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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. Identification of genetic networks.

    PubMed Central

    Xiong, Momiao; Li, Jun; Fang, Xiangzhong

    2004-01-01

    In this report, we propose the use of structural equations as a tool for identifying and modeling genetic networks and genetic algorithms for searching the most likely genetic networks that best fit the data. After genetic networks are identified, it is fundamental to identify those networks influencing cell phenotypes. To accomplish this task we extend the concept of differential expression of the genes, widely used in gene expression data analysis, to genetic networks. We propose a definition for the differential expression of a genetic network and use the generalized T2 statistic to measure the ability of genetic networks to distinguish different phenotypes. However, describing the differential expression of genetic networks is not enough for understanding biological systems because differences in the expression of genetic networks do not directly reflect regulatory strength between gene activities. Therefore, in this report we also introduce the concept of differentially regulated genetic networks, which has the potential to assess changes of gene regulation in response to perturbation in the environment and may provide new insights into the mechanism of diseases and biological processes. We propose five novel statistics to measure the differences in regulation of genetic networks. To illustrate the concepts and methods for reconstruction of genetic networks and identification of association of genetic networks with function, we applied the proposed models and algorithms to three data sets. PMID:15020486

  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. Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis.

    PubMed

    Xing, Heming; McDonagh, Paul D; Bienkowska, Jadwiga; Cashorali, Tanya; Runge, Karl; Miller, Robert E; Decaprio, Dave; Church, Bruce; Roubenoff, Ronenn; Khalil, Iya G; Carulli, John

    2011-03-01

    Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86--a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28. PMID:21423713

  5. Causal Modeling Using Network Ensemble Simulations of Genetic and Gene Expression Data Predicts Genes Involved in Rheumatoid Arthritis

    PubMed Central

    Xing, Heming; McDonagh, Paul D.; Bienkowska, Jadwiga; Cashorali, Tanya; Runge, Karl; Miller, Robert E.; DeCaprio, Dave; Church, Bruce; Roubenoff, Ronenn; Khalil, Iya G.; Carulli, John

    2011-01-01

    Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86 - a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28. PMID:21423713

  6. Understanding genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Kauffman, Stuart

    2003-04-01

    Random Boolean networks (RBM) were introduced about 35 years ago as first crude models of genetic regulatory networks. RBNs are comprised of N on-off genes, connected by a randomly assigned regulatory wiring diagram where each gene has K inputs, and each gene is controlled by a randomly assigned Boolean function. This procedure samples at random from the ensemble of all possible NK Boolean networks. The central ideas are to study the typical, or generic properties of this ensemble, and see 1) whether characteristic differences appear as K and biases in Boolean functions are introducted, and 2) whether a subclass of this ensemble has properties matching real cells. Such networks behave in an ordered or a chaotic regime, with a phase transition, "the edge of chaos" between the two regimes. Networks with continuous variables exhibit the same two regimes. Substantial evidence suggests that real cells are in the ordered regime. A key concept is that of an attractor. This is a reentrant trajectory of states of the network, called a state cycle. The central biological interpretation is that cell types are attractors. A number of properties differentiate the ordered and chaotic regimes. These include the size and number of attractors, the existence in the ordered regime of a percolating "sea" of genes frozen in the on or off state, with a remainder of isolated twinkling islands of genes, a power law distribution of avalanches of gene activity changes following perturbation to a single gene in the ordered regime versus a similar power law distribution plus a spike of enormous avalanches of gene changes in the chaotic regime, and the existence of branching pathway of "differentiation" between attractors induced by perturbations in the ordered regime. Noise is serious issue, since noise disrupts attractors. But numerical evidence suggests that attractors can be made very stable to noise, and meanwhile, metaplasias may be a biological manifestation of noise. As we learn more

  7. Genetic Networks in Osseointegration

    PubMed Central

    Nishimura, I.

    2013-01-01

    Osseointegration-based dental implants have become a well-accepted treatment modality for complete and partial edentulism. The success of this treatment largely depends on the stable integration and maintenance of implant fixtures in alveolar bone; however, the molecular and cellular mechanisms regulating this unique tissue reaction have not yet been fully uncovered. Radiographic and histologic observations suggest the sustained retention of peri-implant bone without an apparent susceptibility to catabolic bone remodeling; therefore, implant-induced bone formation continues to be intensively investigated. Increasing numbers of whole-genome transcriptome studies suggest complex molecular pathways that may play putative roles in osseointegration. This review highlights genetic networks related to bone quality, the transient chondrogenic phase, the vitamin D axis, and the peripheral circadian rhythm to elute the regulatory mechanisms underlying the establishment and maintenance of osseointegration. PMID:24158334

  8. 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

  9. 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.

  10. 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)

  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. Variable Size Genetic Network Programming

    NASA Astrophysics Data System (ADS)

    Katagiri, Hironobu; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

    Genetic Network Programming (GNP) is a kind of volutionary methods, which evolves arbitrary directed graph programs. Previously, the program size of GNP was fixed. In the paper, a new method is proposed, where the program size is adaptively changed depending on the frequency of the use of nodes. To control and to decide a program size are important and difficult problems in Evolutionary Computation, especially, a well-known crossover operator tends to cause bloat. We introduce two additional operators, add operator and delete operator, that can change the number of each kind of nodes based on whether a node function is important in the environment or not. Simulation results shows that the proposed method brings about extremely better results compared with ordinary fixed size GNP.

  13. Synchronization of electronic genetic networks.

    PubMed

    Wagemakers, Alexandre; Buldú, Javier M; García-Ojalvo, Jordi; Sanjuán, Miguel A F

    2006-03-01

    We describe a simple analog electronic circuit that mimics the behavior of a well-known synthetic gene oscillator, the repressilator, which represents a set of three genes repressing one another. Synchronization of a population of such units is thoroughly studied, with the aim to compare the role of global coupling with that of global forcing on the population. Our results show that coupling is much more efficient than forcing in leading the gene population to synchronized oscillations. Furthermore, a modification of the proposed analog circuit leads to a simple electronic version of a genetic toggle switch, which is a simple network of two mutual repressor genes, where control by external forcing is also analyzed. PMID:16599758

  14. Propagation of genetic variation in gene regulatory networks

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

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

  15. Genetic Network Inference Using Hierarchical Structure.

    PubMed

    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

  16. 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.

  17. 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)

  18. 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

  19. Splitting strategy for simulating genetic regulatory networks.

    PubMed

    You, Xiong; Liu, Xueping; Musa, Ibrahim Hussein

    2014-01-01

    The splitting approach is developed for the numerical simulation of genetic regulatory networks with a stable steady-state structure. The numerical results of the simulation of a one-gene network, a two-gene network, and a p53-mdm2 network show that the new splitting methods constructed in this paper are remarkably more effective and more suitable for long-term computation with large steps than the traditional general-purpose Runge-Kutta methods. The new methods have no restriction on the choice of stepsize due to their infinitely large stability regions. PMID:24624223

  20. 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

  1. Bridging genetic networks and queueing theory

    NASA Astrophysics Data System (ADS)

    Arazi, Arnon; Ben-Jacob, Eshel; Yechiali, Uri

    2004-02-01

    One of the main challenges facing biology today is the understanding of the joint action of genes, proteins and RNA molecules, interwoven in intricate interdependencies commonly known as genetic networks. To this end, several mathematical approaches have been introduced to date. In addition to developing the analytical tools required for this task anew, one can utilize knowledge found in existing disciplines, specializing in the representation and analysis of systems featuring similar aspects. We suggest queueing theory as a possible source of such knowledge. This discipline, which focuses on the study of workloads forming in a variety of scenarios, offers an assortment of tools allowing for the derivation of the statistical properties of the inspected systems. We argue that a proper adaptation of modeling techniques and analytical methods used in queueing theory can contribute to the study of genetic regulatory networks. This is demonstrated by presenting a queueing-inspired model of a genetic network of arbitrary size and structure, for which the probability distribution function is derived. This model is further applied to the description of the lac operon regulation mechanism. In addition, we discuss the possible benefits stemming for queueing theory from the interdisciplinary dialogue with molecular biology-in particular, the incorporation of various dynamical behaviours into queueing networks.

  2. 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.

  3. Genetic factors involved in risk for methamphetamine intake and sensitization

    PubMed Central

    Belknap, John K.; McWeeney, Shannon; Reed, Cheryl; Burkhart-Kasch, Sue; McKinnon, Carrie S.; Li, Na; Baba, Harue; Scibelli, Angela C.; Hitzemann, Robert; Phillips, Tamara J.

    2013-01-01

    Lines of mice were created by selective breeding for the purpose of identifying genetic mechanisms that influence magnitude of the selected trait and to explore genetic correlations for additional traits thought to be influenced by shared mechanisms. DNA samples from high and low methamphetamine drinking (MADR) and high and low methamphetamine sensitization lines were used for quantitative trait locus (QTL) mapping. Significant additive genetic correlations between the two traits indicated common genetic influence, and a QTL on chromosome X was detected for both traits, suggesting one source of this commonality. For MADR mice, a QTL on chromosome 10 accounted for more than 50% of the genetic variance in that trait. Microarray gene expression analyses were performed for 3 brain regions for methamphetamine-naïve MADR line mice: nucleus accumbens, prefrontal cortex and ventral midbrain. Many of the genes that were differentially expressed between the high and low MADR lines were shared in common across the 3 brain regions. A gene network highly enriched in transcription factor genes was identified as being relevant to genetically-determined differences in methamphetamine intake. When the mu opioid receptor gene (Oprm1), located on chromosome 10 in the QTL region, was added to this top ranked transcription factor network, it became a hub in the network. These data are consistent with previously published findings of opioid response and intake differences between the MADR lines and suggest that Oprm1 or a gene that impacts activity of the opioid system, plays a role in genetically–determined differences in methamphetamine intake. PMID:24217691

  4. 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

  5. Exploring drug combinations in genetic interaction network

    PubMed Central

    2012-01-01

    Background Drug combination that consists of distinctive agents is an attractive strategy to combat complex diseases and has been widely used clinically with improved therapeutic effects. However, the identification of efficacious drug combinations remains a non-trivial and challenging task due to the huge number of possible combinations among the candidate drugs. As an important factor, the molecular context in which drugs exert their functions can provide crucial insights into the mechanism underlying drug combinations. Results In this work, we present a network biology approach to investigate drug combinations and their target proteins in the context of genetic interaction networks and the related human pathways, in order to better understand the underlying rules of effective drug combinations. Our results indicate that combinatorial drugs tend to have a smaller effect radius in the genetic interaction networks, which is an important parameter to describe the therapeutic effect of a drug combination from the network perspective. We also find that drug combinations are more likely to modulate functionally related pathways. Conclusions This study confirms that the molecular networks where drug combinations exert their functions can indeed provide important insights into the underlying rules of effective drug combinations. We hope that our findings can help shortcut the expedition of the future discovery of novel drug combinations. PMID:22595004

  6. Effects of macromolecular crowding on genetic networks.

    PubMed

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

    2011-12-21

    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

  7. 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

  8. How Are Television Networks Involved in Distance Learning?

    ERIC Educational Resources Information Center

    Bucher, Katherine

    1996-01-01

    Reviews the involvement of various television networks in distance learning, including public broadcasting stations, Cable in the Classroom, Arts and Entertainment Network, Black Entertainment Television, C-SPAN, CNN (Cable News Network), The Discovery Channel, The Learning Channel, Mind Extension University, The Weather Channel, National Teacher…

  9. 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

  10. Get involved in the Young Vet Network.

    PubMed

    2016-07-01

    BVA's Young Vet Network (YVN) supports members from their final year at vet school to eight years after graduation. It is during this period that graduates particularly benefit from access to peer support. Here Tim Keen, BVA marketing manager, provides an update on what's happening. PMID:27365247

  11. 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

  12. 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.

  13. 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.

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

    PubMed

    Wu, Fang-Xiang

    2007-01-01

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

  15. Training product unit neural networks with genetic algorithms

    NASA Technical Reports Server (NTRS)

    Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

    1991-01-01

    The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

  16. 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

  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. Identification of genetic and epigenetic marks involved in population structure.

    PubMed

    Liu, Jingyu; Hutchison, Kent; Perrone-Bizzozero, Nora; Morgan, Marilee; Sui, Jing; Calhoun, Vince

    2010-01-01

    Population structure is well known as a prevalent and important factor in genetic studies, but its relevance in epigenetics is unclear. Very little is known about the affected epigenetic markers and their connections with genetics. In this study we assessed the impact of population diversity on genome wide single nucleotide polymorphisms (SNPs) and DNA methylation levels in 196 participants from five ethnic groups, using principle and independent component analyses. Three population stratification factors (PSFs) were identified in the genomic SNP dataset, accounting for a relatively large portion of total variance (6%). In contrast, only one PSF was identified in genomic methylation dataset accounting for 0.2% of total variance. This methylation PSF, however, was significantly correlated with the largest SNP PSF (r = 0.72, p<1E-23). We then investigated the top contributing markers in these two linked PSFs. The SNP PSF predominantly consists of 8 SNPs from three genes, SLC45A2, HERC2 and CTNNA2, known to encode skin/hair/eye color. The methylation PSF includes 48 methylated sites in 44 genes coding for basic molecular functions, including transcription regulation, DNA binding, cytokine, and transferase activity. Among them, 8 sites are either hypo- or hyper-methylated correlating to minor alleles of SNPs in the SNP PSF. We found that the genes in SNP and methylation PSFs share common biological processes including sexual/multicellular organism reproduction, cell-cell signaling and cytoskeleton organization. We further investigated the transcription regulatory network operating at these genes and identified that most of genes closely interact with ID2, which encodes for a helix-loop-helix inhibitor of DNA binding. Overall, our results show a significant correlation between genetic and epigenetic population stratification, and suggest that the interrelationship between genetic and epigenetic population structure is mediated via complex multiple gene interactions

  19. Are genetic variants for tobacco smoking associated with cannabis involvement?

    PubMed Central

    Agrawal, Arpana; Lynskey, Michael T.; Kapoor, Manav; Bucholz, Kathleen K.; Edenberg, Howard J.; Schuckit, Marc; Brooks, Andrew; Hesselbrock, Victor; Kramer, John; Saccone, Nancy; Tischfield, Jay; Bierut, Laura J.

