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Sample records for genetic networks involved

  1. Chemical genetic screen for AMPKα2 substrates uncovers a network of proteins involved in mitosis

    PubMed Central

    Banko, Max R.; Allen, Jasmina J.; Schaffer, Bethany E.; Wilker, Erik W.; Tsou, Peiling; White, Jamie L.; Villén, Judit; Wang, Beatrice; Kim, Sara R.; Sakamoto, Kei; Gygi, Steven P.; Cantley, Lewis C.; Yaffe, Michael B.; Shokat, Kevan M.; Brunet, Anne

    2011-01-01

    SUMMARY The energy-sensing AMP-activated protein kinase (AMPK) is activated by low nutrient levels. Functions of AMPK, other than its role in cellular metabolism, are just beginning to emerge. Here we use a chemical genetics screen to identify direct substrates of AMPK in human cells. We find that AMPK phosphorylates 28 previously unidentified substrates, several of which are involved in mitosis and cytokinesis. We identify the residues phosphorylated by AMPK in vivo in several substrates, including protein phosphatase 1 regulatory subunit 12C (PPP1R12C) and p21 -activated protein kinase (PAK2). AMPK-induced phosphorylation is necessary for PPP1R12C interaction with 14-3-3 and phosphorylation of myosin regulatory light chain. Both AMPK activity and PPP1R12C phosphorylation are increased in mitotic cells and are important for mitosis completion. These findings suggest that AMPK coordinates nutrient status with mitosis completion, which may be critical for the organism’s response to low nutrients during development, or in adult stem and cancer cells. PMID:22137581

  2. The genetic regulatory network centered on Pto-Wuschela and its targets involved in wood formation revealed by association studies.

    PubMed

    Yang, Xiaohui; Wei, Zunzheng; Du, Qingzhang; Chen, Jinhui; Wang, Qingshi; Quan, Mingyang; Song, Yuepeng; Xie, Jianbo; Zhang, Deqiang

    2015-11-09

    Transcription factors (TFs) regulate gene expression and can strongly affect phenotypes. However, few studies have examined TF variants and TF interactions with their targets in plants. Here, we used genetic association in 435 unrelated individuals of Populus tomentosa to explore the variants in Pto-Wuschela and its targets to decipher the genetic regulatory network of Pto-Wuschela. Our bioinformatics and co-expression analysis identified 53 genes with the motif TCACGTGA as putative targets of Pto-Wuschela. Single-marker association analysis showed that Pto-Wuschela was associated with wood properties, which is in agreement with the observation that it has higher expression in stem vascular tissues in Populus. Also, SNPs in the 53 targets were associated with growth or wood properties under additive or dominance effects, suggesting these genes and Pto-Wuschela may act in the same genetic pathways that affect variation in these quantitative traits. Epistasis analysis indicated that 75.5% of these genes directly or indirectly interacted Pto-Wuschela, revealing the coordinated genetic regulatory network formed by Pto-Wuschela and its targets. Thus, our study provides an alternative method for dissection of the interactions between a TF and its targets, which will strength our understanding of the regulatory roles of TFs in complex traits in plants.

  3. The genetic regulatory network centered on Pto-Wuschela and its targets involved in wood formation revealed by association studies

    PubMed Central

    Yang, Xiaohui; Wei, Zunzheng; Du, Qingzhang; Chen, Jinhui; Wang, Qingshi; Quan, Mingyang; Song, Yuepeng; Xie, Jianbo; Zhang, Deqiang

    2015-01-01

    Transcription factors (TFs) regulate gene expression and can strongly affect phenotypes. However, few studies have examined TF variants and TF interactions with their targets in plants. Here, we used genetic association in 435 unrelated individuals of Populus tomentosa to explore the variants in Pto-Wuschela and its targets to decipher the genetic regulatory network of Pto-Wuschela. Our bioinformatics and co-expression analysis identified 53 genes with the motif TCACGTGA as putative targets of Pto-Wuschela. Single-marker association analysis showed that Pto-Wuschela was associated with wood properties, which is in agreement with the observation that it has higher expression in stem vascular tissues in Populus. Also, SNPs in the 53 targets were associated with growth or wood properties under additive or dominance effects, suggesting these genes and Pto-Wuschela may act in the same genetic pathways that affect variation in these quantitative traits. Epistasis analysis indicated that 75.5% of these genes directly or indirectly interacted Pto-Wuschela, revealing the coordinated genetic regulatory network formed by Pto-Wuschela and its targets. Thus, our study provides an alternative method for dissection of the interactions between a TF and its targets, which will strength our understanding of the regulatory roles of TFs in complex traits in plants. PMID:26549216

  4. Genetic disorders involving adrenal development.

    PubMed

    Lin, Lin; Ferraz-de-Souza, Bruno; Achermann, John C

    2007-01-01

    The past decade has seen significant advances in our understanding of the genetic aetiology of several forms of adrenal failure that present in infancy or childhood. Several of these disorders affect adrenal development and are termed 'adrenal hypoplasia'. These conditions can be broadly divided into: (1) secondary forms of adrenal hypoplasia due to panhypopituitarism (e.g. HESX1, LHX4, SOX3) or abnormalities in ACTH synthesis (TPIT) or processing (e.g. POMC or PC1); (2) adrenal hypoplasia as part of an ACTH resistance syndrome [MC2R/ACTH receptor, MRAP, AAAS (triple A syndrome)], and (3) primary defects in the development of the adrenal gland itself (primary adrenal hypoplasia). Primary adrenal hypoplasia most commonly occurs in an X-linked form due to mutations in the nuclear receptor DAX1 (NR0B1) but can occur in a poorly understood recessive form or as part of the IMAGe (intrauterine growth retardation, metaphyseal dysplasia, adrenal hypoplasia, genitourinary anomalies) syndrome. Defining the molecular basis of these conditions can have significant clinical implications for management, counselling and presymptomatic diagnosis, as well as providing fascinating insight into normal and abnormal mechanisms of adrenal development in humans.

  5. Investigating the specific core genetic-and-epigenetic networks of cellular mechanisms involved in human aging in peripheral blood mononuclear cells

    PubMed Central

    Li, Cheng-Wei; Wang, Wen-Hsin; Chen, Bor-Sen

    2016-01-01

    Aging is an inevitable part of life for humans, and slowing down the aging process has become a main focus of human endeavor. Here, we applied a systems biology approach to construct protein-protein interaction networks, gene regulatory networks, and epigenetic networks, i.e. genetic and epigenetic networks (GENs), of elderly individuals and young controls. We then compared these GENs to extract aging mechanisms using microarray data in peripheral blood mononuclear cells, microRNA (miRNA) data, and database mining. The core GENs of elderly individuals and young controls were obtained by applying principal network projection to GENs based on Principal Component Analysis. By comparing the core networks, we identified that to overcome the accumulated mutation of genes in the aging process the transcription factor JUN can be activated by stress signals, including the MAPK signaling, T-cell receptor signaling, and neurotrophin signaling pathways through DNA methylation of BTG3, G0S2, and AP2B1 and the regulations of mir-223 let-7d, and mir-130a. We also address the aging mechanisms in old men and women. Furthermore, we proposed that drugs designed to target these DNA methylated genes or miRNAs may delay aging. A multiple drug combination comprising phenylalanine, cholesterol, and palbociclib was finally designed for delaying the aging process. PMID:26895224

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

    PubMed

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

    2014-04-01

    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.

  7. Identification of genetic networks involved in the cell injury accompanying endoplasmic reticulum stress induced by bisphenol A in testicular Sertoli cells

    SciTech Connect

    Tabuchi, Yoshiaki . E-mail: ytabu@cts.u-toyama.ac.jp; Takasaki, Ichiro; Kondo, Takashi

    2006-07-07

    To identify detailed mechanisms by which bisphenol A (BPA), an endocrine-disrupting chemical, induces cell injury in mouse testicular Sertoli TTE3 cells, we performed genome-wide microarray and computational gene network analyses. BPA (200 {mu}M) significantly decreased cell viability and simultaneously induced an increase in mRNA levels of HSPA5 and DDIT3, endoplasmic reticulum (ER) stress marker genes. Of the 22,690 probe sets analyzed, BPA down-regulated 661 probe sets and up-regulated 604 probe sets by >2.0-fold. Hierarchical cluster analysis demonstrated nine gene clusters. In decreased gene clusters, two significant genetic networks were associated with cell growth and proliferation and the cell cycle. In increased gene clusters, two significant genetic networks including many basic-region leucine zipper transcription factors were associated with cell death and DNA replication, recombination, and repair. The present results will provide additional novel insights into the detailed molecular mechanisms of cell injury accompanying ER stress induced by BPA in Sertoli cells.

  8. From gene expressions to genetic networks

    NASA Astrophysics Data System (ADS)

    Cieplak, Marek

    2009-03-01

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

  9. Functional brain networks involved in reality monitoring.

    PubMed

    Metzak, Paul D; Lavigne, Katie M; Woodward, Todd S

    2015-08-01

    Source monitoring refers to the recollection of variables that specify the context and conditions in which a memory episode was encoded. This process involves using the qualitative and quantitative features of a memory trace to distinguish its source. One specific class of source monitoring is reality monitoring, which involves distinguishing internally generated from externally generated information, that is, memories of imagined events from real events. The purpose of the present study was to identify functional brain networks that underlie reality monitoring, using an alternative type of source monitoring as a control condition. On the basis of previous studies on self-referential thinking, it was expected that a medial prefrontal cortex (mPFC) based network would be more active during reality monitoring than the control condition, due to the requirement to focus on a comparison of internal (self) and external (other) source information. Two functional brain networks emerged from this analysis, one reflecting increasing task-related activity, and one reflecting decreasing task-related activity. The second network was mPFC based, and was characterized by task-related deactivations in areas resembling the default-mode network; namely, the mPFC, middle temporal gyri, lateral parietal regions, and the precuneus, and these deactivations were diminished during reality monitoring relative to source monitoring, resulting in higher activity during reality monitoring. This result supports previous research suggesting that self-referential thinking involves the mPFC, but extends this to a network-level interpretation of reality monitoring.

  10. Propagation of genetic variation in gene regulatory networks.

    PubMed

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

    2013-08-01

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

  11. Population Dynamics of Genetic Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Braun, Erez

    2005-03-01

    Unlike common objects in physics, a biological cell processes information. The cell interprets its genome and transforms the genomic information content, through the action of genetic regulatory networks, into proteins which in turn dictate its metabolism, functionality and morphology. Understanding the dynamics of a population of biological cells presents a unique challenge. It requires to link the intracellular dynamics of gene regulation, through the mechanism of cell division, to the level of the population. We present experiments studying adaptive dynamics of populations of genetically homogeneous microorganisms (yeast), grown for long durations under steady conditions. We focus on population dynamics that do not involve random genetic mutations. Our experiments follow the long-term dynamics of the population distributions and allow to quantify the correlations among generations. We focus on three interconnected issues: adaptation of genetically homogeneous populations following environmental changes, selection processes on the population and population variability and expression distributions. We show that while the population exhibits specific short-term responses to environmental inputs, it eventually adapts to a robust steady-state, largely independent of external conditions. Cycles of medium-switch show that the adapted state is imprinted in the population and that this memory is maintained for many generations. To further study population adaptation, we utilize the process of gene recruitment whereby a gene naturally regulated by a specific promoter is placed under a different regulatory system. This naturally occurring process has been recognized as a major driving force in evolution. We have recruited an essential gene to a foreign regulatory network and followed the population long-term dynamics. Rewiring of the regulatory network allows us to expose their complex dynamics and phase space structure.

  12. Propagation of genetic variation in gene regulatory networks

    PubMed Central

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

    2013-01-01

    A future quantitative genetics theory should link genetic variation to phenotypic variation in a causally cohesive way based on how genes actually work and interact. We provide a theoretical framework for predicting and understanding the manifestation of genetic variation in haploid and diploid regulatory networks with arbitrary feedback structures and intra-locus and inter-locus functional dependencies. Using results from network and graph theory, we define propagation functions describing how genetic variation in a locus is propagated through the network, and show how their derivatives are related to the network’s feedback structure. Similarly, feedback functions describe the effect of genotypic variation of a locus on itself, either directly or mediated by the network. A simple sign rule relates the sign of the derivative of the feedback function of any locus to the feedback loops involving that particular locus. We show that the sign of the phenotypically manifested interaction between alleles at a diploid locus is equal to the sign of the dominant feedback loop involving that particular locus, in accordance with recent results for a single locus system. Our results provide tools by which one can use observable equilibrium concentrations of gene products to disclose structural properties of the network architecture. Our work is a step towards a theory capable of explaining the pleiotropy and epistasis features of genetic variation in complex regulatory networks as functions of regulatory anatomy and functional location of the genetic variation. PMID:23997378

  13. Genetic Network Inference Using Hierarchical Structure

    PubMed Central

    Kimura, Shuhei; Tokuhisa, Masato; Okada-Hatakeyama, Mariko

    2016-01-01

    Many methods for inferring genetic networks have been proposed, but the regulations they infer often include false-positives. Several researchers have attempted to reduce these erroneous regulations by proposing the use of a priori knowledge about the properties of genetic networks such as their sparseness, scale-free structure, and so on. This study focuses on another piece of a priori knowledge, namely, that biochemical networks exhibit hierarchical structures. Based on this idea, we propose an inference approach that uses the hierarchical structure in a target genetic network. To obtain a reasonable hierarchical structure, the first step of the proposed approach is to infer multiple genetic networks from the observed gene expression data. We take this step using an existing method that combines a genetic network inference method with a bootstrap method. The next step is to extract a hierarchical structure from the inferred networks that is consistent with most of the networks. Third, we use the hierarchical structure obtained to assign confidence values to all candidate regulations. Numerical experiments are also performed to demonstrate the effectiveness of using the hierarchical structure in the genetic network inference. The improvement accomplished by the use of the hierarchical structure is small. However, the hierarchical structure could be used to improve the performances of many existing inference methods. PMID:26941653

  14. Network analyses structure genetic diversity in independent genetic worlds.

    PubMed

    Halary, Sébastien; Leigh, Jessica W; Cheaib, Bachar; Lopez, Philippe; Bapteste, Eric

    2010-01-01

    DNA flows between chromosomes and mobile elements, following rules that are poorly understood. This limited knowledge is partly explained by the limits of current approaches to study the structure and evolution of genetic diversity. Network analyses of 119,381 homologous DNA families, sampled from 111 cellular genomes and from 165,529 phage, plasmid, and environmental virome sequences, offer challenging insights. Our results support a disconnected yet highly structured network of genetic diversity, revealing the existence of multiple "genetic worlds." These divides define multiple isolated groups of DNA vehicles drawing on distinct gene pools. Mathematical studies of the centralities of these worlds' subnetworks demonstrate that plasmids, not viruses, were key vectors of genetic exchange between bacterial chromosomes, both recently and in the past. Furthermore, network methodology introduces new ways of quantifying current sampling of genetic diversity.

  15. Childhood obesity: are genetic differences involved?

    PubMed

    Bouchard, Claude

    2009-05-01

    This brief review focuses on the genetic contribution to childhood obesity. Evidence for a genetic component to excess body weight during growth is presented from the perspective of genetic epidemiology studies. Parental obesity is a predictor of childhood excess weight. The familial risk ratio for childhood obesity when a parent is obese reaches >2.5. Birth weight is characterized by a genetic heritability component on the order of 30%, with significant maternal and paternal effects in addition to the newborn genes. About 5% of childhood obesity cases are caused by a defect that impairs function in a gene, and >/=5 of these genes have been uncovered. However, the common forms of childhood obesity seem to result from a predisposition that primarily favors obesogenic behaviors in an obesogenic environment. Candidate gene and genomewide association studies reveal that these obesogenic genes have small effect sizes but that the risk alleles for obesity are quite common in populations. The latter may translate into a highly significant population-attributable risk of obesity. Gene-environment interaction studies suggest that the effects of predisposing genes can be enhanced or diminished by exposure to relevant behaviors. It is possible that the prevalence of childhood obesity is increasing across generations as a result of positive assortative mating with obese husbands and wives contributing more obese offspring than normal-weight parents.

  16. Human Handedness: More Evidence for Genetic Involvement.

    ERIC Educational Resources Information Center

    Longstreth, Langdon E.

    1980-01-01

    A series of environmental-genetical analyses of the left-handedness of 1,950 college students indicates that left-handedness is familial: it is more frequent in families in which at least one parent is left-handed. (Author/CM)

  17. Inferring genetic networks from microarray data.

    SciTech Connect

    May, Elebeoba Eni; Davidson, George S.; Martin, Shawn Bryan; Werner-Washburne, Margaret C.; Faulon, Jean-Loup Michel

    2004-06-01

    In theory, it should be possible to infer realistic genetic networks from time series microarray data. In practice, however, network discovery has proved problematic. The three major challenges are: (1) inferring the network; (2) estimating the stability of the inferred network; and (3) making the network visually accessible to the user. Here we describe a method, tested on publicly available time series microarray data, which addresses these concerns. The inference of genetic networks from genome-wide experimental data is an important biological problem which has received much attention. Approaches to this problem have typically included application of clustering algorithms [6]; the use of Boolean networks [12, 1, 10]; the use of Bayesian networks [8, 11]; and the use of continuous models [21, 14, 19]. Overviews of the problem and general approaches to network inference can be found in [4, 3]. Our approach to network inference is similar to earlier methods in that we use both clustering and Boolean network inference. However, we have attempted to extend the process to better serve the end-user, the biologist. In particular, we have incorporated a system to assess the reliability of our network, and we have developed tools which allow interactive visualization of the proposed network.

  18. Systematic mapping of genetic interaction networks.

    PubMed

    Dixon, Scott J; Costanzo, Michael; Baryshnikova, Anastasia; Andrews, Brenda; Boone, Charles

    2009-01-01

    Genetic interactions influencing a phenotype of interest can be identified systematically using libraries of genetic tools that perturb biological systems in a defined manner. Systematic screens conducted in the yeast Saccharomyces cerevisiae have identified thousands of genetic interactions and provided insight into the global structure of biological networks. Techniques enabling systematic genetic interaction mapping have been extended to other single-celled organisms, the bacteria Escherichia coli and the yeast Schizosaccharomyces pombe, opening the way to comparative investigations of interaction networks. Genetic interaction screens in Caenorhabditis elegans, Drosophila melanogaster, and mammalian models are helping to improve our understanding of metazoan-specific signaling pathways. Together, our emerging knowledge of the genetic wiring diagrams of eukaryotic and prokaryotic cells is providing a new understanding of the relationship between genotype and phenotype.

  19. Genetic Dissection of Rhythmic Motor Networks in Mice

    PubMed Central

    Grossmann, Katja S.; Giraudin, Aurore; Britz, Olivier; Zhang, Jingming; Goulding, Martyn

    2011-01-01

    Simple motor behaviors such as locomotion and respiration involve rhythmic and coordinated muscle movements that are generated by central pattern generator (CPG) networks in the spinal cord and hindbrain. These CPG networks produce measurable behavioral outputs, and thus represent ideal model systems for studying the operational principles that the nervous system uses to produce specific behaviors. Recent advances in our understanding of the transcriptional code that controls neuronal development have provided an entry point into identifying and targeting distinct neuronal populations that make up locomotor CPG networks in the spinal cord. This has spurred the development of new genetic approaches to dissect and manipulate neuronal networks both in the spinal cord and hindbrain. Here we discuss how the advent of molecular genetics together with anatomical and physiological methods has begun to revolutionize studies of the neuronal networks controlling rhythmic motor behaviors in mice. PMID:21111198

  20. Networks of spatial genetic variation across species

    PubMed Central

    Fortuna, Miguel A.; Albaladejo, Rafael G.; Fernández, Laura; Aparicio, Abelardo; Bascompte, Jordi

    2009-01-01

    Spatial patterns of genetic variation provide information central to many ecological, evolutionary, and conservation questions. This spatial variability has traditionally been analyzed through summary statistics between pairs of populations, therefore missing the simultaneous influence of all populations. More recently, a network approach has been advocated to overcome these limitations. This network approach has been applied to a few cases limited to a single species at a time. The question remains whether similar patterns of spatial genetic variation and similar functional roles for specific patches are obtained for different species. Here we study the networks of genetic variation of four Mediterranean woody plant species inhabiting the same habitat patches in a highly fragmented forest mosaic in Southern Spain. Three of the four species show a similar pattern of genetic variation with well-defined modules or groups of patches holding genetically similar populations. These modules can be thought of as the long-sought-after, evolutionarily significant units or management units. The importance of each patch for the cohesion of the entire network, though, is quite different across species. This variation creates a tremendous challenge for the prioritization of patches to conserve the genetic variation of multispecies assemblages. PMID:19861546

  1. Evolutionary design of oscillatory genetic networks

    NASA Astrophysics Data System (ADS)

    Kobayashi, Y.; Shibata, T.; Kuramoto, Y.; Mikhailov, A. S.

    2010-07-01

    The present study is devoted to the design and statistical investigations of dynamical gene expression networks. In our model problem, we aim to design genetic networks which would exhibit stable periodic oscillations with a prescribed temporal period. While no rational solution of this problem is available, we show that it can be effectively solved by running a computer evolution of the network models. In this process, structural rewiring mutations are applied to the networks with inhibitory interactions between genes and the evolving networks are selected depending on whether, after a mutation, they closer approach the targeted dynamics. We show that, by using this method, networks with required oscillation periods, varying by up to three orders of magnitude, can be constructed by changing the architecture of regulatory connections between the genes. Statistical properties of designed networks, including motif distributions and Laplacian spectra, are considered.

  2. Molecular and genetic inflammation networks in major human diseases.

    PubMed

    Zhao, Yongzhong; Forst, Christian V; Sayegh, Camil E; Wang, I-Ming; Yang, Xia; Zhang, Bin

    2016-07-19

    It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured the most critical inflammation-involved molecules, genetic susceptibilities, epigenetic factors, and environmental factors, our schemata on the role of inflammation in complex diseases remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the molecular and genetic inflammation networks underlying major human diseases. In this review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer's disease, Parkinson's disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2 immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic

  3. Boolean Modelingof Genetic Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Albert, Réka

    Biological systems form complex networks of interaction on several scales, ranging from the molecular to the ecosystem level. On the subcellular scale, interaction between genes and gene products (mRNAs, proteins) forms the basis of essential processes like signal transduction, cell metabolism or embryonic development. Recent experimental advances helped uncover the qualitative structure of many gene control networks, creating a surge of interest in the quantitative description of gene regulation. We give a brief description of the main frameworks and methods used in modeling gene regulatory networks, then focus on a recent model of the segment polarity genes of the fruit fly Drosophila melanogaster. The basis of this model is the known interactions between the products of the segment polarity genes, and the network topology these interactions form. The interactions between mRNAs and proteins are described as logical (Boolean) functions. The success in reproducing both wild type and mutant gene expression patterns suggests that the kinetic details of the interactions are not essential as long as the network of interactions is unperturbed. The model predicts the gene patterns for cases that were not yet studied experimentally, and implies a remarkable robustness toward changes in internal parameters, initial conditions and even some mutations.

  4. Probabilistic belief networks for genetic counseling.

    PubMed

    Harris, N L

    1990-05-01

    This paper describes a program, GenInfer, which uses belief networks to calculate risks of inheriting genetic disorders. GenInfer is based on Pearl's (J. Pearl, Artif. Intell. 29 (1986) 241-288) algorithm for fusion and propagation in probabilistic belief networks. It is written in Common Lisp. GenInfer can calculate genotypes for any family affected with any single-gene inherited disorder. Besides considering both negative and positive information in the pedigree. GenInfer takes into account additional information about the specific disorder as well as supplementary information for family members. The output consists of genotype probabilities for all family members and estimated genetic risks for prospective children of the consultands. Belief networks provide a way to calculate probabilities for systems of conditionally dependent variables. The impacts of various pieces of information are propagated and fused in such a way that, when equilibrium is reached, each proposition can be assigned a degree of belief consistent with the axioms of probability theory. In Pearl's algorithm, information is communicated through the network by messages sent between nodes. Pearl's basic algorithm cannot directly handle multiple-connected networks, which arise in the genetic counseling domain whenever a family pedigree includes consanguinity or more than one child per couple. GenInfer makes use of two cycle breaking methods, clustering and conditioning, to handle these situations. PMID:2401132

  5. Genetic Networks Governing Heart Development

    PubMed Central

    Waardenberg, Ashley J.; Ramialison, Mirana; Bouveret, Romaric; Harvey, Richard P.

    2014-01-01

    Animal genomes contain a code for construction of the body plan from a fertilized egg. Understanding how genome information is deciphered to create the complex multilayered regulatory systems that drive organismal development, and which become altered in disease, is one of the greatest challenges in the biological sciences. The development of methods that effectively represent and communicate the complexity inherent in gene regulatory networks remains a major barrier. This review introduces the philosophy of systems biology and discusses recent progress in understanding the development of the heart at a systems biology level. PMID:25280899

  6. Genetic flexibility of regulatory networks.

    PubMed

    Hunziker, Alexander; Tuboly, Csaba; Horváth, Péter; Krishna, Sandeep; Semsey, Szabolcs

    2010-07-20

    Gene regulatory networks are based on simple building blocks such as promoters, transcription factors (TFs) and their binding sites on DNA. But how diverse are the functions that can be obtained by different arrangements of promoters and TF binding sites? In this work we constructed synthetic regulatory regions using promoter elements and binding sites of two noninteracting TFs, each sensing a single environmental input signal. We show that simply by combining these three kinds of elements, we can obtain 11 of the 16 Boolean logic gates that integrate two environmental signals in vivo. Further, we demonstrate how combination of logic gates can result in new logic functions. Our results suggest that simple elements of transcription regulation form a highly flexible toolbox that can generate diverse functions under natural selection.

  7. Genetic networks controlling retinal injury

    PubMed Central

    Vazquez-Chona, Felix R.; Khan, Amna N.; Chan, Chun K.; Moore, Anthony N.; Dash, Pramod K.; Hernandez, M. Rosario; Lu, Lu; Chesler, Elissa J.; Manly, Kenneth F.; Williams, Robert W.; Geisert, Eldon E.

    2010-01-01

    Purpose The present study defines genomic loci underlying coordinate changes in gene expression following retinal injury. Methods A group of acute phase genes expressed in diverse nervous system tissues was defined by combining microarray results from injury studies from rat retina, brain, and spinal cord. Genomic loci regulating the brain expression of acute phase genes were identified using a panel of BXD recombinant inbred (RI) mouse strains. Candidate upstream regulators within a locus were defined using single nucleotide polymorphism databases and promoter motif databases. Results The acute phase response of rat retina, brain, and spinal cord was dominated by transcription factors. Three genomic loci control transcript expression of acute phase genes in brains of BXD RI mouse strains. One locus was identified on chromosome 12 and was highly correlated with the expression of classic acute phase genes. Within the locus we identified the inhibitor of DNA binding 2 (Id2) as a candidate upstream regulator. Id2 was upregulated as an acute phase transcript in injury models of rat retina, brain, and spinal cord. Conclusions We defined a group of transcriptional changes associated with the retinal acute injury response. Using genetic linkage analysis of natural transcript variation, we identified regulatory loci and candidate regulators that control transcript levels of acute phase genes. PMID:16288200

  8. Adaptation by Plasticity of Genetic Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Brenner, Naama

    2007-03-01

    Genetic regulatory networks have an essential role in adaptation and evolution of cell populations. This role is strongly related to their dynamic properties over intermediate-to-long time scales. We have used the budding yeast as a model Eukaryote to study the long-term dynamics of the genetic regulatory system and its significance in evolution. A continuous cell growth technique (chemostat) allows us to monitor these systems over long times under controlled condition, enabling a quantitative characterization of dynamics: steady states and their stability, transients and relaxation. First, we have demonstrated adaptive dynamics in the GAL system, a classic model for a Eukaryotic genetic switch, induced and repressed by different carbon sources in the environment. We found that both induction and repression are only transient responses; over several generations, the system converges to a single robust steady state, independent of external conditions. Second, we explored the functional significance of such plasticity of the genetic regulatory network in evolution. We used genetic engineering to mimic the natural process of gene recruitment, placing the gene HIS3 under the regulation of the GAL system. Such genetic rewiring events are important in the evolution of gene regulation, but little is known about the physiological processes supporting them and the dynamics of their assimilation in a cell population. We have shown that cells carrying the rewired genome adapted to a demanding change of environment and stabilized a population, maintaining the adaptive state for hundreds of generations. Using genome-wide expression arrays we showed that underlying the observed adaptation is a global transcriptional programming that allowed tuning expression of the recruited gene to demands. Our results suggest that non-specific properties reflecting the natural plasticity of the regulatory network support adaptation of cells to novel challenges and enhance their evolvability.

  9. Genetic interaction networks: better understand to better predict

    PubMed Central

    Boucher, Benjamin; Jenna, Sarah

    2013-01-01

    A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances. PMID:24381582

  10. Fluctuations and Slow Variables in Genetic Networks

    PubMed Central

    Bundschuh, R.; Hayot, F.; Jayaprakash, C.

    2003-01-01

    Computer simulations of large genetic networks are often extremely time consuming because, in addition to the biologically interesting translation and transcription reactions, many less interesting reactions like DNA binding and dimerizations have to be simulated. It is desirable to use the fact that the latter occur on much faster timescales than the former to eliminate the fast and uninteresting reactions and to obtain effective models of the slow reactions only. We use three examples of self-regulatory networks to show that the usual reduction methods where one obtains a system of equations of the Hill type fail to capture the fluctuations that these networks exhibit due to the small number of molecules; moreover, they may even miss describing the behavior of the average number of proteins. We identify the inclusion of fast-varying variables in the effective description as the cause for the failure of the traditional schemes. We suggest a different effective description, which entails the introduction of an additional species, not present in the original networks, that is slowly varying. We show that this description allows for a very efficient simulation of the reduced system while retaining the correct fluctuations and behavior of the full system. This approach ought to be applicable to a wide range of genetic networks. PMID:12609864

  11. Genetic Network Programming with Reconstructed Individuals

    NASA Astrophysics Data System (ADS)

    Ye, Fengming; Mabu, Shingo; Wang, Lutao; Eto, Shinji; Hirasawa, Kotaro

    A lot of research on evolutionary computation has been done and some significant classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Programming (EP), and Evolution Strategies (ES) have been studied. Recently, a new approach named Genetic Network Programming (GNP) has been proposed. GNP can evolve itself and find the optimal solution. It is based on the idea of Genetic Algorithm and uses the data structure of directed graphs. Many papers have demonstrated that GNP can deal with complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is used in many different areas such as data mining, extracting trading rules of stock markets, elevator supervised control systems, etc., and GNP has obtained some outstanding results. Focusing on the GNP's distinguished expression ability of the graph structure, this paper proposes a method named Genetic Network Programming with Reconstructed Individuals (GNP-RI). The aim of GNP-RI is to balance the exploitation and exploration of GNP, that is, to strengthen the exploitation ability by using the exploited information extensively during the evolution process of GNP and finally obtain better performances than that of GNP. In the proposed method, the worse individuals are reconstructed and enhanced by the elite information before undergoing genetic operations (mutation and crossover). The enhancement of worse individuals mimics the maturing phenomenon in nature, where bad individuals can become smarter after receiving a good education. In this paper, GNP-RI is applied to the tile-world problem which is an excellent bench mark for evaluating the proposed architecture. The performance of GNP-RI is compared with that of the conventional GNP. The simulation results show some advantages of GNP-RI demonstrating its superiority over the conventional GNPs.

  12. Effects of Macromolecular Crowding on Genetic Networks

    PubMed Central

    Morelli, Marco J.; Allen, Rosalind J.; Rein ten Wolde, Pieter

    2011-01-01

    The intracellular environment is crowded with proteins, DNA, and other macromolecules. Under physiological conditions, macromolecular crowding can alter both molecular diffusion and the equilibria of bimolecular reactions and therefore is likely to have a significant effect on the function of biochemical networks. We propose a simple way to model the effects of macromolecular crowding on biochemical networks via an appropriate scaling of bimolecular association and dissociation rates. We use this approach, in combination with kinetic Monte Carlo simulations, to analyze the effects of crowding on a constitutively expressed gene, a repressed gene, and a model for the bacteriophage λ genetic switch, in the presence and absence of nonspecific binding of transcription factors to genomic DNA. Our results show that the effects of crowding are mainly caused by the shift of association-dissociation equilibria rather than the slowing down of protein diffusion, and that macromolecular crowding can have relevant and counterintuitive effects on biochemical network performance. PMID:22208186

  13. Time-course of cortical networks involved in working memory

    PubMed Central

    Luu, Phan; Caggiano, Daniel M.; Geyer, Alexandra; Lewis, Jenn; Cohn, Joseph; Tucker, Don M.

    2014-01-01

    Working memory (WM) is one of the most studied cognitive constructs. Although many neuroimaging studies have identified brain networks involved in WM, the time course of these networks remains unclear. In this paper we use dense-array electroencephalography (dEEG) to capture neural signals during performance of a standard WM task, the n-back task, and a blend of principal components analysis and independent components analysis (PCA/ICA) to statistically identify networks of WM and their time courses. Results reveal a visual cortex centric network, that also includes the posterior cingulate cortex, that is active prior to stimulus onset and that appears to reflect anticipatory, attention-related processes. After stimulus onset, the ventromedial prefrontal cortex, lateral prefrontal prefrontal cortex, and temporal poles become associated with the prestimulus network. This second network appears to reflect executive control processes. Following activation of the second network, the cortices of the temporo-parietal junction with the temporal lobe structures seen in the first and second networks re-engage. This third network appears to reflect activity of the ventral attention network involved in control of attentional reorientation. The results point to important temporal features of network dynamics that integrate multiple subsystems of the ventral attention network with the default mode network in the performance of working memory tasks. PMID:24523686

  14. Character Recognition Using Genetically Trained Neural Networks

    SciTech Connect

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the amount of

  15. Demonstrations of neural network computations involving students.

    PubMed

    May, Christopher J

    2010-01-01

    David Marr famously proposed three levels of analysis (implementational, algorithmic, and computational) for understanding information processing systems such as the brain. While two of these levels are commonly taught in neuroscience courses (the implementational level through neurophysiology and the computational level through systems/cognitive neuroscience), the algorithmic level is typically neglected. This leaves an explanatory gap in students' understanding of how, for example, the flow of sodium ions enables cognition. Neural networks bridge these two levels by demonstrating how collections of interacting neuron-like units can give rise to more overtly cognitive phenomena. The demonstrations in this paper are intended to facilitate instructors' introduction and exploration of how neurons "process information."

  16. Genetic, environmental, and epigenetic factors involved in CAKUT.

    PubMed

    Nicolaou, Nayia; Renkema, Kirsten Y; Bongers, Ernie M H F; Giles, Rachel H; Knoers, Nine V A M

    2015-12-01

    Congenital anomalies of the kidney and urinary tract (CAKUT) refer to a spectrum of structural renal malformations and are the leading cause of end-stage renal disease in children. The genetic diagnosis of CAKUT has proven to be challenging due to genetic and phenotypic heterogeneity and incomplete genetic penetrance. Monogenic causes of CAKUT have been identified using different approaches, including single gene screening, and gene panel and whole exome sequencing. The majority of the identified mutations, however, lack substantial evidence to support a pathogenic role in CAKUT. Copy number variants or single nucleotide variants that are associated with CAKUT have also been identified. Numerous studies support the influence of epigenetic and environmental factors on kidney development and the natural history of CAKUT, suggesting that the pathogenesis of this syndrome is multifactorial. In this Review we describe the current knowledge regarding the genetic susceptibility underlying CAKUT and the approaches used to investigate the genetic basis of CAKUT. We outline the associated environmental risk factors and epigenetic influences on CAKUT and discuss the challenges and strategies used to fully address the involvement and interplay of these factors in the pathogenesis of the disease. PMID:26281895

  17. Demonstrations of Neural Network Computations Involving Students

    PubMed Central

    May, Christopher J.

    2010-01-01

    David Marr famously proposed three levels of analysis (implementational, algorithmic, and computational) for understanding information processing systems such as the brain. While two of these levels are commonly taught in neuroscience courses (the implementational level through neurophysiology and the computational level through systems/cognitive neuroscience), the algorithmic level is typically neglected. This leaves an explanatory gap in students’ understanding of how, for example, the flow of sodium ions enables cognition. Neural networks bridge these two levels by demonstrating how collections of interacting neuron-like units can give rise to more overtly cognitive phenomena. The demonstrations in this paper are intended to facilitate instructors’ introduction and exploration of how neurons “process information.” PMID:23493501

  18. Functional Localization of Genetic Network Programming

    NASA Astrophysics Data System (ADS)

    Eto, Shinji; Hirasawa, Kotaro; Hu, Jinglu

    According to the knowledge of brain science, it is suggested that there exists cerebral functional localization, which means that a specific part of the cerebrum is activated depending on various kinds of information human receives. The aim of this paper is to build an artificial model to realize functional localization based on Genetic Network Programming (GNP), a new evolutionary computation method recently developed. GNP has a directed graph structure suitable for realizing functional localization. We studied the basic characteristics of the proposed system by making GNP work in a functionally localized way.

  19. Genetic Regulatory Networks in Embryogenesis and Evolution

    NASA Technical Reports Server (NTRS)

    1998-01-01

    The article introduces a series of papers that were originally presented at a workshop titled Genetic Regulatory Network in Embryogenesis and Evaluation. Contents include the following: evolution of cleavage programs in relationship to axial specification and body plan evolution, changes in cell lineage specification elucidate evolutionary relations in spiralia, axial patterning in the leech: developmental mechanisms and evolutionary implications, hox genes in arthropod development and evolution, heterochronic genes in development and evolution, a common theme for LIM homeobox gene function across phylogeny, and mechanisms of specification in ascidian embryos.

  20. Actor-network theory: a tool to support ethical analysis of commercial genetic testing.

    PubMed

    Williams-Jones, Bryn; Graham, Janice E

    2003-12-01

    Social, ethical and policy analysis of the issues arising from gene patenting and commercial genetic testing is enhanced by the application of science and technology studies, and Actor-Network Theory (ANT) in particular. We suggest the potential for transferring ANT's flexible nature to an applied heuristic methodology for gathering empirical information and for analysing the complex networks involved in the development of genetic technologies. Three concepts are explored in this paper--actor-networks, translation, and drift--and applied to the case of Myriad Genetics and their commercial BRACAnalysis genetic susceptibility test for hereditary breast cancer. Treating this test as an active participant in socio-technical networks clarifies the extent to which it interacts with, shapes and is shaped by people, other technologies, and institutions. Such an understanding enables more sophisticated and nuanced technology assessment, academic analysis, as well as public debate about the social, ethical and policy implications of the commercialization of new genetic technologies. PMID:15115034

  1. Frontoparietal networks involved in categorization and item working memory.

    PubMed

    Braunlich, Kurt; Gomez-Lavin, Javier; Seger, Carol A

    2015-02-15

    Categorization and memory for specific items are fundamental processes that allow us to apply knowledge to novel stimuli. This study directly compares categorization and memory using delay match to category (DMC) and delay match to sample (DMS) tasks. In DMC participants view and categorize a stimulus, maintain the category across a delay, and at the probe phase view another stimulus and indicate whether it is in the same category or not. In DMS, a standard item working memory task, participants encode and maintain a specific individual item, and at probe decide if the stimulus is an exact match or not. Constrained Principal Components Analysis was used to identify and compare activity within neural networks associated with these tasks, and we relate these networks to those that have been identified with resting state-fMRI. We found that two frontoparietal networks of particular interest. The first network included regions associated with the dorsal attention network and frontoparietal salience network; this network showed patterns of activity consistent with a role in rapid orienting to and processing of complex stimuli. The second uniquely involved regions of the frontoparietal central-executive network; this network responded more slowly following each stimulus and showed a pattern of activity consistent with a general role in role in decision-making across tasks. Additional components were identified that were associated with visual, somatomotor and default mode networks. PMID:25482265

  2. Postpartum depression: A systematic review of the genetics involved

    PubMed Central

    Couto, Tiago Castro e; Brancaglion, Mayra Yara Martins; Alvim-Soares, António; Moreira, Lafaiete; Garcia, Frederico Duarte; Nicolato, Rodrigo; Aguiar, Regina Amélia Lopes P; Leite, Henrique Vitor; Corrêa, Humberto

    2015-01-01

    Postpartum depression is one of the most prevalent psychopathologies. Its prevalence is estimated to be between 10% and 15%. Despite its multifactorial etiology, it is known that genetics play an important role in the genesis of this disorder. This paper reviews epidemiological evidence supporting the role of genetics in postpartum depression (PPD). The main objectives of this review are to determine which genes and polymorphisms are associated with PPD and discuss how this association may occur. In addition, this paper explores whether these genes are somehow related to or even the same as those linked to Major Depression (MD). To identify gaps in the current knowledge that require investigation, a systematic review was conducted in the electronic databases PubMed, LILACS and SciELO using the index terms “postpartum depression” and “genetics”. Literature searches for articles in peer-reviewed journals were made until April 2014. PPD was indexed 56 times with genetics. The inclusion criteria were articles in Portuguese, Spanish or English that were available by institutional means or sent by authors upon request; this search resulted in 20 papers. Genes and polymorphisms traditionally related to MD, which are those involved in the serotonin, catecholamine, brain-derived neurotrophic factor and tryptophan metabolism, have been the most studied, and some have been related to PPD. The results are conflicting and some depend on epigenetics, which makes the data incipient. Further studies are required to determine the genes that are involved in PPD and establish the nature of the relationship between these genes and PPD. PMID:25815259

  3. Genetic demographic networks: Mathematical model and applications.

    PubMed

    Kimmel, Marek; Wojdyła, Tomasz

    2016-10-01

    Recent improvement in the quality of genetic data obtained from extinct human populations and their ancestors encourages searching for answers to basic questions regarding human population history. The most common and successful are model-based approaches, in which genetic data are compared to the data obtained from the assumed demography model. Using such approach, it is possible to either validate or adjust assumed demography. Model fit to data can be obtained based on reverse-time coalescent simulations or forward-time simulations. In this paper we introduce a computational method based on mathematical equation that allows obtaining joint distributions of pairs of individuals under a specified demography model, each of them characterized by a genetic variant at a chosen locus. The two individuals are randomly sampled from either the same or two different populations. The model assumes three types of demographic events (split, merge and migration). Populations evolve according to the time-continuous Moran model with drift and Markov-process mutation. This latter process is described by the Lyapunov-type equation introduced by O'Brien and generalized in our previous works. Application of this equation constitutes an original contribution. In the result section of the paper we present sample applications of our model to both simulated and literature-based demographies. Among other we include a study of the Slavs-Balts-Finns genetic relationship, in which we model split and migrations between the Balts and Slavs. We also include another example that involves the migration rates between farmers and hunters-gatherers, based on modern and ancient DNA samples. This latter process was previously studied using coalescent simulations. Our results are in general agreement with the previous method, which provides validation of our approach. Although our model is not an alternative to simulation methods in the practical sense, it provides an algorithm to compute pairwise

  4. Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks.

    PubMed

    Sherif, Fayroz F; Zayed, Nourhan; Fakhr, Mahmoud

    2015-01-01

    Single nucleotide polymorphisms (SNPs) contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer's disease (AD). Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between genes or single SNPs. Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS) data for detecting the causal AD SNPs and gene-SNP interactions. We focused on polymorphisms in the top ten genes associated with AD and identified by genome-wide association (GWA) studies. New SNP biomarkers were observed to be significantly associated with Alzheimer's disease. These SNPs are rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134. The obtained results demonstrated the effectiveness of using BN for identifying AD causal SNPs with acceptable accuracy. The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes. Minimal augmented Markov blanket reaches accuracy of 66.13% and sensitivity of 88.87% versus 61.58% and 59.43% in naïve Bayes, respectively. PMID:26366461

  5. Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks

    PubMed Central

    Sherif, Fayroz F.; Zayed, Nourhan; Fakhr, Mahmoud

    2015-01-01

    Single nucleotide polymorphisms (SNPs) contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer's disease (AD). Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between genes or single SNPs. Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS) data for detecting the causal AD SNPs and gene-SNP interactions. We focused on polymorphisms in the top ten genes associated with AD and identified by genome-wide association (GWA) studies. New SNP biomarkers were observed to be significantly associated with Alzheimer's disease. These SNPs are rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134. The obtained results demonstrated the effectiveness of using BN for identifying AD causal SNPs with acceptable accuracy. The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes. Minimal augmented Markov blanket reaches accuracy of 66.13% and sensitivity of 88.87% versus 61.58% and 59.43% in naïve Bayes, respectively. PMID:26366461

  6. Information transmission in genetic regulatory networks: a review.

    PubMed

    Tkačik, Gašper; Walczak, Aleksandra M

    2011-04-20

    Genetic regulatory networks enable cells to respond to changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform, and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between a network's inputs and outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary for understanding recent work. We then discuss the functional complexity of gene regulation, which arises from the molecular nature of the regulatory interactions. We end by reviewing some experiments that support the view that genetic networks responsible for early development of multicellular organisms might be maximizing transmitted 'positional information'.

  7. A genetic regulatory network for Xenopus mesendoderm formation.

    PubMed

    Loose, Matthew; Patient, Roger

    2004-07-15

    We have constructed a genetic regulatory network (GRN) summarising the functional relationships between the transcription factors (TFs) and embryonic signals involved in Xenopus mesendoderm formation. It is supported by a relational database containing the experimental evidence and both are available in interactive form via the World Wide Web. This network highlights areas for further study and provides a framework for systematic interrogation of new data. Comparison with the equivalent network for the sea urchin identifies conserved features of the deuterostome ancestral pathway, including positive feedback loops, GATA factors, SoxB, Brachyury and a previously underemphasised role for beta-catenin. In contrast, some features central to one species have not yet been found in the other, for example, Krox and Otx in sea urchin, and Mix and Nodal in Xenopus. Such differences may represent evolved features or may eventually be resolved. For example, in Xenopus, Nodal-related genes are positively regulated by beta-catenin and at least one of them is repressed by Sox3, as is the uncharacterised early signal (ES) inducing endomesoderm in the sea urchin, suggesting that ES may be a Nodal-like TGF-beta. Wider comparisons of such networks will inform our understanding of developmental evolution.

  8. Genetic demographic networks: Mathematical model and applications.

    PubMed

    Kimmel, Marek; Wojdyła, Tomasz

    2016-10-01

    Recent improvement in the quality of genetic data obtained from extinct human populations and their ancestors encourages searching for answers to basic questions regarding human population history. The most common and successful are model-based approaches, in which genetic data are compared to the data obtained from the assumed demography model. Using such approach, it is possible to either validate or adjust assumed demography. Model fit to data can be obtained based on reverse-time coalescent simulations or forward-time simulations. In this paper we introduce a computational method based on mathematical equation that allows obtaining joint distributions of pairs of individuals under a specified demography model, each of them characterized by a genetic variant at a chosen locus. The two individuals are randomly sampled from either the same or two different populations. The model assumes three types of demographic events (split, merge and migration). Populations evolve according to the time-continuous Moran model with drift and Markov-process mutation. This latter process is described by the Lyapunov-type equation introduced by O'Brien and generalized in our previous works. Application of this equation constitutes an original contribution. In the result section of the paper we present sample applications of our model to both simulated and literature-based demographies. Among other we include a study of the Slavs-Balts-Finns genetic relationship, in which we model split and migrations between the Balts and Slavs. We also include another example that involves the migration rates between farmers and hunters-gatherers, based on modern and ancient DNA samples. This latter process was previously studied using coalescent simulations. Our results are in general agreement with the previous method, which provides validation of our approach. Although our model is not an alternative to simulation methods in the practical sense, it provides an algorithm to compute pairwise

  9. Social Network Influence on Father Involvement in Childrearing.

    ERIC Educational Resources Information Center

    Riley, Dave

    Predictors of fathers' involvement in childrearing activities were investigated as part of a study of the ecology of urban childrearing in five western societies. Selected on the basis of a stratified random sample technique, participants were 96 white fathers from intact families having a 3-year-old child. Network theory provided three hypotheses…

  10. Menkes' kinky hair syndrome: a genetic disease involving copper.

    PubMed

    Holtzman, N A

    1976-09-01

    The kinky hair syndrome (KHS) is an X-linked defect of copper transport in man. An animal model is available in mutants at the X-linked mottled locus in mice. The defect does not involve the uptake of copper from the intestinal lumen but rather the transport of copper from intestinal cells. The reduced activity of several copper-dependent enzymes and the lower copper content of serum, liver, and probably brain account for the manifestations of the disorder which are evident at, or shortly after, birth. Intrauterine involvement is likely but prenatal diagnosis is not yet possible. Although the delivery of iron to the erythropoietic system, and its utilization, are impaired in nutritionally induced copper deficiency, as is neutrophil production, these processes appear normal in KHS. thus, adequate copper to carry them out is available in KHS. While there may be more than one transport system for copper (only one of which is affected in KHS) it is also possible that the hematopoietic tissue in KHS, like the intestinal cells, has abnormally high afficity for copper. The presence of multiple alleles at the KHS locus (and at other genetic loci) in man, which cause different degrees of reduction in copper transport, could account for variations in the susceptibility to copper deficiency observed in infant populations.

  11. Delay-independent stability of genetic regulatory networks.

    PubMed

    Wu, Fang-Xiang

    2011-11-01

    Genetic regulatory networks can be described by nonlinear differential equations with time delays. In this paper, we study both locally and globally delay-independent stability of genetic regulatory networks, taking messenger ribonucleic acid alternative splicing into consideration. Based on nonnegative matrix theory, we first develop necessary and sufficient conditions for locally delay-independent stability of genetic regulatory networks with multiple time delays. Compared to the previous results, these conditions are easy to verify. Then we develop sufficient conditions for global delay-independent stability for genetic regulatory networks. Compared to the previous results, this sufficient condition is less conservative. To illustrate theorems developed in this paper, we analyze delay-independent stability of two genetic regulatory networks: a real-life repressilatory network with three genes and three proteins, and a synthetic gene regulatory network with five genes and seven proteins. The simulation results show that the theorems developed in this paper can effectively determine the delay-independent stability of genetic regulatory networks.

  12. Introduction to Focus Issue: Quantitative Approaches to Genetic Networks

    NASA Astrophysics Data System (ADS)

    Albert, Réka; Collins, James J.; Glass, Leon

    2013-06-01

    All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks

  13. Bistable responses in bacterial genetic networks: Designs and dynamical consequences

    PubMed Central

    Tiwari, Abhinav; Ray, J. Christian J.; Narula, Jatin; Igoshin, Oleg A.

    2011-01-01

    A key property of living cells is their ability to react to stimuli with specific biochemical responses. These responses can be understood through the dynamics of underlying biochemical and genetic networks. Evolutionary design principles have been well studied in networks that display graded responses, with a continuous relationship between input signal and system output. Alternatively, biochemical networks can exhibit bistable responses so that over a range of signals the network possesses two stable steady states. In this review, we discuss several conceptual examples illustrating network designs that can result in a bistable response of the biochemical network. Next, we examine manifestations of these designs in bacterial master-regulatory genetic circuits. In particular, we discuss mechanisms and dynamic consequences of bistability in three circuits: two-component systems, sigma-factor networks, and a multistep phosphorelay. Analyzing these examples allows us to expand our knowledge of evolutionary design principles for networks with bistable responses. PMID:21385588

  14. Genetic Algorithm Based Neural Networks for Nonlinear Optimization

    1994-09-28

    This software develops a novel approach to nonlinear optimization using genetic algorithm based neural networks. To our best knowledge, this approach represents the first attempt at applying both neural network and genetic algorithm techniques to solve a nonlinear optimization problem. The approach constructs a neural network structure and an appropriately shaped energy surface whose minima correspond to optimal solutions of the problem. A genetic algorithm is employed to perform a parallel and powerful search ofmore » the energy surface.« less

  15. Genetic "code": representations and dynamical models of genetic components and networks.

    PubMed

    Gilman, Alex; Arkin, Adam P

    2002-01-01

    Dynamical modeling of biological systems is becoming increasingly widespread as people attempt to grasp biological phenomena in their full complexity and make sense of an accelerating stream of experimental data. We review a number of recent modeling studies that focus on systems specifically involving gene expression and regulation. These systems include bacterial metabolic operons and phase-variable piliation, bacteriophages T7 and lambda, and interacting networks of eukaryotic developmental genes. A wide range of conceptual and mathematical representations of genetic components and phenomena appears in these works. We discuss these representations in depth and give an overview of the tools currently available for creating and exploring dynamical models. We argue that for modeling to realize its full potential as a mainstream biological research technique the tools must become more general and flexible, and formal, standardized representations of biological knowledge and data must be developed.

  16. Solving deterministic non-linear programming problem using Hopfield artificial neural network and genetic programming techniques

    NASA Astrophysics Data System (ADS)

    Vasant, P.; Ganesan, T.; Elamvazuthi, I.

    2012-11-01

    A fairly reasonable result was obtained for non-linear engineering problems using the optimization techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the non-linear problems to obtain better output. This paper discusses the use of neuro-genetic hybrid technique to optimize the geological structure mapping which is known as seismic survey. It involves the minimization of objective function subject to the requirement of geophysical and operational constraints. In this work, the optimization was initially performed using genetic programming, and followed by hybrid neuro-genetic programming approaches. Comparative studies and analysis were then carried out on the optimized results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method.

  17. Optimizing the controllability of arbitrary networks with genetic algorithm

    NASA Astrophysics Data System (ADS)

    Li, Xin-Feng; Lu, Zhe-Ming

    2016-04-01

    Recently, as the controllability of complex networks attracts much attention, how to optimize networks' controllability has become a common and urgent problem. In this paper, we develop an efficient genetic algorithm oriented optimization tool to optimize the controllability of arbitrary networks consisting of both state nodes and control nodes under Popov-Belevitch-Hautus rank condition. The experimental results on a number of benchmark networks show the effectiveness of this method and the evolution of network topology is captured. Furthermore, we explore how network structure affects its controllability and find that the sparser a network is, the more control nodes are needed to control it and the larger the differences between node degrees, the more control nodes are needed to achieve the full control. Our framework provides an alternative to controllability optimization and can be applied to arbitrary networks without any limitations.

  18. Reliability of genetic networks is evolvable

    NASA Astrophysics Data System (ADS)

    Braunewell, Stefan; Bornholdt, Stefan

    2008-06-01

    Control of the living cell functions with remarkable reliability despite the stochastic nature of the underlying molecular networks—a property presumably optimized by biological evolution. We ask here to what extent the ability of a stochastic dynamical network to produce reliable dynamics is an evolvable trait. Using an evolutionary algorithm based on a deterministic selection criterion for the reliability of dynamical attractors, we evolve networks of noisy discrete threshold nodes. We find that, starting from any random network, reliability of the attractor landscape can often be achieved with only a few small changes to the network structure. Further, the evolvability of networks toward reliable dynamics while retaining their function is investigated and a high success rate is found.

  19. Genetic Networks of Complex Disorders: from a Novel Search Engine for PubMed Article Database.

    PubMed

    Jung, Jae-Yoon; Wall, Dennis Paul

    2013-01-01

    Finding genetic risk factors of complex disorders may involve reviewing hundreds of genes or thousands of research articles iteratively, but few tools have been available to facilitate this procedure. In this work, we built a novel publication search engine that can identify target-disorder specific, genetics-oriented research articles and extract the genes with significant results. Preliminary test results showed that the output of this engine has better coverage in terms of genes or publications, than other existing applications. We consider it as an essential tool for understanding genetic networks of complex disorders.

  20. Genetic and Molecular Network Analysis of Behavior

    PubMed Central

    Williams, Robert W.; Mulligan, Megan K.

    2014-01-01

    This chapter provides an introduction into the genetic control and analysis of behavioral variation using powerful online resources. We introduce you to the new field of systems genetics using "case studies" drawn from the world of behavioral genetics that exploit populations of genetically diverse lines of mice. These lines differ very widely in patterns of gene and protein expression in the brain and in patterns of behavior. In this chapter we address the following set of related questions: (1) Can we combine massive genomic data sets with large aggregates of precise quantitative data on behavior? (2) Can we map causal relations between gene variants and behavioral differences? (3) Can we simultaneously use these highly coherent data sets to understand more about the underlying molecular and cellular basis of behavior? PMID:23195314

  1. Genetic variants in Alzheimer disease - molecular and brain network approaches.

    PubMed

    Gaiteri, Chris; Mostafavi, Sara; Honey, Christopher J; De Jager, Philip L; Bennett, David A

    2016-07-01

    Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models. PMID:27282653

  2. Genetic oscillation deduced from Hopf bifurcation in a genetic regulatory network with delays.

    PubMed

    Xiao, Min; Cao, Jinde

    2008-09-01

    To understand how a gene regulatory network functioning as an oscillator is built, a genetic regulatory network with two transcriptional delays is investigated. We show by mathematical analysis and simulation that autorepression of mRNA and protein can provide a mechanism for the intracellular oscillator. Based on the linear stability approach and bifurcation theory, sufficient conditions for the oscillation of the genetic networks are derived, and critical values of Hopf bifurcation are assessed. In particular, the genetic network can exhibit Hopf bifurcation(oscillation appears) as the sum of delays or transcriptional rate passes through some critical values. Moreover, the robustness of amplitudes against change in delay can also be obtained from the delayed genetic network; period of oscillation increases with the total time delay in an almost linear way. While it is exactly opposite for transcriptional rate, the amplitude of oscillations always increases as the transcriptional rate increases; the robustness of period against change in the transcriptional rate occurs. Some simple genetic regulatory networks are used to study the impact of delays and transcriptional rate on the system dynamics where there are delays.

  3. Genetic Mechanisms Involved in the Phenotype of Down Syndrome

    ERIC Educational Resources Information Center

    Patterson, David

    2007-01-01

    Down syndrome (DS) is the most common genetic cause of significant intellectual disability in the human population, occurring in roughly 1 in 700 live births. The ultimate cause of DS is trisomy of all or part of the set of genes located on chromosome 21. How this trisomy leads to the phenotype of DS is unclear. The completion of the DNA…

  4. Formation mechanism for a hybrid supramolecular network involving cooperative interactions.

    PubMed

    Mura, Manuela; Silly, Fabien; Burlakov, Victor; Castell, Martin R; Briggs, G Andrew D; Kantorovich, Lev N

    2012-04-27

    A novel mechanism of hybrid assembly of molecules on surfaces is proposed stemming from interactions between molecules and on-surface metal atoms which eventually got trapped inside the network pores. Based on state-of-the-art theoretical calculations, we find that the new mechanism relies on formation of molecule-metal atom pairs which, together with molecules themselves, participate in the assembly growth. Most remarkably, the dissociation of pairs is facilitated by a cooperative interaction involving many molecules. This new mechanism is illustrated on a low coverage Melamine hexagonal network on the Au(111) surface where multiple events of gold atoms trapping via a set of so-called "gate" transitions are found by kinetic Monte Carlo simulations based on transition rates obtained using ab initio density functional theory calculations and the nudged elastic band method. Simulated STM images of gold atoms trapped in the pores of the Melamine network predict that the atoms should appear as bright spots inside Melamine hexagons. No trapping was found at large Melamine coverages, however. These predictions have been supported by preliminary STM experiments which show bright spots inside Melamine hexagons at low Melamine coverages, while empty pores are mostly observed at large coverages. Therefore, we suggest that bright spots sometimes observed in the pores of molecular assemblies on metal surfaces may be attributed to trapped substrate metal atoms. We believe that this type of mechanism could be used for delivering adatom species of desired functionality (e.g., magnetic) into the pores of hydrogen-bonded networks serving as templates for their capture. PMID:22680886

  5. Assessing the robustness of networks of spatial genetic variation.

    PubMed

    Albert, Eva M; Fortuna, Miguel A; Godoy, José A; Bascompte, Jordi

    2013-05-01

    Habitat transformation is one of the leading drivers of biodiversity loss. The ecological effects of this transformation have mainly been addressed at the demographic level, for example, finding extinction thresholds. However, interpopulation genetic variability and the subsequent potential for adaptation can be eroded before effects are noticed on species abundances. To what degree this is the case has been difficult to evaluate, partly because of the lack of both spatially extended genetic data and an appropriate framework to map and analyse such data. Here, we extend recent work on the analysis of networks of spatial genetic variation to address the robustness of these networks in the face of perturbations. We illustrate the potential of this framework using the case study of an amphibian metapopulation. Our results show that while the disappearance of some spatial sites barely changes the modular structure of the genetic network, other sites have a much stronger effect. Interestingly, these consequences can not be anticipated using topological, static measures. Mapping these networks of spatial genetic variation will allow identifying significant evolutionary units and how they vanish, merge and reorganise following perturbations.

  6. Template learning of cellular neural network using genetic programming.

    PubMed

    Radwan, Elsayed; Tazaki, Eiichiro

    2004-08-01

    A new learning algorithm for space invariant Uncoupled Cellular Neural Network is introduced. Learning is formulated as an optimization problem. Genetic Programming has been selected for creating new knowledge because they allow the system to find new rules both near to good ones and far from them, looking for unknown good control actions. According to the lattice Cellular Neural Network architecture, Genetic Programming will be used in deriving the Cloning Template. Exploration of any stable domain is possible by the current approach. Details of the algorithm are discussed and several application results are shown.

  7. Asymptotic stability of delayed stochastic genetic regulatory networks with impulses

    NASA Astrophysics Data System (ADS)

    Sakthivel, R.; Raja, R.; Anthoni, S. Marshal

    2010-11-01

    In this paper, the asymptotic stability analysis problem is considered for a class of delayed stochastic genetic regulatory networks with impulses. Based on the Lyapunov stability technique and stochastic analysis theory, stability criteria are proposed in terms of linear matrix inequalities (LMI). It is shown that the addressed stochastic genetic regulatory networks are globally asymptotically stable if four LMIs are feasible, where the feasibility of LMIs can be readily checked by Matlab LMI toolbox. Finally, a numerical example is given to demonstrate the usefulness of the proposed result.

  8. Genetic Networks in Mouse Retinal Ganglion Cells

    PubMed Central

    Struebing, Felix L.; Lee, Richard K.; Williams, Robert W.; Geisert, Eldon E.

    2016-01-01

    Retinal ganglion cells (RGCs) are the output neuron of the eye, transmitting visual information from the retina through the optic nerve to the brain. The importance of RGCs for vision is demonstrated in blinding diseases where RGCs are lost, such as in glaucoma or after optic nerve injury. In the present study, we hypothesize that normal RGC function is transcriptionally regulated. To test our hypothesis, we examine large retinal expression microarray datasets from recombinant inbred mouse strains in GeneNetwork and define transcriptional networks of RGCs and their subtypes. Two major and functionally distinct transcriptional networks centering around Thy1 and Tubb3 (Class III beta-tubulin) were identified. Each network is independently regulated and modulated by unique genomic loci. Meta-analysis of publically available data confirms that RGC subtypes are differentially susceptible to death, with alpha-RGCs and intrinsically photosensitive RGCs (ipRGCs) being less sensitive to cell death than other RGC subtypes in a mouse model of glaucoma. PMID:27733864

  9. Genetic Variation Shapes Protein Networks Mainly through Non-transcriptional Mechanisms

    PubMed Central

    Foss, Eric J.; Radulovic, Dragan; Shaffer, Scott A.; Goodlett, David R.; Kruglyak, Leonid; Bedalov, Antonio

    2011-01-01

    Networks of co-regulated transcripts in genetically diverse populations have been studied extensively, but little is known about the degree to which these networks cause similar co-variation at the protein level. We quantified 354 proteins in a genetically diverse population of yeast segregants, which allowed for the first time construction of a coherent protein co-variation matrix. We identified tightly co-regulated groups of 36 and 93 proteins that were made up predominantly of genes involved in ribosome biogenesis and amino acid metabolism, respectively. Even though the ribosomal genes were tightly co-regulated at both the protein and transcript levels, genetic regulation of proteins was entirely distinct from that of transcripts, and almost no genes in this network showed a significant correlation between protein and transcript levels. This result calls into question the widely held belief that in yeast, as opposed to higher eukaryotes, ribosomal protein levels are regulated primarily by regulating transcript levels. Furthermore, although genetic regulation of the amino acid network was more similar for proteins and transcripts, regression analysis demonstrated that even here, proteins vary predominantly as a result of non-transcriptional variation. We also found that cis regulation, which is common in the transcriptome, is rare at the level of the proteome. We conclude that most inter-individual variation in levels of these particular high abundance proteins in this genetically diverse population is not caused by variation of their underlying transcripts. PMID:21909241

  10. Genetic Network Programming with Intron-Like Nodes

    NASA Astrophysics Data System (ADS)

    Mabu, Shingo; Chen, Yan; Eto, Shinji; Shimada, Kaoru; Hirasawa, Kotaro

    Recently, Genetic Network Programming (GNP) has been proposed, which is an extension of Genetic Algorithm(GA) and Genetic Programming(GP). GNP can make compact programs and can memorize the past history in it implicitly, because it expresses the solution by directed graphs and therefore, it can reuse the nodes. In this research, intron-like nodes are introduced for improving the performance of GNP. The aim of introducing intron-like nodes is to use every node as much as possible. It is found from simulations that the intron-like nodes are useful for improving the training speed and generalization ability.

