regulatory network and spatial gene expression patterns

    PubMed Central

    2014-01-01

    Background During embryogenesis, signaling molecules produced by one cell population direct gene regulatory changes in neighboring cells and influence their developmental fates and spatial organization. One of the earliest events in the development of the vertebrate embryo is the establishment of three germ layers, consisting of the ectoderm, mesoderm and endoderm. Attempts to measure gene expression in vivo in different germ layers and cell types are typically complicated by the heterogeneity of cell types within biological samples (i.e., embryos), as the responses of individual cell types are intermingled into an aggregate observation of heterogeneous cell types. Here, we propose a novel method to elucidate gene regulatory circuits from these aggregate measurements in embryos of the frog Xenopus tropicalis using gene network inference algorithms and then test the ability of the inferred networks to predict spatial gene expression patterns. Results We use two inference models with different underlying assumptions that incorporate existing network information, an ODE model for steady-state data and a Markov model for time series data, and contrast the performance of the two models. We apply our method to both control and knockdown embryos at multiple time points to reconstruct the core mesoderm and endoderm regulatory circuits. Those inferred networks are then used in combination with known dorsal-ventral spatial expression patterns of a subset of genes to predict spatial expression patterns for other genes. Both models are able to predict spatial expression patterns for some of the core mesoderm and endoderm genes, but interestingly of different gene subsets, suggesting that neither model is sufficient to recapitulate all of the spatial patterns, yet they are complementary for the patterns that they do capture. Conclusion The presented methodology of gene network inference combined with spatial pattern prediction provides an additional layer of validation to

  1. Characterizing Multiple Wireless Sensor Networks for Large-Scale Radio Tomography

    DTIC Science & Technology

    2015-03-01

    Network in Home Automation Network and Smart Grid,” in 2012 International Conference on Complex Systems, Agadir, Morocco, Nov. 2012, pp. 1–6. [13] D. Maas...CHARACTERIZING MULTIPLE WIRELESS SENSOR NETWORKS FOR LARGE-SCALE RADIO TOMOGRAPHY THESIS Tan Van, Captain, USAF AFIT-ENG-MS-15-M-057 DEPARTMENT OF...subject to copyright protection in the United States. AFIT-ENG-MS-15-M-057 CHARACTERIZING MULTIPLE WIRELESS SENSOR NETWORKS FOR LARGE-SCALE RADIO

  2. 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

  3. 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

  4. Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

    DOE PAGES

    Faria, Jose P.; Overbeek, Ross; Taylor, Ronald C.; ...

    2016-03-18

    Here, we introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of B. 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, wemore » 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 approximately 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 atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons 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

  5. 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

  6. A rapid and reliable strategy for chromosomal integration of gene(s) with multiple copies

    PubMed Central

    Gu, Pengfei; Yang, Fan; Su, Tianyuan; Wang, Qian; Liang, Quanfeng; Qi, Qingsheng

    2015-01-01

    Direct optimization of the metabolic pathways on the chromosome requires tools that can fine tune the overexpression of a desired gene or optimize the combination of multiple genes. Although plasmid-dependent overexpression has been used for this task, fundamental issues concerning its genetic stability and operational repeatability have not been addressed. Here, we describe a rapid and reliable strategy for chromosomal integration of gene(s) with multiple copies (CIGMC), which uses the flippase from the yeast 2-μm plasmid. Using green fluorescence protein as a model, we verified that the fluorescent intensity was in accordance with the integration copy number of the target gene. When a narrow-host-range replicon, R6K, was used in the integrative plasmid, the maximum integrated copy number of Escherichia coli reached 15. Applying the CIGMC method to optimize the overexpression of single or multiple genes in amino acid biosynthesis, we successfully improved the product yield and stability of the production. As a flexible strategy, CIGMC can be used in various microorganisms other than E. coli. PMID:25851494

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

    PubMed

    Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

    2009-01-01

    The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations show cell-to-cell variability that can manifest significant phenotypic differences. Noise-induced stochastic fluctuations in cellular constituents 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 show 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.

  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.

  9. Inferring gene correlation networks from transcription factor binding sites.

    PubMed

    Mahdevar, Ghasem; Nowzari-Dalini, Abbas; Sadeghi, Mehdi

    2013-01-01

    Gene expression is a highly regulated biological process that is fundamental to the existence of phenotypes of any living organism. The regulatory relations are usually modeled as a network; simply, every gene is modeled as a node and relations are shown as edges between two related genes. This paper presents a novel method for inferring correlation networks, networks constructed by connecting co-expressed genes, through predicting co-expression level from genes promoter's sequences. According to the results, this method works well on biological data and its outcome is comparable to the methods that use microarray as input. The method is written in C++ language and is available upon request from the corresponding author.

  10. Dynamics of gene regulatory networks with cell division cycle

    NASA Astrophysics Data System (ADS)

    Chen, Luonan; Wang, Ruiqi; Kobayashi, Tetsuya J.; Aihara, Kazuyuki

    2004-07-01

    This paper focuses on modeling and analyzing the nonlinear dynamics of gene regulatory networks with the consideration of a cell division cycle with duplication process of DNA , in particular for switches and oscillators of synthetic networks. We derive two models that may correspond to the eukaryotic and prokaryotic cells, respectively. A biologically plausible three-gene model ( lac,tetR , and cI ) and a repressilator as switch and oscillator examples are used to illustrate our theoretical results. We show that the cell cycle may play a significant role in gene regulation due to the nonlinear dynamics of a gene regulatory network although gene expressions are usually tightly controlled by transcriptional factors.

  11. Network of Cancer Genes (NCG 3.0): integration and analysis of genetic and network properties of cancer genes.

    PubMed

    D'Antonio, Matteo; Pendino, Vera; Sinha, Shruti; Ciccarelli, Francesca D

    2012-01-01

    The identification of a constantly increasing number of genes whose mutations are causally implicated in tumor initiation and progression (cancer genes) requires the development of tools to store and analyze them. The Network of Cancer Genes (NCG 3.0) collects information on 1494 cancer genes that have been found mutated in 16 different cancer types. These genes were collected from the Cancer Gene Census as well as from 18 whole exome and 11 whole-genome screenings of cancer samples. For each cancer gene, NCG 3.0 provides a summary of the gene features and the cross-reference to other databases. In addition, it describes duplicability, evolutionary origin, orthology, network properties, interaction partners, microRNA regulation and functional roles of cancer genes and of all genes that are related to them. This integrated network of information can be used to better characterize cancer genes in the context of the system in which they act. The data can also be used to identify novel candidates that share the same properties of known cancer genes and may therefore play a similar role in cancer. NCG 3.0 is freely available at http://bio.ifom-ieo-campus.it/ncg.

  12. Functional analysis of sirtuin genes in multiple Plasmodium falciparum strains.

    PubMed

    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.

  13. 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

  14. 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.

  15. GENIUS: web server to predict local gene networks and key genes for biological functions.

    PubMed

    Puelma, Tomas; Araus, Viviana; Canales, Javier; Vidal, Elena A; Cabello, Juan M; Soto, Alvaro; Gutiérrez, Rodrigo A

    2016-12-19

    GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods.

  16. Expression profiling reveals functionally redundant multiple-copy genes related to zinc, iron and cadmium responses in Brassica rapa.

    PubMed

    Li, Jimeng; Liu, Bo; Cheng, Feng; Wang, Xiaowu; Aarts, Mark G M; Wu, Jian

    2014-07-01

    Genes underlying environmental adaptability tend to be over-retained in polyploid plant species. Zinc deficiency (ZnD) and iron deficiency (FeD), excess Zn (ZnE) and cadmium exposure (CdE) are major environmental problems for crop cultivation, but little is known about the differential expression of duplicated genes upon these stress conditions. Applying Tag-Seq technology to leaves of Brassica rapa grown under FeD, ZnD, ZnE or CdE conditions, with normal conditions as a control, we examined global gene expression changes and compared the expression patterns of multiple paralogs. We identified 812, 543, 331 and 447 differentially expressed genes under FeD, ZnD, ZnE and CdE conditions, respectively, in B. rapa leaves. Genes involved in regulatory networks centered on the transcription factors bHLH038 or bHLH100 were differentially expressed under (ZnE-induced) FeD. Further analysis revealed that genes associated with Zn, Fe and Cd responses tended to be over-retained in the B. rapa genome. Most of these multiple-copy genes showed the same direction of expression change under stress conditions. We conclude that the duplicated genes involved in trace element responses in B. rapa are functionally redundant, making the regulatory network more complex in B. rapa than in Arabidopsis thaliana.

  17. Gene regulation: hacking the network on a sugar high.

    PubMed

    Ellis, Tom; Wang, Xiao; Collins, James J

    2008-04-11

    In a recent issue of Molecular Cell, Kaplan et al. (2008) determine the input functions for 19 E. coli sugar-utilization genes by using a two-dimensional high-throughput approach. The resulting input-function map reveals that gene network regulation follows non-Boolean, and often nonmonotonic, logic.

  18. 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.

  19. 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

  20. Gene regulatory network clustering for graph layout based on microarray gene expression data.

    PubMed

    Kojima, Kaname; Imoto, Seiya; Nagasaki, Masao; Miyano, Satoru

    2010-01-01

    We propose a statistical model realizing simultaneous estimation of gene regulatory network and gene module identification from time series gene expression data from microarray experiments. Under the assumption that genes in the same module are densely connected, the proposed method detects gene modules based on the variational Bayesian technique. The model can also incorporate existing biological prior knowledge such as protein subcellular localization. We apply the proposed model to the time series data from a synthetically generated network and verified the effectiveness of the proposed model. The proposed model is also applied the time series microarray data from HeLa cell. Detected gene module information gives the great help on drawing the estimated gene network.

  1. Optimal finite horizon control in gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Liu, Qiuli

    2013-06-01

    As a paradigm for modeling gene regulatory networks, probabilistic Boolean networks (PBNs) form a subclass of Markov genetic regulatory networks. To date, many different stochastic optimal control approaches have been developed to find therapeutic intervention strategies for PBNs. A PBN is essentially a collection of constituent Boolean networks via a probability structure. Most of the existing works assume that the probability structure for Boolean networks selection is known. Such an assumption cannot be satisfied in practice since the presence of noise prevents the probability structure from being accurately determined. In this paper, we treat a case in which we lack the governing probability structure for Boolean network selection. Specifically, in the framework of PBNs, the theory of finite horizon Markov decision process is employed to find optimal constituent Boolean networks with respect to the defined objective functions. In order to illustrate the validity of our proposed approach, an example is also displayed.

  2. Identification of cancer-related genes and motifs in the human gene regulatory network.

    PubMed

    Carson, Matthew B; Gu, Jianlei; Yu, Guangjun; Lu, Hui

    2015-08-01

    The authors investigated the regulatory network motifs and corresponding motif positions of cancer-related genes. First, they mapped disease-related genes to a transcription factor regulatory network. Next, they calculated statistically significant motifs and subsequently identified positions within these motifs that were enriched in cancer-related genes. Potential mechanisms of these motifs and positions are discussed. These results could be used to identify other disease- and cancer-related genes and could also suggest mechanisms for how these genes relate to co-occurring diseases.

  3. Beyond Genomics: Studying Evolution with Gene Coexpression Networks.

    PubMed

    Ruprecht, Colin; Vaid, Neha; Proost, Sebastian; Persson, Staffan; Mutwil, Marek

    2017-04-01

    Understanding how genomes change as organisms become more complex is a central question in evolution. Molecular evolutionary studies typically correlate the appearance of genes and gene families with the emergence of biological pathways and morphological features. While such approaches are of great importance to understand how organisms evolve, they are also limited, as functionally related genes work together in contexts of dynamic gene networks. Since functionally related genes are often transcriptionally coregulated, gene coexpression networks present a resource to study the evolution of biological pathways. In this opinion article, we discuss recent developments in this field and how coexpression analyses can be merged with existing genomic approaches to transfer functional knowledge between species to study the appearance or extension of pathways.

  4. 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.

  5. REGULATION OF MULTIPLE RENIN-ANGIOTENSIN SYSTEM GENES BY SRY

    PubMed Central

    Milsted, Amy; Underwood, Adam C.; Dunmire, Jeff; DelPuerto, Helen L.; Martins, Almir S.; Ely, Daniel L.; Turner, Monte E.

    2010-01-01

    We demonstrated that the Sry gene complex on the SHR Y chromosome is a candidate locus for hypertension that accounts for the SHR Y chromosome blood pressure effect. All rat strains examined to date share 6 Sry loci, and a seventh Sry locus (Sry3) appears to be unique to SHR males. Previously, we showed that Sry1 increased activity of the tyrosine hydroxylase promoter in transfected PC12 cells, and Sry1 delivered to adrenal gland of WKY rats increased blood pressure and sympathetic nervous system activity. The objective of this study was to determine whether renin-angiotensin system genes participate in Sry-mediated effects. Sry expression vectors were co-transfected into CHO cells with luciferase reporter constructs containing promoters of angiotensinogen (Agt −1430/+22), renin (Ren −1050/−1), ACE (ACE −1677/+21) and ACE2 (ACE2 −1091/+83). Sry1, Sry2 and Sry3 differentially up-regulated activity of the promoters of angiotensinogen, renin and ACE genes, and down-regulated ACE2 promoter activity. The largest effect was seen with Sry3, which increased activity of angiotensinogen promoter by 1.7 fold, renin promoter by 1.3 fold, ACE promoter by 2.6 fold, and decreased activity of ACE2 promoter by 0.5 fold. The effect of Sry1 on promoter activity was significantly less than Sry3. Sry2 activated promoters at a significantly lower level than Sry1. The result of either an additive effect of Sry regulation of multiple genes in the renin-angiotensin system or alterations in expression of a single gene could favor increased levels of Ang II and decreased levels of Ang-(1-7). These actions of Sry could result in increased blood pressure in males and contribute to gender differences in blood pressure. PMID:19809364

  6. 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

  7. Interfering ribonucleic acids that suppress expression of multiple unrelated genes

    PubMed Central

    Passioura, Toby; Gozar, Mary M; Goodchild, Amber; King, Andrew; Arndt, Greg M; Poidinger, Michael; Birkett, Donald J; Rivory, Laurent P

    2009-01-01

    Background Short interfering RNAs (siRNAs) have become the research tool of choice for gene suppression, with human clinical trials ongoing. The emphasis so far in siRNA therapeutics has been the design of one siRNA with complete complementarity to the intended target. However, there is a need for multi-targeting interfering RNA in diseases in which multiple gene products are of importance. We have investigated the possibility of using a single short synthetic duplex RNA to suppress the expression of VEGF-A and ICAM-1; genes implicated in the progression of ocular neovascular diseases such as diabetic retinopathy. Results Duplex RNA were designed to have incomplete complementarity with the 3'UTR sequences of both target genes. One such duplex, CODEMIR-1, was found to suppress VEGF and ICAM-1 by 90 and 60%, respectively in ARPE-19 cells at a transfected concentration of 40 ng/mL. Use of a cyan fusion reporter with target sites constructed in its 3'UTR demonstrated that the repression of VEGF and ICAM-1 by CODEMIR-1 was indeed due to interaction with the target sequence. An exhaustive analysis of sequence variants of CODEMIR-1 demonstrated a clear positive correlation between activity against VEGF (but not ICAM-1) and the length of the contiguous complementary region (from the 5' end of the guide strand). Various strategies, including the use of inosine bases at the sites of divergence of the target sequences were investigated. Conclusion Our work demonstrates the possibility of designing multitargeting dsRNA to suppress more than one disease-altering gene. This warrants further investigation as a possible therapeutic approach. PMID:19531249

  8. An extensive network of coupling among gene expression machines.

    PubMed

    Maniatis, Tom; Reed, Robin

    2002-04-04

    Gene expression in eukaryotes requires several multi-component cellular machines. Each machine carries out a separate step in the gene expression pathway, which includes transcription, several pre-messenger RNA processing steps and the export of mature mRNA to the cytoplasm. Recent studies lead to the view that, in contrast to a simple linear assembly line, a complex and extensively coupled network has evolved to coordinate the activities of the gene expression machines. The extensive coupling is consistent with a model in which the machines are tethered to each other to form 'gene expression factories' that maximize the efficiency and specificity of each step in gene expression.

  9. Replaying the evolutionary tape: biomimetic reverse engineering of gene networks.

    PubMed

    Marbach, Daniel; Mattiussi, Claudio; Floreano, Dario

    2009-03-01

    In this paper, we suggest a new approach for reverse engineering gene regulatory networks, which consists of using a reconstruction process that is similar to the evolutionary process that created these networks. The aim is to integrate prior knowledge into the reverse-engineering procedure, thus biasing the search toward biologically plausible solutions. To this end, we propose an evolutionary method that abstracts and mimics the natural evolution of gene regulatory networks. Our method can be used with a wide range of nonlinear dynamical models. This allows us to explore novel model types such as the log-sigmoid model introduced here. We apply the biomimetic method to a gold-standard dataset from an in vivo gene network. The obtained results won a reverse engineering competition of the second DREAM conference (Dialogue on Reverse Engineering Assessments and Methods 2007, New York, NY).