    2015-01-01

    Background Cannabis users are highly likely to also be tobacco cigarette smokers and a proportion of this comorbidity is attributable to shared genetic influences. Three large meta-analyses of genomewide association studies (GWAS) of tobacco smoking have identified multiple genomewide significant (p<5 × 10−8) single nucleotide polymorphisms (SNPs). We examine whether these SNPs are associated with tobacco smoking and with cannabis involvement in an independent sample. Method Eleven SNPs associated with cigarettes per day (CPD), ever versus never smoking and current smoking/smoking cessation at p < 5 ×10−8 were selected from three published meta-analyses. Association analyses were conducted with similar tobacco smoking measures in 2,716 European-American subjects from the Study of Addictions Genes and Environment (SAGE) and with lifetime and current cannabis use and DSM-IV cannabis abuse/dependence. Results Cannabis use and tobacco smoking correlated at 0.54. Rs16969968 in CHRNA5 (and its proxy, rs1051730 in CHRNA3) and rs1451240, a proxy for rs13280604 in CHRNB3, were associated with CPD after Bonferroni correction (p<.006). rs1451240 was also associated with DSM-IV cannabis abuse/dependence. Rs6265 in BDNF was associated with smoking initiation, as in the original meta-analysis and also with lifetime cannabis use. Associations with cannabis involvement were no longer significant upon adjustment for the tobacco smoking measures. Conclusions The modest associations between cannabis involvement and SNPs for tobacco smoking were not independent of the comorbidity between tobacco and cannabis involvement. Larger samples of individuals might be required to articulate the specific genetic architecture of cannabis involvement. PMID:25770649

  20. 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

  1. 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

  2. Genetic-algorithm-based tri-state neural networks

    NASA Astrophysics Data System (ADS)

    Uang, Chii-Maw; Chen, Wen-Gong; Horng, Ji-Bin

    2002-09-01

    A new method, using genetic algorithms, for constructing a tri-state neural network is presented. The global searching features of the genetic algorithms are adopted to help us easily find the interconnection weight matrix of a bipolar neural network. The construction method is based on the biological nervous systems, which evolve the parameters encoded in genes. Taking the advantages of conventional (binary) genetic algorithms, a two-level chromosome structure is proposed for training the tri-state neural network. A Matlab program is developed for simulating the network performances. The results show that the proposed genetic algorithms method not only has the features of accurate of constructing the interconnection weight matrix, but also has better network performance.

  3. 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

  4. Genetic Algorithm Based Neural Networks for Nonlinear Optimization

    Energy Science and Technology Software Center (ESTSC)

    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

  5. Cellular and network mechanisms of genetically-determined absence seizures.

    PubMed

    Pinault, Didier; O'Brien, Terence J

    2005-01-01

    The absence epilepsies are characterized by recurrent episodes of loss of consciousness associated with generalized spike-and-wave discharges, with an abrupt onset and offset, in the thalamocortical system. In the absence of detailed neurophysiological studies in humans, many of the concepts regarding the pathophysiological basis of absence seizures are based on studies in animal models. Each of these models has its particular strengths and limitations, and the validity of findings from these models for the human condition cannot be assumed. Consequently, studies in different models have produced some conflicting findings and conclusions. A long-standing concept, based primarily from studies in vivo in cats and in vitro brain slices, is that these paroxysmal electrical events develop suddenly from sleep-related spindle oscillations. More specifically, it is proposed that the initial mechanisms that underlie absence-related spike-and-wave discharges are located in the thalamus, involving especially the thalamic reticular nucleus. By contrast, more recent studies in well-established, genetic models of absence epilepsy in rats demonstrate that spike-and-wave discharges originate in a cortical focus and develop from a wake-related natural corticothalamic sensorimotor rhythm. In this review we integrate recent findings showing that, in both the thalamus and the neocortex, genetically-determined, absence-related spike-and-wave discharges are the manifestation of hypersynchronized, cellular, rhythmic excitations and inhibitions that result from a combination of complex, intrinsic, synaptic mechanisms. Arguments are put forward supporting the hypothesis that layer VI corticothalamic neurons act as 'drivers' in the generation of spike-and-wave discharges in the somatosensory thalamocortical system that result in corticothalamic resonances particularly initially involving the thalamic reticular nucleus. However an important unresolved question is: what are the cellular and

  6. 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. PMID:12142360

  7. 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.

  8. 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.

  9. Environmental and genetic perturbations reveal different networks of metabolic regulation

    PubMed Central

    Greenberg, Anthony J; Hackett, Sean R; Harshman, Lawrence G; Clark, Andrew G

    2011-01-01

    Progress in systems biology depends on accurate descriptions of biological networks. Connections in a regulatory network are identified as correlations of gene expression across a set of environmental or genetic perturbations. To use this information to predict system behavior, we must test how the nature of perturbations affects topologies of networks they reveal. To probe this question, we focused on metabolism of Drosophila melanogaster. Our source of perturbations is a set of crosses among 92 wild-derived lines from five populations, replicated in a manner permitting separate assessment of the effects of genetic variation and environmental fluctuation. We directly assayed activities of enzymes and levels of metabolites. Using a multivariate Bayesian model, we estimated covariance among metabolic parameters and built fine-grained probabilistic models of network topology. The environmental and genetic co-regulation networks are substantially the same among five populations. However, genetic and environmental perturbations reveal qualitative differences in metabolic regulation, suggesting that environmental shifts, such as diet modifications, produce different systemic effects than genetic changes, even if the primary targets are the same. PMID:22186737

  10. Characterization of Genetic Networks Associated with Alzheimer's Disease.

    PubMed

    Zhang, Bin; Tran, Linh; Emilsson, Valur; Zhu, Jun

    2016-01-01

    At the molecular level, the genetics of complex disease such as Alzheimer's disease (AD) manifests itself as series of alterations in the molecular interactions in pathways and networks that define biological processes underlying the pathophysiological states of disease. While large-scale genome-wide association (GWA) studies of late-onset alzheimer's disease (LOAD) have uncovered prominent genomic regions linked to the disease, the cause for the vast majority of LOAD cases still remains unknown. Increasingly available large-scale genomic and genetic data related to LOAD has made it possible to comprehensively uncover the mechanisms causally lined to LOAD in a completely data-driven manner. Here we review the various aspects of systems/network biology approaches and methodology in constructing genetic networks associated with AD from large sampling of postmortem brain tissues. We describe in detail a multiscale network modeling approach (MNMA) that integrates interaction and causal gene networks to analyze large-scale DNA, gene expression and pathophysiological data from multiple post-mortem brain regions of LOAD patients as well non-demented normal controls. MNMA first employs weighted gene co-expression network analysis (WGCNA) to construct multi-tissue networks that simultaneously capture intra-tissue and inter-tissue gene-gene interactions and then quantifies the change in connectivity among highly co-expressed genes in LOAD with respect to the normal state. Co-expressed gene modules are then rank ordered by relevance to pathophysiological traits and enrichment of genes differentially expressed in LOAD. Causal regulatory relationships among the genes in each module are then determined by a Bayesian network inference framework that is used to formally integrate genetic and gene expression information. MNMA has uncovered a massive remodeling of network structures in LOAD and identified novel subnetworks and key regulators that are causally linked to LOAD. In the

  11. 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

  12. 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.

  13. Hybrid algorithm for NARX network parameters' determination using differential evolution and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Salami, M. J. E.; Tijani, I. B.; Abdullateef, A. I.; Aibinu, M. A.

    2013-12-01

    A hybrid optimization algorithm using Differential Evolution (DE) and Genetic Algorithm (GA) is proposed in this study to address the problem of network parameters determination associated with the Nonlinear Autoregressive with eXogenous inputs Network (NARX-network). The proposed algorithm involves a two level optimization scheme to search for both optimal network architecture and weights. The DE at the upper level is formulated as combinatorial optimization to search for the network architecture while the associated network weights that minimize the prediction error is provided by the GA at the lower level. The performance of the algorithm is evaluated on identification of a laboratory rotary motion system. The system identification results show the effectiveness of the proposed algorithm for nonparametric model development.

  14. MicroRNA-dependent Genetic Networks During Neural Development

    PubMed Central

    Abernathy, Daniel G.; Yoo, Andrew S.

    2014-01-01

    The development of the structurally and functionally diverse mammalian nervous system requires the integration of numerous levels of gene regulation. Accumulating evidence suggests that microRNAs are key mediators of genetic networks during neural development. Importantly, microRNAs are found to regulate both feedback and feedforward loops during neural development leading to large changes in gene expression. These repressive interactions provide an additional mechanism that facilitates the establishment of complexity within the nervous system. Here, we review studies that have enabled the identification of brain-enriched microRNAs and discuss how genetic networks in neural development depend on microRNAs. PMID:24865244

  15. Involving study populations in the review of genetic research.

    PubMed

    Sharp, R R; Foster, M W

    2000-01-01

    Genetic research can present risks to all members of a study population, not just those who choose to participate in research. The authors suggest that community-based reviews of research protocols can help identify and minimize such research-related risks. PMID:11067631

  16. Genetic mechanisms involved in the phenotype of Down syndrome.

    PubMed

    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 sequencing and annotation of the long arm of chromosome 21 was a critical step towards understanding the genetics of the phenotype. However, annotation of the chromosome continues and the functions of many genes on chromosome 21 remain uncertain. Recent findings about the structure of the human genome and of chromosome 21, in particular, and studies on mechanisms of gene regulation indicate that various genetic mechanisms may be contributors to the phenotype of DS and to the variability of the phenotype. These include variability of gene expression, the activity of transcription factors both encoded on chromosome 21 and encoded elsewhere in the genome, copy number polymorphisms, the function of conserved nongenic regions, microRNA activities, RNA editing, and perhaps DNA methylation. In this manuscript, we describe current knowledge about these genetic complexities and their likely importance in the context of DS. We identify gaps in current knowledge and suggest priorities to fill these gaps. PMID:17910086

  17. 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…

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

    PubMed

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

    2012-09-01

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

  19. 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

  20. 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.

  1. Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine

    PubMed Central

    Davis, N A; Crowe, J E; Pajewski, N M; McKinney, B A

    2010-01-01

    The variation in antibody response to vaccination likely involves small contributions of numerous genetic variants, such as single-nucleotide polymorphisms (SNPs), which interact in gene networks and pathways. To accumulate the bits of genetic information relevant to the phenotype that are distributed throughout the interaction network, we develop a network eigenvector centrality algorithm (SNPrank) that is sensitive to the weak main effects, gene–gene interactions and small higher-order interactions through hub effects. Analogous to Google PageRank, we interpret the algorithm as the simulation of a random SNP surfer (RSS) that accumulates bits of information in the network through a dynamic probabilistic Markov chain. The transition matrix for the RSS is based on a data-driven genetic association interaction network (GAIN), the nodes of which are SNPs weighted by the main-effect strength and edges weighted by the gene–gene interaction strength. We apply SNPrank to a GAIN analysis of a candidate-gene association study on human immune response to smallpox vaccine. SNPrank implicates a SNP in the retinoid X receptor α (RXRA) gene through a network interaction effect on antibody response. This vitamin A- and D-signaling mediator has been previously implicated in human immune responses, although it would be neglected in a standard analysis because its significance is unremarkable outside the context of its network centrality. This work suggests SNPrank to be a powerful method for identifying network effects in genetic association data and reveals a potential vitamin regulation network association with antibody response. PMID:20613780

  2. Formation Mechanism for a Hybrid Supramolecular Network Involving Cooperative Interactions

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

    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.

  3. Genetic architecture and regulatory networks in oilseed development

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Genetic analysis of global gene expression level variation provides evidence for transcriptional regulators and gene network relationships. Plant seeds are an important source of oil and protein, and a genome-wide assessment of transcriptional regulation during seed development offers insight into t...

  4. Using genetic algorithms to construct a network for financial prediction

    NASA Astrophysics Data System (ADS)

    Patel, Devesh

    1996-03-01

    Traditional forecasting models such as the Box-Jenkins ARIMA model are almost all based on models that assume a linear relationship amongst variables and cannot approximate the non- linear relationship that exists amongst variables in real-world data such as stock-price data. Artificial neural networks, on the other hand, consist of two or more levels of nonlinearity that have been successfully used to approximate the underlying relationships of time series data. Neural networks however, pose a design problem: their optimum topology and training rule parameters including learning rate and momentum, for the problem at hand need to be determined. In this paper, we use genetic algorithms to determine these design parameters. In general genetic algorithms are an optimization method that find solutions to a problem by an evolutionary process based on natural selection. The genetic algorithm searches through the network parameter space and the neural network learning algorithm evaluates the selected parameters. We then use the optimally configured network to predict the stock market price of a blue-chip company on the UK market.