  11. Characterizing and prototyping genetic networks with cell-free transcription-translation reactions.

    PubMed

    Takahashi, Melissa K; Hayes, Clarmyra A; Chappell, James; Sun, Zachary Z; Murray, Richard M; Noireaux, Vincent; Lucks, Julius B

    2015-09-15

    A central goal of synthetic biology is to engineer cellular behavior by engineering synthetic gene networks for a variety of biotechnology and medical applications. The process of engineering gene networks often involves an iterative 'design-build-test' cycle, whereby the parts and connections that make up the network are built, characterized and varied until the desired network function is reached. Many advances have been made in the design and build portions of this cycle. However, the slow process of in vivo characterization of network function often limits the timescale of the testing step. Cell-free transcription-translation (TX-TL) systems offer a simple and fast alternative to performing these characterizations in cells. Here we provide an overview of a cell-free TX-TL system that utilizes the native Escherichia coli TX-TL machinery, thereby allowing a large repertoire of parts and networks to be characterized. As a way to demonstrate the utility of cell-free TX-TL, we illustrate the characterization of two genetic networks: an RNA transcriptional cascade and a protein regulated incoherent feed-forward loop. We also provide guidelines for designing TX-TL experiments to characterize new genetic networks. We end with a discussion of current and emerging applications of cell free systems.

  12. Dynamic functional brain networks involved in simple visual discrimination learning.

    PubMed

    Fidalgo, Camino; Conejo, Nélida María; González-Pardo, Héctor; Arias, Jorge Luis

    2014-10-01

    Visual discrimination tasks have been widely used to evaluate many types of learning and memory processes. However, little is known about the brain regions involved at different stages of visual discrimination learning. We used cytochrome c oxidase histochemistry to evaluate changes in regional brain oxidative metabolism during visual discrimination learning in a water-T maze at different time points during training. As compared with control groups, the results of the present study reveal the gradual activation of cortical (prefrontal and temporal cortices) and subcortical brain regions (including the striatum and the hippocampus) associated to the mastery of a simple visual discrimination task. On the other hand, the brain regions involved and their functional interactions changed progressively over days of training. Regions associated with novelty, emotion, visuo-spatial orientation and motor aspects of the behavioral task seem to be relevant during the earlier phase of training, whereas a brain network comprising the prefrontal cortex was found along the whole learning process. This study highlights the relevance of functional interactions among brain regions to investigate learning and memory processes. PMID:24937013

  13. Complex and unexpected dynamics in simple genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Borg, Yanika; Ullner, Ekkehard; Alagha, Afnan; Alsaedi, Ahmed; Nesbeth, Darren; Zaikin, Alexey

    2014-03-01

    One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.

  14. Genetic Algorithm Application in Optimization of Wireless Sensor Networks

    PubMed Central

    Norouzi, Ali; Zaim, A. Halim

    2014-01-01

    There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235

  15. Classifying epilepsy diseases using artificial neural networks and genetic algorithm.

    PubMed

    Koçer, Sabri; Canal, M Rahmi

    2011-08-01

    In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop (QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers of hidden layer in the training process. This study shows that the artificial neural network increases the classification performance using genetic algorithm.

  16. Association genetics and transcriptome analysis reveal a gibberellin-responsive pathway involved in regulating photosynthesis.

    PubMed

    Xie, Jianbo; Tian, Jiaxing; Du, Qingzhang; Chen, Jinhui; Li, Ying; Yang, Xiaohui; Li, Bailian; Zhang, Deqiang

    2016-05-01

    Gibberellins (GAs) regulate a wide range of important processes in plant growth and development, including photosynthesis. However, the mechanism by which GAs regulate photosynthesis remains to be understood. Here, we used multi-gene association to investigate the effect of genes in the GA-responsive pathway, as constructed by RNA sequencing, on photosynthesis, growth, and wood property traits, in a population of 435 Populus tomentosa By analyzing changes in the transcriptome following GA treatment, we identified many key photosynthetic genes, in agreement with the observed increase in measurements of photosynthesis. Regulatory motif enrichment analysis revealed that 37 differentially expressed genes related to photosynthesis shared two essential GA-related cis-regulatory elements, the GA response element and the pyrimidine box. Thus, we constructed a GA-responsive pathway consisting of 47 genes involved in regulating photosynthesis, including GID1, RGA, GID2, MYBGa, and 37 photosynthetic differentially expressed genes. Single nucleotide polymorphism (SNP)-based association analysis showed that 142 SNPs, representing 40 candidate genes in this pathway, were significantly associated with photosynthesis, growth, and wood property traits. Epistasis analysis uncovered interactions between 310 SNP-SNP pairs from 37 genes in this pathway, revealing possible genetic interactions. Moreover, a structural gene-gene matrix based on a time-course of transcript abundances provided a better understanding of the multi-gene pathway affecting photosynthesis. The results imply a functional role for these genes in mediating photosynthesis, growth, and wood properties, demonstrating the potential of combining transcriptome-based regulatory pathway construction and genetic association approaches to detect the complex genetic networks underlying quantitative traits.

  17. Association genetics and transcriptome analysis reveal a gibberellin-responsive pathway involved in regulating photosynthesis.

    PubMed

    Xie, Jianbo; Tian, Jiaxing; Du, Qingzhang; Chen, Jinhui; Li, Ying; Yang, Xiaohui; Li, Bailian; Zhang, Deqiang

    2016-05-01

    Gibberellins (GAs) regulate a wide range of important processes in plant growth and development, including photosynthesis. However, the mechanism by which GAs regulate photosynthesis remains to be understood. Here, we used multi-gene association to investigate the effect of genes in the GA-responsive pathway, as constructed by RNA sequencing, on photosynthesis, growth, and wood property traits, in a population of 435 Populus tomentosa By analyzing changes in the transcriptome following GA treatment, we identified many key photosynthetic genes, in agreement with the observed increase in measurements of photosynthesis. Regulatory motif enrichment analysis revealed that 37 differentially expressed genes related to photosynthesis shared two essential GA-related cis-regulatory elements, the GA response element and the pyrimidine box. Thus, we constructed a GA-responsive pathway consisting of 47 genes involved in regulating photosynthesis, including GID1, RGA, GID2, MYBGa, and 37 photosynthetic differentially expressed genes. Single nucleotide polymorphism (SNP)-based association analysis showed that 142 SNPs, representing 40 candidate genes in this pathway, were significantly associated with photosynthesis, growth, and wood property traits. Epistasis analysis uncovered interactions between 310 SNP-SNP pairs from 37 genes in this pathway, revealing possible genetic interactions. Moreover, a structural gene-gene matrix based on a time-course of transcript abundances provided a better understanding of the multi-gene pathway affecting photosynthesis. The results imply a functional role for these genes in mediating photosynthesis, growth, and wood properties, demonstrating the potential of combining transcriptome-based regulatory pathway construction and genetic association approaches to detect the complex genetic networks underlying quantitative traits. PMID:27091876

  18. Genetic disorders involving molecular-chaperone genes: a perspective.

    PubMed

    Macario, Alberto J L; Grippo, Tomas M; Conway de Macario, Everly

    2005-01-01

    Molecular chaperones are important for maintaining a functional set of proteins in all cellular compartments. Together with protein degradation machineries (e.g., the ubiquitin-proteasome system), chaperones form the core of the cellular protein-quality control mechanism. Chaperones are proteins, and as such, they can be affected by mutations. At least 15 disorders have been identified that are associated with mutations in genes encoding chaperones, or molecules with features suggesting that they function as chaperones. These chaperonopathies and a few other candidates are presented in this article. In most cases, the mechanisms by which the defective genes contribute to the observed phenotypes are still uncharacterized. However, the reported observations definitely point to the possibility that abnormal chaperones participate in pathogenesis. The available data open novel perspectives and should encourage searches for new genetic chaperonopathies, as well as further analyses of the disorders discussed in this article, including detection of new cases.

  19. Modular genetic regulatory networks increase organization during pattern formation.

    PubMed

    Mohamadlou, Hamid; Podgorski, Gregory J; Flann, Nicholas S

    2016-08-01

    Studies have shown that genetic regulatory networks (GRNs) consist of modules that are densely connected subnetworks that function quasi-autonomously. Modules may be recognized motifs that comprise of two or three genes with particular regulatory functions and connectivity or be purely structural and identified through connection density. It is unclear what evolutionary and developmental advantages modular structure and in particular motifs provide that have led to this enrichment. This study seeks to understand how modules within developmental GRNs influence the complexity of multicellular patterns that emerge from the dynamics of the regulatory networks. We apply an algorithmic complexity to measure the organization of the patterns. A computational study was performed by creating Boolean intracellular networks within a simulated epithelial field of embryonic cells, where each cell contains the same network and communicates with adjacent cells using contact-mediated signaling. Intracellular networks with random connectivity were compared to those with modular connectivity and with motifs. Results show that modularity effects network dynamics and pattern organization significantly. In particular: (1) modular connectivity alone increases complexity in network dynamics and patterns; (2) bistable switch motifs simplify both the pattern and network dynamics; (3) all other motifs with feedback loops increase multicellular pattern complexity while simplifying the network dynamics; (4) negative feedback loops affect the dynamics complexity more significantly than positive feedback loops.

  20. Information theory and the ethylene genetic network

    PubMed Central

    González-García, José S

    2011-01-01

    The original aim of the Information Theory (IT) was to solve a purely technical problem: to increase the performance of communication systems, which are constantly affected by interferences that diminish the quality of the transmitted information. That is, the theory deals only with the problem of transmitting with the maximal precision the symbols constituting a message. In Shannon's theory messages are characterized only by their probabilities, regardless of their value or meaning. As for its present day status, it is generally acknowledged that Information Theory has solid mathematical foundations and has fruitful strong links with Physics in both theoretical and experimental areas. However, many applications of Information Theory to Biology are limited to using it as a technical tool to analyze biopolymers, such as DNA, RNA or protein sequences. The main point of discussion about the applicability of IT to explain the information flow in biological systems is that in a classic communication channel, the symbols that conform the coded message are transmitted one by one in an independent form through a noisy communication channel, and noise can alter each of the symbols, distorting the message; in contrast, in a genetic communication channel the coded messages are not transmitted in the form of symbols but signaling cascades transmit them. Consequently, the information flow from the emitter to the effector is due to a series of coupled physicochemical processes that must ensure the accurate transmission of the message. In this review we discussed a novel proposal to overcome this difficulty, which consists of the modeling of gene expression with a stochastic approach that allows Shannon entropy (H) to be directly used to measure the amount of uncertainty that the genetic machinery has in relation to the correct decoding of a message transmitted into the nucleus by a signaling pathway. From the value of H we can define a function I that measures the amount of

  1. Information theory and the ethylene genetic network.

    PubMed

    González-García, José S; Díaz, José

    2011-10-01

    The original aim of the Information Theory (IT) was to solve a purely technical problem: to increase the performance of communication systems, which are constantly affected by interferences that diminish the quality of the transmitted information. That is, the theory deals only with the problem of transmitting with the maximal precision the symbols constituting a message. In Shannon's theory messages are characterized only by their probabilities, regardless of their value or meaning. As for its present day status, it is generally acknowledged that Information Theory has solid mathematical foundations and has fruitful strong links with Physics in both theoretical and experimental areas. However, many applications of Information Theory to Biology are limited to using it as a technical tool to analyze biopolymers, such as DNA, RNA or protein sequences. The main point of discussion about the applicability of IT to explain the information flow in biological systems is that in a classic communication channel, the symbols that conform the coded message are transmitted one by one in an independent form through a noisy communication channel, and noise can alter each of the symbols, distorting the message; in contrast, in a genetic communication channel the coded messages are not transmitted in the form of symbols but signaling cascades transmit them. Consequently, the information flow from the emitter to the effector is due to a series of coupled physicochemical processes that must ensure the accurate transmission of the message. In this review we discussed a novel proposal to overcome this difficulty, which consists of the modeling of gene expression with a stochastic approach that allows Shannon entropy (H) to be directly used to measure the amount of uncertainty that the genetic machinery has in relation to the correct decoding of a message transmitted into the nucleus by a signaling pathway. From the value of H we can define a function I that measures the amount of

  2. Information theory and the ethylene genetic network.

    PubMed

    González-García, José S; Díaz, José

    2011-10-01

    The original aim of the Information Theory (IT) was to solve a purely technical problem: to increase the performance of communication systems, which are constantly affected by interferences that diminish the quality of the transmitted information. That is, the theory deals only with the problem of transmitting with the maximal precision the symbols constituting a message. In Shannon's theory messages are characterized only by their probabilities, regardless of their value or meaning. As for its present day status, it is generally acknowledged that Information Theory has solid mathematical foundations and has fruitful strong links with Physics in both theoretical and experimental areas. However, many applications of Information Theory to Biology are limited to using it as a technical tool to analyze biopolymers, such as DNA, RNA or protein sequences. The main point of discussion about the applicability of IT to explain the information flow in biological systems is that in a classic communication channel, the symbols that conform the coded message are transmitted one by one in an independent form through a noisy communication channel, and noise can alter each of the symbols, distorting the message; in contrast, in a genetic communication channel the coded messages are not transmitted in the form of symbols but signaling cascades transmit them. Consequently, the information flow from the emitter to the effector is due to a series of coupled physicochemical processes that must ensure the accurate transmission of the message. In this review we discussed a novel proposal to overcome this difficulty, which consists of the modeling of gene expression with a stochastic approach that allows Shannon entropy (H) to be directly used to measure the amount of uncertainty that the genetic machinery has in relation to the correct decoding of a message transmitted into the nucleus by a signaling pathway. From the value of H we can define a function I that measures the amount of

  3. Studying genetic regulatory networks at the molecular level: delayed reaction stochastic models.

    PubMed

    Zhu, Rui; Ribeiro, Andre S; Salahub, Dennis; Kauffman, Stuart A

    2007-06-21

    Current advances in molecular biology enable us to access the rapidly increasing body of genetic information. It is still challenging to model gene systems at the molecular level. Here, we propose two types of reaction kinetic models for constructing genetic networks. Time delays involved in transcription and translation are explicitly considered to explore the effects of delays, which may be significant in genetic networks featured with feedback loops. One type of model is based on delayed effective reactions, each reaction modeling a biochemical process like transcription without involving intermediate reactions. The other is based on delayed virtual reactions, each reaction being converted from a mathematical function to model a biochemical function like gene inhibition. The latter stochastic models are derived from the corresponding mean-field models. The former ones are composed of single gene expression modules. We thus design a model of gene expression. This model is verified by our simulations using a delayed stochastic simulation algorithm, which accurately reproduces the stochastic kinetics in a recent experimental study. Various simplified versions of the model are given and evaluated. We then use the two methods to study the genetic toggle switch and the repressilator. We define the "on" and "off" states of genes and extract a binary code from the stochastic time series. The binary code can be described by the corresponding Boolean network models in certain conditions. We discuss these conditions, suggesting a method to connect Boolean models, mean-field models, and stochastic chemical models. PMID:17350653

  4. Predicting genetic interactions with random walks on biological networks

    PubMed Central

    Chipman, Kyle C; Singh, Ambuj K

    2009-01-01

    Background Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse

  5. A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology

    PubMed Central

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

    Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental

  6. In silico Transcriptional Regulatory Networks Involved in Tomato Fruit Ripening

    PubMed Central

    Arhondakis, Stilianos; Bita, Craita E.; Perrakis, Andreas; Manioudaki, Maria E.; Krokida, Afroditi; Kaloudas, Dimitrios; Kalaitzis, Panagiotis

    2016-01-01

    Tomato fruit ripening is a complex developmental programme partly mediated by transcriptional regulatory networks. Several transcription factors (TFs) which are members of gene families such as MADS-box and ERF were shown to play a significant role in ripening through interconnections into an intricate network. The accumulation of large datasets of expression profiles corresponding to different stages of tomato fruit ripening and the availability of bioinformatics tools for their analysis provide an opportunity to identify TFs which might regulate gene clusters with similar co-expression patterns. We identified two TFs, a SlWRKY22-like and a SlER24 transcriptional activator which were shown to regulate modules by using the LeMoNe algorithm for the analysis of our microarray datasets representing four stages of fruit ripening, breaker, turning, pink and red ripe. The WRKY22-like module comprised a subgroup of six various calcium sensing transcripts with similar to the TF expression patterns according to real time PCR validation. A promoter motif search identified a cis acting element, the W-box, recognized by WRKY TFs that was present in the promoter region of all six calcium sensing genes. Moreover, publicly available microarray datasets of similar ripening stages were also analyzed with LeMoNe resulting in TFs such as SlERF.E1, SlERF.C1, SlERF.B2, SLERF.A2, SlWRKY24, SLWRKY37, and MADS-box/TM29 which might also play an important role in regulation of ripening. These results suggest that the SlWRKY22-like might be involved in the coordinated regulation of expression of the six calcium sensing genes. Conclusively the LeMoNe tool might lead to the identification of putative TF targets for further physiological analysis as regulators of tomato fruit ripening. PMID:27625653

  7. In silico Transcriptional Regulatory Networks Involved in Tomato Fruit Ripening

    PubMed Central

    Arhondakis, Stilianos; Bita, Craita E.; Perrakis, Andreas; Manioudaki, Maria E.; Krokida, Afroditi; Kaloudas, Dimitrios; Kalaitzis, Panagiotis

    2016-01-01

    Tomato fruit ripening is a complex developmental programme partly mediated by transcriptional regulatory networks. Several transcription factors (TFs) which are members of gene families such as MADS-box and ERF were shown to play a significant role in ripening through interconnections into an intricate network. The accumulation of large datasets of expression profiles corresponding to different stages of tomato fruit ripening and the availability of bioinformatics tools for their analysis provide an opportunity to identify TFs which might regulate gene clusters with similar co-expression patterns. We identified two TFs, a SlWRKY22-like and a SlER24 transcriptional activator which were shown to regulate modules by using the LeMoNe algorithm for the analysis of our microarray datasets representing four stages of fruit ripening, breaker, turning, pink and red ripe. The WRKY22-like module comprised a subgroup of six various calcium sensing transcripts with similar to the TF expression patterns according to real time PCR validation. A promoter motif search identified a cis acting element, the W-box, recognized by WRKY TFs that was present in the promoter region of all six calcium sensing genes. Moreover, publicly available microarray datasets of similar ripening stages were also analyzed with LeMoNe resulting in TFs such as SlERF.E1, SlERF.C1, SlERF.B2, SLERF.A2, SlWRKY24, SLWRKY37, and MADS-box/TM29 which might also play an important role in regulation of ripening. These results suggest that the SlWRKY22-like might be involved in the coordinated regulation of expression of the six calcium sensing genes. Conclusively the LeMoNe tool might lead to the identification of putative TF targets for further physiological analysis as regulators of tomato fruit ripening.

  8. In silico Transcriptional Regulatory Networks Involved in Tomato Fruit Ripening.

    PubMed

    Arhondakis, Stilianos; Bita, Craita E; Perrakis, Andreas; Manioudaki, Maria E; Krokida, Afroditi; Kaloudas, Dimitrios; Kalaitzis, Panagiotis

    2016-01-01

    Tomato fruit ripening is a complex developmental programme partly mediated by transcriptional regulatory networks. Several transcription factors (TFs) which are members of gene families such as MADS-box and ERF were shown to play a significant role in ripening through interconnections into an intricate network. The accumulation of large datasets of expression profiles corresponding to different stages of tomato fruit ripening and the availability of bioinformatics tools for their analysis provide an opportunity to identify TFs which might regulate gene clusters with similar co-expression patterns. We identified two TFs, a SlWRKY22-like and a SlER24 transcriptional activator which were shown to regulate modules by using the LeMoNe algorithm for the analysis of our microarray datasets representing four stages of fruit ripening, breaker, turning, pink and red ripe. The WRKY22-like module comprised a subgroup of six various calcium sensing transcripts with similar to the TF expression patterns according to real time PCR validation. A promoter motif search identified a cis acting element, the W-box, recognized by WRKY TFs that was present in the promoter region of all six calcium sensing genes. Moreover, publicly available microarray datasets of similar ripening stages were also analyzed with LeMoNe resulting in TFs such as SlERF.E1, SlERF.C1, SlERF.B2, SLERF.A2, SlWRKY24, SLWRKY37, and MADS-box/TM29 which might also play an important role in regulation of ripening. These results suggest that the SlWRKY22-like might be involved in the coordinated regulation of expression of the six calcium sensing genes. Conclusively the LeMoNe tool might lead to the identification of putative TF targets for further physiological analysis as regulators of tomato fruit ripening.

  9. In silico Transcriptional Regulatory Networks Involved in Tomato Fruit Ripening.

    PubMed

    Arhondakis, Stilianos; Bita, Craita E; Perrakis, Andreas; Manioudaki, Maria E; Krokida, Afroditi; Kaloudas, Dimitrios; Kalaitzis, Panagiotis

    2016-01-01

    Tomato fruit ripening is a complex developmental programme partly mediated by transcriptional regulatory networks. Several transcription factors (TFs) which are members of gene families such as MADS-box and ERF were shown to play a significant role in ripening through interconnections into an intricate network. The accumulation of large datasets of expression profiles corresponding to different stages of tomato fruit ripening and the availability of bioinformatics tools for their analysis provide an opportunity to identify TFs which might regulate gene clusters with similar co-expression patterns. We identified two TFs, a SlWRKY22-like and a SlER24 transcriptional activator which were shown to regulate modules by using the LeMoNe algorithm for the analysis of our microarray datasets representing four stages of fruit ripening, breaker, turning, pink and red ripe. The WRKY22-like module comprised a subgroup of six various calcium sensing transcripts with similar to the TF expression patterns according to real time PCR validation. A promoter motif search identified a cis acting element, the W-box, recognized by WRKY TFs that was present in the promoter region of all six calcium sensing genes. Moreover, publicly available microarray datasets of similar ripening stages were also analyzed with LeMoNe resulting in TFs such as SlERF.E1, SlERF.C1, SlERF.B2, SLERF.A2, SlWRKY24, SLWRKY37, and MADS-box/TM29 which might also play an important role in regulation of ripening. These results suggest that the SlWRKY22-like might be involved in the coordinated regulation of expression of the six calcium sensing genes. Conclusively the LeMoNe tool might lead to the identification of putative TF targets for further physiological analysis as regulators of tomato fruit ripening. PMID:27625653

  10. Learning in rich networks involves both positive and negative associations.

    PubMed

    Roembke, Tanja C; Wasserman, Edward A; McMurray, Bob

    2016-08-01

    Adaptive behaviors are believed to be shaped by both positive (the strengthening of correct associations) and negative (the pruning of incorrect associations or the building of inhibitory associations) forms of associative learning. However, there has been little direct documentation of how these basic processes participate in the learning of rich associative networks that support cognitive behaviors like categorization. Although negative associative learning is an important component of theories of development, it is not clear whether it involves acquiring specific (experience-dependent) content or represents a more general aspect of (experience-expectant) development. The authors thus trained pigeons on a complex many-to-many learning paradigm previously established as an analog to human word learning. Pigeons learned to map 16 objects onto 16 distinct report tokens; the authors manipulated the amount of negative associative learning that could occur by restricting which tokens were available as incorrect options. In testing, accuracy was lower on trials with foils that had not been presented with a target than on trials with previously experienced foils. Moreover, when the correct token was withheld, pigeons preferred foils novel to the target object over previously experienced foils. A second experiment replicated these results and further found that these effects only emerged after some positive associations had been acquired. Findings indicate that the learning of rich associative networks does not depend solely on positive associative learning, but also on negative associative learning; this conclusion has important implications for basic learning theories in both animals and humans, as well as for theories of development. (PsycINFO Database Record PMID:27336324

  11. Genetic mapping of tumor susceptibility genes involved in mouse plasmacytomagenesis

    SciTech Connect

    Mock, B.A.; Krall, M.M.; Dosik, J.K. )

    1993-10-15

    Plasmacytomas (PCTs) were induced in 47% of BALB/cAnPt mice by the intraperitoneal injection of pristane, in 2% of (BALB/c [times] DBA/2N)F[sub 1], and in 11% of 773 BALB/cAnPt [times] (BALB/cAnPt [times] DBA/2N)F[sub 1]N[sub 2] backcross mice. This result indicates a multigenic mode of inheritance for PCT susceptibility. To locate genes controlling this complex genetic trait, tumor susceptibility in backcross progeny generated from BALB/c and DBA/2N (resistant) mice was correlated with alleles of 83 marker loci. The genotypes of the PCT-susceptible progeny displayed an excess homozygosity for BALB/c alleles with a 32-centimorgan stretch of mouse chromosome 4 (>95% probability of linkage) with minimal recombination (12%) near Gt10. Another susceptibility gene on mouse chromosome 1 may be linked to Fcgr2 (90% probability of linkage); there were excess heterozygotes for Fcgr2 among the susceptible progeny and excess homozygotes among the resistant progeny. Regions of mouse chromosomes 4 and 1 that are correlated with PCT susceptibility share extensive linkage homology with regions of human chromosome 1 that have been associated with cytogenetic abnormalities in multiple myeloma and lymphoid, breast, and endocrine tumors. 68 refs., 2 figs., 1 tab.

  12. A Review of Modeling Techniques for Genetic Regulatory Networks

    PubMed Central

    Yaghoobi, Hanif; Haghipour, Siyamak; Hamzeiy, Hossein; Asadi-Khiavi, Masoud

    2012-01-01

    Understanding the genetic regulatory networks, the discovery of interactions between genes and understanding regulatory processes in a cell at the gene level are the major goals of system biology and computational biology. Modeling gene regulatory networks and describing the actions of the cells at the molecular level are used in medicine and molecular biology applications such as metabolic pathways and drug discovery. Modeling these networks is also one of the important issues in genomic signal processing. After the advent of microarray technology, it is possible to model these networks using time–series data. In this paper, we provide an extensive review of methods that have been used on time–series data and represent the features, advantages and disadvantages of each. Also, we classify these methods according to their nature. A parallel study of these methods can lead to the discovery of new synthetic methods or improve previous methods. PMID:23493097

  13. Identification of Resting State Networks Involved in Executive Function.

    PubMed

    Connolly, Joanna; McNulty, Jonathan P; Boran, Lorraine; Roche, Richard A P; Delany, David; Bokde, Arun L W

    2016-06-01

    The structural networks in the human brain are consistent across subjects, and this is reflected also in that functional networks across subjects are relatively consistent. These findings are not only present during performance of a goal oriented task but there are also consistent functional networks during resting state. It suggests that goal oriented activation patterns may be a function of component networks identified using resting state. The current study examines the relationship between resting state networks measured and patterns of neural activation elicited during a Stroop task. The association between the Stroop-activated networks and the resting state networks was quantified using spatial linear regression. In addition, we investigated if the degree of spatial association of resting state networks with the Stroop task may predict performance on the Stroop task. The results of this investigation demonstrated that the Stroop activated network can be decomposed into a number of resting state networks, which were primarily associated with attention, executive function, visual perception, and the default mode network. The close spatial correspondence between the functional organization of the resting brain and task-evoked patterns supports the relevance of resting state networks in cognitive function. PMID:26935902

  14. Identification of Resting State Networks Involved in Executive Function.

    PubMed

    Connolly, Joanna; McNulty, Jonathan P; Boran, Lorraine; Roche, Richard A P; Delany, David; Bokde, Arun L W

    2016-06-01

    The structural networks in the human brain are consistent across subjects, and this is reflected also in that functional networks across subjects are relatively consistent. These findings are not only present during performance of a goal oriented task but there are also consistent functional networks during resting state. It suggests that goal oriented activation patterns may be a function of component networks identified using resting state. The current study examines the relationship between resting state networks measured and patterns of neural activation elicited during a Stroop task. The association between the Stroop-activated networks and the resting state networks was quantified using spatial linear regression. In addition, we investigated if the degree of spatial association of resting state networks with the Stroop task may predict performance on the Stroop task. The results of this investigation demonstrated that the Stroop activated network can be decomposed into a number of resting state networks, which were primarily associated with attention, executive function, visual perception, and the default mode network. The close spatial correspondence between the functional organization of the resting brain and task-evoked patterns supports the relevance of resting state networks in cognitive function.

  15. Dissociable networks involved in spatial and temporal order source retrieval.

    PubMed

    Ekstrom, Arne D; Copara, Milagros S; Isham, Eve A; Wang, Wei-chun; Yonelinas, Andrew P

    2011-06-01

    Space and time are important components of our episodic memories. Without this information, we cannot determine the "where and when" of our recent memories, rendering it difficult to disambiguate individual episodes from each other. The neural underpinnings of spatial and temporal order memory in humans remain unclear, in part because of difficulties in disentangling the contributions of these two types of source information. To address this issue, we conducted an experiment in which participants first navigated a virtual city, experiencing unique routes in a specific temporal order and learning about the spatial layout of the city. Spatial and temporal order information were dissociated in our task such that learning one type of information did not facilitate the other behaviorally. This allowed us to then address the extent to which the two types of information involved functionally distinct or overlapping brain areas. During functional magnetic resonance imaging (fMRI), participants retrieved information about the relative distance of stores within the city (spatial task) and the temporal order of stores from each other (temporal task). Comparable hippocampal activity was observed during these two tasks, but greater prefrontal activity was seen during temporal order retrieval whereas greater parahippocampal activity was seen during spatial retrieval. We suggest that while the brain possesses dissociable networks for maintaining and representing spatial layout and temporal order components of episodic memory, this information may converge into a common representation for source memory in areas such as the hippocampus.

  16. Immune allied genetic algorithm for Bayesian network structure learning

    NASA Astrophysics Data System (ADS)

    Song, Qin; Lin, Feng; Sun, Wei; Chang, KC

    2012-06-01

    Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.

  17. Enhanced energy transport in genetically engineered excitonic networks

    NASA Astrophysics Data System (ADS)

    Park, Heechul; Heldman, Nimrod; Rebentrost, Patrick; Abbondanza, Luigi; Iagatti, Alessandro; Alessi, Andrea; Patrizi, Barbara; Salvalaggio, Mario; Bussotti, Laura; Mohseni, Masoud; Caruso, Filippo; Johnsen, Hannah C.; Fusco, Roberto; Foggi, Paolo; Scudo, Petra F.; Lloyd, Seth; Belcher, Angela M.

    2016-02-01

    One of the challenges for achieving efficient exciton transport in solar energy conversion systems is precise structural control of the light-harvesting building blocks. Here, we create a tunable material consisting of a connected chromophore network on an ordered biological virus template. Using genetic engineering, we establish a link between the inter-chromophoric distances and emerging transport properties. The combination of spectroscopy measurements and dynamic modelling enables us to elucidate quantum coherent and classical incoherent energy transport at room temperature. Through genetic modifications, we obtain a significant enhancement of exciton diffusion length of about 68% in an intermediate quantum-classical regime.

  18. Critical Dynamics in Genetic Regulatory Networks: Examples from Four Kingdoms

    PubMed Central

    Balleza, Enrique; Alvarez-Buylla, Elena R.; Chaos, Alvaro; Kauffman, Stuart; Shmulevich, Ilya; Aldana, Maximino

    2008-01-01

    The coordinated expression of the different genes in an organism is essential to sustain functionality under the random external perturbations to which the organism might be subjected. To cope with such external variability, the global dynamics of the genetic network must possess two central properties. (a) It must be robust enough as to guarantee stability under a broad range of external conditions, and (b) it must be flexible enough to recognize and integrate specific external signals that may help the organism to change and adapt to different environments. This compromise between robustness and adaptability has been observed in dynamical systems operating at the brink of a phase transition between order and chaos. Such systems are termed critical. Thus, criticality, a precise, measurable, and well characterized property of dynamical systems, makes it possible for robustness and adaptability to coexist in living organisms. In this work we investigate the dynamical properties of the gene transcription networks reported for S. cerevisiae, E. coli, and B. subtilis, as well as the network of segment polarity genes of D. melanogaster, and the network of flower development of A. thaliana. We use hundreds of microarray experiments to infer the nature of the regulatory interactions among genes, and implement these data into the Boolean models of the genetic networks. Our results show that, to the best of the current experimental data available, the five networks under study indeed operate close to criticality. The generality of this result suggests that criticality at the genetic level might constitute a fundamental evolutionary mechanism that generates the great diversity of dynamically robust living forms that we observe around us. PMID:18560561

  19. Multivariate analysis of noise in genetic regulatory networks.

    PubMed

    Tomioka, Ryota; Kimura, Hidenori; J Kobayashi, Tetsuya; Aihara, Kazuyuki

    2004-08-21

    Stochasticity is an intrinsic property of genetic regulatory networks due to the low copy numbers of the major molecular species, such as, DNA, mRNA, and regulatory proteins. Therefore, investigation of the mechanisms that reduce the stochastic noise is essential in understanding the reproducible behaviors of real organisms and is also a key to design synthetic genetic regulatory networks that can reliably work. We use an analytical and systematic method, the linear noise approximation of the chemical master equation along with the decoupling of a stoichiometric matrix. In the analysis of fluctuations of multiple molecular species, the covariance is an important measure of noise. However, usually the representation of a covariance matrix in the natural coordinate system, i.e. the copy numbers of the molecular species, is intractably complicated because reactions change copy numbers of more than one molecular species simultaneously. Decoupling of a stoichiometric matrix, which is a transformation of variables, significantly simplifies the representation of a covariance matrix and elucidates the mechanisms behind the observed fluctuations in the copy numbers. We apply our method to three types of fundamental genetic regulatory networks, that is, a single-gene autoregulatory network, a two-gene autoregulatory network, and a mutually repressive network. We have found that there are multiple noise components differently originating. Each noise component produces fluctuation in the characteristic direction. The resulting fluctuations in the copy numbers of the molecular species are the sum of these fluctuations. In the examples, the limitation of the negative feedback in noise reduction and the trade-off of fluctuations in multiple molecular species are clearly explained. The analytical representations show the full parameter dependence. Additionally, the validity of our method is tested by stochastic simulations. PMID:15246787

  20. Discovery of novel genetic networks associated with 19 economically important traits in beef cattle

    PubMed Central

    Jiang, Zhihua; Michal, Jennifer J.; Chen, Jie; Daniels, Tyler F.; Kunej, Tanja; Garcia, Matthew D.; Gaskins, Charles T.; Busboom, Jan R.; Alexander, Leeson J.; Wright Jr., Raymond W.; MacNeil, Michael D.

    2009-01-01

    Quantitative or complex traits are determined by the combined effects of many loci, and are affected by genetic networks or molecular pathways. In the present study, we genotyped a total of 138 mutations, mainly single nucleotide polymorphisms derived from 71 functional genes on a Wagyu x Limousin reference population. Two hundred forty six F2 animals were measured for 5 carcass, 6 eating quality and 8 fatty acid composition traits. A total of 2,280 single marker-trait association runs with 120 tagged mutations selected based on the HAPLOVIEW analysis revealed 144 significant associations (P < 0.05), but 50 of them were removed from the analysis due to the small number of animals (≤ 9) in one genotype group or absence of one genotype among three genotypes. The remaining 94 single-trait associations were then placed into three groups of quantitative trait modes (QTMs) with additive, dominant and overdominant effects. All significant markers and their QTMs associated with each of these 19 traits were involved in a linear regression model analysis, which confirmed single-gene associations for 4 traits, but revealed two-gene networks for 8 traits and three-gene networks for 5 traits. Such genetic networks involving both genotypes and QTMs resulted in high correlations between predicted and actual values of performance, thus providing evidence that the classical Mendelian principles of inheritance can be applied in understanding genetic complexity of complex phenotypes. Our present study also indicated that carcass, eating quality and fatty acid composition traits rarely share genetic networks. Therefore, marker-assisted selection for improvement of one category of these traits would not interfere with improvement of another. PMID:19727437

  1. Discovery of novel genetic networks associated with 19 economically important traits in beef cattle.

    PubMed

    Jiang, Zhihua; Michal, Jennifer J; Chen, Jie; Daniels, Tyler F; Kunej, Tanja; Garcia, Matthew D; Gaskins, Charles T; Busboom, Jan R; Alexander, Leeson J; Wright, Raymond W; Macneil, Michael D

    2009-07-29

    Quantitative or complex traits are determined by the combined effects of many loci, and are affected by genetic networks or molecular pathways. In the present study, we genotyped a total of 138 mutations, mainly single nucleotide polymorphisms derived from 71 functional genes on a Wagyu x Limousin reference population. Two hundred forty six F(2) animals were measured for 5 carcass, 6 eating quality and 8 fatty acid composition traits. A total of 2,280 single marker-trait association runs with 120 tagged mutations selected based on the HAPLOVIEW analysis revealed 144 significant associations (P < 0.05), but 50 of them were removed from the analysis due to the small number of animals (< or = 9) in one genotype group or absence of one genotype among three genotypes. The remaining 94 single-trait associations were then placed into three groups of quantitative trait modes (QTMs) with additive, dominant and overdominant effects. All significant markers and their QTMs associated with each of these 19 traits were involved in a linear regression model analysis, which confirmed single-gene associations for 4 traits, but revealed two-gene networks for 8 traits and three-gene networks for 5 traits. Such genetic networks involving both genotypes and QTMs resulted in high correlations between predicted and actual values of performance, thus providing evidence that the classical Mendelian principles of inheritance can be applied in understanding genetic complexity of complex phenotypes. Our present study also indicated that carcass, eating quality and fatty acid composition traits rarely share genetic networks. Therefore, marker-assisted selection for improvement of one category of these traits would not interfere with improvement of another.

  2. Simulating and Synthesizing Substructures Using Neural Network and Genetic Algorithms

    NASA Technical Reports Server (NTRS)

    Liu, Youhua; Kapania, Rakesh K.; VanLandingham, Hugh F.

    1997-01-01

    The feasibility of simulating and synthesizing substructures by computational neural network models is illustrated by investigating a statically indeterminate beam, using both a 1-D and a 2-D plane stress modelling. The beam can be decomposed into two cantilevers with free-end loads. By training neural networks to simulate the cantilever responses to different loads, the original beam problem can be solved as a match-up between two subsystems under compatible interface conditions. The genetic algorithms are successfully used to solve the match-up problem. Simulated results are found in good agreement with the analytical or FEM solutions.

  3. The role of certain Post classes in Boolean network models of genetic networks.

    PubMed

    Shmulevich, Ilya; Lähdesmäki, Harri; Dougherty, Edward R; Astola, Jaakko; Zhang, Wei

    2003-09-16

    A topic of great interest and debate concerns the source of order and remarkable robustness observed in genetic regulatory networks. The study of the generic properties of Boolean networks has proven to be useful for gaining insight into such phenomena. The main focus, as regards ordered behavior in networks, has been on canalizing functions, internal homogeneity or bias, and network connectivity. Here we examine the role that certain classes of Boolean functions that are closed under composition play in the emergence of order in Boolean networks. The closure property implies that any gene at any number of steps in the future is guaranteed to be governed by a function from the same class. By means of Derrida curves on random Boolean networks and percolation simulations on square lattices, we demonstrate that networks constructed from functions belonging to these classes have a tendency toward ordered behavior. Thus they are not overly sensitive to initial conditions, and damage does not readily spread throughout the network. In addition, the considered classes are significantly larger than the class of canalizing functions as the connectivity increases. The functions in these classes exhibit the same kind of preference toward biased functions as do canalizing functions, meaning that functions from this class are likely to be biased. Finally, functions from this class have a natural way of ensuring robustness against noise and perturbations, thus representing plausible evolutionarily selected candidates for regulatory rules in genetic networks. PMID:12963822

  4. Optimal Design of Geodetic Network Using Genetic Algorithms

    NASA Astrophysics Data System (ADS)

    Vajedian, Sanaz; Bagheri, Hosein

    2010-05-01

    A geodetic network is a network which is measured exactly by techniques of terrestrial surveying based on measurement of angles and distances and can control stability of dams, towers and their around lands and can monitor deformation of surfaces. The main goals of an optimal geodetic network design process include finding proper location of control station (First order Design) as well as proper weight of observations (second order observation) in a way that satisfy all the criteria considered for quality of the network with itself is evaluated by the network's accuracy, reliability (internal and external), sensitivity and cost. The first-order design problem, can be dealt with as a numeric optimization problem. In this designing finding unknown coordinates of network stations is an important issue. For finding these unknown values, network geodetic observations that are angle and distance measurements must be entered in an adjustment method. In this regard, using inverse problem algorithms is needed. Inverse problem algorithms are methods to find optimal solutions for given problems and include classical and evolutionary computations. The classical approaches are analytical methods and are useful in finding the optimum solution of a continuous and differentiable function. Least squares (LS) method is one of the classical techniques that derive estimates for stochastic variables and their distribution parameters from observed samples. The evolutionary algorithms are adaptive procedures of optimization and search that find solutions to problems inspired by the mechanisms of natural evolution. These methods generate new points in the search space by applying operators to current points and statistically moving toward more optimal places in the search space. Genetic algorithm (GA) is an evolutionary algorithm considered in this paper. This algorithm starts with definition of initial population, and then the operators of selection, replication and variation are applied

  5. MAC protocol for ad hoc networks using a genetic algorithm.

    PubMed

    Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L; Reyna, Alberto

    2014-01-01

    The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput.

  6. MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm

    PubMed Central

    Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.

    2014-01-01

    The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339

  7. [Networks involving quorum sensing, cyclic-di-GMP and nitric oxide on biofilm production in bacteria].

    PubMed

    Ramírez-Mata, Alberto; Fernández-Domínguez, Ileana J; Nuñez-Reza, Karen J; Xiqui-Vázquez, María L; Baca, Beatriz E

    2014-01-01

    Bacterial biofilms are ubiquitous in nature, and their flexibility is derived in part from a complex extracellular matrix that can be made-to-order to cope with environmental demand. Although common developmental stages leading to biofilm formation have been described, an in-depth knowledge of genetic and signaling is required to understand biofilm formation. Bacteria detect changes in population density by quorum sensing and particular environmental conditions, using signals such as cyclic di-GMP or nitric oxide. The significance of understanding these signaling pathways lies in that they control a broad variety of functions such as biofilm formation, and motility, providing benefits to bacteria as regards host colonization, defense against competitors, and adaptation to changing environments. Due to the importance of these features, we here review the signaling network and regulatory connections among quorum sensing, c-di-GMP and nitric oxide involving biofilm formation.

  8. Towards a predictive theory for genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Tkacik, Gasper

    When cells respond to changes in the environment by regulating the expression levels of their genes, we often draw parallels between these biological processes and engineered information processing systems. One can go beyond this qualitative analogy, however, by analyzing information transmission in biochemical ``hardware'' using Shannon's information theory. Here, gene regulation is viewed as a transmission channel operating under restrictive constraints set by the resource costs and intracellular noise. We present a series of results demonstrating that a theory of information transmission in genetic regulatory circuits feasibly yields non-trivial, testable predictions. These predictions concern strategies by which individual gene regulatory elements, e.g., promoters or enhancers, read out their signals; as well as strategies by which small networks of genes, independently or in spatially coupled settings, respond to their inputs. These predictions can be quantitatively compared to the known regulatory networks and their function, and can elucidate how reproducible biological processes, such as embryonic development, can be orchestrated by networks built out of noisy components. Preliminary successes in the gap gene network of the fruit fly Drosophila indicate that a full ab initio theoretical prediction of a regulatory network is possible, a feat that has not yet been achieved for any real regulatory network. We end by describing open challenges on the path towards such a prediction.

  9. Molecular genetic analysis of activation-tagged transcription factors thought to be involved in photomorphogenesis

    SciTech Connect

    Neff, Michael M.

    2011-06-23

    This is a final report for Department of Energy Grant No. DE-FG02-08ER15927 entitled “Molecular Genetic Analysis of Activation-Tagged Transcription Factors Thought to be Involved in Photomorphogenesis”. Based on our preliminary photobiological and genetic analysis of the sob1-D mutant, we hypothesized that OBP3 is a transcription factor involved in both phytochrome and cryptochrome-mediated signal transduction. In addition, we hypothesized that OBP3 is involved in auxin signaling and root development. Based on our preliminary photobiological and genetic analysis of the sob2-D mutant, we also hypothesized that a related gene, LEP, is involved in hormone signaling and seedling development.

  10. Genetic Programming Neural Networks: A Powerful Bioinformatics Tool for Human Genetics

    PubMed Central

    Ritchie, Marylyn D; Motsinger, Alison A.; Bush, William S; Coffey, Christopher S; Moore, Jason H

    2010-01-01

    The identification of genes that influence the risk of common, complex disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of genetic and gene-environment combinations associated with disease risk. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene and gene-environment interactions. The goal of this study was to compare the power of GPNN to stepwise logistic regression (SLR) and classification and regression trees (CART) for identifying gene-gene and gene-environment interactions. SLR and CART are standard methods of analysis for genetic association studies. Using simulated data, we show that GPNN has higher power to identify gene-gene and gene-environment interactions than SLR and CART. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions in studies of human disease. PMID:20948988

  11. Genetic Programming Neural Networks: A Powerful Bioinformatics Tool for Human Genetics.

    PubMed

    Ritchie, Marylyn D; Motsinger, Alison A; Bush, William S; Coffey, Christopher S; Moore, Jason H

    2007-01-01

    The identification of genes that influence the risk of common, complex disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of genetic and gene-environment combinations associated with disease risk. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene and gene-environment interactions. The goal of this study was to compare the power of GPNN to stepwise logistic regression (SLR) and classification and regression trees (CART) for identifying gene-gene and gene-environment interactions. SLR and CART are standard methods of analysis for genetic association studies. Using simulated data, we show that GPNN has higher power to identify gene-gene and gene-environment interactions than SLR and CART. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions in studies of human disease.

  12. Quantitative Genetics Identifies Cryptic Genetic Variation Involved in the Paternal Regulation of Seed Development.

    PubMed

    Pires, Nuno D; Bemer, Marian; Müller, Lena M; Baroux, Célia; Spillane, Charles; Grossniklaus, Ueli

    2016-01-01

    Embryonic development requires a correct balancing of maternal and paternal genetic information. This balance is mediated by genomic imprinting, an epigenetic mechanism that leads to parent-of-origin-dependent gene expression. The parental conflict (or kinship) theory proposes that imprinting can evolve due to a conflict between maternal and paternal alleles over resource allocation during seed development. One assumption of this theory is that paternal alleles can regulate seed growth; however, paternal effects on seed size are often very low or non-existent. We demonstrate that there is a pool of cryptic genetic variation in the paternal control of Arabidopsis thaliana seed development. Such cryptic variation can be exposed in seeds that maternally inherit a medea mutation, suggesting that MEA acts as a maternal buffer of paternal effects. Genetic mapping using recombinant inbred lines, and a novel method for the mapping of parent-of-origin effects using whole-genome sequencing of segregant bulks, indicate that there are at least six loci with small, paternal effects on seed development. Together, our analyses reveal the existence of a pool of hidden genetic variation on the paternal control of seed development that is likely shaped by parental conflict.

  13. Quantitative Genetics Identifies Cryptic Genetic Variation Involved in the Paternal Regulation of Seed Development

    PubMed Central

    Pires, Nuno D.; Bemer, Marian; Müller, Lena M.; Baroux, Célia; Spillane, Charles; Grossniklaus, Ueli

    2016-01-01

    Embryonic development requires a correct balancing of maternal and paternal genetic information. This balance is mediated by genomic imprinting, an epigenetic mechanism that leads to parent-of-origin-dependent gene expression. The parental conflict (or kinship) theory proposes that imprinting can evolve due to a conflict between maternal and paternal alleles over resource allocation during seed development. One assumption of this theory is that paternal alleles can regulate seed growth; however, paternal effects on seed size are often very low or non-existent. We demonstrate that there is a pool of cryptic genetic variation in the paternal control of Arabidopsis thaliana seed development. Such cryptic variation can be exposed in seeds that maternally inherit a medea mutation, suggesting that MEA acts as a maternal buffer of paternal effects. Genetic mapping using recombinant inbred lines, and a novel method for the mapping of parent-of-origin effects using whole-genome sequencing of segregant bulks, indicate that there are at least six loci with small, paternal effects on seed development. Together, our analyses reveal the existence of a pool of hidden genetic variation on the paternal control of seed development that is likely shaped by parental conflict. PMID:26811909

  14. Quantitative Genetics Identifies Cryptic Genetic Variation Involved in the Paternal Regulation of Seed Development.

    PubMed

    Pires, Nuno D; Bemer, Marian; Müller, Lena M; Baroux, Célia; Spillane, Charles; Grossniklaus, Ueli

    2016-01-01

    Embryonic development requires a correct balancing of maternal and paternal genetic information. This balance is mediated by genomic imprinting, an epigenetic mechanism that leads to parent-of-origin-dependent gene expression. The parental conflict (or kinship) theory proposes that imprinting can evolve due to a conflict between maternal and paternal alleles over resource allocation during seed development. One assumption of this theory is that paternal alleles can regulate seed growth; however, paternal effects on seed size are often very low or non-existent. We demonstrate that there is a pool of cryptic genetic variation in the paternal control of Arabidopsis thaliana seed development. Such cryptic variation can be exposed in seeds that maternally inherit a medea mutation, suggesting that MEA acts as a maternal buffer of paternal effects. Genetic mapping using recombinant inbred lines, and a novel method for the mapping of parent-of-origin effects using whole-genome sequencing of segregant bulks, indicate that there are at least six loci with small, paternal effects on seed development. Together, our analyses reveal the existence of a pool of hidden genetic variation on the paternal control of seed development that is likely shaped by parental conflict. PMID:26811909

  15. Genetic network properties of the human cortex based on regional thickness and surface area measures

    PubMed Central

    Docherty, Anna R.; Sawyers, Chelsea K.; Panizzon, Matthew S.; Neale, Michael C.; Eyler, Lisa T.; Fennema-Notestine, Christine; Franz, Carol E.; Chen, Chi-Hua; McEvoy, Linda K.; Verhulst, Brad; Tsuang, Ming T.; Kremen, William S.

    2015-01-01

    We examined network properties of genetic covariance between average cortical thickness (CT) and surface area (SA) within genetically-identified cortical parcellations that we previously derived from human cortical genetic maps using vertex-wise fuzzy clustering analysis with high spatial resolution. There were 24 hierarchical parcellations based on vertex-wise CT and 24 based on vertex-wise SA expansion/contraction; in both cases the 12 parcellations per hemisphere were largely symmetrical. We utilized three techniques—biometrical genetic modeling, cluster analysis, and graph theory—to examine genetic relationships and network properties within and between the 48 parcellation measures. Biometrical modeling indicated significant shared genetic covariance between size of several of the genetic parcellations. Cluster analysis suggested small distinct groupings of genetic covariance; networks highlighted several significant negative and positive genetic correlations between bilateral parcellations. Graph theoretical analysis suggested that small world, but not rich club, network properties may characterize the genetic relationships between these regional size measures. These findings suggest that cortical genetic parcellations exhibit short characteristic path lengths across a broad network of connections. This property may be protective against network failure. In contrast, previous research with structural data has observed strong rich club properties with tightly interconnected hub networks. Future studies of these genetic networks might provide powerful phenotypes for genetic studies of normal and pathological brain development, aging, and function. PMID:26347632

  16. A candidate multimodal functional genetic network for thermal adaptation

    PubMed Central

    Pathak, Rachana; Prajapati, Indira; Bankston, Shannon; Thompson, Aprylle; Usher, Jaytriece; Isokpehi, Raphael D.

    2014-01-01

    Vertebrate ectotherms such as reptiles provide ideal organisms for the study of adaptation to environmental thermal change. Comparative genomic and exomic studies can recover markers that diverge between warm and cold adapted lineages, but the genes that are functionally related to thermal adaptation may be difficult to identify. We here used a bioinformatics genome-mining approach to predict and identify functions for suitable candidate markers for thermal adaptation in the chicken. We first established a framework of candidate functions for such markers, and then compiled the literature on genes known to adapt to the thermal environment in different lineages of vertebrates. We then identified them in the genomes of human, chicken, and the lizard Anolis carolinensis, and established a functional genetic interaction network in the chicken. Surprisingly, markers initially identified from diverse lineages of vertebrates such as human and fish were all in close functional relationship with each other and more associated than expected by chance. This indicates that the general genetic functional network for thermoregulation and/or thermal adaptation to the environment might be regulated via similar evolutionarily conserved pathways in different vertebrate lineages. We were able to identify seven functions that were statistically overrepresented in this network, corresponding to four of our originally predicted functions plus three unpredicted functions. We describe this network as multimodal: central regulator genes with the function of relaying thermal signal (1), affect genes with different cellular functions, namely (2) lipoprotein metabolism, (3) membrane channels, (4) stress response, (5) response to oxidative stress, (6) muscle contraction and relaxation, and (7) vasodilation, vasoconstriction and regulation of blood pressure. This network constitutes a novel resource for the study of thermal adaptation in the closely related nonavian reptiles and other

  17. A candidate multimodal functional genetic network for thermal adaptation.

    PubMed

    Wollenberg Valero, Katharina C; Pathak, Rachana; Prajapati, Indira; Bankston, Shannon; Thompson, Aprylle; Usher, Jaytriece; Isokpehi, Raphael D

    2014-01-01

    Vertebrate ectotherms such as reptiles provide ideal organisms for the study of adaptation to environmental thermal change. Comparative genomic and exomic studies can recover markers that diverge between warm and cold adapted lineages, but the genes that are functionally related to thermal adaptation may be difficult to identify. We here used a bioinformatics genome-mining approach to predict and identify functions for suitable candidate markers for thermal adaptation in the chicken. We first established a framework of candidate functions for such markers, and then compiled the literature on genes known to adapt to the thermal environment in different lineages of vertebrates. We then identified them in the genomes of human, chicken, and the lizard Anolis carolinensis, and established a functional genetic interaction network in the chicken. Surprisingly, markers initially identified from diverse lineages of vertebrates such as human and fish were all in close functional relationship with each other and more associated than expected by chance. This indicates that the general genetic functional network for thermoregulation and/or thermal adaptation to the environment might be regulated via similar evolutionarily conserved pathways in different vertebrate lineages. We were able to identify seven functions that were statistically overrepresented in this network, corresponding to four of our originally predicted functions plus three unpredicted functions. We describe this network as multimodal: central regulator genes with the function of relaying thermal signal (1), affect genes with different cellular functions, namely (2) lipoprotein metabolism, (3) membrane channels, (4) stress response, (5) response to oxidative stress, (6) muscle contraction and relaxation, and (7) vasodilation, vasoconstriction and regulation of blood pressure. This network constitutes a novel resource for the study of thermal adaptation in the closely related nonavian reptiles and other

  18. Both common and specific genetic factors are involved in polygenic resistance of pepper to several potyviruses.

    PubMed

    Caranta, C; Palloix, A

    1996-01-01

    Absolute resistance to potato virus Y pathotype 0 (PVY 0), potyvirus E and chili veinal mottle virus (CVMV) and a partial resistance to potato virus Y pathotype 1,2 (PVY 1,2) were found in an Indian pepper line, 'Perennial'. In the doubled haploid (DH) progeny from the F1 of a cross 'Perennial' by 'Yolo Wonder', resistance to CVMV was confered by two independent genes, one with a clear dominant effect. Resistance to PVY and potyvirus E was quantitatively expressed and controlled by several recessive genetic factors. Genetic analysis showed that fewer resistance factors were necessary to explain resistance to PVY (0) and potyvirus E than resistance to PVY(1,2). Genetic correlations between resistances to the different potyviruses in the DH progeny showed that most of genetic factors involved in PVY(0) resistance appear to be also involved in potyvirus E resistance, and some of these polyvalent factors may be also involved in PVY(1,2) resistance but, in this case, additional specific genes were necessary. One of the two CVMV resistance genes seems to be implicated in potyvirus E resistance. Thus, the polygenic resistance of 'Perennial' to these potyviruses was due both to polyvalent genetic factors, i.e. factors that apparently interact with several viruses, and strain-specific genetic factors. PMID:24166111

  19. Non-coding RNAs and complex distributed genetic networks

    NASA Astrophysics Data System (ADS)

    Zhdanov, Vladimir

    2011-08-01

    In eukaryotic cells, the mRNA-protein interplay can be dramatically influenced by non-coding RNAs (ncRNAs). Although this new paradigm is now widely accepted, an understanding of the effect of ncRNAs on complex genetic networks is lacking. To clarify what may happen in this case, we propose a mean-field kinetic model describing the influence of ncRNA on a complex genetic network with a distributed architecture including mutual protein-mediated regulation of many genes transcribed into mRNAs. ncRNA is considered to associate with mRNAs and inhibit their translation and/or facilitate degradation. Our results are indicative of the richness of the kinetics under consideration. The main complex features are found to be bistability and oscillations. One could expect to find kinetic chaos as well. The latter feature has however not been observed in our calculations. In addition, we illustrate the difference in the regulation of distributed networks by mRNA and ncRNA.

  20. Networks of genetic loci and the scientific literature

    NASA Astrophysics Data System (ADS)

    Semeiks, J. R.; Grate, L. R.; Mian, I. S.

    This work considers biological information graphs, networks in which nodes corre-spond to genetic loci (or "genes") and an (undirected) edge signifies that two genes are discussed in the same article(s) in the scientific literature ("documents"). Operations that utilize the topology of these graphs can assist researchers in the scientific discovery process. For example, a shortest path between two nodes defines an ordered series of genes and documents that can be used to explore the relationship(s) between genes of interest. This work (i) describes how topologies in which edges are likely to reflect genuine relationship(s) can be constructed from human-curated corpora of genes an-notated with documents (or vice versa), and (ii) illustrates the potential of biological information graphs in synthesizing knowledge in order to formulate new hypotheses and generate novel predictions for subsequent experimental study. In particular, the well-known LocusLink corpus is used to construct a biological information graph consisting of 10,297 nodes and 21,910 edges. The large-scale statistical properties of this gene-document network suggest that it is a new example of a power-law network. The segregation of genes on the basis of species and encoded protein molecular function indicate the presence of assortativity, the preference for nodes with similar attributes to be neighbors in a network. The practical utility of a gene-document network is illustrated by using measures such as shortest paths and centrality to analyze a subset of nodes corresponding to genes implicated in aging. Each release of a curated biomedical corpus defines a particular static graph. The topology of a gene-document network changes over time as curators add and/or remove nodes and/or edges. Such a dynamic, evolving corpus provides both the foundation for analyzing the growth and behavior of large complex networks and a substrate for examining trends in biological research.

  1. Plasticity of genetic interactions in metabolic networks of yeast.

    PubMed

    Harrison, Richard; Papp, Balázs; Pál, Csaba; Oliver, Stephen G; Delneri, Daniela

    2007-02-13

    Why are most genes dispensable? The impact of gene deletions may depend on the environment (plasticity), the presence of compensatory mechanisms (mutational robustness), or both. Here, we analyze the interaction between these two forces by exploring the condition-dependence of synthetic genetic interactions that define redundant functions and alternative pathways. We performed systems-level flux balance analysis of the yeast (Saccharomyces cerevisiae) metabolic network to identify genetic interactions and then tested the model's predictions with in vivo gene-deletion studies. We found that the majority of synthetic genetic interactions are restricted to certain environmental conditions, partly because of the lack of compensation under some (but not all) nutrient conditions. Moreover, the phylogenetic cooccurrence of synthetically interacting pairs is not significantly different from random expectation. These findings suggest that these gene pairs have at least partially independent functions, and, hence, compensation is only a byproduct of their evolutionary history. Experimental analyses that used multiple gene deletion strains not only confirmed predictions of the model but also showed that investigation of false predictions may both improve functional annotation within the model and also lead to the discovery of higher-order genetic interactions. Our work supports the view that functional redundancy may be more apparent than real, and it offers a unified framework for the evolution of environmental adaptation and mutational robustness. PMID:17284612

  2. The role of social networking sites in medical genetics research.

    PubMed

    Reaves, Allison Cook; Bianchi, Diana W

    2013-05-01

    Social networking sites (SNS) have potential value in the field of medical genetics as a means of research subject recruitment and source of data. This article examines the current role of SNS in medical genetics research and potential applications for these sites in future studies. Facebook is the primary SNS considered, given the prevalence of its use in the United States and role in a small but growing number of studies. To date, utilization of SNS in medical genetics research has been primarily limited to three studies that recruited subjects from populations of Facebook users [McGuire et al. (2009); Am J Bioeth 9: 3-10; Janvier et al. (2012); Pediatrics 130: 293-298; Leighton et al. (2012); Public Health Genomics 15: 11-21]. These studies and a number of other medical and public health studies that have used Facebook as a context for recruiting research subjects are discussed. Approaches for Facebook-based subject recruitment are identified, including paid Facebook advertising, snowball sampling, targeted searching and posting. The use of these methods in medical genetics research has the potential to facilitate cost-effective research on both large, heterogeneous populations and small, hard-to-access sub-populations. PMID:23554131

  3. A network of enzymes involved in repair of oxidative DNA damage in Neisseria meningitidis

    PubMed Central

    Li, Yanwen; Pelicic, Vladimir; Freemont, Paul S.; Baldwin, Geoff S.; Tang, Christoph M.

    2013-01-01

    Although oxidative stress is a key aspect of innate immunity, little is known about how host-restricted pathogens successfully repair DNA damage. Base excision repair (BER) is responsible for correcting nucleobases damaged by oxidative stress, and is essential for bloodstream infection caused by the human pathogen, Neisseria meningitidis. We have characterised meningococcal BER enzymes involved in the recognition and removal of damaged nucleobases, and incision of the DNA backbone. We demonstrate that the bi-functional glycosylase/lyases Nth and MutM share several overlapping activities and functional redundancy. However MutM and other members of the GO system, which deal with 8-oxoG, a common lesion of oxidative damage, are not required for survival of N. meningitidis under oxidative stress. Instead, the mismatch repair pathway provides back-up for the GO system, while the lyase activity of Nth can substitute for the meningococcal AP endonuclease, NApe. Our genetic and biochemical evidence show that DNA repair is achieved through a robust network of enzymes that provides a flexible system of DNA repair. This network is likely to reflect successful adaptation to the human nasopharynx, and might provide a paradigm for DNA repair in other prokaryotes. PMID:22296581

  4. A new approach to self-organizing fuzzy polynomial neural networks guided by genetic optimization

    NASA Astrophysics Data System (ADS)

    Oh, Sung-Kwun; Pedrycz, Witold

    2005-09-01

    In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology. The underlying methodology involves mechanisms of genetic optimization, especially genetic algorithms (GAs). Let us recall that the design of the “conventional” FPNNs uses an extended Group Method of Data Handling (GMDH) and exploits a fixed fuzzy inference type located at each FPN of the FPNN as well as considers a fixed number of input nodes at FPNs (or nodes) located in each layer. The proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. The performance of the proposed gFPNN is quantified through experimentation that exploits standard data already being used in fuzzy modeling. The results reveal superiority of the proposed networks over the existing fuzzy and neural models.

  5. Diffusion tensor imaging to determine the potential motor network connectivity between the involved and non-involved hemispheres in stroke.

    PubMed

    Lee, Min-Hee; Shin, Yong-Il; Lee, Sang Hyeon; Cha, Young Joo; Kim, Dong Youn; Han, Bong Soo; You, Sung H

    2015-01-01

    Hemiparetic stroke is a common motor network disorder that affects a wide range of functional movements due to cortical and subcortical network lesions in stroke patients. Conventional magnetic resonance imaging (MRI) has been used to examine structural brain damage, but the integrity and connectivity of the whole brain are poorly understood. Hence, advanced neuroimaging with diffusion tensor imaging (DTI) has been developed to better localize fiber architecture and connectivity in the motor network or pathways that are responsible for motor impairments in hemiparetic stroke. To ascertain motor network connectivity between the involved and non-involved hemispheres in stroke patients, we analyzed the DTI data from all right hemiparetic stroke patients using fractional anisotropy (FA) and network parameters, including node degree and edge betweenness centrality (EBC). The FA values were substantially lower in the left hemisphere than the right hemisphere. Similarly, the node degree and EBC were significantly lower in the left hemisphere than the right hemisphere. The present brain network analysis may provide a useful neuropathway marker for accurate diagnosis and therapeutic intervention.