  10. Linear control theory for gene network modeling.

    PubMed

    Shin, Yong-Jun; Bleris, Leonidas

    2010-09-16

    Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

  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. Identification of Gene Networks: An Approach Based on Mathematical Modeling

    DTIC Science & Technology

    2014-08-21

    applied the sensitivity method to a five-gene subnetwork of Escherichia coli and obtained promising preliminary experimental results. (a) Papers... Escherichia coli : ompR, flhC, flhD, flgA, and flgC. The regulatory interactions among these genes have been previously discovered and are part of the...Network Identification. (Submitted). 3 Datsenko, K. A. & Wanner, B. L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR

  14. 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

  15. 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

  16. The neurobiology of multiple sclerosis: genes, inflammation, and neurodegeneration.

    PubMed

    Hauser, Stephen L; Oksenberg, Jorge R

    2006-10-05

    The autoimmune model of multiple sclerosis (MS) pathogenesis provided for many years a useful but incomplete conceptual framework for understanding the complex array of factors that lead to the loss of immune homeostasis, myelin and axonal injury, and progressive neurological symptoms. The availability of novel tools in molecular neurogenetics and increasingly sophisticated neuroimaging technologies, together with the revitalization of MS neuropathology, has created a new paradigm for the multidisciplinary study of this disease. This is reflected by the growing resolution of the MS genomic map, discovery of delicate inflammatory networks that are perturbed in MS, identification of mediators of demyelination, and recognition that cumulative axonal loss and neuronal injury are the histological correlates of neurological disability. Together, these advances have set the stage for the development of therapeutic approaches designed to target the demyelinating and neurodegenerative components of the disease and promote repair.

  17. [Aberrant micro RNA and epigenetic network are associated with progression from MGUS to multiple myeloma].

    PubMed

    Handa, Hiroshi

    2015-08-01

    In recent years, attention has been drawn to aberrant epigenetics as well as coding gene mutations in cancers. DNA methylation, histone acetylation and methylation, and micro RNA (miRNA) are included in the field of epigenetics. miRNAs are small RNAs of only 19-25 bases in length which do not encode protein but do they control gene expression by destroying mRNA or inhibiting translation. In multiple myeloma (MM), several miRNA expressions were markedly decreased, while in contrast their target genes, associated with apoptosis, the cell cycle and DNA methylation, were markedly increased. Negative correlations were found between miRNA and target genes expressions. The miR-34 family in itself was methylated, and expression was epigenetically controlled. miRNA and other epigenetic mechanisms underlie network formation, thought to be associated with MM progression. Thus, examining miRNA of MM is currently an important issue in terms of predicting patient outcomes and developing novel therapies.

  18. 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

  19. 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.

  20. microRNA and gene networks in human pancreatic cancer

    PubMed Central

    ZHU, MINGHUI; XU, ZHIWEN; WANG, KUNHAO; WANG, NING; LI, YANG

    2013-01-01

    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

  1. FastGCN: a GPU accelerated tool for fast gene co-expression networks.

    PubMed

    Liang, Meimei; Zhang, Futao; Jin, Gulei; Zhu, Jun

    2015-01-01

    Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.

  2. 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

  3. 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

  4. Hidden hysteresis – population dynamics can obscure gene network dynamics

    PubMed Central

    2013-01-01

    Background Positive feedback is a common motif in gene regulatory networks. It can be used in synthetic networks as an amplifier to increase the level of gene expression, as well as a nonlinear module to create bistable gene networks that display hysteresis in response to a given stimulus. Using a synthetic positive feedback-based tetracycline sensor in E. coli, we show that the population dynamics of a cell culture has a profound effect on the observed hysteretic response of a population of cells with this synthetic gene circuit. Results The amount of observable hysteresis in a cell culture harboring the gene circuit depended on the initial concentration of cells within the culture. The magnitude of the hysteresis observed was inversely related to the dilution procedure used to inoculate the subcultures; the higher the dilution of the cell culture, lower was the observed hysteresis of that culture at steady state. Although the behavior of the gene circuit in individual cells did not change significantly in the different subcultures, the proportion of cells exhibiting high levels of steady-state gene expression did change. Although the interrelated kinetics of gene expression and cell growth are unpredictable at first sight, we were able to resolve the surprising dilution-dependent hysteresis as a result of two interrelated phenomena - the stochastic switching between the ON and OFF phenotypes that led to the cumulative failure of the gene circuit over time, and the nonlinear, logistic growth of the cell in the batch culture. Conclusions These findings reinforce the fact that population dynamics cannot be ignored in analyzing the dynamics of gene networks. Indeed population dynamics may play a significant role in the manifestation of bistability and hysteresis, and is an important consideration when designing synthetic gene circuits intended for long-term application. PMID:23800122

  5. Solution of the quasispecies model for an arbitrary gene network

    NASA Astrophysics Data System (ADS)

    Tannenbaum, Emmanuel; Shakhnovich, Eugene I.

    2004-08-01

    In this paper, we study the equilibrium behavior of Eigen’s quasispecies equations for an arbitrary gene network. We consider a genome consisting of N genes, so that the full genome sequence σ may be written as σ=σ1σ2⋯σN , where σi are sequences of individual genes. We assume a single fitness peak model for each gene, so that gene i has some “master” sequence σi,0 for which it is functioning. The fitness landscape is then determined by which genes in the genome are functioning and which are not. The equilibrium behavior of this model may be solved in the limit of infinite sequence length. The central result is that, instead of a single error catastrophe, the model exhibits a series of localization to delocalization transitions, which we term an “error cascade.” As the mutation rate is increased, the selective advantage for maintaining functional copies of certain genes in the network disappears, and the population distribution delocalizes over the corresponding sequence spaces. The network goes through a series of such transitions, as more and more genes become inactivated, until eventually delocalization occurs over the entire genome space, resulting in a final error catastrophe. This model provides a criterion for determining the conditions under which certain genes in a genome will lose functionality due to genetic drift. It also provides insight into the response of gene networks to mutagens. In particular, it suggests an approach for determining the relative importance of various genes to the fitness of an organism, in a more accurate manner than the standard “deletion set” method. The results in this paper also have implications for mutational robustness and what C.O. Wilke termed “survival of the flattest.”

  6. Stability Depends on Positive Autoregulation in Boolean Gene Regulatory Networks

    PubMed Central

    Pinho, Ricardo; Garcia, Victor; Irimia, Manuel; Feldman, Marcus W.

    2014-01-01

    Network motifs have been identified as building blocks of regulatory networks, including gene regulatory networks (GRNs). The most basic motif, autoregulation, has been associated with bistability (when positive) and with homeostasis and robustness to noise (when negative), but its general importance in network behavior is poorly understood. Moreover, how specific autoregulatory motifs are selected during evolution and how this relates to robustness is largely unknown. Here, we used a class of GRN models, Boolean networks, to investigate the relationship between autoregulation and network stability and robustness under various conditions. We ran evolutionary simulation experiments for different models of selection, including mutation and recombination. Each generation simulated the development of a population of organisms modeled by GRNs. We found that stability and robustness positively correlate with autoregulation; in all investigated scenarios, stable networks had mostly positive autoregulation. Assuming biological networks correspond to stable networks, these results suggest that biological networks should often be dominated by positive autoregulatory loops. This seems to be the case for most studied eukaryotic transcription factor networks, including those in yeast, flies and mammals. PMID:25375153

  7. 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

  8. 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

  9. Learning gene regulatory networks from next generation sequencing data.

    PubMed

    Jia, Bochao; Xu, Suwa; Xiao, Guanghua; Lamba, Vishal; Liang, Faming

    2017-03-10

    In recent years, next generation sequencing (NGS) has gradually replaced microarray as the major platform in measuring gene expressions. Compared to microarray, NGS has many advantages, such as less noise and higher throughput. However, the discreteness of NGS data also challenges the existing statistical methodology. In particular, there still lacks an appropriate statistical method for reconstructing gene regulatory networks using NGS data in the literature. The existing local Poisson graphical model method is not consistent and can only infer certain local structures of the network. In this article, we propose a random effect model-based transformation to continuize NGS data and then we transform the continuized data to Gaussian via a semiparametric transformation and apply an equivalent partial correlation selection method to reconstruct gene regulatory networks. The proposed method is consistent. The numerical results indicate that the proposed method can lead to much more accurate inference of gene regulatory networks than the local Poisson graphical model and other existing methods. The proposed data-continuized transformation fills the theoretical gap for how to transform discrete data to continuous data and facilitates NGS data analysis. The proposed data-continuized transformation also makes it feasible to integrate different types of data, such as microarray and RNA-seq data, in reconstruction of gene regulatory networks.

  10. Identifying gene networks underlying the neurobiology of ethanol and alcoholism.

    PubMed

    Wolen, Aaron R; Miles, Michael F

    2012-01-01

    For complex disorders such as alcoholism, identifying the genes linked to these diseases and their specific roles is difficult. Traditional genetic approaches, such as genetic association studies (including genome-wide association studies) and analyses of quantitative trait loci (QTLs) in both humans and laboratory animals already have helped identify some candidate genes. However, because of technical obstacles, such as the small impact of any individual gene, these approaches only have limited effectiveness in identifying specific genes that contribute to complex diseases. The emerging field of systems biology, which allows for analyses of entire gene networks, may help researchers better elucidate the genetic basis of alcoholism, both in humans and in animal models. Such networks can be identified using approaches such as high-throughput molecular profiling (e.g., through microarray-based gene expression analyses) or strategies referred to as genetical genomics, such as the mapping of expression QTLs (eQTLs). Characterization of gene networks can shed light on the biological pathways underlying complex traits and provide the functional context for identifying those genes that contribute to disease development.

  11. 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.

  12. Detectability models and waveform design for multiple access Low-Probability-of-Intercept networks

    NASA Astrophysics Data System (ADS)

    Mills, Robert F.

    1994-04-01

    Increased connectivity demands in the tactical battlefield have led to the development of multiple access low probability-of-intercept (LPI) communication networks. Most detectability studies of LPI networks have focused on the individual network links, in which detectability calculations are carried out for a single network emitter. This report, however, presents a different approach to network detectability analysis: it is assumed that the interceptor does not attempt to distinguish one emitter from another, but rather decides only if a network is operating or not. What distinguishes this approach from conventional link intercept analysis is that detection decisions are based on energy received from multiple sources. The following multiple access schemes are considered: frequency division, time division, direct sequence code division, and frequency hop code division. The wideband radiometer and its hybrids, such as the channelized radiometer, are used as potential network intercept receivers.

  13. Stochastic S-system modeling of gene regulatory network.

    PubMed

    Chowdhury, Ahsan Raja; Chetty, Madhu; Evans, Rob

    2015-10-01

    Microarray gene expression data can provide insights into biological processes at a system-wide level and is commonly used for reverse engineering gene regulatory networks (GRN). Due to the amalgamation of noise from different sources, microarray expression profiles become inherently noisy leading to significant impact on the GRN reconstruction process. Microarray replicates (both biological and technical), generated to increase the reliability of data obtained under noisy conditions, have limited influence in enhancing the accuracy of reconstruction . Therefore, instead of the conventional GRN modeling approaches which are deterministic, stochastic techniques are becoming increasingly necessary for inferring GRN from noisy microarray data. In this paper, we propose a new stochastic GRN model by investigating incorporation of various standard noise measurements in the deterministic S-system model. Experimental evaluations performed for varying sizes of synthetic network, representing different stochastic processes, demonstrate the effect of noise on the accuracy of genetic network modeling and the significance of stochastic modeling for GRN reconstruction . The proposed stochastic model is subsequently applied to infer the regulations among genes in two real life networks: (1) the well-studied IRMA network, a real-life in-vivo synthetic network constructed within the Saccharomyces cerevisiae yeast, and (2) the SOS DNA repair network in Escherichia coli.

  14. 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

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

    PubMed

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

    2016-01-26

    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.

  16. Multiple Networks of Public School Administrators: An Analysis of Network Content and Structure

    ERIC Educational Resources Information Center

    Hite, Julie M.; Williams, Ellen J.; Baugh, Steven C.

    2005-01-01

    This study examines a public school administrator network from a qualitative paradigm using network theory and methods. Findings identify and describe four distinct networks emerging from administrators' relationships: the innovation network, the resource network, the social/emotional support network, and the university-school partnership network.…

  17. Epigenetic modulation of brain gene networks for cocaine and alcohol abuse

    PubMed Central

    Farris, Sean P.; Harris, Robert A.; Ponomarev, Igor

    2015-01-01

    Cocaine and alcohol are two substances of abuse that prominently affect the central nervous system (CNS). Repeated exposure to cocaine and alcohol leads to longstanding changes in gene expression, and subsequent functional CNS plasticity, throughout multiple brain regions. Epigenetic modifications of histones are one proposed mechanism guiding these enduring changes to the transcriptome. Characterizing the large number of available biological relationships as network models can reveal unexpected biochemical relationships. Clustering analysis of variation from whole-genome sequencing of gene expression (RNA-Seq) and histone H3 lysine 4 trimethylation (H3K4me3) events (ChIP-Seq) revealed the underlying structure of the transcriptional and epigenomic landscape within hippocampal postmortem brain tissue of drug abusers and control cases. Distinct sets of interrelated networks for cocaine and alcohol abuse were determined for each abusive substance. The network approach identified subsets of functionally related genes that are regulated in agreement with H3K4me3 changes, suggesting cause and effect relationships between this epigenetic mark and gene expression. Gene expression networks consisted of recognized substrates for addiction, such as the dopamine- and cAMP-regulated neuronal phosphoprotein PPP1R1B/DARPP-32 and the vesicular glutamate transporter SLC17A7/VGLUT1 as well as potentially novel molecular targets for substance abuse. Through a systems biology based approach our results illustrate the utility of integrating epigenetic and transcript expression to establish relevant biological networks in the human brain for addiction. Future work with laboratory models may clarify the functional relevance of these gene networks for cocaine and alcohol, and provide a framework for the development of medications for the treatment of addiction. PMID:26041984

  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.

  19. An efficient algorithm to integrate network and attribute data for gene function prediction.

    PubMed

    Vembu, Shankar; Morris, Quaid

    2014-01-01

    Label propagation methods are extremely well-suited for a variety of biomedical prediction tasks based on network data. However, these algorithms cannot be used to integrate feature-based data sources with networks. We propose an efficient learning algorithm to integrate these two types of heterogeneous data sources to perform binary prediction tasks on node features (e.g., gene prioritization, disease gene prediction). Our method, LMGraph, consists of two steps. In the first step, we extract a small set of "network features" from the nodes of networks that represent connectivity with labeled nodes in the prediction tasks. In the second step, we apply a simple weighting scheme in conjunction with linear classifiers to combine these network features with other feature data. This two-step procedure allows us to (i) learn highly scalable and computationally efficient linear classifiers, (ii) and seamlessly combine feature-based data sources with networks. Our method is much faster than label propagation which is already known to be computationally efficient on large-scale prediction problems. Experiments on multiple functional interaction networks from three species (mouse, y, C.elegans) with tens of thousands of nodes and hundreds of binary prediction tasks demonstrate the efficacy of our method.

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

    PubMed

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

    2014-10-01

    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.

  1. 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.

  2. 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.

  3. Inferring orthologous gene regulatory networks using interspecies data fusion

    PubMed Central

    Penfold, Christopher A.; Millar, Jonathan B. A.; Wild, David L.

    2015-01-01

    Motivation: The ability to jointly learn gene regulatory networks (GRNs) in, or leverage GRNs between related species would allow the vast amount of legacy data obtained in model organisms to inform the GRNs of more complex, or economically or medically relevant counterparts. Examples include transferring information from Arabidopsis thaliana into related crop species for food security purposes, or from mice into humans for medical applications. Here we develop two related Bayesian approaches to network inference that allow GRNs to be jointly inferred in, or leveraged between, several related species: in one framework, network information is directly propagated between species; in the second hierarchical approach, network information is propagated via an unobserved ‘hypernetwork’. In both frameworks, information about network similarity is captured via graph kernels, with the networks additionally informed by species-specific time series gene expression data, when available, using Gaussian processes to model the dynamics of gene expression. Results: Results on in silico benchmarks demonstrate that joint inference, and leveraging of known networks between species, offers better accuracy than standalone inference. The direct propagation of network information via the non-hierarchical framework is more appropriate when there are relatively few species, while the hierarchical approach is better suited when there are many species. Both methods are robust to small amounts of mislabelling of orthologues. Finally, the use of Saccharomyces cerevisiae data and networks to inform inference of networks in the budding yeast Schizosaccharomyces pombe predicts a novel role in cell cycle regulation for Gas1 (SPAC19B12.02c), a 1,3-beta-glucanosyltransferase. Availability and implementation: MATLAB code is available from http://go.warwick.ac.uk/systemsbiology/software/. Contact: d.l.wild@warwick.ac.uk Supplementary information: Supplementary data are available at Bioinformatics

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

    PubMed

    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.

  5. Yin and Yang of disease genes and death genes between reciprocally scale-free biological networks.

    PubMed

    Han, Hyun Wook; Ohn, Jung Hun; Moon, Jisook; Kim, Ju Han

    2013-11-01

    Biological networks often show a scale-free topology with node degree following a power-law distribution. Lethal genes tend to form functional hubs, whereas non-lethal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. We created and investigated two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation sensitivity network (PSN). The hubs of both networks exhibit a low molecular evolutionary rate (P < 8 × 10(-12), P < 2 × 10(-4)) and a high codon adaptation index (P < 2 × 10(-16), P < 2 × 10(-8)), indicating that both hubs have been shaped under high evolutionary selective pressure. Moreover, the topologies of PPI and PSN are inversely proportional: hubs of PPI tend to be located at the periphery of PSN and vice versa. PPI hubs are highly enriched with lethal genes but not with disease genes, whereas PSN hubs are highly enriched with disease genes and drug targets but not with lethal genes. PPI hub genes are enriched with essential cellular processes, but PSN hub genes are enriched with environmental interaction processes, having more TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are seemingly opposite to each other but work in concert between death and disease.

  6. 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.

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

    PubMed

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

    2015-09-15

    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.