  5. Genetic factors involves in intracranial aneurysms – actualities

    PubMed Central

    Mohan, D; Munteanu, V; Coman, T; Ciurea, AV

    2015-01-01

    Intracranial aneurysm (IA) is a common vascular disorder, which frequently leads to fatal vascular rupture leading to subarachnoid hemorrhage (SAH). Although various acquired risk factors associated with IAs have been identified, heritable conditions are associated with IAs formation but these syndromes account for less than 1% of all IAs in the population. Cerebral aneurysm disease is related to hemodynamic and genetic factors, associated with structural weakness in the arterial wall, which was acquired by a specific, often unknown, event. Possibly, the trigger moment of aneurysm formation may depend on the dynamic arterial growth, which is closely related to aging/ atherosclerosis. Genetic factors are known to have an important role in IA pathogenesis. Literature data provide complementary evidence that the variants on chromosomes 8q and 9p are associated with IA and that the risk of IA in patients with these variants is greatly increased with cigarette smoking. Intracranial aneurysms are acquired lesions (5-10% of the population). In comparison with sporadic aneurysms, familial aneurysms tend to be larger, more often located in the middle cerebral artery, and more likely to be multiple. Abbreviations: DNA = deoxyribonucleic acid, FIA = familial Intracranial Aneurysm, GWAS = genome-wide association studies, IL-6 = interleukin-6, ISUIA = International Study of Unruptured Intracranial Aneurysms, IA = Intracranial aneurysm, mRNA = Messager ribonucleic acid, SNPs = single-nucleotide polymorphisms, SMCs = smooth muscle cells, sIAs = sporadic IAs, SAH = subarachnoid hemorrhage, TNF-α = tumor necrosis factor-alpha, COL4A1 = type IV collagen alpha-1 PMID:26351537

  6. Genetic algorithm application in optimization of wireless sensor networks.

    PubMed

    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

  7. 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

  8. 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

  9. 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

  10. Genetic interaction network of the Saccharomyces cerevisiae type 1 phosphatase Glc7

    PubMed Central

    Logan, Michael R; Nguyen, Thao; Szapiel, Nicolas; Knockleby, James; Por, Hanting; Zadworny, Megan; Neszt, Michael; Harrison, Paul; Bussey, Howard; Mandato, Craig A; Vogel, Jackie; Lesage, Guillaume

    2008-01-01

    Background Protein kinases and phosphatases regulate protein phosphorylation, a critical means of modulating protein function, stability and localization. The identification of functional networks for protein phosphatases has been slow due to their redundant nature and the lack of large-scale analyses. We hypothesized that a genome-scale analysis of genetic interactions using the Synthetic Genetic Array could reveal protein phosphatase functional networks. We apply this approach to the conserved type 1 protein phosphatase Glc7, which regulates numerous cellular processes in budding yeast. Results We created a novel glc7 catalytic mutant (glc7-E101Q). Phenotypic analysis indicates that this novel allele exhibits slow growth and defects in glucose metabolism but normal cell cycle progression and chromosome segregation. This suggests that glc7-E101Q is a hypomorphic glc7 mutant. Synthetic Genetic Array analysis of glc7-E101Q revealed a broad network of 245 synthetic sick/lethal interactions reflecting that many processes are required when Glc7 function is compromised such as histone modification, chromosome segregation and cytokinesis, nutrient sensing and DNA damage. In addition, mitochondrial activity and inheritance and lipid metabolism were identified as new processes involved in buffering Glc7 function. An interaction network among 95 genes genetically interacting with GLC7 was constructed by integration of genetic and physical interaction data. The obtained network has a modular architecture, and the interconnection among the modules reflects the cooperation of the processes buffering Glc7 function. Conclusion We found 245 genes required for the normal growth of the glc7-E101Q mutant. Functional grouping of these genes and analysis of their physical and genetic interaction patterns bring new information on Glc7-regulated processes. PMID:18627629

  11. Methods for Mapping of Interaction Networks Involving Membrane Proteins

    SciTech Connect

    Hooker, Brian S.; Bigelow, Diana J.; Lin, Chiann Tso

    2007-11-23

    Numerous approaches have been taken to study protein interactions, such as tagged protein complex isolation followed by mass spectrometry, yeast two-hybrid methods, fluorescence resonance energy transfer, surface plasmon resonance, site-directed mutagenesis, and crystallography. Membrane protein interactions pose significant challenges due to the need to solubilize membranes without disrupting protein-protein interactions. Traditionally, analysis of isolated protein complexes by high-resolution 2D gel electrophoresis has been the main method used to obtain an overall picture of proteome constituents and interactions. However, this method is time consuming, labor intensive, detects only abundant proteins and is not suitable for the coverage required to elucidate large interaction networks. In this review, we discuss the application of various methods to elucidate interactions involving membrane proteins. These techniques include methods for the direct isolation of single complexes or interactors as well as methods for characterization of entire subcellular and cellular interactomes.

  12. 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

  13. 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

    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

  14. Predicting genetic interactions from Boolean models of biological networks.

    PubMed

    Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei

    2015-08-01

    Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing us to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and the phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed. PMID:25958956

  15. 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.

  16. 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

  17. Node-based measures of connectivity in genetic networks.

    PubMed

    Koen, Erin L; Bowman, Jeff; Wilson, Paul J

    2016-01-01

    At-site environmental conditions can have strong influences on genetic connectivity, and in particular on the immigration and settlement phases of dispersal. However, at-site processes are rarely explored in landscape genetic analyses. Networks can facilitate the study of at-site processes, where network nodes are used to model site-level effects. We used simulated genetic networks to compare and contrast the performance of 7 node-based (as opposed to edge-based) genetic connectivity metrics. We simulated increasing node connectivity by varying migration in two ways: we increased the number of migrants moving between a focal node and a set number of recipient nodes, and we increased the number of recipient nodes receiving a set number of migrants. We found that two metrics in particular, the average edge weight and the average inverse edge weight, varied linearly with simulated connectivity. Conversely, node degree was not a good measure of connectivity. We demonstrated the use of average inverse edge weight to describe the influence of at-site habitat characteristics on genetic connectivity of 653 American martens (Martes americana) in Ontario, Canada. We found that highly connected nodes had high habitat quality for marten (deep snow and high proportions of coniferous and mature forest) and were farther from the range edge. We recommend the use of node-based genetic connectivity metrics, in particular, average edge weight or average inverse edge weight, to model the influences of at-site habitat conditions on the immigration and settlement phases of dispersal. PMID:25917123

  18. 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

  19. 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.

  20. Enhanced energy transport in genetically engineered excitonic networks.

    PubMed

    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. PMID:26461447

  1. 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.

  2. 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

  3. Genetic networks with canalyzing Boolean rules are always stable

    PubMed Central

    Kauffman, Stuart; Peterson, Carsten; Samuelsson, Björn; Troein, Carl

    2004-01-01

    We determine stability and attractor properties of random Boolean genetic network models with canalyzing rules for a variety of architectures. For all power law, exponential, and flat in-degree distributions, we find that the networks are dynamically stable. Furthermore, for architectures with few inputs per node, the dynamics of the networks is close to critical. In addition, the fraction of genes that are active decreases with the number of inputs per node. These results are based upon investigating ensembles of networks using analytical methods. Also, for different in-degree distributions, the numbers of fixed points and cycles are calculated, with results intuitively consistent with stability analysis; fewer inputs per node implies more cycles, and vice versa. There are hints that genetic networks acquire broader degree distributions with evolution, and hence our results indicate that for single cells, the dynamics should become more stable with evolution. However, such an effect is very likely compensated for by multicellular dynamics, because one expects less stability when interactions among cells are included. We verify this by simulations of a simple model for interactions among cells. PMID:15572453

  4. 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.

  5. 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

    2009-01-01

    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 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. PMID:19727437

  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. 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

  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. PMID:27625653

  9. How social and genetic factors predict friendship networks

    PubMed Central

    Boardman, Jason D.; Domingue, Benjamin W.; Fletcher, Jason M.

    2012-01-01

    Recent research suggests that the genotype of one individual in a friendship pair is predictive of the genotype of his/her friend. These results provide tentative support for the genetic homophily perspective, which has important implications for social and genetic epidemiology because it substantiates a particular form of gene–environment correlation. This process may also have important implications for social scientists who study the social factors related to health and health-related behaviors. We extend this work by considering the ways in which school context shapes genetically similar friendships. Using the network, school, and genetic information from the National Longitudinal Study of Adolescent Health, we show that genetic homophily for the TaqI A polymorphism within the DRD2 gene is stronger in schools with greater levels of inequality. Our results suggest that individuals with similar genotypes may not actively select into friendships; rather, they may be placed into these contexts by institutional mechanisms outside of their control. Our work highlights the fundamental role played by broad social structures in the extent to which genetic factors explain complex behaviors, such as friendships. PMID:23045663

  10. Bicriteria Network Optimization Problem using Priority-based Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Gen, Mitsuo; Lin, Lin; Cheng, Runwei

    Network optimization is being an increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. In many applications, however, there are several criteria associated with traversing each edge of a network. For example, cost and flow measures are both important in the networks. As a result, there has been recent interest in solving Bicriteria Network Optimization Problem. The Bicriteria Network Optimization Problem is known a NP-hard. The efficient set of paths may be very large, possibly exponential in size. Thus the computational effort required to solve it can increase exponentially with the problem size in the worst case. In this paper, we propose a genetic algorithm (GA) approach used a priority-based chromosome for solving the bicriteria network optimization problem including maximum flow (MXF) model and minimum cost flow (MCF) model. The objective is to find the set of Pareto optimal solutions that give possible maximum flow with minimum cost. This paper also combines Adaptive Weight Approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. Computer simulations show the several numerical experiments by using some difficult-to-solve network design problems, and show the effectiveness of the proposed method.

  11. 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

  12. 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. PMID:25140339

  13. 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

  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. PMID:26935902

  15. 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.

  16. 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.

  17. The independent spreaders involved SIR Rumor model in complex networks

    NASA Astrophysics Data System (ADS)

    Qian, Zhen; Tang, Shaoting; Zhang, Xiao; Zheng, Zhiming

    2015-07-01

    Recent studies of rumor or information diffusion process in complex networks show that in contrast to traditional comprehension, individuals who participate in rumor spreading within one network do not always get the rumor from their neighbors. They can obtain the rumor from different sources like online social networks and then publish it on their personal sites. In our paper, we discuss this phenomenon in complex networks by adopting the concept of independent spreaders. Rather than getting the rumor from neighbors, independent spreaders learn it from other channels. We further develop the classic "ignorant-spreaders-stiflers" or SIR model of rumor diffusion process in complex networks. A steady-state analysis is conducted to investigate the final spectrum of the rumor spreading under various spreading rate, stifling rate, density of independent spreaders and average degree of the network. Results show that independent spreaders effectively enhance the rumor diffusion process, by delivering the rumor to regions far away from the current rumor infected regions. And though the rumor spreading process in SF networks is faster than that in ER networks, the final size of rumor spreading in ER networks is larger than that in SF networks.

  18. 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

  19. 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

  20. 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

  1. [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. PMID:25444134

  2. 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

  3. 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

  4. Non-coding RNAs and complex distributed genetic networks

    NASA Astrophysics Data System (ADS)

    Zhdanov, Vladimir P.

    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.

  5. Superiority of artificial neural networks for a genetic classification procedure.

    PubMed

    Sant'Anna, I C; Tomaz, R S; Silva, G N; Nascimento, M; Bhering, L L; Cruz, C D

    2015-01-01

    The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions. PMID:26345924

  6. Modifier Genes and the Plasticity of Genetic Networks in Mice

    PubMed Central

    Hamilton, Bruce A.; Yu, Benjamin D.

    2012-01-01

    Modifier genes are an integral part of the genetic landscape in both humans and experimental organisms, but have been less well explored in mammals than other systems. A growing number of modifier genes in mouse models of disease nonetheless illustrate the potential for novel findings, while new technical advances promise many more to come. Modifier genes in mouse models include induced mutations and spontaneous or wild-derived variations captured in inbred strains. Identification of modifiers among wild-derived variants in particular should detect disease modifiers that have been shaped by selection and might therefore be compatible with high fitness and function. Here we review selected examples and argue that modifier genes derived from natural variation may provide a bias for nodes in genetic networks that have greater intrinsic plasticity and whose therapeutic manipulation may therefore be more resilient to side effects than conventional targets. PMID:22511884

  7. Combining neural networks and genetic algorithms for hydrological flow forecasting

    NASA Astrophysics Data System (ADS)

    Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr

    2010-05-01

    We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and

  8. Applications of genetic algorithms and neural networks to interatomic potentials

    NASA Astrophysics Data System (ADS)

    Hobday, Steven; Smith, Roger; BelBruno, Joe

    1999-06-01

    Applications of two modern artificial intelligence (AI) techniques, genetic algorithms (GA) and neural networks (NN) to computer simulations are reported. It is shown that the GA are very useful tools for determining the minimum energy structures of clusters of atoms described by interatomic potential functions and generally outperform other optimisation methods for this task. A number of applications are given including covalent, and close packed structures of single or multi-component atomic species. It is also shown that (many body) interatomic potential functions for multi-component systems can be derived by training a specially constructed NN on a variety of structural data.

  9. NACE: A web-based tool for prediction of intercompartmental efficiency of human molecular genetic networks.

    PubMed

    Popik, Olga V; Ivanisenko, Timofey V; Saik, Olga V; Petrovskiy, Evgeny D; Lavrik, Inna N; Ivanisenko, Vladimir A

    2016-06-15

    Molecular genetic processes generally involve proteins from distinct intracellular localisations. Reactions that follow the same process are distributed among various compartments within the cell. In this regard, the reaction rate and the efficiency of biological processes can depend on the subcellular localisation of proteins. Previously, the authors proposed a method of evaluating the efficiency of biological processes based on the analysis of the distribution of protein subcellular localisation (Popik et al., 2014). Here, NACE is presented, which is an open access web-oriented program that implements this method and allows the user to evaluate the intercompartmental efficiency of human molecular genetic networks. The method has been extended by a new feature that provides the evaluation of the tissue-specific efficiency of networks for more than 2800 anatomical structures. Such assessments are important in cases when molecular genetic pathways in different tissues proceed with the participation of various proteins with a number of intracellular localisations. For example, an analysis of KEGG pathways, conducted using the developed program, showed that the efficiencies of many KEGG pathways are tissue-specific. Analysis of efficiencies of regulatory pathways in the liver, linking proteins of the hepatitis C virus with human proteins involved in the KEGG apoptosis pathway, showed that intercompartmental efficiency might play an important role in host-pathogen interactions. Thus, the developed tool can be useful in the study of the effectiveness of functioning of various molecular genetic networks, including metabolic, regulatory, host-pathogen interactions and others taking into account tissue-specific gene expression. The tool is available via the following link: http://www-bionet.sscc.ru/nace/. PMID:27109913

  10. Intricate environment-modulated genetic networks control isoflavone accumulation in soybean seeds

    PubMed Central

    2010-01-01

    Background Soybean (Glycine max [L] Merr.) seed isoflavones have long been considered a desirable trait to target in selection programs for their contribution to human health and plant defense systems. However, attempts to modify seed isoflavone contents have not always produced the expected results because their genetic basis is polygenic and complex. Undoubtedly, the extreme variability that seed isoflavones display over environments has obscured our understanding of the genetics involved. Results In this study, a mapping population of RILs with three replicates was analyzed in four different environments (two locations over two years). We found a total of thirty-five main-effect genomic regions and many epistatic interactions controlling genistein, daidzein, glycitein and total isoflavone accumulation in seeds. The use of distinct environments permitted detection of a great number of environment-modulated and minor-effect QTL. Our findings suggest that isoflavone seed concentration is controlled by a complex network of multiple minor-effect loci interconnected by a dense epistatic map of interactions. The magnitude and significance of the effects of many of the nodes and connections in the network varied depending on the environmental conditions. In an attempt to unravel the genetic architecture underlying the traits studied, we searched on a genome-wide scale for genomic regions homologous to the most important identified isoflavone biosynthetic genes. We identified putative candidate genes for several of the main-effect and epistatic QTL and for QTL reported by other groups. Conclusions To better understand the underlying genetics of isoflavone accumulation, we performed a large scale analysis to identify genomic regions associated with isoflavone concentrations. We not only identified a number of such regions, but also found that they can interact with one another and with the environment to form a complex adaptable network controlling seed isoflavone levels

  11. 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

  12. 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

  13. 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

  14. 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. PMID:26858398

  15. 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…

  16. 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.