  6. Involvement of Genetic and Environmental Factors in the Onset of Depression

    PubMed Central

    Kim, Hyoung-Chun

    2013-01-01

    First, this article provides a brief overview of the previous hypotheses regarding depression and then focuses on involvement of genetic and environmental factors in development of depression. According to epidemiological research, 30~40% of occurrences of bipolar disorder involve a genetic factor. Therefore, environmental factors play a more important role in development of depression. Resilience and resistance to stress are common; therefore, although a certain extent of stress might be received during the embryonic or perinatal period, having a genetic predisposition to mental disorders does not imply that a mental disorder will develop. However, having a genetic predisposition to disorders does weaken resistance to stresses received during puberty, and without the ability to recover, a mental disorder is triggered. The importance of epigenetics in maintaining normal development and biology is reflected by the observation that development of many diseases occurs when the wrong type of epigenetic marks are introduced or are added at the wrong time or in the wrong place. Involvement of genetic and environmental factors in the onset of depression was investigated in relation to epigenetics. When mice with the disrupted in schizophrenia 1 (DISC1) abnormal gene received isolated rearing stress, depression-like abnormal behaviors and decreased gene expression of tyrosine hydroxylase in the frontal cortex by epigenetical suppression via DNA methylation were observed. Decrease of dopamine in the frontal cortex triggers behavioral disorders. Administration of a glucocorticoid receptor antagonist resulted in full recovery from neurological and behavioral disorders. These results suggest a new therapeutic approach to depression. PMID:24465138

  7. Design of artificial genetic regulatory networks with multiple delayed adaptive responses*

    NASA Astrophysics Data System (ADS)

    Kaluza, Pablo; Inoue, Masayo

    2016-06-01

    Genetic regulatory networks with adaptive responses are widely studied in biology. Usually, models consisting only of a few nodes have been considered. They present one input receptor for activation and one output node where the adaptive response is computed. In this work, we design genetic regulatory networks with many receptors and many output nodes able to produce delayed adaptive responses. This design is performed by using an evolutionary algorithm of mutations and selections that minimizes an error function defined by the adaptive response in signal shapes. We present several examples of network constructions with a predefined required set of adaptive delayed responses. We show that an output node can have different kinds of responses as a function of the activated receptor. Additionally, complex network structures are presented since processing nodes can be involved in several input-output pathways. Supplementary material in the form of one nets file available from the Journal web page at http://dx.doi.org/10.1140/epjb/e2016-70172-9

  8. A cost-benefit analysis of the Quebec Network of Genetic Medicine.

    PubMed

    Dagenais, D L; Courville, L; Dagenais, M G

    1985-01-01

    Certain serious diseases, including several major genetic disorders, cannot be treated effectively unless they are detected before symptoms appear. In such cases, only systematic population screening can ensure that the necessary preventive treatment can be administered to affected individuals. The question of whether to establish such screening programs, which may be relatively costly, is a pressing problem for many public administrations. This study of the costs and benefits of the Quebec Network of Genetic Medicine has as its main objective the development of an analytical framework which can be generally applied to such problems. In this article, we attempt to evaluate the profitability of the Network to society. For the evaluation of the less tangible costs and benefits, we adopted the minimum profitability principle, which essentially involves establishing a lower bound on the value of the profitability of the Network. The net benefits assessed by this study, although certainly underestimated, are still very significant. Since the Network is administered by a team of researchers, the study also throws some light on the links existing between research and development activities on the one hand and public services on the other, and hence on the general question of the socioeconomic profitability of biomedical research.

  9. Does Religious Involvement Protect against Early Drinking? A Behavior Genetic Approach

    ERIC Educational Resources Information Center

    Harden, K. Paige

    2010-01-01

    Background: Adolescent involvement in religious organizations has been hypothesized to protect against early age at first drink. However, the correlation between adolescent religiosity and later age at first drink may be confounded by environmental or genetic differences between families. This study tests whether, after controlling for shared…

  10. Scaling up: human genetics as a Cold War network.

    PubMed

    Lindee, Susan

    2014-09-01

    In this commentary I explore how the papers here illuminate the processes of collection that have been so central to the history of human genetics since 1945. The development of human population genetics in the Cold War period produced databases and biobanks that have endured into the present, and that continue to be used and debated. In the decades after the bomb, scientists collected and transferred human biological materials and information from populations of interest, and as they moved these biological resources or biosocial resources acquired new meanings and uses. The papers here collate these practices and map their desires and ironies. They explore how a large international network of geneticists, biological anthropologists, virologists and other physicians and scientists interacted with local informants, research subjects and public officials. They also track the networks and standards that mobilized the transfer of information, genealogies, tissue and blood samples. As Joanna Radin suggests here, the massive collections of human biological materials and data were often understood to be resources for an "as-yet-unknown" future. The stories told here contain elements of surveillance, extraction, salvage and eschatology.

  11. Scaling up: human genetics as a Cold War network.

    PubMed

    Lindee, Susan

    2014-09-01

    In this commentary I explore how the papers here illuminate the processes of collection that have been so central to the history of human genetics since 1945. The development of human population genetics in the Cold War period produced databases and biobanks that have endured into the present, and that continue to be used and debated. In the decades after the bomb, scientists collected and transferred human biological materials and information from populations of interest, and as they moved these biological resources or biosocial resources acquired new meanings and uses. The papers here collate these practices and map their desires and ironies. They explore how a large international network of geneticists, biological anthropologists, virologists and other physicians and scientists interacted with local informants, research subjects and public officials. They also track the networks and standards that mobilized the transfer of information, genealogies, tissue and blood samples. As Joanna Radin suggests here, the massive collections of human biological materials and data were often understood to be resources for an "as-yet-unknown" future. The stories told here contain elements of surveillance, extraction, salvage and eschatology. PMID:24954362

  12. Genetic specificity of a plant–insect food web: Implications for linking genetic variation to network complexity

    PubMed Central

    Barbour, Matthew A.; Fortuna, Miguel A.; Bascompte, Jordi; Nicholson, Joshua R.; Julkunen-Tiitto, Riitta; Jules, Erik S.; Crutsinger, Gregory M.

    2016-01-01

    Theory predicts that intraspecific genetic variation can increase the complexity of an ecological network. To date, however, we are lacking empirical knowledge of the extent to which genetic variation determines the assembly of ecological networks, as well as how the gain or loss of genetic variation will affect network structure. To address this knowledge gap, we used a common garden experiment to quantify the extent to which heritable trait variation in a host plant determines the assembly of its associated insect food web (network of trophic interactions). We then used a resampling procedure to simulate the additive effects of genetic variation on overall food-web complexity. We found that trait variation among host-plant genotypes was associated with resistance to insect herbivores, which indirectly affected interactions between herbivores and their insect parasitoids. Direct and indirect genetic effects resulted in distinct compositions of trophic interactions associated with each host-plant genotype. Moreover, our simulations suggest that food-web complexity would increase by 20% over the range of genetic variation in the experimental population of host plants. Taken together, our results indicate that intraspecific genetic variation can play a key role in structuring ecological networks, which may in turn affect network persistence. PMID:26858398

  13. Genetic specificity of a plant-insect food web: Implications for linking genetic variation to network complexity.

    PubMed

    Barbour, Matthew A; Fortuna, Miguel A; Bascompte, Jordi; Nicholson, Joshua R; Julkunen-Tiitto, Riitta; Jules, Erik S; Crutsinger, Gregory M

    2016-02-23

    Theory predicts that intraspecific genetic variation can increase the complexity of an ecological network. To date, however, we are lacking empirical knowledge of the extent to which genetic variation determines the assembly of ecological networks, as well as how the gain or loss of genetic variation will affect network structure. To address this knowledge gap, we used a common garden experiment to quantify the extent to which heritable trait variation in a host plant determines the assembly of its associated insect food web (network of trophic interactions). We then used a resampling procedure to simulate the additive effects of genetic variation on overall food-web complexity. We found that trait variation among host-plant genotypes was associated with resistance to insect herbivores, which indirectly affected interactions between herbivores and their insect parasitoids. Direct and indirect genetic effects resulted in distinct compositions of trophic interactions associated with each host-plant genotype. Moreover, our simulations suggest that food-web complexity would increase by 20% over the range of genetic variation in the experimental population of host plants. Taken together, our results indicate that intraspecific genetic variation can play a key role in structuring ecological networks, which may in turn affect network persistence.

  14. Two-edge disjoint survivable network design problem with relays: a hybrid genetic algorithm and Lagrangian heuristic approach

    NASA Astrophysics Data System (ADS)

    Konak, Abdullah

    2014-01-01

    This article presents a network design problem with relays considering the two-edge network connectivity. The problem arises in telecommunications and logistic networks where a constraint is imposed on the distance that a commodity can travel on a route without being processed by a relay, and the survivability of the network is critical in case of a component failure. The network design problem involves selecting two-edge disjoint paths between source and destination node pairs and determining the location of relays to minimize the network design cost. The formulated problem is solved by a hybrid approach of a genetic algorithm (GA) and a Lagrangian heuristic such that the GA searches for two-edge disjoint paths for each commodity, and the Lagrangian heuristic is used to determine relays on these paths. The performance of the proposed hybrid approach is compared to the previous approaches from the literature, with promising results.

  15. Neural-network-biased genetic algorithms for materials design

    NASA Astrophysics Data System (ADS)

    Patra, Tarak; Meenakshisundaram, Venkatesh; Simmons, David

    Machine learning tools have been progressively adopted by the materials science community to accelerate design of materials with targeted properties. However, in the search for new materials exhibiting properties and performance beyond that previously achieved, machine learning approaches are frequently limited by two major shortcomings. First, they are intrinsically interpolative. They are therefore better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require the availability of large datasets, which in some fields are not available and would be prohibitively expensive to produce. Here we describe a new strategy for combining genetic algorithms, neural networks and other machine learning tools, and molecular simulation to discover materials with extremal properties in the absence of pre-existing data. Predictions from progressively constructed machine learning tools are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct molecular dynamics simulation. We survey several initial materials design problems we have addressed with this framework and compare its performance to that of standard genetic algorithm approaches. We acknowledge the W. M. Keck Foundation for support of this work.

  16. Safety assessment, detection and traceability, and societal aspects of genetically modified foods. European Network on Safety Assessment of Genetically Modified Food Crops (ENTRANSFOOD). Concluding remarks.

    PubMed

    Kuiper, H A; König, A; Kleter, G A; Hammes, W P; Knudsen, I

    2004-07-01

    The most important results from the EU-sponsored ENTRANSFOOD Thematic Network project are reviewed, including the design of a detailed step-wise procedure for the risk assessment of foods derived from genetically modified crops based on the latest scientific developments, evaluation of topical risk assessment issues, and the formulation of proposals for improved risk management and public involvement in the risk analysis process.

  17. Safety assessment, detection and traceability, and societal aspects of genetically modified foods. European Network on Safety Assessment of Genetically Modified Food Crops (ENTRANSFOOD). Concluding remarks.

    PubMed

    Kuiper, H A; König, A; Kleter, G A; Hammes, W P; Knudsen, I

    2004-07-01

    The most important results from the EU-sponsored ENTRANSFOOD Thematic Network project are reviewed, including the design of a detailed step-wise procedure for the risk assessment of foods derived from genetically modified crops based on the latest scientific developments, evaluation of topical risk assessment issues, and the formulation of proposals for improved risk management and public involvement in the risk analysis process. PMID:15123387

  18. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    SciTech Connect

    Bornholdt, S.; Graudenz, D.

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

  19. Genetic Background Specific Hypoxia Resistance in Rat is Correlated with Balanced Activation of a Cross-Chromosomal Genetic Network Centering on Physiological Homeostasis

    PubMed Central

    Mao, Lei

    2012-01-01

    Genetic background of an individual can drastically influence an organism’s response upon environmental stress and pathological stimulus. Previous studies in inbred rats showed that compared to Brown Norway (BN), Dahl salt-sensitive (SS) rat exerts strong hypoxia susceptibility. However, despite extensive narrow-down approaches via the chromosome substitution methodology, this genome-based physiological predisposition could not be traced back to distinct quantitative trait loci. Upon the completion and public data availability of PhysGen SS-BN consomic (CS) rat platform, I employed systems biology approach attempting to further our understanding of the molecular basis of genetic background effect in light of hypoxia response. I analyzed the physiological screening data of 22 CS rat strains under normoxia and 2-weeks of hypoxia, and cross-compared them to the parental strains. The analyses showed that SS-9BN and SS-18BN represent the most hypoxia-resistant CS strains with phenotype similar to BN, whereas SS-6BN and SS-YBN segregated to the direction of SS. A meta-analysis on the transcriptomic profiles of these CS rat strains under hypoxia treatment showed that although polymorphisms on the substituted BN chromosomes could be directly involved in hypoxia resistance, this seems to be embedded in a more complex trans-chromosomal genetic regulatory network. Via information theory based modeling approach, this hypoxia relevant core genetic network was reverse engineered. Network analyses showed that the protective effects of BN chromosome 9 and 18 were reflected by a balanced activation of this core network centering on physiological homeostasis. Presumably, it is the system robustness constituted on such differential network activation that acts as hypoxia response modifier. Understanding of the intrinsic link between the individual genetic background and the network robustness will set a basis in the current scientific efforts toward personalized medicine. PMID

  20. Genetic Background Specific Hypoxia Resistance in Rat is Correlated with Balanced Activation of a Cross-Chromosomal Genetic Network Centering on Physiological Homeostasis.

    PubMed

    Mao, Lei

    2012-01-01

    Genetic background of an individual can drastically influence an organism's response upon environmental stress and pathological stimulus. Previous studies in inbred rats showed that compared to Brown Norway (BN), Dahl salt-sensitive (SS) rat exerts strong hypoxia susceptibility. However, despite extensive narrow-down approaches via the chromosome substitution methodology, this genome-based physiological predisposition could not be traced back to distinct quantitative trait loci. Upon the completion and public data availability of PhysGen SS-BN consomic (CS) rat platform, I employed systems biology approach attempting to further our understanding of the molecular basis of genetic background effect in light of hypoxia response. I analyzed the physiological screening data of 22 CS rat strains under normoxia and 2-weeks of hypoxia, and cross-compared them to the parental strains. The analyses showed that SS-9(BN) and SS-18(BN) represent the most hypoxia-resistant CS strains with phenotype similar to BN, whereas SS-6(BN) and SS-Y(BN) segregated to the direction of SS. A meta-analysis on the transcriptomic profiles of these CS rat strains under hypoxia treatment showed that although polymorphisms on the substituted BN chromosomes could be directly involved in hypoxia resistance, this seems to be embedded in a more complex trans-chromosomal genetic regulatory network. Via information theory based modeling approach, this hypoxia relevant core genetic network was reverse engineered. Network analyses showed that the protective effects of BN chromosome 9 and 18 were reflected by a balanced activation of this core network centering on physiological homeostasis. Presumably, it is the system robustness constituted on such differential network activation that acts as hypoxia response modifier. Understanding of the intrinsic link between the individual genetic background and the network robustness will set a basis in the current scientific efforts toward personalized medicine.

  1. Building Leadership Capacity in the Involving Network State

    ERIC Educational Resources Information Center

    Pedersen, Dorthe; Tangkjaer, Christian

    2013-01-01

    New partnerships, cross-organisational collaborations and co-creation, digitalisation, involvement of citizens, public design and innovation stand out as new and emerging solutions in welfare delivery. However, New Public Management (NPM) seems to represent a historical repertoire of perspectives and tools that falls short of dealing with public…

  2. Unpacking Parent Involvement: Korean American Parents' Collective Networking

    ERIC Educational Resources Information Center

    Lim, Minjung

    2012-01-01

    This study examines the ways in which a group of Korean American parents perceived and responded to institutional inequalities in a family-school partnership. In their school, which had a growing Asian population, the dominant group's middle-class perspective on parent involvement became normal and operated as an overarching structure. Drawing…

  3. Rett networked database: an integrated clinical and genetic network of Rett syndrome databases.

    PubMed

    Grillo, Elisa; Villard, Laurent; Clarke, Angus; Ben Zeev, Bruria; Pineda, Mercedes; Bahi-Buisson, Nadia; Hryniewiecka-Jaworska, Anna; Bienvenu, Thierry; Armstrong, Judith; Roche-Martinez, Ana; Mari, Francesca; Veneselli, Edvige; Russo, Silvia; Vignoli, Aglaia; Pini, Giorgio; Djuric, Milena; Bisgaard, Anne-Marie; Mejaški Bošnjak, Vlatka; Polgár, Noémi; Cogliati, Francesca; Ravn, Kirstine; Pintaudi, Maria; Melegh, Béla; Craiu, Dana; Djukic, Aleksandra; Renieri, Alessandra

    2012-07-01

    Rett syndrome (RTT) is a neurodevelopmental disorder with one principal phenotype and several distinct, atypical variants (Zappella, early seizure onset and congenital variants). Mutations in MECP2 are found in most cases of classic RTT but at least two additional genes, CDKL5 and FOXG1, can underlie some (usually variant) cases. There is only limited correlation between genotype and phenotype. The Rett Networked Database (http://www.rettdatabasenetwork.org/) has been established to share clinical and genetic information. Through an "adaptor" process of data harmonization, a set of 293 clinical items and 16 genetic items was generated; 62 clinical and 7 genetic items constitute the core dataset; 23 clinical items contain longitudinal information. The database contains information on 1838 patients from 11 countries (December 2011), with or without mutations in known genes. These numbers can expand indefinitely. Data are entered by a clinician in each center who supervises accuracy. This network was constructed to make available pooled international data for the study of RTT natural history and genotype-phenotype correlation and to indicate the proportion of patients with specific clinical features and mutations. We expect that the network will serve for the recruitment of patients into clinical trials and for developing quality measures to drive up standards of medical management. PMID:22415763

  4. Information theoretical methods to deconvolute genetic regulatory networks applied to thyroid neoplasms

    NASA Astrophysics Data System (ADS)

    Hernández-Lemus, Enrique; Velázquez-Fernández, David; Estrada-Gil, Jesús K.; Silva-Zolezzi, Irma; Herrera-Hernández, Miguel F.; Jiménez-Sánchez, Gerardo

    2009-12-01

    Most common pathologies in humans are not caused by the mutation of a single gene, rather they are complex diseases that arise due to the dynamic interaction of many genes and environmental factors. This plethora of interacting genes generates a complexity landscape that masks the real effects associated with the disease. To construct dynamic maps of gene interactions (also called genetic regulatory networks) we need to understand the interplay between thousands of genes. Several issues arise in the analysis of experimental data related to gene function: on the one hand, the nature of measurement processes generates highly noisy signals; on the other hand, there are far more variables involved (number of genes and interactions among them) than experimental samples. Another source of complexity is the highly nonlinear character of the underlying biochemical dynamics. To overcome some of these limitations, we generated an optimized method based on the implementation of a Maximum Entropy Formalism (MaxEnt) to deconvolute a genetic regulatory network based on the most probable meta-distribution of gene-gene interactions. We tested the methodology using experimental data for Papillary Thyroid Cancer (PTC) and Thyroid Goiter tissue samples. The optimal MaxEnt regulatory network was obtained from a pool of 25,593,993 different probability distributions. The group of observed interactions was validated by several (mostly in silico) means and sources. For the associated Papillary Thyroid Cancer Gene Regulatory Network (PTC-GRN) the majority of the nodes (genes) have very few links (interactions) whereas a small number of nodes are highly connected. PTC-GRN is also characterized by high clustering coefficients and network heterogeneity. These properties have been recognized as characteristic of topological robustness, and they have been largely described in relation to biological networks. A number of biological validity outcomes are discussed with regard to both the

  5. Genetic effects on behavior are mediated by neurotransmitters and large-scale neural networks.

    PubMed

    Dang, Linh C; O'Neil, James P; Jagust, William J

    2013-02-01

    Claims of gene-behavior associations are complex and sometimes difficult to replicate because these relationships involve many downstream endogenous and environmental processes that mediate genetic effects. Knowing these mediating processes is critical to understanding the links between genes and behavior and how these factors differ between people. We identified and characterized the effects of a gene on neurochemistry and neural networks to elucidate the mechanism, at the systems level, whereby genes influence cognition. Catechol-O-methyltransferase (COMT) degrades dopamine in the prefrontal cortex (PFC) and is polymorphic with alleles differing in enzymatic activity. We found that COMT genotype determined dopamine synthesis, such that individuals with greater COMT activity synthesized more dopamine. Dopamine synthesis in the midbrain and ventral striatum affected functional connectivity in the default mode network, likely through the mesocorticolimbic pathway, in an inverted-U pattern with greater functional connectivity in medial PFC associated with intermediate levels of COMT activity and dopamine. Greater functional connectivity correlated with greater deactivation during performance of a set-shifting task that engaged the PFC. Greater deactivation was in turn associated with better performance. The integration of these results yields a model whereby COMT affects prefrontal function by a mechanism involving dopaminergic modulation of the default mode network. The model features the well-known inverted-U function between dopamine and performance and supports the hypothesis that dopamine and the default mode network shift attentional resources to influence prefrontal cognition.

  6. [Genetic and biochemical mechanisms of involvement of antioxidant defense enzymes in the development of bronchial asthma].

    PubMed

    Polonikov, A V; Ivanov, V P; Bogomazov, A D; Solodilova, M A

    2015-01-01

    In the present review we have analyzed and summarized recent literature data on genetic and biochemical mechanisms responsible for involvement of antioxidant defense enzymes in the etiology and pathogenesis of bronchial asthma. It has been shown that the mechanisms of asthma development are linked with genetically determined abnormalities in the functioning of antioxidant defense enzymes. These alterations are accompanied by a systemic imbalance between oxidative and anti-oxidative reactions with the shift of the redox state toward increased free radical production and oxidative stress, a key element in the pathogenesis of bronchial asthma. PMID:26350733

  7. [Genetic and biochemical mechanisms of involvement of antioxidant defense enzymes in the development of bronchial asthma].

    PubMed

    Polonikov, A V; Ivanov, V P; Bogomazov, A D; Solodilova, M A

    2015-01-01

    In the present review we have analyzed and summarized recent literature data on genetic and biochemical mechanisms responsible for involvement of antioxidant defense enzymes in the etiology and pathogenesis of bronchial asthma. It has been shown that the mechanisms of asthma development are linked with genetically determined abnormalities in the functioning of antioxidant defense enzymes. These alterations are accompanied by a systemic imbalance between oxidative and anti-oxidative reactions with the shift of the redox state toward increased free radical production and oxidative stress, a key element in the pathogenesis of bronchial asthma.

  8. Parent involvement, sibling companionship, and adolescent substance use: A longitudinal, genetically informed design.

    PubMed

    Samek, Diana R; Rueter, Martha A; Keyes, Margaret A; McGue, Matt; Iacono, William G

    2015-08-01

    A large literature shows that parent and sibling relationship factors are associated with an increased likelihood of adolescent substance use. Less is known about the etiology of these associations. Using a genetically informed sibling design, we examined the prospective associations between parent involvement, sibling companionship, and adolescent substance use at 2 points in mid- and late-adolescence. Adolescents were adopted (n = 568) or the biological offspring of both parents (n = 412). Cross-lagged panel results showed that higher levels of parent involvement in early adolescence were associated with lower levels of substance use later in adolescence. Results did not significantly differ across adoption status, suggesting this association cannot be due to passive gene-environment correlation. Adolescent substance use at Time 1 was not significantly associated with parent involvement at Time 2, suggesting this association does not appear to be solely due to evocative (i.e., "child-driven") effects either. Together, results support a protective influence of parent involvement on subsequent adolescent substance use that is environmental in nature. The cross-paths between sibling companionship and adolescent substance use were significant and negative in direction (i.e., protective) for sisters, but positive for brothers (in line with a social contagion hypothesis). These effects were consistent across genetically related and unrelated pairs, and thus appear to be environmentally mediated. For mixed gender siblings, results were consistent with environmentally driven, protective influence hypothesis for genetically unrelated pairs, but in line with a genetically influenced, social contagion hypothesis for genetically related pairs. Implications are discussed. PMID:26030026

  9. A social network analysis of communication about hereditary nonpolyposis colorectal cancer genetic testing and family functioning.

    PubMed

    Koehly, Laura M; Peterson, Susan K; Watts, Beatty G; Kempf, Kari K G; Vernon, Sally W; Gritz, Ellen R

    2003-04-01

    Hereditary cancers are relational diseases. A primary focus of research in the past has been the biological relations that exist within the families and how genes are passed along family lines. However, hereditary cancers are relational in a psychosocial sense, as well. They can impact communication relationships within a family, as well as support relationships among family members. Furthermore, the familial culture can affect an individual's participation in genetic counseling and testing endeavors. Our aims are (a) to describe the composition of familial networks, (b) to characterize the patterns of family functioning within families, (c) to analyze how these patterns relate to communications about genetic counseling and testing among family members, and (d) to identify influential family members. Specifically, we asked how the relationship between mutation status, kinship ties, and family functioning constructs, e.g., communication, cohesion, affective involvement, leadership, and conflict, was associated with discussions about genetic counseling and testing. We used social network analysis and random graph techniques to examine 783 dyadic relationships in 36 members of 5 hereditary nonpolyposis colorectal cancer (HNPCC) families interviewed from 1999-2000. Results suggest that in these five HNPCC families, two family members are more likely to discuss genetic counseling and testing if either one carries the mutation, if either one is a spouse or a first-degree relative of the other, or if the relationship is defined by positive cohesion, leadership, or lack of conflict. Furthermore, the family functioning patterns suggest that mothers tend to be the most influential persons in the family network. Results of this study suggest encouraging family members who act in the mother role to take a "team approach" with the family proband when discussing HNPCC risks and management with family members.

  10. Does self-construal predict activity in the social brain network? A genetic moderation effect.

    PubMed

    Ma, Yina; Wang, Chenbo; Li, Bingfeng; Zhang, Wenxia; Rao, Yi; Han, Shihui

    2014-09-01

    Neural activity in the social brain network varies across individuals with different cultural traits and different genetic polymorphisms. It remains unknown whether a specific genetic polymorphism may influence the association between cultural traits and neural activity in the social brain network. We tested whether the serotonin transporter promoter polymorphism (5-HTTLPR) affects the association between self-construals and neural activity involved in reflection of personal attributes of oneself and a significant other (i.e., mother). Using functional MRI, we scanned Chinese adults with short/short (s/s) or long/long (l/l) variants of the 5-HTTLPR during reflection of personal attributes of oneself and one's mother. We found that, while s/s and l/l genotype groups did not differ significantly in self-construals measured by the Self-Construal Scale, the relationship between self-construal scores and neural responses to reflection of oneself and mother was significantly different between the two genotype groups. Specifically, l/l but not s/s genotype group showed significant association between self-construal scores and activity in the medial prefrontal cortex, bilateral middle frontal cortex, temporoparietal junction, insula and hippocampus during reflection on mental attributes of oneself and mother. Our findings suggest that a specific genetic polymorphism may interact with a cultural trait to shape the neural substrates underlying social cognition.

  11. Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility

    NASA Astrophysics Data System (ADS)

    Wang, H. B.; Li, J. W.; Zhou, B.; Yuan, Z. Q.; Chen, Y. P.

    2013-03-01

    In the last few decades, the development of Geographical Information Systems (GIS) technology has provided a method for the evaluation of landslide susceptibility and hazard. Slope units were found to be appropriate for the fundamental morphological elements in landslide susceptibility evaluation. Following the DEM construction in a loess area susceptible to landslides, the direct-reverse DEM technology was employed to generate 216 slope units in the studied area. After a detailed investigation, the landslide inventory was mapped in which 39 landslides, including paleo-landslides, old landslides and recent landslides, were present. Of the 216 slope units, 123 involved landslides. To analyze the mechanism of these landslides, six environmental factors were selected to evaluate landslide occurrence: slope angle, aspect, the height and shape of the slope, distance to river and human activities. These factors were extracted in terms of the slope unit within the ArcGIS software. The spatial analysis demonstrates that most of the landslides are located on convex slopes at an elevation of 100-150 m with slope angles from 135°-225° and 40°-60°. Landslide occurrence was then checked according to these environmental factors using an artificial neural network with back propagation, optimized by genetic algorithms. A dataset of 120 slope units was chosen for training the neural network model, i.e., 80 units with landslide presence and 40 units without landslide presence. The parameters of genetic algorithms and neural networks were then set: population size of 100, crossover probability of 0.65, mutation probability of 0.01, momentum factor of 0.60, learning rate of 0.7, max learning number of 10 000, and target error of 0.000001. After training on the datasets, the susceptibility of landslides was mapped for the land-use plan and hazard mitigation. Comparing the susceptibility map with landslide inventory, it was noted that the prediction accuracy of landslide occurrence

  12. Genetic polymorphism of serotonin transporter 5-HTTLPR: involvement in smoking behaviour.

    PubMed

    Watanabe, Maria Angelica Ehara; Nunes, Sandra Odebrecht Vargas; Nunes, Sandra Odebrechet Vargas; Amarante, Marla Karine; Guembarovski, Roberta Losi; Oda, Julie Massayo Maeda; Lima, Kalil William Alves De; Fungaro, Maria Helena Pelegrinelli

    2011-04-01

    Data suggest that the serotonin (5-hydroxytryptamine, 5-HT) system is implicated in the pathogenesis of multiple neuropsychiatric disorders and may also be involved in smoking behaviour since nicotine increases brain serotonin secretion. It is known that smoking behaviour is influenced by both genetic and environmental factors. The present review examines the role of the serotonin transporter gene (5-HTT) in smoking behaviour and investigating studies that showed association of 5-HTT gene with smoking. This study discusses a polymorphism which has been investigated by many researchers, as the bi-allelic insertion/deletion polymorphism in the 5'- flanking promoter region (5-HTTLPR). This gene has received considerable attention in attempts to understand the molecular determinants of smoking. Therefore, in the present study, the relationship between genetic polymorphism of serotonin transporter in smoking behaviour is reviewed considering the interactive effect of genetic factors.

  13. Molecular networks involved in the immune control of BK polyomavirus.

    PubMed

    Girmanova, Eva; Brabcova, Irena; Klema, Jiri; Hribova, Petra; Wohlfartova, Mariana; Skibova, Jelena; Viklicky, Ondrej

    2012-01-01

    BK polyomavirus infection is the important cause of virus-related nephropathy following kidney transplantation. BK virus reactivates in 30%-80% of kidney transplant recipients resulting in BK virus-related nephropathy in 1%-10% of cases. Currently, the molecular processes associated with asymptomatic infections in transplant patients infected with BK virus remain unclear. In this study we evaluate intrarenal molecular processes during different stages of BKV infection. The gene expression profiles of 90 target genes known to be associated with immune response were evaluated in kidney graft biopsy material using TaqMan low density array. Three patient groups were examined: control patients with no evidence of BK virus reactivation (n = 11), infected asymptomatic patients (n = 9), and patients with BK virus nephropathy (n = 10). Analysis of biopsies from asymptomatic viruria patients resulted in the identification of 5 differentially expressed genes (CD3E, CD68, CCR2, ICAM-1, and SKI) (P < 0.05), and functional analysis showed a significantly heightened presence of costimulatory signals (e.g., CD40/CD40L; P < 0.05). Gene ontology analysis revealed several biological networks associated with BKV immune control in comparison to the control group. This study demonstrated that asymptomatic BK viruria is associated with a different intrarenal regulation of several genes implicating in antiviral immune response.

  14. Strawberry Maturity Neural Network Detectng System Based on Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Xu, Liming

    The quick and non-detective detection of agriculture product is one of the measures to increase the precision and productivity of harvesting and grading. Having analyzed H frequency of different maturities in different light intensities, the results show that H frequency for the same maturity has little influence in different light intensities; Under the same light intensity, three strawberry maturities are changing in order. After having confirmed the H frequency section to distinguish the different strawberry maturity, the triplelayer feed-forward neural network system to detect strawberry maturity was designed by using genetic algorithm. The test results show that the detecting precision ratio is 91.7%, it takes 160ms to distinguish one strawberry. Therefore, the online non-detective detecting the strawberry maturity could be realized.

  15. A parallel attractor-finding algorithm based on Boolean satisfiability for genetic regulatory networks.

    PubMed

    Guo, Wensheng; Yang, Guowu; Wu, Wei; He, Lei; Sun, Mingyu

    2014-01-01

    In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures.

  16. Two networks involved in producing and realizing plans.

    PubMed

    Crescentini, Cristiano; Seyed-Allaei, Shima; Vallesi, Antonino; Shallice, Tim

    2012-06-01

    Planning is essential for normal daily activities. Although the dorsolateral prefrontal cortex (DLPFC) is thought to be crucially involved in planning, it remains to be understood whether this contribution is attributable to working memory requirements of the tasks and when it occurs, whether during initial planning or during subsequent plan execution. Here, we compared patterns of activation observed when participants planned and executed their plans to solve Tower of Hanoi problems to when they had to memorize and reproduce externally presented sequences of moves. The DLPFC was preferentially active during initial planning relative to both plan execution and initial memorization of sequences of moves. By contrast, plan execution relied on posterior temporal areas, inferior frontal regions and the dorsolateral premotor cortex. We attribute activation in DLPFC to generation and evaluation of abstract sequences of responses, and activation in the regions underlying plan execution to rehearsal of planned sequences of moves. PMID:22433287

  17. Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis).

    PubMed

    VanderWaal, Kimberly L; Atwill, Edward R; Isbell, Lynne A; McCowan, Brenda

    2014-03-01

    Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in

  18. Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways.

    PubMed

    Boucher, Benjamin; Lee, Anna Y; Hallett, Michael; Jenna, Sarah

    2016-02-01

    A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution. PMID:26871911

  19. Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

    PubMed Central

    Boucher, Benjamin; Lee, Anna Y.; Hallett, Michael; Jenna, Sarah

    2016-01-01

    A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution. PMID:26871911

  20. Network-centric Analysis of Genetic Predisposition in Diabetic Nephropathy

    PubMed Central

    Ntemka, A; Iliadis, F; Papanikolaou, NA; Grekas, D

    2011-01-01

    Diabetic nephropathy is a serious, long-term complication of diabetes and the leading cause of end-stage renal disease throughout the world. Although this disease is progressively imposing a heavier burden on the health care system, in many aspects it remains poorly understood. In addition to environmental influences, there is abundant evidence in support of genetic susceptibility to microvascular complications of nephropathy in diabetic patients. Familial clustering of phenotypes such as end-stage renal disease, albuminuria and kidney disease have been reported in large scale population studies throughout the world demonstrating strong contribution of inherited factors. Recent genome-wide linkage scans identified several chromosomal regions that are likely to contain diabetic nephropathy susceptibility genes, and association analyses have evaluated positional candidate genes under linkage peaks. In this review we have extracted from the literature the most promising candidate genes thought to confer susceptibility to diabetic nephropathy and mapped them to affected pathways by using network-centric analysis. Several of the top susceptibility genes have been identified as network hubs and bottlenecks suggesting that they might be important agents in the onset of diabetic nephropathy. PMID:22435020

  1. Accurate measurements of dynamics and reproducibility in small genetic networks

    PubMed Central

    Dubuis, Julien O; Samanta, Reba; Gregor, Thomas

    2013-01-01

    Quantification of gene expression has become a central tool for understanding genetic networks. In many systems, the only viable way to measure protein levels is by immunofluorescence, which is notorious for its limited accuracy. Using the early Drosophila embryo as an example, we show that careful identification and control of experimental error allows for highly accurate gene expression measurements. We generated antibodies in different host species, allowing for simultaneous staining of four Drosophila gap genes in individual embryos. Careful error analysis of hundreds of expression profiles reveals that less than ∼20% of the observed embryo-to-embryo fluctuations stem from experimental error. These measurements make it possible to extract not only very accurate mean gene expression profiles but also their naturally occurring fluctuations of biological origin and corresponding cross-correlations. We use this analysis to extract gap gene profile dynamics with ∼1 min accuracy. The combination of these new measurements and analysis techniques reveals a twofold increase in profile reproducibility owing to a collective network dynamics that relays positional accuracy from the maternal gradients to the pair-rule genes. PMID:23340845

  2. Calibration of neural networks using genetic algorithms, with application to optimal path planning

    NASA Technical Reports Server (NTRS)

    Smith, Terence R.; Pitney, Gilbert A.; Greenwood, Daniel

    1987-01-01

    Genetic algorithms (GA) are used to search the synaptic weight space of artificial neural systems (ANS) for weight vectors that optimize some network performance function. GAs do not suffer from some of the architectural constraints involved with other techniques and it is straightforward to incorporate terms into the performance function concerning the metastructure of the ANS. Hence GAs offer a remarkably general approach to calibrating ANS. GAs are applied to the problem of calibrating an ANS that finds optimal paths over a given surface. This problem involves training an ANS on a relatively small set of paths and then examining whether the calibrated ANS is able to find good paths between arbitrary start and end points on the surface.

  3. Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

    NASA Astrophysics Data System (ADS)

    Zu, Yun-Xiao; Zhou, Jie

    2012-01-01

    Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.

  4. Molecular Genetic Analysis of Activation-tagged Transcription Factors Thought to be Involved in Photomorphogenesis

    SciTech Connect

    Neff, Michael

    2011-06-23

    Plants utilize light as a source of information via families of photoreceptors such as the red/far-red absorbing phytochromes (PHY) and the blue/UVA absorbing cryptochromes (CRY). The main goal of the Neff lab is to use molecular-genetic mutant screens to elucidate signaling components downstream of these photoreceptors. Activation-tagging mutagenesis led to the identification of two putative transcription factors that may be involved in both photomorphogenesis and hormone signaling pathways. sob1-D (suppressor of phyB-dominant) mutant phenotypes are caused by the over-expression of a Dof transcription factor previously named OBP3. Our previous studies indicate that OBP3 is a negative regulator of light-mediated cotyledon expansion and may be involved in modulating responsiveness to the growth-regulating hormone auxin. The sob2-D mutant uncovers a role for LEP, a putative AP2/EREBP-like transcription factor, in seed germination, hypocotyl elongation and responsiveness to the hormone abscisic acid. Based on photobiological and genetic analysis of OBP3-knockdown and LEP-null mutations, we hypothesize that these transcription factors are involved in both light-mediated seedling development and hormone signaling. To examine the role that these genes play in photomorphogenesis we will: 1) Further explore the genetic role of OBP3 in cotyledon/leaf expansion and other photomorphogenic processes as well as examine potential physical interactions between OBP3 and CRY1 or other signaling components that genetically interact with this transcription factor 2) Test the hypothesis that OBP3 is genetically involved in auxin signaling and root development as well as examine the affects of this hormone and light on OBP3 protein accumulation. 3) Test the hypothesis that LEP is involved in seed germination, seedling photomorphogenesis and hormone signaling. Together these experiments will lead to a greater understanding of the complexity of interactions between photoreceptors and DNA

  5. Modifications of a conserved regulatory network involving INDEHISCENT controls multiple aspects of reproductive tissue development in Arabidopsis.

    PubMed

    Kay, P; Groszmann, M; Ross, J J; Parish, R W; Swain, S M

    2013-01-01

    Disrupting pollen tube growth and fertilization in Arabidopsis plants leads to reduced seed set and silique size, providing a powerful genetic system with which to identify genes with important roles in plant fertility. A transgenic Arabidopsis line with reduced pollen tube growth, seed set and silique growth was used as the progenitor in a genetic screen to isolate suppressors with increased seed set and silique size. This screen generated a new allele of INDEHISCENT (IND), a gene originally identified by its role in valve margin development and silique dehiscence (pod shatter). IND forms part of a regulatory network that involves several other transcriptional regulators and involves the plant hormones GA and auxin. Using GA and auxin mutants that alter various aspects of reproductive development, we have identified novel roles for IND, its paralogue HECATE3, and the MADS box proteins SHATTERPROOF1/2 in flower and fruit development. These results suggest that modified forms of the regulatory network originally described for the Arabidopsis valve margin, which include these genes and/or their recently evolved paralogs, function in multiple components of GA/auxin-regulated reproductive development. PMID:23126654

  6. Characterizing individual differences in reward sensitivity from the brain networks involved in response inhibition.

    PubMed

    Fuentes-Claramonte, Paola; Ávila, César; Rodríguez-Pujadas, Aina; Costumero, Víctor; Ventura-Campos, Noelia; Bustamante, Juan Carlos; Rosell-Negre, Patricia; Barrós-Loscertales, Alfonso

    2016-01-01

    A "disinhibited" cognitive profile has been proposed for individuals with high reward sensitivity, characterized by increased engagement in goal-directed responses and reduced processing of negative or unexpected cues, which impairs adequate behavioral regulation after feedback in these individuals. This pattern is manifested through deficits in inhibitory control and/or increases in RT variability. In the present work, we aimed to test whether this profile is associated with the activity of functional networks during a stop-signal task using independent component analysis (ICA). Sixty-one participants underwent fMRI while performing a stop-signal task, during which a manual response had to be inhibited. ICA was used to mainly replicate the functional networks involved in the task (Zhang and Li, 2012): two motor networks involved in the go response, the left and right fronto-parietal networks for stopping, a midline error-processing network, and the default-mode network (DMN), which was further subdivided into its anterior and posterior parts. Reward sensitivity was mainly associated with greater activity of motor networks, reduced activity in the midline network during correct stop trials and, behaviorally, increased RT variability. All these variables explained 36% of variance of the SR scores. This pattern of associations suggests that reward sensitivity involves greater motor engagement in the dominant response, more distractibility and reduced processing of salient or unexpected events, which may lead to disinhibited behavior.

  7. Characterizing individual differences in reward sensitivity from the brain networks involved in response inhibition.

    PubMed

    Fuentes-Claramonte, Paola; Ávila, César; Rodríguez-Pujadas, Aina; Costumero, Víctor; Ventura-Campos, Noelia; Bustamante, Juan Carlos; Rosell-Negre, Patricia; Barrós-Loscertales, Alfonso

    2016-01-01

    A "disinhibited" cognitive profile has been proposed for individuals with high reward sensitivity, characterized by increased engagement in goal-directed responses and reduced processing of negative or unexpected cues, which impairs adequate behavioral regulation after feedback in these individuals. This pattern is manifested through deficits in inhibitory control and/or increases in RT variability. In the present work, we aimed to test whether this profile is associated with the activity of functional networks during a stop-signal task using independent component analysis (ICA). Sixty-one participants underwent fMRI while performing a stop-signal task, during which a manual response had to be inhibited. ICA was used to mainly replicate the functional networks involved in the task (Zhang and Li, 2012): two motor networks involved in the go response, the left and right fronto-parietal networks for stopping, a midline error-processing network, and the default-mode network (DMN), which was further subdivided into its anterior and posterior parts. Reward sensitivity was mainly associated with greater activity of motor networks, reduced activity in the midline network during correct stop trials and, behaviorally, increased RT variability. All these variables explained 36% of variance of the SR scores. This pattern of associations suggests that reward sensitivity involves greater motor engagement in the dominant response, more distractibility and reduced processing of salient or unexpected events, which may lead to disinhibited behavior. PMID:26343318

  8. Increasing public involvement in enriching our fish stocks through genetic enhancement.

    PubMed

    Halvorson, H O; Quezada, F

    1999-11-01

    A total of 70%, of the world's conventional commercial fish species are now fully exploited, overexploited, depleted or recovering from depletion. This dramatic crash in the capture world fisheries production has led to problems in foods distribution, balance of payments, employment, and ecological depletion. Public support for breeding programs with terrestrial farm animals and plants in agriculture have revolutionized this industry over the past few hundred years. However, new genetic rearing technologies to improve marine animal production through aquaculture that utilize modern biology to obtain sustainable aquaculture and preserve biodiversity provide a promise to address these problems. However aquaculture has not been subject to public discussion and approval. Public involvement, not necessarily acquiescence, provide value added in the decision making process. Public understanding and involvement involves three stages. (i) Public concern over the pool of genetic information; (ii) if aquaculture is to respond to the fisheries crises with innovation, the knowledge gap between public understanding and scientific information must be bridged; and (iii) strategies must be developed for achieving this. Release of recombinant DNA to the environment, and handling exotic species, are useful case studies. Illustrations will be given of communication bridges to the public and ways to involve the public in making policy decisions.

  9. Systematic review of genetic association studies involving histologically confirmed non-alcoholic fatty liver disease

    PubMed Central

    Wood, Kayleigh L; Miller, Michael H; Dillon, John F

    2015-01-01

    Non-alcoholic fatty liver disease has an increasing prevalence in Western countries, affecting up to 20% of the population. Objective The aim of this project was to systematically review and summarise the genetic association studies that investigate possible genetic influences that confer susceptibility to non-alcoholic fatty liver disease and non-alcoholic steatohepatitis. Design The MEDLINE and SCOPUS databases were searched to identify candidate gene studies on histologically diagnosed non-alcoholic fatty liver disease. Results A total of 85 articles have been summarised and categorised on the basis of the general pathway each candidate gene is involved in, including lipid metabolism, lipoprotein processing, cholesterol synthesis, glucose homoeostasis, inflammatory response, protection against oxidative stress and whole body metabolism. Conclusions The main findings demonstrate a small but consistent association of PNPLA3 with non-alcoholic fatty liver disease and non-alcoholic steatohepatitis. Genetic association studies have investigated general disease susceptibility, histological characteristics, severity and progression. However, further study is required to better elucidate the genetic factors influencing fatty liver disease. PMID:26462272

  10. Genes and quantitative genetic variation involved with senescence in cells, organs, and the whole plant

    PubMed Central

    Pujol, Benoit

    2015-01-01

    Senescence, the deterioration of morphological, physiological, and reproductive functions with age that ends with the death of the organism, was widely studied in plants. Genes were identified that are linked to the deterioration of cells, organs and the whole plant. It is, however, unclear whether those genes are the source of age dependent deterioration or get activated to regulate such deterioration. Furthermore, it is also unclear whether such genes are active as a direct consequence of age or because they are specifically involved in some developmental stages. At the individual level, it is the relationship between quantitative genetic variation, and age that can be used to detect the genetic signature of senescence. Surprisingly, the latter approach was only scarcely applied to plants. This may be the consequence of the demanding requirements for such approaches and/or the fact that most research interest was directed toward plants that avoid senescence. Here, I review those aspects in turn and call for an integrative genetic theory of senescence in plants. Such conceptual development would have implications for the management of plant genetic resources and generate progress on fundamental questions raised by aging research. PMID:25755664

  11. Fins into limbs: Autopod acquisition and anterior elements reduction by modifying gene networks involving 5'Hox, Gli3, and Shh.

    PubMed

    Tanaka, Mikiko

    2016-05-01

    Two major morphological changes occurred during the fin-to-limb transition: the appearance of the autopod, and the reduction of anterior skeletal elements. In the past decades, numerous approaches to the study of genetic developmental systems involved in patterning of fins/limbs among different taxa have provided clues to better understand the mechanism of the fin-to-limb transition. In this article, I discuss recent progress toward elucidating the evolutionary origin of the autopod and the mechanism through which the multiple-basal bones of ancestral fins were reduced into a single bone (humerus/femur). A particular focus of this article is the patterning mechanism of the tetrapod limb and chondrichthyan fin controlled by gene networks involving the 5'Hox genes, Gli3 and Shh. These recent data provide possible scenarios that could have led to the transformation of fins into limbs. PMID:26992366

  12. Etiologic Ischemic Stroke Phenotypes in the NINDS Stroke Genetics Network

    PubMed Central

    Ay, Hakan; Arsava, Ethem Murat; Andsberg, Gunnar; Benner, Thomas; Brown, Robert D.; Chapman, Sherita N.; Cole, John W.; Delavaran, Hossein; Dichgans, Martin; Engström, Gunnar; Giralt-Steinhauer, Eva; Grewal, Raji P.; Gwinn, Katrina; Jern, Christina; Jimenez-Conde, Jordi; Jood, Katarina; Katsnelson, Michael; Kissela, Brett; Kittner, Steven J.; Kleindorfer, Dawn O.; Labovitz, Daniel L.; Lanfranconi, Silvia; Lee, Jin-Moo; Lehm, Manuel; Lemmens, Robin; Levi, Chris; Li, Linxin; Lindgren, Arne; Markus, Hugh S.; McArdle, Patrick F.; Melander, Olle; Norrving, Bo; Peddareddygari, Leema Reddy; Pedersén, Annie; Pera, Joanna; Rannikmäe, Kristiina; Rexrode, Kathryn M.; Rhodes, David; Rich, Stephen S.; Roquer, Jaume; Rosand, Jonathan; Rothwell, Peter M.; Rundek, Tatjana; Sacco, Ralph L.; Schmidt, Reinhold; Schürks, Markus; Seiler, Stephan; Sharma, Pankaj; Slowik, Agnieszka; Sudlow, Cathie; Thijs, Vincent; Woodfield, Rebecca; Worrall, Bradford B.; Meschia, James F.

    2014-01-01

    Background and Purpose NINDS Stroke Genetics Network (SiGN) is an international consortium of ischemic stroke studies that aims to generate high quality phenotype data to identify the genetic basis of etiologic stroke subtypes. This analysis characterizes the etiopathogenetic basis of ischemic stroke and reliability of stroke classification in the consortium. Methods Fifty-two trained and certified adjudicators determined both phenotypic (abnormal test findings categorized in major etiologic groups without weighting towards the most likely cause) and causative ischemic stroke subtypes in 16,954 subjects with imaging-confirmed ischemic stroke from 12 US studies and 11 studies from 8 European countries using the web-based Causative Classification of Stroke System. Classification reliability was assessed with blinded re-adjudication of 1509 randomly selected cases. Results The distribution of etiologic categories varied by study, age, sex, and race (p<0.001 for each). Overall, only 40% to 54% of cases with a given major ischemic stroke etiology (phenotypic subtype) were classified into the same final causative category with high confidence. There was good agreement for both causative (kappa 0.72, 95%CI:0.69-0.75) and phenotypic classifications (kappa 0.73, 95%CI:0.70-0.75). Conclusions This study demonstrates that etiologic subtypes can be determined with good reliability in studies that include investigators with different expertise and background, institutions with different stroke evaluation protocols and geographic location, and patient populations with different epidemiological characteristics. The discordance between phenotypic and causative stroke subtypes highlights the fact that the presence of an abnormality in a stroke patient does not necessarily mean that it is the cause of stroke. PMID:25378430

  13. A Co-Association Network Analysis of the Genetic Determination of Pig Conformation, Growth and Fatness

    PubMed Central

    Puig-Oliveras, Anna; Ballester, Maria; Corominas, Jordi; Revilla, Manuel; Estellé, Jordi; Fernández, Ana I.; Ramayo-Caldas, Yuliaxis; Folch, Josep M.

    2014-01-01

    Background Several QTLs have been identified for major economically relevant traits in livestock, such as growth and meat quality, revealing the complex genetic architecture of these traits. The use of network approaches considering the interactions of multiple molecules and traits provides useful insights into the molecular underpinnings of complex traits. Here, a network based methodology, named Association Weight Matrix, was applied to study gene interactions and pathways affecting pig conformation, growth and fatness traits. Results The co-association network analysis underpinned three transcription factors, PPARγ, ELF1, and PRDM16 involved in mesoderm tissue differentiation. Fifty-four genes in the network belonged to growth-related ontologies and 46 of them were common with a similar study for growth in cattle supporting our results. The functional analysis uncovered the lipid metabolism and the corticotrophin and gonadotrophin release hormone pathways among the most important pathways influencing these traits. Our results suggest that the genes and pathways here identified are important determining either the total body weight of the animal and the fat content. For instance, a switch in the mesoderm tissue differentiation may determinate the age-related preferred pathways being in the puberty stage those related with the miogenic and osteogenic lineages; on the contrary, in the maturity stage cells may be more prone to the adipocyte fate. Hence, our results demonstrate that an integrative genomic co-association analysis is a powerful approach for identifying new connections and interactions among genes. Conclusions This work provides insights about pathways and key regulators which may be important determining the animal growth, conformation and body proportions and fatness traits. Molecular information concerning genes and pathways here described may be crucial for the improvement of genetic breeding programs applied to pork meat production. PMID:25503799

  14. Genetic algorithms and their application to in silico evolution of genetic regulatory networks.

    PubMed

    Knabe, Johannes F; Wegner, Katja; Nehaniv, Chrystopher L; Schilstra, Maria J

    2010-01-01

    A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution to solve computational problems. A GA introduces variation through "mutation" and "recombination" in a "population" of possible solutions to a problem, encoded as strings of characters in "genomes," and allows this population to evolve, using selection procedures that favor the gradual enrichment of the gene pool with the genomes of the "fitter" individuals. GAs are particularly suitable for optimization problems in which an effective system design or set of parameter values is sought.In nature, genetic regulatory networks (GRNs) form the basic control layer in the regulation of gene expression levels. GRNs are composed of regulatory interactions between genes and their gene products, and are, inter alia, at the basis of the development of single fertilized cells into fully grown organisms. This paper describes how GAs may be applied to find functional regulatory schemes and parameter values for models that capture the fundamental GRN characteristics. The central ideas behind evolutionary computation and GRN modeling, and the considerations in GA design and use are discussed, and illustrated with an extended example. In this example, a GRN-like controller is sought for a developmental system based on Lewis Wolpert's French flag model for positional specification, in which cells in a growing embryo secrete and detect morphogens to attain a specific spatial pattern of cellular differentiation. PMID:20835807

  15. A chemical-genetic screen to unravel the genetic network of CDC28/CDK1 links ubiquitin and Rad6–Bre1 to cell cycle progression

    PubMed Central

    Zimmermann, Christine; Chymkowitch, Pierre; Eldholm, Vegard; Putnam, Christopher D.; Lindvall, Jessica M.; Omerzu, Manja; Bjørås, Magnar; Kolodner, Richard D.; Enserink, Jorrit M.

    2011-01-01

    Cyclin-dependent kinases (CDKs) control the eukaryotic cell cycle, and a single CDK, Cdc28 (also known as Cdk1), is necessary and sufficient for cell cycle regulation in the budding yeast Saccharomyces cerevisiae. Cdc28 regulates cell cycle-dependent processes such as transcription, DNA replication and repair, and chromosome segregation. To gain further insight into the functions of Cdc28, we performed a high-throughput chemical-genetic array (CGA) screen aimed at unraveling the genetic network of CDC28. We identified 107 genes that strongly genetically interact with CDC28. Although these genes serve multiple cellular functions, genes involved in cell cycle regulation, transcription, and chromosome metabolism were overrepresented. DOA1, which is involved in maintaining free ubiquitin levels, as well as the RAD6–BRE1 pathway, which is involved in transcription, displayed particularly strong genetic interactions with CDC28. We discovered that DOA1 is important for cell cycle entry by supplying ubiquitin. Furthermore, we found that the RAD6–BRE1 pathway functions downstream of DOA1/ubiquitin but upstream of CDC28, by promoting transcription of cyclins. These results link cellular ubiquitin levels and the Rad6–Bre1 pathway to cell cycle progression. PMID:22042866

  16. Transcriptome comparison reveals a genetic network regulating the lower temperature limit in fish.

    PubMed

    Hu, Peng; Liu, Mingli; Liu, Yimeng; Wang, Jinfeng; Zhang, Dong; Niu, Hongbo; Jiang, Shouwen; Wang, Jian; Zhang, Dongsheng; Han, Bingshe; Xu, Qianghua; Chen, Liangbiao

    2016-01-01

    Transcriptional plasticity is a major driver of phenotypic differences between species. The lower temperature limit (LTL), namely the lower end of survival temperature, is an important trait delimiting the geographical distribution of a species, however, the genetic mechanisms are poorly understood. We investigated the inter-species transcriptional diversification in cold responses between zebrafish Danio rerio and tilapia Oreochromis niloticus, which were reared at a common temperature (28 °C) but have distinct LTLs. We identified significant expressional divergence between the two species in the orthologous genes from gills when the temperature cooled to the LTL of tilapia (8 °C). Five KEGG pathways were found sequentially over-represented in the zebrafish/tilapia divergently expressed genes in the duration (12 hour) of 8 °C exposure, forming a signaling cascade from metabolic regulation to apoptosis via FoxO signaling. Consistently, we found differential progression of apoptosis in the gills of the two species in which zebrafish manifested a delayed and milder apoptotic phenotype than tilapia, corresponding with a lower LTL of zebrafish. We identified diverged expression in 25 apoptosis-related transcription factors between the two species which forms an interacting network with diverged factors involving the FoxO signaling and metabolic regulation. We propose a genetic network which regulates LTL in fishes. PMID:27356472

  17. Transcriptome comparison reveals a genetic network regulating the lower temperature limit in fish

    PubMed Central

    Hu, Peng; Liu, Mingli; Liu, Yimeng; Wang, Jinfeng; Zhang, Dong; Niu, Hongbo; Jiang, Shouwen; Wang, Jian; Zhang, Dongsheng; Han, Bingshe; Xu, Qianghua; Chen, Liangbiao

    2016-01-01

    Transcriptional plasticity is a major driver of phenotypic differences between species. The lower temperature limit (LTL), namely the lower end of survival temperature, is an important trait delimiting the geographical distribution of a species, however, the genetic mechanisms are poorly understood. We investigated the inter-species transcriptional diversification in cold responses between zebrafish Danio rerio and tilapia Oreochromis niloticus, which were reared at a common temperature (28 °C) but have distinct LTLs. We identified significant expressional divergence between the two species in the orthologous genes from gills when the temperature cooled to the LTL of tilapia (8 °C). Five KEGG pathways were found sequentially over-represented in the zebrafish/tilapia divergently expressed genes in the duration (12 hour) of 8 °C exposure, forming a signaling cascade from metabolic regulation to apoptosis via FoxO signaling. Consistently, we found differential progression of apoptosis in the gills of the two species in which zebrafish manifested a delayed and milder apoptotic phenotype than tilapia, corresponding with a lower LTL of zebrafish. We identified diverged expression in 25 apoptosis-related transcription factors between the two species which forms an interacting network with diverged factors involving the FoxO signaling and metabolic regulation. We propose a genetic network which regulates LTL in fishes. PMID:27356472

  18. Prediction of plasma processes using neural network and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Kim, Byungwhan; Bae, Jungki

    2005-10-01

    Using genetic algorithm (GA) and backpropagation neural network (BPNN), computer models of plasma processes were constructed. The GA was applied to optimize five training factors simultaneously. The presented technique was evaluated with plasma etch data, characterized by a statistical experimental design. The etching was conducted in an inductively coupled plasma etch system. The etch outputs to model include aluminum (Al) etch rate, Al selectivity, silica profile angle, and DC bias. GA-BPNN models demonstrated improved predictions of more than 20% for all etch outputs but the DC bias. This indicates that a simultaneous optimization of training factors is more effective in improving the prediction performance of BPNN model than a sequential optimization of individual training factor. Compared to GA-BPNN models constructed in a previous training set, the presented models also yielded a much improved prediction of more than 35% for all etch outputs. The proven improvement indicates that the presented training set is more effective to improve GA-BPNN models.

  19. Global dynamics of bidirectional associative memory neural networks involving transmission delays and dead zones.

    PubMed

    Sree Hari Rao, V; Phaneendra, Bh R.M.

    1999-04-01

    In this article, a model describing the activation dynamics of bidirectional associative memory (BAM) neural networks involving transmission delays was considered. The concept of BAM networks employed in this work is improved and it includes the earlier notions known in the literature and is applied to a wider class of networks. Further, we introduced a new notion, as a measure of restoring stability and termed it as a dead zone. In this article, the influence of the presence of dead zones on the global asymptotic stability of the equilibrium pattern was investigated. Existence and uniqueness of an equilibrium pattern under fairly general and easily verifiable conditions were also established.

  20. Identification of plant genetic loci involved in a posttranscriptional mechanism for meiotically reversible transgene silencing.

    PubMed Central

    Dehio, C; Schell, J

    1994-01-01

    Numerous reports describe phenomena of transgene silencing in plants, yet the underlying genetic and molecular mechanisms are poorly understood. We observed that regeneration of Arabidopsis thaliana plants transgenic for the rolB gene of Agrobacterium rhizogenes results in a selection for transgene silencing. Transgene silencing could be monitored in this system by reversion of the visible RolB phenotype. We report a phenotypic, molecular, and genetic characterization of a meiotically reversible transgene silencing phenomenon observed in a rolB transgenic line. In this line, the rolB gene is expressed strongly and uniformly in seedlings, but in the course of further development, the rolB gene is silenced erratically at a frequency that depends on the dosage of rolB. The silenced state is mitotically stable, while complete resetting of rolB gene expression occurs in seedlings of the following generation. The silencing of rolB correlates with a dramatic reduction of steady-state rolB transcripts, while rolB nuclear run-off transcripts are only moderately reduced. Therefore, rolB gene silencing seems to act predominantly at the posttranscriptional level. The process of rolB gene silencing was found to be affected by two extragenic modifier loci that influence both the frequency and the timing of rolB gene silencing during plant development. These genetic data demonstrate a direct involvement of defined plant genes in this form of gene silencing. Images PMID:8202523

  1. Genetic, metabolic and environmental factors involved in the development of liver cirrhosis in Mexico

    PubMed Central

    Ramos-Lopez, Omar; Martinez-Lopez, Erika; Roman, Sonia; Fierro, Nora A; Panduro, Arturo

    2015-01-01

    Liver cirrhosis (LC) is a chronic illness caused by inflammatory responses and progressive fibrosis. Globally, the most common causes of chronic liver disease include persistent alcohol abuse, followed by viral hepatitis infections and nonalcoholic fatty liver disease. However, regardless of the etiological factors, the susceptibility and degree of liver damage may be influenced by genetic polymorphisms that are associated with distinct ethnic and cultural backgrounds. Consequently, metabolic genes are influenced by variable environmental lifestyle factors, such as diet, physical inactivity, and emotional stress, which are associated with regional differences among populations. This Topic Highlight will focus on the genetic and environmental factors that may influence the metabolism of alcohol and nutrients in the setting of distinct etiologies of liver disease. The interaction between genes and environment in the current-day admixed population, Mestizo and Native Mexican, will be described. Additionally, genes involved in immune regulation, insulin sensitivity, oxidative stress and extracellular matrix deposition may modulate the degree of severity. In conclusion, LC is a complex disease. The onset, progression, and clinical outcome of LC among the Mexican population are influenced by specific genetic and environmental factors. Among these are an admixed genome with a heterogenic distribution of European, Amerindian and African ancestry; a high score of alcohol consumption; viral infections; a hepatopathogenic diet; and a high prevalence of obesity. The variance in risk factors among populations suggests that intervention strategies directed towards the prevention and management of LC should be tailored according to such population-based features. PMID:26556986

  2. [Constructing the network of classic genetic knowledge and developing self-learning ability of students in genetic classroom].

    PubMed

    Luo, Pei-Gao

    2010-04-01

    With the quick increase of new knowledge in genetics, undergraduate teaching of genetics is becoming a challenge for many teachers. In this paper, the author suggested that it would be important to construct the knowledge network of genetics and to develop the self-learning ability of students. This could help students to read textbooks "from the thicker to the thinner in classroom" and "from the thinner to the thicker outside classroom", so that students would turn to be the talents with new ideas and have more competent ability in biology-related fields.

  3. Identification of host genes involved in geminivirus infection using a reverse genetics approach.

    PubMed

    Lozano-Durán, Rosa; Rosas-Díaz, Tábata; Luna, Ana P; Bejarano, Eduardo R

    2011-01-01

    Geminiviruses, like all viruses, rely on the host cell machinery to establish a successful infection, but the identity and function of these required host proteins remain largely unknown. Tomato yellow leaf curl Sardinia virus (TYLCSV), a monopartite geminivirus, is one of the causal agents of the devastating Tomato yellow leaf curl disease (TYLCD). The transgenic 2IRGFP N. benthamiana plants, used in combination with Virus Induced Gene Silencing (VIGS), entail an important potential as a tool in reverse genetics studies to identify host factors involved in TYLCSV infection. Using these transgenic plants, we have made an accurate description of the evolution of TYLCSV replication in the host in both space and time. Moreover, we have determined that TYLCSV and Tobacco rattle virus (TRV) do not dramatically influence each other when co-infected in N. benthamiana, what makes the use of TRV-induced gene silencing in combination with TYLCSV for reverse genetic studies feasible. Finally, we have tested the effect of silencing candidate host genes on TYLCSV infection, identifying eighteen genes potentially involved in this process, fifteen of which had never been implicated in geminiviral infections before. Seven of the analyzed genes have a potential anti-viral effect, whereas the expression of the other eleven is required for a full infection. Interestingly, almost half of the genes altering TYLCSV infection play a role in postranslational modifications. Therefore, our results provide new insights into the molecular mechanisms underlying geminivirus infections, and at the same time reveal the 2IRGFP/VIGS system as a powerful tool for functional reverse genetics studies. PMID:21818318

  4. Identification of Host Genes Involved in Geminivirus Infection Using a Reverse Genetics Approach

    PubMed Central

    Luna, Ana P.; Bejarano, Eduardo R.