  8. Genome-Scale Networks Link Neurodegenerative Disease Genes to α-Synuclein through Specific Molecular Pathways.

    PubMed

    Khurana, Vikram; Peng, Jian; Chung, Chee Yeun; Auluck, Pavan K; Fanning, Saranna; Tardiff, Daniel F; Bartels, Theresa; Koeva, Martina; Eichhorn, Stephen W; Benyamini, Hadar; Lou, Yali; Nutter-Upham, Andy; Baru, Valeriya; Freyzon, Yelena; Tuncbag, Nurcan; Costanzo, Michael; San Luis, Bryan-Joseph; Schöndorf, David C; Barrasa, M Inmaculada; Ehsani, Sepehr; Sanjana, Neville; Zhong, Quan; Gasser, Thomas; Bartel, David P; Vidal, Marc; Deleidi, Michela; Boone, Charles; Fraenkel, Ernest; Berger, Bonnie; Lindquist, Susan

    2017-02-22

    Numerous genes and molecular pathways are implicated in neurodegenerative proteinopathies, but their inter-relationships are poorly understood. We systematically mapped molecular pathways underlying the toxicity of alpha-synuclein (α-syn), a protein central to Parkinson's disease. Genome-wide screens in yeast identified 332 genes that impact α-syn toxicity. To "humanize" this molecular network, we developed a computational method, TransposeNet. This integrates a Steiner prize-collecting approach with homology assignment through sequence, structure, and interaction topology. TransposeNet linked α-syn to multiple parkinsonism genes and druggable targets through perturbed protein trafficking and ER quality control as well as mRNA metabolism and translation. A calcium signaling hub linked these processes to perturbed mitochondrial quality control and function, metal ion transport, transcriptional regulation, and signal transduction. Parkinsonism gene interaction profiles spatially opposed in the network (ATP13A2/PARK9 and VPS35/PARK17) were highly distinct, and network relationships for specific genes (LRRK2/PARK8, ATXN2, and EIF4G1/PARK18) were confirmed in patient induced pluripotent stem cell (iPSC)-derived neurons. This cross-species platform connected diverse neurodegenerative genes to proteinopathy through specific mechanisms and may facilitate patient stratification for targeted therapy.

  9. 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.

  10. 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

  11. Inference and validation of predictive gene networks from biomedical literature and gene expression data.

    PubMed

    Olsen, Catharina; Fleming, Kathleen; Prendergast, Niall; Rubio, Renee; Emmert-Streib, Frank; Bontempi, Gianluca; Haibe-Kains, Benjamin; Quackenbush, John

    2014-01-01

    Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem. Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments. Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches. Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples.

  12. 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

  13. Localization of multiple pleiotropic genes for lipoprotein metabolism in baboonss⃞

    PubMed Central

    Rainwater, David L.; Cox, Laura A.; Rogers, Jeffrey; VandeBerg, John L.; Mahaney, Michael C.

    2009-01-01

    We employed a novel approach to identify the key loci that harbor genes influencing lipoprotein metabolism in approximately 2,000 pedigreed baboons fed various diets differing in levels of fat and cholesterol. In this study, 126 overlapping traits related to both LDL and HDL metabolism were normalized and subjected to genome-wide linkage screening. As was expected, the traits were highly, but not completely, correlated. We exploited the information in these correlated traits by focusing on those genomic regions harboring quantitative trait loci (QTL) for multiple traits, reasoning that the more influential genes would impact a larger number of traits. This study identified five major QTL clusters (each with at least two significant logarithm of the odds scores >4.7), two of which had not been previously reported in baboons. One of these mapped to the baboon ortholog of human chromosome 1p32-p34 and influenced concentrations of LDL-cholesterol on Basal and high-fat, low-cholesterol diets. The other novel QTL cluster mapped to the baboon ortholog of human chromosome 12q13.13-q14.1 and influenced LDL size properties on high-fat, low-cholesterol and high-fat, high-cholesterol, but not Basal, diets. Confirming the value of this approach, three of the QTL clusters replicated published linkage findings for the same or similar traits. PMID:19270339

  14. Analysis of the prion protein gene in multiple system atrophy.

    PubMed

    Chelban, Viorica; Manole, Andreea; Pihlstrøm, Lasse; Schottlaender, Lucia; Efthymiou, Stephanie; OConnor, Emer; Meissner, Wassilios G; Holton, Janice L; Houlden, Henry

    2017-01-01

    Neurodegenerative diseases are a very diverse group of disorders but they share some common mechanisms such as abnormally misfolded proteins with prion-like propagation and aggregation. Creutzfeldt-Jakob disease (CJD) is the most prevalent prion disease in humans. In the sporadic form of CJD the only known risk factor is the codon 129 polymorphism. Recent reports suggested that α-synuclein in multiple system atrophy (MSA) has similar pathogenic mechanisms as the prion protein. Here we present 1 Italian family with MSA and prion disease. Also, cases of concurrent MSA and prion pathology in the same individual or family suggest the possibility of molecular interaction between prion protein and α-synuclein in the process of protein accumulation and neurodegeneration, warranting further investigations. We assessed the PRNP gene by whole-exome sequencing in 264 pathologically confirmed MSA cases and 462 healthy controls to determine whether the 2 diseases share similar risk factors. We then analyzed codon 129 polymorphism by Sanger sequencing and compared with previously published results in sporadic CJD. Homozygosity at codon 129 was present in 50% of pathologically confirmed MSA cases and in 58% of normal controls (odds ratio, 0.7 (95% confidence interval of 0.5-0.9)) compared with 88.2% in sporadic CJD. Our data show that the homozygous state of position 129 in the PRNP is not a risk factor for MSA. No other variants in the PRNP gene were associated with increased risk for MSA.

  15. Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data

    PubMed Central

    2012-01-01

    Background Using gene co-expression analysis, researchers were able to predict clusters of genes with consistent functions that are relevant to cancer development and prognosis. We applied a weighted gene co-expression network (WGCN) analysis algorithm on glioblastoma multiforme (GBM) data obtained from the TCGA project and predicted a set of gene co-expression networks which are related to GBM prognosis. Methods We modified the Quasi-Clique Merger algorithm (QCM algorithm) into edge-covering Quasi-Clique Merger algorithm (eQCM) for mining weighted sub-network in WGCN. Each sub-network is considered a set of features to separate patients into two groups using K-means algorithm. Survival times of the two groups are compared using log-rank test and Kaplan-Meier curves. Simulations using random sets of genes are carried out to determine the thresholds for log-rank test p-values for network selection. Sub-networks with p-values less than their corresponding thresholds were further merged into clusters based on overlap ratios (>50%). The functions for each cluster are analyzed using gene ontology enrichment analysis. Results Using the eQCM algorithm, we identified 8,124 sub-networks in the WGCN, out of which 170 sub-networks show p-values less than their corresponding thresholds. They were then merged into 16 clusters. Conclusions We identified 16 gene clusters associated with GBM prognosis using the eQCM algorithm. Our results not only confirmed previous findings including the importance of cell cycle and immune response in GBM, but also suggested important epigenetic events in GBM development and prognosis. PMID:22536863

  16. 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

  17. 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

  18. Portrait of Candida Species Biofilm Regulatory Network Genes.

    PubMed

    Araújo, Daniela; Henriques, Mariana; Silva, Sónia

    2017-01-01

    Most cases of candidiasis have been attributed to Candida albicans, but Candida glabrata, Candida parapsilosis and Candida tropicalis, designated as non-C. albicans Candida (NCAC), have been identified as frequent human pathogens. Moreover, Candida biofilms are an escalating clinical problem associated with significant rates of mortality. Biofilms have distinct developmental phases, including adhesion/colonisation, maturation and dispersal, controlled by complex regulatory networks. This review discusses recent advances regarding Candida species biofilm regulatory network genes, which are key components for candidiasis.

  19. 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.

  20. Construction and analysis of regulatory genetic networks in cervical cancer based on involved microRNAs, target genes, transcription factors and host genes

    PubMed Central

    WANG, NING; XU, ZHIWEN; WANG, KUNHAO; ZHU, MINGHUI; LI, YANG

    2014-01-01

    Over recent years, genes and microRNA (miRNA/miR) have been considered as key biological factors in human carcinogenesis. During cancer development, genes may act as multiple identities, including target genes of miRNA, transcription factors and host genes. The present study concentrated on the regulatory networks consisting of the biological factors involved in cervical cancer in order to investigate their features and affect on this specific pathology. Numerous raw data was collected and organized into purposeful structures, and adaptive procedures were defined for application to the prepared data. The networks were therefore built with the factors as basic components according to their interacting associations. The networks were constructed at three levels of interdependency, including a differentially-expressed network, a related network and a global network. Comparisons and analyses were made at a systematic level rather than from an isolated gene or miRNA. Critical hubs were extracted in the core networks and notable features were discussed, including self-adaption feedback regulation. The present study expounds the pathogenesis from a novel point of view and is proposed to provide inspiration for further investigation and therapy. PMID:24944708

  1. The US Network of Pediatric Multiple Sclerosis Centers: Development, Progress, and Next Steps.

    PubMed

    Casper, T Charles; Rose, John W; Roalstad, Shelly; Waubant, Emmanuelle; Aaen, Gregory; Belman, Anita; Chitnis, Tanuja; Gorman, Mark; Krupp, Lauren; Lotze, Timothy E; Ness, Jayne; Patterson, Marc; Rodriguez, Moses; Weinstock-Guttman, Bianca; Browning, Brittan; Graves, Jennifer; Tillema, Jan-Mendelt; Benson, Leslie; Harris, Yolanda

    2015-09-01

    Multiple sclerosis and other demyelinating diseases in the pediatric population have received an increasing level of attention by clinicians and researchers. The low incidence of these diseases in children creates a need for the involvement of multiple clinical centers in research efforts. The Network of Pediatric Multiple Sclerosis Centers was created initially in 2006 to improve the diagnosis and care of children with demyelinating diseases. In 2010, the Network shifted its focus to multicenter research while continuing to advance the care of patients. The Network has obtained support from the National Multiple Sclerosis Society, the Guthy-Jackson Charitable Foundation, and the National Institutes of Health. The Network will continue to serve as a platform for conducting impactful research in pediatric demyelinating diseases of the central nervous system. This article provides a description of the history and development, organization, mission, research priorities, current studies, and future plans of the Network.

  2. 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.

  3. Simultaneous gene quantitation of multiple genes in individual bovine nuclear transfer blastocysts.

    PubMed

    Smith, Craig; Berg, Debbie; Beaumont, Sue; Standley, Neil T; Wells, David N; Pfeffer, Peter L

    2007-01-01

    During somatic cell nuclear transfer (NT), the transcriptional status of the donor cell has to be reprogrammed to reflect that of an embryo. We analysed the accuracy of this process by comparing transcript levels of four developmentally important genes (Oct4, Otx2, Ifitm3, GATA6), a gene involved in epigenetic regulation (Dnmt3a) and three housekeeping genes (beta-actin, beta-tubulin and GAPDH) in 21 NT blastocysts with that in genetically half-identical in vitro produced (IVP, n=19) and in vivo (n=15) bovine embryos. We have optimised an RNA-isolation and SYBR-green-based real-time RT-PCR procedure allowing the reproducible absolute quantification of multiple genes from a single blastocyst. Our data indicated that transcript levels did not differ significantly between stage and grade-matched zona-free NT and IVP embryos except for Ifitm3/Fragilis, which was expressed at twofold higher levels in NT blastocysts. Ifitm3 expression is confined to the inner cell mass at day 7 blastocysts and to the epiblast in day 14 embryos. No ectopic expression in the trophectoderm was seen in NT embryos. Gene expression in NT and IVP embryos increased between two- and threefold for all eight genes from early to late blastocyst stages. This increase exceeded the increase in cell number over this time period indicating an increase in transcript number per cell. Embryo quality (morphological grading) was correlated to cell number for NT and IVP embryos with grade 3 blastocysts containing 30% fewer cells. However, only NT embryos displayed a significant reduction in gene expression (50%) with loss of quality. Variability in gene expression levels was not significantly different in NT, IVP or in vivo embryos but differed among genes, suggesting that the stringency of regulation is intrinsic to a gene and not affected by culture or nuclear transfer. Oct4 levels exhibited the lowest variability. Analysing the total variability of all eight genes for individual embryos revealed that in

  4. Chaperone Hsp47 Drives Malignant Growth and Invasion by Modulating an ECM Gene Network.

    PubMed

    Zhu, Jieqing; Xiong, Gaofeng; Fu, Hanjiang; Evers, B Mark; Zhou, Binhua P; Xu, Ren

    2015-04-15

    The extracellular matrix (ECM) is a determining factor in the tumor microenvironment that restrains or promotes malignant growth. In this report, we show how the molecular chaperone protein Hsp47 functions as a nodal hub in regulating an ECM gene transcription network. A transcription network analysis showed that Hsp47 expression was activated during breast cancer development and progression. Hsp47 silencing reprogrammed human breast cancer cells to form growth-arrested and/or noninvasive structures in 3D cultures, and to limit tumor growth in xenograft assays by reducing deposition of collagen and fibronectin. Coexpression network analysis also showed that levels of microRNA(miR)-29b and -29c were inversely correlated with expression of Hsp47 and ECM network genes in human breast cancer tissues. We found that miR-29 repressed expression of Hsp47 along with multiple ECM network genes. Ectopic expression of miR-29b suppressed malignant phenotypes of breast cancer cells in 3D culture. Clinically, increased expression of Hsp47 and reduced levels of miR-29b and -29c were associated with poor survival outcomes in breast cancer patients. Our results show that Hsp47 is regulated by miR-29 during breast cancer development and progression, and that increased Hsp47 expression promotes cancer progression in part by enhancing deposition of ECM proteins.

  5. Distributed Power Allocation for Sink-Centric Clusters in Multiple Sink Wireless Sensor Networks

    PubMed Central

    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

  6. Multiple relaxation modes in associative polymer networks with varying connectivity

    NASA Astrophysics Data System (ADS)

    Bohdan, M.; Sprakel, J.; van der Gucht, J.

    2016-09-01

    The dynamics and mechanics of networks depend sensitively on their spatial connectivity. To explore the effect of connectivity on local network dynamics, we prepare transient polymer networks in which we systematically cut connecting bonds. We do this by creating networks formed from hydrophobically modified difunctionalized polyethylene glycol chains. These form physical gels, consisting of flowerlike micelles that are transiently cross-linked by connecting bridges. By introducing monofunctionalized chains, we can systematically reduce the number of bonds between micelles and thus lower the network connectivity, which strongly reduces the network elasticity and relaxation time. Dynamic light scattering reveals a complex relaxation dynamics that are not apparent in bulk rheology. We observe three distinct relaxation modes. First we find a fast diffusive mode that does not depend on the number of bridges and is attributed to the diffusion of micelles within a cage formed by neighboring micelles. A second, intermediate mode depends strongly on network connectivity but surprisingly is independent of the scattering vector q . We attribute this viscoelastic mode to fluctuations in local connectivity of the network. The third, slowest mode is also diffusive and is attributed to the diffusion of micelle clusters through the viscoelastic matrix. These results shed light on the microscopic dynamics in weakly interconnected transient networks.

  7. 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

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

    PubMed

    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.

  9. Compensatory interactions to stabilize multiple steady states or mitigate the effects of multiple deregulations in biological networks

    NASA Astrophysics Data System (ADS)

    Yang, Gang; Campbell, Colin; Albert, Réka

    2016-12-01

    Complex diseases can be modeled as damage to intracellular networks that results in abnormal cell behaviors. Network-based dynamic models such as Boolean models have been employed to model a variety of biological systems including those corresponding to disease. Previous work designed compensatory interactions to stabilize an attractor of a Boolean network after single node damage. We generalize this method to a multinode damage scenario and to the simultaneous stabilization of multiple steady state attractors. We classify the emergent situations, with a special focus on combinatorial effects, and characterize each class through simulation. We explore how the structural and functional properties of the network affect its resilience and its possible repair scenarios. We demonstrate the method's applicability to two intracellular network models relevant to cancer. This work has implications in designing prevention strategies for complex disease.

  10. A network of genes regulated by light in cyanobacteria.

    PubMed

    Aurora, Rajeev; Hihara, Yukako; Singh, Abhay K; Pakrasi, Himadri B

    2007-01-01

    Oxygenic photosynthetic organisms require light for their growth and development. However, exposure to high light is detrimental to them. Using time series microarray data from a model cyanobacterium, Synechocystis 6803 transferred from low to high light, we generated a gene co-expression network. The network has twelve sub-networks connected hierarchically, each consisting of an interconnected hub-and-spoke architecture. Within each sub-network, edges formed between genes that recapitulate known pathways. Analysis of the expression profiles shows that the cells undergo a phase transition 6-hours post-shift to high light, characterized by core sub-network. The core sub-network is enriched in proteins that (putatively) bind Fe-S clusters and proteins that mediate iron and sulfate homeostasis. At the center of this core is a sulfate permease, suggesting sulfate is rate limiting for cells grown in high light. To validate this novel finding, we demonstrate the limited ability of cell growth in sulfate-depleted medium in high light. This study highlights how understanding the organization of the networks can provide insights into the coordination of physiologic responses.