  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. 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.

  20. Gene network and familial analyses uncover a gene network involving Tbx5/Osr1/Pcsk6 interaction in the second heart field for atrial septation.

    PubMed

    Zhang, Ke K; Xiang, Menglan; Zhou, Lun; Liu, Jielin; Curry, Nathan; Heine Suñer, Damian; Garcia-Pavia, Pablo; Zhang, Xiaohua; Wang, Qin; Xie, Linglin

    2016-03-15

    Atrial septal defects (ASDs) are a common human congenital heart disease (CHD) that can be induced by genetic abnormalities. Our previous studies have demonstrated a genetic interaction between Tbx5 and Osr1 in the second heart field (SHF) for atrial septation. We hypothesized that Osr1 and Tbx5 share a common signaling networking and downstream targets for atrial septation. To identify this molecular networks, we acquired the RNA-Seq transcriptome data from the posterior SHF of wild-type, Tbx5(+/) (-), Osr1(+/-), Osr1(-/-) and Tbx5(+/-)/Osr1(+/-) mutant embryos. Gene set analysis was used to identify the Kyoto Encyclopedia of Genes and Genomes pathways that were affected by the doses of Tbx5 and Osr1. A gene network module involving Tbx5 and Osr1 was identified using a non-parametric distance metric, distance correlation. A subset of 10 core genes and gene-gene interactions in the network module were validated by gene expression alterations in posterior second heart field (pSHF) of Tbx5 and Osr1 transgenic mouse embryos, a time-course gene expression change during P19CL6 cell differentiation. Pcsk6 was one of the network module genes that were linked to Tbx5. We validated the direct regulation of Tbx5 on Pcsk6 using immunohistochemical staining of pSHF, ChIP-quantitative polymerase chain reaction and luciferase reporter assay. Importantly, we identified Pcsk6 as a novel gene associated with ASD via a human genotyping study of an ASD family. In summary, our study implicated a gene network involving Tbx5, Osr1 and Pcsk6 interaction in SHF for atrial septation, providing a molecular framework for understanding the role of Tbx5 in CHD ontogeny. PMID:26744331

  1. Markov Logic Networks in the Analysis of Genetic Data

    PubMed Central

    Sakhanenko, Nikita A.

    2010-01-01

    Abstract Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of influences of each gene and often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying mechanisms. Modeling approaches from the artificial intelligence (AI) field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we can replicate the results of traditional statistical methods, but we also show that we are able to go beyond finding independent markers linked to a phenotype by using joint inference without an independence assumption. The method is applied to genetic data on yeast sporulation, a complex phenotype with gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method identifies four loci with smaller effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics. PMID:20958249

  2. 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

  3. Parent Involvement, Sibling Companionship, and Adolescent Substance Use: A Longitudinal, Genetically-Informed Design

    PubMed Central

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

    2015-01-01

    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 two 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

  4. 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

  5. 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. PMID:24009354

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

    PubMed Central

    Ma, Yina; Han, Shihui

    2014-01-01

    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. PMID:24009354

  7. Combined Simulated Annealing and Genetic Algorithm Approach to Bus Network Design

    NASA Astrophysics Data System (ADS)

    Liu, Li; Olszewski, Piotr; Goh, Pong-Chai

    A new method - combined simulated annealing (SA) and genetic algorithm (GA) approach is proposed to solve the problem of bus route design and frequency setting for a given road network with fixed bus stop locations and fixed travel demand. The method involves two steps: a set of candidate routes is generated first and then the best subset of these routes is selected by the combined SA and GA procedure. SA is the main process to search for a better solution to minimize the total system cost, comprising user and operator costs. GA is used as a sub-process to generate new solutions. Bus demand assignment on two alternative paths is performed at the solution evaluation stage. The method was implemented on four theoretical grid networks of different size and a benchmark network. Several GA operators (crossover and mutation) were utilized and tested for their effectiveness. The results show that the proposed method can efficiently converge to the optimal solution on a small network but computation time increases significantly with network size. The method can also be used for other transport operation management problems.

  8. Genetic network identifies novel pathways contributing to atherosclerosis susceptibility in the innominate artery

    PubMed Central

    2014-01-01

    Background Atherosclerosis, the underlying cause of cardiovascular disease, results from both genetic and environmental factors. Methods In the current study we take a systems-based approach using weighted gene co-expression analysis to identify a candidate pathway of genes related to atherosclerosis. Bioinformatic analyses are performed to identify candidate genes and interactions and several novel genes are characterized using in-vitro studies. Results We identify 1 coexpression module associated with innominate artery atherosclerosis that is also enriched for inflammatory and macrophage gene signatures. Using a series of bioinformatics analysis, we further prioritize the genes in this pathway and identify Cd44 as a critical mediator of the atherosclerosis. We validate our predictions generated by the network analysis using Cd44 knockout mice. Conclusion These results indicate that alterations in Cd44 expression mediate inflammation through a complex transcriptional network involving a number of previously uncharacterized genes. PMID:25115202

  9. 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

  10. 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.

  11. 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…

  12. 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…

  13. Echoed time series predictions, neural networks and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Conway, A.

    This work aims to illustrate a potentially serious and previously unrecognised problem in using Neural Networks (NNs), and possibly other techniques, to predict Time Series (TS). It also demonstrates how a new training scheme using a genetic algorithm can alleviate this problem. Although it is already established that NNs can predict TS such as Sunspot Number (SSN) with reasonable success, the accuracy of these predictions is often judged solely by an RMS or related error. The use of this type of error overlooks the presence of what we have termed echoing, where the NN outputs its most recent input as its prediction. Therefore, a method of detecting echoed predictions is introduced, called time-shifting. Reasons for the presence of echo are discussed and then related to the choice of TS sampling. Finally, a new specially designed training scheme is described, which is a hybrid of a genetic algorithm search and back propagation. With this method we have successfully trained NNs to predict without any echo.

  14. 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

  15. Exhaustive search for conservation networks of populations representing genetic diversity.

    PubMed

    Diniz-Filho, J A F; Diniz, J V B P L; Telles, M P C

    2016-01-01

    Conservation strategies routinely use optimization methods to identify the smallest number of units required to represent a set of features that need to be conserved, including biomes, species, and populations. In this study, we provide R scripts to facilitate exhaustive search for solutions that represent all of the alleles in networks with the smallest possible number of populations. The script also allows other variables to be added to describe the populations, thereby providing the basis for multi-objective optimization and the construction of Pareto curves by averaging the values in the solutions. We applied this algorithm to an empirical dataset that comprised 23 populations of Eugenia dysenterica, which is a tree species with a widespread distribution in the Cerrado biome. We observed that 15 populations would be necessary to represent all 249 alleles based on 11 microsatellite loci, and that the likelihood of representing all of the alleles with random networks is less than 0.0001. We selected the solution (from two with the smallest number of populations) obtained for the populations with a higher level of climatic stability as the best strategy for in situ conservation of genetic diversity of E. dysenterica. The scripts provided in this study are a simple and efficient alternative to more complex optimization methods, especially when the number of populations is relatively small (i.e., <25 populations). PMID:26909939

  16. 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

  17. 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.

  18. Injection Drug Users' Involvement In Drug Economy: Dynamics of Sociometric and Egocentric Social Networks.

    PubMed

    Yang, Cui; Latkin, Carl; Muth, Stephen Q; Rudolph, Abby

    2013-07-01

    The purpose of this analysis was to examine the effect of social network cohesiveness on drug economy involvement, and to test whether this relationship is mediated by drug support network size in a sample of active injection drug users. Involvement in the drug economy was defined by self-report of participation in at least one of the following activities: selling drugs, holding drugs or money for drugs, providing street security for drug sellers, cutting/packaging/cooking drugs, selling or renting drug paraphernalia (e.g., pipes, tools, rigs), and injecting drugs in others' veins. The sample consists of 273 active injection drug users in Baltimore, Maryland who reported having injected drugs in the last 6 months and were recruited through either street outreach or by their network members. Egocentric drug support networks were assessed through a social network inventory at baseline. Sociometric networks were built upon the linkages by selected matching characteristics, and k-plex rank was used to characterize the level of cohesiveness of the individual to others in the social network. Although no direct effect was observed, structural equation modeling indicated k-plex rank was indirectly associated with drug economy involvement through drug support network size. These findings suggest the effects of large-scale sociometric networks on injectors' drug economy involvement may occur through their immediate egocentric networks. Future harm reduction programs for injection drug users (IDUs) should consider providing programs coupled with economic opportunities to those drug users within a cohesive network subgroup. Moreover, individuals with a high connectivity to others in their network may be optimal individuals to train for diffusing HIV prevention messages. PMID:25309015

  19. Injection Drug Users’ Involvement In Drug Economy: Dynamics of Sociometric and Egocentric Social Networks

    PubMed Central

    Yang, Cui; Latkin, Carl; Muth, Stephen Q.; Rudolph, Abby

    2014-01-01

    The purpose of this analysis was to examine the effect of social network cohesiveness on drug economy involvement, and to test whether this relationship is mediated by drug support network size in a sample of active injection drug users. Involvement in the drug economy was defined by self-report of participation in at least one of the following activities: selling drugs, holding drugs or money for drugs, providing street security for drug sellers, cutting/packaging/cooking drugs, selling or renting drug paraphernalia (e.g., pipes, tools, rigs), and injecting drugs in others’ veins. The sample consists of 273 active injection drug users in Baltimore, Maryland who reported having injected drugs in the last 6 months and were recruited through either street outreach or by their network members. Egocentric drug support networks were assessed through a social network inventory at baseline. Sociometric networks were built upon the linkages by selected matching characteristics, and k-plex rank was used to characterize the level of cohesiveness of the individual to others in the social network. Although no direct effect was observed, structural equation modeling indicated k-plex rank was indirectly associated with drug economy involvement through drug support network size. These findings suggest the effects of large-scale sociometric networks on injectors’ drug economy involvement may occur through their immediate egocentric networks. Future harm reduction programs for injection drug users (IDUs) should consider providing programs coupled with economic opportunities to those drug users within a cohesive network subgroup. Moreover, individuals with a high connectivity to others in their network may be optimal individuals to train for diffusing HIV prevention messages. PMID:25309015

  20. 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

  1. Integrative network-based approach identifies key genetic elements in breast invasive carcinoma

    PubMed Central

    2015-01-01

    Background Breast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. In the light of the availability of tumor genomic and epigenomic data from different sources and experiments, new integrative approaches are needed to boost the probability of identifying such genetic key drivers. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets. Results Our results showed strong association between regulatory elements from different data sources in terms of the mutual regulatory influence and genomic proximity. By analyzing different types of regulatory interactions, TF-gene, miRNA-mRNA, and proximity analysis of somatic variants, we identified 106 genes, 68 miRNAs, and 9 mutations that are candidate drivers of oncogenic processes in breast cancer. Moreover, we unraveled regulatory interactions among these key drivers and the other elements in the breast cancer network. Intriguingly, about one third of the identified driver genes are targeted by known anti-cancer drugs and the majority of the identified key miRNAs are implicated in cancerogenesis of multiple organs. Also, the identified driver mutations likely cause damaging effects on protein functions. The constructed gene network and the identified key drivers were compared to well-established network-based methods. Conclusion The integrated molecular analysis enabled by the presented network-based approach substantially expands our knowledge base of prospective genomic drivers of genes, miRNAs, and mutations. For a good part of the identified key drivers

  2. 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

  3. 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

  4. Genetic Interactions Involving Five or More Genes Contribute to a Complex Trait in Yeast

    PubMed Central

    Taylor, Matthew B.; Ehrenreich, Ian M.