    2011-01-01

    Geminiviruses, like all viruses, rely on the host cell machinery to establish a successful infection, but the identity and function of these required host proteins remain largely unknown. Tomato yellow leaf curl Sardinia virus (TYLCSV), a monopartite geminivirus, is one of the causal agents of the devastating Tomato yellow leaf curl disease (TYLCD). The transgenic 2IRGFP N. benthamiana plants, used in combination with Virus Induced Gene Silencing (VIGS), entail an important potential as a tool in reverse genetics studies to identify host factors involved in TYLCSV infection. Using these transgenic plants, we have made an accurate description of the evolution of TYLCSV replication in the host in both space and time. Moreover, we have determined that TYLCSV and Tobacco rattle virus (TRV) do not dramatically influence each other when co-infected in N. benthamiana, what makes the use of TRV-induced gene silencing in combination with TYLCSV for reverse genetic studies feasible. Finally, we have tested the effect of silencing candidate host genes on TYLCSV infection, identifying eighteen genes potentially involved in this process, fifteen of which had never been implicated in geminiviral infections before. Seven of the analyzed genes have a potential anti-viral effect, whereas the expression of the other eleven is required for a full infection. Interestingly, almost half of the genes altering TYLCSV infection play a role in postranslational modifications. Therefore, our results provide new insights into the molecular mechanisms underlying geminivirus infections, and at the same time reveal the 2IRGFP/VIGS system as a powerful tool for functional reverse genetics studies. PMID:21818318

  5. State estimation for delayed genetic regulatory networks based on passivity theory.

    PubMed

    Vembarasan, V; Nagamani, G; Balasubramaniam, P; Park, Ju H

    2013-08-01

    This paper is concerned with the state estimation problem for delayed genetic regulatory networks (GRNs) based on passivity analysis approach. The main purpose of the problem is to design the estimator to approximate the true concentrations of the mRNA and protein through available measurement outputs. Time-varying delays are explicitly assumed to be non-differentiable and constraint on the derivative of the time-varying delay is less than one can be removed. Based on the Lyapunov-Krasovskii functionals involving triple integral terms, using some integral inequalities and convex combination technique, a delay-dependent passivity criterion is established for GRNs in terms of linear matrix inequalities (LMIs) that can efficiently be solved by any available LMI solvers. Finally, numerical examples and simulation are presented to demonstrate the efficiency of the proposed estimation schemes.

  6. Genetically Modified Networks: A Genetic Algorithm contribution to Space Geodesy. Application to the transformation of SLR and DORIS EOP time series into ITRF2005.

    NASA Astrophysics Data System (ADS)

    Coulot, D.; Collilieux, X.; Pollet, A.; Berio, P.; Gobinddass, M. L.; Soudarin, L.; Willis, P.

    2009-04-01

    In this study, we apply Genetic Algorithms (GAs) in order to optimize the referencing (and consequently the precision - stability - and the accuracy) of the EOPs with respect to ITRF2005. These EOPs are derived from SLR or DORIS data at a daily sampling, simultaneously with weekly station positions. GAs are evolutionary algorithms, i.e. stochastic algorithms based on the evolution theory and using some genetic operators such as chromosome crossover and gene mutations. They are currently used for a broad spectrum of activities, from medicine to defence to finance. They have also been used in Earth and Space sciences (remote sensing, geophysics, meteorology, astrophysics, astronomy, etc.) since the early nineties. But, as far as we know, the present work is the first application of GAs in the framework of Space Geodesy. In this work, we use an algorithm based on GAs to find weekly optimal sub-networks over which applying minimum constraints in order to reference EOPs. Each week, the three rotations of the involved terrestrial frames are forced to be zero with respect to ITRF2005 through minimum constraints applied over these sub-networks, which are called Genetically Modified Networks (GMNs). The reference system effects are used as objectives to optimize with GAs. Regarding SLR, our approach provides an improvement of 10 % in accuracy for polar motion in comparison to the results obtained with the network specially designed for EOP referencing by the Analysis Working Group of the International Laser Ranging Service. This improvement of nearly 25 as represents 50 % of the current precision of the IERS 05 C04 reference series. We also show preliminary results regarding such GMNs for the DORIS technique using two different solutions (IGN and CNES/CLS solutions). Finally, for practical applications, we also test, for the SLR and the DORIS techniques, the possible emergence of global core networks to be used for EOP referencing on the basis of GAs.

  7. Prediction of Genetic Interactions Using Machine Learning and Network Properties

    PubMed Central

    Madhukar, Neel S.; Elemento, Olivier; Pandey, Gaurav

    2015-01-01

    A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI – synthetic sickness or synthetic lethality – involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases. PMID:26579514

  8. Telethon Network of Genetic Biobanks: a key service for diagnosis and research on rare diseases.

    PubMed

    Filocamo, Mirella; Baldo, Chiara; Goldwurm, Stefano; Renieri, Alessandra; Angelini, Corrado; Moggio, Maurizio; Mora, Marina; Merla, Giuseppe; Politano, Luisa; Garavaglia, Barbara; Casareto, Lorena; Bricarelli, Francesca Dagna

    2013-08-30

    Several examples have always illustrated how access to large numbers of biospecimens and associated data plays a pivotal role in the identification of disease genes and the development of pharmaceuticals. Hence, allowing researchers to access to significant numbers of quality samples and data, genetic biobanks are a powerful tool in basic, translational and clinical research into rare diseases. Recently demand for well-annotated and properly-preserved specimens is growing at a high rate, and is expected to grow for years to come. The best effective solution to this issue is to enhance the potentialities of well-managed biobanks by building a network.Here we report a 5-year experience of the Telethon Network of Genetic Biobanks (TNGB), a non-profit association of Italian repositories created in 2008 to form a virtually unique catalogue of biospecimens and associated data, which presently lists more than 750 rare genetic defects. The process of TNGB harmonisation has been mainly achieved through the adoption of a unique, centrally coordinated, IT infrastructure, which has enabled (i) standardisation of all the TNGB procedures and activities; (ii) creation of an updated TNGB online catalogue, based on minimal data set and controlled terminologies; (iii) sample access policy managed via a shared request control panel at web portal. TNGB has been engaged in disseminating information on its services into both scientific/biomedical - national and international - contexts, as well as associations of patients and families. Indeed, during the last 5-years national and international scientists extensively used the TNGB with different purposes resulting in more than 250 scientific publications. In addition, since its inception the TNGB is an associated member of the Biobanking and Biomolecular Resources Research Infrastructure and recently joined the EuroBioBank network. Moreover, the involvement of patients and families, leading to the formalization of various agreements

  9. Evolution of genetic diversity using networks: the human gut microbiome as a case study.

    PubMed

    Bapteste, E; Bicep, C; Lopez, P

    2012-07-01

    In order to study complex microbial communities and their associated mobile genetic elements, such as the human gut microbiome, evolutionists could explore their genetic diversity with shared sequence networks. In particular, the detection of remarkable structures in gene networks of the gut microbiome could serve to identify important functions within the community, and would ease comparison of data sets from microbiomes of various sources (human, ape, mouse etc.) in a single analysis.

  10. Online Social Networks for Crowdsourced Multimedia-Involved Behavioral Testing: An Empirical Study.

    PubMed

    Choi, Jun-Ho; Lee, Jong-Seok

    2015-01-01

    Online social networks have emerged as effective crowdsourcing media to recruit participants in recent days. However, issues regarding how to effectively exploit them have not been adequately addressed yet. In this paper, we investigate the reliability and effectiveness of multimedia-involved behavioral testing via social network-based crowdsourcing, especially focused on Facebook as a medium to recruit participants. We conduct a crowdsourcing-based experiment for a music recommendation problem. It is shown that different advertisement methods yield different degrees of efficiency and there exist significant differences in behavioral patterns across different genders and different age groups. In addition, we perform a comparison of our experiment with other multimedia-involved crowdsourcing experiments built on Amazon Mechanical Turk (MTurk), which suggests that crowdsourcing-based experiments using social networks for recruitment can achieve comparable efficiency. Based on the analysis results, advantages and disadvantages of social network-based crowdsourcing and suggestions for successful experiments are also discussed. We conclude that social networks have the potential to support multimedia-involved behavioral tests to gather in-depth data even for long-term periods. PMID:26793137

  11. Online Social Networks for Crowdsourced Multimedia-Involved Behavioral Testing: An Empirical Study.

    PubMed

    Choi, Jun-Ho; Lee, Jong-Seok

    2015-01-01

    Online social networks have emerged as effective crowdsourcing media to recruit participants in recent days. However, issues regarding how to effectively exploit them have not been adequately addressed yet. In this paper, we investigate the reliability and effectiveness of multimedia-involved behavioral testing via social network-based crowdsourcing, especially focused on Facebook as a medium to recruit participants. We conduct a crowdsourcing-based experiment for a music recommendation problem. It is shown that different advertisement methods yield different degrees of efficiency and there exist significant differences in behavioral patterns across different genders and different age groups. In addition, we perform a comparison of our experiment with other multimedia-involved crowdsourcing experiments built on Amazon Mechanical Turk (MTurk), which suggests that crowdsourcing-based experiments using social networks for recruitment can achieve comparable efficiency. Based on the analysis results, advantages and disadvantages of social network-based crowdsourcing and suggestions for successful experiments are also discussed. We conclude that social networks have the potential to support multimedia-involved behavioral tests to gather in-depth data even for long-term periods.

  12. Online Social Networks for Crowdsourced Multimedia-Involved Behavioral Testing: An Empirical Study

    PubMed Central

    Choi, Jun-Ho; Lee, Jong-Seok

    2016-01-01

    Online social networks have emerged as effective crowdsourcing media to recruit participants in recent days. However, issues regarding how to effectively exploit them have not been adequately addressed yet. In this paper, we investigate the reliability and effectiveness of multimedia-involved behavioral testing via social network-based crowdsourcing, especially focused on Facebook as a medium to recruit participants. We conduct a crowdsourcing-based experiment for a music recommendation problem. It is shown that different advertisement methods yield different degrees of efficiency and there exist significant differences in behavioral patterns across different genders and different age groups. In addition, we perform a comparison of our experiment with other multimedia-involved crowdsourcing experiments built on Amazon Mechanical Turk (MTurk), which suggests that crowdsourcing-based experiments using social networks for recruitment can achieve comparable efficiency. Based on the analysis results, advantages and disadvantages of social network-based crowdsourcing and suggestions for successful experiments are also discussed. We conclude that social networks have the potential to support multimedia-involved behavioral tests to gather in-depth data even for long-term periods. PMID:26793137

  13. Decreasing Risky Behavior on Social Network Sites: The Impact of Parental Involvement in Secondary Education Interventions.

    PubMed

    Vanderhoven, Ellen; Schellens, Tammy; Valcke, Martin

    2016-06-01

    Teenagers face significant risks when using increasingly popular social network sites. Prevention and intervention efforts to raise awareness about these risks and to change risky behavior (so-called "e-safety" interventions) are essential for the wellbeing of these minors. However, several studies have revealed that while school interventions often affect awareness, they have only a limited impact on pupils' unsafe behavior. Utilizing the Theory of Planned Behavior and theories about parental involvement, we hypothesized that involving parents in an e-safety intervention would positively influence pupils' intentions and behavior. In a quasi-experimental study with pre- and post-test measures involving 207 pupils in secondary education, we compared the impact of an intervention without parental involvement with one that included active parental involvement by means of a homework task. We found that whereas parental involvement was not necessary to improve the intervention's impact on risk awareness, it did change intentions to engage in certain unsafe behavior, such as posting personal and sexual information on the profile page of a social network site, and in reducing existing problematic behavior. This beneficial impact was particularly evident for boys. These findings suggest that developing prevention campaigns with active parental involvement is well worth the effort. Researchers and developers should therefore focus on other efficient strategies to involve parents. PMID:26821548

  14. Complex molecular genetic abnormalities involving three or more genetic mutations are important prognostic factors for acute myeloid leukemia.

    PubMed

    Wakita, S; Yamaguchi, H; Ueki, T; Usuki, K; Kurosawa, S; Kobayashi, Y; Kawata, E; Tajika, K; Gomi, S; Koizumi, M; Fujiwara, Y; Yui, S; Fukunaga, K; Ryotokuji, T; Hirakawa, T; Arai, K; Kitano, T; Kosaka, F; Tamai, H; Nakayama, K; Fukuda, T; Inokuchi, K

    2016-03-01

    We conducted a comprehensive analysis of 28 recurrently mutated genes in acute myeloid leukemia (AML) in 271 patients with de novo AML. Co-mutations were frequently detected in the intermediate cytogenetic risk group, at an average of 2.76 co-mutations per patient. When assessing the prognostic impact of these co-mutations in the intermediate cytogenetic risk group, overall survival (OS) was found to be significantly shorter (P=0.0006) and cumulative incidence of relapse (CIR) significantly higher (P=0.0052) in patients with complex molecular genetic abnormalities (CMGAs) involving three or more mutations. This trend was marked even among patients aged ⩽65 years who were also FLT3-ITD (FMS-like tyrosine kinase 3 internal tandem duplications)-negative (OS: P=0.0010; CIR: P=0.1800). Moreover, the multivariate analysis revealed that CMGA positivity was an independent prognostic factor associated with OS (P=0.0007). In stratification based on FLT3-ITD and CEBPA status and 'simplified analysis of co-mutations' using seven genes that featured frequently in CMGAs, CMGA positivity retained its prognostic value in transplantation-aged patients of the intermediate cytogenetic risk group (OS: P=0.0002. CIR: P<0.0001). In conclusion, CMGAs in AML were found to be strong independent adverse prognostic factors and simplified co-mutation analysis to have clinical usefulness and applicability.

  15. Testing whether Genetic Variation Explains Correlation of Quantitative Measures of Gene Expression, and Application to Genetic Network Analysis

    PubMed Central

    Yu, Zhaoxia; Wang, Leiwei; Hildebrandt, Michelle A.T.; Schaid, Daniel J.

    2009-01-01

    SUMMARY Genetic networks for gene expression data are often built by graphical models, which in turn are built from pairwise correlations of gene expression levels. A key feature of building graphical models is evaluation of conditional independence of two traits, given other traits. When conditional independence can be assumed, the traits that are conditioned on are considered to “explain” the correlation of a pair of traits, allowing efficient building and interpretation of a network. Overlaying genetic polymorphisms, such as single nucleotide polymorphisms (SNPs), on quantitative measures of gene expression provides a much richer set of data to build a genetic network, because it is possible to evaluate whether sets of SNPs “explain” the correlation of gene expression levels. However, there is strong evidence that gene expression levels are controlled by multiple interacting genes, suggesting that it will be difficult to reduce the partial correlation completely to zero. Ignoring the fact that some set of SNPs can explain at least part of the correlation between gene expression levels, if not all, might miss important clues on the genetic control of gene expression. To enrich the assessment of the causes of correlation between gene expression levels, we develop methods to evaluate whether a set of covariates (e.g., SNPs, or even a set of quantitative expression transcripts), explains at least some of the correlation of gene expression levels. These methods can be used to assist the interpretation of regulation of gene expression and the construction of gene regulation networks. PMID:18444230

  16. Stability of dead zone bidirectional associative memory neural networks involving time delays.

    PubMed

    Rao, V Sree Hari; Rao, P Raja Sekhara

    2002-02-01

    A mathematical model describing the dynamical interactions of bidirectional associative memory networks involving transmission delays is considered. The influence of a dead zone or a zone of noactivation on the global stability is investigated and various easily verifiable sets of sufficient conditions are established. The asymptotic nature of solutions when the given system of equations does not possess an equilibrium pattern is discussed.

  17. Community (in) Colleges: The Relationship Between Online Network Involvement and Academic Outcomes at a Community College

    ERIC Educational Resources Information Center

    Evans, Eliza D.; McFarland, Daniel A.; Rios-Aguilar, Cecilia; Deil-Amen, Regina

    2016-01-01

    Objective: This study explores the relationship between online social network involvement and academic outcomes among community college students. Prior theory hypothesizes that socio-academic moments are especially important for the integration of students into community colleges and that integration is related to academic outcomes. Online social…

  18. Students' Involvement in Social Networking and Attitudes towards Its Integration into Teaching

    ERIC Educational Resources Information Center

    Umoh, Ukeme Ekpedeme; Etuk, Etuk Nssien

    2016-01-01

    The study examined Students' Involvement in Social Networking and attitudes towards its Integration into Teaching. The study was carried out in the University of Uyo, Akwa Ibom State, Nigeria. The population of the study consisted of 17,618 undergraduate students enrolled into full time degree programmes in the University of Uyo for 2014/2015…

  19. Cambodian Parental Involvement: The Role of Parental Beliefs, Social Networks, and Trust

    ERIC Educational Resources Information Center

    Eng, Sothy; Szmodis, Whitney; Mulsow, Miriam

    2014-01-01

    The role of social capital (parental beliefs, social networks, and trust) as a predictor of parental involvement in Cambodian children's education was examined, controlling for human capital (family socioeconomic status). Parents of elementary students (n = 273) were interviewed face to face in Cambodia. Teacher contact scored highest,…

  20. Power graph compression reveals dominant relationships in genetic transcription networks.

    PubMed

    Ahnert, Sebastian E

    2013-11-01

    We introduce a framework for the discovery of dominant relationship patterns in transcription networks, by compressing the network into a power graph with overlapping power nodes. Our application of this approach to the transcription networks of S. cerevisiae and E. coli, paired with GO term enrichment analysis, provides a highly informative overview of the most prominent relationships in the gene regulatory networks of these two organisms.

  1. Genetic Network and Breeding Patterns of a Sicklefin Lemon Shark (Negaprion acutidens) Population in the Society Islands, French Polynesia

    PubMed Central

    Mourier, Johann; Buray, Nicolas; Schultz, Jennifer K.; Clua, Eric; Planes, Serge

    2013-01-01

    Human pressures have put many top predator populations at risk of extinction. Recent years have seen alarming declines in sharks worldwide, while their resilience remains poorly understood. Studying the ecology of small populations of marine predators is a priority to better understand their ability to withstand anthropogenic and environmental stressors. In the present study, we monitored a naturally small island population of 40 adult sicklefin lemon sharks in Moorea, French Polynesia over 5 years. We reconstructed the genetic relationships among individuals and determined the population’s mating system. The genetic network illustrates that all individuals, except one, are interconnected at least through one first order genetic relationship. While this species developed a clear inbreeding avoidance strategy involving dispersal and migration, the small population size, low number of breeders, and the fragmented environment characterizing these tropical islands, limits its complete effectiveness. PMID:23967354

  2. Genetic network and breeding patterns of a sicklefin lemon shark (Negaprion acutidens) population in the Society Islands, French Polynesia.

    PubMed

    Mourier, Johann; Buray, Nicolas; Schultz, Jennifer K; Clua, Eric; Planes, Serge

    2013-01-01

    Human pressures have put many top predator populations at risk of extinction. Recent years have seen alarming declines in sharks worldwide, while their resilience remains poorly understood. Studying the ecology of small populations of marine predators is a priority to better understand their ability to withstand anthropogenic and environmental stressors. In the present study, we monitored a naturally small island population of 40 adult sicklefin lemon sharks in Moorea, French Polynesia over 5 years. We reconstructed the genetic relationships among individuals and determined the population's mating system. The genetic network illustrates that all individuals, except one, are interconnected at least through one first order genetic relationship. While this species developed a clear inbreeding avoidance strategy involving dispersal and migration, the small population size, low number of breeders, and the fragmented environment characterizing these tropical islands, limits its complete effectiveness. PMID:23967354

  3. The reverse cholesterol transport pathway improves understanding of genetic networks for fat deposition and muscle growth in beef cattle.

    PubMed

    Daniels, Tyler F; Wu, Xiao-Lin; Pan, Zengxiang; Michal, Jennifer J; Wright, Raymond W; Killinger, Karen M; MacNeil, Michael D; Jiang, Zhihua

    2010-01-01

    In the present study, thirteen genes involved in the reverse cholesterol transport (RCT) pathway were investigated for their associations with three fat depositions, eight fatty acid compositions and two growth-related phenotypes in a Wagyu x Limousin reference population, including 6 F(1) bulls, 113 F(1) dams, and 246 F(2) progeny. A total of 37 amplicons were used to screen single nucleotide polymorphisms (SNPs) on 6 F(1) bulls. Among 36 SNPs detected in 11 of these 13 genes, 19 were selected for genotyping by the Sequenom assay design on all F(2) progeny. Single-marker analysis revealed seven SNPs in ATP binding cassette A1, apolipoproteins A1, B and E, phospholipid transfer protein and paraoxinase 1 genes significantly associated with nine phenotypes (P<0.05). Previously, we reported genetic networks associated with 19 complex phenotypes based on a total of 138 genetic polymorphisms derived from 71 known functional genes. Therefore, after Bonferroni correction, these significant (adjusted P<0.05) and suggestive (adjusted P<0.10) associations were then used to identify genetic networks related to the RCT pathway. Multiple-marker analysis suggested possible genetic networks involving the RCT pathway for kidney-pelvic-heart fat percentage, rib-eye area, and subcutaneous fat depth phenotypes with markers derived from paraoxinase 1, apolipoproteins A1 and E, respectively. The present study confirmed that genes involved in cholesterol homeostasis are useful targets for investigating obesity in humans as well as for improving meat quality phenotypes in a livestock production. PMID:21151936

  4. Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis).

    PubMed

    VanderWaal, Kimberly L; Atwill, Edward R; Isbell, Lynne A; McCowan, Brenda

    2014-03-01

    Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in

  5. The involvement of the interleukin-1 Receptor-Associated Kinases (IRAKs) in cellular signaling networks controlling inflammation

    PubMed Central

    Ringwood, Lorna; Li, Liwu

    2008-01-01

    Innate immunity and inflammation plays a key role in host defense and wound healing. However, Excessive or altered inflammatory processes can contribute to severe and diverse human diseases including cardiovascular disease, diabetes and cancer. The interleukin-1 receptor associated kinases (IRAKs) are critically involved in the regulation of intra-cellular signaling networks controlling inflammation. Collective studies indicate that IRAKs are present in many cell types, and can mediate signals from various cell receptors including Toll-Like-Receptors (TLRs). Consequently, diverse downstream signaling processes can be elicited following the activation of various IRAKs. Given the critical and complex roles IRAK proteins play, it is not surprising that genetic variations in human IRAK genes have been found to be linked with various human inflammatory diseases. This review intends to summarize the recent advances regarding the regulations of various IRAK proteins and their cellular functions in mediating inflammatory signaling processes. PMID:18249132

  6. SYSGENET: a meeting report from a new European network for systems genetics.

    PubMed

    Schughart, Klaus

    2010-08-01

    The first scientific meeting of the newly established European SYSGENET network took place at the Helmholtz Centre for Infection Research (HZI) in Braunschweig, April 7-9, 2010. About 50 researchers working in the field of systems genetics using mouse genetic reference populations (GRP) participated in the meeting and exchanged their results, phenotyping approaches, and data analysis tools for studying systems genetics. In addition, the future of GRP resources and phenotyping in Europe was discussed.

  7. Theta Oscillation Reveals the Temporal Involvement of Different Attentional Networks in Contingent Reorienting

    PubMed Central

    Chang, Chi-Fu; Liang, Wei-Kuang; Lai, Chiou-Lian; Hung, Daisy L.; Juan, Chi-Hung

    2016-01-01

    In the visual world, rapidly reorienting to relevant objects outside the focus of attention is vital for survival. This ability from the interaction between goal-directed and stimulus-driven attentional control is termed contingent reorienting. Neuroimaging studies have demonstrated activations of the ventral and dorsal attentional networks (DANs) which exhibit right hemisphere dominance, but the temporal dynamics of the attentional networks still remain unclear. The present study used event-related potential (ERP) to index the locus of spatial attention and Hilbert-Huang transform (HHT) to acquire the time-frequency information during contingent reorienting. The ERP results showed contingent reorienting induced significant N2pc on both hemispheres. In contrast, our time-frequency analysis found further that, unlike the N2pc, theta oscillation during contingent reorienting differed between hemispheres and experimental sessions. The inter-trial coherence (ITC) of the theta oscillation demonstrated that the two sides of the attentional networks became phase-locked to contingent reorienting at different stages. The left attentional networks were associated with contingent reorienting in the first experimental session whereas the bilateral attentional networks play a more important role in this process in the subsequent session. This phase-locked information suggests a dynamic temporal evolution of the involvement of different attentional networks in contingent reorienting and a potential role of the left ventral network in the first session. PMID:27375459

  8. Theta Oscillation Reveals the Temporal Involvement of Different Attentional Networks in Contingent Reorienting.

    PubMed

    Chang, Chi-Fu; Liang, Wei-Kuang; Lai, Chiou-Lian; Hung, Daisy L; Juan, Chi-Hung

    2016-01-01

    In the visual world, rapidly reorienting to relevant objects outside the focus of attention is vital for survival. This ability from the interaction between goal-directed and stimulus-driven attentional control is termed contingent reorienting. Neuroimaging studies have demonstrated activations of the ventral and dorsal attentional networks (DANs) which exhibit right hemisphere dominance, but the temporal dynamics of the attentional networks still remain unclear. The present study used event-related potential (ERP) to index the locus of spatial attention and Hilbert-Huang transform (HHT) to acquire the time-frequency information during contingent reorienting. The ERP results showed contingent reorienting induced significant N2pc on both hemispheres. In contrast, our time-frequency analysis found further that, unlike the N2pc, theta oscillation during contingent reorienting differed between hemispheres and experimental sessions. The inter-trial coherence (ITC) of the theta oscillation demonstrated that the two sides of the attentional networks became phase-locked to contingent reorienting at different stages. The left attentional networks were associated with contingent reorienting in the first experimental session whereas the bilateral attentional networks play a more important role in this process in the subsequent session. This phase-locked information suggests a dynamic temporal evolution of the involvement of different attentional networks in contingent reorienting and a potential role of the left ventral network in the first session. PMID:27375459

  9. Theta Oscillation Reveals the Temporal Involvement of Different Attentional Networks in Contingent Reorienting.

    PubMed

    Chang, Chi-Fu; Liang, Wei-Kuang; Lai, Chiou-Lian; Hung, Daisy L; Juan, Chi-Hung

    2016-01-01

    In the visual world, rapidly reorienting to relevant objects outside the focus of attention is vital for survival. This ability from the interaction between goal-directed and stimulus-driven attentional control is termed contingent reorienting. Neuroimaging studies have demonstrated activations of the ventral and dorsal attentional networks (DANs) which exhibit right hemisphere dominance, but the temporal dynamics of the attentional networks still remain unclear. The present study used event-related potential (ERP) to index the locus of spatial attention and Hilbert-Huang transform (HHT) to acquire the time-frequency information during contingent reorienting. The ERP results showed contingent reorienting induced significant N2pc on both hemispheres. In contrast, our time-frequency analysis found further that, unlike the N2pc, theta oscillation during contingent reorienting differed between hemispheres and experimental sessions. The inter-trial coherence (ITC) of the theta oscillation demonstrated that the two sides of the attentional networks became phase-locked to contingent reorienting at different stages. The left attentional networks were associated with contingent reorienting in the first experimental session whereas the bilateral attentional networks play a more important role in this process in the subsequent session. This phase-locked information suggests a dynamic temporal evolution of the involvement of different attentional networks in contingent reorienting and a potential role of the left ventral network in the first session.

  10. A study on ionospheric TEC forecast using genetic algorithm and neural network

    NASA Astrophysics Data System (ADS)

    Huang, Zhi; Yuan, Hong

    Back propagation artificial neural network (ANN) augmented by genetic algorithm (GA) is introduced to forecast ionospheric TEC with the dual-frequency GPS measurements from the low and high solar activity years in this paper due to ionosphere space characterizing by the highly nonlinear and time-varying with random variations. First, with different number of neurons in the hidden layer, different transfer function and training function, the training performance of network model is analyzed and then optimized network structure is determined. The ionospheric TEC values one hour in advance are forecasted and further the prediction performance of the developed network model is evaluated at the given criterions. The results show that predicted TEC using BP neural network improved by genetic algorithm has good agreement with observed data. In addition, the prediction errors are smaller in middle and high latitudes than in low latitudes, smaller in low solar activity than in high solar activity. Compared with BP Network with three layers structure, Prediction precision of network model optimized by genetic algorithm is further improved. The resolution quality indicate that the proposed algorithm can offer a powerful and reliable alternative to the design of ionospheric TEC forecast technologies, and provide advice for the regional ionospheric TEC maps. Key words: Neural network, Genetic algorithm, Ionospheric TEC, Forecast,

  11. Idiopathic Pulmonary Fibrosis: A Genetic Disease That Involves Mucociliary Dysfunction of the Peripheral Airways.

    PubMed

    Evans, Christopher M; Fingerlin, Tasha E; Schwarz, Marvin I; Lynch, David; Kurche, Jonathan; Warg, Laura; Yang, Ivana V; Schwartz, David A

    2016-10-01

    Idiopathic pulmonary fibrosis (IPF) is an incurable complex genetic disorder that is associated with sequence changes in 7 genes (MUC5B, TERT, TERC, RTEL1, PARN, SFTPC, and SFTPA2) and with variants in at least 11 novel loci. We have previously found that 1) a common gain-of-function promoter variant in MUC5B rs35705950 is the strongest risk factor (genetic and otherwise), accounting for 30-35% of the risk of developing IPF, a disease that was previously considered idiopathic; 2) the MUC5B promoter variant can potentially be used to identify individuals with preclinical pulmonary fibrosis and is predictive of radiologic progression of preclinical pulmonary fibrosis; and 3) MUC5B may be involved in the pathogenesis of pulmonary fibrosis with MUC5B message and protein expressed in bronchiolo-alveolar epithelia of IPF and the characteristic IPF honeycomb cysts. Based on these considerations, we hypothesize that excessive production of MUC5B either enhances injury due to reduced mucociliary clearance or impedes repair consequent to disruption of normal regenerative mechanisms in the distal lung. In aggregate, these novel considerations should have broad impact, resulting in specific etiologic targets, early detection of disease, and novel biologic pathways for use in the design of future intervention, prevention, and mechanistic studies of IPF. PMID:27630174

  12. Systematic Analysis of the Genetic Variability That Impacts SUMO Conjugation and Their Involvement in Human Diseases

    NASA Astrophysics Data System (ADS)

    Xu, Hao-Dong; Shi, Shao-Ping; Chen, Xiang; Qiu, Jian-Ding

    2015-07-01

    Protein function has been observed to rely on select essential sites instead of requiring all sites to be indispensable. Small ubiquitin-related modifier (SUMO) conjugation or sumoylation, which is a highly dynamic reversible process and its outcomes are extremely diverse, ranging from changes in localization to altered activity and, in some cases, stability of the modified, has shown to be especially valuable in cellular biology. Motivated by the significance of SUMO conjugation in biological processes, we report here on the first exploratory assessment whether sumoylation related genetic variability impacts protein functions as well as the occurrence of diseases related to SUMO. Here, we defined the SUMOAMVR as sumoylation related amino acid variations that affect sumoylation sites or enzymes involved in the process of connectivity, and categorized four types of potential SUMOAMVRs. We detected that 17.13% of amino acid variations are potential SUMOAMVRs and 4.83% of disease mutations could lead to SUMOAMVR with our system. More interestingly, the statistical analysis demonstrates that the amino acid variations that directly create new potential lysine sumoylation sites are more likely to cause diseases. It can be anticipated that our method can provide more instructive guidance to identify the mechanisms of genetic diseases.

  13. Genetic network of the eye in Platyhelminthes: expression and functional analysis of some players during planarian regeneration.

    PubMed

    Saló, Emili; Pineda, David; Marsal, Maria; Gonzalez, Javier; Gremigni, Vittorio; Batistoni, Renata

    2002-04-01

    Planarians are the free-living members (order Tricladida) of the phylum Platyhelminthes. They are triploblastic, acoelomate, unsegmented and located at the base of the Lophotrochozoa clade. Besides their huge regenerative capacity, planarians have simple eyes, considered similar to the prototypic eye suggested by Charles Darwin in his book 'On the Origin of Species'. The conserved genetic network that determines the initial steps of eye development across metazoans supports a monophyletic origin of the various eye types present in the animal kingdom. Here we summarise the pattern of expression of certain genes involved in the eye network that have been isolated in planarians, such as Otx, Pax-6, Six, Rax and opsin. We describe the effects of RNA interference-mediated loss of function on eye regeneration. Finally, we discuss the relevance of these findings for the evolution of the eye gene network. PMID:11992724

  14. Genetic network of the eye in Platyhelminthes: expression and functional analysis of some players during planarian regeneration.

    PubMed

    Saló, Emili; Pineda, David; Marsal, Maria; Gonzalez, Javier; Gremigni, Vittorio; Batistoni, Renata

    2002-04-01

    Planarians are the free-living members (order Tricladida) of the phylum Platyhelminthes. They are triploblastic, acoelomate, unsegmented and located at the base of the Lophotrochozoa clade. Besides their huge regenerative capacity, planarians have simple eyes, considered similar to the prototypic eye suggested by Charles Darwin in his book 'On the Origin of Species'. The conserved genetic network that determines the initial steps of eye development across metazoans supports a monophyletic origin of the various eye types present in the animal kingdom. Here we summarise the pattern of expression of certain genes involved in the eye network that have been isolated in planarians, such as Otx, Pax-6, Six, Rax and opsin. We describe the effects of RNA interference-mediated loss of function on eye regeneration. Finally, we discuss the relevance of these findings for the evolution of the eye gene network.

  15. Genetically Determined Susceptibility to Tuberculosis in Mice Causally Involves Accelerated and Enhanced Recruitment of Granulocytes

    PubMed Central

    Keller, Christine; Hoffmann, Reinhard; Lang, Roland; Brandau, Sven; Hermann, Corinna; Ehlers, Stefan

    2006-01-01

    Classical twin studies and recent linkage analyses of African populations have revealed a potential involvement of host genetic factors in susceptibility or resistance to Mycobacterium tuberculosis infection. In order to identify the candidate genes involved and test their causal implication, we capitalized on the mouse model of tuberculosis, since inbred mouse strains also differ substantially in their susceptibility to infection. Two susceptible and two resistant mouse strains were aerogenically infected with 1,000 CFU of M. tuberculosis, and the regulation of gene expression was examined by Affymetrix GeneChip U74A array with total lung RNA 2 and 4 weeks postinfection. Four weeks after infection, 96 genes, many of which are involved in inflammatory cell recruitment and activation, were regulated in common. One hundred seven genes were differentially regulated in susceptible mouse strains, whereas 43 genes were differentially expressed only in resistant mice. Data mining revealed a bias towards the expression of genes involved in granulocyte pathophysiology in susceptible mice, such as an upregulation of those for the neutrophil chemoattractant LIX (CXCL5), interleukin 17 receptor, phosphoinositide kinase 3 delta, or gamma interferon-inducible protein 10. Following M. tuberculosis challenge in both airways or peritoneum, granulocytes were recruited significantly faster and at higher numbers in susceptible than in resistant mice. When granulocytes were efficiently depleted by either of two regimens at the onset of infection, only susceptible mice survived aerosol challenge with M. tuberculosis significantly longer than control mice. We conclude that initially enhanced recruitment of granulocytes contributes to susceptibility to tuberculosis. PMID:16790804

  16. Untangling genetic networks of panic, phobia, fear and anxiety

    PubMed Central

    Villafuerte, Sandra; Burmeister, Margit

    2003-01-01

    As is the case for normal individual variation in anxiety levels, the conditions panic disorder, agoraphobia and other phobias have a significant genetic basis. Recent reports have started to untangle the genetic relationships between predispositions to anxiety and anxiety disorders. PMID:12914652

  17. Finding friends and enemies in an enemies-only network: A graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions

    PubMed Central

    Qi, Yan; Suhail, Yasir; Lin, Yu-yi; Boeke, Jef D.; Bader, Joel S.

    2008-01-01

    The yeast synthetic lethal genetic interaction network contains rich information about underlying pathways and protein complexes as well as new genetic interactions yet to be discovered. We have developed a graph diffusion kernel as a unified framework for inferring complex/pathway membership analogous to “friends” and genetic interactions analogous to “enemies” from the genetic interaction network. When applied to the Saccharomyces cerevisiae synthetic lethal genetic interaction network, we can achieve a precision around 50% with 20% to 50% recall in the genome-wide prediction of new genetic interactions, supported by experimental validation. The kernels show significant improvement over previous best methods for predicting genetic interactions and protein co-complex membership from genetic interaction data. PMID:18832443

  18. Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational (co)variance.

    PubMed

    Hether, Tyler D; Hohenlohe, Paul A

    2014-04-01

    Systems biology is accumulating a wealth of understanding about the structure of genetic regulatory networks, leading to a more complete picture of the complex genotype-phenotype relationship. However, models of multivariate phenotypic evolution based on quantitative genetics have largely not incorporated a network-based view of genetic variation. Here we model a set of two-node, two-phenotype genetic network motifs, covering a full range of regulatory interactions. We find that network interactions result in different patterns of mutational (co)variance at the phenotypic level (the M-matrix), not only across network motifs but also across phenotypic space within single motifs. This effect is due almost entirely to mutational input of additive genetic (co)variance. Variation in M has the effect of stretching and bending phenotypic space with respect to evolvability, analogous to the curvature of space-time under general relativity, and similar mathematical tools may apply in each case. We explored the consequences of curvature in mutational variation by simulating adaptation under divergent selection with gene flow. Both standing genetic variation (the G-matrix) and rate of adaptation are constrained by M, so that G and adaptive trajectories are curved across phenotypic space. Under weak selection the phenotypic mean at migration-selection balance also depends on M. PMID:24219635

  19. Cellular and genetic mechanisms involved in the generation of protective and pathogenic immune responses in human Chagas disease

    PubMed Central

    Dutra, Walderez Ornelas; Menezes, Cristiane Alves Silva; Villani, Fernanda Nobre Amaral; da Costa, Germano Carneiro; da Silveira, Alexandre Barcelos Morais; d’Ávila Reis, Débora; Gollob, Kenneth J

    2012-01-01

    Perhaps one of the most intriguing aspects of human Chagas disease is the complex network of events that underlie the generation of protective versus pathogenic immune responses during the chronic phase of the disease. While most individuals do not develop patent disease, a large percentage may develop severe forms that eventually lead to death. Although many efforts have been devoted to deciphering these mechanisms, there is still much to be learned before we can fully understand the pathogenesis of Chagas disease. It is clear that the host’s immune response is decisive in this process. While characteristics of the parasite influence the immune response, it is becoming evident that the host genetic background plays a fundamental role in the establishment of pathogenic versus protective responses. The involvement of three complex organisms, host, parasite and vector, is certainly one of the key aspects that calls for multidisciplinary approaches towards the understanding of Chagas disease. We believe that now, one hundred years after the discovery of Chagas disease, it is imperative to continue with highly interactive research in order to elucidate the immune response associated with disease evolution, which will be essential in designing prophylactic or therapeutic interventions. PMID:19753476

  20. Comparative Study of Computational Methods for Reconstructing Genetic Networks of Cancer-Related Pathways

    PubMed Central

    Sedaghat, Nafiseh; Saegusa, Takumi; Randolph, Timothy; Shojaie, Ali

    2014-01-01

    Network reconstruction is an important yet challenging task in systems biology. While many methods have been recently proposed for reconstructing biological networks from diverse data types, properties of estimated networks and differences between reconstruction methods are not well understood. In this paper, we conduct a comprehensive empirical evaluation of seven existing network reconstruction methods, by comparing the estimated networks with different sparsity levels for both normal and tumor samples. The results suggest substantial heterogeneity in networks reconstructed using different reconstruction methods. Our findings also provide evidence for significant differences between networks of normal and tumor samples, even after accounting for the considerable variability in structures of networks estimated using different reconstruction methods. These differences can offer new insight into changes in mechanisms of genetic interaction associated with cancer initiation and progression. PMID:25288880

  1. Selection Shapes Transcriptional Logic and Regulatory Specialization in Genetic Networks

    PubMed Central

    Fogelmark, Karl; Peterson, Carsten; Troein, Carl

    2016-01-01

    Background Living organisms need to regulate their gene expression in response to environmental signals and internal cues. This is a computational task where genes act as logic gates that connect to form transcriptional networks, which are shaped at all scales by evolution. Large-scale mutations such as gene duplications and deletions add and remove network components, whereas smaller mutations alter the connections between them. Selection determines what mutations are accepted, but its importance for shaping the resulting networks has been debated. Methodology To investigate the effects of selection in the shaping of transcriptional networks, we derive transcriptional logic from a combinatorially powerful yet tractable model of the binding between DNA and transcription factors. By evolving the resulting networks based on their ability to function as either a simple decision system or a circadian clock, we obtain information on the regulation and logic rules encoded in functional transcriptional networks. Comparisons are made between networks evolved for different functions, as well as with structurally equivalent but non-functional (neutrally evolved) networks, and predictions are validated against the transcriptional network of E. coli. Principal Findings We find that the logic rules governing gene expression depend on the function performed by the network. Unlike the decision systems, the circadian clocks show strong cooperative binding and negative regulation, which achieves tight temporal control of gene expression. Furthermore, we find that transcription factors act preferentially as either activators or repressors, both when binding multiple sites for a single target gene and globally in the transcriptional networks. This separation into positive and negative regulators requires gene duplications, which highlights the interplay between mutation and selection in shaping the transcriptional networks. PMID:26927540

  2. A Multi-Stage Reverse Logistics Network Problem by Using Hybrid Priority-Based Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Lee, Jeong-Eun; Gen, Mitsuo; Rhee, Kyong-Gu

    Today remanufacturing problem is one of the most important problems regarding to the environmental aspects of the recovery of used products and materials. Therefore, the reverse logistics is gaining become power and great potential for winning consumers in a more competitive context in the future. This paper considers the multi-stage reverse Logistics Network Problem (m-rLNP) while minimizing the total cost, which involves reverse logistics shipping cost and fixed cost of opening the disassembly centers and processing centers. In this study, we first formulate the m-rLNP model as a three-stage logistics network model. Following for solving this problem, we propose a Genetic Algorithm pri (GA) with priority-based encoding method consisting of two stages, and introduce a new crossover operator called Weight Mapping Crossover (WMX). Additionally also a heuristic approach is applied in the 3rd stage to ship of materials from processing center to manufacturer. Finally numerical experiments with various scales of the m-rLNP models demonstrate the effectiveness and efficiency of our approach by comparing with the recent researches.

  3. A Systems Genetic Analysis of High Density Lipoprotein Metabolism and Network Preservation across Mouse Models

    PubMed Central

    Langfelder, Peter; Castellani, Lawrence W.; Zhou, Zhiqiang; Paul, Eric; Davis, Richard; Schadt, Eric E.; Lusis, Aldons J.; Horvath, Steve; Mehrabian, Margarete

    2011-01-01

    We report a systems genetics analysis of high density lipoproteins (HDL) levels in an F2 intercross between inbred strains CAST/EiJ and C57BL/6J. We previously showed that there are dramatic differences in HDL metabolism in a cross between these strains, and we now report co-expression network analysis of HDL that integrates global expression data from liver and adipose with relevant metabolic traits. Using data from a total of 293 F2 intercross mice, we constructed weighted gene co-expression networks and identified modules (subnetworks) associated with HDL and clinical traits. These were examined for genes implicated in HDL levels based on large human genome-wide associations studies (GWAS) and examined with respect to conservation between tissue and sexes in a total of 9 data sets. We identify genes that are consistently ranked high by association with HDL across the 9 data sets. We focus in particular on two genes, Wfdc2 and Hdac3, that are located in close proximity to HDL QTL peaks where causal testing indicates that they may affect HDL. Our results provide a rich resource for studies of complex metabolic interactions involving HDL. PMID:21807117

  4. Kernel-Based Aggregation of Marker-Level Genetic Association Tests Involving Copy-Number Variation

    PubMed Central

    Li, Yinglei; Breheny, Patrick

    2013-01-01

    Genetic association tests involving copy-number variants (CNVs) are complicated by the fact that CNVs span multiple markers at which measurements are taken. The power of an association test at a single marker is typically low, and it is desirable to pool information across the markers spanned by the CNV. However, CNV boundaries are not known in advance, and the best way to proceed with this pooling is unclear. In this article, we propose a kernel-based method for aggregation of marker-level tests and explore several aspects of its implementation. In addition, we explore some of the theoretical aspects of marker-level test aggregation, proposing a permutation-based approach that preserves the family-wise error rate of the testing procedure, while demonstrating that several simpler alternatives fail to do so. The empirical power of the approach is studied in a number of simulations constructed from real data involving a pharmacogenomic study of gemcitabine and compares favorably with several competing approaches.

  5. Autonomous photogrammetric network design based on changing environment genetic algorithms

    NASA Astrophysics Data System (ADS)

    Yang, Jian; Lu, Nai-Guang; Dong, Mingli

    2008-10-01

    In order to get good accuracy, designer used to consider how to place cameras. Usually, cameras placement design is a multidimensional optimal problem, so people used genetic algorithms to solve it. But genetic algorithms could result in premature or convergent problem. Sometime we get local minimum and observe vibrating phenomenon. Those will get inaccurate design. So we try to solve the problem using the changing environment genetic algorithms. The work proposes giving those species groups difference environment during difference stage to improve the property. Computer simulation result shows the acceleration in convergent speed and ability of selecting good individual. This work would be used in other application.

  6. KEGG for representation and analysis of molecular networks involving diseases and drugs.

    PubMed

    Kanehisa, Minoru; Goto, Susumu; Furumichi, Miho; Tanabe, Mao; Hirakawa, Mika

    2010-01-01

    Most human diseases are complex multi-factorial diseases resulting from the combination of various genetic and environmental factors. In the KEGG database resource (http://www.genome.jp/kegg/), diseases are viewed as perturbed states of the molecular system, and drugs as perturbants to the molecular system. Disease information is computerized in two forms: pathway maps and gene/molecule lists. The KEGG PATHWAY database contains pathway maps for the molecular systems in both normal and perturbed states. In the KEGG DISEASE database, each disease is represented by a list of known disease genes, any known environmental factors at the molecular level, diagnostic markers and therapeutic drugs, which may reflect the underlying molecular system. The KEGG DRUG database contains chemical structures and/or chemical components of all drugs in Japan, including crude drugs and TCM (Traditional Chinese Medicine) formulas, and drugs in the USA and Europe. This database also captures knowledge about two types of molecular networks: the interaction network with target molecules, metabolizing enzymes, other drugs, etc. and the chemical structure transformation network in the history of drug development. The new disease/drug information resource named KEGG MEDICUS can be used as a reference knowledge base for computational analysis of molecular networks, especially, by integrating large-scale experimental datasets.

  7. Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty

    NASA Astrophysics Data System (ADS)

    Luo, Qiankun; Wu, Jianfeng; Yang, Yun; Qian, Jiazhong; Wu, Jichun

    2016-03-01

    Optimal design of long term groundwater monitoring (LTGM) network often involves conflicting objectives and substantial uncertainty arising from insufficient hydraulic conductivity (K) data. This study develops a new multi-objective simulation-optimization model involving four objectives: minimizations of (i) the total sampling costs for monitoring contaminant plume, (ii) mass estimation error, (iii) the first moment estimation error, and (iv) the second moment estimation error of the contaminant plume, for LTGM network design problems. Then a new probabilistic Pareto genetic algorithm (PPGA) coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, is developed to search for the Pareto-optimal solutions to the multi-objective LTGM problems under uncertainty of the K-fields. The PPGA integrates the niched Pareto genetic algorithm with probabilistic Pareto sorting scheme to deal with the uncertainty of objectives caused by the uncertain K-field. Also, the elitist selection strategy, the operation library and the Pareto solution set filter are conducted to improve the diversity and reliability of Pareto-optimal solutions by the PPGA. Furthermore, the sampling strategy of noisy genetic algorithm is adopted to cope with the uncertainty of the K-fields and improve the computational efficiency of the PPGA. In particular, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology in finding Pareto-optimal sampling network designs of LTGM systems through a two-dimensional hypothetical example and a three-dimensional field application in Indiana (USA). Comprehensive analysis demonstrates that the proposed PPGA can find Pareto optimal solutions with low variability and high reliability and is a promising tool for optimizing multi-objective LTGM network designs under uncertainty.

  8. The relationship between Clinical Trial Network protocol involvement and quality of substance use disorder (SUD) treatment

    PubMed Central

    Abraham, Amanda J.; Knudsen, Hannah K.; Roman, Paul M.

    2013-01-01

    The National Institute on Drug Abuse’s Clinical Trials Network (CTN) is a practice-based research network that partners academic researchers with community based substance use disorder (SUD) treatment programs designed primarily to conduct effectiveness trials of promising interventions. A secondary goal of the CTN is to widely disseminate results of these trials and thus improve the quality of SUD treatment in the US. Drawing on data from 156 CTN programs, this study examines the association between involvement in CTN protocols and overall treatment quality measured by a comprehensive index of 35 treatment services. Negative binomial regression models show that treatment programs that participated in a greater number of CTN protocols had significantly higher levels of treatment quality, an association that held after controlling for key organizational characteristics. These findings contribute to the growing body of research on the role of practice-based research networks in promoting health care quality. PMID:24080073

  9. De novo deleterious genetic variations target a biological network centered on Aβ peptide in early-onset Alzheimer disease.

    PubMed

    Rovelet-Lecrux, A; Charbonnier, C; Wallon, D; Nicolas, G; Seaman, M N J; Pottier, C; Breusegem, S Y; Mathur, P P; Jenardhanan, P; Le Guennec, K; Mukadam, A S; Quenez, O; Coutant, S; Rousseau, S; Richard, A-C; Boland, A; Deleuze, J-F; Frebourg, T; Hannequin, D; Campion, D

    2015-09-01

    We hypothesized that de novo variants (DNV) might participate in the genetic determinism of sporadic early-onset Alzheimer disease (EOAD, onset before 65 years). We investigated 14 sporadic EOAD trios first by array-comparative genomic hybridization. Two patients carried a de novo copy number variation (CNV). We then performed whole-exome sequencing in the 12 remaining trios and identified 12 non-synonymous DNVs in six patients. The two de novo CNVs (an amyloid precursor protein (APP) duplication and a BACE2 intronic deletion) and 3/12 non-synonymous DNVs (in PSEN1, VPS35 and MARK4) targeted genes from a biological network centered on the Amyloid beta (Aβ) peptide. We showed that this a priori-defined genetic network was significantly enriched in amino acid-altering DNV, compared with the rest of the exome. The causality of the APP de novo duplication (which is the first reported one) was obvious. In addition, we provided evidence of the functional impact of the following three non-synonymous DNVs targeting this network: the novel PSEN1 variant resulted in exon 9 skipping in patient's RNA, leading to a pathogenic missense at exons 8-10 junction; the VPS35 missense variant led to partial loss of retromer function, which may impact neuronal APP trafficking and Aβ secretion; and the MARK4 multiple nucleotide variant resulted into increased Tau phosphorylation, which may trigger enhanced Aβ-induced toxicity. Despite the difficulty to recruit Alzheimer disease (AD) trios owing to age structures of the pedigrees and the genetic heterogeneity of the disease, this strategy allowed us to highlight the role of de novo pathogenic events, the putative involvement of new genes in AD genetics and the key role of Aβ network alteration in AD.

  10. A new approach to dynamic fuzzy modeling of genetic regulatory networks.

    PubMed

    Sun, Yonghui; Feng, Gang; Cao, Jinde

    2010-12-01

    In this paper, the dynamic fuzzy modeling approach is applied for modeling genetic regulatory networks from gene expression data. The parameters of the dynamic fuzzy model and the optimal number of fuzzy rules for the fuzzy gene network can be obtained via the proposed modeling approach from the measured gene expression data. One of the main features of the proposed approach is that the prior qualitative knowledge on the network structure can be easily incorporated in the proposed identification algorithm, so that the faster learning convergence of the algorithm can be achieved. Two sets of data, one the synthetic data, and the other the experimental SOS DNA repair network data with structural knowledge, have been used to validate the proposed modeling approach. It is shown that the proposed approach is effective in modeling genetic regulatory networks. PMID:21041161

  11. Involvement of the CREB5 regulatory network in colorectal cancer metastasis.

    PubMed

    Qi, Lu; Ding, Yanqing

    2014-07-01

    The signal regulatory network involved in colorectal cancer metastasis is complicated and thus the search for key control steps in the network is of great significance for unraveling colorectal cancer metastasis mechanism and finding drug-target site. Previous studies suggested that CREB5 (cAMP responsive element binding protein 5) might play key role in the metastatic signal network of colorectal cancer. Through colorectal cancer expression profile and enriching analysis of the effect of CREB5 gene expression levels on colorectal cancer molecular events, we found that these molecular events are correlated with tumor metastasis. Based on the feature that CREB5 could combine with c-Jun to form heterodimer, together with enriched binding sites for transcription factor AP-1, we identified 16 genes which were up-regulated in the CREB5 high-expression group, contained AP-1 binding sites, and participated in cancer pathway. The molecular network involving these 16 genes, in particular, CSF1R, MMP9, PDGFRB, FIGF and IL6, regulates cell migration. Therefore, CREB5 might accelerate the metastasis of colorectal cancer by regulating these five key genes.

  12. Genome-wide genetic interaction analysis of glaucoma using expert knowledge derived from human phenotype networks.

    PubMed

    Hu, Ting; Darabos, Christian; Cricco, Maria E; Kong, Emily; Moore, Jason H

    2015-01-01

    The large volume of GWAS data poses great computational challenges for analyzing genetic interactions associated with common human diseases. We propose a computational framework for characterizing epistatic interactions among large sets of genetic attributes in GWAS data. We build the human phenotype network (HPN) and focus around a disease of interest. In this study, we use the GLAUGEN glaucoma GWAS dataset and apply the HPN as a biological knowledge-based filter to prioritize genetic variants. Then, we use the statistical epistasis network (SEN) to identify a significant connected network of pairwise epistatic interactions among the prioritized SNPs. These clearly highlight the complex genetic basis of glaucoma. Furthermore, we identify key SNPs by quantifying structural network characteristics. Through functional annotation of these key SNPs using Biofilter, a software accessing multiple publicly available human genetic data sources, we find supporting biomedical evidences linking glaucoma to an array of genetic diseases, proving our concept. We conclude by suggesting hypotheses for a better understanding of the disease.

  13. Evaluation of the efficiency of artificial neural networks for genetic value prediction.

    PubMed

    Silva, G N; Tomaz, R S; Sant'Anna, I C; Carneiro, V Q; Cruz, C D; Nascimento, M

    2016-01-01

    Artificial neural networks have shown great potential when applied to breeding programs. In this study, we propose the use of artificial neural networks as a viable alternative to conventional prediction methods. We conduct a thorough evaluation of the efficiency of these networks with respect to the prediction of breeding values. Therefore, we considered eight simulated scenarios, and for the purpose of genetic value prediction, seven statistical parameters in addition to the phenotypic mean in a network designed as a multilayer perceptron. After an evaluation of different network configurations, the results demonstrated the superiority of neural networks compared to estimation procedures based on linear models, and indicated high predictive accuracy and network efficiency. PMID:27051007

  14. An Examination of Two Policy Networks Involved in Advancing Smokefree Policy Initiatives

    PubMed Central

    Moreland-Russell, Sarah; Carothers, Bobbi J.

    2015-01-01

    This study examines smokefree policy networks in two cities—Kansas City and St. Louis, Missouri—one that was successful in achieving widespread policy success, and one that was not. Descriptive social network analyses and visual network mapping were used to compare importance and contact relationships among actors involved in the smokefree policy initiatives. In Kansas City, where policy adoption was achieved, there was a higher level of connectivity among members, with network members being in contact with an average of more than five people, compared to just over two people for the St. Louis network. For both cities, despite being recognized as important, politicians were in contact with the fewest number of people. Results highlight the critical need to actively engage a variety of stakeholders when attempting city wide public health policy change. As evident by the success in smokefree policy adoption throughout Kansas City compared to St. Louis, closer linkages and continued communication among stakeholders including the media, coalitions, public health agencies, policymakers, and other partners are essential if we are to advance and broaden the impact of public health policy. Results indicate that the presence of champions, or those that play leadership roles in actively promoting policy by linking individuals and organizations, play an important role in advancing public health policy. Those working in public health should examine their level of engagement with the policy process and implement strategies for improving that engagement through relationship building and ongoing interactions with a variety of stakeholders, including policymakers. PMID:26371022

  15. An Examination of Two Policy Networks Involved in Advancing Smokefree Policy Initiatives.

    PubMed

    Moreland-Russell, Sarah; Carothers, Bobbi J

    2015-09-01

    This study examines smokefree policy networks in two cities—Kansas City and St. Louis, Missouri—one that was successful in achieving widespread policy success, and one that was not. Descriptive social network analyses and visual network mapping were used to compare importance and contact relationships among actors involved in the smokefree policy initiatives. In Kansas City, where policy adoption was achieved, there was a higher level of connectivity among members, with network members being in contact with an average of more than five people, compared to just over two people for the St. Louis network. For both cities, despite being recognized as important, politicians were in contact with the fewest number of people. Results highlight the critical need to actively engage a variety of stakeholders when attempting city wide public health policy change. As evident by the success in smokefree policy adoption throughout Kansas City compared to St. Louis, closer linkages and continued communication among stakeholders including the media, coalitions, public health agencies, policymakers, and other partners are essential if we are to advance and broaden the impact of public health policy. Results indicate that the presence of champions, or those that play leadership roles in actively promoting policy by linking individuals and organizations, play an important role in advancing public health policy. Those working in public health should examine their level of engagement with the policy process and implement strategies for improving that engagement through relationship building and ongoing interactions with a variety of stakeholders, including policymakers. PMID:26371022

  16. An Examination of Two Policy Networks Involved in Advancing Smokefree Policy Initiatives.

    PubMed

    Moreland-Russell, Sarah; Carothers, Bobbi J

    2015-09-01

    This study examines smokefree policy networks in two cities—Kansas City and St. Louis, Missouri—one that was successful in achieving widespread policy success, and one that was not. Descriptive social network analyses and visual network mapping were used to compare importance and contact relationships among actors involved in the smokefree policy initiatives. In Kansas City, where policy adoption was achieved, there was a higher level of connectivity among members, with network members being in contact with an average of more than five people, compared to just over two people for the St. Louis network. For both cities, despite being recognized as important, politicians were in contact with the fewest number of people. Results highlight the critical need to actively engage a variety of stakeholders when attempting city wide public health policy change. As evident by the success in smokefree policy adoption throughout Kansas City compared to St. Louis, closer linkages and continued communication among stakeholders including the media, coalitions, public health agencies, policymakers, and other partners are essential if we are to advance and broaden the impact of public health policy. Results indicate that the presence of champions, or those that play leadership roles in actively promoting policy by linking individuals and organizations, play an important role in advancing public health policy. Those working in public health should examine their level of engagement with the policy process and implement strategies for improving that engagement through relationship building and ongoing interactions with a variety of stakeholders, including policymakers.

  17. Application of BP Neural Network Based on Genetic Algorithm in Quantitative Analysis of Mixed GAS

    NASA Astrophysics Data System (ADS)

    Chen, Hongyan; Liu, Wenzhen; Qu, Jian; Zhang, Bing; Li, Zhibin

    Aiming at the problem of mixed gas detection in neural network and analysis on the principle of gas detection. Combining BP algorithm of genetic algorithm with hybrid gas sensors, a kind of quantitative analysis system of mixed gas is designed. The local minimum of network learning is the main reason which affects the precision of gas analysis. On the basis of the network study to improve the learning algorithms, the analyses and tests for CO, CO2 and HC compounds were tested. The results showed that the above measures effectively improve and enhance the accuracy of the neural network for gas analysis.

  18. Bayesian network reconstruction using systems genetics data: comparison of MCMC methods.

    PubMed

    Tasaki, Shinya; Sauerwine, Ben; Hoff, Bruce; Toyoshiba, Hiroyoshi; Gaiteri, Chris; Chaibub Neto, Elias

    2015-04-01

    Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis-Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover, this performance gain is strongest in networks with biologically oriented topology, which indicates that our novel samplers are suitable for inferring biological networks. The performance of MCMC samplers in this simulation framework can guide the choice of methods for network reconstruction using systems genetics data. PMID:25631319

  19. Bayesian Network Reconstruction Using Systems Genetics Data: Comparison of MCMC Methods

    PubMed Central

    Tasaki, Shinya; Sauerwine, Ben; Hoff, Bruce; Toyoshiba, Hiroyoshi; Gaiteri, Chris; Chaibub Neto, Elias

    2015-01-01

    Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis–Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover, this performance gain is strongest in networks with biologically oriented topology, which indicates that our novel samplers are suitable for inferring biological networks. The performance of MCMC samplers in this simulation framework can guide the choice of methods for network reconstruction using systems genetics data. PMID:25631319

  20. Genetic variability of transcript abundance in pig peri-mortem skeletal muscle: eQTL localized genes involved in stress response, cell death, muscle disorders and metabolism

    PubMed Central

    2011-01-01

    Background The genetics of transcript-level variation is an exciting field that has recently given rise to many studies. Genetical genomics studies have mainly focused on cell lines, blood cells or adipose tissues, from human clinical samples or mice inbred lines. Few eQTL studies have focused on animal tissues sampled from outbred populations to reflect natural genetic variation of gene expression levels in animals. In this work, we analyzed gene expression in a whole tissue, pig skeletal muscle sampled from individuals from a half sib F2 family shortly after slaughtering. Results QTL detection on transcriptome measurements was performed on a family structured population. The analysis identified 335 eQTLs affecting the expression of 272 transcripts. The ontologic annotation of these eQTLs revealed an over-representation of genes encoding proteins involved in processes that are expected to be induced during muscle development and metabolism, cell morphology, assembly and organization and also in stress response and apoptosis. A gene functional network approach was used to evidence existing biological relationships between all the genes whose expression levels are influenced by eQTLs. eQTLs localization revealed a significant clustered organization of about half the genes located on segments of chromosome 1, 2, 10, 13, 16, and 18. Finally, the combined expression and genetic approaches pointed to putative cis-drivers of gene expression programs in skeletal muscle as COQ4 (SSC1), LOC100513192 (SSC18) where both the gene transcription unit and the eQTL affecting its expression level were shown to be localized in the same genomic region. This suggests cis-causing genetic polymorphims affecting gene expression levels, with (e.g. COQ4) or without (e.g. LOC100513192) potential pleiotropic effects that affect the expression of other genes (cluster of trans-eQTLs). Conclusion Genetic analysis of transcription levels revealed dependence among molecular phenotypes as being

  1. Recent advances in the molecular genetics of hereditary retinal dystrophies with primary involvement of the macula.

    PubMed

    Weber, B H

    1998-01-01

    Hereditary dystrophies of the central retina and choroid are a heterogeneous group of disorders characterized by preferential loss of macular function and consequently loss of central and color vision. The primary causes leading to the degenerative processes are largely unknown although recent progress in human molecular genetics is most promising in providing novel insights into the basic biochemical mechanisms of these dystrophies. To date, the disease loci of more than 20 maculopathies including cone and cone-rod dystrophies have been mapped to specific chromosomal regions of which seven disease genes have already been identified. As the goals of the Human Genome Initiative approach completion, the cloning of the genes involved in the etiology of human retinopathies will be greatly simplified providing the basis for a more comprehensive understanding of retinal function and dysfunction. In addition, these advances will facilitate the identification of individuals at risk at a presymptomatic or initial stage of disease, thus creating a unique opportunity to devise novel therapeutic strategies that will primarily be aimed at an early intervention with the potential to either delay or even prevent the development of disease pathology.

  2. A Forward Genetic Screen for Molecules Involved in Pheromone-Induced Dauer Formation in Caenorhabditis elegans

    PubMed Central

    Neal, Scott J.; Park, JiSoo; DiTirro, Danielle; Yoon, Jason; Shibuya, Mayumi; Choi, Woochan; Schroeder, Frank C.; Butcher, Rebecca A.; Kim, Kyuhyung; Sengupta, Piali

    2016-01-01

    Animals must constantly assess their surroundings and integrate sensory cues to make appropriate behavioral and developmental decisions. Pheromones produced by conspecific individuals provide critical information regarding environmental conditions. Ascaroside pheromone concentration and composition are instructive in the decision of Caenorhabditis elegans to either develop into a reproductive adult or enter into the stress-resistant alternate dauer developmental stage. Pheromones are sensed by a small set of sensory neurons, and integrated with additional environmental cues, to regulate neuroendocrine signaling and dauer formation. To identify molecules required for pheromone-induced dauer formation, we performed an unbiased forward genetic screen and identified phd (pheromone response-defective dauer) mutants. Here, we describe new roles in dauer formation for previously identified neuronal molecules such as the WD40 domain protein QUI-1 and MACO-1 Macoilin, report new roles for nociceptive neurons in modulating pheromone-induced dauer formation, and identify tau tubulin kinases as new genes involved in dauer formation. Thus, phd mutants define loci required for the detection, transmission, or integration of pheromone signals in the regulation of dauer formation. PMID:26976437

  3. Genetic Polymorphisms Involved in Folate Metabolism and Maternal Risk for Down Syndrome: A Meta-Analysis

    PubMed Central

    Balduino Victorino, Daniella; de Godoy, Moacir Fernandes; Goloni-Bertollo, Eny Maria; Pavarino, Érika Cristina

    2014-01-01

    Inconclusive results of the association between genetic polymorphisms involved in folate metabolism and maternal risk for Down syndrome (DS) have been reported. Therefore, this meta-analysis was conducted. We searched electronic databases through May, 2014, for eligible studies. Pooled odds ratios with 95% confidence intervals were used to assess the strength of the association, which was estimated by fixed or random effects models. Heterogeneity among studies was evaluated using Q-test and I2 statistic. Subgroup and sensitivity analyses were also conducted. Publication bias was estimated using Begg's and Egger's tests. A total of 17 case-controls studies were included. There was evidence for an association between the MTRR c.66A>G (rs1801394) polymorphism and maternal risk for DS. In the subgroup analysis, increased maternal risk for DS was found in Caucasians. Additionally, the polymorphic heterozygote MTHFD1 1958GA genotype was associated significantly with maternal risk for DS, when we limit the analysis by studies conformed to Hardy-Weinberg equilibrium. Finally, considering MTR c.2756A>G (rs1805087), TC2 c.776C>G (rs1801198), and CBS c.844ins68, no significant associations have been found, neither in the overall analyses nor in the stratified analyses by ethnicity. In conclusion, our meta-analysis suggested that the MTRR c.66A>G (rs1801394) polymorphism and MTHFD1 c.1958G>A (rs2236225) were associated with increased maternal risk for DS. PMID:25544792

  4. PGTandMe: social networking-based genetic testing and the evolving research model.

    PubMed

    Koch, Valerie Gutmann

    2012-01-01

    The opportunity to use extensive genetic data, personal information, and family medical history for research purposes may be naturally appealing to the personal genetic testing (PGT) industry, which is already coupling direct-to-consumer (DTC) products with social networking technologies, as well as to potential industry or institutional partners. This article evaluates the transformation in research that the hybrid of PGT and social networking will bring about, and--highlighting the challenges associated with a new paradigm of "patient-driven" genomic research--focuses on the consequences of shifting the structure, locus, timing, and scope of research through genetic crowd-sourcing. This article also explores potential ethical, legal, and regulatory issues that arise from the hybrid between personal genomic research and online social networking, particularly regarding informed consent, institutional review board (IRB) oversight, and ownership/intellectual property (IP) considerations.