  11. In Silico Evolution of Gene Cooption in Pattern-Forming Gene Networks

    PubMed Central

    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

  12. 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

  13. A gene regulatory network controlling the embryonic specification of endoderm.

    PubMed

    Peter, Isabelle S; Davidson, Eric H

    2011-05-29

    Specification of endoderm is the prerequisite for gut formation in the embryogenesis of bilaterian organisms. Modern lineage labelling studies have shown that in the sea urchin embryo model system, descendants of the veg1 and veg2 cell lineages produce the endoderm, and that the veg2 lineage also gives rise to mesodermal cell types. It is known that Wnt/β-catenin signalling is required for endoderm specification and Delta/Notch signalling is required for mesoderm specification. Some direct cis-regulatory targets of these signals have been found and various phenomenological patterns of gene expression have been observed in the pre-gastrular endomesoderm. However, no comprehensive, causal explanation of endoderm specification has been conceived for sea urchins, nor for any other deuterostome. Here we propose a model, on the basis of the underlying genomic control system, that provides such an explanation, built at several levels of biological organization. The hardwired core of the control system consists of the cis-regulatory apparatus of endodermal regulatory genes, which determine the relationship between the inputs to which these genes are exposed and their outputs. The architecture of the network circuitry controlling the dynamic process of endoderm specification then explains, at the system level, a sequence of developmental logic operations, which generate the biological process. The control system initiates non-interacting endodermal and mesodermal gene regulatory networks in veg2-derived cells and extinguishes the endodermal gene regulatory network in mesodermal precursors. It also generates a cross-regulatory network that specifies future anterior endoderm in veg2 descendants and institutes a distinct network specifying posterior endoderm in veg1-derived cells. The network model provides an explanatory framework that relates endoderm specification to the genomic regulatory code.

  14. Harnessing diversity towards the reconstructing of large scale gene regulatory networks.

    PubMed

    Hase, Takeshi; Ghosh, Samik; 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.

  15. Large-scale analysis of genome and transcriptome alterations in multiple tumors unveils novel cancer-relevant splicing networks

    PubMed Central

    Sebestyén, Endre; Singh, Babita; Miñana, Belén; Pagès, Amadís; Mateo, Francesca; Pujana, Miguel Angel; Valcárcel, Juan; Eyras, Eduardo

    2016-01-01

    Alternative splicing is regulated by multiple RNA-binding proteins and influences the expression of most eukaryotic genes. However, the role of this process in human disease, and particularly in cancer, is only starting to be unveiled. We systematically analyzed mutation, copy number, and gene expression patterns of 1348 RNA-binding protein (RBP) genes in 11 solid tumor types, together with alternative splicing changes in these tumors and the enrichment of binding motifs in the alternatively spliced sequences. Our comprehensive study reveals widespread alterations in the expression of RBP genes, as well as novel mutations and copy number variations in association with multiple alternative splicing changes in cancer drivers and oncogenic pathways. Remarkably, the altered splicing patterns in several tumor types recapitulate those of undifferentiated cells. These patterns are predicted to be mainly controlled by MBNL1 and involve multiple cancer drivers, including the mitotic gene NUMA1. We show that NUMA1 alternative splicing induces enhanced cell proliferation and centrosome amplification in nontumorigenic mammary epithelial cells. Our study uncovers novel splicing networks that potentially contribute to cancer development and progression. PMID:27197215

  16. Additive Functions in Boolean Models of Gene Regulatory Network Modules

    PubMed Central

    Darabos, Christian; Di Cunto, Ferdinando; Tomassini, Marco; Moore, Jason H.; Provero, Paolo; Giacobini, Mario

    2011-01-01

    Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity

  17. Additive functions in boolean models of gene regulatory network modules.

    PubMed

    Darabos, Christian; Di Cunto, Ferdinando; Tomassini, Marco; Moore, Jason H; Provero, Paolo; Giacobini, Mario

    2011-01-01

    Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity

  18. 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.

  19. 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

  20. Network Biomarkers Constructed from Gene Expression and Protein-Protein Interaction Data for Accurate Prediction of Leukemia

    PubMed Central

    Yuan, Xuye; Chen, Jiajia; Lin, Yuxin; Li, Yin; Xu, Lihua; Chen, Luonan; Hua, Haiying; Shen, Bairong

    2017-01-01

    Leukemia is a leading cause of cancer deaths in the developed countries. Great efforts have been undertaken in search of diagnostic biomarkers of leukemia. However, leukemia is highly complex and heterogeneous, involving interaction among multiple molecular components. Individual molecules are not necessarily sensitive diagnostic indicators. Network biomarkers are considered to outperform individual molecules in disease characterization. We applied an integrative approach that identifies active network modules as putative biomarkers for leukemia diagnosis. We first reconstructed the leukemia-specific PPI network using protein-protein interactions from the Protein Interaction Network Analysis (PINA) and protein annotations from GeneGo. The network was further integrated with gene expression profiles to identify active modules with leukemia relevance. Finally, the candidate network-based biomarker was evaluated for the diagnosing performance. A network of 97 genes and 400 interactions was identified for accurate diagnosis of leukemia. Functional enrichment analysis revealed that the network biomarkers were enriched in pathways in cancer. The network biomarkers could discriminate leukemia samples from the normal controls more effectively than the known biomarkers. The network biomarkers provide a useful tool to diagnose leukemia and also aids in further understanding the molecular basis of leukemia. PMID:28243332

  1. Graphlet Based Metrics for the Comparison of Gene Regulatory Networks

    PubMed Central

    Martin, Alberto J. M.; Dominguez, Calixto; Contreras-Riquelme, Sebastián; Holmes, David S.; Perez-Acle, Tomas

    2016-01-01

    Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to environmental conditions. However, computational tools to identify how regulation changes are scarce. Regulation of gene expression is usually depicted in the form of a gene regulatory network (GRN). Structural changes in a GRN over time and conditions represent variations in the regulation of gene expression. Like other biological networks, GRNs are composed of basic building blocks called graphlets. As a consequence, two new metrics based on graphlets are proposed in this work: REConstruction Rate (REC) and REC Graphlet Degree (RGD). REC determines the rate of graphlet similarity between different states of a network and RGD identifies the subset of nodes with the highest topological variation. In other words, RGD discerns how th GRN was rewired. REC and RGD were used to compare the local structure of nodes in condition-specific GRNs obtained from gene expression data of Escherichia coli, forming biofilms and cultured in suspension. According to our results, most of the network local structure remains unaltered in the two compared conditions. Nevertheless, changes reported by RGD necessarily imply that a different cohort of regulators (i.e. transcription factors (TFs)) appear on the scene, shedding light on how the regulation of gene expression occurs when E. coli transits from suspension to biofilm. Consequently, we propose that both metrics REC and RGD should be adopted as a quantitative approach to conduct differential analyses of GRNs. A tool that implements both metrics is available as an on-line web server (http://dlab.cl/loto). PMID:27695050

  2. Graphlet Based Metrics for the Comparison of Gene Regulatory Networks.

    PubMed

    Martin, Alberto J M; Dominguez, Calixto; Contreras-Riquelme, Sebastián; Holmes, David S; Perez-Acle, Tomas

    2016-01-01

    Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to environmental conditions. However, computational tools to identify how regulation changes are scarce. Regulation of gene expression is usually depicted in the form of a gene regulatory network (GRN). Structural changes in a GRN over time and conditions represent variations in the regulation of gene expression. Like other biological networks, GRNs are composed of basic building blocks called graphlets. As a consequence, two new metrics based on graphlets are proposed in this work: REConstruction Rate (REC) and REC Graphlet Degree (RGD). REC determines the rate of graphlet similarity between different states of a network and RGD identifies the subset of nodes with the highest topological variation. In other words, RGD discerns how th GRN was rewired. REC and RGD were used to compare the local structure of nodes in condition-specific GRNs obtained from gene expression data of Escherichia coli, forming biofilms and cultured in suspension. According to our results, most of the network local structure remains unaltered in the two compared conditions. Nevertheless, changes reported by RGD necessarily imply that a different cohort of regulators (i.e. transcription factors (TFs)) appear on the scene, shedding light on how the regulation of gene expression occurs when E. coli transits from suspension to biofilm. Consequently, we propose that both metrics REC and RGD should be adopted as a quantitative approach to conduct differential analyses of GRNs. A tool that implements both metrics is available as an on-line web server (http://dlab.cl/loto).

  3. Detecting Essential Proteins Based on Network Topology, Gene Expression Data and Gene Ontology Information.

    PubMed

    Zhang, Wei; Xu, Jia; Li, Yuanyuan; Zou, Xiufen

    2016-10-07

    The identification of essential proteins in protein-protein interaction (PPI) networks is of great significance for understanding cellular processes. With the increasing availability of large-scale PPI data, numerous centrality measures based on network topology have been proposed to detect essential proteins from PPI networks. However, most of the current approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology annotation information. In this paper, we propose a novel centrality measure, called TEO, for identifying essential proteins by combining network topology, gene expression profiles and GO information. To evaluate the performance of the TEO method, we compare it with five other methods (degree, betweenness, NC, Pec, CowEWC) in detecting essential proteins from two different yeast PPI datasets. The simulation results show that adding GO information can effectively improve the predicted precision and that our method outperforms the others in predicting essential proteins.

  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.

  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. 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...

  7. 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.

  8. PDCD10 gene mutations in multiple cerebral cavernous malformations.

    PubMed

    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.

  9. A gene network simulator to assess reverse engineering algorithms.

    PubMed

    Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio

    2009-03-01

    In the context of reverse engineering of biological networks, simulators are helpful to test and compare the accuracy of different reverse-engineering approaches in a variety of experimental conditions. A novel gene-network simulator is presented that resembles some of the main features of transcriptional regulatory networks related to topology, interaction among regulators of transcription, and expression dynamics. The simulator generates network topology according to the current knowledge of biological network organization, including scale-free distribution of the connectivity and clustering coefficient independent of the number of nodes in the network. It uses fuzzy logic to represent interactions among the regulators of each gene, integrated with differential equations to generate continuous data, comparable to real data for variety and dynamic complexity. Finally, the simulator accounts for saturation in the response to regulation and transcription activation thresholds and shows robustness to perturbations. It therefore provides a reliable and versatile test bed for reverse engineering algorithms applied to microarray data. Since the simulator describes regulatory interactions and expression dynamics as two distinct, although interconnected aspects of regulation, it can also be used to test reverse engineering approaches that use both microarray and protein-protein interaction data in the process of learning. A first software release is available at http://www.dei.unipd.it/~dicamill/software/netsim as an R programming language package.

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

    PubMed Central

    Lee, Hangnoh; Cho, Dong-Yeon; Roote, John; Kaufman, Thomas; Cook, Kevin; Przytycka, Teresa; Oliver, Brian

    2016-01-01

    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

  11. Analysis of genetic interaction networks shows that alternatively spliced genes are highly versatile.

    PubMed

    Talavera, David; Sheoran, Ritika; Lovell, Simon C

    2013-01-01

    Alternative splicing has the potential to increase the diversity of the transcriptome and proteome. Where more than one transcript arises from a gene they are often so different that they are quite unlikely to have the same function. However, it remains unclear if alternative splicing generally leads to a gene being involved in multiple biological processes or whether it alters the function within a single process. Knowing that genetic interactions occur between functionally related genes, we have used them as a proxy for functional versatility, and have analysed the sets of genes of two well-characterised model organisms: Caenorhabditis elegans and Drosophila melanogaster. Using network analyses we find that few genes are functionally homogenous (only involved in a few functionally-related biological processes). Moreover, there are differences between alternatively spliced genes and genes with a single transcript; specifically, genes with alternatively splicing are, on average, involved in more biological processes. Finally, we suggest that factors other than specific functional classes determine whether a gene is alternatively spliced.

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

    PubMed

    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.

  13. 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

  14. 3D visualization of gene clusters and networks

    NASA Astrophysics Data System (ADS)

    Zhang, Leishi; Sheng, Weiguo; Liu, Xiaohui

    2005-03-01

    In this paper, we try to provide a global view of DNA microarray gene expression data analysis and modeling process by combining novel and effective visualization techniques with data mining algorithms. An integrated framework has been proposed to model and visualize short, high-dimensional gene expression data. The framework reduces the dimensionality of variables before applying appropriate temporal modeling method. Prototype has been built using Java3D to visualize the framework. The prototype takes gene expression data as input, clusters the genes, displays the clustering results using a novel graph layout algorithm, models individual gene clusters using Dynamic Bayesian Network and then visualizes the modeling results using simple but effective visualization techniques.

  15. Floral Morphogenesis: Stochastic Explorations of a Gene Network Epigenetic Landscape

    PubMed Central

    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

  16. Multiple Path Static Routing Protocols for Packet Switched Networks.

    DTIC Science & Technology

    1983-09-01

    has not yet been used in routing schemes implemented in operating networks, although there are plans to incorporate this procedure in future routing...rules and procedures used to develop loop-frea! routing scheses, are extensions of techniques used in network graph theory and flow pattern analysis. The...parameters of interest, of a process that resembles the actual bahavior of a specific ’ system. Thus an evaluation of its performance and other important

  17. 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

  18. Predicting Heat Transport across Multiple Devices with Neural Networks

    NASA Astrophysics Data System (ADS)

    Luna, C. J.; Budny, R. V.; Meneghini, O.; Smith, S. P.; Penna, J.

    2014-10-01

    Three multi-layer, feed-forward, back-propagation neural networks have been built and trained on heat transport data from DIII-D, TFTR, and JET, respectively. A comparative analysis shows that previous success of neural networks in predicting heat transport in DIII-D is reproduced for both TFTR and JET. The effect of using different neural network topologies has been investigated across all of the devices. It is found that the neural networks can consistently predict the total species' heat fluxes for all of the devices, however they have difficulty in predicting the individual components of the heat fluxes in presence of significant transient variations in stored energy (i.e. non steady-state conditions). Such limitation has been addressed by providing the time-derivative information of the plasma parameters that are input to the neural network. Finally, an attempt is made to draw a connection between the most consistently successful neural network topologies and their relevance to the physics of heat transport in tokamak plasmas. Supported in part by U.S. DoE Contracts No. DE-AC02-09CH1146 and No. DE-FG02-95ER54309.

  19. 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

  20. Analysis of microRNA and Gene Expression Profiles in Multiple Sclerosis: Integrating Interaction Data to Uncover Regulatory Mechanisms

    PubMed Central

    Freiesleben, Sherry; Hecker, Michael; Zettl, Uwe Klaus; Fuellen, Georg; Taher, Leila

    2016-01-01

    MicroRNAs (miRNAs) have been reported to contribute to the pathophysiology of multiple sclerosis (MS), an inflammatory disorder of the central nervous system. Here, we propose a new consensus-based strategy to analyse and integrate miRNA and gene expression data in MS as well as other publically available data to gain a deeper understanding of the role of miRNAs in MS and to overcome the challenges posed by studies with limited patient sample sizes. We processed and analysed microarray datasets, and compared the expression of genes and miRNAs in the blood of MS patients and controls. We then used our consensus and integration approach to construct two molecular networks dysregulated in MS: a miRNA- and a gene-based network. We identified 18 differentially expressed (DE) miRNAs and 128 DE genes that may contribute to the regulatory alterations behind MS. The miRNAs were linked to immunological and neurological pathways, and we exposed let-7b-5p and miR-345-5p as promising blood-derived disease biomarkers in MS. The results suggest that DE miRNAs are more informative than DE genes in uncovering pathways potentially involved in MS. Our findings provide novel insights into the regulatory mechanisms and networks underlying MS. PMID:27694855

  1. Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes.

    PubMed

    Guney, Emre; Oliva, Baldo

    2014-01-01

    Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO) analysis highlighted the role of functional diversity for such diseases.

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

    DOE PAGES

    Peng, Jiajie; Uygun, Sahra; Kim, Taehyong; ...

    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

  4. 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.

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

    PubMed

    Narang, Vipin; Ramli, Muhamad Azfar; 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.

  6. 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

  7. Selection for distinct gene expression properties favours the evolution of mutational robustness in gene regulatory networks.

    PubMed

    Espinosa-Soto, C

    2016-11-01

    Mutational robustness is a genotype's tendency to keep a phenotypic trait with little and few changes in the face of mutations. Mutational robustness is both ubiquitous and evolutionarily important as it affects in different ways the probability that new phenotypic variation arises. Understanding the origins of robustness is specially relevant for systems of development that are phylogenetically widespread and that construct phenotypic traits with a strong impact on fitness. Gene regulatory networks are examples of this class of systems. They comprise sets of genes that, through cross-regulation, build the gene activity patterns that define cellular responses, different tissues or distinct cell types. Several empirical observations, such as a greater robustness of wild-type phenotypes, suggest that stabilizing selection underlies the evolution of mutational robustness. However, the role of selection in the evolution of robustness is still under debate. Computer simulations of the dynamics and evolution of gene regulatory networks have shown that selection for any gene activity pattern that is steady and self-sustaining is sufficient to promote the evolution of mutational robustness. Here, I generalize this scenario using a computational model to show that selection for different aspects of a gene activity phenotype increases mutational robustness. Mutational robustness evolves even when selection favours properties that conflict with the stationarity of a gene activity pattern. The results that I present support an important role for stabilizing selection in the evolution of robustness in gene regulatory networks.

  8. Cerebral blood flow modulation insufficiency in brain networks in multiple sclerosis: A hypercapnia MRI study.

    PubMed

    Marshall, Olga; Chawla, Sanjeev; Lu, Hanzhang; Pape, Louise; Ge, Yulin

    2016-12-01

    Cerebrovascular reactivity measures vascular regulation of cerebral blood flow and is responsible for maintaining healthy neurovascular coupling. Multiple sclerosis exhibits progressive neurodegeneration and global cerebrovascular reactivity deficits. This study investigates varied degrees of cerebrovascular reactivity impairment in different brain networks, which may be an underlying cause for functional changes in the brain, affecting long-distance projection integrity and cognitive function; 28 multiple sclerosis and 28 control subjects underwent pseudocontinuous arterial spin labeling perfusion MRI to measure cerebral blood flow under normocapnia (room air) and hypercapnia (5% carbon dioxide gas mixture) breathing. Cerebrovascular reactivity, measured as normocapnic to hypercapnic cerebral blood flow percent increase normalized by end-tidal carbon dioxide change, was determined from seven functional networks (default mode, frontoparietal, somatomotor, visual, limbic, dorsal, and ventral attention networks). Group analysis showed significantly decreased cerebrovascular reactivity in patients compared to controls within the default mode, frontoparietal, somatomotor, and ventral attention networks after multiple comparison correction. Regression analysis showed a significant correlation of cerebrovascular reactivity with lesion load in the default mode and ventral attention networks and with gray matter atrophy in the default mode network. Functional networks in multiple sclerosis patients exhibit varied amounts of cerebrovascular reactivity deficits. Such blood flow regulation abnormalities may contribute to functional communication disruption in multiple sclerosis.