    2014-01-01

    Recent research suggests that genetic interactions involving more than two loci may influence a number of complex traits. How these ‘higher-order’ interactions arise at the genetic and molecular levels remains an open question. To provide insights into this problem, we dissected a colony morphology phenotype that segregates in a yeast cross and results from synthetic higher-order interactions. Using backcrossing and selective sequencing of progeny, we found five loci that collectively produce the trait. We fine-mapped these loci to 22 genes in total and identified a single gene at each locus that caused loss of the phenotype when deleted. Complementation tests or allele replacements provided support for functional variation in these genes, and revealed that pre-existing genetic variants and a spontaneous mutation interact to cause the trait. The causal genes have diverse functions in endocytosis (END3), oxidative stress response (TRR1), RAS-cAMP signalling (IRA2), and transcriptional regulation of multicellular growth (FLO8 and MSS11), and for the most part have not previously been shown to exhibit functional relationships. Further efforts uncovered two additional loci that together can complement the non-causal allele of END3, suggesting that multiple genotypes in the cross can specify the same phenotype. Our work sheds light on the complex genetic and molecular architecture of higher-order interactions, and raises questions about the broader contribution of such interactions to heritable trait variation. PMID:24784154

  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. 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

  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. 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

  9. 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

  10. 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

  11. Genetic determination of nephrogenesis: the Pax/Eya/Six gene network.

    PubMed

    Brodbeck, Stephan; Englert, Christoph

    2004-03-01

    Development of the kidney serves as a paradigm to understand the mechanisms underlying the formation of an organ. The first sign of kidney development is the interaction between two tissues derived from the intermediate mesoderm, the metanephrogenic mesenchyme and the nephric duct. Many of the genes that play a crucial role in early kidney development, such as Pax2, Eya1, Six1, Six2, Sall1, Foxc1, Wt1, and the Hox11 genes, are expressed in the mesenchyme and encode transcription factors that--with few exceptions--are involved in regulation of the Gdnf gene. Moreover, mutations in a number of these genes in humans are associated with kidney diseases. Interestingly, many of the components regulating early kidney development are conserved throughout evolution and are also involved in eye and muscle formation in mammals, as well as in eye development in Drosophila. Genetic and biochemical studies in Drosophila and mice indicate that these genes and their respective products act in a complex network of interdependencies and positive and negative feedback loops. Genetic experiments have allowed us to begin to characterize the complex interactions between the individual components, but it will require additional biochemical and functional experiments to eventually understand the molecular functions of each of the participating proteins. PMID:14673635

  12. Digit and command interpretation for electronic book using neural network and genetic algorithm.

    PubMed

    Lam, H K; Leung, Frank H F

    2004-12-01

    This paper presents the interpretation of digits and commands using a modified neural network and the genetic algorithm. The modified neural network exhibits a node-to-node relationship which enhances its learning and generalization abilities. A digit-and-command interpreter constructed by the modified neural networks is proposed to recognize handwritten digits and commands. A genetic algorithm is employed to train the parameters of the modified neural networks of the digit-and-command interpreter. The proposed digit-and-command interpreter is successfully realized in an electronic book. Simulation and experimental results will be presented to show the applicability and merits of the proposed approach. PMID:15619928

  13. 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

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

    PubMed

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

    2015-11-01

    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

  15. Genetic associations with micronutrient levels identified in immune and gastrointestinal networks.

    PubMed

    Morine, Melissa J; Monteiro, Jacqueline Pontes; Wise, Carolyn; Teitel, Candee; Pence, Lisa; Williams, Anna; Ning, Baitang; McCabe-Sellers, Beverly; Champagne, Catherine; Turner, Jerome; Shelby, Beatrice; Bogle, Margaret; Beger, Richard D; Priami, Corrado; Kaput, Jim

    2014-07-01

    The discovery of vitamins and clarification of their role in preventing frank essential nutrient deficiencies occurred in the early 1900s. Much vitamin research has understandably focused on public health and the effects of single nutrients to alleviate acute conditions. The physiological processes for maintaining health, however, are complex systems that depend upon interactions between multiple nutrients, environmental factors, and genetic makeup. To analyze the relationship between these factors and nutritional health, data were obtained from an observational, community-based participatory research program of children and teens (age 6-14) enrolled in a summer day camp in the Delta region of Arkansas. Assessments of erythrocyte S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), plasma homocysteine (Hcy) and 6 organic micronutrients (retinol, 25-hydroxy vitamin D3, pyridoxal, thiamin, riboflavin, and vitamin E), and 1,129 plasma proteins were performed at 3 time points in each of 2 years. Genetic makeup was analyzed with 1 M SNP genotyping arrays, and nutrient status was assessed with 24-h dietary intake questionnaires. A pattern of metabolites (met_PC1) that included the ratio of erythrocyte SAM/SAH, Hcy, and 5 vitamins were identified by principal component analysis. Met_PC1 levels were significantly associated with (1) single-nucleotide polymorphisms, (2) levels of plasma proteins, and (3) multilocus genotypes coding for gastrointestinal and immune functions, as identified in a global network of metabolic/protein-protein interactions. Subsequent mining of data from curated pathway, network, and genome-wide association studies identified genetic and functional relationships that may be explained by gene-nutrient interactions. The systems nutrition strategy described here has thus associated a multivariate metabolite pattern in blood with genes involved in immune and gastrointestinal functions. PMID:24879315

  16. Genetic Networking of the Bemisia tabaci Cryptic Species Complex Reveals Pattern of Biological Invasions

    PubMed Central

    De Barro, Paul; Ahmed, Muhammad Z.

    2011-01-01

    Background A challenge within the context of cryptic species is the delimitation of individual species within the complex. Statistical parsimony network analytics offers the opportunity to explore limits in situations where there are insufficient species-specific morphological characters to separate taxa. The results also enable us to explore the spread in taxa that have invaded globally. Methodology/Principal Findings Using a 657 bp portion of mitochondrial cytochrome oxidase 1 from 352 unique haplotypes belonging to the Bemisia tabaci cryptic species complex, the analysis revealed 28 networks plus 7 unconnected individual haplotypes. Of the networks, 24 corresponded to the putative species identified using the rule set devised by Dinsdale et al. (2010). Only two species proposed in Dinsdale et al. (2010) departed substantially from the structure suggested by the analysis. The analysis of the two invasive members of the complex, Mediterranean (MED) and Middle East – Asia Minor 1 (MEAM1), showed that in both cases only a small number of haplotypes represent the majority that have spread beyond the home range; one MEAM1 and three MED haplotypes account for >80% of the GenBank records. Israel is a possible source of the globally invasive MEAM1 whereas MED has two possible sources. The first is the eastern Mediterranean which has invaded only the USA, primarily Florida and to a lesser extent California. The second are western Mediterranean haplotypes that have spread to the USA, Asia and South America. The structure for MED supports two home range distributions, a Sub-Saharan range and a Mediterranean range. The MEAM1 network supports the Middle East - Asia Minor region. Conclusion/Significance The network analyses show a high level of congruence with the species identified in a previous phylogenetic analysis. The analysis of the two globally invasive members of the complex support the view that global invasion often involve very small portions of the available

  17. Robust Finite-Time Passivity for Discrete-Time Genetic Regulatory Networks with Markovian Jumping Parameters

    NASA Astrophysics Data System (ADS)

    Sakthivel, R.; Sathishkumar, M.; Kaviarasan, B.; Anthoni, S. Marshal

    2016-04-01

    This article addresses the issue of robust finite-time passivity for a class of uncertain discrete-time genetic regulatory networks (GRNs) with time-varying delays and Markovian jumping parameters. By constructing a proper Lyapunov-Krasovskii functional involving the lower and upper bounds of time delays, a new set of sufficient conditions is obtained in terms of linear matrix inequalities (LMIs), which guarantees the finite-time boundedness and finite-time passivity of the addressed GRNs for all admissible uncertainties and satisfies the given passive performance index. More precisely, the conditions are obtained with respect to the finite-time interval, while the exogenous disturbances are unknown but energy bounded. Furthermore, the Schur complement together with reciprocally convex optimisation approach is used to simplify the derivation in the main results. Finally, three numerical examples are provided to illustrate the validity of the obtained results.

  18. 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.

  19. Towards the discovery of novel genetic component involved in stress resistance in Arabidopsis thaliana.

    PubMed

    Juraniec, Michal; Lequeux, Hélène; Hermans, Christian; Willems, Glenda; Nordborg, Magnus; Schneeberger, Korbinian; Salis, Pietrino; Vromant, Maud; Lutts, Stanley; Verbruggen, Nathalie

    2014-02-01

    The exposure of plants to high concentrations of trace metallic elements such as copper involves a remodeling of the root system, characterized by a primary root growth inhibition and an increase in the lateral root density. These characteristics constitute easy and suitable markers for screening mutants altered in their response to copper excess. A forward genetic approach was undertaken in order to discover novel genetic factors involved in the response to copper excess. A Cu(2+) -sensitive mutant named copper modified resistance1 (cmr1) was isolated and a causative mutation in the CMR1 gene was identified by using positional cloning and next-generation sequencing. CMR1 encodes a plant-specific protein of unknown function. The analysis of the cmr1 mutant indicates that the CMR1 protein is required for optimal growth under normal conditions and has an essential role in the stress response. Impairment of the CMR1 activity alters root growth through aberrant activity of the root meristem, and modifies potassium concentration and hormonal balance (ethylene production and auxin accumulation). Our data support a putative role for CMR1 in cell division regulation and meristem maintenance. Research on the role of CMR1 will contribute to the understanding of the plasticity of plants in response to changing environments. PMID:24134393

  20. 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

  1. 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

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

    PubMed

    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

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

    PubMed Central

    2013-01-01

    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

  4. Modeling the evolution of complex genetic systems: the gene network family tree.

    PubMed

    Fierst, Janna L; Phillips, Patrick C

    2015-01-01

    In 1994 and 1996, Andreas Wagner introduced a novel model in two papers addressing the evolution of genetic regulatory networks. This work, and a suite of papers that followed using similar models, helped integrate network thinking into biology and motivate research focused on the evolution of genetic networks. The Wagner network has its mathematical roots in the Ising model, a statistical physics model describing the activity of atoms on a lattice, and in neural networks. These models have given rise to two branches of applications, one in physics and biology and one in artificial intelligence and machine learning. Here, we review development along these branches, outline similarities and differences between biological models of genetic regulatory circuits and neural circuits models used in machine learning, and identify ways in which these models can provide novel insights into biological systems. PMID:25504926

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

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

    2008-12-01

    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

  10. 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

  11. 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.

  12. Sudden death of an infant with cardiac, nervous system and genetic involvement – a case report

    PubMed Central

    2013-01-01

    Abstract We present a case of sudden death of a 1-month-old male infant with heart, brainstem and genetic polymorphism involvement. Previously considered quite healthy, the child died suddenly and unexpectedly during sleep. The autopsy protocol included an in-depth anatomopathological examination of both the autonomic nervous system and the cardiac conduction system, and molecular analysis of the serotonin transporter gene promoter region, in which a specific genetic condition seems to be associated with sudden infant death. Histological examination revealed the presence of congenital cardiac alterations (hypertrophic cardiomyopathy and an accessory Mahaim fiber in the cardiac conduction system), severe hypodevelopment of all the raphe nuclei and a heterozygous genotype L/S related to the serotonin transporter gene. The sudden death of this infant was the unavoidable outcome of a complex series of congenital anomalies, each predisposing to SIDS. Virtual slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/3480540091031788 PMID:24053176

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

    PubMed Central

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

    2015-01-01

    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. PMID:26154679

  14. 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

  15. 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

  16. Attitudes toward Genetic Research Review: Results from a National Survey of Professionals involved in Human Subjects Protection

    PubMed Central

    Lemke, Amy A.; Trinidad, Susan B.; Edwards, Karen L.; Starks, Helene; Wiesner, Georgia L.

    2010-01-01

    The recent expansion of human genetics research has raised complex ethical and regulatory issues. However, few published reports describe the views of professionals involved in human subjects protection (HSP) regarding the risks and benefits of genetic research. This anonymous, web-based study elicited the opinions of 208 HSP professionals about review of genetic research. The majority of respondents felt that different guidance is needed for various aspects of genetic protocol review compared with other types of human subjects research. Importantly, opinions were divided on specific genetic research issues such as what constitutes human subjects research, when to re-consent, and the likelihood and risks of research participant identification. Findings from this study illustrate the need for a collaborative approach to ethics oversight in the conduct and review of genetic research. PMID:20235866

  17. Speech recognition method based on genetic vector quantization and BP neural network

    NASA Astrophysics Data System (ADS)

    Gao, Li'ai; Li, Lihua; Zhou, Jian; Zhao, Qiuxia

    2009-07-01

    Vector Quantization is one of popular codebook design methods for speech recognition at present. In the process of codebook design, traditional LBG algorithm owns the advantage of fast convergence, but it is easy to get the local optimal result and be influenced by initial codebook. According to the understanding that Genetic Algorithm has the capability of getting the global optimal result, this paper proposes a hybrid clustering method GA-L based on Genetic Algorithm and LBG algorithm to improve the codebook.. Then using genetic neural networks for speech recognition. consequently search a global optimization codebook of the training vector space. The experiments show that neural network identification method based on genetic algorithm can extricate from its local maximum value and the initial restrictions, it can show superior to the standard genetic algorithm and BP neural network algorithm from various sources, and the genetic BP neural networks has a higher recognition rate and the unique application advantages than the general BP neural network in the same GA-VQ codebook, it can achieve a win-win situation in the time and efficiency.

  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. 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

  20. 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,…

  1. 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…

  2. 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

  3. 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

  4. Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight

    PubMed Central

    Zhang, Bin; Wang, Susanna; Plaisier, Christopher; Castellanos, Ruth; Brozell, Alec; Schadt, Eric E; Drake, Thomas A

    2006-01-01

    Systems biology approaches that are based on the genetics of gene expression have been fruitful in identifying genetic regulatory loci related to complex traits. We use microarray and genetic marker data from an F2 mouse intercross to examine the large-scale organization of the gene co-expression network in liver, and annotate several gene modules in terms of 22 physiological traits. We identify chromosomal loci (referred to as module quantitative trait loci, mQTL) that perturb the modules and describe a novel approach that integrates network properties with genetic marker information to model gene/trait relationships. Specifically, using the mQTL and the intramodular connectivity of a body weight–related module, we describe which factors determine the relationship between gene expression profiles and weight. Our approach results in the identification of genetic targets that influence gene modules (pathways) that are related to the clinical phenotypes of interest. PMID:16934000

  5. Integrating genetic and network analysis to characterize genes related to mouse weight.

    PubMed

    Ghazalpour, Anatole; Doss, Sudheer; Zhang, Bin; Wang, Susanna; Plaisier, Christopher; Castellanos, Ruth; Brozell, Alec; Schadt, Eric E; Drake, Thomas A; Lusis, Aldons J; Horvath, Steve

    2006-08-18

    Systems biology approaches that are based on the genetics of gene expression have been fruitful in identifying genetic regulatory loci related to complex traits. We use microarray and genetic marker data from an F2 mouse intercross to examine the large-scale organization of the gene co-expression network in liver, and annotate several gene modules in terms of 22 physiological traits. We identify chromosomal loci (referred to as module quantitative trait loci, mQTL) that perturb the modules and describe a novel approach that integrates network properties with genetic marker information to model gene/trait relationships. Specifically, using the mQTL and the intramodular connectivity of a body weight-related module, we describe which factors determine the relationship between gene expression profiles and weight. Our approach results in the identification of genetic targets that influence gene modules (pathways) that are related to the clinical phenotypes of interest. PMID:16934000

  6. 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.