  5. A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

    NASA Technical Reports Server (NTRS)

    Kobayashi, Takahisa; Simon, Donald L.

    2001-01-01

    In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

  6. Searching for causal networks involving latent variables in complex traits: Application to growth, carcass, and meat quality traits in pigs.

    PubMed

    Peñagaricano, F; Valente, B D; Steibel, J P; Bates, R O; Ernst, C W; Khatib, H; Rosa, G J M

    2015-10-01

    Structural equation models (SEQM) can be used to model causal relationships between multiple variables in multivariate systems. Among the strengths of SEQM is its ability to consider causal links between latent variables. The use of latent variables allows modeling complex phenomena while reducing at the same time the dimensionality of the data. One relevant aspect in the quantitative genetics context is the possibility of correlated genetic effects influencing sets of variables under study. Under this scenario, if one aims at inferring causality among latent variables, genetic covariances act as confounders if ignored. Here we describe a methodology for assessing causal networks involving latent variables underlying complex phenotypic traits. The first step of the method consists of the construction of latent variables defined on the basis of prior knowledge and biological interest. These latent variables are jointly evaluated using confirmatory factor analysis. The estimated factor scores are then used as phenotypes for fitting a multivariate mixed model to obtain the covariance matrix of latent variables conditional on the genetic effects. Finally, causal relationships between the adjusted latent variables are evaluated using different SEQM with alternative causal specifications. We have applied this method to a data set with pigs for which several phenotypes were recorded over time. Five different latent variables were evaluated to explore causal links between growth, carcass, and meat quality traits. The measurement model, which included 5 latent variables capturing the information conveyed by 19 different phenotypic traits, showed an acceptable fit to data (e.g., χ/df = 1.3, root-mean-square error of approximation = 0.028, standardized root-mean-square residual = 0.041). Causal links between latent variables were explored after removing genetic confounders. Interestingly, we found that both growth (-0.160) and carcass traits (-0.500) have a significant

  7. Informed consent, participation in, and withdrawal from a population based cohort study involving genetic analysis

    PubMed Central

    Matsui, K; Kita, Y; Ueshima, H

    2005-01-01

    Design: Descriptive analyses. Setting and participants: The study evaluated two non-genetic subcohorts comprising 3166 people attending for a health checkup during 2002, and two genetic subcohorts comprising 2195 people who underwent a checkup during 2003. Main outcome measurements: Analysis endpoints were differences in participation rates between the non-genetic and genetic subcohorts, differences between providing non-extensive and extensive preliminary information, and changes in participation status between baseline and at 6 months. Results: Participation rates in the genetic subcohorts were 4·7–9·3% lower than those in the non-genetic subcohorts. The odds ratios (OR) of participation in genetic research were between 0·60 and 0·77, and the OR for withdrawal from the research was over 7·70; providing preliminary extensive information about genetic research reduced the withdrawal risks (OR 0·15 for all dependent variables) but worsened participation rates (OR 0·63–0·74). Conclusions: The general population responded sceptically towards genetic research. It is crucial that genetic researchers utilise an informative and educational consent process worthy of public trust. PMID:15994356

  8. Functional and genetic analysis of the colon cancer network.

    PubMed

    Emmert-Streib, Frank; de Matos Simoes, Ricardo; Glazko, Galina; McDade, Simon; Haibe-Kains, Benjamin; Holzinger, Andreas; Dehmer, Matthias; Campbell, Frederick

    2014-01-01

    Cancer is a complex disease that has proven to be difficult to understand on the single-gene level. For this reason a functional elucidation needs to take interactions among genes on a systems-level into account. In this study, we infer a colon cancer network from a large-scale gene expression data set by using the method BC3Net. We provide a structural and a functional analysis of this network and also connect its molecular interaction structure with the chromosomal locations of the genes enabling the definition of cis- and trans-interactions. Furthermore, we investigate the interaction of genes that can be found in close neighborhoods on the chromosomes to gain insight into regulatory mechanisms. To our knowledge this is the first study analyzing the genome-scale colon cancer network. PMID:25079297

  9. Genetic variants and their interactions in disease risk prediction – machine learning and network perspectives

    PubMed Central

    2013-01-01

    A central challenge in systems biology and medical genetics is to understand how interactions among genetic loci contribute to complex phenotypic traits and human diseases. While most studies have so far relied on statistical modeling and association testing procedures, machine learning and predictive modeling approaches are increasingly being applied to mining genotype-phenotype relationships, also among those associations that do not necessarily meet statistical significance at the level of individual variants, yet still contributing to the combined predictive power at the level of variant panels. Network-based analysis of genetic variants and their interaction partners is another emerging trend by which to explore how sub-network level features contribute to complex disease processes and related phenotypes. In this review, we describe the basic concepts and algorithms behind machine learning-based genetic feature selection approaches, their potential benefits and limitations in genome-wide setting, and how physical or genetic interaction networks could be used as a priori information for providing improved predictive power and mechanistic insights into the disease networks. These developments are geared toward explaining a part of the missing heritability, and when combined with individual genomic profiling, such systems medicine approaches may also provide a principled means for tailoring personalized treatment strategies in the future. PMID:23448398

  10. Parents, friends, and romantic partners: enmeshment in deviant networks and adolescent delinquency involvement.

    PubMed

    Lonardo, Robert A; Giordano, Peggy C; Longmore, Monica A; Manning, Wendy D

    2009-03-01

    Adolescent networks include parents, friends, and romantic partners, but research on the social learning mechanisms related to delinquency has not typically examined the characteristics of all three domains simultaneously. Analyses draw on data from the Toledo Adolescent Relationships Study (n = 957), and our analytic sample contains 51% male and 49% female as well as 69% white, 24% African-American, and 7% Latino respondents. Parents,' peers,' and partners' deviance are each related to respondents' delinquency, and affiliation with a greater number of deviant networks is associated with higher self-reported involvement. Analyses that consider enmeshment type indicate that those with both above average romantic partner and friend delinquency report especially high levels of self-reported involvement. In all comparisons, adolescents with deviant romantic partners are more delinquent than those youths with more prosocial partners, regardless of friends' and parents' behavior. Findings highlight the importance of capturing the adolescent's entire network of affiliations, rather than viewing these in isolation, and suggest the need for additional research on romantic partner influences on delinquent behavior and other adolescent outcomes.

  11. Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

    NASA Technical Reports Server (NTRS)

    Rajkumar, T.; Aragon, Cecilia; Bardina, Jorge; Britten, Roy

    2002-01-01

    A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

  12. Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

    NASA Technical Reports Server (NTRS)

    Kobayashi, Takahisa; Simon, Donald L.

    2002-01-01

    As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

  13. Stochasticity, Bistability and the Wisdom of Crowds: A Model for Associative Learning in Genetic Regulatory Networks

    PubMed Central

    Sorek, Matan; Balaban, Nathalie Q.; Loewenstein, Yonatan

    2013-01-01

    It is generally believed that associative memory in the brain depends on multistable synaptic dynamics, which enable the synapses to maintain their value for extended periods of time. However, multistable dynamics are not restricted to synapses. In particular, the dynamics of some genetic regulatory networks are multistable, raising the possibility that even single cells, in the absence of a nervous system, are capable of learning associations. Here we study a standard genetic regulatory network model with bistable elements and stochastic dynamics. We demonstrate that such a genetic regulatory network model is capable of learning multiple, general, overlapping associations. The capacity of the network, defined as the number of associations that can be simultaneously stored and retrieved, is proportional to the square root of the number of bistable elements in the genetic regulatory network. Moreover, we compute the capacity of a clonal population of cells, such as in a colony of bacteria or a tissue, to store associations. We show that even if the cells do not interact, the capacity of the population to store associations substantially exceeds that of a single cell and is proportional to the number of bistable elements. Thus, we show that even single cells are endowed with the computational power to learn associations, a power that is substantially enhanced when these cells form a population. PMID:23990765

  14. Genetic influences on resting-state functional networks: A twin study.

    PubMed

    Fu, Yixiao; Ma, Zhiwei; Hamilton, Christina; Liang, Zhifeng; Hou, Xiao; Ma, Xingshun; Hu, Xiaomei; He, Qian; Deng, Wei; Wang, Yingcheng; Zhao, Liansheng; Meng, Huaqing; Li, Tao; Zhang, Nanyin

    2015-10-01

    Alterations in resting-state networks (RSNs) are often associated with psychiatric and neurologic disorders. Given this critical linkage, it has been hypothesized that RSNs can potentially be used as endophenotypes for brain diseases. To validate this notion, a critical step is to show that RSNs exhibit heritability. However, the investigation of the genetic basis of RSNs has only been attempted in the default-mode network at the region-of-interest level, while the genetic control on other RSNs has not been determined yet. Here, we examined the genetic and environmental influences on eight well-characterized RSNs using a twin design. Resting-state functional magnetic resonance imaging data in 56 pairs of twins were collected. The genetic and environmental effects on each RSN were estimated by fitting the functional connectivity covariance of each voxel in the RSN to the classic ACE twin model. The data showed that although environmental effects accounted for the majority of variance in wide-spread areas, there were specific brain sites that showed significant genetic control for individual RSNs. These results suggest that part of the human brain functional connectome is shaped by genomic constraints. Importantly, this information can be useful for bridging genetic analysis and network-level assessment of brain disorders.

  15. Design and Implementation of the International Genetics and Translational Research in Transplantation Network

    PubMed Central

    2015-01-01

    Background Genetic association studies of transplantation outcomes have been hampered by small samples and highly complex multifactorial phenotypes, hindering investigations of the genetic architecture of a range of comorbidities which significantly impact graft and recipient life expectancy. We describe here the rationale and design of the International Genetics & Translational Research in Transplantation Network. The network comprises 22 studies to date, including 16494 transplant recipients and 11669 donors, of whom more than 5000 are of non-European ancestry, all of whom have existing genomewide genotype data sets. Methods We describe the rich genetic and phenotypic information available in this consortium comprising heart, kidney, liver, and lung transplant cohorts. Results We demonstrate significant power in International Genetics & Translational Research in Transplantation Network to detect main effect association signals across regions such as the MHC region as well as genomewide for transplant outcomes that span all solid organs, such as graft survival, acute rejection, new onset of diabetes after transplantation, and for delayed graft function in kidney only. Conclusions This consortium is designed and statistically powered to deliver pioneering insights into the genetic architecture of transplant-related outcomes across a range of different solid-organ transplant studies. The study design allows a spectrum of analyses to be performed including recipient-only analyses, donor-recipient HLA mismatches with focus on loss-of-function variants and nonsynonymous single nucleotide polymorphisms. PMID:26479416

  16. Genetic Evaluation of Children with Global Developmental Delay--Current Status of Network Systems in Taiwan.

    PubMed

    Foo, Yong-Lin; Chow, Julie Chi; Lai, Ming-Chi; Tsai, Wen-Hui; Tung, Li-Chen; Kuo, Mei-Chin; Lin, Shio-Jean

    2015-08-01

    This review article aims to introduce the screening and referral network of genetic evaluation for children with developmental delay in Taiwan. For these children, integrated systems provide services from the medical, educational, and social welfare sectors. All cities and counties in Taiwan have established a network for screening, detection, referral, evaluation, and intervention services. Increased awareness improves early detection and intervention. There remains a gap between supply and demand, especially with regard to financial resources and professional manpower. Genetic etiology has a major role in prenatal causes of developmental delay. A summary of reports on some related genetic disorders in the Taiwanese population is included in this review. Genetic diagnosis allows counseling with regard to recurrence risk and prevention. Networking with neonatal screening, laboratory diagnosis, genetic counseling, and orphan drugs logistics systems can provide effective treatment for patients. In Taiwan, several laboratories provide genetic tests for clinical diagnosis. Accessibility to advanced expensive tests such as gene chips or whole exome sequencing is limited because of funding problems; however, the service system in Taiwan can still operate in a relatively cost-effective manner. This experience in Taiwan may serve as a reference for other countries.

  17. Criticality Is an Emergent Property of Genetic Networks that Exhibit Evolvability

    PubMed Central

    Torres-Sosa, Christian; Huang, Sui; Aldana, Maximino

    2012-01-01

    Accumulating experimental evidence suggests that the gene regulatory networks of living organisms operate in the critical phase, namely, at the transition between ordered and chaotic dynamics. Such critical dynamics of the network permits the coexistence of robustness and flexibility which are necessary to ensure homeostatic stability (of a given phenotype) while allowing for switching between multiple phenotypes (network states) as occurs in development and in response to environmental change. However, the mechanisms through which genetic networks evolve such critical behavior have remained elusive. Here we present an evolutionary model in which criticality naturally emerges from the need to balance between the two essential components of evolvability: phenotype conservation and phenotype innovation under mutations. We simulated the Darwinian evolution of random Boolean networks that mutate gene regulatory interactions and grow by gene duplication. The mutating networks were subjected to selection for networks that both (i) preserve all the already acquired phenotypes (dynamical attractor states) and (ii) generate new ones. Our results show that this interplay between extending the phenotypic landscape (innovation) while conserving the existing phenotypes (conservation) suffices to cause the evolution of all the networks in a population towards criticality. Furthermore, the networks produced by this evolutionary process exhibit structures with hubs (global regulators) similar to the observed topology of real gene regulatory networks. Thus, dynamical criticality and certain elementary topological properties of gene regulatory networks can emerge as a byproduct of the evolvability of the phenotypic landscape. PMID:22969419

  18. Criticality is an emergent property of genetic networks that exhibit evolvability.

    PubMed

    Torres-Sosa, Christian; Huang, Sui; Aldana, Maximino

    2012-01-01

    Accumulating experimental evidence suggests that the gene regulatory networks of living organisms operate in the critical phase, namely, at the transition between ordered and chaotic dynamics. Such critical dynamics of the network permits the coexistence of robustness and flexibility which are necessary to ensure homeostatic stability (of a given phenotype) while allowing for switching between multiple phenotypes (network states) as occurs in development and in response to environmental change. However, the mechanisms through which genetic networks evolve such critical behavior have remained elusive. Here we present an evolutionary model in which criticality naturally emerges from the need to balance between the two essential components of evolvability: phenotype conservation and phenotype innovation under mutations. We simulated the Darwinian evolution of random Boolean networks that mutate gene regulatory interactions and grow by gene duplication. The mutating networks were subjected to selection for networks that both (i) preserve all the already acquired phenotypes (dynamical attractor states) and (ii) generate new ones. Our results show that this interplay between extending the phenotypic landscape (innovation) while conserving the existing phenotypes (conservation) suffices to cause the evolution of all the networks in a population towards criticality. Furthermore, the networks produced by this evolutionary process exhibit structures with hubs (global regulators) similar to the observed topology of real gene regulatory networks. Thus, dynamical criticality and certain elementary topological properties of gene regulatory networks can emerge as a byproduct of the evolvability of the phenotypic landscape.

  19. Simulating Visual Learning and Optical Illusions via a Network-Based Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Siu, Theodore; Vivar, Miguel; Shinbrot, Troy

    We present a neural network model that uses a genetic algorithm to identify spatial patterns. We show that the model both learns and reproduces common visual patterns and optical illusions. Surprisingly, we find that the illusions generated are a direct consequence of the network architecture used. We discuss the implications of our results and the insights that we gain on how humans fall for optical illusions

  20. Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks.

    PubMed

    Chen, S; Wu, Y; Luk, B L

    1999-01-01

    The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and prediction is used as an example to demonstrate the effectiveness of this hierarchical learning approach.

  1. Differential involvement of oriens/pyramidale interneurones in hippocampal network oscillations in vitro.

    PubMed

    Gloveli, Tengis; Dugladze, Tamar; Saha, Sikha; Monyer, Hannah; Heinemann, Uwe; Traub, Roger D; Whittington, Miles A; Buhl, Eberhard H

    2005-01-01

    Using whole-cell patch-clamp recordings in conjunction with post hoc anatomy we investigated the physiological properties of hippocampal stratum oriens and stratum pyramidale inhibitory interneurones, before and following the induction of pharmacologically evoked gamma frequency network oscillations. Prior to kainate-induced transient epochs of gamma activity, two distinct classes of oriens interneurones, oriens lacunosum-moleculare (O-LM) and trilaminar cells, showed prominent differences in their membrane and firing properties, as well as in the amplitude and kinetics of their excitatory postsynaptic events. In the active network both types of neurone received a phasic barrage of gamma frequency excitatory inputs but, due to their differential functional integration, showed clear differences in their output patterns. While O-LM cells fired intermittently at theta frequency, trilaminar interneurones discharged on every gamma cycle and showed a propensity to fire spike doublets. Two other classes of fast spiking interneurones, perisomatic targeting basket and bistratified cells, in the active network discharged predominantly single action potentials on every gamma cycle. Thus, within a locally excited network, O-LM cells are likely to provide a theta-frequency patterned output to distal dendritic segments, whereas basket and bistratified cells are involved in the generation of locally synchronous gamma band oscillations. The anatomy and output profile of trilaminar cells suggest they are involved in the projection of locally generated gamma rhythms to distal sites. Therefore a division of labour appears to exist whereby different frequencies and spatiotemporal properties of hippocampal rhythms are mediated by different interneurone subtypes. PMID:15486016

  2. Epidemiological and genetic clues for molecular mechanisms involved in uterine leiomyoma development and growth

    PubMed Central

    Commandeur, Arno E.; Styer, Aaron K.; Teixeira, Jose M.

    2015-01-01

    BACKGROUND Uterine leiomyomas (fibroids) are highly prevalent benign smooth muscle tumors of the uterus. In the USA, the lifetime risk for women developing uterine leiomyomas is estimated as up to 75%. Except for hysterectomy, most therapies or treatments often provide only partial or temporary relief and are not successful in every patient. There is a clear racial disparity in the disease; African-American women are estimated to be three times more likely to develop uterine leiomyomas and generally develop more severe symptoms. There is also familial clustering between first-degree relatives and twins, and multiple inherited syndromes in which fibroid development occurs. Leiomyomas have been described as clonal and hormonally regulated, but despite the healthcare burden imposed by the disease, the etiology of uterine leiomyomas remains largely unknown. The mechanisms involved in their growth are also essentially unknown, which has contributed to the slow progress in development of effective treatment options. METHODS A comprehensive PubMed search for and critical assessment of articles related to the epidemiological, biological and genetic clues for uterine leiomyoma development was performed. The individual functions of some of the best candidate genes are explained to provide more insight into their biological function and to interconnect and organize genes and pathways in one overarching figure that represents the current state of knowledge about uterine leiomyoma development and growth. RESULTS In this review, the widely recognized roles of estrogen and progesterone in uterine leiomyoma pathobiology on the basis of clinical and experimental data are presented. This is followed by fundamental aspects and concepts including the possible cellular origin of uterine fibroids. The central themes in the subsequent parts are cytogenetic aberrations in leiomyomas and the racial/ethnic disparities in uterine fibroid biology. Then, the attributes of various in vitro and

  3. Landscape attributes and life history variability shape genetic structure of trout populations in a stream network

    USGS Publications Warehouse

    Neville, H.M.; Dunham, J.B.; Peacock, M.M.

    2006-01-01

    Spatial and temporal landscape patterns have long been recognized to influence biological processes, but these processes often operate at scales that are difficult to study by conventional means. Inferences from genetic markers can overcome some of these limitations. We used a landscape genetics approach to test hypotheses concerning landscape processes influencing the demography of Lahontan cutthroat trout in a complex stream network in the Great Basin desert of the western US. Predictions were tested with population- and individual-based analyses of microsatellite DNA variation, reflecting patterns of dispersal, population stability, and local effective population sizes. Complementary genetic inferences suggested samples from migratory corridors housed a mixture of fish from tributaries, as predicted based on assumed migratory life histories in those habitats. Also as predicted, populations presumed to have greater proportions of migratory fish or from physically connected, large, or high quality habitats had higher genetic variability and reduced genetic differentiation from other populations. Populations thought to contain largely non-migratory individuals generally showed the opposite pattern, suggesting behavioral isolation. Estimated effective sizes were small, and we identified significant and severe genetic bottlenecks in several populations that were isolated, recently founded, or that inhabit streams that desiccate frequently. Overall, this work suggested that Lahontan cutthroat trout populations in stream networks are affected by a combination of landscape and metapopulation processes. Results also demonstrated that genetic patterns can reveal unexpected processes, even within a system that is well studied from a conventional ecological perspective. ?? Springer 2006.

  4. Connectivity rescues genetic diversity after a demographic bottleneck in a butterfly population network.

    PubMed

    Jangjoo, Maryam; Matter, Stephen F; Roland, Jens; Keyghobadi, Nusha

    2016-09-27

    Demographic bottlenecks that occur when populations fluctuate in size erode genetic diversity, but that diversity can be recovered through immigration. Connectivity among populations and habitat patches in the landscape enhances immigration and should in turn facilitate recovery of genetic diversity after a sudden reduction in population size. For the conservation of genetic diversity, it may therefore be particularly important to maintain connectivity in the face of factors that increase demographic instability, such as climate change. However, a direct link between connectivity and recovery of genetic diversity after a demographic bottleneck has not been clearly demonstrated in an empirical system. Here, we show that connectivity of habitat patches in the landscape contributes to the maintenance of genetic diversity after a demographic bottleneck. We were able to monitor genetic diversity in a network of populations of the alpine butterfly, Parnassius smintheus, before, during, and after a severe reduction in population size that lasted two generations. We found that allelic diversity in the network declined after the demographic bottleneck but that less allelic diversity was lost from populations occupying habitat patches with higher connectivity. Furthermore, the effect of connectivity on allelic diversity was important during the demographic recovery phase. Our results demonstrate directly the ability of connectivity to mediate the rescue of genetic diversity in a natural system. PMID:27621433

  5. Inference and Analysis of Population Structure Using Genetic Data and Network Theory.

    PubMed

    Greenbaum, Gili; Templeton, Alan R; Bar-David, Shirli

    2016-04-01

    Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition's modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free method for inference about population structure that does not entail assumption of

  6. Inference and Analysis of Population Structure Using Genetic Data and Network Theory.

    PubMed

    Greenbaum, Gili; Templeton, Alan R; Bar-David, Shirli

    2016-04-01

    Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition's modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free method for inference about population structure that does not entail assumption of

  7. Sensory-motor networks involved in speech production and motor control: an fMRI study.

    PubMed

    Behroozmand, Roozbeh; Shebek, Rachel; Hansen, Daniel R; Oya, Hiroyuki; Robin, Donald A; Howard, Matthew A; Greenlee, Jeremy D W

    2015-04-01

    Speaking is one of the most complex motor behaviors developed to facilitate human communication. The underlying neural mechanisms of speech involve sensory-motor interactions that incorporate feedback information for online monitoring and control of produced speech sounds. In the present study, we adopted an auditory feedback pitch perturbation paradigm and combined it with functional magnetic resonance imaging (fMRI) recordings in order to identify brain areas involved in speech production and motor control. Subjects underwent fMRI scanning while they produced a steady vowel sound /a/ (speaking) or listened to the playback of their own vowel production (playback). During each condition, the auditory feedback from vowel production was either normal (no perturbation) or perturbed by an upward (+600 cents) pitch-shift stimulus randomly. Analysis of BOLD responses during speaking (with and without shift) vs. rest revealed activation of a complex network including bilateral superior temporal gyrus (STG), Heschl's gyrus, precentral gyrus, supplementary motor area (SMA), Rolandic operculum, postcentral gyrus and right inferior frontal gyrus (IFG). Performance correlation analysis showed that the subjects produced compensatory vocal responses that significantly correlated with BOLD response increases in bilateral STG and left precentral gyrus. However, during playback, the activation network was limited to cortical auditory areas including bilateral STG and Heschl's gyrus. Moreover, the contrast between speaking vs. playback highlighted a distinct functional network that included bilateral precentral gyrus, SMA, IFG, postcentral gyrus and insula. These findings suggest that speech motor control involves feedback error detection in sensory (e.g. auditory) cortices that subsequently activate motor-related areas for the adjustment of speech parameters during speaking.

  8. Encore: Genetic Association Interaction Network Centrality Pipeline and Application to SLE Exome Data

    PubMed Central

    Davis, Nicholas A.; Lareau, Caleb A.; White, Bill C.; Pandey, Ahwan; Wiley, Graham; Montgomery, Courtney G.; Gaffney, Patrick M.; McKinney, B.A.

    2014-01-01

    Open source tools are needed to facilitate the construction, analysis, and visualization of gene-gene interaction networks for sequencing data. To address this need, we present Encore, an open source network analysis pipeline for GWAS and rare variant data. Encore constructs Genetic Association Interaction Networks or Epistasis Networks using two optional approaches: our previous information-theory method or a generalized linear model approach. Additionally, Encore includes multiple data filtering options, including Random Forest/Random Jungle for main effect enrichment and Evaporative Cooling and Relief-F filters for enrichment of interaction effects. Encore implements SNPrank network centrality for identifying susceptibility hubs (nodes containing a large amount of disease susceptibility information through the combination of multivariate main effects and multiple gene-gene interactions in the network), and it provides appropriate files for interactive visualization of a network using tools from our online Galaxy instance. We implemented these algorithms in C++ using OpenMP for shared-memory parallel analysis on a server or desktop. To demonstrate Encore’s utility in analysis of genetic sequencing data, we present an analysis of exome resequencing data from healthy individuals and those with Systemic Lupus Erythematous (SLE). Our results verify the importance of the previously associated SLE genes HLA-DRB and NCF2, and these two genes had the highest gene-gene interaction degrees among the susceptibility hubs. An additional 14 genes previously associated with SLE emerged in our epistasis network model of the exome data, and three novel candidate genes, ST8SIA4, CMTM4, and C2CD4B, were implicated in the model. In summary, we present a comprehensive tool for epistasis network analysis and the first such analysis of exome data from a genetic study of SLE. Software Availability: http://insilico.utulsa.edu/encore.php. PMID:23740754

  9. Encore: Genetic Association Interaction Network centrality pipeline and application to SLE exome data.

    PubMed

    Davis, Nicholas A; Lareau, Caleb A; White, Bill C; Pandey, Ahwan; Wiley, Graham; Montgomery, Courtney G; Gaffney, Patrick M; McKinney, B A

    2013-09-01

    Open source tools are needed to facilitate the construction, analysis, and visualization of gene-gene interaction networks for sequencing data. To address this need, we present Encore, an open source network analysis pipeline for genome-wide association studies and rare variant data. Encore constructs Genetic Association Interaction Networks or epistasis networks using two optional approaches: our previous information-theory method or a generalized linear model approach. Additionally, Encore includes multiple data filtering options, including Random Forest/Random Jungle for main effect enrichment and Evaporative Cooling and Relief-F filters for enrichment of interaction effects. Encore implements SNPrank network centrality for identifying susceptibility hubs (nodes containing a large amount of disease susceptibility information through the combination of multivariate main effects and multiple gene-gene interactions in the network), and it provides appropriate files for interactive visualization of a network using tools from our online Galaxy instance. We implemented these algorithms in C++ using OpenMP for shared-memory parallel analysis on a server or desktop. To demonstrate Encore's utility in analysis of genetic sequencing data, we present an analysis of exome resequencing data from healthy individuals and those with Systemic Lupus Erythematous (SLE). Our results verify the importance of the previously associated SLE genes HLA-DRB and NCF2, and these two genes had the highest gene-gene interaction degrees among the susceptibility hubs. An additional 14 genes previously associated with SLE emerged in our epistasis network model of the exome data, and three novel candidate genes, ST8SIA4, CMTM4, and C2CD4B, were implicated in the model. In summary, we present a comprehensive tool for epistasis network analysis and the first such analysis of exome data from a genetic study of SLE. PMID:23740754

  10. Environmental Moderators of Genetic Influences on Adolescent Delinquent Involvement and Victimization

    ERIC Educational Resources Information Center

    Beaver, Kevin M.

    2011-01-01

    A growing body of empirical research reveals that genetic factors account for a substantial amount of variance in measures of antisocial behaviors. At the same time, evidence is also emerging indicating that certain environmental factors moderate the effects that genetic factors have on antisocial outcomes. Despite this line of research, much…

  11. Robust dynamics in minimal hybrid models of genetic networks.

    PubMed

    Perkins, Theodore J; Wilds, Roy; Glass, Leon

    2010-11-13

    Many gene-regulatory networks necessarily display robust dynamics that are insensitive to noise and stable under evolution. We propose that a class of hybrid systems can be used to relate the structure of these networks to their dynamics and provide insight into the origin of robustness. In these systems, the genes are represented by logical functions, and the controlling transcription factor protein molecules are real variables, which are produced and destroyed. As the transcription factor concentrations cross thresholds, they control the production of other transcription factors. We discuss mathematical analysis of these systems and show how the concepts of robustness and minimality can be used to generate putative logical organizations based on observed symbolic sequences. We apply the methods to control of the cell cycle in yeast.

  12. Chain functions and scoring functions in genetic networks.

    PubMed

    Gat-Viks, I; Shamir, R

    2003-01-01

    One of the grand challenges of system biology is to reconstruct the network of regulatory control among genes and proteins. High throughput data, particularly from expression experiments, may gradually make this possible in the future. Here we address two key ingredients in any such 'reverse engineering' effort: The choice of a biologically relevant, yet restricted, set of potential regulation functions, and the appropriate score to evaluate candidate regulatory relations. We propose a set of regulation functions which we call chain functions, and argue for their ubiquity in biological networks. We analyze their complexity and show that their number is exponentially smaller than all boolean functions of the same dimension. We define two new scores: one evaluating the fitness of a candidate set of regulators of a particular gene, and the other evaluating a candidate function. Both scores use established statistical methods. Finally, we test our methods on experimental gene expression data from the yeast galactose pathway. We show the utility of using chain functions and the improved inference using our scores in comparison to several extant scores. We demonstrate that the combined use of the two scores gives an extra advantage. We expect both chain functions and the new scores to be helpful in future attempts to infer regulatory networks. PMID:12855446

  13. Exact and Heuristic Methods for Network Completion for Time-Varying Genetic Networks

    PubMed Central

    Nakajima, Natsu

    2014-01-01

    Robustness in biological networks can be regarded as an important feature of living systems. A system maintains its functions against internal and external perturbations, leading to topological changes in the network with varying delays. To understand the flexibility of biological networks, we propose a novel approach to analyze time-dependent networks, based on the framework of network completion, which aims to make the minimum amount of modifications to a given network so that the resulting network is most consistent with the observed data. We have developed a novel network completion method for time-varying networks by extending our previous method for the completion of stationary networks. In particular, we introduce a double dynamic programming technique to identify change time points and required modifications. Although this extended method allows us to guarantee the optimality of the solution, this method has relatively low computational efficiency. In order to resolve this difficulty, we developed a heuristic method for speeding up the calculation of minimum least squares errors. We demonstrate the effectiveness of our proposed methods through computational experiments using synthetic data and real microarray gene expression data. The results indicate that our methods exhibit good performance in terms of completing and inferring gene association networks with time-varying structures. PMID:24738067

  14. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    USGS Publications Warehouse

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  15. Pattern recognition in lithology classification: modeling using neural networks, self-organizing maps and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Sahoo, Sasmita; Jha, Madan K.

    2016-10-01

    Effective characterization of lithology is vital for the conceptualization of complex aquifer systems, which is a prerequisite for the development of reliable groundwater-flow and contaminant-transport models. However, such information is often limited for most groundwater basins. This study explores the usefulness and potential of a hybrid soft-computing framework; a traditional artificial neural network with gradient descent-momentum training (ANN-GDM) and a traditional genetic algorithm (GA) based ANN (ANN-GA) approach were developed and compared with a novel hybrid self-organizing map (SOM) based ANN (SOM-ANN-GA) method for the prediction of lithology at a basin scale. This framework is demonstrated through a case study involving a complex multi-layered aquifer system in India, where well-log sites were clustered on the basis of sand-layer frequencies; within each cluster, subsurface layers were reclassified into four depth classes based on the maximum drilling depth. ANN models for each depth class were developed using each of the three approaches. Of the three, the hybrid SOM-ANN-GA models were able to recognize incomplete geologic pattern more reasonably, followed by ANN-GA and ANN-GDM models. It is concluded that the hybrid soft-computing framework can serve as a promising tool for characterizing lithology in groundwater basins with missing lithologic patterns.

  16. Identification of a complex genetic network underlying Saccharomyces cerevisiae colony morphology

    PubMed Central

    Voordeckers, Karin; De Maeyer, Dries; Zande, Elisa; Vinces, Marcelo D; Meert, Wim; Cloots, Lore; Ryan, Owen; Marchal, Kathleen; Verstrepen, Kevin J

    2012-01-01

    When grown on solid substrates, different microorganisms often form colonies with very specific morphologies. Whereas the pioneers of microbiology often used colony morphology to discriminate between species and strains, the phenomenon has not received much attention recently. In this study, we use a genome-wide assay in the model yeast Saccharomyces cerevisiae to identify all genes that affect colony morphology. We show that several major signalling cascades, including the MAPK, TORC, SNF1 and RIM101 pathways play a role, indicating that morphological changes are a reaction to changing environments. Other genes that affect colony morphology are involved in protein sorting and epigenetic regulation. Interestingly, the screen reveals only few genes that are likely to play a direct role in establishing colony morphology, with one notable example being FLO11, a gene encoding a cell-surface adhesin that has already been implicated in colony morphology, biofilm formation, and invasive and pseudohyphal growth. Using a series of modified promoters for fine-tuning FLO11 expression, we confirm the central role of Flo11 and show that differences in FLO11 expression result in distinct colony morphologies. Together, our results provide a first comprehensive look at the complex genetic network that underlies the diversity in the morphologies of yeast colonies. PMID:22882838

  17. Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms.

    ERIC Educational Resources Information Center

    Chen, Hsinchun

    1995-01-01

    Presents an overview of artificial-intelligence-based inductive learning techniques and their use in information science research. Three methods are discussed: the connectionist Hopfield network; the symbolic ID3/ID5R; evolution-based genetic algorithms. The knowledge representations and algorithms of these methods are examined in the context of…

  18. Molecular evolution and population genetics of two Drosophila mettleri cytochrome P450 genes involved in host plant utilization.

    PubMed

    Bono, Jeremy M; Matzkin, Luciano M; Castrezana, Sergio; Markow, Therese A

    2008-07-01

    Understanding the genetic basis of adaptation is one of the primary goals of evolutionary biology. The evolution of xenobiotic resistance in insects has proven to be an especially suitable arena for studying the genetics of adaptation, and resistant phenotypes are known to result from both coding and regulatory changes. In this study, we examine the evolutionary history and population genetics of two Drosophila mettleri cytochrome P450 genes that are putatively involved in the detoxification of alkaloids present in two of its cactus hosts: saguaro (Carnegiea gigantea) and senita (Lophocereus schottii). Previous studies demonstrated that Cyp28A1 was highly up-regulated following exposure to rotting senita tissue while Cyp4D10 was highly up-regulated following exposure to rotting saguaro tissue. Here, we show that a subset of sites in Cyp28A1 experienced adaptive evolution specifically in the D. mettleri lineage. Moreover, neutrality tests in several populations were also consistent with a history of selection on Cyp28A1. In contrast, we did not find evidence for positive selection on Cyp4D10, although this certainly does not preclude its involvement in host plant use. A surprising result that emerged from our population genetic analyses was the presence of significant genetic differentiation between flies collected from different host plant species (saguaro and senita) at Organ Pipe National Monument, Arizona, USA. This preliminary evidence suggests that D. mettleri may have evolved into distinctive host races that specialize on different hosts, a possibility that warrants further investigation. PMID:18510584

  19. Searching Online Mendelian Inheritance in Man (OMIM) for information on genetic loci involved in human disease.

    PubMed

    Baxevanis, Andreas D

    2012-04-01

    Online Mendelian Inheritance in Man (OMIM) is a comprehensive compendium of information on human genes and genetic disorders, with a particular emphasis on the interplay between observed phenotypes and underlying genotypes. This unit focuses on the basic methodology for formulating OMIM searches and illustrates the types of information that can be retrieved from OMIM, including descriptions of clinical manifestations resulting from genetic abnormalities. This unit also provides information on additional relevant medical and molecular biology databases. A basic knowledge of OMIM should be part of the armamentarium of physicians and scientists with an interest in research on the clinical aspects of genetic disorders.

  20. Improved Cost-Base Design of Water Distribution Networks using Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Moradzadeh Azar, Foad; Abghari, Hirad; Taghi Alami, Mohammad; Weijs, Steven

    2010-05-01

    Population growth and progressive extension of urbanization in different places of Iran cause an increasing demand for primary needs. The water, this vital liquid is the most important natural need for human life. Providing this natural need is requires the design and construction of water distribution networks, that incur enormous costs on the country's budget. Any reduction in these costs enable more people from society to access extreme profit least cost. Therefore, investment of Municipal councils need to maximize benefits or minimize expenditures. To achieve this purpose, the engineering design depends on the cost optimization techniques. This paper, presents optimization models based on genetic algorithm(GA) to find out the minimum design cost Mahabad City's (North West, Iran) water distribution network. By designing two models and comparing the resulting costs, the abilities of GA were determined. the GA based model could find optimum pipe diameters to reduce the design costs of network. Results show that the water distribution network design using Genetic Algorithm could lead to reduction of at least 7% in project costs in comparison to the classic model. Keywords: Genetic Algorithm, Optimum Design of Water Distribution Network, Mahabad City, Iran.

  1. Occipital and occipital "plus" epilepsies: A study of involved epileptogenic networks through SEEG quantification.

    PubMed

    Marchi, Angela; Bonini, Francesca; Lagarde, Stanislas; McGonigal, Aileen; Gavaret, Martine; Scavarda, Didier; Carron, Romain; Aubert, Sandrine; Villeneuve, Nathalie; Médina Villalon, Samuel; Bénar, Christian; Trebuchon, Agnes; Bartolomei, Fabrice

    2016-09-01

    Compared with temporal or frontal lobe epilepsies, the occipital lobe epilepsies (OLE) remain poorly characterized. In this study, we aimed at classifying the ictal networks involving OLE and investigated clinical features of the OLE network subtypes. We studied 194 seizures from 29 consecutive patients presenting with OLE and investigated by stereoelectroencephalography (SEEG). Epileptogenicity of occipital and extraoccipital regions was quantified according to the 'epileptogenicity index' (EI) method. We found that 79% of patients showed widespread epileptogenic zone organization, involving parietal or temporal regions in addition to the occipital lobe. Two main groups of epileptogenic zone organization within occipital lobe seizures were identified: a pure occipital group and an occipital "plus" group, the latter including two further subgroups, occipitotemporal and occipitoparietal. In 29% of patients, the epileptogenic zone was found to have a bilateral organization. The most epileptogenic structure was the fusiform gyrus (mean EI: 0.53). Surgery was proposed in 18/29 patients, leading to seizure freedom in 55% (Engel Class I). Results suggest that, in patient candidates for surgery, the majority of cases are characterized by complex organization of the EZ, corresponding to the occipital plus group. PMID:27454330

  2. Occipital and occipital "plus" epilepsies: A study of involved epileptogenic networks through SEEG quantification.

    PubMed

    Marchi, Angela; Bonini, Francesca; Lagarde, Stanislas; McGonigal, Aileen; Gavaret, Martine; Scavarda, Didier; Carron, Romain; Aubert, Sandrine; Villeneuve, Nathalie; Médina Villalon, Samuel; Bénar, Christian; Trebuchon, Agnes; Bartolomei, Fabrice

    2016-09-01

    Compared with temporal or frontal lobe epilepsies, the occipital lobe epilepsies (OLE) remain poorly characterized. In this study, we aimed at classifying the ictal networks involving OLE and investigated clinical features of the OLE network subtypes. We studied 194 seizures from 29 consecutive patients presenting with OLE and investigated by stereoelectroencephalography (SEEG). Epileptogenicity of occipital and extraoccipital regions was quantified according to the 'epileptogenicity index' (EI) method. We found that 79% of patients showed widespread epileptogenic zone organization, involving parietal or temporal regions in addition to the occipital lobe. Two main groups of epileptogenic zone organization within occipital lobe seizures were identified: a pure occipital group and an occipital "plus" group, the latter including two further subgroups, occipitotemporal and occipitoparietal. In 29% of patients, the epileptogenic zone was found to have a bilateral organization. The most epileptogenic structure was the fusiform gyrus (mean EI: 0.53). Surgery was proposed in 18/29 patients, leading to seizure freedom in 55% (Engel Class I). Results suggest that, in patient candidates for surgery, the majority of cases are characterized by complex organization of the EZ, corresponding to the occipital plus group.

  3. Mechanisms and neuronal networks involved in reactive and proactive cognitive control of interference in working memory.

    PubMed

    Irlbacher, Kerstin; Kraft, Antje; Kehrer, Stefanie; Brandt, Stephan A

    2014-10-01

    Cognitive control can be reactive or proactive in nature. Reactive control mechanisms, which support the resolution of interference, start after its onset. Conversely, proactive control involves the anticipation and prevention of interference prior to its occurrence. The interrelation of both types of cognitive control is currently under debate: Are they mediated by different neuronal networks? Or are there neuronal structures that have the potential to act in a proactive as well as in a reactive manner? This review illustrates the way in which integrating knowledge gathered from behavioral studies, functional imaging, and human electroencephalography proves useful in answering these questions. We focus on studies that investigate interference resolution at the level of working memory representations. In summary, different mechanisms are instrumental in supporting reactive and proactive control. Distinct neuronal networks are involved, though some brain regions, especially pre-SMA, possess functions that are relevant to both control modes. Therefore, activation of these brain areas could be observed in reactive, as well as proactive control, but at different times during information processing.

  4. Scale invariance analysis for genetic networks applying homogeneity.

    PubMed

    Bernuau, Emmanuel; Efimov, Denis; Perruquetti, Wilfrid

    2016-05-01

    Scalability is a property describing the change of the trajectory of a dynamical system under a scaling of the input stimulus and of the initial conditions. Particular cases of scalability include the scale invariance and fold change detection (when the scaling of the input does not influence the system output). In the present paper it is shown that homogeneous systems have this scalability property while locally homogeneous systems approximately possess this property. These facts are used for detecting scale invariance or approximate scalability (far from a steady state) in several biological systems. The results are illustrated by various regulatory networks. PMID:26304616

  5. Image feature analysis for classification of microcalcifications in digital mammography: neural networks and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Wu, Chris Y.; Tsujii, Osamu; Freedman, Matthew T.; Mun, Seong K.

    1997-04-01

    We have developed an image feature-based algorithm to classify microcalcifications associated with benign and malignant processes in digital mammograms for the diagnosis of breast cancer. The feature-based algorithm is an alternative approach to image based method for classification of microcalcifications in digital mammograms. Microcalcifications can be characterized by a number of quantitative variables describing the underling key features of a suspicious region such as the size, shape, and number of microcalcifications in a cluster. These features are calculated by an automated extraction scheme for each of the selected regions. The features are then used as input to a backpropagation neural network to make a decision regarding the probability of malignancy of a selected region. The initial selection of image features set is a rough estimation that may include redundant and non-discriminant features. A genetic algorithm is employed to select an optimal image feature set from the initial feature set and select an optimized structure of the neural network for the optimal input features. The performance of neural network is compared with that of radiologists in classifying the clusters of microcalcifications. Two set of mammogram cases are used in this study. The first set is from the digital mammography database from the Mammographic Image Analysis Society (MIAS). The second set is from cases collected at Georgetown University Medical Center (GUMC). The diagnostic truth of the cases have been verified by biopsy. The performance of the neural network system is evaluated by ROC analysis. The system of neural network and genetic algorithms improves performance of our previous TRBF neural network. The neural network system was able to classify benign and malignant microcalcifications at a level favorably compared to experienced radiologists. The use of the neural network system can be used to help radiologists reducing the number biopsies in clinical applications

  6. Obesity is marked by distinct functional connectivity in brain networks involved in food reward and salience.

    PubMed

    Wijngaarden, M A; Veer, I M; Rombouts, S A R B; van Buchem, M A; Willems van Dijk, K; Pijl, H; van der Grond, J

    2015-01-01

    We hypothesized that brain circuits involved in reward and salience respond differently to fasting in obese versus lean individuals. We compared functional connectivity networks related to food reward and saliency after an overnight fast (baseline) and after a prolonged fast of 48 h in lean versus obese subjects. We included 13 obese (2 males, 11 females, BMI 35.4 ± 1.2 kg/m(2), age 31 ± 3 years) and 11 lean subjects (2 males, 9 females, BMI 23.2 ± 0.5 kg/m(2), age 28 ± 3 years). Resting-state functional magnetic resonance imaging scans were made after an overnight fast (baseline) and after a prolonged 48 h fast. Functional connectivity of the amygdala, hypothalamus and posterior cingulate cortex (default-mode) networks was assessed using seed-based correlations. At baseline, we found a stronger connectivity between hypothalamus and left insula in the obese subjects. This effect diminished upon the prolonged fast. After prolonged fasting, connectivity of the hypothalamus with the dorsal anterior cingulate cortex (dACC) increased in lean subjects and decreased in obese subjects. Amygdala connectivity with the ventromedial prefrontal cortex was stronger in lean subjects at baseline, which did not change upon the prolonged fast. No differences in posterior cingulate cortex connectivity were observed. In conclusion, obesity is marked by alterations in functional connectivity networks involved in food reward and salience. Prolonged fasting differentially affected hypothalamic connections with the dACC and the insula between obese and lean subjects. Our data support the idea that food reward and nutrient deprivation are differently perceived and/or processed in obesity.

  7. Disease-aging network reveals significant roles of aging genes in connecting genetic diseases.

    PubMed

    Wang, Jiguang; Zhang, Shihua; Wang, Yong; Chen, Luonan; Zhang, Xiang-Sun

    2009-09-01

    One of the challenging problems in biology and medicine is exploring the underlying mechanisms of genetic diseases. Recent studies suggest that the relationship between genetic diseases and the aging process is important in understanding the molecular mechanisms of complex diseases. Although some intricate associations have been investigated for a long time, the studies are still in their early stages. In this paper, we construct a human disease-aging network to study the relationship among aging genes and genetic disease genes. Specifically, we integrate human protein-protein interactions (PPIs), disease-gene associations, aging-gene associations, and physiological system-based genetic disease classification information in a single graph-theoretic framework and find that (1) human disease genes are much closer to aging genes than expected by chance; and (2) diseases can be categorized into two types according to their relationships with aging. Type I diseases have their genes significantly close to aging genes, while type II diseases do not. Furthermore, we examine the topological characters of the disease-aging network from a systems perspective. Theoretical results reveal that the genes of type I diseases are in a central position of a PPI network while type II are not; (3) more importantly, we define an asymmetric closeness based on the PPI network to describe relationships between diseases, and find that aging genes make a significant contribution to associations among diseases, especially among type I diseases. In conclusion, the network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases.

  8. Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism.

    PubMed

    Corominas, Roser; Yang, Xinping; Lin, Guan Ning; Kang, Shuli; Shen, Yun; Ghamsari, Lila; Broly, Martin; Rodriguez, Maria; Tam, Stanley; Trigg, Shelly A; Fan, Changyu; Yi, Song; Tasan, Murat; Lemmens, Irma; Kuang, Xingyan; Zhao, Nan; Malhotra, Dheeraj; Michaelson, Jacob J; Vacic, Vladimir; Calderwood, Michael A; Roth, Frederick P; Tavernier, Jan; Horvath, Steve; Salehi-Ashtiani, Kourosh; Korkin, Dmitry; Sebat, Jonathan; Hill, David E; Hao, Tong; Vidal, Marc; Iakoucheva, Lilia M

    2014-04-11

    Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases.

  9. Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism

    PubMed Central

    Corominas, Roser; Yang, Xinping; Lin, Guan Ning; Kang, Shuli; Shen, Yun; Ghamsari, Lila; Broly, Martin; Rodriguez, Maria; Tam, Stanley; Trigg, Shelly A.; Fan, Changyu; Yi, Song; Tasan, Murat; Lemmens, Irma; Kuang, Xingyan; Zhao, Nan; Malhotra, Dheeraj; Michaelson, Jacob J.; Vacic, Vladimir; Calderwood, Michael A.; Roth, Frederick P.; Tavernier, Jan; Horvath, Steve; Salehi-Ashtiani, Kourosh; Korkin, Dmitry; Sebat, Jonathan; Hill, David E.; Hao, Tong; Vidal, Marc; Iakoucheva, Lilia M.

    2014-01-01

    Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases. PMID:24722188

  10. Magnetoencephalography Reveals a Widespread Increase in Network Connectivity in Idiopathic/Genetic Generalized Epilepsy.

    PubMed

    Elshahabi, Adham; Klamer, Silke; Sahib, Ashish Kaul; Lerche, Holger; Braun, Christoph; Focke, Niels K

    2015-01-01

    Idiopathic/genetic generalized epilepsy (IGE/GGE) is characterized by seizures, which start and rapidly engage widely distributed networks, and result in symptoms such as absences, generalized myoclonic and primary generalized tonic-clonic seizures. Although routine magnetic resonance imaging is apparently normal, many studies have reported structural alterations in IGE/GGE patients using diffusion tensor imaging and voxel-based morphometry. Changes have also been reported in functional networks during generalized spike wave discharges. However, network function in the resting-state without epileptiforme discharges has been less well studied. We hypothesize that resting-state networks are more representative of the underlying pathophysiology and abnormal network synchrony. We studied functional network connectivity derived from whole-brain magnetoencephalography recordings in thirteen IGE/GGE and nineteen healthy controls. Using graph theoretical network analysis, we found a widespread increase in connectivity in patients compared to controls. These changes were most pronounced in the motor network, the mesio-frontal and temporal cortex. We did not, however, find any significant difference between the normalized clustering coefficients, indicating preserved gross network architecture. Our findings suggest that increased resting state connectivity could be an important factor for seizure spread and/or generation in IGE/GGE, and could serve as a biomarker for the disease.

  11. Magnetoencephalography Reveals a Widespread Increase in Network Connectivity in Idiopathic/Genetic Generalized Epilepsy

    PubMed Central

    Elshahabi, Adham; Klamer, Silke; Sahib, Ashish Kaul; Lerche, Holger; Braun, Christoph; Focke, Niels K.

    2015-01-01

    Idiopathic/genetic generalized epilepsy (IGE/GGE) is characterized by seizures, which start and rapidly engage widely distributed networks, and result in symptoms such as absences, generalized myoclonic and primary generalized tonic-clonic seizures. Although routine magnetic resonance imaging is apparently normal, many studies have reported structural alterations in IGE/GGE patients using diffusion tensor imaging and voxel-based morphometry. Changes have also been reported in functional networks during generalized spike wave discharges. However, network function in the resting-state without epileptiforme discharges has been less well studied. We hypothesize that resting-state networks are more representative of the underlying pathophysiology and abnormal network synchrony. We studied functional network connectivity derived from whole-brain magnetoencephalography recordings in thirteen IGE/GGE and nineteen healthy controls. Using graph theoretical network analysis, we found a widespread increase in connectivity in patients compared to controls. These changes were most pronounced in the motor network, the mesio-frontal and temporal cortex. We did not, however, find any significant difference between the normalized clustering coefficients, indicating preserved gross network architecture. Our findings suggest that increased resting state connectivity could be an important factor for seizure spread and/or generation in IGE/GGE, and could serve as a biomarker for the disease. PMID:26368933

  12. An approach for in vitro genetic networks assembly

    NASA Astrophysics Data System (ADS)

    Noireaux, Vincent; Bar-Ziv, Roy; Libchaber, Albert

    2004-03-01

    A cell-free expression extract has been used to assemble genetic circuits in vitro. The extract, which does not contained endogenous DNA and RNA, is used as a battery to carry out transcription and translation of genes inserted into plasmids. We engineered transcriptional activation and repression cascades, in which the protein product of each stage is the input required to drive or block the following stage. Although we can find regions of linear response for single stages, cascading to subsequent stages requires working in non-linear regimes. Substantial time delays and dramatic decreases in output production are incurred with each additional stage, due to a bottleneck at the translation machinery. Faster turnover of RNA message can relieve competition between genes and stabilize output against variations in input and parameters.

  13. M-matrix-based stability conditions for genetic regulatory networks with time-varying delays and noise perturbations.

    PubMed

    Tian, Li-Ping; Shi, Zhong-Ke; Liu, Li-Zhi; Wu, Fang-Xiang

    2013-10-01

    Stability is essential for designing and controlling any dynamic systems. Recently, the stability of genetic regulatory networks has been widely studied by employing linear matrix inequality (LMI) approach, which results in checking the existence of feasible solutions to high-dimensional LMIs. In the previous study, the authors present several stability conditions for genetic regulatory networks with time-varying delays, based on M-matrix theory and using the non-smooth Lyapunov function, which results in determining whether a low-dimensional matrix is a non-singular M-matrix. However, the previous approach cannot be applied to analyse the stability of genetic regulatory networks with noise perturbations. Here, the authors design a smooth Lyapunov function quadratic in state variables and employ M-matrix theory to derive new stability conditions for genetic regulatory networks with time-varying delays. Theoretically, these conditions are less conservative than existing ones in some genetic regulatory networks. Then the results are extended to genetic regulatory networks with time-varying delays and noise perturbations. For genetic regulatory networks with n genes and n proteins, the derived conditions are to check if an n × n matrix is a non-singular M-matrix. To further present the new theories proposed in this study, three example regulatory networks are analysed.

  14. From simple to complex oscillatory behavior in metabolic and genetic control networks

    NASA Astrophysics Data System (ADS)

    Goldbeter, Albert; Gonze, Didier; Houart, Gérald; Leloup, Jean-Christophe; Halloy, José; Dupont, Geneviève

    2001-03-01

    We present an overview of mechanisms responsible for simple or complex oscillatory behavior in metabolic and genetic control networks. Besides simple periodic behavior corresponding to the evolution toward a limit cycle we consider complex modes of oscillatory behavior such as complex periodic oscillations of the bursting type and chaos. Multiple attractors are also discussed, e.g., the coexistence between a stable steady state and a stable limit cycle (hard excitation), or the coexistence between two simultaneously stable limit cycles (birhythmicity). We discuss mechanisms responsible for the transition from simple to complex oscillatory behavior by means of a number of models serving as selected examples. The models were originally proposed to account for simple periodic oscillations observed experimentally at the cellular level in a variety of biological systems. In a second stage, these models were modified to allow for complex oscillatory phenomena such as bursting, birhythmicity, or chaos. We consider successively (1) models based on enzyme regulation, proposed for glycolytic oscillations and for the control of successive phases of the cell cycle, respectively; (2) a model for intracellular Ca2+ oscillations based on transport regulation; (3) a model for oscillations of cyclic AMP based on receptor desensitization in Dictyostelium cells; and (4) a model based on genetic regulation for circadian rhythms in Drosophila. Two main classes of mechanism leading from simple to complex oscillatory behavior are identified, namely (i) the interplay between two endogenous oscillatory mechanisms, which can take multiple forms, overt or more subtle, depending on whether the two oscillators each involve their own regulatory feedback loop or share a common feedback loop while differing by some related process, and (ii) self-modulation of the oscillator through feedback from the system's output on one of the parameters controlling oscillatory behavior. However, the latter

  15. On Path Attractors, Stochastic Bifurcation and Dephasing In Genetic Networks

    NASA Astrophysics Data System (ADS)

    Potoyan, Davit

    2015-03-01

    Gene regulatory networks are driven stochastic systems with the noise having two distinct components due to the to birth and death of metabolite molecules and dichotomous nature of gene state switching. Presence of dichotomous gene noise alone has the capacity to significantly perturb the optimal transition paths and steady state probability distributions compared to the macroscopic models and their weak noise approximations. Most importantly dichotomous gene noise can also lead to multimodal distributions due to stochastic bifurcation of the underlying nonlinear dynamical system, which underlies the mechanism of formation of population heterogeneity. In this note we derive approximate path based expression of the time dependent probability of gene circuits which enables deeper exploration of the role of gene noise in formation of epigenetic states and dephasing-like phenomena.

  16. An integrated approach to characterize genetic interaction networks in yeast metabolism

    PubMed Central

    Szappanos, Balázs; Kovács, Károly; Szamecz, Béla; Honti, Frantisek; Costanzo, Michael; Baryshnikova, Anastasia; Gelius-Dietrich, Gabriel; Lercher, Martin J.; Jelasity, Márk; Myers, Chad L.; Andrews, Brenda J.; Boone, Charles; Oliver, Stephen G.; Pál, Csaba; Papp, Balázs

    2011-01-01

    Intense experimental and theoretical efforts have been made to globally map genetic interactions, yet we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we: i) quantitatively measure genetic interactions between ~185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii) superpose the data on a detailed systems biology model of metabolism, and iii) introduce a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigate the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy, and gene dispensability. Last, we demonstrate the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments. PMID:21623372

  17. Genetic variation in genes involved in hormones, inflammation and energetic factors and breast cancer risk in an admixed population.

    PubMed

    Slattery, Martha L; John, Esther M; Torres-Mejia, Gabriela; Lundgreen, Abbie; Herrick, Jennifer S; Baumgartner, Kathy B; Hines, Lisa M; Stern, Mariana C; Wolff, Roger K

    2012-08-01

    Breast cancer incidence rates are characterized by unique racial and ethnic differences. Native American ancestry has been associated with reduced breast cancer risk. We explore the biological basis of disparities in breast cancer risk in Hispanic and non-Hispanic white women by evaluating genetic variation in genes involved in inflammation, insulin and energy homeostasis in conjunction with genetic ancestry. Hispanic (2111 cases, 2597 controls) and non-Hispanic white (1481 cases, 1586 controls) women enrolled in the 4-Corner's Breast Cancer Study, the Mexico Breast Cancer Study and the San Francisco Bay Area Breast Cancer Study were included. Genetic admixture was determined from 104 ancestral informative markers that discriminate between European and Native American ancestry. Twenty-one genes in the CHIEF candidate pathway were evaluated. Higher Native American ancestry was associated with reduced risk of breast cancer (odds ratio = 0.79, 95% confidence interval 0.65, 0.95) but was limited to postmenopausal women (odds ratio = 0.66, 95% confidence interval 0.52, 0.85). After adjusting for genetic ancestry and multiple comparisons, four genes were significantly associated with breast cancer risk, NFκB1, NFκB1A, PTEN and STK11. Within admixture strata, breast cancer risk among women with low Native American ancestry was associated with IkBKB, NFκB1, PTEN and RPS6KA2, whereas among women with high Native American ancestry, breast cancer risk was associated with IkBKB, mTOR, PDK2, PRKAA1, RPS6KA2 and TSC1. Higher Native American ancestry was associated with reduced breast cancer risk. Breast cancer risk differed by genetic ancestry along with genetic variation in genes involved in inflammation, insulin, and energy homeostasis. PMID:22562547

  18. Genetic Adaptation to Climate in White Spruce Involves Small to Moderate Allele Frequency Shifts in Functionally Diverse Genes

    PubMed Central

    Hornoy, Benjamin; Pavy, Nathalie; Gérardi, Sébastien; Beaulieu, Jean; Bousquet, Jean

    2015-01-01

    Understanding the genetic basis of adaptation to climate is of paramount importance for preserving and managing genetic diversity in plants in a context of climate change. Yet, this objective has been addressed mainly in short-lived model species. Thus, expanding knowledge to nonmodel species with contrasting life histories, such as forest trees, appears necessary. To uncover the genetic basis of adaptation to climate in the widely distributed boreal conifer white spruce (Picea glauca), an environmental association study was conducted using 11,085 single nucleotide polymorphisms representing 7,819 genes, that is, approximately a quarter of the transcriptome. Linear and quadratic regressions controlling for isolation-by-distance, and the Random Forest algorithm, identified several dozen genes putatively under selection, among which 43 showed strongest signals along temperature and precipitation gradients. Most of them were related to temperature. Small to moderate shifts in allele frequencies were observed. Genes involved encompassed a wide variety of functions and processes, some of them being likely important for plant survival under biotic and abiotic environmental stresses according to expression data. Literature mining and sequence comparison also highlighted conserved sequences and functions with angiosperm homologs. Our results are consistent with theoretical predictions that local adaptation involves genes with small frequency shifts when selection is recent and gene flow among populations is high. Accordingly, genetic adaptation to climate in P. glauca appears to be complex, involving many independent and interacting gene functions, biochemical pathways, and processes. From an applied perspective, these results shall lead to specific functional/association studies in conifers and to the development of markers useful for the conservation of genetic resources. PMID:26560341

  19. An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.

    PubMed

    Yoon, Yourim; Kim, Yong-Hyuk

    2013-10-01

    Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.