  9. Elucidating the genotype-phenotype relationships and network perturbations of human shared and specific disease genes from an evolutionary perspective.

    PubMed

    Begum, Tina; Ghosh, Tapash Chandra

    2014-10-05

    To date, numerous studies have been attempted to determine the extent of variation in evolutionary rates between human disease and nondisease (ND) genes. In our present study, we have considered human autosomal monogenic (Mendelian) disease genes, which were classified into two groups according to the number of phenotypic defects, that is, specific disease (SPD) gene (one gene: one defect) and shared disease (SHD) gene (one gene: multiple defects). Here, we have compared the evolutionary rates of these two groups of genes, that is, SPD genes and SHD genes with respect to ND genes. We observed that the average evolutionary rates are slow in SHD group, intermediate in SPD group, and fast in ND group. Group-to-group evolutionary rate differences remain statistically significant regardless of their gene expression levels and number of defects. We demonstrated that disease genes are under strong selective constraint if they emerge through edgetic perturbation or drug-induced perturbation of the interactome network, show tissue-restricted expression, and are involved in transmembrane transport. Among all the factors, our regression analyses interestingly suggest the independent effects of 1) drug-induced perturbation and 2) the interaction term of expression breadth and transmembrane transport on protein evolutionary rates. We reasoned that the drug-induced network disruption is a combination of several edgetic perturbations and, thus, has more severe effect on gene phenotypes.

  10. 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

  11. Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks

    PubMed Central

    2012-01-01

    Background Various computational models have been of interest due to their use in the modelling of gene regulatory networks (GRNs). As a logical model, probabilistic Boolean networks (PBNs) consider molecular and genetic noise, so the study of PBNs provides significant insights into the understanding of the dynamics of GRNs. This will ultimately lead to advances in developing therapeutic methods that intervene in the process of disease development and progression. The applications of PBNs, however, are hindered by the complexities involved in the computation of the state transition matrix and the steady-state distribution of a PBN. For a PBN with n genes and N Boolean networks, the complexity to compute the state transition matrix is O(nN22n) or O(nN2n) for a sparse matrix. Results This paper presents a novel implementation of PBNs based on the notions of stochastic logic and stochastic computation. This stochastic implementation of a PBN is referred to as a stochastic Boolean network (SBN). An SBN provides an accurate and efficient simulation of a PBN without and with random gene perturbation. The state transition matrix is computed in an SBN with a complexity of O(nL2n), where L is a factor related to the stochastic sequence length. Since the minimum sequence length required for obtaining an evaluation accuracy approximately increases in a polynomial order with the number of genes, n, and the number of Boolean networks, N, usually increases exponentially with n, L is typically smaller than N, especially in a network with a large number of genes. Hence, the computational efficiency of an SBN is primarily limited by the number of genes, but not directly by the total possible number of Boolean networks. Furthermore, a time-frame expanded SBN enables an efficient analysis of the steady-state distribution of a PBN. These findings are supported by the simulation results of a simplified p53 network, several randomly generated networks and a network inferred from a T

  12. Literature Mining and Ontology based Analysis of Host-Brucella Gene-Gene Interaction Network.

    PubMed

    Karadeniz, İlknur; Hur, Junguk; He, Yongqun; Özgür, Arzucan

    2015-01-01

    Brucella is an intracellular bacterium that causes chronic brucellosis in humans and various mammals. The identification of host-Brucella interaction is crucial to understand host immunity against Brucella infection and Brucella pathogenesis against host immune responses. Most of the information about the inter-species interactions between host and Brucella genes is only available in the text of the scientific publications. Many text-mining systems for extracting gene and protein interactions have been proposed. However, only a few of them have been designed by considering the peculiarities of host-pathogen interactions. In this paper, we used a text mining approach for extracting host-Brucella gene-gene interactions from the abstracts of articles in PubMed. The gene-gene interactions here represent the interactions between genes and/or gene products (e.g., proteins). The SciMiner tool, originally designed for detecting mammalian gene/protein names in text, was extended to identify host and Brucella gene/protein names in the abstracts. Next, sentence-level and abstract-level co-occurrence based approaches, as well as sentence-level machine learning based methods, originally designed for extracting intra-species gene interactions, were utilized to extract the interactions among the identified host and Brucella genes. The extracted interactions were manually evaluated. A total of 46 host-Brucella gene interactions were identified and represented as an interaction network. Twenty four of these interactions were identified from sentence-level processing. Twenty two additional interactions were identified when abstract-level processing was performed. The Interaction Network Ontology (INO) was used to represent the identified interaction types at a hierarchical ontology structure. Ontological modeling of specific gene-gene interactions demonstrates that host-pathogen gene-gene interactions occur at experimental conditions which can be ontologically represented. Our

  13. Innovation and robustness in complex regulatory gene networks

    PubMed Central

    Ciliberti, S.; Martin, O. C.; Wagner, A.

    2007-01-01

    The history of life involves countless evolutionary innovations, a steady stream of ingenuity that has been flowing for more than 3 billion years. Very little is known about the principles of biological organization that allow such innovation. Here, we examine these principles for evolutionary innovation in gene expression patterns. To this end, we study a model for the transcriptional regulation networks that are at the heart of embryonic development. A genotype corresponds to a regulatory network of a given topology, and a phenotype corresponds to a steady-state gene expression pattern. Networks with the same phenotype form a connected graph in genotype space, where two networks are immediate neighbors if they differ by one regulatory interaction. We show that an evolutionary search on this graph can reach genotypes that are as different from each other as if they were chosen at random in genotype space, allowing evolutionary access to different kinds of innovation while staying close to a viable phenotype. Thus, although robustness to mutations may hinder innovation in the short term, we conclude that long-term innovation in gene expression patterns can only emerge in the presence of the robustness caused by connected genotype graphs. PMID:17690244

  14. Disease Risk Assessment Using a Voronoi-Based Network Analysis of Genes and Variants Scores

    PubMed Central

    Chen, Lin; Mukerjee, Gouri; Dorfman, Ruslan; Moghadas, Seyed M.

    2017-01-01

    Much effort has been devoted to assess disease risk based on large-scale protein-protein network and genotype-phenotype associations. However, the challenge of risk prediction for complex diseases remains unaddressed. Here, we propose a framework to quantify the risk based on a Voronoi tessellation network analysis, taking into account the disease association scores of both genes and variants. By integrating ClinVar, SNPnexus, and DISEASES databases, we introduce a gene-variant map that is based on the pairwise disease-associated gene-variant scores. This map is clustered using Voronoi tessellation and network analysis with a threshold obtained from fitting the background Voronoi cell density distribution. We define the relative risk of disease that is inferred from the scores of the data points within the related clusters on the gene-variant map. We identify autoimmune-associated clusters that may interact at the system-level. The proposed framework can be used to determine the clusters that are specific to a subtype or contribute to multiple subtypes of complex diseases. PMID:28326099

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

    PubMed

    Liu, Ji-Long; Zhao, Miao

    2016-02-01

    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.

  16. Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory

    PubMed Central

    Luo, Feng; Yang, Yunfeng; Zhong, Jianxin; Gao, Haichun; Khan, Latifur; Thompson, Dorothea K; Zhou, Jizhong

    2007-01-01

    Background Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. Results Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the

  17. Toward an orofacial gene regulatory network.

    PubMed

    Kousa, Youssef A; Schutte, Brian C

    2016-03-01

    Orofacial clefting is a common birth defect with significant morbidity. A panoply of candidate genes have been discovered through synergy of animal models and human genetics. Among these, variants in interferon regulatory factor 6 (IRF6) cause syndromic orofacial clefting and contribute risk toward isolated cleft lip and palate (1/700 live births). Rare variants in IRF6 can lead to Van der Woude syndrome (1/35,000 live births) and popliteal pterygium syndrome (1/300,000 live births). Furthermore, IRF6 regulates GRHL3 and rare variants in this downstream target can also lead to Van der Woude syndrome. In addition, a common variant (rs642961) in the IRF6 locus is found in 30% of the world's population and contributes risk for isolated orofacial clefting. Biochemical studies revealed that rs642961 abrogates one of four AP-2alpha binding sites. Like IRF6 and GRHL3, rare variants in TFAP2A can also lead to syndromic orofacial clefting with lip pits (branchio-oculo-facial syndrome). The literature suggests that AP-2alpha, IRF6 and GRHL3 are part of a pathway that is essential for lip and palate development. In addition to updating the pathways, players and pursuits, this review will highlight some of the current questions in the study of orofacial clefting.

  18. 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.

  19. Listening to the Noise: Random Fluctuations Reveal Gene Network Parameters

    NASA Astrophysics Data System (ADS)

    Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

    2010-03-01

    The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant prototypical differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, single molecule fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques. 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. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.

  20. 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

  1. Dynamic Gene Regulatory Networks of Human Myeloid Differentiation.

    PubMed

    Ramirez, Ricardo N; El-Ali, Nicole C; Mager, Mikayla Anne; Wyman, Dana; Conesa, Ana; Mortazavi, Ali

    2017-03-27

    The reconstruction of gene regulatory networks underlying cell differentiation from high-throughput gene expression and chromatin data remains a challenge. Here, we derive dynamic gene regulatory networks for human myeloid differentiation using a 5-day time series of RNA-seq and ATAC-seq data. We profile HL-60 promyelocytes differentiating into macrophages, neutrophils, monocytes, and monocyte-derived macrophages. We find a rapid response in the expression of key transcription factors and lineage markers that only regulate a subset of their targets at a given time, which is followed by chromatin accessibility changes that occur later along with further gene expression changes. We observe differences between promyelocyte- and monocyte-derived macrophages at both the transcriptional and chromatin landscape level, despite using the same differentiation stimulus, which suggest that the path taken by cells in the differentiation landscape defines their end cell state. More generally, our approach of combining neighboring time points and replicates to achieve greater sequencing depth can efficiently infer footprint-based regulatory networks from long series data.

  2. Gene regulation networks generate diverse pigmentation patterns in plants.

    PubMed

    Albert, Nick W; Davies, Kevin M; Schwinn, Kathy E

    2014-01-01

    The diversity of pigmentation patterns observed in plants occurs due to the spatial distribution and accumulation of colored compounds, which may also be associated with structural changes to the tissue. Anthocyanins are flavonoids that provide red/purple/blue coloration to plants, often forming complex patterns such as spots, stripes, and vein-associated pigmentation, particularly in flowers. These patterns are determined by the activity of MYB-bHLH-WDR (MBW) transcription factor complexes, which activate the anthocyanin biosynthesis genes, resulting in anthocyanin pigment accumulation. Recently, we established that the MBW complex controlling anthocyanin synthesis acts within a gene regulation network that is conserved within at least the Eudicots. This network involves hierarchy, reinforcement, and feedback mechanisms that allow for stringent and responsive regulation of the anthocyanin biosynthesis genes. The gene network and mobile nature of the WDR and R3-MYB proteins provide exciting new opportunities to explore the basis of pigmentation patterning, and to investigate the evolutionary history of the MBW components in land plants.

  3. Reverse engineering of gene regulatory network using restricted gene expression programming.

    PubMed

    Yang, Bin; Liu, Sanrong; Zhang, Wei

    2016-10-01

    Inference of gene regulatory networks has been becoming a major area of interest in the field of systems biology over the past decade. In this paper, we present a novel representation of S-system model, named restricted gene expression programming (RGEP), to infer gene regulatory network. A new hybrid evolutionary algorithm based on structure-based evolutionary algorithm and cuckoo search (CS) is proposed to optimize the architecture and corresponding parameters of model, respectively. Two synthetic benchmark datasets and one real biological dataset from SOS DNA repair network in E. coli are used to test the validity of our method. Experimental results demonstrate that our proposed method performs better than previously proposed popular methods.

  4. 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

  5. The use of gene interaction networks to improve the identification of cancer driver genes

    PubMed Central

    Walkins, Kheston; Tripathi, Vrijesh; John, Melford

    2017-01-01

    Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes. PMID:28149674

  6. The use of gene interaction networks to improve the identification of cancer driver genes.

    PubMed

    Ramsahai, Emilie; Walkins, Kheston; Tripathi, Vrijesh; John, Melford

    2017-01-01

    Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.

  7. An Arabidopsis gene regulatory network for secondary cell wall synthesis

    SciTech Connect

    Taylor-Teeples, M.; Lin, L.; de Lucas, M.; Turco, G.; Toal, T. W.; Gaudinier, A.; Young, N. F.; Trabucco, G. M.; Veling, M. T.; Lamothe, R.; Handakumbura, P. P.; Xiong, G.; Wang, C.; Corwin, J.; Tsoukalas, A.; Zhang, L.; Ware, D.; Pauly, M.; Kliebenstein, D. J.; Dehesh, K.; Tagkopoulos, I.; Breton, G.; Pruneda-Paz, J. L.; Ahnert, S. E.; Kay, S. A.; Hazen, S. P.; Brady, S. M.

    2014-12-24

    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. In this paper, we present a protein–DNA network between Arabidopsis thaliana 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. Finally, these interactions will serve as a foundation for understanding the regulation of a complex, integral plant component.

  8. Multiple Measures of Network TV News Bias in Campaign '72.

    ERIC Educational Resources Information Center

    Lowry, Dennis T.

    Divided into two parts, this study includes an analysis of the verbal content of Nixon and McGovern news stories carried by the three networks and an analysis of selected types of nonverbal content. The universe for the study was the 53 days, Monday through Friday, between the end of the Republican national convention and election day, 1972. A…

  9. 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.

  10. Recurrent neural network based hybrid model for reconstructing gene regulatory network.

    PubMed

    Raza, Khalid; Alam, Mansaf

    2016-10-01

    One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model.

  11. Network component analysis provides quantitative insights on an Arabidopsis transcription factor-gene regulatory network

    PubMed Central

    2013-01-01

    Background Gene regulatory networks (GRNs) are models of molecule-gene interactions instrumental in the coordination of gene expression. Transcription factor (TF)-GRNs are an important subset of GRNs that characterize gene expression as the effect of TFs acting on their target genes. Although such networks can qualitatively summarize TF-gene interactions, it is highly desirable to quantitatively determine the strengths of the interactions in a TF-GRN as well as the magnitudes of TF activities. To our knowledge, such analysis is rare in plant biology. A computational methodology developed for this purpose is network component analysis (NCA), which has been used for studying large-scale microbial TF-GRNs to obtain nontrivial, mechanistic insights. In this work, we employed NCA to quantitatively analyze a plant TF-GRN important in floral development using available regulatory information from AGRIS, by processing previously reported gene expression data from four shoot apical meristem cell types. Results The NCA model satisfactorily accounted for gene expression measurements in a TF-GRN of seven TFs (LFY, AG, SEPALLATA3 [SEP3], AP2, AGL15, HY5 and AP3/PI) and 55 genes. NCA found strong interactions between certain TF-gene pairs including LFY → MYB17, AG → CRC, AP2 → RD20, AGL15 → RAV2 and HY5 → HLH1, and the direction of the interaction (activation or repression) for some AGL15 targets for which this information was not previously available. The activity trends of four TFs - LFY, AG, HY5 and AP3/PI as deduced by NCA correlated well with the changes in expression levels of the genes encoding these TFs across all four cell types; such a correlation was not observed for SEP3, AP2 and AGL15. Conclusions For the first time, we have reported the use of NCA to quantitatively analyze a plant TF-GRN important in floral development for obtaining nontrivial information about connectivity strengths between TFs and their target genes as well as TF

  12. 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

  13. 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

  14. 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.

  15. Vertebrate segmentation: from cyclic gene networks to scoliosis.

    PubMed

    Pourquié, Olivier

    2011-05-27

    One of the most striking features of the human vertebral column is its periodic organization along the anterior-posterior axis. This pattern is established when segments of vertebrates, called somites, bud off at a defined pace from the anterior tip of the embryo's presomitic mesoderm (PSM). To trigger this rhythmic production of somites, three major signaling pathways--Notch, Wnt/β-catenin, and fibroblast growth factor (FGF)--integrate into a molecular network that generates a traveling wave of gene expression along the embryonic axis, called the "segmentation clock." Recent systems approaches have begun identifying specific signaling circuits within the network that set the pace of the oscillations, synchronize gene expression cycles in neighboring cells, and contribute to the robustness and bilateral symmetry of somite formation. These findings establish a new model for vertebrate segmentation and provide a conceptual framework to explain human diseases of the spine, such as congenital scoliosis.

  16. Solution Approaches for Network Flow Problems with Multiple Criteria,

    DTIC Science & Technology

    1979-12-01

    34 Journal of Mathematical Analysis and Applications , 69, 2 (1979) 283-303. 12. R. Steuer and A. Schuler, "An Interactive Multiple-Objective Linear...Solutions in Linear Cases and a Multicriteria Siuplex Method," Journal of Mathematical Analysis and Applications , 49, 2 (1975) 430-468. 16. M. Zeleny, Linear

  17. 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.