  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. 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.

  9. Genetic evidence for involvement of membrane trafficking in the action of 5-fluorouracil.

    PubMed

    Hu, Lingling; Yao, Fan; Ma, Yan; Liu, Qiannan; Chen, Si; Hayafuji, Tsutomu; Kuno, Takayoshi; Fang, Yue

    2016-08-01

    To identify novel genes that mediate cellular sensitivity and resistance to 5-fluorouracil (5-FU), we performed a genome-wide genetic screening to identify altered susceptibility to 5-FU by Schizosaccharomyces pombe haploid nonessential gene deletion library containing 3004 deletion mutants. We identified 50 hypersensitive and 12 resistant mutants to this drug. Mutants sensitive or resistant to 5-FU were classified into various categories based on their putative functions. The largest group of the genes whose disruption renders cells altered susceptibility to 5-FU is involved in nucleic acid metabolism, but to our surprise, the second largest group is involved in membrane trafficking. In addition, several other membrane traffic mutants examined including gdi1-i11, ypt3-i5, Δryh1, Δric1, and Δaps1 exhibited hypersensitivity to 5-FU. Furthermore, we found that 5-FU in low concentration that generally do not affect cell growth altered the localization of Syb1, a secretory vesicle SNARE synaptobrevin which is cycled between the plasma membrane and the endocytic pathway. Notably, 5-FU at such low concentration also significantly inhibited the secretion of acid phosphatase. Altogether, our findings revealed the first evidence that 5-FU influences membrane trafficking as the potential underlying mechanism of the drug action. PMID:27255861

  10. Systems genetics of obesity in an F2 pig model by genome-wide association, genetic network, and pathway analyses

    PubMed Central

    Kogelman, Lisette J. A.; Pant, Sameer D.; Fredholm, Merete; Kadarmideen, Haja N.

    2014-01-01

    Obesity is a complex condition with world-wide exponentially rising prevalence rates, linked with severe diseases like Type 2 Diabetes. Economic and welfare consequences have led to a raised interest in a better understanding of the biological and genetic background. To date, whole genome investigations focusing on single genetic variants have achieved limited success, and the importance of including genetic interactions is becoming evident. Here, the aim was to perform an integrative genomic analysis in an F2 pig resource population that was constructed with an aim to maximize genetic variation of obesity-related phenotypes and genotyped using the 60K SNP chip. Firstly, Genome Wide Association (GWA) analysis was performed on the Obesity Index to locate candidate genomic regions that were further validated using combined Linkage Disequilibrium Linkage Analysis and investigated by evaluation of haplotype blocks. We built Weighted Interaction SNP Hub (WISH) and differentially wired (DW) networks using genotypic correlations amongst obesity-associated SNPs resulting from GWA analysis. GWA results and SNP modules detected by WISH and DW analyses were further investigated by functional enrichment analyses. The functional annotation of SNPs revealed several genes associated with obesity, e.g., NPC2 and OR4D10. Moreover, gene enrichment analyses identified several significantly associated pathways, over and above the GWA study results, that may influence obesity and obesity related diseases, e.g., metabolic processes. WISH networks based on genotypic correlations allowed further identification of various gene ontology terms and pathways related to obesity and related traits, which were not identified by the GWA study. In conclusion, this is the first study to develop a (genetic) obesity index and employ systems genetics in a porcine model to provide important insights into the complex genetic architecture associated with obesity and many biological pathways that underlie

  11. 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.

  12. Using genetic markers to orient the edges in quantitative trait networks: The NEO software

    PubMed Central

    Aten, Jason E; Fuller, Tova F; Lusis, Aldons J; Horvath, Steve

    2008-01-01

    Background Systems genetic studies have been used to identify genetic loci that affect transcript abundances and clinical traits such as body weight. The pairwise correlations between gene expression traits and/or clinical traits can be used to define undirected trait networks. Several authors have argued that genetic markers (e.g expression quantitative trait loci, eQTLs) can serve as causal anchors for orienting the edges of a trait network. The availability of hundreds of thousands of genetic markers poses new challenges: how to relate (anchor) traits to multiple genetic markers, how to score the genetic evidence in favor of an edge orientation, and how to weigh the information from multiple markers. Results We develop and implement Network Edge Orienting (NEO) methods and software that address the challenges of inferring unconfounded and directed gene networks from microarray-derived gene expression data by integrating mRNA levels with genetic marker data and Structural Equation Model (SEM) comparisons. The NEO software implements several manual and automatic methods for incorporating genetic information to anchor traits. The networks are oriented by considering each edge separately, thus reducing error propagation. To summarize the genetic evidence in favor of a given edge orientation, we propose Local SEM-based Edge Orienting (LEO) scores that compare the fit of several competing causal graphs. SEM fitting indices allow the user to assess local and overall model fit. The NEO software allows the user to carry out a robustness analysis with regard to genetic marker selection. We demonstrate the utility of NEO by recovering known causal relationships in the sterol homeostasis pathway using liver gene expression data from an F2 mouse cross. Further, we use NEO to study the relationship between a disease gene and a biologically important gene co-expression module in liver tissue. Conclusion The NEO software can be used to orient the edges of gene co

  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. Kin in space: social viscosity in a spatially and genetically substructured network

    PubMed Central

    Wolf, Jochen B.W; Trillmich, Fritz

    2008-01-01

    Population substructuring is a fundamental aspect of animal societies. A growing number of theoretical studies recognize that who-meets-whom is not random, but rather determined by spatial relationships or illustrated by social networks. Structural properties of large highly dynamic social systems are notoriously difficult to unravel. Network approaches provide powerful ways to analyse the intricate relationships between social behaviour, dispersal strategies and genetic structure. Applying network analytical tools to a colony of the highly gregarious Galápagos sea lion (Zalophus wollebaeki), we find several genetic clusters that correspond to spatially determined ‘network communities’. Overall relatedness was low, and genetic structure in the network can be interpreted as an emergent property of philopatry and seems not to be primarily driven by targeted interactions among highly related individuals in family groups. Nevertheless, social relationships between directly adjacent individuals in the network were stronger among genetically more similar individuals. Taken together, these results suggest that even small differences in the degree of relatedness can influence behavioural decisions. This raises the fascinating prospect that kin selection may also apply to low levels of relatedness within densely packed animal groups where less obvious co-operative interactions such as increased tolerance and stress reduction are important. PMID:18522913

  15. 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.

  16. 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

  17. 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

  18. 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. PMID:22616542

  19. 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.

  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 characterization of genetic factors involved in human susceptibility to infection by schistosomiasis.

    PubMed

    Isnard, Amandine; Chevillard, Christophe

    2008-01-01

    Human resistance to infection by schistosomes is associated to a strong Th2 immune. However a persistent Th2 response can cause severe kidney and liver disease in human. In this review, we mainly focused on the control of infection levels caused by schistosomes. Several experimental models allowed us to better understand the immunological mechanisms of the host against schistosome infection. High IgE and eosinophil levels are associated with resistance to infection by schistosomes and this effect is counterbalanced by IgG4. IgE and eosinophils are highly dependent on IL-4, IL-13, and Il-5, which are three main Th2 cytokines. We also examined the genetic factors involved in human susceptibility to infection by schistosomiasis. Infection levels are mainly regulated by a major locus SM1, in 5q31-q33 region, which contains the genes encoding for the IL-4, IL-13, and Il-5 cytokines. An association between an IL13 polymorphism, rs1800925, and infection levels has been shown. This polymorphism synergistically acts with another polymorphism (rs324013) in the STAT6 gene, encoding for the signal transducer of the IL13 pathway. This pathway has also been involved in atopic disorders. As helminthiasis, atopy is the result of aberrant Th2 cytokine response to allergens, with an increased production of IL-4, IL-13, Il-9 and Il-5, with high amounts of allergen-specific and total IgE and eosinophilia. However, the Th2 immune response is protective in helminthiasis but aggravating in atopic disorders. Several studies reported interplay between helminthic infections and allergic reactions. The different results are discussed here. PMID:19471606

  2. Dual-Trait Selection for Ethanol Consumption and Withdrawal: Genetic and Transcriptional Network Effects

    PubMed Central

    Metten, Pamela; Dan Iancu, Ovidiu; Spence, Stephanie E.; Walter, Nicole A. R.; Oberbeck, Denesa; Harrington, Christina A.; Colville, Alexandre; McWeeney, Shannon; Phillips, Tamara J.; Buck, Kari J.; Crabbe, John C.; Belknap, John K.; Hitzemann, Robert J.

    2015-01-01

    Background Data from C57BL/6J (B6) × DBA/2J (D2) F2 intercrosses (B6×D2 F2), standard and recombinant inbred strains, and heterogeneous stock mice indicate that a reciprocal (or inverse) genetic relationship exists between alcohol consumption and withdrawal severity. Furthermore, some genetic studies have detected reciprocal quantitative trait loci (QTLs) for these traits. We used a novel mouse model developed by simultaneous selection for both high alcohol consumption/low withdrawal and low alcohol consumption/high withdrawal and analyzed the gene expression and genome-wide genotypic differences. Methods Randomly chosen third selected generation (S3) mice (N=24/sex/line), bred from a B6×D2 F2, were genotyped using the Mouse Universal Genotyping Array, which provided 2,760 informative markers. QTL analysis used a marker-by-marker strategy with the threshold for a significant log of the odds (LOD) set at 10. Gene expression in the ventral striatum was measured using the Illumina Mouse 8.2 array. Differential gene expression and the weighted gene coexpression network analysis (WGCNA) were implemented. Results Significant QTLs for consumption/withdrawal were detected on Chromosomes (Chr) 2, 4, 9, and 12. A suggestive QTL mapped to Chr 6. Some of the QTLs overlapped with known QTLs mapped for one of the traits individually. 1745 transcripts were detected as being differentially expressed between the lines; there was some overlap with known withdrawal genes (e.g. Mpdz) located within QTL regions. WGCNA revealed several modules of co-expressed genes showing significant effects in both differential expression and intramodular connectivity; a module richly annotated with kinase-related annotations was most affected. Discussion Marked effects of selection on expression and network structure were detected. QTLs overlapping with differentially expressed genes on Chr 2 (distal) and 4 suggest that these are cis-eQTLs (Chr 2: Kif3b, Kcnq2; Chr 4: Mpdz, Snapc3). Other QTLs

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

    PubMed

    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

  4. Genetic polymorphisms involved in folate metabolism and maternal risk for down syndrome: a meta-analysis.

    PubMed

    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 I (2) 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

  5. 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

  6. 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

  7. Genetic Analysis of Transvection Effects Involving Cis-Regulatory Elements of the Drosophila Ultrabithorax Gene

    PubMed Central

    Micol, J. L.; Castelli-Gair, J. E.; Garcia-Bellido, A.

    1990-01-01

    The Ultrabithorax (Ubx) gene of Drosophila melanogaster contains two functionally distinguishable regions: the protein-coding Ubx transcription unit and, upstream of it, the transcribed but non-protein-coding bxd region. Numerous recessive, partial loss-of-function mutations which appear to be regulatory mutations map within the bxd region and within the introns of the Ubx transcription unit. In addition, mutations within the Ubx unit exons are known and most of these behave as null alleles. Ubx(1) is one such allele. We have confirmed that, although the Ubx(1) allele does not produce detectable Ubx proteins (UBX), it does retain other genetic functions detectable by their effects on the expression of a paired, homologous Ubx allele, i.e., by transvection. We have extended previous analyses made by E. B. Lewis by mapping the critical elements of the Ubx gene which participate in transvection effects. Our results show that the Ubx(1) allele retains wild-type functions whose effectiveness can be reduced (1) by additional cis mutations in the bxd region or in introns of the Ubx transcription unit, as well as (2) by rearrangements disturbing pairing between homologous Ubx genes. Our results suggest that those remnant functions in Ubx(1) are able to modulate the activity of the allele located in the homologous chromosome. We discuss the normal cis regulatory role of these functions involved in trans interactions between homologous Ubx genes, as well as the implications of our results for the current models on transvection. PMID:2123161

  8. Functional and genetic analysis of the colon cancer network

    PubMed Central

    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. 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

  10. 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

  11. Genetic Influences on Resting-state Functional Networks: A Twin Study

    PubMed Central

    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

    2015-01-01

    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 by 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 widespread 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. PMID:26147340

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. 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

  18. Dopamine modulates neural networks involved in effort-based decision-making.

    PubMed

    Assadi, Seyed M; Yücel, Murat; Pantelis, Christos

    2009-03-01

    Recent animal and human studies suggest that the dorsal anterior cingulate cortex (dACC) and its related subcortical structures including nucleus accumbens (NAc) are in the center of a brain network that determines and pursues the best option from available alternatives. Specifically, the involvement of the dACC network in decision-making can be categorized under two broad processes of evaluation and execution. The former aims to determine the most cost-effective option while the latter aims to attain the preferred option. The present article reviews neural and molecular findings to show that the dopamine system might modulate this dACC network at multiple levels to optimize both processes. Several lines of evidence suggest that the dopamine system has a bimodal effect, allows the network to compare different representations in the evaluation phase, and focuses the network on the preferred representation in the execution phase. This is apparently achieved by modulating other neurotransmission systems and by transmitting different signals via D1 vs. D2 receptor subtypes and phasic vs. tonic firing. PMID:19046987