  20. The effect of network topology on the stability of discrete state models of genetic control

    PubMed Central

    Pomerance, Andrew; Ott, Edward; Girvan, Michelle; Losert, Wolfgang

    2009-01-01

    Boolean networks have been proposed as potentially useful models for genetic control. An important aspect of these networks is the stability of their dynamics in response to small perturbations. Previous approaches to stability have assumed uncorrelated random network structure. Real gene networks typically have nontrivial topology significantly different from the random network paradigm. To address such situations, we present a general method for determining the stability of large Boolean networks of any specified network topology and predicting their steady-state behavior in response to small perturbations. Additionally, we generalize to the case where individual genes have a distribution of “expression biases,” and we consider a nonsynchronous update, as well as extension of our method to non-Boolean models in which there are more than two possible gene states. We find that stability is governed by the maximum eigenvalue of a modified adjacency matrix, and we test this result by comparison with numerical simulations. We also discuss the possible application of our work to experimentally inferred gene networks. PMID:19416903

  1. The genetic diversity of triticale genotypes involved in Polish breeding programs.

    PubMed

    Niedziela, Agnieszka; Orłowska, Renata; Machczyńska, Joanna; Bednarek, Piotr T

    2016-01-01

    Genetic diversity analysis of triticale populations is useful for breeding programs, as it helps to select appropriate genetic material for classifying the parental lines, heterotic groups and predicting hybrid performance. In our study 232 breeding forms were analyzed using diversity arrays technology markers. Principal coordinate analysis followed by model-based Bayesian analysis of population structure revealed the presence of weak data structuring with three groups of data. In the first group, 17 spring and 17 winter forms were clustered. The second and the third groups were represented by 101 and 26 winter forms, respectively. Polymorphic information content values, as well as Shannon's Information Index, were higher for the first (0.319) and second (0.309) than for third (0.234) group. AMOVA analysis demonstrated a higher level of within variation (86 %) than among populations (14 %). This study provides the basic information on the presence of structure within a genetic pool of triticale breeding forms. PMID:27066368

  2. Using a genetic network to parameterize a landscape resistance surface for fishers, Martes pennanti.

    PubMed

    Garroway, Colin J; Bowman, Jeff; Wilson, Paul J

    2011-10-01

    Knowledge of dispersal-related gene flow is important for addressing many basic and applied questions in ecology and evolution. We used landscape genetics to understand the recovery of a recently expanded population of fishers (Martes pennanti) in Ontario, Canada. An important focus of landscape genetics is modelling the effects of landscape features on gene flow. Most often resistance surfaces in landscape genetic studies are built a priori based upon nongenetic field data or expert opinion. The resistance surface that best fits genetic data is then selected and interpreted. Given inherent biases in using expert opinion or movement data to model gene flow, we sought an alternative approach. We used estimates of conditional genetic distance derived from a network of genetic connectivity to parameterize landscape resistance and build a final resistance surface based upon information-theoretic model selection and multi-model averaging. We sampled 657 fishers from 31 landscapes, genotyped them at 16 microsatellite loci, and modelled the effects of snow depth, road density, river density, and coniferous forest on gene flow. Our final model suggested that road density, river density, and snow depth impeded gene flow during the fisher population expansion demonstrating that both human impacts and seasonal habitat variation affect gene flow for fishers. Our approach to building landscape genetic resistance surfaces mitigates many of the problems and caveats associated with using either nongenetic field data or expert opinion to derive resistance surfaces. PMID:21883589

  3. A Genetic Algorithm for the Bi-Level Topological Design of Local Area Networks

    PubMed Central

    Camacho-Vallejo, José-Fernando; Mar-Ortiz, Julio; López-Ramos, Francisco; Rodríguez, Ricardo Pedraza

    2015-01-01

    Local access networks (LAN) are commonly used as communication infrastructures which meet the demand of a set of users in the local environment. Usually these networks consist of several LAN segments connected by bridges. The topological LAN design bi-level problem consists on assigning users to clusters and the union of clusters by bridges in order to obtain a minimum response time network with minimum connection cost. Therefore, the decision of optimally assigning users to clusters will be made by the leader and the follower will make the decision of connecting all the clusters while forming a spanning tree. In this paper, we propose a genetic algorithm for solving the bi-level topological design of a Local Access Network. Our solution method considers the Stackelberg equilibrium to solve the bi-level problem. The Stackelberg-Genetic algorithm procedure deals with the fact that the follower’s problem cannot be optimally solved in a straightforward manner. The computational results obtained from two different sets of instances show that the performance of the developed algorithm is efficient and that it is more suitable for solving the bi-level problem than a previous Nash-Genetic approach. PMID:26102502

  4. Optimization of cocoa butter analog synthesis variables using neural networks and genetic algorithm.

    PubMed

    Shekarchizadeh, Hajar; Tikani, Reza; Kadivar, Mahdi

    2014-09-01

    Cocoa butter analog was prepared from camel hump fat and tristearin by enzymatic interesterification in supercritical carbon dioxide (SC-CO2) using immobilized Thermomyces lanuginosus lipase (Lipozyme TL IM) as a biocatalyst. Optimal process conditions were determined using neural networks and genetic algorithm optimization. Response surfaces methodology was used to design the experiments to collect data for the neural network modelling. A general regression neural network model was developed to predict the response of triacylglycerol (TAG) distribution of cocoa butter analog from the process pressure, temperature, tristearin/camel hump fat ratio, water content, and incubation time. A genetic algorithm was used to search for a combination of the process variables for production of most similar cocoa butter analog to the corresponding cocoa butter. The combinations of the process variables during genetic algorithm optimization were evaluated using the neural network model. The pressure of 10 MPa; temperature of 40 °C; SSS/CHF ratio of 0.6:1; water content of 13 % (w/w); and incubation time of 4.5 h were found to be the optimum conditions to achieve the most similar cocoa butter analog to the corresponding cocoa butter. PMID:25190869

  5. Optimization of cocoa butter analog synthesis variables using neural networks and genetic algorithm.

    PubMed

    Shekarchizadeh, Hajar; Tikani, Reza; Kadivar, Mahdi

    2014-09-01

    Cocoa butter analog was prepared from camel hump fat and tristearin by enzymatic interesterification in supercritical carbon dioxide (SC-CO2) using immobilized Thermomyces lanuginosus lipase (Lipozyme TL IM) as a biocatalyst. Optimal process conditions were determined using neural networks and genetic algorithm optimization. Response surfaces methodology was used to design the experiments to collect data for the neural network modelling. A general regression neural network model was developed to predict the response of triacylglycerol (TAG) distribution of cocoa butter analog from the process pressure, temperature, tristearin/camel hump fat ratio, water content, and incubation time. A genetic algorithm was used to search for a combination of the process variables for production of most similar cocoa butter analog to the corresponding cocoa butter. The combinations of the process variables during genetic algorithm optimization were evaluated using the neural network model. The pressure of 10 MPa; temperature of 40 °C; SSS/CHF ratio of 0.6:1; water content of 13 % (w/w); and incubation time of 4.5 h were found to be the optimum conditions to achieve the most similar cocoa butter analog to the corresponding cocoa butter.

  6. A Genetic Algorithm for the Bi-Level Topological Design of Local Area Networks.

    PubMed

    Camacho-Vallejo, José-Fernando; Mar-Ortiz, Julio; López-Ramos, Francisco; Rodríguez, Ricardo Pedraza

    2015-01-01

    Local access networks (LAN) are commonly used as communication infrastructures which meet the demand of a set of users in the local environment. Usually these networks consist of several LAN segments connected by bridges. The topological LAN design bi-level problem consists on assigning users to clusters and the union of clusters by bridges in order to obtain a minimum response time network with minimum connection cost. Therefore, the decision of optimally assigning users to clusters will be made by the leader and the follower will make the decision of connecting all the clusters while forming a spanning tree. In this paper, we propose a genetic algorithm for solving the bi-level topological design of a Local Access Network. Our solution method considers the Stackelberg equilibrium to solve the bi-level problem. The Stackelberg-Genetic algorithm procedure deals with the fact that the follower's problem cannot be optimally solved in a straightforward manner. The computational results obtained from two different sets of instances show that the performance of the developed algorithm is efficient and that it is more suitable for solving the bi-level problem than a previous Nash-Genetic approach.

  7. Conserved genetic basis of a quantitative plumage trait involved in mate choice.

    PubMed

    Mundy, Nicholas I; Badcock, Nichola S; Hart, Tom; Scribner, Kim; Janssen, Kirstin; Nadeau, Nicola J

    2004-03-19

    A key question in evolutionary genetics is whether shared genetic mechanisms underlie the independent evolution of similar phenotypes across phylogenetically divergent lineages. Here we show that in two classic examples of melanic plumage polymorphisms in birds, lesser snow geese (Anser c. caerulescens) and arctic skuas (Stercorarius parasiticus), melanism is perfectly associated with variation in the melanocortin-1 receptor (MC1R) gene. In both species, the degree of melanism correlates with the number of copies of variant MC1R alleles. Phylogenetic reconstructions of variant MC1R alleles in geese and skuas show that melanism is a derived trait that evolved in the Pleistocene. PMID:15031505

  8. Discordant patterns of genetic and phenotypic differentiation in five grasshopper species codistributed across a microreserve network.

    PubMed

    Ortego, Joaquín; García-Navas, Vicente; Noguerales, Víctor; Cordero, Pedro J

    2015-12-01

    Conservation plans can be greatly improved when information on the evolutionary and demographic consequences of habitat fragmentation is available for several codistributed species. Here, we study spatial patterns of phenotypic and genetic variation among five grasshopper species that are codistributed across a network of microreserves but show remarkable differences in dispersal-related morphology (body size and wing length), degree of habitat specialization and extent of fragmentation of their respective habitats in the study region. In particular, we tested the hypothesis that species with preferences for highly fragmented microhabitats show stronger genetic and phenotypic structure than codistributed generalist taxa inhabiting a continuous matrix of suitable habitat. We also hypothesized a higher resemblance of spatial patterns of genetic and phenotypic variability among species that have experienced a higher degree of habitat fragmentation due to their more similar responses to the parallel large-scale destruction of their natural habitats. In partial agreement with our first hypothesis, we found that genetic structure, but not phenotypic differentiation, was higher in species linked to highly fragmented habitats. We did not find support for congruent patterns of phenotypic and genetic variability among any studied species, indicating that they show idiosyncratic evolutionary trajectories and distinctive demographic responses to habitat fragmentation across a common landscape. This suggests that conservation practices in networks of protected areas require detailed ecological and evolutionary information on target species to focus management efforts on those taxa that are more sensitive to the effects of habitat fragmentation. PMID:26475782

  9. Assembling global maps of cellular function through integrative analysis of physical and genetic networks

    PubMed Central

    Srivas, Rohith; Hannum, Gregory; Ruscheinski, Johannes; Ono, Keiichiro; Wang, Peng-Liang; Smoot, Michael; Ideker, Trey

    2012-01-01

    To take full advantage of high-throughput genetic and physical interaction mapping projects, the raw interactions must first be assembled into models of cell structure and function. PanGIA (for physical and genetic interaction alignment) is a plug-in for the bioinformatics platform Cytoscape, designed to integrate physical and genetic interactions into hierarchical module maps. PanGIA identifies ‘modules’ as sets of proteins whose physical and genetic interaction data matches that of known protein complexes. Higher-order functional cooperativity and redundancy is identified by enrichment for genetic interactions across modules. This protocol begins with importing interaction networks into Cytoscape, followed by filtering and basic network visualization. Next, PanGIA is used to infer a set of modules and their functional inter-relationships. This module map is visualized in a number of intuitive ways, and modules are tested for functional enrichment and overlap with known complexes. The full protocol can be completed between 10 and 30 min, depending on the size of the data set being analyzed. PMID:21886098

  10. Shared Genetic Factors Involved in Celiac Disease, Type 2 Diabetes and Anorexia Nervosa Suggest Common Molecular Pathways for Chronic Diseases

    PubMed Central

    Mostowy, Joanna; Montén, Caroline; Gudjonsdottir, Audur H.; Arnell, Henrik; Browaldh, Lars; Nilsson, Staffan; Agardh, Daniel

    2016-01-01

    Background and Objectives Genome-wide association studies (GWAS) have identified several genetic regions involved in immune-regulatory mechanisms to be associated with celiac disease. Previous GWAS also revealed an over-representation of genes involved in type 2 diabetes and anorexia nervosa associated with celiac disease, suggesting involvement of common metabolic pathways for development of these chronic diseases. The aim of this study was to extend these previous analyses to study the gene expression in the gut from children with active celiac disease. Material and Methods Thirty six target genes involved in type 2 diabetes and four genes associated with anorexia nervosa were investigated for gene expression in small intestinal biopsies from 144 children with celiac disease at median (range) age of 7.4 years (1.6–17.8) and from 154 disease controls at a median (range) age 11.4.years (1.4–18.3). Results A total of eleven of genes were differently expressed in celiac patients compared with disease controls of which CD36, CD38, FOXP1, SELL, PPARA, PPARG, AGT previously associated with type 2 diabetes and AKAP6, NTNG1 with anorexia nervosa remained significant after correction for multiple testing. Conclusion Shared genetic factors involved in celiac disease, type 2 diabetes and anorexia nervosa suggest common underlying molecular pathways for these diseases. PMID:27483138

  11. A network of assembly factors is involved in remodeling rRNA elements during preribosome maturation

    PubMed Central

    Baßler, Jochen; Paternoga, Helge; Holdermann, Iris; Thoms, Matthias; Granneman, Sander; Barrio-Garcia, Clara; Nyarko, Afua; Stier, Gunter; Clark, Sarah A.; Schraivogel, Daniel; Kallas, Martina; Beckmann, Roland; Tollervey, David

    2014-01-01

    Eukaryotic ribosome biogenesis involves ∼200 assembly factors, but how these contribute to ribosome maturation is poorly understood. Here, we identify a network of factors on the nascent 60S subunit that actively remodels preribosome structure. At its hub is Rsa4, a direct substrate of the force-generating ATPase Rea1. We show that Rsa4 is connected to the central protuberance by binding to Rpl5 and to ribosomal RNA (rRNA) helix 89 of the nascent peptidyl transferase center (PTC) through Nsa2. Importantly, Nsa2 binds to helix 89 before relocation of helix 89 to the PTC. Structure-based mutations of these factors reveal the functional importance of their interactions for ribosome assembly. Thus, Rsa4 is held tightly in the preribosome and can serve as a “distribution box,” transmitting remodeling energy from Rea1 into the developing ribosome. We suggest that a relay-like factor network coupled to a mechano-enzyme is strategically positioned to relocate rRNA elements during ribosome maturation. PMID:25404745

  12. A network of assembly factors is involved in remodeling rRNA elements during preribosome maturation.

    PubMed

    Baßler, Jochen; Paternoga, Helge; Holdermann, Iris; Thoms, Matthias; Granneman, Sander; Barrio-Garcia, Clara; Nyarko, Afua; Lee, Woonghee; Stier, Gunter; Clark, Sarah A; Schraivogel, Daniel; Kallas, Martina; Beckmann, Roland; Tollervey, David; Barbar, Elisar; Sinning, Irmi; Hurt, Ed

    2014-11-24

    Eukaryotic ribosome biogenesis involves ∼200 assembly factors, but how these contribute to ribosome maturation is poorly understood. Here, we identify a network of factors on the nascent 60S subunit that actively remodels preribosome structure. At its hub is Rsa4, a direct substrate of the force-generating ATPase Rea1. We show that Rsa4 is connected to the central protuberance by binding to Rpl5 and to ribosomal RNA (rRNA) helix 89 of the nascent peptidyl transferase center (PTC) through Nsa2. Importantly, Nsa2 binds to helix 89 before relocation of helix 89 to the PTC. Structure-based mutations of these factors reveal the functional importance of their interactions for ribosome assembly. Thus, Rsa4 is held tightly in the preribosome and can serve as a "distribution box," transmitting remodeling energy from Rea1 into the developing ribosome. We suggest that a relay-like factor network coupled to a mechano-enzyme is strategically positioned to relocate rRNA elements during ribosome maturation.

  13. High acceptance of an early dyslexia screening test involving genetic analyses in Germany.

    PubMed

    Wilcke, Arndt; Müller, Bent; Schaadt, Gesa; Kirsten, Holger; Boltze, Johannes

    2016-02-01

    Dyslexia is a developmental disorder characterized by severe problems in the acquisition of reading and writing skills. It has a strong neurobiological basis. Genetic influence is estimated at 50-70%. One of the central problems with dyslexia is its late diagnosis, normally not before the end of the 2nd grade, resulting in the loss of several years for early therapy. Currently, research is focusing on the development of early tests for dyslexia, which may be based on EEG and genetics. Our aim was to determine the acceptance of such a future test among parents. We conducted a representative survey in Germany with 1000 parents of children aged 3-7 years, with and without experience of dyslexia. 88.7% of the parents supported the introduction of an early test for dyslexia based on EEG and genetics; 82.8% would have their own children tested, and 57.9% were willing to pay for the test if health insurance did not cover the costs. Test acceptance was significantly higher if parents had prior experience with dyslexia. The perceived benefits of such a test were early recognition and remediation and, preventing deficits. Concerns regarded the precision of the test, its potentially stigmatizing effect and its costs. The high overall support for the test leads to the conclusion that parents would accept a test for dyslexia based on EEG and genetics.

  14. Stochastic gene expression in single cells: exploring the importance of noise in genetic networks

    NASA Astrophysics Data System (ADS)

    van Oudenaarden, Alexander

    2003-03-01

    Cells are intrinsically noisy biochemical reactors. This leads to random cell to cell variation (noise) in gene expression levels. First, I will address the source of this noise at the level of transcription and translation of a single gene. Our experimental results demonstrate that the intrinsic noise of a single gene is predominantly controlled at the translational level, and that increased translational efficiency leads to increased noise strength. This observation is consistent with a theoretical model in which proteins are randomly produced in sharp bursts followed by periods of slow decay. Second, I will explore the importance of genetic noise for a naturally occuring network: the lac operon. The classic lactose utilization network of E. coli has been under investigation for several decades and, in its simplest form the network may be modeled as a single positive feedback module. However, this simplicity is deceptive, as even this basic network is capable of complex metabolic behavior, including adaptation, amplification, and graded-to-binary response conversion. I will present single cell measurements on the expression of key genes in lactose uptake network and explore the importance of genetic noise on the regulation of these genes.

  15. A Target Coverage Scheduling Scheme Based on Genetic Algorithms in Directional Sensor Networks

    PubMed Central

    Gil, Joon-Min; Han, Youn-Hee

    2011-01-01

    As a promising tool for monitoring the physical world, directional sensor networks (DSNs) consisting of a large number of directional sensors are attracting increasing attention. As directional sensors in DSNs have limited battery power and restricted angles of sensing range, maximizing the network lifetime while monitoring all the targets in a given area remains a challenge. A major technique to conserve the energy of directional sensors is to use a node wake-up scheduling protocol by which some sensors remain active to provide sensing services, while the others are inactive to conserve their energy. In this paper, we first address a Maximum Set Covers for DSNs (MSCD) problem, which is known to be NP-complete, and present a greedy algorithm-based target coverage scheduling scheme that can solve this problem by heuristics. This scheme is used as a baseline for comparison. We then propose a target coverage scheduling scheme based on a genetic algorithm that can find the optimal cover sets to extend the network lifetime while monitoring all targets by the evolutionary global search technique. To verify and evaluate these schemes, we conducted simulations and showed that the schemes can contribute to extending the network lifetime. Simulation results indicated that the genetic algorithm-based scheduling scheme had better performance than the greedy algorithm-based scheme in terms of maximizing network lifetime. PMID:22319387

  16. Extracting directed information flow networks: An application to genetics and semantics

    NASA Astrophysics Data System (ADS)

    Masucci, A. P.; Kalampokis, A.; Eguíluz, V. M.; Hernández-García, E.

    2011-02-01

    We introduce a general method to infer the directional information flow between populations whose elements are described by n-dimensional vectors of symbolic attributes. The method is based on the Jensen-Shannon divergence and on the Shannon entropy and has a wide range of application. We show here the results of two applications: first we extract the network of genetic flow between meadows of the seagrass Poseidonia oceanica, where the meadow elements are specified by sets of microsatellite markers, and then we extract the semantic flow network from a set of Wikipedia pages, showing the semantic channels between different areas of knowledge.

  17. Systematic genetic analysis of transcription factors to map the fission yeast transcription-regulatory network.

    PubMed

    Chua, Gordon

    2013-12-01

    Mapping transcriptional-regulatory networks requires the identification of target genes, binding specificities and signalling pathways of transcription factors. However, the characterization of each transcription factor sufficiently for deciphering such networks remains laborious. The recent availability of overexpression and deletion strains for almost all of the transcription factor genes in the fission yeast Schizosaccharomyces pombe provides a valuable resource to better investigate transcription factors using systematic genetics. In the present paper, I review and discuss the utility of these strain collections combined with transcriptome profiling and genome-wide chromatin immunoprecipitation to identify the target genes of transcription factors.

  18. Mean square exponential and robust stability of stochastic discrete-time genetic regulatory networks with uncertainties

    PubMed Central

    Cui, Baotong

    2010-01-01

    This paper aims to analyze global robust exponential stability in the mean square sense of stochastic discrete-time genetic regulatory networks with stochastic delays and parameter uncertainties. Comparing to the previous research works, time-varying delays are assumed to be stochastic whose variation ranges and probability distributions of the time-varying delays are explored. Based on the stochastic analysis approach and some analysis techniques, several sufficient criteria for the global robust exponential stability in the mean square sense of the networks are derived. Moreover, two numerical examples are presented to show the effectiveness of the obtained results. PMID:21629588

  19. A multi-agent genetic algorithm for community detection in complex networks

    NASA Astrophysics Data System (ADS)

    Li, Zhangtao; Liu, Jing

    2016-05-01

    Complex networks are popularly used to represent a lot of practical systems in the domains of biology and sociology, and the structure of community is one of the most important network attributes which has received an enormous amount of attention. Community detection is the process of discovering the community structure hidden in complex networks, and modularity Q is one of the best known quality functions measuring the quality of communities of networks. In this paper, a multi-agent genetic algorithm, named as MAGA-Net, is proposed to optimize modularity value for the community detection. An agent, coded by a division of a network, represents a candidate solution. All agents live in a lattice-like environment, with each agent fixed on a lattice point. A series of operators are designed, namely split and merging based neighborhood competition operator, hybrid neighborhood crossover, adaptive mutation and self-learning operator, to increase modularity value. In the experiments, the performance of MAGA-Net is validated on both well-known real-world benchmark networks and large-scale synthetic LFR networks with 5000 nodes. The systematic comparisons with GA-Net and Meme-Net show that MAGA-Net outperforms these two algorithms, and can detect communities with high speed, accuracy and stability.

  20. Discovering link communities in complex networks by an integer programming model and a genetic algorithm.

    PubMed

    Li, Zhenping; Zhang, Xiang-Sun; Wang, Rui-Sheng; Liu, Hongwei; Zhang, Shihua

    2013-01-01

    Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks.

  1. Computational, Integrative, and Comparative Methods for the Elucidation of Genetic Coexpression Networks

    DOE PAGESBeta

    Baldwin, Nicole E.; Chesler, Elissa J.; Kirov, Stefan; Langston, Michael A.; Snoddy, Jay R.; Williams, Robert W.; Zhang, Bing

    2005-01-01

    Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively co-regulated genes and their annotation using gene ontology analysis and cis -regulatory element discovery.more » The causal basis for co-regulation is detected through the use of quantitative trait locus mapping.« less

  2. [Predicting genetic modification targets based on metabolic network analysis--a review].

    PubMed

    Li, Peishun; Ma, Hongwu; Zhao, Xueming; Chen, Tao

    2016-01-01

    Construction of artificial cell factory to produce specific compounds of interest needs wild strain to be genetically engineered. In recent years, with the reconstruction of many genome-scale metabolic networks, a number of methods have been proposed based on metabolic network analysis for predicting genetic modification targets that lead to overproduction of compounds of interest. These approaches use constraints of stoichiometry and reaction reversibility in genome-scale models of metabolism and adopt different mathematical algorithms to predict modification targets, and thus can discover new targets that are difficult to find through traditional intuitive methods. In this review, we introduce the principle, merit, demerit and application of various strain optimization methods in detail. The main problems in existing methods and perspectives on this emerging research field are also discussed, aiming to provide guidance to choose the appropriate methods according to different types of products and the reliability of the predicted results. PMID:27363195

  3. Multiple Genetic Modifiers of Bilirubin Metabolism Involvement in Significant Neonatal Hyperbilirubinemia in Patients of Chinese Descent

    PubMed Central

    Yang, Hui; Zheng, Lei; Lin, Min; Zheng, Xiang-bin; Lin, Fen

    2015-01-01

    The potential for genetic variation to modulate neonatal hyperbilirubinemia risk is increasingly being recognized. A case-control study was designed to assess comprehensive contributions of the multiple genetic modifiers of bilirubin metabolism on significant neonatal hyperbilirubinemia in Chinese descendents. Eleven common mutations and polymorphisms across five bilirubin metabolism genes, namely those encoding UGT1A1, HMOX1, BLVRA, SLCO1B1 and SLCO1B3, were determined using the high resolution melt (HRM) assay or PCR-capillary electrophoresis analysis. A total of 129 hyperbilirubinemic infants and 108 control subjects were evaluated. Breastfeeding and the presence of the minor A allele of rs4148323 (UGTA*6) were correlated with an increased risk of hyperbilirubinemia (OR=2.17, P=0.02 for breastfeeding; OR=9.776, P=0.000 for UGTA*6 homozygote; OR=3.151, P=0.000 for UGTA*6 heterozygote); whereas, increasing gestational age and the presence of –TA7 repeat variant of UGT1A1 decreased the risk (OR=0.721, P=0.003 for gestational age; OR=0.313, P=0.002 for heterozygote TA6/TA7). In addition, the SLCO1B1 and SLCO1B3 polymorphisms also contributed to an increased risk of hyperbilirubinemia. This detailed analysis revealed the impact of multiple genetic modifiers on neonatal hyperbilirubinemia. This may support the use of genetic tests for clinical risk assessment. Furthermore, the established HRM assay can serve as an effective method for large-scale investigation. PMID:26146841

  4. The immunoglobulin-like genetic predetermination of the brain: the protocadherins, blueprint of the neuronal network

    NASA Astrophysics Data System (ADS)

    Hilschmann, N.; Barnikol, H. U.; Barnikol-Watanabe, S.; Götz, H.; Kratzin, H.; Thinnes, F. P.

    2001-01-01

    The morphogenesis of the brain is governed by synaptogenesis. Synaptogenesis in turn is determined by cell adhesion molecules, which bridge the synaptic cleft and, by homophilic contact, decide which neurons are connected and which are not. Because of their enormous diversification in specificities, protocadherins (pcdhα, pcdhβ, pcdhγ), a new class of cadherins, play a decisive role. Surprisingly, the genetic control of the protocadherins is very similar to that of the immunoglobulins. There are three sets of variable (V) genes followed by a corresponding constant (C) gene. Applying the rules of the immunoglobulin genes to the protocadherin genes leads, despite of this similarity, to quite different results in the central nervous system. The lymphocyte expresses one single receptor molecule specifically directed against an outside stimulus. In contrast, there are three specific recognition sites in each neuron, each expressing a different protocadherin. In this way, 4,950 different neurons arising from one stem cell form a neuronal network, in which homophilic contacts can be formed in 52 layers, permitting an enormous number of different connections and restraints between neurons. This network is one module of the central computer of the brain. Since the V-genes are generated during evolution and V-gene translocation during embryogenesis, outside stimuli have no influence on this network. The network is an inborn property of the protocadherin genes. Every circuit produced, as well as learning and memory, has to be based on this genetically predetermined network. This network is so universal that it can cope with everything, even the unexpected. In this respect the neuronal network resembles the recognition sites of the immunoglobulins.

  5. Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

    NASA Technical Reports Server (NTRS)

    Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

    2002-01-01

    Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic

  6. Scale-free properties of information flux networks in genetic algorithms

    NASA Astrophysics Data System (ADS)

    Wu, Jieyu; Shao, Xinyu; Li, Jinhang; Huang, Gang

    2012-02-01

    In this study, we present empirical analysis of statistical properties of mating networks in genetic algorithms (GAs). Under the framework of GAs, we study a class of interaction network model-information flux network (IFN), which describes the information flow among generations during evolution process. The IFNs are found to be scale-free when the selection operator uses a preferential strategy rather than a random. The topology structure of IFN is remarkably affected by operations used in genetic algorithms. The experimental results suggest that the scaling exponent of the power-law degree distribution is shown to decrease when crossover rate increases, but increase when mutation rate increases, and the reason may be that high crossover rate leads to more edges that are shared between nodes and high mutation rate leads to many individuals in a generation possessing low fitness. The magnitude of the out-degree exponent is always more than the in-degree exponent for the systems tested. These results may provide a new viewpoint with which to view GAs and guide the dissemination process of genetic information throughout a population.

  7. Cuba's Salgen: a provincial informatics network for genetic services to pregnant women and newborns.

    PubMed

    Rodríguez-Vázquez, Miguel; Pérez, Rubén; Valero, Damicel; Santiago, Darío G

    2014-01-01

    The Sancti Spíritus Provincial Medical Genetics Network has been using the Salgen IT platform since 2009 for health care, administrative and research activities concerning pregnant mothers and newborns. The network uses the national Infomed backbone to provide real-time connection between community-based polyclinics in primary health care and the Provincial Medical Genetics Reference Center. The platform has records for 23,025 pregnant women and sequential clinical data on genetic risk assessment in early pregnancy, first trimester ultrasound, sickle cell anemia screening, alpha-fetoprotein levels, cytogenetic antenatal diagnosis, second trimester ultrasound, delivery and newborn characteristics, neonatal metabolic screening, and infant clinical assessment. The system makes health care results immediately available and provides health alerts to enable timely preventive care for pregnant women. It also provides guidelines for processes and practices, and streamlines administrative and monitoring activities through statistical reports. The database generates indicators for assessing fetal growth and applies international standards for antenatal ultrasound quality control. Salgen provides a new source of information for medical research and knowledge management, and its use in this case fulfills Cuba's criteria for an integrated health services network. PMID:25208122

  8. Identification of causal genetic drivers of human disease through systems-level analysis of regulatory networks.

    PubMed

    Chen, James C; Alvarez, Mariano J; Talos, Flaminia; Dhruv, Harshil; Rieckhof, Gabrielle E; Iyer, Archana; Diefes, Kristin L; Aldape, Kenneth; Berens, Michael; Shen, Michael M; Califano, Andrea

    2014-10-01

    Identification of driver mutations in human diseases is often limited by cohort size and availability of appropriate statistical models. We propose a framework for the systematic discovery of genetic alterations that are causal determinants of disease, by prioritizing genes upstream of functional disease drivers, within regulatory networks inferred de novo from experimental data. We tested this framework by identifying the genetic determinants of the mesenchymal subtype of glioblastoma. Our analysis uncovered KLHL9 deletions as upstream activators of two previously established master regulators of the subtype, C/EBPβ and C/EBPδ. Rescue of KLHL9 expression induced proteasomal degradation of C/EBP proteins, abrogated the mesenchymal signature, and reduced tumor viability in vitro and in vivo. Deletions of KLHL9 were confirmed in > 50% of mesenchymal cases in an independent cohort, thus representing the most frequent genetic determinant of the subtype. The method generalized to study other human diseases, including breast cancer and Alzheimer's disease.

  9. Genetic variation among 82 pharmacogenes: The PGRNseq data from the eMERGE network.

    PubMed

    Bush, W S; Crosslin, D R; Owusu-Obeng, A; Wallace, J; Almoguera, B; Basford, M A; Bielinski, S J; Carrell, D S; Connolly, J J; Crawford, D; Doheny, K F; Gallego, C J; Gordon, A S; Keating, B; Kirby, J; Kitchner, T; Manzi, S; Mejia, A R; Pan, V; Perry, C L; Peterson, J F; Prows, C A; Ralston, J; Scott, S A; Scrol, A; Smith, M; Stallings, S C; Veldhuizen, T; Wolf, W; Volpi, S; Wiley, K; Li, R; Manolio, T; Bottinger, E; Brilliant, M H; Carey, D; Chisholm, R L; Chute, C G; Haines, J L; Hakonarson, H; Harley, J B; Holm, I A; Kullo, I J; Jarvik, G P; Larson, E B; McCarty, C A; Williams, M S; Denny, J C; Rasmussen-Torvik, L J; Roden, D M; Ritchie, M D

    2016-08-01

    Genetic variation can affect drug response in multiple ways, although it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE-PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of "precision medicine." The February 2015 eMERGE-PGx data release includes sequence-derived data from ∼5,000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled Combined Annotation-Dependent Depletion score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine. PMID:26857349

  10. Environmentally induced changes in correlated responses to selection reveal variable pleiotropy across a complex genetic network.

    PubMed

    Sikkink, Kristin L; Reynolds, Rose M; Cresko, William A; Phillips, Patrick C

    2015-05-01

    Selection in novel environments can lead to a coordinated evolutionary response across a suite of characters. Environmental conditions can also potentially induce changes in the genetic architecture of complex traits, which in turn could alter the pattern of the multivariate response to selection. We describe a factorial selection experiment using the nematode Caenorhabditis remanei in which two different stress-related phenotypes (heat and oxidative stress resistance) were selected under three different environmental conditions. The pattern of covariation in the evolutionary response between phenotypes or across environments differed depending on the environment in which selection occurred, including asymmetrical responses to selection in some cases. These results indicate that variation in pleiotropy across the stress response network is highly sensitive to the external environment. Our findings highlight the complexity of the interaction between genes and environment that influences the ability of organisms to acclimate to novel environments. They also make clear the need to identify the underlying genetic basis of genetic correlations in order understand how patterns of pleiotropy are distributed across complex genetic networks.

  11. Genetic Variation among 82 Pharmacogenes: the PGRN-Seq data from the eMERGE Network

    PubMed Central

    Obeng, Aniwaa Owusu; Wallace, John; Almoguera, Berta; Basford, Melissa A.; Bielinski, Suzette J.; Carrell, David S.; Connolly, John J.; Crawford, Dana; Doheny, Kimberly F.; Gallego, Carlos J.; Gordon, Adam S.; Keating, Brendan; Kirby, Jacqueline; Kitchner, Terrie; Manzi, Shannon; Mejia, Ana R.; Pan, Vivian; Perry, Cassandra L.; Peterson, Josh F.; Prows, Cynthia A.; Ralston, James; Scott, Stuart A.; Scrol, Aaron; Smith, Maureen; Stallings, Sarah C.; Veldhuizen, Tamra; Wolf, Wendy; Volpi, Simona; Wiley, Ken; Li, Rongling; Manolio, Teri; Bottinger, Erwin; Brilliant, Murray H.; Carey, David; Chisholm, Rex L.; Chute, Christopher G.; Haines, Jonathan L.; Hakonarson, Hakon; Harley, John B.; Holm, Ingrid A.; Kullo, Iftikhar J.; Jarvik, Gail P.; Larson, Eric B.; McCarty, Catherine A.; Williams, Marc S.; Denny, Joshua C.; Rasmussen-Torvik, Laura J.; Roden, Dan M.; Ritchie, Marylyn D.

    2016-01-01

    Genetic variation can affect drug response in multiple ways, though it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE-PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of “precision medicine.” The February 2015 eMERGE-PGx data release includes sequence-derived data from ~5000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled CADD score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine. PMID:26857349

  12. Strain Dependent Genetic Networks for Antibiotic-Sensitivity in a Bacterial Pathogen with a Large Pan-Genome

    PubMed Central

    van Opijnen, Tim; Bento, José

    2016-01-01

    The interaction between an antibiotic and bacterium is not merely restricted to the drug and its direct target, rather antibiotic induced stress seems to resonate through the bacterium, creating selective pressures that drive the emergence of adaptive mutations not only in the direct target, but in genes involved in many different fundamental processes as well. Surprisingly, it has been shown that adaptive mutations do not necessarily have the same effect in all species, indicating that the genetic background influences how phenotypes are manifested. However, to what extent the genetic background affects the manner in which a bacterium experiences antibiotic stress, and how this stress is processed is unclear. Here we employ the genome-wide tool Tn-Seq to construct daptomycin-sensitivity profiles for two strains of the bacterial pathogen Streptococcus pneumoniae. Remarkably, over half of the genes that are important for dealing with antibiotic-induced stress in one strain are dispensable in another. By confirming over 100 genotype-phenotype relationships, probing potassium-loss, employing genetic interaction mapping as well as temporal gene-expression experiments we reveal genome-wide conditionally important/essential genes, we discover roles for genes with unknown function, and uncover parts of the antibiotic’s mode-of-action. Moreover, by mapping the underlying genomic network for two query genes we encounter little conservation in network connectivity between strains as well as profound differences in regulatory relationships. Our approach uniquely enables genome-wide fitness comparisons across strains, facilitating the discovery that antibiotic responses are complex events that can vary widely between strains, which suggests that in some cases the emergence of resistance could be strain specific and at least for species with a large pan-genome less predictable. PMID:27607357

  13. Strain Dependent Genetic Networks for Antibiotic-Sensitivity in a Bacterial Pathogen with a Large Pan-Genome.

    PubMed

    van Opijnen, Tim; Dedrick, Sandra; Bento, José

    2016-09-01

    The interaction between an antibiotic and bacterium is not merely restricted to the drug and its direct target, rather antibiotic induced stress seems to resonate through the bacterium, creating selective pressures that drive the emergence of adaptive mutations not only in the direct target, but in genes involved in many different fundamental processes as well. Surprisingly, it has been shown that adaptive mutations do not necessarily have the same effect in all species, indicating that the genetic background influences how phenotypes are manifested. However, to what extent the genetic background affects the manner in which a bacterium experiences antibiotic stress, and how this stress is processed is unclear. Here we employ the genome-wide tool Tn-Seq to construct daptomycin-sensitivity profiles for two strains of the bacterial pathogen Streptococcus pneumoniae. Remarkably, over half of the genes that are important for dealing with antibiotic-induced stress in one strain are dispensable in another. By confirming over 100 genotype-phenotype relationships, probing potassium-loss, employing genetic interaction mapping as well as temporal gene-expression experiments we reveal genome-wide conditionally important/essential genes, we discover roles for genes with unknown function, and uncover parts of the antibiotic's mode-of-action. Moreover, by mapping the underlying genomic network for two query genes we encounter little conservation in network connectivity between strains as well as profound differences in regulatory relationships. Our approach uniquely enables genome-wide fitness comparisons across strains, facilitating the discovery that antibiotic responses are complex events that can vary widely between strains, which suggests that in some cases the emergence of resistance could be strain specific and at least for species with a large pan-genome less predictable. PMID:27607357

  14. Jimena: efficient computing and system state identification for genetic regulatory networks

    PubMed Central

    2013-01-01

    Background Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed. Results (i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones). Conclusions Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior. PMID:24118878

  15. Variations in the transcriptome of Alzheimer's disease reveal molecular networks involved in cardiovascular diseases

    PubMed Central

    Ray, Monika; Ruan, Jianhua; Zhang, Weixiong

    2008-01-01

    Background Because of its polygenic nature, Alzheimer's disease is believed to be caused not by defects in single genes, but rather by variations in a large number of genes and their complex interactions. A systems biology approach, such as the generation of a network of co-expressed genes and the identification of functional modules and cis-regulatory elements, to extract insights and knowledge from microarray data will lead to a better understanding of complex diseases such as Alzheimer's disease. In this study, we perform a series of analyses using co-expression networks, cis-regulatory elements, and functions of co-expressed gene modules to analyze single-cell gene expression data from normal and Alzheimer's disease-affected subjects. Results We identified six co-expressed gene modules, each of which represented a biological process perturbed in Alzheimer's disease. Alzheimer's disease-related genes, such as APOE, A2M, PON2 and MAP4, and cardiovascular disease-associated genes, including COMT, CBS and WNK1, all congregated in a single module. Some of the disease-related genes were hub genes while many of them were directly connected to one or more hub genes. Further investigation of this disease-associated module revealed cis-regulatory elements that match to the binding sites of transcription factors involved in Alzheimer's disease and cardiovascular disease. Conclusion Our results show the extensive links between Alzheimer's disease and cardiovascular disease at the co-expression and co-regulation levels, providing further evidence for the hypothesis that cardiovascular disease and Alzheimer's disease are linked. Our results support the notion that diseases in which the same set of biochemical pathways are affected may tend to co-occur with each other. PMID:18842138

  16. IN-STREAM AND WATERSHED PREDICTORS OF GENETIC DIVERSITY, EFFECTIVE POPULATION SIZE AND IMMIGRATION ACROSS RIVER-STREAM NETWORKS

    EPA Science Inventory

    The influence of spatial processes on population dynamics within river-stream networks is poorly understood. Utilizing spatially explicit analyses of temporal genetic variance, we examined whether persistence of Central Stonerollers (Campostoma anomalum) reflects differences in h...

  17. Evolutionary genetics as a tool to target genes involved in phenotypes of medical relevance

    PubMed Central

    Heyer, Evelyne; Quintana-Murci, Lluis

    2009-01-01

    There is an increasing interest in detecting genes, or genomic regions, that have been targeted by natural selection. Indeed, the evolutionary approach for inferring the action of natural selection in the human genome represents a powerful tool for predicting regions of the genome potentially associated with disease and of interest in epidemiological genetic studies. Here, we review several examples going from candidate gene studies associated with specific phenotypes, including nutrition, infectious disease and climate adaptation, to whole genome scans for natural selection. All these studies illustrate the power of the evolutionary approach in identifying regions of the genome having played a major role in human survival and adaptation. PMID:25567848

  18. Elucidation of Genetic Interactions in the Yeast GATA-Factor Network Using Bayesian Model Selection

    PubMed Central

    Milias-Argeitis, Andreas; Oliveira, Ana Paula; Gerosa, Luca; Falter, Laura; Sauer, Uwe; Lygeros, John

    2016-01-01

    Understanding the structure and function of complex gene regulatory networks using classical genetic assays is an error-prone procedure that frequently generates ambiguous outcomes. Even some of the best-characterized gene networks contain interactions whose validity is not conclusively proven. Founded on dynamic experimental data, mechanistic mathematical models are able to offer detailed insights that would otherwise require prohibitively large numbers of genetic experiments. Here we attempt mechanistic modeling of the transcriptional network formed by the four GATA-factor proteins, a well-studied system of central importance for nitrogen-source regulation of transcription in the yeast Saccharomyces cerevisiae. To resolve ambiguities in the network organization, we encoded a set of five interactions hypothesized in the literature into a set of 32 mathematical models, and employed Bayesian model selection to identify the most plausible set of interactions based on dynamic gene expression data. The top-ranking model was validated on newly generated GFP reporter dynamic data and was subsequently used to gain a better understanding of how yeast cells organize their transcriptional response to dynamic changes of nitrogen sources. Our work constitutes a necessary and important step towards obtaining a holistic view of the yeast nitrogen regulation mechanisms; on the computational side, it provides a demonstration of how powerful Monte Carlo techniques can be creatively combined and used to address the great challenges of large-scale dynamical system inference. PMID:26967983

  19. Genetic Factors Involved in Fumonisin Accumulation in Maize Kernels and Their Implications in Maize Agronomic Management and Breeding.

    PubMed

    Santiago, Rogelio; Cao, Ana; Butrón, Ana

    2015-08-20

    Contamination of maize with fumonisins depends on the environmental conditions; the maize resistance to contamination and the interaction between both factors. Although the effect of environmental factors is a determinant for establishing the risk of kernel contamination in a region, there is sufficient genetic variability among maize to develop resistance to fumonisin contamination and to breed varieties with contamination at safe levels. In addition, ascertaining which environmental factors are the most important in a region will allow the implementation of risk monitoring programs and suitable cultural practices to reduce the impact of such environmental variables. The current paper reviews all works done to address the influence of environmental variables on fumonisin accumulation, the genetics of maize resistance to fumonisin accumulation, and the search for the biochemical and/or structural mechanisms of the maize plant that could be involved in resistance to fumonisin contamination. We also explore the outcomes of breeding programs and risk monitoring of undertaken projects.

  20. Genetic Factors Involved in Fumonisin Accumulation in Maize Kernels and Their Implications in Maize Agronomic Management and Breeding.

    PubMed

    Santiago, Rogelio; Cao, Ana; Butrón, Ana

    2015-08-01

    Contamination of maize with fumonisins depends on the environmental conditions; the maize resistance to contamination and the interaction between both factors. Although the effect of environmental factors is a determinant for establishing the risk of kernel contamination in a region, there is sufficient genetic variability among maize to develop resistance to fumonisin contamination and to breed varieties with contamination at safe levels. In addition, ascertaining which environmental factors are the most important in a region will allow the implementation of risk monitoring programs and suitable cultural practices to reduce the impact of such environmental variables. The current paper reviews all works done to address the influence of environmental variables on fumonisin accumulation, the genetics of maize resistance to fumonisin accumulation, and the search for the biochemical and/or structural mechanisms of the maize plant that could be involved in resistance to fumonisin contamination. We also explore the outcomes of breeding programs and risk monitoring of undertaken projects. PMID:26308050

  1. Genetic Factors Involved in Fumonisin Accumulation in Maize Kernels and Their Implications in Maize Agronomic Management and Breeding

    PubMed Central

    Santiago, Rogelio; Cao, Ana; Butrón, Ana

    2015-01-01

    Contamination of maize with fumonisins depends on the environmental conditions; the maize resistance to contamination and the interaction between both factors. Although the effect of environmental factors is a determinant for establishing the risk of kernel contamination in a region, there is sufficient genetic variability among maize to develop resistance to fumonisin contamination and to breed varieties with contamination at safe levels. In addition, ascertaining which environmental factors are the most important in a region will allow the implementation of risk monitoring programs and suitable cultural practices to reduce the impact of such environmental variables. The current paper reviews all works done to address the influence of environmental variables on fumonisin accumulation, the genetics of maize resistance to fumonisin accumulation, and the search for the biochemical and/or structural mechanisms of the maize plant that could be involved in resistance to fumonisin contamination. We also explore the outcomes of breeding programs and risk monitoring of undertaken projects. PMID:26308050

  2. SND1 transcription factor-directed quantitative functional hierarchical genetic regulatory network in wood formation in Populus trichocarpa.

    PubMed

    Lin, Ying-Chung; Li, Wei; Sun, Ying-Hsuan; Kumari, Sapna; Wei, Hairong; Li, Quanzi; Tunlaya-Anukit, Sermsawat; Sederoff, Ronald R; Chiang, Vincent L

    2013-11-01

    Wood is an essential renewable raw material for industrial products and energy. However, knowledge of the genetic regulation of wood formation is limited. We developed a genome-wide high-throughput system for the discovery and validation of specific transcription factor (TF)-directed hierarchical gene regulatory networks (hGRNs) in wood formation. This system depends on a new robust procedure for isolation and transfection of Populus trichocarpa stem differentiating xylem protoplasts. We overexpressed Secondary Wall-Associated NAC Domain 1s (Ptr-SND1-B1), a TF gene affecting wood formation, in these protoplasts and identified differentially expressed genes by RNA sequencing. Direct Ptr-SND1-B1-DNA interactions were then inferred by integration of time-course RNA sequencing data and top-down Graphical Gaussian Modeling-based algorithms. These Ptr-SND1-B1-DNA interactions were verified to function in differentiating xylem by anti-PtrSND1-B1 antibody-based chromatin immunoprecipitation (97% accuracy) and in stable transgenic P. trichocarpa (90% accuracy). In this way, we established a Ptr-SND1-B1-directed quantitative hGRN involving 76 direct targets, including eight TF and 61 enzyme-coding genes previously unidentified as targets. The network can be extended to the third layer from the second-layer TFs by computation or by overexpression of a second-layer TF to identify a new group of direct targets (third layer). This approach would allow the sequential establishment, one two-layered hGRN at a time, of all layers involved in a more comprehensive hGRN. Our approach may be particularly useful to study hGRNs in complex processes in plant species resistant to stable genetic transformation and where mutants are unavailable.

  3. Stimulatory Influences of Far Infrared Therapy on the Transcriptome and Genetic Networks of Endothelial Progenitor Cells Receiving High Glucose Treatment

    PubMed Central

    Lin, Tzu-Chiao; Lin, Chin-Sheng; Tsai, Tsung-Neng; Cheng, Shu-Meng; Lin, Wei-Shiang; Cheng, Cheng-Chung; Wu, Chun-Hsien; Hsu, Chih-Hsueng

    2015-01-01

    Background Endothelial progenitor cells (EPCs) play a fundamental role in vascular repair and angiogenesis- related diseases. It is well-known that the process of angiogenesis is faulty in patients with diabetes. Long-term exposure of peripheral blood EPCs to high glucose (HG-EPCs) has been shown to impair cell proliferation and other functional competencies. Far infrared (FIR) therapy can promote ischemia-induced angiogenesis in diabetic mice and restore high glucose-suppressed endothelial progenitor cell functions both in vitro and in vivo. However, the detail mechanisms and global transcriptome alternations are still unclear. Methods In this study, we investigated the influences of FIR upon HG-EPC gene expressions. EPCs were obtained from the peripheral blood and treated with high glucose. These cells were then subjected to FIR irradiation and functional assays. Results Those genes responsible for fibroblast growth factors, Mitogen-activated protein kinases (MAPK), Janus kinase/signal transducer and activator of transcription and prostaglandin signaling pathways were significantly induced in HG-EPCs after FIR treatment. On the other hand, mouse double minute 2 homolog, genes involved in glycogen metabolic process, and genes involved in cardiac fibrosis were down-regulated. We also observed complex genetic networks functioning in FIR-treated HG-EPCs, in which several genes, such as GATA binding protein 3, hairy and enhancer of split-1, Sprouty Homolog 2, MAPK and Sirtuin 1, acted as hubs to maintain the stability and connectivity of the whole genetic network. Conclusions Deciphering FIR-affected genes will not only provide us with new knowledge regarding angiogenesis, but also help to develop new biomarkers for evaluating the effects of FIR therapy. Our findings may also be adapted to develop new methods to increase EPC activities for treating diabetes-related ischemia and metabolic syndrome-associated cardiovascular disorders. PMID:27122901

  4. Chromatin remodeling gene EZH2 involved in the genetic etiology of autism in Chinese Han population.

    PubMed

    Li, Jun; You, Yang; Yue, Weihua; Yu, Hao; Lu, Tianlan; Wu, Zhiliu; Jia, Meixiang; Ruan, Yanyan; Liu, Jing; Zhang, Dai; Wang, Lifang

    2016-01-01

    Autism spectrum disorder (ASD) is a group of severe neurodevelopmental disorders. Epigenetic factors play a critical role in the etiology of ASD. Enhancer of zest homolog 2 (EZH2), which encodes a histone methyltransferase, plays an important role in the process of chromatin remodeling during neurodevelopment. Further, EZH2 is located in chromosome 7q35-36, which is one of the linkage regions for autism. However, the genetic relationship between autism and EZH2 remains unclear. To investigate the association between EZH2 and autism in Chinese Han population, we performed a family-based association study between autism and three tagged single nucleotide polymorphisms (SNPs) that covered 95.4% of the whole region of EZH2. In the discovery cohort of 239 trios, two SNPs (rs740949 and rs6464926) showed a significant association with autism. To decrease false positive results, we expanded the sample size to 427 trios. A SNP (rs6464926) was significantly associated with autism even after Bonferroni correction (p=0.008). Haplotype G-T (rs740949 and rs6464926) was a risk factor for autism (Z=2.655, p=0.008, Global p=0.024). In silico function prediction for SNPs indicated that these two SNPs might be regulatory SNPs. Expression pattern of EZH2 showed that it is highly expressed in human embryonic brains. In conclusion, our findings demonstrate that EZH2 might contribute to the genetic etiology of autism in Chinese Han population.

  5. A Mosaic Genetic Screen for Genes Involved in the Early Steps of Drosophila Oogenesis

    PubMed Central

    Jagut, Marlène; Mihaila-Bodart, Ludivine; Molla-Herman, Anahi; Alin, Marie-Françoise; Lepesant, Jean-Antoine; Huynh, Jean-René

    2013-01-01

    The first hours of Drosophila embryogenesis rely exclusively on maternal information stored within the egg during oogenesis. The formation of the egg chamber is thus a crucial step for the development of the future adult. It has emerged that many key developmental decisions are made during the very first stages of oogenesis. We performed a clonal genetic screen on the left arm of chromosome 2 for mutations affecting early oogenesis. During the first round of screening, we scored for defects in egg chambers morphology as an easy read-out of early abnormalities. In a second round of screening, we analyzed the localization of centrosomes and Orb protein within the oocyte, the position of the oocyte within the egg chamber, and the progression through meiosis. We have generated a collection of 71 EMS-induced mutants that affect oocyte determination, polarization, or localization. We also recovered mutants affecting the number of germline cyst divisions or the differentiation of follicle cells. Here, we describe the analysis of nine complementation groups and eight single alleles. We mapped several mutations and identified alleles of Bicaudal-D, lethal(2) giant larvae, kuzbanian, GDP-mannose 4,6-dehydratase, tho2, and eiF4A. We further report the molecular identification of two alleles of the Drosophila homolog of Che-1/AATF and demonstrate its antiapoptotic activity in vivo. This collection of mutants will be useful to investigate further the early steps of Drosophila oogenesis at a genetic level. PMID:23450845

  6. Identification of Genetic Loci Involved in Diabetes using a Rat Model of Depression

    PubMed Central

    Woods, Leah C Solberg; Ahmadiyeh, Nasim; Baum, Amber; Shimomura, Kazuhiro; Li, Qian; Steiner, Donald F; Turek, Fred W; Takahashi, Joseph S; Churchill, Gary A; Redei, Eva E

    2009-01-01

    While diabetic patients often present with comorbid depression, the underlying mechanisms linking diabetes and depression are unknown. The Wistar Kyoto (WKY) rat is a well-known animal model of depression and stress hyper-reactivity. In addition, the WKY rat is glucose intolerant and likely harbors diabetes susceptibility alleles. We conducted a quantitative trait loci (QTL) analysis in the segregating F2 population of a WKY × Fischer 344 (F344) inter-cross. We have previously published QTL analyses for depressive behavior and hypothalamic-pituitary-adrenal (HPA) activity in this cross. In the current study, we report results from the QTL analysis for multiple metabolic phenotypes, including fasting glucose, post-restraint stress glucose, post-prandial glucose and insulin, and body weight. We identified multiple QTLs for each trait and many of the QTLs overlap with those previously identified using inbred models of type 2 diabetes (T2D). Significant correlations were found between metabolic traits and HPA axis measures and several metabolic loci overlap with loci previously identified for HPA activity in this F2 intercross, suggesting the genetic mechanisms underlying these traits may be similar. These results indicate that WKY rats harbor diabetes susceptibility alleles and suggest that this strain may be useful for dissecting the underlying genetic mechanisms linking diabetes, HPA activity and depression. PMID:19697080

  7. Genetic susceptibility to Chagas disease cardiomyopathy: involvement of several genes of the innate immunity and chemokine-dependent migration pathways

    PubMed Central

    2013-01-01

    Background Chagas disease, caused by the protozoan Trypanosoma cruzi is endemic in Latin America. Thirty percent of infected individuals develop chronic Chagas cardiomyopathy (CCC), an inflammatory dilated cardiomyopathy that is, by far, the most important clinical consequence of T. cruzi infection. The others remain asymptomatic (ASY). A possible genetic component to disease progression was suggested by familial aggregation of cases and the association of markers of innate and adaptive immunity genes with CCC development. Migration of Th1-type T cells play a major role in myocardial damage. Methods Our genetic analysis focused on CCR5, CCL2 and MAL/TIRAP genes. We used the Tag SNPs based approach, defined to catch all the genetic information from each gene. The study was conducted on a large Brazilian population including 315 CCC cases and 118 ASY subjects. Results The CCL2rs2530797A/A and TIRAPrs8177376A/A were associated to an increase susceptibility whereas the CCR5rs3176763C/C genotype is associated to protection to CCC. These associations were confirmed when we restricted the analysis to severe CCC, characterized by a left ventricular ejection fraction under 40%. Conclusions Our data show that polymorphisms affecting key molecules involved in several immune parameters (innate immunity signal transduction and T cell/monocyte migration) play a role in genetic susceptibility to CCC development. This also points out to the multigenic character of CCC, each polymorphism imparting a small contribution. The identification of genetic markers for CCC will provide information for pathogenesis as well as therapeutic targets. PMID:24330528

  8. Human amniotic fluid stem cells as a model for functional studies of genes involved in human genetic diseases or oncogenesis.

    PubMed

    Rosner, Margit; Dolznig, Helmut; Schipany, Katharina; Mikula, Mario; Brandau, Oliver; Hengstschläger, Markus

    2011-09-01

    Besides their putative usage for therapies, stem cells are a promising tool for functional studies of genes involved in human genetic diseases or oncogenesis. For this purpose induced pluripotent stem (iPS) cells can be derived from patients harbouring specific mutations. In contrast to adult stem cells, iPS cells are pluripotent and can efficiently be grown in culture. However, iPS cells are modulated due to the ectopic induction of pluripotency, harbour other somatic mutations accumulated during the life span of the source cells, exhibit only imperfectly cleared epigenetic memory of the source cell, and are often genomically instable. In addition, iPS cells from patients only allow the investigation of mutations, which are not prenatally lethal. Embryonic stem (ES) cells have a high proliferation and differentiation potential, but raise ethical issues. Human embryos, which are not transferred in the course of in vitro fertilization, because of preimplantation genetic diagnosis of a genetic defect, are still rarely donated for the establishment of ES cell lines. In addition, their usage for studies on gene functions for oncogenesis is hampered by the fact the ES cells are already tumorigenic per se. In 2003 amniotic fluid stem (AFS) cells have been discovered, which meanwhile have been demonstrated to harbour the potential to differentiate into cells of all three germ layers. Monoclonal human AFS cell lines derived from amniocenteses have a high proliferative potential, are genomically stable and are not associated with ethical controversies. Worldwide amniocenteses are performed for routine human genetic diagnosis. We here discuss how generation and banking of monoclonal human AFS cell lines with specific chromosomal aberrations or monogenic disease mutations would allow to study the functional consequences of disease causing mutations. In addition, recently a protocol for efficient and highly reproducible siRNA-mediated long-term knockdown of endogenous gene

  9. The Congenital Heart Disease Genetic Network Study: rationale, design, and early results.

    PubMed

    Gelb, Bruce; Brueckner, Martina; Chung, Wendy; Goldmuntz, Elizabeth; Kaltman, Jonathan; Kaski, Juan Pablo; Kim, Richard; Kline, Jennie; Mercer-Rosa, Laura; Porter, George; Roberts, Amy; Rosenberg, Ellen; Seiden, Howard; Seidman, Christine; Sleeper, Lynn; Tennstedt, Sharon; Kaltman, Jonathan; Schramm, Charlene; Burns, Kristin; Pearson, Gail; Rosenberg, Ellen

    2013-02-15

    Congenital heart defects (CHD) are the leading cause of infant mortality among birth defects, and later morbidities and premature mortality remain problematic. Although genetic factors contribute significantly to cause CHD, specific genetic lesions are unknown for most patients. The National Heart, Lung, and Blood Institute-funded Pediatric Cardiac Genomics Consortium established the Congenital Heart Disease Genetic Network Study to investigate relationships between genetic factors, clinical features, and outcomes in CHD. The Pediatric Cardiac Genomics Consortium comprises 6 main and 4 satellite sites at which subjects are recruited, and medical data and biospecimens (blood, saliva, cardiovascular tissue) are collected. Core infrastructure includes an administrative/data-coordinating center, biorepository, data hub, and core laboratories (genotyping, whole-exome sequencing, candidate gene evaluation, and variant confirmation). Eligibility includes all forms of CHD. Annual follow-up is obtained for probands <1-year-old. Parents are enrolled whenever available. Enrollment from December 2010 to June 2012 comprised 3772 probands. One or both parents were enrolled for 72% of probands. Proband median age is 5.5 years. The one third enrolled at age <1 year are contacted annually for follow-up information. The distribution of CHD favors more complex lesions. Approximately, 11% of probands have a genetic diagnosis. Adequate DNA is available from 97% and 91% of blood and saliva samples, respectively. Genomic analyses of probands with heterotaxy, atrial septal defects, conotruncal, and left ventricular outflow tract obstructive lesions are underway. The scientific community's use of Pediatric Cardiac Genomics Consortium resources is welcome.

  10. Global and robust stability analysis of genetic regulatory networks with time-varying delays and parameter uncertainties.

    PubMed

    Fang-Xiang Wu

    2011-08-01

    The study of stability is essential for designing or controlling genetic regulatory networks. This paper addresses global and robust stability of genetic regulatory networks with time delays and parameter uncertainties. Most existing results on this issue are based on the linear matrix inequalities (LMIs) approach, which results in checking the existence of a feasible solution to high dimensional LMIs. Based on M-matrix theory, we will present several novel global stability conditions for genetic regulatory networks with time-varying and time-invariant delays. All of these stability conditions are given in terms of M-matrices, for which there are many and very easy ways to be verified. Then, we extend these results to genetic regulatory networks with time delays and parameter uncertainties. To illustrate the effectiveness of our theoretical results, several genetic regulatory networks are analyzed. Compared with existing results in the literature, we also show that our results are less conservative than existing ones with these illustrative genetic regulatory networks.

  11. Rapid sensitive analysis of IDH1 mutation in lower-grade gliomas by automated genetic typing involving a quenching probe.

    PubMed

    Kurimoto, Michihiro; Suzuki, Hiromichi; Aoki, Kosuke; Ohka, Fumiharu; Kondo, Goro; Motomura, Kazuya; Iijima, Kentaro; Yamamichi, Akane; Ranjit, Melissa; Wakabayashi, Toshihiko; Kimura, Shinya; Natsume, Atsushi

    2016-01-01

    The authors recently found that 80% of lower-grade gliomas (LGGs) harbored a mutation in IDH1. Intraoperative detection of the mutated IDH1 helps not only differentiate LGGs from other type of brain tumors, but determine the resection border. In the current study, the authors have applied an automated genetic typing involving a quenching probe to detect the mutated IDH1. If tumor cells with the mutated IDH1 contained 10% or more in the mixture of normal and tumor cells, the device could detect it sensitively. The intraoperative assessment of IDH1 mutation is useful in brain tumor surgeries.

  12. Alcohol-Induced Histone Acetylation Reveals a Gene Network Involved in Alcohol Tolerance

    PubMed Central

    Ghezzi, Alfredo; Krishnan, Harish R.; Lew, Linda; Prado, Francisco J.; Ong, Darryl S.; Atkinson, Nigel S.

    2013-01-01

    Sustained or repeated exposure to sedating drugs, such as alcohol, triggers homeostatic adaptations in the brain that lead to the development of drug tolerance and dependence. These adaptations involve long-term changes in the transcription of drug-responsive genes as well as an epigenetic restructuring of chromosomal regions that is thought to signal and maintain the altered transcriptional state. Alcohol-induced epigenetic changes have been shown to be important in the long-term adaptation that leads to alcohol tolerance and dependence endophenotypes. A major constraint impeding progress is that alcohol produces a surfeit of changes in gene expression, most of which may not make any meaningful contribution to the ethanol response under study. Here we used a novel genomic epigenetic approach to find genes relevant for functional alcohol tolerance by exploiting the commonalities of two chemically distinct alcohols. In Drosophila melanogaster, ethanol and benzyl alcohol induce mutual cross-tolerance, indicating that they share a common mechanism for producing tolerance. We surveyed the genome-wide changes in histone acetylation that occur in response to these drugs. Each drug induces modifications in a large number of genes. The genes that respond similarly to either treatment, however, represent a subgroup enriched for genes important for the common tolerance response. Genes were functionally tested for behavioral tolerance to the sedative effects of ethanol and benzyl alcohol using mutant and inducible RNAi stocks. We identified a network of genes that are essential for the development of tolerance to sedation by alcohol. PMID:24348266

  13. Genetic diversity, dynamics, and activity of Lactobacillus community involved in traditional processing of artisanal Manchego cheese.

    PubMed

    Sánchez, Isabel; Seseña, Susana; Poveda, Justa M; Cabezas, Lourdes; Palop, Llanos

    2006-04-01

    A total of 248 strains of predominant lactobacilli isolated during the manufacture and ripening of artisanal Manchego cheeses obtained from two dairies were obtained and the genetic diversity of 197 investigated using random amplified polymorphic DNA (RAPD-PCR). 51 isolates could not be lysed and were therefore not genotyped. Forty-two distinct RAPD patterns, grouped in six major clusters at a similarity level of 54%, were obtained. Phenotypic characterization of isolates enabled their assignment to the species L. plantarum, L. brevis, L. paracasei subsp. paracasei, L. fermentum, L. pentosus, L. acidophilus and L. curvatus. In samples from both dairies, the species L. plantarum, L. brevis and L. paracasei subsp. paracasei dominated during ripening. Three genotypes showed excellent physiological characteristics and were therefore proposed as adjunct cultures for Manchego cheese manufacture. PMID:16481060

  14. An integrated approach to defining genetic and environmental determinants for major clinical outcomes involving vitamin D.

    PubMed

    Berlanga-Taylor, Antonio J; Knight, Julian C

    2014-06-01

    There is substantial genetic and epidemiological evidence implicating vitamin D in the pathogenesis of many common diseases. A number of studies have sought to define an association for disease with sequence variation in the VDR gene, encoding the ligand-activated nuclear hormone receptor for vitamin D. The results of such studies have been difficult to replicate and are likely to need to account for specific environmental exposures. Here, we review recent work that has begun to study the interactions between VDR gene polymorphisms, vitamin D blood levels, and complex disease susceptibility, notably in the context of major clinical outcomes. We highlight the challenges moving forward in this area and its importance for effective clinical translation of current research.

  15. Integument pattern formation involves genetic and epigenetic controls: feather arrays simulated by digital hormone models

    PubMed Central

    Jiang, Ting-Xin; Widelitz, Randall B.; Shen, Wei-Min; Will, Peter; Wu, Da-Yu; Lin, Chih-Min; Jung, Han-Sung; Chuong, Cheng-Ming

    2015-01-01

    Pattern formation is a fundamental morphogenetic process. Models based on genetic and epigenetic control have been proposed but remain controversial. Here we use feather morphogenesis for further evaluation. Adhesion molecules and/or signaling molecules were first expressed homogenously in feather tracts (restrictive mode, appear earlier) or directly in bud or inter-bud regions (de novo mode, appear later). They either activate or inhibit bud formation, but paradoxically co-localize in the bud. Using feather bud reconstitution, we showed that completely dissociated cells can reform periodic patterns without reference to previous positional codes. The patterning process has the characteristics of being self-organizing, dynamic and plastic. The final pattern is an equilibrium state reached by competition, and the number and size of buds can be altered based on cell number and activator/inhibitor ratio, respectively. We developed a Digital Hormone Model which consists of (1) competent cells without identity that move randomly in a space, (2) extracellular signaling hormones which diffuse by a reaction-diffusion mechanism and activate or inhibit cell adhesion, and (3) cells which respond with topological stochastic actions manifested as changes in cell adhesion. Based on probability, the results are cell clusters arranged in dots or stripes. Thus genetic control provides combinational molecular information which defines the properties of the cells but not the final pattern. Epigenetic control governs interactions among cells and their environment based on physical-chemical rules (such as those described in the Digital Hormone Model). Complex integument patterning is the sum of these two components of control and that is why integument patterns are usually similar but non-identical. These principles may be shared by other pattern formation processes such as barb ridge formation, fingerprints, pigmentation patterning, etc. The Digital Hormone Model can also be applied to

  16. Genetics

    MedlinePlus

    ... Inheritance; Heterozygous; Inheritance patterns; Heredity and disease; Heritable; Genetic markers ... The chromosomes are made up of strands of genetic information called DNA. Each chromosome contains sections of ...