  18. Gene network reconstruction reveals cell cycle and antiviral genes as major drivers of cervical cancer.

    PubMed

    Mine, Karina L; Shulzhenko, Natalia; Yambartsev, Anatoly; Rochman, Mark; Sanson, Gerdine F O; Lando, Malin; Varma, Sudhir; Skinner, Jeff; Volfovsky, Natalia; Deng, Tao; Brenna, Sylvia M F; Carvalho, Carmen R N; Ribalta, Julisa C L; Bustin, Michael; Matzinger, Polly; Silva, Ismael D C G; Lyng, Heidi; Gerbase-DeLima, Maria; Morgun, Andrey

    2013-01-01

    Although human papillomavirus was identified as an aetiological factor in cervical cancer, the key human gene drivers of this disease remain unknown. Here we apply an unbiased approach integrating gene expression and chromosomal aberration data. In an independent group of patients, we reconstruct and validate a gene regulatory meta-network, and identify cell cycle and antiviral genes that constitute two major subnetworks upregulated in tumour samples. These genes are located within the same regions as chromosomal amplifications, most frequently on 3q. We propose a model in which selected chromosomal gains drive activation of antiviral genes contributing to episomal virus elimination, which synergizes with cell cycle dysregulation. These findings may help to explain the paradox of episomal human papillomavirus decline in women with invasive cancer who were previously unable to clear the virus.

  19. Systems Biology Approach to Identify Gene Network Signatures for Colorectal Cancer

    PubMed Central

    Sonachalam, Madhankumar; Shen, Jeffrey; Huang, Hui; Wu, Xiaogang

    2012-01-01

    In this work, we integrated prior knowledge from gene signatures and protein interactions with gene set enrichment analysis (GSEA), and gene/protein network modeling together to identify gene network signatures from gene expression microarray data. We demonstrated how to apply this approach into discovering gene network signatures for colorectal cancer (CRC) from microarray datasets. First, we used GSEA to analyze the microarray data through enriching differential genes in different CRC-related gene sets from two publicly available up-to-date gene set databases – Molecular Signatures Database (MSigDB) and Gene Signatures Database (GeneSigDB). Second, we compared the enriched gene sets through enrichment score, false-discovery rate, and nominal p-value. Third, we constructed an integrated protein–protein interaction (PPI) network through connecting these enriched genes by high-quality interactions from a human annotated and predicted protein interaction database, with a confidence score labeled for each interaction. Finally, we mapped differential gene expressions onto the constructed network to build a comprehensive network model containing visualized transcriptome and proteome data. The results show that although MSigDB has more CRC-relevant gene sets than GeneSigDB, the integrated PPI network connecting the enriched genes from both MSigDB and GeneSigDB can provide a more complete view for discovering gene network signatures. We also found several important sub-network signatures for CRC, such as TP53 sub-network, PCNA sub-network, and IL8 sub-network, corresponding to apoptosis, DNA repair, and immune response, respectively. PMID:22629282

  20. Analyzing stationary states of gene regulatory network using petri nets.

    PubMed

    Gambin, Anna; Lasota, Sławomir; Rutkowski, Michał

    2006-01-01

    We introduce and formally define the notion of a stationary state for Petri nets. We also propose a fully automatic method for finding such states. The procedure makes use of the Presburger arithmetic to describe all the stationary states. Finally we apply this novel approach to find stationary states of a gene regulatory network describing the flower morphogenesis of A. thaliana. This shows that the proposed method can be successfully applied in the study of biological systems.

  1. Analyzing stationary States of gene regulatory network using petri nets.

    PubMed

    Gambin, Anna; Lasota, Sławomir; Rutkowski, Michał

    2011-01-01

    We introduce and formally define the notion of a stationary state for Petri nets. We also propose a fully automatic method for finding such states. The procedure makes use of the Presburger arithmetic to describe all the stationary states. Finally we apply this novel approach to find stationary states of a gene regulatory network describing the flower morphogenesis of A. thaliana. This shows that the proposed method can be successfully applied in the study of biological systems.

  2. Comprehensive Analysis of Interaction Networks of Telomerase Reverse Transcriptase with Multiple Bioinformatic Approach: Deep Mining the Potential Functions of Telomere and Telomerase.

    PubMed

    Hou, Chunyu; Wang, Fei; Liu, Xuewen; Chang, Guangming; Wang, Feng; Geng, Xin

    2017-03-06

    Telomerase reverse transcriptase (TERT)is the protein component of telomerase complex. Evidence has accumulated showing that the non-telomeric functions of TERT are independent of telomere elongation. However, the mechanisms governing the interaction between TERT and its target genes are not revealed clear. The biological functions of TERT are not fully elucidated and have thus far been underestimated. To further explore these functions, we investigated TERT interaction networks using multiple bioinformatic databases, including BioGRID, STRING, DAVID, GeneCards, GeneMANIA, PANTHER, miRWalk, mirTarBase, miRNet, miRDB and TargetScan. In addition, network diagrams were built using Cytoscape software. Since competing endogenous RNAs (ceRNAs) are endogenous transcripts that compete for the binding of microRNAs by using shared microRNA Recognition Elements (MREs), they are involved in create widespread regulatory networks. Therefore, the ceRNA regulatory networks of TERT were also investigated in the present study. Interestingly, we found that the three genes PABPC1, SLC7A11, TP53 were present in both TERT interaction networks and ceRNAs target genes. It was predicted that TERT might play non-telomeric roles in the generation or development of some rare diseases, such as Rift Valley Fever and Dyscalculia. Thus, our data will help to decipher the interaction networks of TERT and reveal the unknown functions of telomerase in cancer and aging -related diseases.

  3. 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

  4. 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

  5. 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.

  6. Inferring Gene Networks for Strains of Dehalococcoides Highlights Conserved Relationships between Genes Encoding Core Catabolic and Cell-Wall Structural Proteins

    PubMed Central

    Mansfeldt, Cresten B.; Heavner, Gretchen W.; Rowe, Annette R.; Hayete, Boris; Church, Bruce W.; Richardson, Ruth E.

    2016-01-01

    The interpretation of high-throughput gene expression data for non-model microorganisms remains obscured because of the high fraction of hypothetical genes and the limited number of methods for the robust inference of gene networks. Therefore, to elucidate gene-gene and gene-condition linkages in the bioremediation-important genus Dehalococcoides, we applied a Bayesian inference strategy called Reverse Engineering/Forward Simulation (REFS™) on transcriptomic data collected from two organohalide-respiring communities containing different Dehalococcoides mccartyi strains: the Cornell University mixed community D2 and the commercially available KB-1® bioaugmentation culture. In total, 49 and 24 microarray datasets were included in the REFS™ analysis to generate an ensemble of 1,000 networks for the Dehalococcoides population in the Cornell D2 and KB-1® culture, respectively. Considering only linkages that appeared in the consensus network for each culture (exceeding the determined frequency cutoff of ≥ 60%), the resulting Cornell D2 and KB-1® consensus networks maintained 1,105 nodes (genes or conditions) with 974 edges and 1,714 nodes with 1,455 edges, respectively. These consensus networks captured multiple strong and biologically informative relationships. One of the main highlighted relationships shared between these two cultures was a direct edge between the transcript encoding for the major reductive dehalogenase (tceA (D2) or vcrA (KB-1®)) and the transcript for the putative S-layer cell wall protein (DET1407 (D2) or KB1_1396 (KB-1®)). Additionally, transcripts for two key oxidoreductases (a [Ni Fe] hydrogenase, Hup, and a protein with similarity to a formate dehydrogenase, “Fdh”) were strongly linked, generalizing a strong relationship noted previously for Dehalococcoides mccartyi strain 195 to multiple strains of Dehalococcoides. Notably, the pangenome array utilized when monitoring the KB-1® culture was capable of resolving signals from

  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 C4/Slp genes distinguished by expression after transfection.

    PubMed Central

    Robins, D M; Malissen, M; Hood, L; Ferreira, A; Walthall, D; Mitchell, M

    1986-01-01

    The S region of the murine major histocompatibility complex contains two closely related genes: C4, encoding the fourth component of complement, and Slp, encoding sex-limited protein. We cloned these genes from a cosmid library of the B10.W7R strain that does not show androgen regulation of the Slp protein. Restriction site polymorphisms revealed at least four C4-like genes within the Sw7 locus, indicating evolutionary amplification of this region. Transfection of these genes into L cells resulted in expression, processing, and secretion of immunologically correct C4 and Slp proteins. At least two different Slp genes and one C4 gene were capable, after transfection, of expressing C4 and Slp indistinguishable from macrophage-derived protein. A third Slp gene exists within this locus whose recombinant cognate did not express in L cells. Thus, the B10.W7R S region includes one C4 gene and at least three Slp-like genes. Images PMID:3023818

  9. Gene networks in skeletal muscle following endurance exercise are coexpressed in blood neutrophils and linked with blood inflammation markers.

    PubMed

    Broadbent, James; Sampson, Dayle; Sabapathy, Surendran; Haseler, Luke J; Wagner, Karl-Heinz; Bulmer, Andrew C; Peake, Jonathan M; Neubauer, Oliver

    2017-04-01

    It remains incompletely understood whether there is an association between the transcriptome profiles of skeletal muscle and blood leukocytes in response to exercise or other physiological stressors. We have previously analyzed the changes in the muscle and blood neutrophil transcriptome in eight trained men before and 3, 48, and 96 h after 2 h cycling and running. Because we collected muscle and blood in the same individuals and under the same conditions, we were able to directly compare gene expression between the muscle and blood neutrophils. Applying weighted gene coexpression network analysis (WGCNA) as an advanced network-driven method to these original data sets enabled us to compare the muscle and neutrophil transcriptomes in a rigorous and systematic manner. Two gene networks were identified that were preserved between skeletal muscle and blood neutrophils, functionally related to mitochondria and posttranslational processes. Strong preservation measures (Zsummary > 10) for both muscle-neutrophil gene networks were evident within the postexercise recovery period. Muscle and neutrophil gene coexpression was strongly correlated in the mitochondria-related network (r = 0.97; P = 3.17E-2). We also identified multiple correlations between muscular gene subnetworks and exercise-induced changes in blood leukocyte counts, inflammation, and muscle damage markers. These data reveal previously unidentified gene coexpression between skeletal muscle and blood neutrophils following exercise, showing the value of WGCNA to understand exercise physiology. Furthermore, these findings provide preliminary evidence in support of the notion that blood neutrophil gene networks may potentially help us to track physiological and pathophysiological changes in the muscle.NEW & NOTEWORTHY By using weighted gene coexpression network analysis, an advanced bioinformatics method, we have identified previously unknown, functional gene networks that are preserved between skeletal muscle

  10. 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.

  11. Gene Network Reconstruction using Global-Local Shrinkage Priors*

    PubMed Central

    Leday, Gwenaël G.R.; de Gunst, Mathisca C.M.; Kpogbezan, Gino B.; van der Vaart, Aad W.; van Wieringen, Wessel N.; van de Wiel, Mark A.

    2016-01-01

    Reconstructing a gene network from high-throughput molecular data is an important but challenging task, as the number of parameters to estimate easily is much larger than the sample size. A conventional remedy is to regularize or penalize the model likelihood. In network models, this is often done locally in the neighbourhood of each node or gene. However, estimation of the many regularization parameters is often difficult and can result in large statistical uncertainties. In this paper we propose to combine local regularization with global shrinkage of the regularization parameters to borrow strength between genes and improve inference. We employ a simple Bayesian model with non-sparse, conjugate priors to facilitate the use of fast variational approximations to posteriors. We discuss empirical Bayes estimation of hyper-parameters of the priors, and propose a novel approach to rank-based posterior thresholding. Using extensive model- and data-based simulations, we demonstrate that the proposed inference strategy outperforms popular (sparse) methods, yields more stable edges, and is more reproducible. The proposed method, termed ShrinkNet, is then applied to Glioblastoma to investigate the interactions between genes associated with patient survival.

  12. 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

  13. Simple and Efficient Targeting of Multiple Genes Through CRISPR-Cas9 in Physcomitrella patens

    PubMed Central

    Lopez-Obando, Mauricio; Hoffmann, Beate; Géry, Carine; Guyon-Debast, Anouchka; Téoulé, Evelyne; Rameau, Catherine; Bonhomme, Sandrine; Nogué, Fabien

    2016-01-01

    Powerful genome editing technologies are needed for efficient gene function analysis. The CRISPR-Cas9 system has been adapted as an efficient gene-knock-out technology in a variety of species. However, in a number of situations, knocking out or modifying a single gene is not sufficient; this is particularly true for genes belonging to a common family, or for genes showing redundant functions. Like many plants, the model organism Physcomitrella patens has experienced multiple events of polyploidization during evolution that has resulted in a number of families of duplicated genes. Here, we report a robust CRISPR-Cas9 system, based on the codelivery of a CAS9 expressing cassette, multiple sgRNA vectors, and a cassette for transient transformation selection, for gene knock-out in multiple gene families. We demonstrate that CRISPR-Cas9-mediated targeting of five different genes allows the selection of a quintuple mutant, and all possible subcombinations of mutants, in one experiment, with no mutations detected in potential off-target sequences. Furthermore, we confirmed the observation that the presence of repeats in the vicinity of the cutting region favors deletion due to the alternative end joining pathway, for which induced frameshift mutations can be potentially predicted. Because the number of multiple gene families in Physcomitrella is substantial, this tool opens new perspectives to study the role of expanded gene families in the colonization of land by plants. PMID:27613750

  14. First passage time of multiple Brownian particles on networks with applications

    NASA Astrophysics Data System (ADS)

    Wang, Shao-Ping; Pei, Wen-Jiang

    2008-07-01

    In this article, we study some theoretical and technological problems with relation to multiple Brownian particles on networks. We are especially interested in the behavior of the first arriving Brownian particle when all the Brownian particles start out from the source s simultaneously and head to the destination h randomly. We analyze the first passage time (FPT) Y(z) and the mean first passage time (MFPT) of multiple Brownian particles on complex networks. Equations of Y(z) and are obtained. On a variety of commonly encountered networks, we observe first passage properties of multiple Brownian particles from different aspects. We find that drops substantially when particle number z increases at the first stage, and converges to d, the distance between the source and the destination when z→∞. The distribution of FPT Prob{Y(z)=t},t=0,1,2,… is also analyzed in these networks. The distribution curve peaks up towards t=d when z increases. Consequently, if particle number z is set appropriately large, the first arriving Brownian particle will go along the shortest or near shortest paths between the source and the destination with high probability. Simulations confirm our analysis. Based on theoretical studies, we also investigate some practical problems using multiple Brownian particles, such as communication on P2P networks, optimal routing in small world networks, phenomenon of asymmetry in scale-free networks, information spreading in social networks, pervasion of viruses on the Internet, and so on. Our analytic and experimental results on multiple Brownian particles provide useful evidence for further understanding and properly tackling these problems.

  15. Differentially expressed genes and gene networks involved in pig ovarian follicular atresia.

    PubMed

    Terenina, Elena; Fabre, Stephane; Bonnet, Agnès; Monniaux, Danielle; Robert-Granié, Christèle; SanCristobal, Magali; Sarry, Julien; Vignoles, Florence; Gondret, Florence; Monget, Philippe; Tosser-Klopp, Gwenola

    2017-02-01

    Ovarian folliculogenesis corresponds to the development of follicles leading to either ovulation or degeneration, this latter process being called atresia. Even if atresia involves apoptosis, its mechanism is not well understood. The objective of this study was to analyze global gene expression in pig granulosa cells of ovarian follicles during atresia. The transcriptome analysis was performed on a 9,216 cDNA microarray to identify gene networks and candidate genes involved in pig ovarian follicular atresia. We found 1,684 significantly regulated genes to be differentially regulated between small healthy follicles and small atretic follicles. Among them, 287 genes had a fold-change higher than two between the two follicle groups. Eleven genes (DKK3, GADD45A, CAMTA2, CCDC80, DAPK2, ECSIT, MSMB, NUPR1, RUNX2, SAMD4A, and ZNF628) having a fold-change higher than five between groups could likely serve as markers of follicular atresia. Moreover, automatic confrontation of deregulated genes with literature data highlighted 93 genes as regulatory candidates of pig granulosa cell atresia. Among these genes known to be inhibitors of apoptosis, stimulators of apoptosis, or tumor suppressors INHBB, HNF4, CLU, different interleukins (IL5, IL24), TNF-associated receptor (TNFR1), and cytochrome-c oxidase (COX) were suggested as playing an important role in porcine atresia. The present study also enlists key upstream regulators in follicle atresia based on our results and on a literature review. The novel gene candidates and gene networks identified in the current study lead to a better understanding of the molecular regulation of ovarian follicular atresia.

  16. A Network of Genes Antagonistic to the LIN-35 Retinoblastoma Protein of Caenorhabditis elegans

    PubMed Central

    Polley, Stanley R. G.; Fay, David S.