  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. BisoGenet: a new tool for gene network building, visualization and analysis

    PubMed Central

    2010-01-01

    Background The increasing availability and diversity of omics data in the post-genomic era offers new perspectives in most areas of biomedical research. Graph-based biological networks models capture the topology of the functional relationships between molecular entities such as gene, protein and small compounds and provide a suitable framework for integrating and analyzing omics-data. The development of software tools capable of integrating data from different sources and to provide flexible methods to reconstruct, represent and analyze topological networks is an active field of research in bioinformatics. Results BisoGenet is a multi-tier application for visualization and analysis of biomolecular relationships. The system consists of three tiers. In the data tier, an in-house database stores genomics information, protein-protein interactions, protein-DNA interactions, gene ontology and metabolic pathways. In the middle tier, a global network is created at server startup, representing the whole data on bioentities and their relationships retrieved from the database. The client tier is a Cytoscape plugin, which manages user input, communication with the Web Service, visualization and analysis of the resulting network. Conclusion BisoGenet is able to build and visualize biological networks in a fast and user-friendly manner. A feature of Bisogenet is the possibility to include coding relations to distinguish between genes and their products. This feature could be instrumental to achieve a finer grain representation of the bioentities and their relationships. The client application includes network analysis tools and interactive network expansion capabilities. In addition, an option is provided to allow other networks to be converted to BisoGenet. This feature facilitates the integration of our software with other tools available in the Cytoscape platform. BisoGenet is available at http://bio.cigb.edu.cu/bisogenet-cytoscape/. PMID:20163717

  1. A comparison of the genetic pathways involved in the pathogenesis of three types of colorectal cancer.

    PubMed

    Tomlinson, I; Ilyas, M; Johnson, V; Davies, A; Clark, G; Talbot, I; Bodmer, W

    1998-02-01

    Patterns of allele loss (loss of heterozygosity, LOH) have been studied in order to investigate the genetic pathways involved in the pathogenesis of three types of colorectal cancer (CRC): sporadic CRC without replication errors (RER-) (32 cases); sporadic RER+ CRC (23 cases); and ulcerative colitis-associated CRC (UCACRC) (16 cases). Each tumour was assessed for allele loss at ten microsatellite markers which map close to known or putative tumour-suppressor genes: APC (5q21-q22); DCC (18q21.1); 1p35-p36; p16 (9p21); 22q; 8p; E-cadherin (16q22.1); beta-catenin (3p22-p21.3); RB1 (13q14.1-q14.2); and HLA. Overall, high frequencies of allele loss (> 30 per cent) were found near DCC (42 per cent), p16 (38 per cent), 22q (37 per cent), 1p35-p36 (34 per cent) and APC (31 per cent), and low frequencies (< 20 per cent) near RB1 (16 per cent) and E-cadherin (13 per cent). LOH near beta-catenin, HLA, and on 8p occurred at frequencies between 20 and 30 per cent. The overall frequency of allele loss did not differ among the three tumour groups, but some variation was seen at individual loci. There was a significantly higher frequency of LOH at 1p35-36 in RER+ tumours compared to RER- tumours. Allele loss at this site was also associated with a more advanced Dukes' stage at presentation. In addition, RER- tumours showed a higher frequency of allele loss at p16 than RER+ tumours. No significant difference existed at any locus between the frequency of LOH in sporadic CRC and in UCACRC. Pairwise analysis showed a negative association between LOH at APC and DCC, and between LOH at chromosome 22p and p53 overexpression. Thus, there may be specific differences between the mutation spectra of RER+ and RER- CRCs, but there are large degrees of overlap among the underlying genetic pathways of these cancers and UCACRCs. PMID:9602705

  2. Network-based Prediction of Cancer under Genetic Storm

    PubMed Central

    Ay, Ahmet; Gong, Dihong; Kahveci, Tamer

    2014-01-01

    Classification of cancer patients using traditional methods is a challenging task in the medical practice. Owing to rapid advances in microarray technologies, currently expression levels of thousands of genes from individual cancer patients can be measured. The classification of cancer patients by supervised statistical learning algorithms using the gene expression datasets provides an alternative to the traditional methods. Here we present a new network-based supervised classification technique, namely the NBC method. We compare NBC to five traditional classification techniques (support vector machines (SVM), k-nearest neighbor (kNN), naïve Bayes (NB), C4.5, and random forest (RF)) using 50–300 genes selected by five feature selection methods. Our results on five large cancer datasets demonstrate that NBC method outperforms traditional classification techniques. Our analysis suggests that using symmetrical uncertainty (SU) feature selection method with NBC method provides the most accurate classification strategy. Finally, in-depth analysis of the correlation-based co-expression networks chosen by our network-based classifier in different cancer classes shows that there are drastic changes in the network models of different cancer types. PMID:25368507

  3. [The international network and Italian modernization. Ruggero Ceppellini, genetics, and HLA].

    PubMed

    Capocci, Mauro

    2014-01-01

    The paper reconstructs the scientific career of Ruggero Ceppellini, focusing especially on his role in the discovery of the genetic system underlying the Human Leucocyte Antigen. From his earliest investigations in blood group genetics, Ceppellini quickly became an internationally acknowledged authority in the field of immunogenetics--the study of genetics by means of immunological tools--and participated to the endeavor that ultimately yelded a new meaning for the word: thanks to the pioneering research in the HLA field, immunogenetics became the study of the genetic control of immune system. The paper will also place Ceppellini's scientific work against the backdrop of the modernization of Italian genetics after WWII, resulting from the efforts of a handful of scientists to connect to international networks and adopting new methodologies in life sciences. PMID:26292523

  4. 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.

  5. Relevant Networks involving the p53 Signalling Pathway in Renal Cell Carcinoma

    PubMed Central

    Villaamil, V. Medina; Gallego, G. Aparicio; Caínzos, I. Santamarina; Ruvira, L. Valbuena; Valladares-Ayerbes, M.; Aparicio, L. M. Antón

    2011-01-01

    Introduction: Renal cell carcinoma is the most common type of kidney cancer. A better understanding of the critical pathways and interactions associated with alterations in renal function and renal tumour properties is required. Our final goal is to combine the knowledge provided by a regulatory network with experimental observations provided by the dataset. Methods: In this study, a systems biology approach was used, integrating immunohistochemistry protein expression profiles and protein interaction information with the STRING and MeV bioinformatics tools. A group consisting of 80 patients with renal cell carcinoma was studied. The expression of selected markers was assessed using tissue microarray technology on immunohistochemically stained slides. The immunohistochemical data of the molecular factors studied were analysed using a parametric statistical test, Pearson’s correlation coefficient test. Results: Bioinformatics analysis of tumour samples resulted in 2 protein networks. The first network consists of proteins involved in the angiogenesis pathway and the apoptosis suppressor, BCL2, and includes both positive and negative correlations. The second network shows a negative interaction between the p53 tumour suppressor protein and the glucose transporter type 4. Conclusion: The comprehensive pathway network will help us to realise the cooperative behaviours among pathways. Regulation of metabolic pathways is an important role of p53. The pathway involving the tumour suppressor gene p53 could regulate tumour angiogenesis. Further investigation of the proteins that interact with this pathway in this type of tumour may provide new strategies for cancer therapies to specifically inhibit the molecules that play crucial roles in tumour progression. PMID:23675247

  6. Alzheimer's as a Systems-Level Disease Involving the Interplay of Multiple Cellular Networks.

    PubMed

    Castrillo, Juan I; Oliver, Stephen G

    2016-01-01

    Alzheimer's disease (AD), and many neurodegenerative disorders, are multifactorial in nature. They involve a combination of genomic, epigenomic, interactomic and environmental factors. Progress is being made, and these complex diseases are beginning to be understood as having their origin in altered states of biological networks at the cellular level. In the case of AD, genomic susceptibility and mechanisms leading to (or accompanying) the impairment of the central Amyloid Precursor Protein (APP) processing and tau networks are widely accepted as major contributors to the diseased state. The derangement of these networks may result in both the gain and loss of functions, increased generation of toxic species (e.g., toxic soluble oligomers and aggregates) and imbalances, whose effects can propagate to supra-cellular levels. Although well sustained by empirical data and widely accepted, this global perspective often overlooks the essential roles played by the main counteracting homeostatic networks (e.g., protein quality control/proteostasis, unfolded protein response, protein folding chaperone networks, disaggregases, ER-associated degradation/ubiquitin proteasome system, endolysosomal network, autophagy, and other stress-protective and clearance networks), whose relevance to AD is just beginning to be fully realized. In this chapter, an integrative perspective is presented. Alzheimer's disease is characterized to be a result of: (a) intrinsic genomic/epigenomic susceptibility and, (b) a continued dynamic interplay between the deranged networks and the central homeostatic networks of nerve cells. This interplay of networks will underlie both the onset and rate of progression of the disease in each individual. Integrative Systems Biology approaches are required to effect its elucidation. Comprehensive Systems Biology experiments at different 'omics levels in simple model organisms, engineered to recapitulate the basic features of AD may illuminate the onset and

  7. 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

  8. 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…

  9. 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.

  10. Predicting and exploring network components involved in pathogenesis in the malaria parasite via novel subnetwork alignments

    PubMed Central

    2015-01-01

    Background Malaria is a major health threat, affecting over 40% of the world's population. The latest report released by the World Health Organization estimated about 207 million cases of malaria infection, and about 627,000 deaths in 2012 alone. During the past decade, new therapeutic targets have been identified and are at various stages of characterization, thanks to the emerging omics-based technologies. However, the mechanism of malaria pathogenesis remains largely unknown. In this paper, we employ a novel neighborhood subnetwork alignment approach to identify network components that are potentially involved in pathogenesis. Results Our module-based subnetwork alignment approach identified 24 functional homologs of pathogenesis-related proteins in the malaria parasite P. falciparum, using the protein-protein interaction networks in Escherichia coli as references. Eighteen out of these 24 proteins are associated with 418 other proteins that are related to DNA replication, transcriptional regulation, translation, signaling, metabolism, cell cycle regulation, as well as cytoadherence and entry to the host. Conclusions The subnetwork alignments and subsequent protein-protein association network mining predicted a group of malarial proteins that may be involved in parasite development and parasite-host interaction, opening a new systems-level view of parasite pathogenesis and virulence. PMID:26100579

  11. Stress-sensitive neurosignalling in depression: an integrated network biology approach to candidate gene selection for genetic association analysis

    PubMed Central

    van Eekelen, J. Anke M.; Ellis, Justine A.; Pennell, Craig E.; Craig, Jeff; Saffery, Richard; Mattes, Eugen; Olsson, Craig A.

    2012-01-01

    Genetic risk for depressive disorders is poorly understood despite consistent suggestions of a high heritable component. Most genetic studies have focused on risk associated with single variants, a strategy which has so far only yielded small (often non-replicable) risks for depressive disorders. In this paper we argue that more substantial risks are likely to emerge from genetic variants acting in synergy within and across larger neurobiological systems (polygenic risk factors). We show how knowledge of major integrated neurobiological systems provides a robust basis for defining and testing theoretically defensible polygenic risk factors. We do this by describing the architecture of the overall stress response. Maladaptation via impaired stress responsiveness is central to the aetiology of depression and anxiety and provides a framework for a systems biology approach to candidate gene selection. We propose principles for identifying genes and gene networks within the neurosystems involved in the stress response and for defining polygenic risk factors based on the neurobiology of stress-related behaviour. We conclude that knowledge of the neurobiology of the stress response system is likely to play a central role in future efforts to improve genetic prediction of depression and related disorders. PMID:25478122

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Quantitative or complex traits are determined by the combined effects of many loci, and are affected by gene-gene interactions, genetic networks or molecular pathways. In the present study, we genotyped a total of 138 mutations, mainly single nucleotide polymorphisms derived from 71 functional gene...

  13. Applying Monte Carlo Simulation to Biomedical Literature to Approximate Genetic Network.

    PubMed

    Al-Dalky, Rami; Taha, Kamal; Al Homouz, Dirar; Qasaimeh, Murad

    2016-01-01

    Biologists often need to know the set of genes associated with a given set of genes or a given disease. We propose in this paper a classifier system called Monte Carlo for Genetic Network (MCforGN) that can construct genetic networks, identify functionally related genes, and predict gene-disease associations. MCforGN identifies functionally related genes based on their co-occurrences in the abstracts of biomedical literature. For a given gene g , the system first extracts the set of genes found within the abstracts of biomedical literature associated with g. It then ranks these genes to determine the ones with high co-occurrences with g . It overcomes the limitations of current approaches that employ analytical deterministic algorithms by applying Monte Carlo Simulation to approximate genetic networks. It does so by conducting repeated random sampling to obtain numerical results and to optimize these results. Moreover, it analyzes results to obtain the probabilities of different genes' co-occurrences using series of statistical tests. MCforGN can detect gene-disease associations by employing a combination of centrality measures (to identify the central genes in disease-specific genetic networks) and Monte Carlo Simulation. MCforGN aims at enhancing state-of-the-art biological text mining by applying novel extraction techniques. We evaluated MCforGN by comparing it experimentally with nine approaches. Results showed marked improvement. PMID:26415184

  14. 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…

  15. An alternative approach for neural network evolution with a genetic algorithm: crossover by combinatorial optimization.