  17. Genetic Characterization of the Klebsiella pneumoniae waa Gene Cluster, Involved in Core Lipopolysaccharide Biosynthesis

    PubMed Central

    Regué, Miguel; Climent, Núria; Abitiu, Nihal; Coderch, Núria; Merino, Susana; Izquierdo, Luis; Altarriba, Maria; Tomás, Juan M.

    2001-01-01

    A recombinant cosmid containing genes involved in Klebsiella pneumoniae C3 core lipopolysaccharide biosynthesis was identified by its ability to confer bacteriocin 28b resistance to Escherichia coli K-12. The recombinant cosmid contains 12 genes, the whole waa gene cluster, flanked by kbl and coaD genes, as was found in E. coli K-12. PCR amplification analysis showed that this cluster is conserved in representative K. pneumoniae strains. Partial nucleotide sequence determination showed that the same genes and gene order are found in K. pneumoniae subsp. ozaenae, for which the core chemical structure is known. Complementation analysis of known waa mutants from E. coli K-12 and/or Salmonella enterica led to the identification of genes involved in biosynthesis of the inner core backbone that are shared by these three members of the Enterobacteriaceae. K. pneumoniae orf10 mutants showed a two-log-fold reduction in a mice virulence assay and a strong decrease in capsule amount. Analysis of a constructed K. pneumoniae waaE deletion mutant suggests that the WaaE protein is involved in the transfer of the branch β-d-Glc to the O-4 position of l-glycero-d-manno-heptose I, a feature shared by K. pneumoniae, Proteus mirabilis, and Yersinia enterocolitica. PMID:11371519

  18. XROUTE: A knowledge-based routing system using neural networks and genetic algorithms

    SciTech Connect

    Kadaba, N.

    1990-01-01

    This dissertation is concerned with applying alternative methods of artificial intelligence (AI) in conjunction with mathematical methods to Vehicle Routing Problems. The combination of good mathematical models, knowledge-based systems, artificial neural networks, and adaptive genetic algorithms (GA) - which are shown to be synergistic - produces near-optimal results, which none of the individual methods can produce on its own. A significant problem associated with application of the Back Propagation learning paradigm for pattern classification with neural networks is the lack of high accuracy in generalization when the domain is large. In this work, a multiple neural network system is employed, using two self-organizing neural networks that work as feature extractors, producing information that is used to train a generalization neural network. The technique was successfully applied to the selection of control rules for a Traveling Salesman Problem heuristic, thus making it adaptive to the input problem instance. XROUTE provides an interactive visualization system, using state-of-the-art vehicle routing models and AI tools, yet allows an interactive environment for human expertise to be utilized in powerful ways. XROUTE provides an experimental, exploratory framework that allows many variations, and alternatives to problems with different characteristics. XROUTE is dynamic, expandable, and adaptive, and typically outperforms alternative methods in computer-aided vehicle routing.

  19. Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

    PubMed

    Barton, Alan J; Valdés, Julio J; Orchard, Robert

    2009-01-01

    Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

  20. Combining social and genetic networks to study HIV transmission in mixing risk groups

    NASA Astrophysics Data System (ADS)

    Zarrabi, Narges; Prosperi, Mattia C. F.; Belleman, Robbert G.; Di Giambenedetto, Simona; Fabbiani, Massimiliano; De Luca, Andrea; Sloot, Peter M. A.

    2013-09-01

    Reconstruction of HIV transmission networks is important for understanding and preventing the spread of the virus and drug resistant variants. Mixing risk groups is important in network analysis of HIV in order to assess the role of transmission between risk groups in the HIV epidemic. Most of the research focuses on the transmission within HIV risk groups, while transmission between different risk groups has been less studied. We use a proposed filter-reduction method to infer hypothetical transmission networks of HIV by combining data from social and genetic scales. We modified the filtering process in order to include mixing risk groups in the model. For this, we use the information on phylogenetic clusters obtained through phylogenetic analysis. A probability matrix is also defined to specify contact rates between individuals form the same and different risk groups. The method converts the data form each scale into network forms and combines them by overlaying and computing their intersection. We apply this method to reconstruct networks of HIV infected patients in central Italy, including mixing between risk groups. Our results suggests that bisexual behavior among Italian MSM and IDU partnership are relatively important in heterosexual transmission of HIV in central Italy.

  1. An improved localization algorithm based on genetic algorithm in wireless sensor networks.

    PubMed

    Peng, Bo; Li, Lei

    2015-04-01

    Wireless sensor network (WSN) are widely used in many applications. A WSN is a wireless decentralized structure network comprised of nodes, which autonomously set up a network. The node localization that is to be aware of position of the node in the network is an essential part of many sensor network operations and applications. The existing localization algorithms can be classified into two categories: range-based and range-free. The range-based localization algorithm has requirements on hardware, thus is expensive to be implemented in practice. The range-free localization algorithm reduces the hardware cost. Because of the hardware limitations of WSN devices, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is a typical range-free localization algorithm utilizing hop-distance estimation. In this paper, we propose an improved DV-Hop algorithm based on genetic algorithm. Simulation results show that our proposed algorithm improves the localization accuracy compared with previous algorithms.

  2. Genetic algorithm-support vector regression for high reliability SHM system based on FBG sensor network

    NASA Astrophysics Data System (ADS)

    Zhang, XiaoLi; Liang, DaKai; Zeng, Jie; Asundi, Anand

    2012-02-01

    Structural Health Monitoring (SHM) based on fiber Bragg grating (FBG) sensor network has attracted considerable attention in recent years. However, FBG sensor network is embedded or glued in the structure simply with series or parallel. In this case, if optic fiber sensors or fiber nodes fail, the fiber sensors cannot be sensed behind the failure point. Therefore, for improving the survivability of the FBG-based sensor system in the SHM, it is necessary to build high reliability FBG sensor network for the SHM engineering application. In this study, a model reconstruction soft computing recognition algorithm based on genetic algorithm-support vector regression (GA-SVR) is proposed to achieve the reliability of the FBG-based sensor system. Furthermore, an 8-point FBG sensor system is experimented in an aircraft wing box. The external loading damage position prediction is an important subject for SHM system; as an example, different failure modes are selected to demonstrate the SHM system's survivability of the FBG-based sensor network. Simultaneously, the results are compared with the non-reconstruct model based on GA-SVR in each failure mode. Results show that the proposed model reconstruction algorithm based on GA-SVR can still keep the predicting precision when partial sensors failure in the SHM system; thus a highly reliable sensor network for the SHM system is facilitated without introducing extra component and noise.

  3. Quantitative analysis of cefalexin based on artificial neural networks combined with modified genetic algorithm using short near-infrared spectroscopy

    NASA Astrophysics Data System (ADS)

    Huan, Yanfu; Feng, Guodong; Wang, Bin; Ren, Yulin; Fei, Qiang

    2013-05-01

    In this paper, a novel chemometric method was developed for rapid, accurate, and quantitative analysis of cefalexin in samples. The experiments were carried out by using the short near-infrared spectroscopy coupled with artificial neural networks. In order to enhancing the predictive ability of artificial neural networks model, a modified genetic algorithm was used to select fixed number of wavelength.

  4. Application of wavelet neural network model based on genetic algorithm in the prediction of high-speed railway settlement

    NASA Astrophysics Data System (ADS)

    Tang, Shihua; Li, Feida; Liu, Yintao; Lan, Lan; Zhou, Conglin; Huang, Qing

    2015-12-01

    With the advantage of high speed, big transport capacity, low energy consumption, good economic benefits and so on, high-speed railway is becoming more and more popular all over the world. It can reach 350 kilometers per hour, which requires high security performances. So research on the prediction of high-speed railway settlement that as one of the important factors affecting the safety of high-speed railway becomes particularly important. This paper takes advantage of genetic algorithms to seek all the data in order to calculate the best result and combines the advantage of strong learning ability and high accuracy of wavelet neural network, then build the model of genetic wavelet neural network for the prediction of high-speed railway settlement. By the experiment of back propagation neural network, wavelet neural network and genetic wavelet neural network, it shows that the absolute value of residual errors in the prediction of high-speed railway settlement based on genetic algorithm is the smallest, which proves that genetic wavelet neural network is better than the other two methods. The correlation coefficient of predicted and observed value is 99.9%. Furthermore, the maximum absolute value of residual error, minimum absolute value of residual error-mean value of relative error and value of root mean squared error(RMSE) that predicted by genetic wavelet neural network are all smaller than the other two methods'. The genetic wavelet neural network in the prediction of high-speed railway settlement is more stable in terms of stability and more accurate in the perspective of accuracy.

  5. Genetic Variation and Divergence of Genes Involved in Leaf Adaxial-Abaxial Polarity Establishment in Brassica rapa

    PubMed Central

    Liang, Jianli; Liu, Bo; Wu, Jian; Cheng, Feng; Wang, Xiaowu

    2016-01-01

    Alterations in leaf adaxial-abaxial (ad-ab) polarity are one of the main factors that influence leaf curvature. In Chinese cabbage, leaf incurvature is an essential prerequisite to the formation of a leafy head. Identifying ad-ab patterning genes and investigating their genetic variation may facilitate elucidation of the mechanisms underlying leaf incurvature during head formation. Comparative genomic analysis of 45 leaf ad-ab patterning genes in Brassica rapa based on 26 homologs of Arabidopsis thaliana indicated that these genes underwent expansion and were retained after whole genome triplication (WGT). We also assessed the nucleotide diversity and selection footprints of these 45 genes in a collection of 94 Brassica rapa accessions that were composed of heading and non-heading morphotypes. Six of the 45 genes showed significant negative Tajima's D indices and nucleotide diversity reduction in heading accessions compared to those in non-heading accessions, indicating that they underwent purifying selection. Further testing of the BrARF3.1 gene, which was one of the selection signals from a larger collection, confirmed that purifying selection did occur. Our results provide genetic evidence that ad-ab patterning genes are involved in leaf incurvature, which is associated with formation of a leafy head, as well as promote an understanding of the genetic mechanism underlying leafy head formation in Chinese cabbage. PMID:26904064

  6. Genetic Variation and Divergence of Genes Involved in Leaf Adaxial-Abaxial Polarity Establishment in Brassica rapa.

    PubMed

    Liang, Jianli; Liu, Bo; Wu, Jian; Cheng, Feng; Wang, Xiaowu

    2016-01-01

    Alterations in leaf adaxial-abaxial (ad-ab) polarity are one of the main factors that influence leaf curvature. In Chinese cabbage, leaf incurvature is an essential prerequisite to the formation of a leafy head. Identifying ad-ab patterning genes and investigating their genetic variation may facilitate elucidation of the mechanisms underlying leaf incurvature during head formation. Comparative genomic analysis of 45 leaf ad-ab patterning genes in Brassica rapa based on 26 homologs of Arabidopsis thaliana indicated that these genes underwent expansion and were retained after whole genome triplication (WGT). We also assessed the nucleotide diversity and selection footprints of these 45 genes in a collection of 94 Brassica rapa accessions that were composed of heading and non-heading morphotypes. Six of the 45 genes showed significant negative Tajima's D indices and nucleotide diversity reduction in heading accessions compared to those in non-heading accessions, indicating that they underwent purifying selection. Further testing of the BrARF3.1 gene, which was one of the selection signals from a larger collection, confirmed that purifying selection did occur. Our results provide genetic evidence that ad-ab patterning genes are involved in leaf incurvature, which is associated with formation of a leafy head, as well as promote an understanding of the genetic mechanism underlying leafy head formation in Chinese cabbage. PMID:26904064

  7. Genetic Variation and Divergence of Genes Involved in Leaf Adaxial-Abaxial Polarity Establishment in Brassica rapa.

    PubMed

    Liang, Jianli; Liu, Bo; Wu, Jian; Cheng, Feng; Wang, Xiaowu

    2016-01-01

    Alterations in leaf adaxial-abaxial (ad-ab) polarity are one of the main factors that influence leaf curvature. In Chinese cabbage, leaf incurvature is an essential prerequisite to the formation of a leafy head. Identifying ad-ab patterning genes and investigating their genetic variation may facilitate elucidation of the mechanisms underlying leaf incurvature during head formation. Comparative genomic analysis of 45 leaf ad-ab patterning genes in Brassica rapa based on 26 homologs of Arabidopsis thaliana indicated that these genes underwent expansion and were retained after whole genome triplication (WGT). We also assessed the nucleotide diversity and selection footprints of these 45 genes in a collection of 94 Brassica rapa accessions that were composed of heading and non-heading morphotypes. Six of the 45 genes showed significant negative Tajima's D indices and nucleotide diversity reduction in heading accessions compared to those in non-heading accessions, indicating that they underwent purifying selection. Further testing of the BrARF3.1 gene, which was one of the selection signals from a larger collection, confirmed that purifying selection did occur. Our results provide genetic evidence that ad-ab patterning genes are involved in leaf incurvature, which is associated with formation of a leafy head, as well as promote an understanding of the genetic mechanism underlying leafy head formation in Chinese cabbage.

  8. Genetic variants involved in gallstone formation and capsaicin metabolism, and the risk of gallbladder cancer in Chilean women

    PubMed Central

    Báez, Sergio; Tsuchiya, Yasuo; Calvo, Alfonso; Pruyas, Martha; Nakamura, Kazutoshi; Kiyohara, Chikako; Oyama, Mari; Yamamoto, Masaharu

    2010-01-01

    AIM: To determine the effects of genetic variants associated with gallstone formation and capsaicin (a pungent component of chili pepper) metabolism on the risk of gallbladder cancer (GBC). METHODS: A total of 57 patients with GBC, 119 patients with gallstones, and 70 controls were enrolled in this study. DNA was extracted from their blood or paraffin block sample using standard commercial kits. The statuses of the genetic variants were assayed using Taqman® SNP Genotyping Assays or Custom Taqman® SNP Genotyping Assays. RESULTS: The non-ancestral T/T genotype of apolipoprotein B rs693 polymorphism was associated with a decreased risk of GBC (OR: 0.14, 95% CI: 0.03-0.63). The T/T genotype of cholesteryl ester transfer protein (CETP) rs708272 polymorphism was associated with an increased risk of GBC (OR: 5.04, 95% CI: 1.43-17.8). CONCLUSION: Genetic variants involved in gallstone formation such as the apolipoprotein B rs693 and CETP rs708272 polymorphisms may be related to the risk of developing GBC in Chilean women. PMID:20082485

  9. Engineering modular and tunable genetic amplifiers for scaling transcriptional signals in cascaded gene networks.

    PubMed

    Wang, Baojun; Barahona, Mauricio; Buck, Martin

    2014-08-01

    Synthetic biology aims to control and reprogram signal processing pathways within living cells so as to realize repurposed, beneficial applications. Here we report the design and construction of a set of modular and gain-tunable genetic amplifiers in Escherichia coli capable of amplifying a transcriptional signal with wide tunable-gain control in cascaded gene networks. The devices are engineered using orthogonal genetic components (hrpRS, hrpV and PhrpL) from the hrp (hypersensitive response and pathogenicity) gene regulatory network in Pseudomonas syringae. The amplifiers can linearly scale up to 21-fold the transcriptional input with a large output dynamic range, yet not introducing significant time delay or significant noise during signal amplification. The set of genetic amplifiers achieves different gains and input dynamic ranges by varying the expression levels of the underlying ligand-free activator proteins in the device. As their electronic counterparts, these engineered transcriptional amplifiers can act as fundamental building blocks in the design of biological systems by predictably and dynamically modulating transcriptional signal flows to implement advanced intra- and extra-cellular control functions.

  10. Genomic and network patterns of schizophrenia genetic variation in human evolutionary accelerated regions.

    PubMed

    Xu, Ke; Schadt, Eric E; Pollard, Katherine S; Roussos, Panos; Dudley, Joel T

    2015-05-01

    The population persistence of schizophrenia despite associated reductions in fitness and fecundity suggests that the genetic basis of schizophrenia has a complex evolutionary history. A recent meta-analysis of schizophrenia genome-wide association studies offers novel opportunities for assessment of the evolutionary trajectories of schizophrenia-associated loci. In this study, we hypothesize that components of the genetic architecture of schizophrenia are attributable to human lineage-specific evolution. Our results suggest that schizophrenia-associated loci enrich in genes near previously identified human accelerated regions (HARs). Specifically, we find that genes near HARs conserved in nonhuman primates (pHARs) are enriched for schizophrenia-associated loci, and that pHAR-associated schizophrenia genes are under stronger selective pressure than other schizophrenia genes and other pHAR-associated genes. We further evaluate pHAR-associated schizophrenia genes in regulatory network contexts to investigate associated molecular functions and mechanisms. We find that pHAR-associated schizophrenia genes significantly enrich in a GABA-related coexpression module that was previously found to be differentially regulated in schizophrenia affected individuals versus healthy controls. In another two independent networks constructed from gene expression profiles from prefrontal cortex samples, we find that pHAR-associated schizophrenia genes are located in more central positions and their average path lengths to the other nodes are significantly shorter than those of other schizophrenia genes. Together, our results suggest that HARs are associated with potentially important functional roles in the genetic architecture of schizophrenia.

  11. Using a genetic algorithm to optimize a water-monitoring network for accuracy and cost effectiveness

    NASA Astrophysics Data System (ADS)

    Julich, R. J.

    2004-05-01

    The purpose of this project is to determine the optimal spatial distribution of water-monitoring wells to maximize important data collection and to minimize the cost of managing the network. We have employed a genetic algorithm (GA) towards this goal. The GA uses a simple fitness measure with two parts: the first part awards a maximal score to those combinations of hydraulic head observations whose net uncertainty is closest to the value representing all observations present, thereby maximizing accuracy; the second part applies a penalty function to minimize the number of observations, thereby minimizing the overall cost of the monitoring network. We used the linear statistical inference equation to calculate standard deviations on predictions from a numerical model generated for the 501-observation Death Valley Regional Flow System as the basis for our uncertainty calculations. We have organized the results to address the following three questions: 1) what is the optimal design strategy for a genetic algorithm to optimize this problem domain; 2) what is the consistency of solutions over several optimization runs; and 3) how do these results compare to what is known about the conceptual hydrogeology? Our results indicate the genetic algorithms are a more efficient and robust method for solving this class of optimization problems than have been traditional optimization approaches.

  12. Temperature drift modeling of MEMS gyroscope based on genetic-Elman neural network

    NASA Astrophysics Data System (ADS)

    Chong, Shen; Rui, Song; Jie, Li; Xiaoming, Zhang; Jun, Tang; Yunbo, Shi; Jun, Liu; Huiliang, Cao

    2016-05-01

    In order to improve the temperature drift modeling precision of a tuning fork micro-electromechanical system (MEMS) gyroscope, a novel multiple inputs/single output model based on genetic algorithm (GA) and Elman neural network (Elman NN) is proposed. First, the temperature experiment of MEMS gyroscope is carried out and the outputs of MEMS gyroscope and temperature sensors are collected; then the temperature drift model based on temperature, temperature variation rate and the coupling term is proposed, and the Elman NN is employed to guarantee the generalization ability of the model; at last the genetic algorithm is used to tune the parameters of Elman NN in order to improve the modeling precision. The Allan analysis results validate that, compared to traditional single input/single output model, the novel multiple inputs/single output model can guarantee high accurate fitting ability because the proposed model can provide more plentiful controllable information. By the way, the generalization ability of the Elman neural network can be improved significantly due to the parameters are optimized by genetic algorithm.

  13. Molecular stripping, targets and decoys as modulators of oscillations in the NF-κB/IκBα/DNA genetic network

    PubMed Central

    Wang, Zhipeng; Wolynes, Peter G.

    2016-01-01

    Eukaryotic transcription factors in the NF-κB family are central components of an extensive genetic network that activates cellular responses to inflammation and to a host of other external stressors. This network consists of feedback loops that involve the inhibitor IκBα, numerous downstream functional targets, and still more numerous binding sites that do not appear to be directly functional. Under steady stimulation, the regulatory network of NF-κB becomes oscillatory, and temporal patterns of NF-κB pulses appear to govern the patterns of downstream gene expression needed for immune response. Understanding how the information from external stress passes to oscillatory signals and is then ultimately relayed to gene expression is a general issue in systems biology. Recently, in vitro kinetic experiments as well as molecular simulations suggest that active stripping of NF-κB by IκBα from its binding sites can modify the traditional systems biology view of NF-κB/IκBα gene circuits. In this work, we revise the commonly adopted minimal model of the NF-κB regulatory network to account for the presence of the large number of binding sites for NF-κB along with dissociation from these sites that may proceed either by passive unbinding or by active molecular stripping. We identify regimes where the kinetics of target and decoy unbinding and molecular stripping enter a dynamic tug of war that may either compensate each other or amplify nuclear NF-κB activity, leading to distinct oscillatory patterns. Our finding that decoys and stripping play a key role in shaping the NF-κB oscillations suggests strategies to control NF-κB responses by introducing artificial decoys therapeutically. PMID:27683001

  14. Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations

    SciTech Connect

    Gupta, Chinmaya; López, José Manuel; Azencott, Robert; Ott, William; Bennett, Matthew R.; Josić, Krešimir

    2014-05-28

    Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here, we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove a quantitative bound on the error between the pathwise realizations of these two processes. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemical Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the oscillatory behavior in negative feedback circuits, cross-correlations between nodes in a network, and spatial and temporal information in two commonly studied motifs of metastability in biochemical systems. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay.

  15. a Modified Genetic Algorithm for Finding Fuzzy Shortest Paths in Uncertain Networks

    NASA Astrophysics Data System (ADS)

    Heidari, A. A.; Delavar, M. R.

    2016-06-01

    In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP) due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA) to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP) with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA) strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical instances are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.

  16. Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease.

    PubMed

    Johnson, Michael R; Shkura, Kirill; Langley, Sarah R; Delahaye-Duriez, Andree; Srivastava, Prashant; Hill, W David; Rackham, Owen J L; Davies, Gail; Harris, Sarah E; Moreno-Moral, Aida; Rotival, Maxime; Speed, Doug; Petrovski, Slavé; Katz, Anaïs; Hayward, Caroline; Porteous, David J; Smith, Blair H; Padmanabhan, Sandosh; Hocking, Lynne J; Starr, John M; Liewald, David C; Visconti, Alessia; Falchi, Mario; Bottolo, Leonardo; Rossetti, Tiziana; Danis, Bénédicte; Mazzuferi, Manuela; Foerch, Patrik; Grote, Alexander; Helmstaedter, Christoph; Becker, Albert J; Kaminski, Rafal M; Deary, Ian J; Petretto, Enrico

    2016-02-01

    Genetic determinants of cognition are poorly characterized, and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here we performed a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities, including memory. Using exome sequence data from 6,871 trios, we found that M3 genes were also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease-associated genes in the developed human brain, and provide empirical support for a convergent gene-regulatory network influencing cognition and neurodevelopmental disease. PMID:26691832

  17. Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations

    NASA Astrophysics Data System (ADS)

    Gupta, Chinmaya; López, José Manuel; Azencott, Robert; Bennett, Matthew R.; Josić, Krešimir; Ott, William

    2014-05-01

    Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here, we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove a quantitative bound on the error between the pathwise realizations of these two processes. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemical Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the oscillatory behavior in negative feedback circuits, cross-correlations between nodes in a network, and spatial and temporal information in two commonly studied motifs of metastability in biochemical systems. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay.

  18. Modeling delay in genetic networks: from delay birth-death processes to delay stochastic differential equations.

    PubMed

    Gupta, Chinmaya; López, José Manuel; Azencott, Robert; Bennett, Matthew R; Josić, Krešimir; Ott, William

    2014-05-28

    Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here, we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove a quantitative bound on the error between the pathwise realizations of these two processes. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemical Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the oscillatory behavior in negative feedback circuits, cross-correlations between nodes in a network, and spatial and temporal information in two commonly studied motifs of metastability in biochemical systems. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay.

  19. A Simple Genetic Algorithm for Calibration of Stochastic Rock Discontinuity Networks

    NASA Astrophysics Data System (ADS)

    Jimenez, R.; Jurado-Piña, R.

    2012-07-01

    We present a novel approach for calibration of stochastic discontinuity network parameters based on genetic algorithms (GAs). To validate the approach, examples of application of the method to cases with known parameters of the original Poisson discontinuity network are presented. Parameters of the model are encoded as chromosomes using a binary representation, and such chromosomes evolve as successive generations of a randomly generated initial population, subjected to GA operations of selection, crossover and mutation. Such back-calculated parameters are employed to make assessments about the inference capabilities of the model using different objective functions with different probabilities of crossover and mutation. Results show that the predictive capabilities of GAs significantly depend on the type of objective function considered; and they also show that the calibration capabilities of the genetic algorithm can be acceptable for practical engineering applications, since in most cases they can be expected to provide parameter estimates with relatively small errors for those parameters of the network (such as intensity and mean size of discontinuities) that have the strongest influence on many engineering applications.

  20. Genetic risk for schizophrenia: convergence on synaptic pathways involved in plasticity.

    PubMed

    Hall, Jeremy; Trent, Simon; Thomas, Kerrie L; O'Donovan, Michael C; Owen, Michael J

    2015-01-01

    Recent large-scale genomic studies have revealed two broad classes of risk alleles for schizophrenia: a polygenic component of risk mediated through multiple common risk variants and rarer more highly penetrant submicroscopic chromosomal deletions and duplications, known as copy number variants. The focus of this review is on the emerging findings from the latter and subsequent exome sequencing data of smaller, deleterious single nucleotide variants and indels. In these studies, schizophrenia patients were found to have enriched de novo mutations in genes belonging to the postsynaptic density at glutamatergic synapses, particularly components of the N-methyl-D-aspartate receptor signaling complex, including the PSD-95 complex, activity-regulated cytoskeleton-associated protein interactors, the fragile X mental retardation protein complex, voltage-gated calcium channels, and genes implicated in actin cytoskeletal dynamics. The convergence of these implicated genes onto a coherent biological pathway at the synapse, with a specific role in plasticity, provides a significant advance in understanding pathogenesis and points to new targets for biological investigation. We consider the implications of these studies in the context of existing genetic data and the potential need to reassess diagnostic boundaries of neuropsychiatric disorders before discussing ways forward for more directed mechanistic studies to develop stratified, novel therapeutic approaches in the future. PMID:25152434

  1. Genetic risk for schizophrenia: convergence on synaptic pathways involved in plasticity.

    PubMed

    Hall, Jeremy; Trent, Simon; Thomas, Kerrie L; O'Donovan, Michael C; Owen, Michael J

    2015-01-01

    Recent large-scale genomic studies have revealed two broad classes of risk alleles for schizophrenia: a polygenic component of risk mediated through multiple common risk variants and rarer more highly penetrant submicroscopic chromosomal deletions and duplications, known as copy number variants. The focus of this review is on the emerging findings from the latter and subsequent exome sequencing data of smaller, deleterious single nucleotide variants and indels. In these studies, schizophrenia patients were found to have enriched de novo mutations in genes belonging to the postsynaptic density at glutamatergic synapses, particularly components of the N-methyl-D-aspartate receptor signaling complex, including the PSD-95 complex, activity-regulated cytoskeleton-associated protein interactors, the fragile X mental retardation protein complex, voltage-gated calcium channels, and genes implicated in actin cytoskeletal dynamics. The convergence of these implicated genes onto a coherent biological pathway at the synapse, with a specific role in plasticity, provides a significant advance in understanding pathogenesis and points to new targets for biological investigation. We consider the implications of these studies in the context of existing genetic data and the potential need to reassess diagnostic boundaries of neuropsychiatric disorders before discussing ways forward for more directed mechanistic studies to develop stratified, novel therapeutic approaches in the future.

  2. Combining gene expression and genetic analyses to identify candidate genes involved in cold responses in pea.

    PubMed

    Legrand, Sylvain; Marque, Gilles; Blassiau, Christelle; Bluteau, Aurélie; Canoy, Anne-Sophie; Fontaine, Véronique; Jaminon, Odile; Bahrman, Nasser; Mautord, Julie; Morin, Julie; Petit, Aurélie; Baranger, Alain; Rivière, Nathalie; Wilmer, Jeroen; Delbreil, Bruno; Lejeune-Hénaut, Isabelle

    2013-09-01

    Cold stress affects plant growth and development. In order to better understand the responses to cold (chilling or freezing tolerance), we used two contrasted pea lines. Following a chilling period, the Champagne line becomes tolerant to frost whereas the Terese line remains sensitive. Four suppression subtractive hybridisation libraries were obtained using mRNAs isolated from pea genotypes Champagne and Terese. Using quantitative polymerase chain reaction (qPCR) performed on 159 genes, 43 and 54 genes were identified as differentially expressed at the initial time point and during the time course study, respectively. Molecular markers were developed from the differentially expressed genes and were genotyped on a population of 164 RILs derived from a cross between Champagne and Terese. We identified 5 candidate genes colocalizing with 3 different frost damage quantitative trait loci (QTL) intervals and a protein quantity locus (PQL) rich region previously reported. This investigation revealed the role of constitutive differences between both genotypes in the cold responses, in particular with genes related to glycine degradation pathway that could confer to Champagne a better frost tolerance. We showed that freezing tolerance involves a decrease of expression of genes related to photosynthesis and the expression of a gene involved in the production of cysteine and methionine that could act as cryoprotectant molecules. Although it remains to be confirmed, this study could also reveal the involvement of the jasmonate pathway in the cold responses, since we observed that two genes related to this pathway were mapped in a frost damage QTL interval and in a PQL rich region interval, respectively.

  3. Genetic diversity of variants involved in drug response and metabolism in Sri Lankan populations: implications for clinical implementation of pharmacogenomics

    PubMed Central

    Chan, Sze Ling; Samaranayake, Nilakshi; Ross, Colin J.D.; Toh, Meng Tiak; Carleton, Bruce; Hayden, Michael R.; Teo, Yik Ying; Dissanayake, Vajira H.W.

    2016-01-01

    Background Interpopulation differences in drug responses are well documented, and in some cases they correspond to differences in the frequency of associated genetic markers. Understanding the diversity of genetic markers associated with drug response across different global populations is essential to infer population rates of drug response or risk for adverse drug reactions, and to guide implementation of pharmacogenomic testing. Sri Lanka is a culturally and linguistically diverse nation, but little is known about the population genetics of the major Sri Lankan ethnic groups. The objective of this study was to investigate the diversity of pharmacogenomic variants in the major Sri Lankan ethnic groups. Methods We examined the allelic diversity of more than 7000 variants in genes involved in drug biotransformation and response in the three major ethnic populations of Sri Lanka (Sinhalese, Sri Lankan Tamils, and Moors), and compared them with other South Asian, South East Asian, and European populations using Wright’s Fixation Index, principal component analysis, and STRUCTURE analysis. Results We observed overall high levels of similarity within the Sri Lankan populations (median FST=0.0034), and between Sri Lankan and other South Asian populations (median FST=0.0064). Notably, we observed substantial differentiation between Sri Lankan and European populations for important pharmacogenomic variants related to warfarin (VKORC1 rs9923231) and clopidogrel (CYP2C19 rs4986893) response. Conclusion These data expand our understanding of the population structure of Sri Lanka, provide a resource for pharmacogenomic research, and have implications for the clinical use of genetic testing of pharmacogenomic variants in these populations. PMID:26444257

  4. Molecular genetics of Erwinia amylovora involved in the development of fire blight.

    PubMed

    Oh, Chang-Sik; Beer, Steven V

    2005-12-15

    The bacterial plant pathogen, Erwinia amylovora, causes the devastating disease known as fire blight in some Rosaceous plants like apple, pear, quince, raspberry and several ornamentals. Knowledge of the factors affecting the development of fire blight has mushroomed in the last quarter century. On the molecular level, genes encoding a Hrp type III secretion system, genes encoding enzymes involved in synthesis of extracellular polysaccharides and genes facilitating the growth of E. amylovora in its host plants have been characterized. The Hrp pathogenicity island, delimited by genes suggesting horizontal gene transfer, is composed of four distinct regions, the hrp/hrc region, the HEE (Hrp effectors and elicitors) region, the HAE (Hrp-associated enzymes) region, and the IT (Island transfer) region. The Hrp pathogenicity island encodes a Hrp type III secretion system (TTSS), which delivers several proteins from bacteria to plant apoplasts or cytoplasm. E. amylovora produces two exopolysaccharides, amylovoran and levan, which cause the characteristic fire blight wilting symptom in host plants. In addition, other genes, and their encoded proteins, have been characterized as virulence factors of E. amylovora that encode enzymes facilitating sorbitol metabolism, proteolytic activity and iron harvesting. This review summarizes our understanding of the genes and gene products of E. amylovora that are involved in the development of the fire blight disease. PMID:16253442

  5. Using Long-Distance Scientist Involvement to Enhance NASA Volunteer Network Educational Activities

    NASA Astrophysics Data System (ADS)

    Ferrari, K.

    2012-12-01

    Since 1999, the NASA/JPL Solar System Ambassadors (SSA) and Solar System Educators (SSEP) programs have used specially-trained volunteers to expand education and public outreach beyond the immediate NASA center regions. Integrating nationwide volunteers in these highly effective programs has helped optimize agency funding set aside for education. Since these volunteers were trained by NASA scientists and engineers, they acted as "stand-ins" for the mission team members in communities across the country. Through the efforts of these enthusiastic volunteers, students gained an increased awareness of NASA's space exploration missions through Solar System Ambassador classroom visits, and teachers across the country became familiarized with NASA's STEM (Science, Technology, Engineering and Mathematics) educational materials through Solar System Educator workshops; however the scientist was still distant. In 2003, NASA started the Digital Learning Network (DLN) to bring scientists into the classroom via videoconferencing. The first equipment was expensive and only schools that could afford the expenditure were able to benefit; however, recent advancements in software allow classrooms to connect to the DLN via personal computers and an internet connection. Through collaboration with the DLN at NASA's Jet Propulsion Laboratory and the Goddard Spaceflight Center, Solar System Ambassadors and Solar System Educators in remote parts of the country are able to bring scientists into their classroom visits or workshops as guest speakers. The goals of this collaboration are to provide special elements to the volunteers' event, allow scientists opportunities for education involvement with minimal effort, acquaint teachers with DLN services and enrich student's classroom learning experience.;

  6. Yeast Genetic Analysis Reveals the Involvement of Chromatin Reassembly Factors in Repressing HIV-1 Basal Transcription

    PubMed Central

    Respaldiza, Iñaki; Rodríguez-Gil, Alfonso; Gómez-Herreros, Fernando; Jimeno-González, Silvia; Jordan, Albert; Chávez, Sebastián

    2009-01-01

    Rebound of HIV viremia after interruption of anti-retroviral therapy is due to the small population of CD4+ T cells that remain latently infected. HIV-1 transcription is the main process controlling post-integration latency. Regulation of HIV-1 transcription takes place at both initiation and elongation levels. Pausing of RNA polymerase II at the 5′ end of HIV-1 transcribed region (5′HIV-TR), which is immediately downstream of the transcription start site, plays an important role in the regulation of viral expression. The activation of HIV-1 transcription correlates with the rearrangement of a positioned nucleosome located at this region. These two facts suggest that the 5′HIV-TR contributes to inhibit basal transcription of those HIV-1 proviruses that remain latently inactive. However, little is known about the cell elements mediating the repressive role of the 5′HIV-TR. We performed a genetic analysis of this phenomenon in Saccharomyces cerevisiae after reconstructing a minimal HIV-1 transcriptional system in this yeast. Unexpectedly, we found that the critical role played by the 5′HIV-TR in maintaining low levels of basal transcription in yeast is mediated by FACT, Spt6, and Chd1, proteins so far associated with chromatin assembly and disassembly during ongoing transcription. We confirmed that this group of factors plays a role in HIV-1 postintegration latency in human cells by depleting the corresponding human orthologs with shRNAs, both in HIV latently infected cell populations and in particular single-integration clones, including a latent clone with a provirus integrated in a highly transcribed gene. Our results indicate that chromatin reassembly factors participate in the establishment of the equilibrium between activation and repression of HIV-1 when it integrates into the human genome, and they open the possibility of considering these factors as therapeutic targets of HIV-1 latency. PMID:19148280

  7. Probabilistic Model Building Genetic Programming based on Estimation of Bayesian Network

    NASA Astrophysics Data System (ADS)

    Hasegawa, Yoshihiko; Iba, Hitoshi

    Genetic Programming (GP) is a powerful optimization algorithm, which employs the crossover for genetic operation. Because the crossover operator in GP randomly selects sub-trees, the building blocks may be destroyed by the crossover. Recently, algorithms called PMBGPs (Probabilistic Model Building GP) based on probabilistic techniques have been proposed in order to improve the problem mentioned above. We propose a new PMBGP employing Bayesian network for generating new individuals with a special chromosome called expanded parse tree, which much reduces a number of possible symbols at each node. Although the large number of symbols gives rise to the large conditional probability table and requires a lot of samples to estimate the interactions among nodes, a use of the expanded parse tree overcomes these problems. Computational experiments on two subjects demonstrate that our new PMBGP is much superior to prior probabilistic models.

  8. Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks

    NASA Astrophysics Data System (ADS)

    Ozcan, H. Kurtulus; Bilgili, Erdem; Sahin, Ulku; Ucan, O. Nuri; Bayat, Cuma

    2007-09-01

    Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network (ML-CNN) is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.

  9. Development of the carapacial ridge: implications for the evolution of genetic networks in turtle shell development.

    PubMed

    Moustakas, Jacqueline E

    2008-01-01

    Paleontologists and neontologists have long looked to development to understand the homologies of the dermal bones that form the "armor" of turtles, crocodiles, armadillos, and other vertebrates. This study shows molecular evidence supporting a dermomyotomal identity for the mesenchyme of the turtle carapacial ridge. The mesenchyme of the carapace primordium expresses Pax3, Twist1, Dermo1, En1, Sim1, and Gremlin at early stages and before overt ossification expresses Pax1. A hypothesis is proposed that this mesenchyme forms dermal bone in the turtle carapace. A comparison of regulatory gene expression in the primordia of the turtle carapace, the vertebrate limb, and the vertebral column implies the exaptation of key genetic networks in the development of the turtle shell. This work establishes a new role for this mesodermal compartment and highlights the importance of changes in genetic regulation in the evolution of morphology. PMID:18184355

  10. Transnational science and collaborative networks. The case of Genetics and Radiobiology in Mexico, 1950-1970.

    PubMed

    Barahona, Ana

    2015-01-01

    The transnational approach of the science and technology studies (S&TS) abandons the nation as a unit of analysis in order to understand the development of science history. It also abandons Euro-US-centred narratives in order to explain the role of international collaborative networks and the circulation of knowledge, people, artefacts and scientific practices. It is precisely under this perspective that the development of genetics and radiobiology in Mexico shall be analyzed, together with the pioneering work of the Mexican physician-turned-geneticist Alfonso León de Garay who spent two years in the Galton Laboratory in London under the supervision of Lionel Penrose. Upon his return de Garay funded the Genetics and Radiobiology Program of the National Commission of Nuclear Energy based on local needs and the aim of working beyond geographical limitations to thus facilitate the circulation of knowledge, practices and people. The three main lines of research conducted in the years after its foundation that were in line with international projects while responding to the national context were, first, cytogenetic studies of certain abnormalities, and the cytogenetics and anthropological studies of the Olympic Games held in Mexico in 1968; second, the study of the effects of radiation on hereditary material; and third, the study of population genetics in Drosophila and in Mexican indigenous groups. The program played a key role in reshaping the scientific careers of Mexican geneticists, and in transferring locally sourced research into broader networks. This case shows the importance of international collaborative networks and circulation in the constitution of national scientific elites, and also shows the national and transnational concerns that shaped local practices.

  11. Analysis and interpretation of RNA splicing alterations in genes involved in genetic disorders.

    PubMed

    Vreeswijk, Maaike P G; van der Klift, Heleen M

    2012-01-01

    Germ line mutations in genes involved in hereditary cancer syndromes, such as BRCA1 and BRCA2 in breast cancer and MSH2, MSH6, MLH1, and PSM2 in hereditary nonpolyposis colorectal cancer (HNPCC, more recently indicated as Lynch syndrome), confer a high risk to develop cancer. Mutation analysis in these genes has resulted in the identification of a large number of sequence variants, of which mutations causing frame shifts and nonsense codons are considered undoubtedly to be pathogenic. Many variants, however, cannot be classified as either disease-causing mutations or neutral variants and are therefore called unclassified variants (UVs). A subset of these variants may have an effect on RNA splicing. Appropriate RNA analysis will enable the characterization of the exact molecular nature of this effect and hence, is essential to determine the clinical relevance of the genomic variant. This chapter describes the design and implementation of RNA analysis as an indispensible tool in today's clinical diagnostic setting.

  12. The genetic basis of inherited anomalies of the teeth. Part 2: syndromes with significant dental involvement.

    PubMed

    Bailleul-Forestier, Isabelle; Berdal, Ariane; Vinckier, Frans; de Ravel, Thomy; Fryns, Jean Pierre; Verloes, Alain

    2008-01-01

    Teeth are specialized structural components of the craniofacial skeleton. Developmental defects occur either alone or in combination with other birth defects. In this paper, we review the dental anomalies in several multiple congenital anomaly (MCA) syndromes, in which the dental component is pivotal in the recognition of the phenotype and/or the molecular basis of the disorder is known. We will consider successively syndromic forms of amelogenesis imperfecta or enamel defects, dentinogenesis imperfecta (i.e. osteogenesis imperfecta) and other dentine anomalies. Focusing on dental aspects, we will review a selection of MCA syndromes associated with teeth number and/or shape anomalies. A better knowledge of the dental phenotype may contribute to an earlier diagnosis of some MCA syndromes involving teeth anomalies. They may serve as a diagnostic indicator or help confirm a syndrome diagnosis. PMID:18599376

  13. Genetic Algorithm for Solving Fuzzy Shortest Path Problem in a Network with mixed fuzzy arc lengths

    NASA Astrophysics Data System (ADS)

    Mahdavi, Iraj; Tajdin, Ali; Hassanzadeh, Reza; Mahdavi-Amiri, Nezam; Shafieian, Hosna

    2011-06-01

    We are concerned with the design of a model and an algorithm for computing a shortest path in a network having various types of fuzzy arc lengths. First, we develop a new technique for the addition of various fuzzy numbers in a path using α -cuts by proposing a linear least squares model to obtain membership functions for the considered additions. Then, using a recently proposed distance function for comparison of fuzzy numbers. we propose a new approach to solve the fuzzy APSPP using of genetic algorithm. Examples are worked out to illustrate the applicability of the proposed model.

  14. Sufficient and necessary conditions for Lyapunov stability of genetic networks with SUM regulatory logic.

    PubMed

    Zhou, Guopeng; Huang, Jinhua; Tian, Fengxia; Liao, Xiaoxin

    2015-08-01

    In this paper, a nonlinear model for genetic regulator networks (GRNs) with SUM regulatory logic is presented. Four sufficient and necessary conditions of global asymptotical stability and global exponential stability for the equilibrium point of the GRNs are proposed, respectively. Specifically, three weak sufficient conditions and corresponding corollaries are derived by using comparing theorem and Dini derivative method. Then, a famous GRN model is used as the example to illustrate the effectiveness of our theoretical results. Comparing to the results in the previous literature, some novel ideas, study methods and interesting results are explored.

  15. Color tongue image segmentation using fuzzy Kohonen networks and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Aimin; Shen, Lansun; Zhao, Zhongxu

    2000-04-01

    A Tongue Imaging and Analysis System is being developed to acquire digital color tongue images, and to automatically classify and quantify the tongue characteristics for traditional Chinese medical examinations. An important processing step is to segment the tongue pixels into two categories, the tongue body (no coating) and the coating. In this paper, we present a two-stage clustering algorithm that combines Fuzzy Kohonen Clustering Networks and Genetic Algorithm for the segmentation, of which the major concern is to increase the interclass distance and at the same time decrease the intraclass distance. Experimental results confirm the effectiveness of this algorithm.

  16. Noise-Aided Logic in an Electronic Analog of Synthetic Genetic Networks

    PubMed Central

    Hellen, Edward H.; Dana, Syamal K.; Kurths, Jürgen; Kehler, Elizabeth; Sinha, Sudeshna

    2013-01-01

    We report the experimental verification of noise-enhanced logic behaviour in an electronic analog of a synthetic genetic network, composed of two repressors and two constitutive promoters. We observe good agreement between circuit measurements and numerical prediction, with the circuit allowing for robust logic operations in an optimal window of noise. Namely, the input-output characteristics of a logic gate is reproduced faithfully under moderate noise, which is a manifestation of the phenomenon known as Logical Stochastic Resonance. The two dynamical variables in the system yield complementary logic behaviour simultaneously. The system is easily morphed from AND/NAND to OR/NOR logic. PMID:24124531

  17. Grinding precision forecasting in optical aspheric grinding using artificial neural network and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Jiang, Chen; Guo, Yinbiao; Yang, Qingqing; Han, Chunguang

    2010-10-01

    A new approach based on an artificial neural network (ANN) was presented for the prediction of machining precision of optical aspheric grinding. The ANN model is based on Globally Convergent Adaptive Quick Back Propagation algorithm (GCAOBP). A genetic algorithm (GA) was then applied to the trained ANN model to predict the gridding precision. The integrated GCAOBP-GA algorithm was successful in predicting the Root Mean Square of profile error (RMS) of optical aspheric workpiece in parallel grinding method using machining parameters. The results of experiments have shown that RMS of machined workpiece in parallel grinding can be predicted effectively through this approach.

  18. AN ARTIFICIAL NEURAL NETWORK EVALUATION OF TUBERCULOSIS USING GENETIC AND PHYSIOLOGICAL PATIENT DATA

    SciTech Connect

    Griffin, William O.; Darsey, Jerry A.; Hanna, Josh; Razorilova, Svetlana; Kitaev, Mikhael; Alisherov, Avtandiil; Tarasenko, Olga

    2010-04-12

    When doctors see more cases of patients with tell-tale symptoms of a disease, it is hoped that they will be able to recognize an infection administer treatment appropriately, thereby speeding up recovery for sick patients. We hope that our studies can aid in the detection of tuberculosis by using a computer model called an artificial neural network. Our model looks at patients with and without tuberculosis (TB). The data that the neural network examined came from the following: patient' age, gender, place, of birth, blood type, Rhesus (Rh) factor, and genes of the human Leukocyte Antigens (HLA) system (9q34.1) present in the Major Histocompatibility Complex. With availability in genetic data and good research, we hope to give them an advantage in the detection of tuberculosis. We try to mimic the doctor's experience with a computer test, which will learn from patient data the factors that contribute to TB.

  19. On the transient and steady-state estimates of interval genetic regulatory networks.

    PubMed

    Li, Ping; Lam, James; Shu, Zhan

    2010-04-01

    This paper is concerned with the transient and steady-state estimates of a class of genetic regulatory networks (GRNs). Some sufficient conditions, which do not only present the transient estimate but also provide the estimates of decay rate and decay coefficient of the GRN with interval parameter uncertainties (interval GRN), are established by means of linear matrix inequality (LMI) and Lyapunov-Krasovskii functional. Moreover, the steady-state estimate of the proposed GRN model is also investigated. Furthermore, it is well known that gene regulation is an intrinsically noisy process due to intracellular and extracellular noise perturbations and environmental fluctuations. Then, by utilizing stochastic differential equation theory, the obtained results are extended to the case with noise perturbations due to natural random fluctuations. All the conditions are expressed within the framework of LMIs, which can easily be computed by using standard numerical software. A three-gene network is provided to illustrate the effectiveness of the theoretical results.

  20. Optimal groundwater remediation using artificial neural networks and the genetic algorithm

    SciTech Connect

    Rogers, L.L.

    1992-08-01

    An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ``recycle`` or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models.

  1. An Artificial Neural Network Evaluation of Tuberculosis Using Genetic and Physiological Patient Data

    NASA Astrophysics Data System (ADS)

    Griffin, William O.; Hanna, Josh; Razorilova, Svetlana; Kitaev, Mikhael; Alisherov, Avtandiil; Darsey, Jerry A.; Tarasenko, Olga

    2010-04-01

    When doctors see more cases of patients with tell-tale symptoms of a disease, it is hoped that they will be able to recognize an infection administer treatment appropriately, thereby speeding up recovery for sick patients. We hope that our studies can aid in the detection of tuberculosis by using a computer model called an artificial neural network. Our model looks at patients with and without tuberculosis (TB). The data that the neural network examined came from the following: patient' age, gender, place, of birth, blood type, Rhesus (Rh) factor, and genes of the human Leukocyte Antigens (HLA) system (9q34.1) present in the Major Histocompatibility Complex. With availability in genetic data and good research, we hope to give them an advantage in the detection of tuberculosis. We try to mimic the doctor's experience with a computer test, which will learn from patient data the factors that contribute to TB.

  2. An integrated genetic, genomic and systems approach defines gene networks regulated by the interaction of light and carbon signaling pathways in Arabidopsis

    PubMed Central

    Thum, Karen E; Shin, Michael J; Gutiérrez, Rodrigo A; Mukherjee, Indrani; Katari, Manpreet S; Nero, Damion; Shasha, Dennis; Coruzzi, Gloria M

    2008-01-01

    Background Light and carbon are two important interacting signals affecting plant growth and development. The mechanism(s) and/or genes involved in sensing and/or mediating the signaling pathways involving these interactions are unknown. This study integrates genetic, genomic and systems approaches to identify a genetically perturbed gene network that is regulated by the interaction of carbon and light signaling in Arabidopsis. Results Carbon and light insensitive (cli) mutants were isolated. Microarray data from cli186 is analyzed to identify the genes, biological processes and gene networks affected by the integration of light and carbon pathways. Analysis of this data reveals 966 genes regulated by light and/or carbon signaling in wild-type. In cli186, 216 of these light/carbon regulated genes are misregulated in response to light and/or carbon treatments where 78% are misregulated in response to light and carbon interactions. Analysis of the gene lists show that genes in the biological processes "energy" and "metabolism" are over-represented among the 966 genes regulated by carbon and/or light in wild-type, and the 216 misregulated genes in cli186. To understand connections among carbon and/or light regulated genes in wild-type and the misregulated genes in cli186, the microarray data is interpreted in the context of metabolic and regulatory networks. The network created from the 966 light/carbon regulated genes in wild-type, reveals that cli186 is affected in the light and/or carbon regulation of a network of 60 connected genes, including six transcription factors. One transcription factor, HAT22 appears to be a regulatory "hub" in the cli186 network as it shows regulatory connections linking a metabolic network of genes involved in "amino acid metabolism", "C-compound/carbohydrate metabolism" and "glycolysis/gluconeogenesis". Conclusion The global misregulation of gene networks controlled by light and carbon signaling in cli186 indicates that it represents

  3. Network dysfunction of emotional and cognitive processes in those at genetic risk of bipolar disorder.

    PubMed

    Breakspear, Michael; Roberts, Gloria; Green, Melissa J; Nguyen, Vinh T; Frankland, Andrew; Levy, Florence; Lenroot, Rhoshel; Mitchell, Philip B

    2015-11-01

    The emotional and cognitive vulnerabilities that precede the development of bipolar disorder are poorly understood. The inferior frontal gyrus-a key cortical hub for the integration of cognitive and emotional processes-exhibits both structural and functional changes in bipolar disorder, and is also functionally impaired in unaffected first-degree relatives, showing diminished engagement during inhibition of threat-related emotional stimuli. We hypothesized that this functional impairment of the inferior frontal gyrus in those at genetic risk of bipolar disorder reflects the dysfunction of broader network dynamics underlying the coordination of emotion perception and cognitive control. To test this, we studied effective connectivity in functional magnetic resonance imaging data acquired from 41 first-degree relatives of patients with bipolar disorder, 45 matched healthy controls and 55 participants with established bipolar disorder. Dynamic causal modelling was used to model the neuronal interaction between key regions associated with fear perception (the anterior cingulate), inhibition (the left dorsolateral prefrontal cortex) and the region upon which these influences converge, namely the inferior frontal gyrus. Network models that embodied non-linear, hierarchical relationships were the most strongly supported by data from our healthy control and bipolar participants. We observed a marked difference in the hierarchical influence of the anterior cingulate on the effective connectivity from the dorsolateral prefrontal cortex to the inferior frontal gyrus that is unique to the at-risk cohort. Non-specific, non-hierarchical mechanisms appear to compensate for this network disturbance. We thus establish a specific network disturbance suggesting dysfunction in the processes that support hierarchical relationships between emotion and cognitive control in those at high genetic risk for bipolar disorder. PMID:26373604

  4. Network dysfunction of emotional and cognitive processes in those at genetic risk of bipolar disorder.

    PubMed

    Breakspear, Michael; Roberts, Gloria; Green, Melissa J; Nguyen, Vinh T; Frankland, Andrew; Levy, Florence; Lenroot, Rhoshel; Mitchell, Philip B

    2015-11-01

    The emotional and cognitive vulnerabilities that precede the development of bipolar disorder are poorly understood. The inferior frontal gyrus-a key cortical hub for the integration of cognitive and emotional processes-exhibits both structural and functional changes in bipolar disorder, and is also functionally impaired in unaffected first-degree relatives, showing diminished engagement during inhibition of threat-related emotional stimuli. We hypothesized that this functional impairment of the inferior frontal gyrus in those at genetic risk of bipolar disorder reflects the dysfunction of broader network dynamics underlying the coordination of emotion perception and cognitive control. To test this, we studied effective connectivity in functional magnetic resonance imaging data acquired from 41 first-degree relatives of patients with bipolar disorder, 45 matched healthy controls and 55 participants with established bipolar disorder. Dynamic causal modelling was used to model the neuronal interaction between key regions associated with fear perception (the anterior cingulate), inhibition (the left dorsolateral prefrontal cortex) and the region upon which these influences converge, namely the inferior frontal gyrus. Network models that embodied non-linear, hierarchical relationships were the most strongly supported by data from our healthy control and bipolar participants. We observed a marked difference in the hierarchical influence of the anterior cingulate on the effective connectivity from the dorsolateral prefrontal cortex to the inferior frontal gyrus that is unique to the at-risk cohort. Non-specific, non-hierarchical mechanisms appear to compensate for this network disturbance. We thus establish a specific network disturbance suggesting dysfunction in the processes that support hierarchical relationships between emotion and cognitive control in those at high genetic risk for bipolar disorder.

  5. Genetic Networks of Liver Metabolism Revealed by Integration of Metabolic and Transcriptional Profiling

    PubMed Central

    Ferrara, Christine T.; Wang, Ping; Neto, Elias Chaibub; Stevens, Robert D.; Bain, James R.; Wenner, Brett R.; Ilkayeva, Olga R.; Keller, Mark P.; Blasiole, Daniel A.; Kendziorski, Christina; Yandell, Brian S.; Newgard, Christopher B.; Attie, Alan D.

    2008-01-01

    Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes. PMID:18369453

  6. Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

    NASA Technical Reports Server (NTRS)

    Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

    1998-01-01

    Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

  7. Reveal, a general reverse engineering algorithm for inference of genetic network architectures.

    PubMed

    Liang, S; Fuhrman, S; Somogyi, R

    1998-01-01

    Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

  8. Non-coding RNAs and a layered architecture of genetic networks

    NASA Astrophysics Data System (ADS)

    Zhdanov, Vladimir P.

    2010-12-01

    In eukaryotic cells, protein-coding sequences constitute a relatively small part of the genome. The rest of the genome is transcribed to non-coding RNAs (ncRNAs). Such RNAs form the cornerstone of a regulatory network that operates in parallel with the protein network. Their biological functions are based primarily on the ability to pair with and deactivate target messenger RNAs (mRNAs). To clarify the likely role of ncRNAs in complex genetic networks, we present and comprehensively analyze a kinetic model of one of the key counterparts of the network architectures. Specifically, the genes transcribed to ncRNAs are considered to interplay with a hierarchical two-layer set of genes transcribed to mRNAs. The genes forming the bottom layer are regulated from the top and negatively self-regulated. If the former regulation is positive, the dependence of the RNA populations on the governing parameters is found to be often non-monotonous. Specifically, the model predicts bistability. If the regulation is negative, the dependence of the RNA populations on the governing parameters is monotonous. In particular, the population of the mRNAs, corresponding to the genes forming the bottom layer, is nearly constant.

  9. On a vector space representation in genetic algorithms for sensor scheduling in wireless sensor networks.

    PubMed

    Martins, F V C; Carrano, E G; Wanner, E F; Takahashi, R H C; Mateus, G R; Nakamura, F G

    2014-01-01

    Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system's dynamics. To the authors' knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances. PMID:24102647

  10. On a vector space representation in genetic algorithms for sensor scheduling in wireless sensor networks.

    PubMed

    Martins, F V C; Carrano, E G; Wanner, E F; Takahashi, R H C; Mateus, G R; Nakamura, F G

    2014-01-01

    Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system's dynamics. To the authors' knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances.

  11. Genetic analysis of functions involved in adhesion of Pseudomonas putida to seeds.

    PubMed

    Espinosa-Urgel, M; Salido, A; Ramos, J L

    2000-05-01

    Many agricultural uses of bacteria require the establishment of efficient bacterial populations in the rhizosphere, for which colonization of plant seeds often constitutes a critical first step. Pseudomonas putida KT2440 is a strain that colonizes the rhizosphere of a number of agronomically important plants at high population densities. To identify the functions involved in initial seed colonization by P. putida KT2440, we subjected this strain to transposon mutagenesis and screened for mutants defective in attachment to corn seeds. Eight different mutants were isolated and characterized. While all of them showed reduced attachment to seeds, only two had strong defects in their adhesion to abiotic surfaces (glass and different plastics). Sequences of the loci affected in all eight mutants were obtained. None of the isolated genes had previously been described in P. putida, although four of them showed clear similarities with genes of known functions in other organisms. They corresponded to putative surface and membrane proteins, including a calcium-binding protein, a hemolysin, a peptide transporter, and a potential multidrug efflux pump. One other showed limited similarities with surface proteins, while the remaining three presented no obvious similarities with known genes, indicating that this study has disclosed novel functions.

  12. Identification of transcriptional networks involved in peroxisome proliferator chemical-induced hepatocyte proliferation

    EPA Science Inventory

    Peroxisome proliferator chemical (PPC) exposure leads to increases in rodent liver tumors through a non-genotoxic mode of action (MOA). The PPC MOA includes increased oxidative stress, hepatocyte proliferation and decreased apoptosis. We investigated the putative genetic regulato...

  13. Dynamical properties of gene regulatory networks involved in long-term potentiation

    PubMed Central

    Nido, Gonzalo S.; Ryan, Margaret M.; Benuskova, Lubica; Williams, Joanna M.

    2015-01-01

    The long-lasting enhancement of synaptic effectiveness known as long-term potentiation (LTP) is considered to be the cellular basis of long-term memory. LTP elicits changes at the cellular and molecular level, including temporally specific alterations in gene networks. LTP can be seen as a biological process in which a transient signal sets a new homeostatic state that is “remembered” by cellular regulatory systems. Previously, we have shown that early growth response (Egr) transcription factors are of fundamental importance to gene networks recruited early after LTP induction. From a systems perspective, we hypothesized that these networks will show less stable architecture, while networks recruited later will exhibit increased stability, being more directly related to LTP consolidation. Using random Boolean network (RBN) simulations we found that the network derived at 24 h was markedly more stable than those derived at 20 min or 5 h post-LTP. This temporal effect on the vulnerability of the networks is mirrored by what is known about the vulnerability of LTP and memory itself. Differential gene co-expression analysis further highlighted the importance of the Egr family and found a rapid enrichment in connectivity at 20 min, followed by a systematic decrease, providing a potential explanation for the down-regulation of gene expression at 24 h documented in our preceding studies. We also found that the architecture exhibited by a control and the 24 h LTP co-expression networks fit well to a scale-free distribution, known to be robust against perturbations. By contrast the 20 min and 5 h networks showed more truncated distributions. These results suggest that a new homeostatic state is achieved 24 h post-LTP. Together, these data present an integrated view of the genomic response following LTP induction by which the stability of the networks regulated at different times parallel the properties observed at the synapse. PMID:26300724

  14. Construction of protein interaction network involved in lung adenocarcinomas using a novel algorithm

    PubMed Central

    Chen, Juan; Yang, Hai-Tao; Li, Zhu; Xu, Ning; Yu, Bo; Xu, Jun-Ping; Zhao, Pei-Ge; Wang, Yan; Zhang, Xiu-Juan; Lin, Dian-Jie

    2016-01-01

    Studies that only assess differentially-expressed (DE) genes do not contain the information required to investigate the mechanisms of diseases. A complete knowledge of all the direct and indirect interactions between proteins may act as a significant benchmark in the process of forming a comprehensive description of cellular mechanisms and functions. The results of protein interaction network studies are often inconsistent and are based on various methods. In the present study, a combined network was constructed using selected gene pairs, following the conversion and combination of the scores of gene pairs that were obtained across multiple approaches by a novel algorithm. Samples from patients with and without lung adenocarcinoma were compared, and the RankProd package was used to identify DE genes. The empirical Bayesian (EB) meta-analysis approach, the search tool for the retrieval of interacting genes/proteins database (STRING), the weighted gene coexpression network analysis (WGCNA) package and the differentially-coexpressed genes and links package (DCGL) were used for network construction. A combined network was also constructed with a novel rank-based algorithm using a combined score. The topological features of the 5 networks were analyzed and compared. A total of 941 DE genes were screened. The topological analysis indicated that the gene interaction network constructed using the WGCNA method was more likely to produce a small-world property, which has a small average shortest path length and a large clustering coefficient, whereas the combined network was confirmed to be a scale-free network. Gene pairs that were identified using the novel combined method were mostly enriched in the cell cycle and p53 signaling pathway. The present study provided a novel perspective to the network-based analysis. Each method has advantages and disadvantages. Compared with single methods, the combined algorithm used in the present study may provide a novel method to

  15. The Role of Human Transportation Networks in Mediating the Genetic Structure of Seasonal Influenza in the United States

    PubMed Central

    Bozick, Brooke A.; Real, Leslie A.

    2015-01-01

    Recent studies have demonstrated the importance of accounting for human mobility networks when modeling epidemics in order to accurately predict spatial dynamics. However, little is known about the impact these movement networks have on the genetic structure of pathogen populations and whether these effects are scale-dependent. We investigated how human movement along the aviation and commuter networks contributed to intra-seasonal genetic structure of influenza A epidemics in the continental United States using spatially-referenced hemagglutinin nucleotide sequences collected from 2003–2013 for both the H3N2 and H1N1 subtypes. Comparative analysis of these transportation networks revealed that the commuter network is highly spatially-organized and more heavily traveled than the aviation network, which instead is characterized by high connectivity between all state pairs. We found that genetic distance between sequences often correlated with distance based on interstate commuter network connectivity for the H1N1 subtype, and that this correlation was not as prevalent when geographic distance or aviation network connectivity distance was assessed against genetic distance. However, these patterns were not as apparent for the H3N2 subtype at the scale of the continental United States. Finally, although sequences were spatially referenced at the level of the US state of collection, a community analysis based on county to county commuter connections revealed that commuting communities did not consistently align with state geographic boundaries, emphasizing the need for the greater availability of more specific sequence location data. Our results highlight the importance of utilizing host movement data in characterizing the underlying genetic structure of pathogen populations and demonstrate a need for a greater understanding of the differential effects of host movement networks on pathogen transmission at various spatial scales. PMID:26086273

  16. A Protein-Based Genetic Screening Uncovers Mutants Involved in Phytochrome Signaling in Arabidopsis

    PubMed Central

    Zhu, Ling; Xin, Ruijiao; Huq, Enamul

    2016-01-01

    Plants perceive red and far-red region of the light spectrum to regulate photomorphogenesis through a family of photoreceptors called phytochromes. Phytochromes transduce the light signals to trigger a cascade of downstream gene regulation in part via a subfamily of bHLH transcription factors called Phytochrome Interacting Factors (PIFs). As the repressors of light signaling pathways, most PIFs are phosphorylated and degraded through the ubiquitin/26S proteasome pathway in response to light. The mechanisms involved in the phosphorylation and degradation of PIFs have not been fully understood yet. Here we used an EMS mutagenesis and luminescent imaging system to identify mutants defective in the degradation of one of the PIFs, called PIF1. We identified five mutants named stable PIF (spf) that showed reduced degradation of PIF1 under light treatment in both luminescent imaging and immunoblot assays. The amounts of PIF1 in spf3, spf4, and spf5 were similar to a PIF1 missense mutant (PIF1–3M) that lacks interactions between PIF1 and phyA/phyB under light. The hypocotyl lengths of spf1 and spf2 were slightly longer under red light compared to the LUC-PIF1 control, while only spf1 displayed weak phenotype under far-red light conditions. Interestingly, the spf3, spf4, and spf5 displayed high abundance of PIF1, yet the hypocotyl lengths were similar to the wild type under these conditions. Cloning and characterization of these mutants will help identify key players in the light signaling pathways including, the light-regulated kinase(s) and the E3 ligase(s) necessary for the light-induced degradation of PIFs. PMID:27499759

  17. A Protein-Based Genetic Screening Uncovers Mutants Involved in Phytochrome Signaling in Arabidopsis.

    PubMed

    Zhu, Ling; Xin, Ruijiao; Huq, Enamul

    2016-01-01

    Plants perceive red and far-red region of the light spectrum to regulate photomorphogenesis through a family of photoreceptors called phytochromes. Phytochromes transduce the light signals to trigger a cascade of downstream gene regulation in part via a subfamily of bHLH transcription factors called Phytochrome Interacting Factors (PIFs). As the repressors of light signaling pathways, most PIFs are phosphorylated and degraded through the ubiquitin/26S proteasome pathway in response to light. The mechanisms involved in the phosphorylation and degradation of PIFs have not been fully understood yet. Here we used an EMS mutagenesis and luminescent imaging system to identify mutants defective in the degradation of one of the PIFs, called PIF1. We identified five mutants named stable PIF (spf) that showed reduced degradation of PIF1 under light treatment in both luminescent imaging and immunoblot assays. The amounts of PIF1 in spf3, spf4, and spf5 were similar to a PIF1 missense mutant (PIF1-3M) that lacks interactions between PIF1 and phyA/phyB under light. The hypocotyl lengths of spf1 and spf2 were slightly longer under red light compared to the LUC-PIF1 control, while only spf1 displayed weak phenotype under far-red light conditions. Interestingly, the spf3, spf4, and spf5 displayed high abundance of PIF1, yet the hypocotyl lengths were similar to the wild type under these conditions. Cloning and characterization of these mutants will help identify key players in the light signaling pathways including, the light-regulated kinase(s) and the E3 ligase(s) necessary for the light-induced degradation of PIFs. PMID:27499759

  18. Genetic Manipulation of Leishmania donovani to Explore the Involvement of Argininosuccinate Synthase in Oxidative Stress Management.