    2012-01-01

    The Caenorhabditis elegans pRb ortholog, LIN-35, functions in a wide range of cellular and developmental processes. This includes a role of LIN-35 in nutrient utilization by the intestine, which it carries out redundantly with SLR-2, a zinc-finger protein. This and other redundant functions of LIN-35 were identified in genetic screens for mutations that display synthetic phenotypes in conjunction with loss of lin-35. To explore the intestinal role of LIN-35, we conducted a genome-wide RNA-interference-feeding screen for suppressors of lin-35; slr-2 early larval arrest. Of the 26 suppressors identified, 17 fall into three functional classes: (1) ribosome biogenesis genes, (2) mitochondrial prohibitins, and (3) chromatin regulators. Further characterization indicates that different categories of suppressors act through distinct molecular mechanisms. We also tested lin-35; slr-2 suppressors, as well as suppressors of the synthetic multivulval phenotype, to determine the spectrum of lin-35-synthetic phenotypes that could be suppressed following inhibition of these genes. We identified 19 genes, most of which are evolutionarily conserved, that can suppress multiple unrelated lin-35-synthetic phenotypes. Our study reveals a network of genes broadly antagonistic to LIN-35 as well as genes specific to the role of LIN-35 in intestinal and vulval development. Suppressors of multiple lin-35 phenotypes may be candidate targets for anticancer therapies. Moreover, screening for suppressors of phenotypically distinct synthetic interactions, which share a common altered gene, may prove to be a novel and effective approach for identifying genes whose activities are most directly relevant to the core functions of the shared gene. PMID:22542970

  17. 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

  18. 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

  19. Gene regulatory networks governing haematopoietic stem cell development and identity.

    PubMed

    Pimanda, John E; Göttgens, Berthold

    2010-01-01

    Development can be viewed as a dynamic progression through regulatory states which characterise the various cell types within a given differentiation cascade. To understand the progression of regulatory states that define the origin and subsequent development of haematopoietic stem cells, the first imperative is to understand the ontogeny of haematopoiesis. We are fortunate that the ontogeny of blood development is one of the best characterized mammalian developmental systems. However, the field is still in its infancy with regard to the reconstruction of gene regulatory networks and their interactions with cell signalling cascades that drive a mesodermal progenitor to adopt the identity of a haematopoietic stem cell and beyond. Nevertheless, a framework to dissect these networks and comprehend the logic of its circuitry does exist and although they may not as yet be available, a sense for the tools that will be required to achieve this aim is also emerging. In this review we cover the fundamentals of network architecture, methods used to reconstruct networks, current knowledge of haematopoietic and related transcriptional networks, current challenges and future outlook.

  20. On a fundamental structure of gene networks in living cells.

    PubMed

    Kravchenko-Balasha, Nataly; Levitzki, Alexander; Goldstein, Andrew; Rotter, Varda; Gross, A; Remacle, F; Levine, R D

    2012-03-20

    Computers are organized into hardware and software. Using a theoretical approach to identify patterns in gene expression in a variety of species, organs, and cell types, we found that biological systems similarly are comprised of a relatively unchanging hardware-like gene pattern. Orthogonal patterns of software-like transcripts vary greatly, even among tumors of the same type from different individuals. Two distinguishable classes could be identified within the hardware-like component: those transcripts that are highly expressed and stable and an adaptable subset with lower expression that respond to external stimuli. Importantly, we demonstrate that this structure is conserved across organisms. Deletions of transcripts from the highly stable core are predicted to result in cell mortality. The approach provides a conceptual thermodynamic-like framework for the analysis of gene-expression levels and networks and their variations in diseased cells.

  1. 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.

  2. 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.

  3. Development of a multiple objective planning theory and system for sustainable air quality monitoring networks.

    PubMed

    Chen, Ching-Ho; Liu, Wei-Lin; Chen, Chia-Hsing

    2006-01-15

    Air quality monitoring data are important bases for air quality management strategies planning and performance assessment. Therefore, the environmental protection authorities need to plan the air quality monitoring network effectively. However, in Taiwan, the national Environmental Protection Administration (EPA) and some county environmental protection bureaus (EPB) separately installed their own monitoring stations. This study developed an integrated methodology and computer system for planning air quality monitoring networks. The environmental, social, and economic objectives and sub-objectives, and their weights were identified using system analysis and multiple objective planning, based on the principles of sustainable development. A multiple objective optimization model and procedure for sustainable air quality monitoring networks planning are developed in this study. According to the procedure, a multiple objective planning system for sustainable air quality monitoring networks (MOPSSAQMN) is developed using computer software based on the modified bounded implicit enumeration algorithm with the constraint arrangement method. The air quality monitoring network of Taoyuan County, in northern Taiwan, was used as a case study to demonstrate the proposed method. Two satisfactory alternatives based on different conditions were generated using MOPSSAQMN. The compared results show that this study generated better alternatives than the current monitoring network. An installation schedule for the alternative was proposed, and its first step is now being implemented by the EPB of Taoyuan County Government. The procedure and computer system developed in this study can be used to assist the competent authorities to devise good and different alternatives for air quality monitoring networks planning.

  4. Pinning Synchronization of Directed Networks With Switching Topologies: A Multiple Lyapunov Functions Approach.

    PubMed

    Wen, Guanghui; Yu, Wenwu; Hu, Guoqiang; Cao, Jinde; Yu, Xinghuo

    2015-12-01

    This paper studies the global pinning synchronization problem for a class of complex networks with switching directed topologies. The common assumption in the existing related literature that each possible network topology contains a directed spanning tree is removed in this paper. Using tools from M -matrix theory and stability analysis of the switched nonlinear systems, a new kind of network topology-dependent multiple Lyapunov functions is proposed for analyzing the synchronization behavior of the whole network. It is theoretically shown that the global pinning synchronization in switched complex networks can be ensured if some nodes are appropriately pinned and the coupling is carefully selected. Interesting issues of how many and which nodes should be pinned for possibly realizing global synchronization are further addressed. Finally, some numerical simulations on coupled neural networks are provided to verify the theoretical results.

  5. Topology association analysis in weighted protein interaction network for gene prioritization

    NASA Astrophysics Data System (ADS)

    Wu, Shunyao; Shao, Fengjing; Zhang, Qi; Ji, Jun; Xu, Shaojie; Sun, Rencheng; Sun, Gengxin; Du, Xiangjun; Sui, Yi

    2016-11-01

    Although lots of algorithms for disease gene prediction have been proposed, the weights of edges are rarely taken into account. In this paper, the strengths of topology associations between disease and essential genes are analyzed in weighted protein interaction network. Empirical analysis demonstrates that compared to other genes, disease genes are weakly connected with essential genes in protein interaction network. Based on this finding, a novel global distance measurement for gene prioritization with weighted protein interaction network is proposed in this paper. Positive and negative flow is allocated to disease and essential genes, respectively. Additionally network propagation model is extended for weighted network. Experimental results on 110 diseases verify the effectiveness and potential of the proposed measurement. Moreover, weak links play more important role than strong links for gene prioritization, which is meaningful to deeply understand protein interaction network.

  6. Next generation communications satellites: Multiple access and network studies

    NASA Technical Reports Server (NTRS)

    Stern, T. E.; Schwartz, M.; Meadows, H. E.; Ahmadi, H. K.; Gadre, J. G.; Gopal, I. S.; Matsmo, K.

    1980-01-01

    Following an overview of issues involved in the choice of promising system architectures for efficient communication with multiple small inexpensive Earth stations serving hetergeneous user populations, performance evaluation via analysis and simulation for six SS/TDMA (satellite-switched/time-division multiple access) system architectures is discussed. These configurations are chosen to exemplify the essential alternatives available in system design. Although the performance evaluation analyses are of fairly general applicability, whenever possible they are considered in the context of NASA's 30/20 GHz studies. Packet switched systems are considered, with the assumption that only a part of transponder capacit is devoted to packets, the integration of circuit and packet switched traffic being reserved for further study. Three types of station access are distinguished: fixed (FA), demand (DA), and random access (RA). Similarly, switching in the satellite can be assigned on a fixed (FS) or demand (DS) basis, or replaced by a buffered store-and-forward system (SF) onboard the satellite. Since not all access/switching combinations are practical, six systems are analyzed in detail: three FS SYSTEMS, FA/FS, DA/ES, RA/FS; one DS system, DA/DS; and two SF systems, FA/SF, DA/SF. Results are presented primarily in terms of delay-throughput characteristics.

  7. Four-path multiple ∞-shaped fiber protection mechanism for cable television networks

    NASA Astrophysics Data System (ADS)

    Shih, Fu-Hung; Lai, Jiunn-Ru; Chen, Wen-Ping; Wang, Luke K.

    2015-03-01

    Traditional cable television (CATV) network configuration is mainly a branch topology. When fibers break, there are always disconnections of CATV and Internet access. Meanwhile, network service providers usually cannot be instantly informed of network breakage; network problems are noticed only after the users call for services. Network breakage location detection and repair are, in general, costly and time-consuming. This paper proposes a new framework to protect CATV fiber optic networks, where a HUB with multiple optical protection switching (OPS) is constructed between the head end and the fiber nodes. The first layer ranging from the head end to the HUB forms a 1+1 protection path ring structure. The second layer ranging from the HUB to the fiber nodes forms multiple 1+1 protection path ring structures. The framework can form a four fiber optic path protection architecture with multiple ∞-shapes between the head end and the fiber nodes. When the CATV network system experiences a malfunction, such as fiber optic cable breakage or poor signal quality, OPS can immediately switch the fiber transmission path in order to maintain uninterrupted CATV network traffic. After simulation tests on fiber path switching time automatic protection switching, frame loss, packet jitter, latency, bit errors, and system traffic flow monitoring by manually switching OPS and pulling broken fiber optic cable, results show that the fiber path protection switching mechanism can react immediately, and test data results show that network traffic is unaffected, so the proposed framework can effectively enhance the survivability and quality of service of CATV network systems.

  8. 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

  9. Prioritization of candidate disease genes by enlarging the seed set and fusing information of the network topology and gene expression.

    PubMed

    Zhang, Shao-Wu; Shao, Dong-Dong; Zhang, Song-Yao; Wang, Yi-Bin

    2014-06-01

    The identification of disease genes is very important not only to provide greater understanding of gene function and cellular mechanisms which drive human disease, but also to enhance human disease diagnosis and treatment. Recently, high-throughput techniques have been applied to detect dozens or even hundreds of candidate genes. However, experimental approaches to validate the many candidates are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Therefore, numerous theoretical and computational methods (e.g. network-based approaches) have been developed to prioritize candidate disease genes. Many network-based approaches implicitly utilize the observation that genes causing the same or similar diseases tend to correlate with each other in gene-protein relationship networks. Of these network approaches, the random walk with restart algorithm (RWR) is considered to be a state-of-the-art approach. To further improve the performance of RWR, we propose a novel method named ESFSC to identify disease-related genes, by enlarging the seed set according to the centrality of disease genes in a network and fusing information of the protein-protein interaction (PPI) network topological similarity and the gene expression correlation. The ESFSC algorithm restarts at all of the nodes in the seed set consisting of the known disease genes and their k-nearest neighbor nodes, then walks in the global network separately guided by the similarity transition matrix constructed with PPI network topological similarity properties and the correlational transition matrix constructed with the gene expression profiles. As a result, all the genes in the network are ranked by weighted fusing the above results of the RWR guided by two types of transition matrices. Comprehensive simulation results of the 10 diseases with 97 known disease genes collected from the Online Mendelian Inheritance in Man (OMIM) database show that ESFSC outperforms existing methods for

  10. 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 loops. Coupling two PFLs with bistability or one NFL with a bistable PFL is most likely to generate tristability, but the underlying mechanisms are largely different. We also interpret published results and make testable predictions. This work sheds new light on interlinking feedback loops to realize tristability. The proposed theoretical framework can be of wide applicability. PMID:27378101

  11. Pitx2 modulates a Tbx5-dependent gene regulatory network to maintain atrial rhythm

    PubMed Central

    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.

    2017-01-01

    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

  12. Identification of multiple independent horizontal gene transfers into poxviruses using a comparative genomics approach

    PubMed Central

    2008-01-01

    Background Poxviruses are important pathogens of humans, livestock and wild animals. These large dsDNA viruses have a set of core orthologs whose gene order is extremely well conserved throughout poxvirus genera. They also contain many genes with sequence and functional similarity to host genes which were probably acquired by horizontal gene transfer. Although phylogenetic trees can indicate the occurrence of horizontal gene transfer and even uncover multiple events, their use may be hampered by uncertainties in both the topology and the rooting of the tree. We propose to use synteny conservation around the horizontally transferred gene (HTgene) to distinguish between single and multiple events. Results Here we devise a method that incorporates comparative genomic information into the investigation of horizontal gene transfer, and we apply this method to poxvirus genomes. We examined the synteny conservation around twenty four pox genes that we identified, or which were reported in the literature, as candidate HTgenes. We found support for multiple independent transfers into poxviruses for five HTgenes. Three of these genes are known to be important for the survival of the virus in or out of the host cell and one of them increases susceptibility to some antiviral drugs. Conclusion In related genomes conserved synteny information can provide convincing evidence for multiple independent horizontal gene transfer events even in the absence of a robust phylogenetic tree for the HTgene. PMID:18304319

  13. Evolution of a core gene network for skeletogenesis in chordates.

    PubMed

    Hecht, Jochen; Stricker, Sigmar; Wiecha, Ulrike; Stiege, Asita; Panopoulou, Georgia; Podsiadlowski, Lars; Poustka, Albert J; Dieterich, Christoph; Ehrich, Siegfried; Suvorova, Julia; Mundlos, Stefan; Seitz, Volkhard

    2008-03-21

    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

  14. Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm

    PubMed Central

    Sun, Baoliang; Jiang, Chunlan; Li, Ming

    2016-01-01

    An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs. PMID:27809271

  15. Augmented lagrange hopfield network for economic dispatch with multiple fuel options

    SciTech Connect

    Dieu, Vo Ngoc; Ongsakul, Weerakorn; Polprasert, Jirawadee

    2011-06-20

    This paper proposes an augmented Lagrange Hopfield network (ALHN) for solving economic dispatch (ED) problem with multiple fuel options. The proposed ALHN method is a continuous Hopfield neural network with its energy function based on augmented Lagrangian function. The advantages of ALHN over the conventional Hopfield neural network are easier use, more general applications, faster convergence, better optimal solution, and larger scale of problem implementation. The method solves the problem by directly searching the most suitable fuel among the available fuels of each unit and finding the optimal solution for the problem based on minimization of the energy function of the continuous Hopfield neural network. The proposed method is tested on systems up to 100 units and the obtained results are compared to those from other methods in the literature. The results have shown that the proposed method is efficient for solving the ED problem with multiple fuel options and favorable for implementation in large scale problems.

  16. Study on multiple-hops performance of MOOC sequences-based optical labels for OPS networks

    NASA Astrophysics Data System (ADS)

    Zhang, Chongfu; Qiu, Kun; Ma, Chunli

    2009-11-01

    In this paper, we utilize a new study method that is under independent case of multiple optical orthogonal codes to derive the probability function of MOOCS-OPS networks, discuss the performance characteristics for a variety of parameters, and compare some characteristics of the system employed by single optical orthogonal code or multiple optical orthogonal codes sequences-based optical labels. The performance of the system is also calculated, and our results verify that the method is effective. Additionally it is found that performance of MOOCS-OPS networks would, negatively, be worsened, compared with single optical orthogonal code-based optical label for optical packet switching (SOOC-OPS); however, MOOCS-OPS networks can greatly enlarge the scalability of optical packet switching networks.

  17. Cerebellar network plasticity: from genes to fast oscillation.

    PubMed

    Cheron, G; Servais, L; Dan, B

    2008-04-22

    The role of the cerebellum has been increasingly recognized not only in motor control but in sensory, cognitive and emotional learning and regulation. Purkinje cells, being the sole output from the cerebellar cortex, occupy an integrative position in this network. Plasticity at this level is known to critically involve calcium signaling. In the last few years, electrophysiological study of genetically engineered mice has demonstrated the topical role of several genes encoding calcium-binding proteins (calretinin, calbindin, parvalbumin). Specific inactivation of these genes results in the emergence of a fast network oscillation (ca. 160 Hz) throughout the cerebellar cortex in alert animals, associated with ataxia. This oscillation is produced by synchronization of Purkinje cells along the parallel fiber beam. It behaves as an electrophysiological arrest rhythm, being blocked by sensorimotor stimulation. Pharmacological manipulations showed that the oscillation is blocked by GABA(A) and NMDA antagonists as well as gap junction blockers. This cerebellar network oscillation has also been documented in mouse models of human conditions with complex developmental cerebellar dysfunction, such as Angelman syndrome and fetal alcohol syndrome. Recent evidence suggests a relationship between fast oscillation and cerebellar long term depression (LTD). This may have major implications for future therapeutic targeting.

  18. Ionically Cross-Linked Polymer Networks for the Multiple-Month Release of Small Molecules

    PubMed Central

    2016-01-01

    Long-term (multiple-week or -month) release of small, water-soluble molecules from hydrogels remains a significant pharmaceutical challenge, which is typically overcome at the expense of more-complicated drug carrier designs. Such approaches are payload-specific and include covalent conjugation of drugs to base materials or incorporation of micro- and nanoparticles. As a simpler alternative, here we report a mild and simple method for achieving multiple-month release of small molecules from gel-like polymer networks. Densely cross-linked matrices were prepared through ionotropic gelation of poly(allylamine hydrochloride) (PAH) with either pyrophosphate (PPi) or tripolyphosphate (TPP), all of which are commonly available commercial molecules. The loading of model small molecules (Fast Green FCF and Rhodamine B dyes) within these polymer networks increases with the payload/network binding strength and with the PAH and payload concentrations used during encapsulation. Once loaded into the PAH/PPi and PAH/TPP ionic networks, only a few percent of the payload is released over multiple months. This extended release is achieved regardless of the payload/network binding strength and likely reflects the small hydrodynamic mesh size within the gel-like matrices. Furthermore, the PAH/TPP networks show promising in vitro cytocompatibility with model cells (human dermal fibroblasts), though slight cytotoxic effects were exhibited by the PAH/PPi networks. Taken together, the above findings suggest that PAH/PPi and (especially) PAH/TPP networks might be attractive materials for the multiple-month delivery of drugs and other active molecules (e.g., fragrances or disinfectants). PMID:26811936

  19. Adaptive Multi-Node Multiple Input and Multiple Output (MIMO) Transmission for Mobile Wireless Multimedia Sensor Networks

    PubMed Central

    Cho, Sunghyun; Choi, Ji-Woong; You, Cheolwoo

    2013-01-01

    Mobile wireless multimedia sensor networks (WMSNs), which consist of mobile sink or sensor nodes and use rich sensing information, require much faster and more reliable wireless links than static wireless sensor networks (WSNs). This paper proposes an adaptive multi-node (MN) multiple input and multiple output (MIMO) transmission to improve the transmission reliability and capacity of mobile sink nodes when they experience spatial correlation. Unlike conventional single-node (SN) MIMO transmission, the proposed scheme considers the use of transmission antennas from more than two sensor nodes. To find an optimal antenna set and a MIMO transmission scheme, a MN MIMO channel model is introduced first, followed by derivation of closed-form ergodic capacity expressions with different MIMO transmission schemes, such as space-time transmit diversity coding and spatial multiplexing. The capacity varies according to the antenna correlation and the path gain from multiple sensor nodes. Based on these statistical results, we propose an adaptive MIMO mode and antenna set switching algorithm that maximizes the ergodic capacity of mobile sink nodes. The ergodic capacity of the proposed scheme is compared with conventional SN MIMO schemes, where the gain increases as the antenna correlation and path gain ratio increase. PMID:24152920

  20. A novel all-optical label processing for OPS networks based on multiple OOC sequences from multiple-groups OOC

    NASA Astrophysics Data System (ADS)

    Qiu, Kun; Zhang, Chongfu; Ling, Yun; Wang, Yibo

    2007-11-01

    This paper proposes an all-optical label processing scheme using multiple optical orthogonal codes sequences (MOOCS) for optical packet switching (OPS) (MOOCS-OPS) networks, for the first time to the best of our knowledge. In this scheme, the multiple optical orthogonal codes (MOOC) from multiple-groups optical orthogonal codes (MGOOC) are permuted and combined to obtain the MOOCS for the optical labels, which are used to effectively enlarge the capacity of available optical codes for optical labels. The optical label processing (OLP) schemes are reviewed and analyzed, the principles of MOOCS-based optical labels for OPS networks are given, and analyzed, then the MOOCS-OPS topology and the key realization units of the MOOCS-based optical label packets are studied in detail, respectively. The performances of this novel all-optical label processing technology are analyzed, the corresponding simulation is performed. These analysis and results show that the proposed scheme can overcome the lack of available optical orthogonal codes (OOC)-based optical labels due to the limited number of single OOC for optical label with the short code length, and indicate that the MOOCS-OPS scheme is feasible.

  1. Analysis of a distributed algorithm to determine multiple routes with path diversity in ad hoc networks.

    SciTech Connect

    Ghosal, Dipak; Mueller, Stephen Ng

    2005-04-01

    With multipath routing in mobile ad hoc networks (MANETs), a source can establish multiple routes to a destination for routing data. In MANETs, mulitpath routing can be used to provide route resilience, smaller end-to-end delay, and better load balancing. However, when the multiple paths are close together, transmissions of different paths may interfere with each other, causing degradation in performance. Besides interference, the physical diversity of paths also improves fault tolerance. We present a purely distributed multipath protocol based on the AODV-Multipath (AODVM) protocol called AODVM with Path Diversity (AODVM/PD) that finds multiple paths with a desired degree of correlation between paths specified as an input parameter to the algorithm. We demonstrate through detailed simulation analysis that multiple paths with low degree of correlation determined by AODVM/PD provides both smaller end-to-end delay than AODVM in networks with low mobility and better route resilience in the presence of correlated node failures.

  2. 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..

  3. Using Morpholinos to Probe Gene Networks in Sea Urchin.

    PubMed

    Materna, Stefan C

    2017-01-01

    The control processes that underlie the progression of development can be summarized in maps of gene regulatory networks (GRNs). A critical step in their assembly is the systematic perturbation of network candidates. In sea urchins the most important method for interfering with expression in a gene-specific way is application of morpholino antisense oligonucleotides (MOs). MOs act by binding to their sequence complement in transcripts resulting in a block in translation or a change in splicing and thus result in a loss of function. Despite the tremendous success of this technology, recent comparisons to mutants generated by genome editing have led to renewed criticism and challenged its reliability. As with all methods based on sequence recognition, MOs are prone to off-target binding that may result in phenotypes that are erroneously ascribed to the loss of the intended target. However, the slow progression of development in sea urchins has enabled extremely detailed studies of gene activity in the embryo. This wealth of knowledge paired with the simplicity of the sea urchin embryo enables careful analysis of MO phenotypes through a variety of methods that do not rely on terminal phenotypes. This article summarizes the use of MOs in probing GRNs and the steps that should be taken to assure their specificity.

  4. Reverse engineering and analysis of genome-wide gene regulatory networks from gene expression profiles using high-performance computing.

    PubMed

    Belcastro, Vincenzo; Gregoretti, Francesco; Siciliano, Velia; Santoro, Michele; D'Angelo, Giovanni; Oliva, Gennaro; di Bernardo, Diego

    2012-01-01

    Regulation of gene expression is a carefully regulated phenomenon in the cell. “Reverse-engineering” algorithms try to reconstruct the regulatory interactions among genes from genome-scale measurements of gene expression profiles (microarrays). Mammalian cells express tens of thousands of genes; hence, hundreds of gene expression profiles are necessary in order to have acceptable statistical evidence of interactions between genes. As the number of profiles to be analyzed increases, so do computational costs and memory requirements. In this work, we designed and developed a parallel computing algorithm to reverse-engineer genome-scale gene regulatory networks from thousands of gene expression profiles. The algorithm is based on computing pairwise Mutual Information between each gene-pair. We successfully tested it to reverse engineer the Mus Musculus (mouse) gene regulatory network in liver from gene expression profiles collected from a public repository. A parallel hierarchical clustering algorithm was implemented to discover “communities” within the gene network. Network communities are enriched for genes involved in the same biological functions. The inferred network was used to identify two mitochondrial proteins.

  5. Delay-induced multiple stochastic resonances on scale-free neuronal networks.

    PubMed

    Wang, Qingyun; Perc, Matjaz; Duan, Zhisheng; Chen, Guanrong

    2009-06-01

    We study the effects of periodic subthreshold pacemaker activity and time-delayed coupling on stochastic resonance over scale-free neuronal networks. As the two extreme options, we introduce the pacemaker, respectively, to the neuron with the highest degree and to one of the neurons with the lowest degree within the network, but we also consider the case when all neurons are exposed to the periodic forcing. In the absence of delay, we show that an intermediate intensity of noise is able to optimally assist the pacemaker in imposing its rhythm on the whole ensemble, irrespective to its placing, thus providing evidences for stochastic resonance on the scale-free neuronal networks. Interestingly thereby, if the forcing in form of a periodic pulse train is introduced to all neurons forming the network, the stochastic resonance decreases as compared to the case when only a single neuron is paced. Moreover, we show that finite delays in coupling can significantly affect the stochastic resonance on scale-free neuronal networks. In particular, appropriately tuned delays can induce multiple stochastic resonances independently of the placing of the pacemaker, but they can also altogether destroy stochastic resonance. Delay-induced multiple stochastic resonances manifest as well-expressed maxima of the correlation measure, appearing at every multiple of the pacemaker period. We argue that fine-tuned delays and locally active pacemakers are vital for assuring optimal conditions for stochastic resonance on complex neuronal networks.

  6. Estimating Single and Multiple Target Locations Using K-Means Clustering with Radio Tomographic Imaging in Wireless Sensor Networks

    DTIC Science & Technology

    2015-03-26

    ESTIMATING SINGLE AND MULTIPLE TARGET LOCATIONS USING K-MEANS CLUSTERING WITH RADIO TOMOGRAPHIC IMAGING IN WIRELESS SENSOR NETWORKS THESIS Jeffrey K...AND MULTIPLE TARGET LOCATIONS USING K-MEANS CLUSTERING WITH RADIO TOMOGRAPHIC IMAGING IN WIRELESS SENSOR NETWORKS THESIS Presented to the Faculty...SINGLE AND MULTIPLE TARGET LOCATIONS USING K-MEANS CLUSTERING WITH RADIO TOMOGRAPHIC IMAGING IN WIRELESS SENSOR NETWORKS Jeffrey K. Nishida, B.S.E.E

  7. Integrating multiple sensory systems to modulate neural networks controlling posture

    PubMed Central

    Gerasimenko, Y.; Burdick, J.; Zhong, H.; Roy, R. R.; Edgerton, V. R.

    2015-01-01

    In this study we investigated the ability of sensory input to produce tonic responses in hindlimb muscles to facilitate standing in adult spinal rats and tested two hypotheses: 1) whether the spinal neural networks below a complete spinal cord transection can produce tonic reactions by activating different sensory inputs and 2) whether facilitation of tonic and rhythmic responses via activation of afferents and with spinal cord stimulation could engage similar neuronal mechanisms. We used a dynamically controlled platform to generate vibration during weight bearing, epidural stimulation (at spinal cord level S1), and/or tail pinching to determine the postural control responses that can be generated by the lumbosacral spinal cord. We observed that a combination of platform displacement, epidural stimulation, and tail pinching produces a cumulative effect that progressively enhances tonic responses in the hindlimbs. Tonic responses produced by epidural stimulation alone during standing were represented mainly by monosynaptic responses, whereas the combination of epidural stimulation and tail pinching during standing or epidural stimulation during stepping on a treadmill facilitated bilaterally both monosynaptic and polysynaptic responses. The results demonstrate that tonic muscle activity after complete spinal cord injury can be facilitated by activation of specific combinations of afferent inputs associated with load-bearing proprioception and cutaneous input in the presence of epidural stimulation and indicate that whether activation of tonic or rhythmic responses is generated depends on the specific combinations of sources and types of afferents activated in the hindlimb muscles. PMID:26445868

  8. Integrating multiple sensory systems to modulate neural networks controlling posture.

    PubMed

    Lavrov, I; Gerasimenko, Y; Burdick, J; Zhong, H; Roy, R R; Edgerton, V R

    2015-12-01

    In this study we investigated the ability of sensory input to produce tonic responses in hindlimb muscles to facilitate standing in adult spinal rats and tested two hypotheses: 1) whether the spinal neural networks below a complete spinal cord transection can produce tonic reactions by activating different sensory inputs and 2) whether facilitation of tonic and rhythmic responses via activation of afferents and with spinal cord stimulation could engage similar neuronal mechanisms. We used a dynamically controlled platform to generate vibration during weight bearing, epidural stimulation (at spinal cord level S1), and/or tail pinching to determine the postural control responses that can be generated by the lumbosacral spinal cord. We observed that a combination of platform displacement, epidural stimulation, and tail pinching produces a cumulative effect that progressively enhances tonic responses in the hindlimbs. Tonic responses produced by epidural stimulation alone during standing were represented mainly by monosynaptic responses, whereas the combination of epidural stimulation and tail pinching during standing or epidural stimulation during stepping on a treadmill facilitated bilaterally both monosynaptic and polysynaptic responses. The results demonstrate that tonic muscle activity after complete spinal cord injury can be facilitated by activation of specific combinations of afferent inputs associated with load-bearing proprioception and cutaneous input in the presence of epidural stimulation and indicate that whether activation of tonic or rhythmic responses is generated depends on the specific combinations of sources and types of afferents activated in the hindlimb muscles.

  9. Multiple actin-based motor genes in Dictyostelium.

    PubMed Central

    Titus, M A; Warrick, H M; Spudich, J A

    1989-01-01

    Dictyostelium cells, devoid of conventional myosin, display a variety of motile activities, consistent with the presence of other molecular motors. The Dictyostelium genome was probed at low stringency with a gene fragment containing the conserved conventional myosin head domain sequences to identify other actin-based motors that may play a role in the observed motility of these mutant cells. One gene (abmA) has been characterized and encodes a polypeptide of approximately 135 kDa with a head region homologous to other myosin head sequences and a tail region that is not predicted to form either an alpha-helical structure of coiled-coil interactions. Comparisons of the amino acid sequences of the tail regions of abmA, Dictyostelium myosin I, and Acanthamoeba myosins IB and IL reveal an area of sequence similarity in the amino terminal half of the tail that may be a membrane-binding domain. The abmA gene, however, does not contain an unusual Gly, Pro, Ala stretch typical of many of the previously described myosin Is. Two additional genes (abmB and abmC) were identified using this approach and also found to contain sequences that encode proteins with typical conserved myosin head sequences. The abm genes may be part of a large family of actin-based motors that play various roles in diverse aspects of cellular motility. Images PMID:2519618

  10. Quantitative assessment of gene expression network module-validation methods.

    PubMed

    Li, Bing; Zhang, Yingying; Yu, Yanan; Wang, Pengqian; Wang, Yongcheng; Wang, Zhong; Wang, Yongyan

    2015-10-16

    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.

  11. Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

    PubMed

    Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

    2015-01-01

    Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

  12. Algebraic model checking for Boolean gene regulatory networks.

    PubMed

    Tran, Quoc-Nam

    2011-01-01

    We present a computational method in which modular and Groebner bases (GB) computation in Boolean rings are used for solving problems in Boolean gene regulatory networks (BN). In contrast to other known algebraic approaches, the degree of intermediate polynomials during the calculation of Groebner bases using our method will never grow resulting in a significant improvement in running time and memory space consumption. We also show how calculation in temporal logic for model checking can be done by means of our direct and efficient Groebner basis computation in Boolean rings. We present our experimental results in finding attractors and control strategies of Boolean networks to illustrate our theoretical arguments. The results are promising. Our algebraic approach is more efficient than the state-of-the-art model checker NuSMV on BNs. More importantly, our approach finds all solutions for the BN problems.

  13. Autonomous Boolean modelling of developmental gene regulatory networks

    PubMed Central

    Cheng, Xianrui; Sun, Mengyang; Socolar, Joshua E. S.

    2013-01-01

    During early embryonic development, a network of regulatory interactions among genes dynamically determines a pattern of differentiated tissues. We show that important timing information associated with the interactions can be faithfully represented in autonomous Boolean models in which binary variables representing expression levels are updated in continuous time, and that such models can provide a direct insight into features that are difficult to extract from ordinary differential equation (ODE) models. As an application, we model the experimentally well-studied network controlling fly body segmentation. The Boolean model successfully generates the patterns formed in normal and genetically perturbed fly embryos, permits the derivation of constraints on the time delay parameters, clarifies the logic associated with different ODE parameter sets and provides a platform for studying connectivity and robustness in parameter space. By elucidating the role of regulatory time delays in pattern formation, the results suggest new types of experimental measurements in early embryonic development. PMID:23034351

  14. Stochastic models and numerical algorithms for a class of regulatory gene networks.

    PubMed

    Fournier, Thomas; Gabriel, Jean-Pierre; Pasquier, Jerôme; Mazza, Christian; Galbete, José; Mermod, Nicolas

    2009-08-01

    Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

  15. Network-based multiple sclerosis pathway analysis with GWAS data from 15,000 cases and 30,000 controls.

    PubMed

    2013-06-06

    Multiple sclerosis (MS) is an inflammatory CNS disease with a substantial genetic component, originally mapped to only the human leukocyte antigen (HLA) region. In the last 5 years, a total of seven genome-wide association studies and one meta-analysis successfully identified 57 non-HLA susceptibility loci. Here, we merged nominal statistical evidence of association and physical evidence of interaction to conduct a protein-interaction-network-based pathway analysis (PINBPA) on two large genetic MS studies comprising a total of 15,317 cases and 29,529 controls. The distribution of nominally significant loci at the gene level matched the patterns of extended linkage disequilibrium in regions of interest. We found that products of genome-wide significantly associated genes are more likely to interact physically and belong to the same or related pathways. We next searched for subnetworks (modules) of genes (and their encoded proteins) enriched with nominally associated loci within each study and identified those modules in common between the two studies. We demonstrate that these modules are more likely to contain genes with bona fide susceptibility variants and, in addition, identify several high-confidence candidates (including BCL10, CD48, REL, TRAF3, and TEC). PINBPA is a powerful approach to gaining further insights into the biology of associated genes and to prioritizing candidates for subsequent genetic studies of complex traits.

  16. Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data.

    PubMed

    Liu, Zhi-Ping

    2015-02-01

    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.

  17. 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

  18. An energy efficient distance-aware routing algorithm with multiple mobile sinks for wireless sensor networks.

    PubMed

    Wang, Jin; Li, Bin; Xia, Feng; Kim, Chang-Seob; Kim, Jeong-Uk

    2014-08-18

    Traffic patterns in wireless sensor networks (WSNs) usually follow a many-to-one model. Sensor nodes close to static sinks will deplete their limited energy more rapidly than other sensors, since they will have more data to forward during multihop transmission. This will cause network partition, isolated nodes and much shortened network lifetime. Thus, how to balance energy consumption for sensor nodes is an important research issue. In recent years, exploiting sink mobility technology in WSNs has attracted much research attention because it can not only improve energy efficiency, but prolong network lifetime. In this paper, we propose an energy efficient distance-aware routing algorithm with multiple mobile sink for WSNs, where sink nodes will move with a certain speed along the network boundary to collect monitored data. We study the influence of multiple mobile sink nodes on energy consumption and network lifetime, and we mainly focus on the selection of mobile sink node number and the selection of parking positions, as well as their impact on performance metrics above. We can see that both mobile sink node number and the selection of parking position have important influence on network performance. Simulation results show that our proposed routing algorithm has better performance than traditional routing ones in terms of energy consumption.

  19. 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.

  20. A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks

    PubMed Central

    Kerkhofs, Johan; Geris, Liesbet

    2015-01-01

    Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model’s scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour. PMID:26067297