    PubMed

    Sardar, Abul Hasan; Jardim, Armando; Ghosh, Ayan Kumar; Mandal, Abhishek; Das, Sushmita; Saini, Savita; Abhishek, Kumar; Singh, Ruby; Verma, Sudha; Kumar, Ajay; Das, Pradeep

    2016-03-01

    Reactive oxygen and nitrogen species (ROS and RNS) produced by the phagocytic cells are the most common arsenals used to kill the intracellular pathogens. However, Leishmania, an intracellular pathogen, has evolved mechanisms to survive by counterbalancing the toxic oxygen metabolites produced during infection. Polyamines, the major contributor in this anti-oxidant machinery, are largely dependent on the availability of L-arginine in the intracellular milieu. Argininosuccinate synthase (ASS) plays an important role as the rate-limiting step required for converting L-citrulline to argininosuccinate to provide arginine for an assortment of metabolic processes. Leishmania produce an active ASS enzyme, yet it has an incomplete urea cycle as it lacks an argininosuccinate lyase (ASL). There is no evidence for endogenous synthesis of L-arginine in Leishmania, which suggests that these parasites salvage L-arginine from extracellular milieu and makes the biological function of ASS and the production of argininosuccinate in Leishmania unclear. Our previous quantitative proteomic analysis of Leishmania promastigotes treated with sub-lethal doses of ROS, RNS, or a combination of both, led to the identification of several differentially expressed proteins which included ASS. To assess the involvement of ASS in stress management, a mutant cell line with greatly reduced ASS activity was created by a double-targeted gene replacement strategy in L. donovani promastigote. Interestingly, LdASS is encoded by three copies of allele, but Western blot analysis showed the third allele did not appear to express ASS. The free thiol levels in the mutant LdASS-/-/+ cell line were decreased. Furthermore, the cell viability in L-arginine depleted medium was greatly attenuated on exposure to different stress environments and was adversely impacted in its ability to infect mice. These findings suggest that ASS is important for Leishmania donovani to counterbalance the stressed environments

  19. Genetic Manipulation of Leishmania donovani to Explore the Involvement of Argininosuccinate Synthase in Oxidative Stress Management.

    PubMed

    Sardar, Abul Hasan; Jardim, Armando; Ghosh, Ayan Kumar; Mandal, Abhishek; Das, Sushmita; Saini, Savita; Abhishek, Kumar; Singh, Ruby; Verma, Sudha; Kumar, Ajay; Das, Pradeep

    2016-03-01

    Reactive oxygen and nitrogen species (ROS and RNS) produced by the phagocytic cells are the most common arsenals used to kill the intracellular pathogens. However, Leishmania, an intracellular pathogen, has evolved mechanisms to survive by counterbalancing the toxic oxygen metabolites produced during infection. Polyamines, the major contributor in this anti-oxidant machinery, are largely dependent on the availability of L-arginine in the intracellular milieu. Argininosuccinate synthase (ASS) plays an important role as the rate-limiting step required for converting L-citrulline to argininosuccinate to provide arginine for an assortment of metabolic processes. Leishmania produce an active ASS enzyme, yet it has an incomplete urea cycle as it lacks an argininosuccinate lyase (ASL). There is no evidence for endogenous synthesis of L-arginine in Leishmania, which suggests that these parasites salvage L-arginine from extracellular milieu and makes the biological function of ASS and the production of argininosuccinate in Leishmania unclear. Our previous quantitative proteomic analysis of Leishmania promastigotes treated with sub-lethal doses of ROS, RNS, or a combination of both, led to the identification of several differentially expressed proteins which included ASS. To assess the involvement of ASS in stress management, a mutant cell line with greatly reduced ASS activity was created by a double-targeted gene replacement strategy in L. donovani promastigote. Interestingly, LdASS is encoded by three copies of allele, but Western blot analysis showed the third allele did not appear to express ASS. The free thiol levels in the mutant LdASS-/-/+ cell line were decreased. Furthermore, the cell viability in L-arginine depleted medium was greatly attenuated on exposure to different stress environments and was adversely impacted in its ability to infect mice. These findings suggest that ASS is important for Leishmania donovani to counterbalance the stressed environments

  20. Genetic Manipulation of Leishmania donovani to Explore the Involvement of Argininosuccinate Synthase in Oxidative Stress Management

    PubMed Central

    Sardar, Abul Hasan; Jardim, Armando; Ghosh, Ayan Kumar; Mandal, Abhishek; Das, Sushmita; Saini, Savita; Abhishek, Kumar; Singh, Ruby; Verma, Sudha; Kumar, Ajay; Das, Pradeep

    2016-01-01

    Reactive oxygen and nitrogen species (ROS and RNS) produced by the phagocytic cells are the most common arsenals used to kill the intracellular pathogens. However, Leishmania, an intracellular pathogen, has evolved mechanisms to survive by counterbalancing the toxic oxygen metabolites produced during infection. Polyamines, the major contributor in this anti-oxidant machinery, are largely dependent on the availability of L-arginine in the intracellular milieu. Argininosuccinate synthase (ASS) plays an important role as the rate-limiting step required for converting L-citrulline to argininosuccinate to provide arginine for an assortment of metabolic processes. Leishmania produce an active ASS enzyme, yet it has an incomplete urea cycle as it lacks an argininosuccinate lyase (ASL). There is no evidence for endogenous synthesis of L-arginine in Leishmania, which suggests that these parasites salvage L-arginine from extracellular milieu and makes the biological function of ASS and the production of argininosuccinate in Leishmania unclear. Our previous quantitative proteomic analysis of Leishmania promastigotes treated with sub-lethal doses of ROS, RNS, or a combination of both, led to the identification of several differentially expressed proteins which included ASS. To assess the involvement of ASS in stress management, a mutant cell line with greatly reduced ASS activity was created by a double-targeted gene replacement strategy in L. donovani promastigote. Interestingly, LdASS is encoded by three copies of allele, but Western blot analysis showed the third allele did not appear to express ASS. The free thiol levels in the mutant LdASS-/-/+ cell line were decreased. Furthermore, the cell viability in L-arginine depleted medium was greatly attenuated on exposure to different stress environments and was adversely impacted in its ability to infect mice. These findings suggest that ASS is important for Leishmania donovani to counterbalance the stressed environments

  1. Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices

    NASA Astrophysics Data System (ADS)

    Chen, Yan; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

    The key in stock trading model is to take the right actions for trading at the right time, primarily based on the accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to creating a stock trading model. In this paper, we propose a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are three important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the candlestick charts according to the real time stock prices. Second, we combine RTU-GNP with a Sarsa learning algorithm to create the programs efficiently. Also, sub-nodes are introduced in each judgment and processing node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. Third, a Real Time Updating system has been firstly introduced in our paper considering the change of the trend of stock prices. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without real time updating. We also compared the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.

  2. Comparison of Deterministic and Stochastic Models of the lac Operon Genetic Network

    PubMed Central

    Stamatakis, Michail; Mantzaris, Nikos V.

    2009-01-01

    The lac operon has been a paradigm for genetic regulation with positive feedback, and several modeling studies have described its dynamics at various levels of detail. However, it has not yet been analyzed how stochasticity can enrich the system's behavior, creating effects that are not observed in the deterministic case. To address this problem we use a comparative approach. We develop a reaction network for the dynamics of the lac operon genetic switch and derive corresponding deterministic and stochastic models that incorporate biological details. We then analyze the effects of key biomolecular mechanisms, such as promoter strength and binding affinities, on the behavior of the models. No assumptions or approximations are made when building the models other than those utilized in the reaction network. Thus, we are able to carry out a meaningful comparison between the predictions of the two models to demonstrate genuine effects of stochasticity. Such a comparison reveals that in the presence of stochasticity, certain biomolecular mechanisms can profoundly influence the region where the system exhibits bistability, a key characteristic of the lac operon dynamics. For these cases, the temporal asymptotic behavior of the deterministic model remains unchanged, indicating a role of stochasticity in modulating the behavior of the system. PMID:19186128

  3. Mitochondrial 12S rRNA A827G mutation is involved in the genetic susceptibility to aminoglycoside ototoxicity

    SciTech Connect

    Xing Guangqian; Chen Zhibin; Wei Qinjun; Tian Huiqin; Li Xiaolu; Zhou Aidong; Bu Xingkuan; Cao Xin . E-mail: caoxin@njmu.edu.cn

    2006-08-11

    We have analyzed the clinical and molecular characterization of a Chinese family with aminoglycoside-induced and non-syndromic hearing impairment. Clinical evaluations revealed that only those family members who had a history of exposure to aminoglycoside antibiotics subsequently developed hearing loss, suggesting mitochondrial genome involvement. Sequence analysis of the mitochondrial 12S rRNA and tRNA{sup Ser(UCN)} genes led to the identification of a homoplasmic A827G mutation in all maternal relatives, a mutation that was identified previously in a few sporadic patients and in another Chinese family with non-syndromic deafness. The pathogenicity of the A827G mutation is strongly supported by the occurrence of the same mutation in two independent families and several genetically unrelated subjects. The A827G mutation is located at the A-site of the mitochondrial 12S rRNA gene which is highly conserved in mammals. It is possible that the alteration of the tertiary or quaternary structure of this rRNA by the A827G mutation may lead to mitochondrial dysfunction, thereby playing a role in the pathogenesis of hearing loss and aminoglycoside hypersensitivity. However, incomplete penetrance of hearing impairment indicates that the A827G mutation itself is not sufficient to produce clinical phenotype but requires the involvement of modifier factors for the phenotypic expression. Indeed, aminoglycosides may contribute to the phenotypic manifestation of the A827G mutation in this family. In contrast with the congenital or early-onset hearing impairment in another Chinese family carrying the A827G mutation, three patients in this pedigree developed hearing loss only after use of aminoglycosides. This discrepancy likely reflects the difference of genetic backgrounds, either mitochondrial haplotypes or nuclear modifier genes, between two families.

  4. Inference of a Transcriptional Network Involved in Chemical Inhibition of Estrogen Synthesis in Fathead Minnow

    EPA Science Inventory

    A variety of chemicals in the environment have the potential to inhibit aromatase, an enzyme critical to estrogen synthesis. We examined the responses of female fathead minnows (Pimephales promelas) to a model aromatase inhibitor, fadrozole, using transcriptional network inferen...

  5. Modeling the Normal and Neoplastic Cell Cycle with 'Realistic Boolean Genetic Networks': Their Application for Understanding Carcinogenesis and Assessing Therapeutic Strategies

    NASA Technical Reports Server (NTRS)

    Szallasi, Zoltan; Liang, Shoudan

    2000-01-01

    In this paper we show how Boolean genetic networks could be used to address complex problems in cancer biology. First, we describe a general strategy to generate Boolean genetic networks that incorporate all relevant biochemical and physiological parameters and cover all of their regulatory interactions in a deterministic manner. Second, we introduce 'realistic Boolean genetic networks' that produce time series measurements very similar to those detected in actual biological systems. Third, we outline a series of essential questions related to cancer biology and cancer therapy that could be addressed by the use of 'realistic Boolean genetic network' modeling.

  6. Identification of causal genetic drivers of human disease through systems-level analysis of regulatory networks

    PubMed Central

    Chen, James C.; Alvarez, Mariano J.; Talos, Flaminia; Dhruv, Harshil; Rieckhof, Gabrielle E.; Iyer, Archana; Diefes, Kristin L.; Aldape, Kenneth; Berens, Michael; Shen, Michael M.; Califano, Andrea

    2014-01-01

    SUMMARY Identification of driver mutations in human diseases is often limited by cohort size and availability of appropriate statistical models. We propose a novel framework for the systematic discovery of genetic alterations that are causal determinants of disease, by prioritizing genes upstream of functional disease drivers, within regulatory networks inferred de novo from experimental data. We tested this framework by identifying the genetic determinants of the mesenchymal subtype of glioblastoma. Our analysis uncovered KLHL9 deletions as upstream activators of two previously established master regulators of the subtype, C/EBPβ and C/EBPδ. Rescue of KLHL9 expression induced proteasomal degradation of C/EBP proteins, abrogated the mesenchymal signature, and reduced tumor viability in vitro and in vivo. Deletions of KLHL9 were confirmed in >50% of mesenchymal cases in an independent cohort, thus representing the most frequent genetic determinant of the subtype. The method generalized to study other human diseases, including breast cancer and Alzheimer’s disease. PMID:25303533

  7. Genetic and environmental factors affecting cryptic variations in gene regulatory networks

    PubMed Central

    2013-01-01

    Background Cryptic genetic variation (CGV) is considered to facilitate phenotypic evolution by producing visible variations in response to changes in the internal and/or external environment. Several mechanisms enabling the accumulation and release of CGVs have been proposed. In this study, we focused on gene regulatory networks (GRNs) as an important mechanism for producing CGVs, and examined how interactions between GRNs and the environment influence the number of CGVs by using individual-based simulations. Results Populations of GRNs were allowed to evolve under various stabilizing selections, and we then measured the number of genetic and phenotypic variations that had arisen. Our results showed that CGVs were not depleted irrespective of the strength of the stabilizing selection for each phenotype, whereas the visible fraction of genetic variation in a population decreased with increasing strength of selection. On the other hand, increasing the number of different environments that individuals encountered within their lifetime (i.e., entailing plastic responses to multiple environments) suppressed the accumulation of CGVs, whereas the GRNs with more genes and interactions were favored in such heterogeneous environments. Conclusions Given the findings that the number of CGVs in a population was largely determined by the size (order) of GRNs, we propose that expansion of GRNs and adaptation to novel environments are mutually facilitating and sustainable sources of evolvability and hence the origins of biological diversity and complexity. PMID:23622056

  8. Genome-level analysis of genetic regulation of liver gene expression networks

    SciTech Connect

    Gatti, Daniel; Maki, Akira; Chesler, Elissa J; Kirova, Roumyana; Kosyk, Oksana; Lu, Lu; Manly, Kenneth; Matthews, Douglas B.; Qu, Yanhua; Williams, Robert; Perkins, Andy; Langston, Michael A; Threadgill, David; Rusyn, Ivan

    2007-01-01

    Liver is the primary site for metabolism of nutrients, drugs and chemical agents. While metabolic pathways are complex and tightly regulated, genetic variation among individuals, reflected in variation in gene expression levels, introduces complexity into research on liver disease. This study aimed to dissect genetic networks that control liver gene expression by combining largescale quantitative mRNA expression analysis with genetic mapping in a reference population of BXD recombinant inbred mouse strains for which extensive SNP, haplotype and phenotypic data is publicly available. We profiled gene expression in livers of naive mice of both sexes from C57BL/6J, DBA/2J, B6D2F1, and 37 BXD strains using Agilent oligonucleotide microarrays. This data was used to map quantitative trait loci (QTLs) responsible for variation in expression of about 19,000 transcripts. We identified polymorphic cis- and trans-acting loci, including several loci that control expression of large numbers of genes in liver, by comparing the physical transcript position with the location of the controlling QTL. The data is available through a public web-based resource (www.genenetwork.org) that allows custom data mining, identification of co-regulated transcripts and correlated phenotypes, cross-tissue and -species comparisons, as well as testing of a broad array of hypotheses.

  9. Genetic regulation analysis reveals involvement of tumor necrosis factor and alpha-induced protein 3 in stress response in mice.

    PubMed

    Xu, Jian; Dai, Aihua; Chen, Qi; Liu, Xiaorong; Zhang, Yu; Wang, Hongmei; Li, Haizhen; Chen, Ying; Cao, Maohong

    2016-01-15

    In order to study whether Tnfaip3 is related to stress response and further to find it's genetic regulation, we use C57BL/6J (B6) and DBA/2 (D2) mice to built the model of chronic unpredictable mild stress. RT-PCR, Western blotting and immunohistochemistry were used for studying the expression variation of Tnfaip3 in hippocampus tissue of B6 and D2 mice after being stressed. We found that the expression of Tnfaip3 was more remarkably increased in chronic unpredictable stress models than that in untreated mice (P<0.05). It is indicated that Tnfaip3 might take part in the process of stress response. The expression of Tnfaip3 is regulated by a cis-acting quantitative trait locus (cis-eQTL). We identified 5 genes are controlled by Tnfaip3 and the expression of 64 genes highly associated with Tnfaip3, 9 of those have formerly been participate in stress related pathways. In order to estimate the relationship between Tnfaip3 and its downstream genes or network members, we transfected SH-SY5Y cells with Tnfaip3 siRNA leading to down-regulation of Tnfaip3 mRNA. We confirmed a significant influence of Tnfaip3 depletion on the expression of Tsc22d3, Pex7, Rap2a, Slc2a3, and Gap43. These validated downstream genes and members of Tnfaip3 gene network provide us new insight into the biological mechanisms of Tnfaip3 in chronic unpredictable stress. PMID:26546835

  10. A Web-based database of genetic association studies in cutaneous melanoma enhanced with network-driven data exploration tools

    PubMed Central

    Athanasiadis, Emmanouil I.; Antonopoulou, Kyriaki; Chatzinasiou, Foteini; Lill, Christina M.; Bourdakou, Marilena M.; Sakellariou, Argiris; Kypreou, Katerina; Stefanaki, Irene; Evangelou, Evangelos; Ioannidis, John P.A.; Bertram, Lars; Stratigos, Alexander J.; Spyrou, George M.

    2014-01-01

    The publicly available online database MelGene provides a comprehensive, regularly updated, collection of data from genetic association studies in cutaneous melanoma (CM), including random-effects meta-analysis results of all eligible polymorphisms. The updated database version includes data from 192 publications with information on 1114 significantly associated polymorphisms across 280 genes, along with new front-end and back-end capabilities. Various types of relationships between data are calculated and visualized as networks. We constructed 13 different networks containing the polymorphisms and the genes included in MelGene. We explored the derived network representations under the following questions: (i) are there nodes that deserve consideration regarding their network connectivity characteristics? (ii) What is the relation of either the genome-wide or nominally significant CM polymorphisms/genes with the ones highlighted by the network representation? We show that our network approach using the MelGene data reveals connections between statistically significant genes/ polymorphisms and other genes/polymorphisms acting as ‘hubs’ in the reconstructed networks. To the best of our knowledge, this is the first database containing data from a comprehensive field synopsis and systematic meta-analyses of genetic polymorphisms in CM that provides user-friendly tools for in-depth molecular network visualization and exploration. The proposed network connections highlight potentially new loci requiring further investigation of their relation to melanoma risk. Database URL: http://www.melgene.org. PMID:25380778

  11. A Web-based database of genetic association studies in cutaneous melanoma enhanced with network-driven data exploration tools.

    PubMed

    Athanasiadis, Emmanouil I; Antonopoulou, Kyriaki; Chatzinasiou, Foteini; Lill, Christina M; Bourdakou, Marilena M; Sakellariou, Argiris; Kypreou, Katerina; Stefanaki, Irene; Evangelou, Evangelos; Ioannidis, John P A; Bertram, Lars; Stratigos, Alexander J; Spyrou, George M

    2014-01-01

    The publicly available online database MelGene provides a comprehensive, regularly updated, collection of data from genetic association studies in cutaneous melanoma (CM), including random-effects meta-analysis results of all eligible polymorphisms. The updated database version includes data from 192 publications with information on 1114 significantly associated polymorphisms across 280 genes, along with new front-end and back-end capabilities. Various types of relationships between data are calculated and visualized as networks. We constructed 13 different networks containing the polymorphisms and the genes included in MelGene. We explored the derived network representations under the following questions: (i) are there nodes that deserve consideration regarding their network connectivity characteristics? (ii) What is the relation of either the genome-wide or nominally significant CM polymorphisms/genes with the ones highlighted by the network representation? We show that our network approach using the MelGene data reveals connections between statistically significant genes/ polymorphisms and other genes/polymorphisms acting as 'hubs' in the reconstructed networks. To the best of our knowledge, this is the first database containing data from a comprehensive field synopsis and systematic meta-analyses of genetic polymorphisms in CM that provides user-friendly tools for in-depth molecular network visualization and exploration. The proposed network connections highlight potentially new loci requiring further investigation of their relation to melanoma risk. Database URL: http://www.melgene.org. PMID:25380778

  12. Regulators of genetic risk of breast cancer identified by integrative network analysis.

    PubMed

    Castro, Mauro A A; de Santiago, Ines; Campbell, Thomas M; Vaughn, Courtney; Hickey, Theresa E; Ross, Edith; Tilley, Wayne D; Markowetz, Florian; Ponder, Bruce A J; Meyer, Kerstin B

    2016-01-01

    Genetic risk for breast cancer is conferred by a combination of multiple variants of small effect. To better understand how risk loci might combine, we examined whether risk-associated genes share regulatory mechanisms. We created a breast cancer gene regulatory network comprising transcription factors and groups of putative target genes (regulons) and asked whether specific regulons are enriched for genes associated with risk loci via expression quantitative trait loci (eQTLs). We identified 36 overlapping regulons that were enriched for risk loci and formed a distinct cluster within the network, suggesting shared biology. The risk transcription factors driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to estrogen receptor (ER)(+) luminal A or luminal B and ER(-) basal-like cancers and to different luminal epithelial cell populations in the adult mammary gland. Our network approach provides a foundation for determining the regulatory circuits governing breast cancer, to identify targets for intervention, and is transferable to other disease settings.

  13. Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

    NASA Technical Reports Server (NTRS)

    Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

    2000-01-01

    Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

  14. Genetic networks and the flow of positional information in embryonic development

    NASA Astrophysics Data System (ADS)

    Bialek, William

    When we study a biological system, we make inferences about the underlying mechanisms and dynamics. But biological systems themselves must also solve inference problems, as when our brains draw conclusions about the world given (often quite limited) data from our eyes and ears. My colleagues and I have been exploring both of these inference problems as they play out in the first hours of development in the fruit fly embryo. In this system, the concentrations of particular molecules encode the position of each cell in the embryo, and these concentrations are the outputs of a genetic network. Putting ourselves in the place of the cells, we have been able to read the code, building a dictionary that maps gene expression levels back into estimates of position. If our dictionary really is the one used by the embryo, then mutants should build predictably distorted body plans, and preliminary results show quantitative agreement with these predictions. Independent of their role as carriers of information, we can also analyze the patterns of gene expression to draw inferences about the underlying network. Finally, it is possible that the network architecture and parameters have been chosen to optimize the flow of information, and we see signatures of this optimization. Joint work with CG Callan, JO Dubuis, T Gregor, D Krotov, M Petkova, TR Sokolowski, G Tkacik, AM Walczak, and EF Wieschaus.

  15. Network-based gene prediction for Plasmodium falciparum malaria towards genetics-based drug discovery

    PubMed Central

    2015-01-01

    Background Malaria is the most deadly parasitic infectious disease. Existing drug treatments have limited efficacy in malaria elimination, and the complex pathogenesis of the disease is not fully understood. Detecting novel malaria-associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for anti-malaria drugs. Methods In this study, we developed a network-based approach to predict malaria-associated genes. We constructed a cross-species network to integrate human-human, parasite-parasite and human-parasite protein interactions. Then we extended the random walk algorithm on this network, and used known malaria genes as the seeds to find novel candidate genes for malaria. Results We validated our algorithms using 77 known malaria genes: 14 human genes and 63 parasite genes were ranked averagely within top 2% and top 4%, respectively among human and parasite genomes. We also evaluated our method for predicting novel malaria genes using a set of 27 genes with literature supporting evidence. Our approach ranked 12 genes within top 1% and 24 genes within top 5%. In addition, we demonstrated that top-ranked candied genes were enriched for drug targets, and identified commonalities underlying top-ranked malaria genes through pathway analysis. In summary, the candidate malaria-associated genes predicted by our data-driven approach have the potential to guide genetics-based anti-malaria drug discovery. PMID:26099491

  16. Genetic regulatory networks programming hematopoietic stem cells and erythroid lineage specification.

    PubMed

    Swiers, Gemma; Patient, Roger; Loose, Matthew

    2006-06-15

    Erythroid cell production results from passage through cellular hierarchies dependent on differential gene expression under the control of transcription factors responsive to changing niches. We have constructed Genetic Regulatory Networks (GRNs) describing this process, based predominantly on mouse data. Regulatory network motifs identified in E. coli and yeast GRNs are found in combination in these GRNs. Feed-forward motifs with autoregulation generate forward momentum and also control its rate, which is at its lowest in hematopoietic stem cells (HSCs). The simultaneous requirement for multiple regulators in multi-input motifs (MIMs) provides tight control over expression of target genes. Combinations of MIMs, exemplified by the SCL/LMO2 complexes, which have variable content and binding sites, explain how individual regulators can have different targets in HSCs and erythroid cells and possibly also how HSCs maintain stem cell functions while expressing lineage-affiliated genes at low level, so-called multi-lineage priming. MIMs combined with cross-antagonism describe the relationship between PU.1 and GATA-1 and between two of their target genes, Fli-1 and EKLF, with victory for GATA-1 and EKLF leading to erythroid lineage specification. These GRNs are useful repositories for current regulatory information, are accessible in interactive form via the internet, enable the consequences of perturbation to be predicted, and can act as seed networks to organize the rapidly accumulating microarray data.

  17. Reverse genetic screening identifies five E-class PPR proteins involved in RNA editing in mitochondria of Arabidopsis thaliana.

    PubMed

    Takenaka, Mizuki; Verbitskiy, Daniil; Zehrmann, Anja; Brennicke, Axel

    2010-08-27

    RNA editing in flowering plant mitochondria post-transcriptionally alters several hundred nucleotides from C to U, mostly in mRNAs. Several factors required for specific RNA-editing events in plant mitochondria and plastids have been identified, all of them PPR proteins of the PLS subclass with a C-terminal E-domain and about half also with an additional DYW domain. Based on this information, we here probe the connection between E-PPR proteins and RNA editing in plant mitochondria. We initiated a reverse genetics screen of T-DNA insertion lines in Arabidopsis thaliana and investigated 58 of the 150 E-PPR-coding genes for a function in RNA editing. Six genes were identified to be involved in mitochondrial RNA editing at specific sites. Homozygous mutants of the five genes MEF18-MEF22 display no gross disturbance in their growth or development patterns, suggesting that the editing sites affected are not crucial at least in the greenhouse. These results show that a considerable percentage of the E-PPR proteins are involved in the functional processing of site-specific RNA editing in plant mitochondria.

  18. Network-assisted genetic dissection of pathogenicity and drug resistance in the opportunistic human pathogenic fungus Cryptococcus neoformans.

    PubMed

    Kim, Hanhae; Jung, Kwang-Woo; Maeng, Shinae; Chen, Ying-Lien; Shin, Junha; Shim, Jung Eun; Hwang, Sohyun; Janbon, Guilhem; Kim, Taeyup; Heitman, Joseph; Bahn, Yong-Sun; Lee, Insuk

    2015-01-01

    Cryptococcus neoformans is an opportunistic human pathogenic fungus that causes meningoencephalitis. Due to the increasing global risk of cryptococcosis and the emergence of drug-resistant strains, the development of predictive genetics platforms for the rapid identification of novel genes governing pathogenicity and drug resistance of C. neoformans is imperative. The analysis of functional genomics data and genome-scale mutant libraries may facilitate the genetic dissection of such complex phenotypes but with limited efficiency. Here, we present a genome-scale co-functional network for C. neoformans, CryptoNet, which covers ~81% of the coding genome and provides an efficient intermediary between functional genomics data and reverse-genetics resources for the genetic dissection of C. neoformans phenotypes. CryptoNet is the first genome-scale co-functional network for any fungal pathogen. CryptoNet effectively identified novel genes for pathogenicity and drug resistance using guilt-by-association and context-associated hub algorithms. CryptoNet is also the first genome-scale co-functional network for fungi in the basidiomycota phylum, as Saccharomyces cerevisiae belongs to the ascomycota phylum. CryptoNet may therefore provide insights into pathway evolution between two distinct phyla of the fungal kingdom. The CryptoNet web server (www.inetbio.org/cryptonet) is a public resource that provides an interactive environment of network-assisted predictive genetics for C. neoformans.

  19. Networks involved in olfaction and their dynamics using independent component analysis and unified structural equation modeling.

    PubMed

    Karunanayaka, Prasanna; Eslinger, Paul J; Wang, Jian-Li; Weitekamp, Christopher W; Molitoris, Sarah; Gates, Kathleen M; Molenaar, Peter C M; Yang, Qing X

    2014-05-01

    The study of human olfaction is complicated by the myriad of processing demands in conscious perceptual and emotional experiences of odors. Combining functional magnetic resonance imaging with convergent multivariate network analyses, we examined the spatiotemporal behavior of olfactory-generated blood-oxygenated-level-dependent signal in healthy adults. The experimental functional magnetic resonance imaging (fMRI) paradigm was found to offset the limitations of olfactory habituation effects and permitted the identification of five functional networks. Analysis delineated separable neuronal circuits that were spatially centered in the primary olfactory cortex, striatum, dorsolateral prefrontal cortex, rostral prefrontal cortex/anterior cingulate, and parietal-occipital junction. We hypothesize that these functional networks subserve primary perceptual, affective/motivational, and higher order olfactory-related cognitive processes. Results provided direct evidence for the existence of parallel networks with top-down modulation for olfactory processing and clearly distinguished brain activations that were sniffing-related versus odor-related. A comprehensive neurocognitive model for olfaction is presented that may be applied to broader translational studies of olfactory function, aging, and neurological disease.

  20. Tinnitus and hyperacusis involve hyperactivity and enhanced connectivity in auditory-limbic-arousal-cerebellar network

    PubMed Central

    Chen, Yu-Chen; Li, Xiaowei; Liu, Lijie; Wang, Jian; Lu, Chun-Qiang; Yang, Ming; Jiao, Yun; Zang, Feng-Chao; Radziwon, Kelly; Chen, Guang-Di; Sun, Wei; Krishnan Muthaiah, Vijaya Prakash; Salvi, Richard; Teng, Gao-Jun

    2015-01-01

    Hearing loss often triggers an inescapable buzz (tinnitus) and causes everyday sounds to become intolerably loud (hyperacusis), but exactly where and how this occurs in the brain is unknown. To identify the neural substrate for these debilitating disorders, we induced both tinnitus and hyperacusis with an ototoxic drug (salicylate) and used behavioral, electrophysiological, and functional magnetic resonance imaging (fMRI) techniques to identify the tinnitus–hyperacusis network. Salicylate depressed the neural output of the cochlea, but vigorously amplified sound-evoked neural responses in the amygdala, medial geniculate, and auditory cortex. Resting-state fMRI revealed hyperactivity in an auditory network composed of inferior colliculus, medial geniculate, and auditory cortex with side branches to cerebellum, amygdala, and reticular formation. Functional connectivity revealed enhanced coupling within the auditory network and segments of the auditory network and cerebellum, reticular formation, amygdala, and hippocampus. A testable model accounting for distress, arousal, and gating of tinnitus and hyperacusis is proposed. DOI: http://dx.doi.org/10.7554/eLife.06576.001 PMID:25962854

  1. Regional contraction of brain surface area involves three large-scale networks in schizophrenia.

    PubMed

    Palaniyappan, Lena; Mallikarjun, Pavan; Joseph, Verghese; White, Thomas P; Liddle, Peter F

    2011-07-01

    In schizophrenia, morphological changes in the cerebral cortex have been primarily investigated using volumetric or cortical thickness measurements. In healthy subjects, as the brain size increases, the surface area expands disproportionately when compared to the scaling of cortical thickness. In this structural MRI study, we investigated the changes in brain surface area in schizophrenia by constructing relative areal contraction/expansion maps showing group differences in surface area using Freesurfer software in 57 patients and 41 controls. We observed relative areal contraction affecting Default Mode Network, Central Executive Network and Salience Network, in addition to other regions in schizophrenia. We confirmed the surface area reduction across these three large-scale brain networks by undertaking further region-of-interest analysis of surface area. We also observed a significant hemispheric asymmetry in the surface area changes, with the left hemisphere showing a greater reduction in the areal contraction maps. Our findings suggest that a fundamental disturbance in cortical expansion is likely in individuals who develop schizophrenia. PMID:21497489

  2. A new type of entangled coordination network: coexistence of polythreading and polyknotting involved molecular braids.

    PubMed

    Gu, Zhi-Guo; Xu, Xin-Xin; Zhou, Wen; Pang, Chun-Yan; Bao, Fei-Fei; Li, Zaijun

    2012-03-28

    A fascinating polythreaded coordination network formed by 1D crankshaft shaped chains threading into a 2D undulated sheet in a one-over/one-under interweaving fashion was reported, in which the 2D layer exhibits an unusual polyknotted entanglement containing triple-stranded molecular braids. PMID:22331293

  3. A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology

    PubMed Central

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

    Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view. PMID:23515240

  4. Altered brain rhythms and functional network disruptions involved in patients with generalized fixation-off epilepsy.

    PubMed

    Solana, Ana Beatriz; Martínez, Kenia; Hernández-Tamames, Juan Antonio; San Antonio-Arce, Victoria; Toledano, Rafael; García-Morales, Irene; Alvárez-Linera, Juan; Gil-Nágel, Antonio; Del Pozo, Francisco

    2016-06-01

    Generalized Fixation-off Sensitivity (CGE-FoS) patients present abnormal EEG patterns when losing fixation. In the present work, we studied two CGE-FoS epileptic patients with simultaneous EEG-fMRI. We aim to identify brain areas that are specifically related to the pathology by identifying the brain networks that are related to the EEG brain altered rhythms. Three main analyses were performed: EEG standalone, where the voltage fluctuations in delta, alpha, and beta EEG bands were obtained; fMRI standalone, where resting-state fMRI ICA analyses for opened and closed eyes conditions were computed per subject; and, EEG-informed fMRI, where EEG delta, alpha and beta oscillations were used to analyze fMRI. Patient 1 showed EEG abnormalities for lower beta band EEG brain rhythm. Fluctuations of this rhythm were correlated with a brain network mainly composed by temporo-frontal areas only found in the closed eyes condition. Patient 2 presented alterations in all the EEG brain rhythms (delta, alpha, beta) under study when closing eyes. Several biologically relevant brain networks highly correlated (r > 0.7) to each other in the closed eyes condition were found. EEG-informed fMRI results in patient 2 showed hypersynchronized patterns in the fMRI correlation spatial maps. The obtained findings allow a differential diagnosis for each patient and different profiles with respect to healthy volunteers. The results suggest a different disruption in the functional brain networks of these patients that depends on their altered brain rhythms. This knowledge could be used to treat these patients by novel brain stimulation approaches targeting specific altered brain networks in each patient.

  5. Further evidence for genetic heterogeneity of distal HMN type V, CMT2 with predominant hand involvement and Silver syndrome

    PubMed Central

    Rohkamm, Barbara; Reilly, Mary M.; Lochmüller, Hanns; Schlotter-Weigel, Beate; Barisic, Nina; Schöls, Ludger; Nicholson, Garth; Pareyson, Davide; Laurà, Matilde; Janecke, Andreas R.; Miltenberger-Miltenyi, Gabriel; John, Elisabeth; Fischer, Carina; Grill, Franz; Wakeling, William; Davis, Mary; Pieber, Thomas R.; Auer-Grumbach, Michaela

    2011-01-01

    Objective Distal hereditary motor neuropathy type V (dHMN-V) and Charcot–Marie–Tooth syndrome (CMT) type 2 presenting with predominant hand involvement, also known as CMT2D and Silver syndrome (SS) are rare phenotypically overlapping diseases which can be caused by mutations in the Berardinelli–Seip Congenital Lipodystrophy 2 (BSCL2) and in the glycyl-tRNA synthetase encoding (GARS) genes. Mutations in the heat-shock proteins HSPB1 and HSPB8 can cause related distal hereditary motor neuropathies (dHMN) and are considered candidates for dHMN-V, CMT2, and SS. Design To define the frequency and distribution of mutations in the GARS, BSCL2, HSPB1 and HSPB8 genes we screened 33 unrelated sporadic and familial patients diagnosed as either dHMN-V, CMT2D or SS. Exon 3 of the BSCL2 gene was screened in further 69 individuals with an unclassified dHMN phenotype or diagnosed as hereditary spastic paraplegia (HSP) complicated by pure motor neuropathy. Results Four patients diagnosed with dHMN-Vor SS carried known heterozygous BSCL2 mutations (N88S and S90L). In one dHMN-V patient we detected a putative GARS mutation (A57V). No mutations were detected in HSPB1 and HSPB8. The diagnostic yield gained in the series of 33 probands was 12% for BSCL2 mutations and 3% for GARS mutations. In the series of unclassified dHMN and complicated HSP cases no mutations were found. Conclusions Our data confirm that most likely only two mutations (N88S, S90L) in exon 3 of BSCL2 may lead to dHMN-V or SS phenotypes. Mutations in GARS, HSPB1 and HSPB8. are not a common cause of dHMN-V, SS and CMT2D. We would therefore suggest that a genetic testing of dHMN-V and SS patients should begin with screening of exon 3 of the BSCL2 gene. Screening of the GARS gene is useful in patients with CMT2 with predominant hand involvement and dHMN-V. The rather low frequencies of BSCL2, GARS, HSPB1 and HSPB8 mutations in dHMN-V, CMT2D and SS patients strongly point to further genetic heterogeneity of these

  6. A bioinformatics analysis of Lamin-A regulatory network: a perspective on epigenetic involvement in Hutchinson-Gilford progeria syndrome.

    PubMed

    Arancio, Walter

    2012-04-01

    Hutchinson-Gilford progeria syndrome (HGPS) is a rare human genetic disease that leads to premature aging. HGPS is caused by mutation in the Lamin-A (LMNA) gene that leads, in affected young individuals, to the accumulation of the progerin protein, usually present only in aging differentiated cells. Bioinformatics analyses of the network of interactions of the LMNA gene and transcripts are presented. The LMNA gene network has been analyzed using the BioGRID database (http://thebiogrid.org/) and related analysis tools such as Osprey (http://biodata.mshri.on.ca/osprey/servlet/Index) and GeneMANIA ( http://genemania.org/). The network of interaction of LMNA transcripts has been further analyzed following the competing endogenous (ceRNA) hypotheses (RNA cross-talk via microRNAs [miRNAs]) and using the miRWalk database and tools (www.ma.uni-heidelberg.de/apps/zmf/mirwalk/). These analyses suggest particular relevance of epigenetic modifiers (via acetylase complexes and specifically HTATIP histone acetylase) and adenosine triphosphate (ATP)-dependent chromatin remodelers (via pBAF, BAF, and SWI/SNF complexes).

  7. Forecasting the Indian summer monsoon intraseasonal oscillations using genetic algorithm and neural network

    NASA Astrophysics Data System (ADS)

    Dwivedi, Suneet; Pandey, Avinash C.

    2011-08-01

    The correct and timely forecast of the Indian summer monsoon Intraseasonal Oscillations (ISOs) is very important. It has great impact on the agriculture and economy of the Indian subcontinent region. The applicability of Genetic Algorithm (GA) is demonstrated for nonlinear curve fitting of the inherently chaotic and noisy Lorenz time series and the ISO data. A robust method is developed for the very long-range prediction of the ISO using a feed-forward time delay backpropagation Artificial Neural Network (ANN). Using an iterative one-step-ahead prediction strategy, five years (120 pentads) of advanced prediction is made for the ISO data with good forecast skill. It is shown that a hybrid GA-ANN model may be used as an early forecast model followed by ANN only model as a more reliable model.

  8. Combining genetic mapping with genome-wide expression in experimental autoimmune encephalomyelitis highlights a gene network enriched for T cell functions and candidate genes regulating autoimmunity

    PubMed Central

    Thessen Hedreul, Melanie; Möller, Steffen; Stridh, Pernilla; Gupta, Yask; Gillett, Alan; Daniel Beyeen, Amennai; Öckinger, Johan; Flytzani, Sevasti; Diez, Margarita; Olsson, Tomas; Jagodic, Maja

    2013-01-01

    The experimental autoimmune encephalomyelitis (EAE) is an autoimmune disease of the central nervous system commonly used to study multiple sclerosis (MS). We combined clinical EAE phenotypes with genome-wide expression profiling in spleens from 150 backcross rats between susceptible DA and resistant PVG rat strains during the chronic EAE phase. This enabled correlation of transcripts with genotypes, other transcripts and clinical EAE phenotypes and implicated potential genetic causes and pathways in EAE. We detected 2285 expression quantitative trait loci (eQTLs). Sixty out of 599 cis-eQTLs overlapped well-known EAE QTLs and constitute positional candidate genes, including Ifit1 (Eae7), Atg7 (Eae20-22), Klrc3 (eEae22) and Mfsd4 (Eae17). A trans-eQTL that overlaps Eae23a regulated a large number of small RNAs and implicates a master regulator of transcription. We defined several disease-correlated networks enriched for pathways involved in cell-mediated immunity. They include C-type lectins, G protein coupled receptors, mitogen-activated protein kinases, transmembrane proteins, suppressors of transcription (Jundp2 and Nr1d1) and STAT transcription factors (Stat4) involved in interferon signaling. The most significant network was enriched for T cell functions, similar to genetic findings in MS, and revealed both established and novel gene interactions. Transcripts in the network have been associated with T cell proliferation and differentiation, the TCR signaling and regulation of regulatory T cells. A number of network genes and their family members have been associated with MS and/or other autoimmune diseases. Combining disease and genome-wide expression phenotypes provides a link between disease risk genes and distinct molecular pathways that are dysregulated during chronic autoimmune inflammation. PMID:23900079

  9. Attention Deficit Hyperactivity Disorder with Reading Disabilities: Preliminary Genetic Findings on the Involvement of the ADRA2A Gene

    ERIC Educational Resources Information Center

    Stevenson, J.; Langley, K.; Pay, H.; Payton, A.; Worthington, J.; Ollier, W.; Thapar, A.

    2005-01-01

    Background: Attention deficit/hyperactivity disorder (ADHD) and reading disability (RD) tend to co-occur and quantitative genetic studies have shown this to arise primarily through shared genetic influences. However, molecular genetic studies have shown different genes to be associated with each of these conditions. Neurobiological studies have…

  10. Coexpression network analysis in abdominal and gluteal adipose tissue reveals regulatory genetic loci for metabolic syndrome and related phenotypes.

    PubMed

    Min, Josine L; Nicholson, George; Halgrimsdottir, Ingileif; Almstrup, Kristian; Petri, Andreas; Barrett, Amy; Travers, Mary; Rayner, Nigel W; Mägi, Reedik; Pettersson, Fredrik H; Broxholme, John; Neville, Matt J; Wills, Quin F; Cheeseman, Jane; Allen, Maxine; Holmes, Chris C; Spector, Tim D; Fleckner, Jan; McCarthy, Mark I; Karpe, Fredrik; Lindgren, Cecilia M; Zondervan, Krina T

    2012-01-01

    Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue, and whole blood (WB), from 29 MetS cases and 44 controls. Co-expression network analysis for each tissue independently identified nine, six, and zero MetS-associated modules of coexpressed genes in ABD, GLU, and WB, respectively. Of 8,992 probesets expressed in ABD or GLU, 685 (7.6%) were expressed in ABD and 51 (0.6%) in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots (D(ABD-GLU) = 0.89), seven of which were associated with MetS (FDR P<0.01). The strongest associated module, significantly enriched for immune response-related processes, contained 94/620 (15%) genes with inter-depot differences. In an independent cohort of 145/141 twins with ABD and WB longitudinal expression data, median variability in ABD due to familiality was greater for MetS-associated versus un-associated modules (ABD: 0.48 versus 0.18, P = 0.08; GLU: 0.54 versus 0.20, P = 7.8×10(-4)). Cis-eQTL analysis of probesets associated with MetS (FDR P<0.01) and/or inter-depot differences (FDR P<0.01) provided evidence for 32 eQTLs. Corresponding eSNPs were tested for association with MetS-related phenotypes in two GWAS of >100,000 individuals; rs10282458, affecting expression of RARRES2 (encoding chemerin), was associated with body mass index (BMI) (P = 6.0×10(-4)); and rs2395185, affecting inter-depot differences of HLA-DRB1 expression, was associated with high-density lipoprotein (P = 8.7×10(-4)) and BMI-adjusted waist-to-hip ratio (P = 2.4×10(-4)). Since many genes and their interactions influence complex traits such as MetS, integrated analysis of genotypes and coexpression

  11. Genetic networks inducing invasive growth in Saccharomyces cerevisiae identified through systematic genome-wide overexpression.

    PubMed

    Shively, Christian A; Eckwahl, Matthew J; Dobry, Craig J; Mellacheruvu, Dattatreya; Nesvizhskii, Alexey; Kumar, Anuj

    2013-04-01

    The budding yeast Saccharomyces cerevisiae can respond to nutritional and environmental stress by implementing a morphogenetic program wherein cells elongate and interconnect, forming pseudohyphal filaments. This growth transition has been studied extensively as a model signaling system with similarity to processes of hyphal development that are linked with virulence in related fungal pathogens. Classic studies have identified core pseudohyphal growth signaling modules in yeast; however, the scope of regulatory networks that control yeast filamentation is broad and incompletely defined. Here, we address the genetic basis of yeast pseudohyphal growth by implementing a systematic analysis of 4909 genes for overexpression phenotypes in a filamentous strain of S. cerevisiae. Our results identify 551 genes conferring exaggerated invasive growth upon overexpression under normal vegetative growth conditions. This cohort includes 79 genes lacking previous phenotypic characterization. Pathway enrichment analysis of the gene set identifies networks mediating mitogen-activated protein kinase (MAPK) signaling and cell cycle progression. In particular, overexpression screening suggests that nuclear export of the osmoresponsive MAPK Hog1p may enhance pseudohyphal growth. The function of nuclear Hog1p is unclear from previous studies, but our analysis using a nuclear-depleted form of Hog1p is consistent with a role for nuclear Hog1p in repressing pseudohyphal growth. Through epistasis and deletion studies, we also identified genetic relationships with the G2 cyclin Clb2p and phenotypes in filamentation induced by S-phase arrest. In sum, this work presents a unique and informative resource toward understanding the breadth of genes and pathways that collectively constitute the molecular basis of filamentation.

  12. Identification of AMPK Phosphorylation Sites Reveals a Network of Proteins Involved in Cell Invasion and Facilitates Large-Scale Substrate Prediction.

    PubMed

    Schaffer, Bethany E; Levin, Rebecca S; Hertz, Nicholas T; Maures, Travis J; Schoof, Michael L; Hollstein, Pablo E; Benayoun, Bérénice A; Banko, Max R; Shaw, Reuben J; Shokat, Kevan M; Brunet, Anne

    2015-11-01

    AMP-activated protein kinase (AMPK) is a central energy gauge that regulates metabolism and has been increasingly involved in non-metabolic processes and diseases. However, AMPK's direct substrates in non-metabolic contexts are largely unknown. To better understand the AMPK network, we use a chemical genetics screen coupled to a peptide capture approach in whole cells, resulting in identification of direct AMPK phosphorylation sites. Interestingly, the high-confidence AMPK substrates contain many proteins involved in cell motility, adhesion, and invasion. AMPK phosphorylation of the RHOA guanine nucleotide exchange factor NET1A inhibits extracellular matrix degradation, an early step in cell invasion. The identification of direct AMPK phosphorylation sites also facilitates large-scale prediction of AMPK substrates. We provide an AMPK motif matrix and a pipeline to predict additional AMPK substrates from quantitative phosphoproteomics datasets. As AMPK is emerging as a critical node in aging and pathological processes, our study identifies potential targets for therapeutic strategies. PMID:26456332

  13. Neuroblastoma Tyrosine Kinase Signaling Networks Involve FYN and LYN in Endosomes and Lipid Rafts

    PubMed Central

    Guo, Ailan; Stokes, Matthew P.; Kuehn, Emily D.; George, Lynn; Comb, Michael; Grimes, Mark L.

    2015-01-01

    Protein phosphorylation plays a central role in creating a highly dynamic network of interacting proteins that reads and responds to signals from growth factors in the cellular microenvironment. Cells of the neural crest employ multiple signaling mechanisms to control migration and differentiation during development. It is known that defects in these mechanisms cause neuroblastoma, but how multiple signaling pathways interact to govern cell behavior is unknown. In a phosphoproteomic study of neuroblastoma cell lines and cell fractions, including endosomes and detergent-resistant membranes, 1622 phosphorylated proteins were detected, including more than half of the receptor tyrosine kinases in the human genome. Data were analyzed using a combination of graph theory and pattern recognition techniques that resolve data structure into networks that incorporate statistical relationships and protein-protein interaction data. Clusters of proteins in these networks are indicative of functional signaling pathways. The analysis indicates that receptor tyrosine kinases are functionally compartmentalized into distinct collaborative groups distinguished by activation and intracellular localization of SRC-family kinases, especially FYN and LYN. Changes in intracellular localization of activated FYN and LYN were observed in response to stimulation of the receptor tyrosine kinases, ALK and KIT. The results suggest a mechanism to distinguish signaling responses to activation of different receptors, or combinations of receptors, that govern the behavior of the neural crest, which gives rise to neuroblastoma. PMID:25884760

  14. Networks in Coronary Heart Disease Genetics As a Step towards Systems Epidemiology.

    PubMed

    Drenos, Fotios; Grossi, Enzo; Buscema, Massimo; Humphries, Steve E

    2015-01-01

    We present the use of innovative machine learning techniques in the understanding of Coronary Heart Disease (CHD) through intermediate traits, as an example of the use of this class of methods as a first step towards a systems epidemiology approach of complex diseases genetics. Using a sample of 252 middle-aged men, of which 102 had a CHD event in 10 years follow-up, we applied machine learning algorithms for the selection of CHD intermediate phenotypes, established markers, risk factors, and their previously associated genetic polymorphisms, and constructed a map of relationships between the selected variables. Of the 52 variables considered, 42 were retained after selection of the most informative variables for CHD. The constructed map suggests that most selected variables were related to CHD in a context dependent manner while only a small number of variables were related to a specific outcome. We also observed that loss of complexity in the network was linked to a future CHD event. We propose that novel, non-linear, and integrative epidemiological approaches are required to combine all available information, in order to truly translate the new advances in medical sciences to gains in preventive measures and patients care.

  15. Multi-input CRISPR/Cas genetic circuits that interface host regulatory networks

    PubMed Central

    Nielsen, Alec AK; Voigt, Christopher A

    2014-01-01

    Genetic circuits require many regulatory parts in order to implement signal processing or execute algorithms in cells. A potentially scalable approach is to use dCas9, which employs small guide RNAs (sgRNAs) to repress genetic loci via the programmability of RNA:DNA base pairing. To this end, we use dCas9 and designed sgRNAs to build transcriptional logic gates and connect them to perform computation in living cells. We constructed a set of NOT gates by designing five synthetic Escherichia coli σ70 promoters that are repressed by corresponding sgRNAs, and these interactions do not exhibit crosstalk between each other. These sgRNAs exhibit high on-target repression (56- to 440-fold) and negligible off-target interactions (< 1.3-fold). These gates were connected to build larger circuits, including the Boolean-complete NOR gate and a 3-gate circuit consisting of four layered sgRNAs. The synthetic circuits were connected to the native E. coli regulatory network by designing output sgRNAs to target an E. coli transcription factor (malT). This converts the output of a synthetic circuit to a switch in cellular phenotype (sugar utilization, chemotaxis, phage resistance). PMID:25422271

  16. Genetic Network Programming with Automatically Generated Macro Nodes of Variable Size

    NASA Astrophysics Data System (ADS)

    Mabu, Shingo; Hatakeyama, Hiroyuki; Nakagoe, Hiroshi; Hirasawa, Kotaro; Furuzuki, Takayuki

    Recently, Genetic Network Programming (GNP) has been proposed as one of the evolutionary algorithms. It represents its solutions as directed graph structures and the distinguished abilities have been shown. However, when we apply GNP to complex problems like the real world one, GNP must have robustness against the changes of environments and evolve quickly. Therefore, we introduced Automatically Generated Macro Nodes (AGMs) to GNP (GNP with AGMs). Actually GNP with AGMs has shown higher performances than the conventional GNP in terms of the fitness and the speed of evolution. In this paper, a new mechanism, AGMs with variable size, is introduced to GNP. Conventional AGMs have the fixed number of nodes and they evolve using only genetic operations, while a new method allows AGM to add nodes by necessity and delete nodes which do not contribute to the evolution of the AGM. The proposed GNP with AGMs of variable size is expected to evolve effectively and efficiently when it is applied to agent systems and also expected to make better behavior sequences of agents more easily than the conventional GNP algorithm. In the simulations, the proposed and conventional methods are applied to a tileworld problem and they are compared with each other. From the results, GNP with AGMs of variable size shows better fitness than GNP with AGMs of fixed size and the conventional GNP when adapting ten different environments.

  17. Networks in Coronary Heart Disease Genetics As a Step towards Systems Epidemiology

    PubMed Central

    Drenos, Fotios; Grossi, Enzo; Buscema, Massimo; Humphries, Steve E.

    2015-01-01

    We present the use of innovative machine learning techniques in the understanding of Coronary Heart Disease (CHD) through intermediate traits, as an example of the use of this class of methods as a first step towards a systems epidemiology approach of complex diseases genetics. Using a sample of 252 middle-aged men, of which 102 had a CHD event in 10 years follow-up, we applied machine learning algorithms for the selection of CHD intermediate phenotypes, established markers, risk factors, and their previously associated genetic polymorphisms, and constructed a map of relationships between the selected variables. Of the 52 variables considered, 42 were retained after selection of the most informative variables for CHD. The constructed map suggests that most selected variables were related to CHD in a context dependent manner while only a small number of variables were related to a specific outcome. We also observed that loss of complexity in the network was linked to a future CHD event. We propose that novel, non-linear, and integrative epidemiological approaches are required to combine all available information, in order to truly translate the new advances in medical sciences to gains in preventive measures and patients care. PMID:25951190

  18. Identifying a Network of Brain Regions Involved in Aversion-Related Processing: A Cross-Species Translational Investigation

    PubMed Central

    Hayes, Dave J.; Northoff, Georg

    2011-01-01

    The ability to detect and respond appropriately to aversive stimuli is essential for all organisms, from fruit flies to humans. This suggests the existence of a core neural network which mediates aversion-related processing. Human imaging studies on aversion have highlighted the involvement of various cortical regions, such as the prefrontal cortex, while animal studies have focused largely on subcortical regions like the periaqueductal gray and hypothalamus. However, whether and how these regions form a core neural network of aversion remains unclear. To help determine this, a translational cross-species investigation in humans (i.e., meta-analysis) and other animals (i.e., systematic review of functional neuroanatomy) was performed. Our results highlighted the recruitment of the anterior cingulate cortex, the anterior insula, and the amygdala as well as other subcortical (e.g., thalamus, midbrain) and cortical (e.g., orbitofrontal) regions in both animals and humans. Importantly, involvement of these regions remained independent of sensory modality. This study provides evidence for a core neural network mediating aversion in both animals and humans. This not only contributes to our understanding of the trans-species neural correlates of aversion but may also carry important implications for psychiatric disorders where abnormal aversive behavior can often be observed. PMID:22102836

  19. Genetics and Gene Expression Involving Stress and Distress Pathways in Fibromyalgia with and without Comorbid Chronic Fatigue Syndrome

    PubMed Central

    Light, Kathleen C.; White, Andrea T.; Tadler, Scott; Iacob, Eli; Light, Alan R.

    2012-01-01

    In complex multisymptom disorders like fibromyalgia syndrome (FMS) and chronic fatigue syndrome (CFS) that are defined primarily by subjective symptoms, genetic and gene expression profiles can provide very useful objective information. This paper summarizes research on genes that may be linked to increased susceptibility in developing and maintaining these disorders, and research on resting and stressor-evoked changes in leukocyte gene expression, highlighting physiological pathways linked to stress and distress. These include the adrenergic nervous system, the hypothalamic-pituitary-adrenal axis and serotonergic pathways, and exercise responsive metabolite-detecting ion channels. The findings to date provide some support for both inherited susceptibility and/or physiological dysregulation in all three systems, particularly for catechol-O-methyl transferase (COMT) genes, the glucocorticoid and the related mineralocorticoid receptors (NR3C1, NR3C2), and the purinergic 2X4 (P2X4) ion channel involved as a sensory receptor for muscle pain and fatigue and also in upregulation of spinal microglia in chronic pain models. Methodological concerns for future research, including potential influences of comorbid clinical depression and antidepressants and other medications, on gene expression are also addressed. PMID:22110941

  20. Simulation and optimization of a pulsating heat pipe using artificial neural network and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad

    2016-11-01

    In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat flux to evaporator (q″), filling ratio (FR) and inclined angle (IA) and its output is thermal resistance of PHP. Finally, based upon the simulation results and considering the heat pipe's operating constraints, the optimum operating point of the system is obtained by using genetic algorithm (GA). The experimental results show that the optimum FR (38.25 %), input heat flux to evaporator (39.93 W) and IA (55°) that obtained from GA are acceptable.

  1. Simulation and optimization of a pulsating heat pipe using artificial neural network and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad

    2016-01-01

    In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat flux to evaporator (q″), filling ratio (FR) and inclined angle (IA) and its output is thermal resistance of PHP. Finally, based upon the simulation results and considering the heat pipe's operating constraints, the optimum operating point of the system is obtained by using genetic algorithm (GA). The experimental results show that the optimum FR (38.25 %), input heat flux to evaporator (39.93 W) and IA (55°) that obtained from GA are acceptable.

  2. Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Ali Ghorbani, Mohammad; Khatibi, Rahman; Aytek, Ali; Makarynskyy, Oleg; Shiri, Jalal

    2010-05-01

    Water level forecasting at various time intervals using records of past time series is of importance in water resources engineering and management. In the last 20 years, emerging approaches over the conventional harmonic analysis techniques are based on using Genetic Programming (GP) and Artificial Neural Networks (ANNs). In the present study, the GP is used to forecast sea level variations, three time steps ahead, for a set of time intervals comprising 12 h, 24 h, 5 day and 10 day time intervals using observed sea levels. The measurements from a single tide gauge at Hillarys Boat Harbor, Western Australia, were used to train and validate the employed GP for the period from December 1991 to December 2002. Statistical parameters, namely, the root mean square error, correlation coefficient and scatter index, are used to measure their performances. These were compared with a corresponding set of published results using an Artificial Neural Network model. The results show that both these artificial intelligence methodologies perform satisfactorily and may be considered as alternatives to the harmonic analysis.

  3. The fatigue life prediction of aluminium alloy using genetic algorithm and neural network

    NASA Astrophysics Data System (ADS)

    Susmikanti, Mike

    2013-09-01

    The behavior of the fatigue life of the industrial materials is very important. In many cases, the material with experiencing fatigue life cannot be avoided, however, there are many ways to control their behavior. Many investigations of the fatigue life phenomena of alloys have been done, but it is high cost and times consuming computation. This paper report the modeling and simulation approaches to predict the fatigue life behavior of Aluminum Alloys and resolves some problems of computation. First, the simulation using genetic algorithm was utilized to optimize the load to obtain the stress values. These results can be used to provide N-cycle fatigue life of the material. Furthermore, the experimental data was applied as input data in the neural network learning, while the samples data were applied for testing of the training data. Finally, the multilayer perceptron algorithm is applied to predict whether the given data sets in accordance with the fatigue life of the alloy. To achieve rapid convergence, the Levenberg-Marquardt algorithm was also employed. The simulations results shows that the fatigue behaviors of aluminum under pressure can be predicted. In addition, implementation of neural networks successfully identified a model for material fatigue life.

  4. Optimal sensor placement for leak location in water distribution networks using genetic algorithms.

    PubMed

    Casillas, Myrna V; Puig, Vicenç; Garza-Castañón, Luis E; Rosich, Albert

    2013-11-04

    This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach.

  5. Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms

    PubMed Central

    Casillas, Myrna V.; Puig, Vicenç; Garza-Castañón, Luis E.; Rosich, Albert

    2013-01-01

    This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach. PMID:24193099

  6. Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

    ERIC Educational Resources Information Center

    Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

    2003-01-01

    Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

  7. Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

    PubMed Central

    Zhang, Lun; Zhang, Meng; Yang, Wenchen; Dong, Decun

    2015-01-01

    This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers' route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity. PMID:25802512

  8. A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

    NASA Astrophysics Data System (ADS)

    Tahmasebi, Pejman; Hezarkhani, Ardeshir

    2012-05-01

    The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.

  9. Optimal groundwater remediation using artificial neural networks and the genetic algorithm

    SciTech Connect

    Rogers, L.L.

    1992-01-01

    An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the trained network searches through realizations or patterns of pumping selected by the GA, predicting the outcome. This approach has advantages of parallel processing of the groundwater simulations and the ability to [open quotes]recycle[close quotes] or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models. The ANN technology, inspired by neurobiological theories of massive interconnection and parallelism, has been applied to a variety of optimization problems. In the ANN groundwater management approach presented here, the behavior of complex groundwater scenarios with spatially-variable transport parameters and multiple contaminant plumes are simulated with 2-D flow and transport codes. An ANN is trained upon a set of examples developed from groundwater simulations. The input of the ANN characterizes the different realizations of pumping. The output characterizes the objectives and constraints of the optimization, such as whether regulatory goals have been met, value of cost functions or cleanup time, and mass of contaminant removal. The supervised learning algorithm of backpropagation is used to train the network. The conjugate gradient method and weight-elimination procedures are used to speed convergence and improve performance, respectively. Then a search is made through possible pumping realizations to find optimal realizations.

  10. Breeding and Genetics Symposium: networks and pathways to guide genomic selection.

    PubMed

    Snelling, W M; Cushman, R A; Keele, J W; Maltecca, C; Thomas, M G; Fortes, M R S; Reverter, A

    2013-02-01

    Many traits affecting profitability and sustainability of meat, milk, and fiber production are polygenic, with no single gene having an overwhelming influence on observed variation. No knowledge of the specific genes controlling these traits has been needed to make substantial improvement through selection. Significant gains have been made through phenotypic selection enhanced by pedigree relationships and continually improving statistical methodology. Genomic selection, recently enabled by assays for dense SNP located throughout the genome, promises to increase selection accuracy and accelerate genetic improvement by emphasizing the SNP most strongly correlated to phenotype although the genes and sequence variants affecting phenotype remain largely unknown. These genomic predictions theoretically rely on linkage disequilibrium (LD) between genotyped SNP and unknown functional variants, but familial linkage may increase effectiveness when predicting individuals related to those in the training data. Genomic selection with functional SNP genotypes should be less reliant on LD patterns shared by training and target populations, possibly allowing robust prediction across unrelated populations. Although the specific variants causing polygenic variation may never be known with certainty, a number of tools and resources can be used to identify those most likely to affect phenotype. Associations of dense SNP genotypes with phenotype provide a 1-dimensional approach for identifying genes affecting specific traits; in contrast, associations with multiple traits allow defining networks of genes interacting to affect correlated traits. Such networks are especially compelling when corroborated by existing functional annotation and established molecular pathways. The SNP occurring within network genes, obtained from public databases or derived from genome and transcriptome sequences, may be classified according to expected effects on gene products. As illustrated by

  11. 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 a