    PubMed

    García-Pedrajas, Nicolás; Ortiz-Boyer, Domingo; Hervás-Martínez, César

    2006-05-01

    In this work we present a new approach to crossover operator in the genetic evolution of neural networks. The most widely used evolutionary computation paradigm for neural network evolution is evolutionary programming. This paradigm is usually preferred due to the problems caused by the application of crossover to neural network evolution. However, crossover is the most innovative operator within the field of evolutionary computation. One of the most notorious problems with the application of crossover to neural networks is known as the permutation problem. This problem occurs due to the fact that the same network can be represented in a genetic coding by many different codifications. Our approach modifies the standard crossover operator taking into account the special features of the individuals to be mated. We present a new model for mating individuals that considers the structure of the hidden layer and redefines the crossover operator. As each hidden node represents a non-linear projection of the input variables, we approach the crossover as a problem on combinatorial optimization. We can formulate the problem as the extraction of a subset of near-optimal projections to create the hidden layer of the new network. This new approach is compared to a classical crossover in 25 real-world problems with an excellent performance. Moreover, the networks obtained are much smaller than those obtained with classical crossover operator. PMID:16343847

  16. Co-evolution of Hetero Multiagent Systems using Genetic Network Programming

    NASA Astrophysics Data System (ADS)

    Hirasawa, Kotaro; Okubo, Masafumi; Hu, Jinglu; Murata, Junichi; Matsuya, Yuko

    Recently, many methods of evolutionary computation such as Genetic Algorithm(GA) and Genetic Programming(GP) have been developed as a basic tool for modeling and optimizing complex systems. GA has the genome of string structure, while the genome in GP is of tree structure. In this paper, a new evolutionary method named Genetic Network Programming(GNP), whose genome has network structure is applied to multiagent sysytems. Hetero Multiagent Sysytems with GNP are studied, where each agent of the same group has its own GNP program in order to build the adaptive agents against changing environments. Specifically, the comparison between Hetero Multiagent Systems and conventional Homo Multiagent Sysytems is carried out in simulations on ants behaviors.

  17. 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

  18. 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.

  19. Combining neural network and genetic algorithm for prediction of lung sounds.

    PubMed

    Güler, Inan; Polat, Hüseyin; Ergün, Uçman

    2005-06-01

    Recognition of lung sounds is an important goal in pulmonary medicine. In this work, we present a study for neural networks-genetic algorithm approach intended to aid in lung sound classification. Lung sound was captured from the chest wall of The subjects with different pulmonary diseases and also from the healthy subjects. Sound intervals with duration of 15-20 s were sampled from subjects. From each interval, full breath cycles were selected. Of each selected breath cycle, a 256-point Fourier Power Spectrum Density (PSD) was calculated. Total of 129 data values calculated by the spectral analysis are selected by genetic algorithm and applied to neural network. Multilayer perceptron (MLP) neural network employing backpropagation training algorithm was used to predict the presence or absence of adventitious sounds (wheeze and crackle). We used genetic algorithms to search for optimal structure and training parameters of neural network for a better predicting of lung sounds. This application resulted in designing of optimum network structure and, hence reducing the processing load and time. PMID:16050077

  20. 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

  1. Transcriptome analysis of genes and gene networks involved in aggressive behavior in mouse and zebrafish.

    PubMed

    Malki, Karim; Du Rietz, Ebba; Crusio, Wim E; Pain, Oliver; Paya-Cano, Jose; Karadaghi, Rezhaw L; Sluyter, Frans; de Boer, Sietse F; Sandnabba, Kenneth; Schalkwyk, Leonard C; Asherson, Philip; Tosto, Maria Grazia

    2016-09-01

    Despite moderate heritability estimates, the molecular architecture of aggressive behavior remains poorly characterized. This study compared gene expression profiles from a genetic mouse model of aggression with zebrafish, an animal model traditionally used to study aggression. A meta-analytic, cross-species approach was used to identify genomic variants associated with aggressive behavior. The Rankprod algorithm was used to evaluated mRNA differences from prefrontal cortex tissues of three sets of mouse lines (N = 18) selectively bred for low and high aggressive behavior (SAL/LAL, TA/TNA, and NC900/NC100). The same approach was used to evaluate mRNA differences in zebrafish (N = 12) exposed to aggressive or non-aggressive social encounters. Results were compared to uncover genes consistently implicated in aggression across both studies. Seventy-six genes were differentially expressed (PFP < 0.05) in aggressive compared to non-aggressive mice. Seventy genes were differentially expressed in zebrafish exposed to a fight encounter compared to isolated zebrafish. Seven genes (Fos, Dusp1, Hdac4, Ier2, Bdnf, Btg2, and Nr4a1) were differentially expressed across both species 5 of which belonging to a gene-network centred on the c-Fos gene hub. Network analysis revealed an association with the MAPK signaling cascade. In human studies HDAC4 haploinsufficiency is a key genetic mechanism associated with brachydactyly mental retardation syndrome (BDMR), which is associated with aggressive behaviors. Moreover, the HDAC4 receptor is a drug target for valproic acid, which is being employed as an effective pharmacological treatment for aggressive behavior in geriatric, psychiatric, and brain-injury patients. © 2016 Wiley Periodicals, Inc. PMID:27090961

  2. [An optimal predicting method based on improved genetic algorithm embedded in neural network and its application to peritoneal dialysis].

    PubMed

    Zhang, Mei; Hu, Yueming; Wang, Tao; Zhu, Jinhui

    2009-12-01

    This paper addresses the predicting problem of peritoneal fluid absorption rate(PFAR). An innovative predicting model was developed, which employed the improved genetic algorithm embedded in neural network for predicting the important PFAR index in the peritoneal dialysis treatment process of renal failure. The significance of PFAR and the complexity of dialysis process were analyzed. The improved genetic algorithm was used for defining the initial weight and bias of neural network, and then the neural network was used for finding out the optimal predicting model of PFAR. This method utilizes the global search capability of genetic algorithm and the local search advantage of neural network completely. For the purpose of showing the validity of the model, the improved optimal predicting model is compared with the standard hybrid method of genetic algorithm and neural network. The simulation results show that the predicting accuracy of the improved optimal neural network is greatly improved and the learning process needs less time. PMID:20095466

  3. 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

  4. 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. PMID:25623499

  5. 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.

  6. Mining functional modules in genetic networks with decomposable graphical models.

    PubMed

    Dejori, Mathäus; Schwaighofer, Anton; Tresp, Volker; Stetter, Martin

    2004-01-01

    In recent years, graphical models have become an increasingly important tool for the structural analysis of genome-wide expression profiles at the systems level. Here we present a new graphical modelling technique, which is based on decomposable graphical models, and apply it to a set of gene expression profiles from acute lymphoblastic leukemia (ALL). The new method explains probabilistic dependencies of expression levels in terms of the concerted action of underlying genetic functional modules, which are represented as so-called "cliques" in the graph. In addition, the method uses continuous-valued (instead of discretized) expression levels, and makes no particular assumption about their probability distribution. We show that the method successfully groups members of known functional modules to cliques. Our method allows the evaluation of the importance of genes for global cellular functions based on both link count and the clique membership count. PMID:15268775

  7. 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

  8. 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. PMID:26368933

  9. Trauma histories among justice-involved youth: findings from the National Child Traumatic Stress Network

    PubMed Central

    Dierkhising, Carly B.; Ko, Susan J.; Woods-Jaeger, Briana; Briggs, Ernestine C.; Lee, Robert; Pynoos, Robert S.

    2013-01-01

    Background Up to 90% of justice-involved youth report exposure to some type of traumatic event. On average, 70% of youth meet criteria for a mental health disorder with approximately 30% of youth meeting criteria for post-traumatic stress disorder (PTSD). Justice-involved youth are also at risk for substance use and academic problems, and child welfare involvement. Yet, less is known about the details of their trauma histories, and associations among trauma details, mental health problems, and associated risk factors. Objective This study describes detailed trauma histories, mental health problems, and associated risk factors (i.e., academic problems, substance/alcohol use, and concurrent child welfare involvement) among adolescents with recent involvement in the juvenile justice system. Method The National Child Traumatic Stress Network Core Data Set (NCTSN-CDS) is used to address these aims, among which 658 adolescents report recent involvement in the juvenile justice system as indexed by being detained or under community supervision by the juvenile court. Results Age of onset of trauma exposure was within the first 5 years of life for 62% of youth and approximately one-third of youth report exposure to multiple or co-occurring trauma types each year into adolescence. Mental health problems are prevalent with 23.6% of youth meeting criteria for PTSD, 66.1% in the clinical range for externalizing problems, and 45.5% in the clinical range for internalizing problems. Early age of onset of trauma exposure was differentially associated with mental health problems and related risk factors among males and females. Conclusions The results indicate that justice-involved youth report high rates of trauma exposure and that this trauma typically begins early in life, is often in multiple contexts, and persists over time. Findings provide support for establishing trauma-informed juvenile justice systems that can respond to the needs of traumatized youth. PMID:23869252

  10. 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.

  11. Genetic counseling in carriers of reciprocal chromosomal translocations involving long arm of chromosome 16.

    PubMed

    Stasiewicz-Jarocka, B; Haus, O; Van Assche, E; Kostyk, E; Constantinou, M; Rybałko, A; Krzykwa, B; Marcinkowska, A; Barisic, I; Kucinskas, V; Katuzewski, B; Schwanitz, G; Midro, A T

    2004-09-01

    Families with balanced chromosomal changes ascertained by unbalanced progeny, miscarriages, or by chance are interested in their probability for unbalanced offspring and other unfavorable pregnancy outcomes. This is usually done based on the original data published by Stengel-Rutkowski et al. several decades ago. That data set has never been updated. It is particularly true for the subgroup with low number of observations, to which belong reciprocal chromosomal translocations (RCTs) with breakpoint in an interstitial segment of 16q. The 11 pedigrees from original data together with the new 18 pedigrees of RCT carriers at risk of single-segment imbalance detected among 100 pedigrees of RCT carriers with breakpoint position at 16q were used for re-evaluation of the probability estimation for unbalanced offspring at birth and at second trimester of prenatal diagnosis, published in 1988. The new probability rate for unbalanced offspring after 2 : 2 disjunction and adjacent-1 segregation for the total group of pedigrees was 4 +/- 3.9% (1/25). In addition, the probability estimate for unbalanced fetuses at second trimester of prenatal diagnosis was calculated as 2/11, i.e. 18.2 +/- 11.6%. The probability rates for miscarriages and stillbirths/early deaths were about 16 +/- 7.3% (4/25) and <2% (0/25), respectively. Considering different segment lengths of 16q, higher probability rate (0/8, i.e. <6.1%) for maternal RCT carriers at risk of distal 16q segment imbalance (shorter segment) was obtained in comparison with the rate (0/10, i.e. <4.8%) for RCT at risk of proximal segment imbalance (longer segment). It supports findings obtained from the original data for RCT with other chromosomes, where the probability for unbalanced offspring generally increased with decreasing length of the segments involved in RCT. Our results were applied for five new families with RCT involving 16q, namely three at risk of single-segment imbalance [t(8;16)(q24.3;q22)GTG, ish(wcp8+,wcp16+;wcp8

  12. Modeling of CMM dynamic error based on optimization of neural network using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Ying, Qu; Zai, Luo; Yi, Lu

    2010-08-01

    By analyzing the dynamic error of CMM, a model is established using BP neural network for CMM .The most important 5 input parameters which affect the dynamic error of CMM are approximate rate, length of rod, diameter of probe, coordinate values of X and coordinate values of Y. But the training of BP neural network can be easily trapped in local minimums and its training speed is slow. In order to overcome these disadvantages, genetic algorithm (GA) is introduced for optimization. So the model of GA-BP network is built up. In order to verify the model, experiments are done on the CMM of type 9158. Experimental results indicate that the entire optimizing capability of genetic algorithm is perfect. Compared with traditional BP network, the GA-BP network has better accuracy and adaptability and the training time is halved using GA-BP network. The average dynamic error can be reduced from 3.5μm to 0.7μm. So the precision is improved by 76%.

  13. Discovering Main Genetic Interactions with LABNet LAsso-Based Network Inference

    PubMed Central

    Gadaleta, Francesco; Van Steen, Kristel

    2014-01-01

    Genome-wide association studies can potentially unravel the mechanisms behind complex traits and common genetic diseases. Despite the valuable results produced thus far, many questions remain unanswered. For instance, which specific genetic compounds are linked to the risk of the disease under investigation; what biological mechanism do they act through; or how do they interact with environmental and other external factors? The driving force of computational biology is the constantly growing amount of big data generated by high-throughput technologies. A practical framework that can deal with this abundance of information and that consent to discovering genetic associations and interactions is provided by means of networks. Unfortunately, high dimensionality, the presence of noise and the geometry of data can make the aforementioned problem extremely challenging. We propose a penalised linear regression approach that can deal with the aforementioned issues that affect genetic data. We analyse the gene expression profiles of individuals with a common trait to infer the network structure of interactions among genes. The permutation-based approach leads to more stable and reliable networks inferred from synthetic microarray data. We show that a higher number of permutations determines the number of predicted edges, improves the overall sensitivity and controls the number of false positives. PMID:25369052

  14. Metabolic mapping reveals sex-dependent involvement of default mode and salience network in alexithymia.

    PubMed

    Colic, L; Demenescu, L R; Li, M; Kaufmann, J; Krause, A L; Metzger, C; Walter, M

    2016-02-01

    Alexithymia, a personality construct marked by difficulties in processing one's emotions, has been linked to the altered activity in the anterior cingulate cortex (ACC). Although longitudinal studies reported sex differences in alexithymia, what mediates them is not known. To investigate sex-specific associations of alexithymia and neuronal markers, we mapped metabolites in four brain regions involved differentially in emotion processing using a point-resolved spectroscopy MRS sequence in 3 Tesla. Both sexes showed negative correlations between alexithymia and N-acetylaspartate (NAA) in pregenual ACC (pgACC). Women showed a robust negative correlation of the joint measure of glutamate and glutamine (Glx) to NAA in posterior cingulate cortex (PCC), whereas men showed a weak positive association of Glx to NAA in dorsal ACC (dACC). Our results suggest that lowered neuronal integrity in pgACC, a region of the default mode network (DMN), might primarily account for the general difficulties in emotional processing in alexithymia. Association of alexithymia in women extends to another region in the DMN-PCC, while in men a region in the salience network (SN) was involved. These observations could be representative of sex specific regulation strategies that include diminished internal evaluation of feelings in women and cognitive emotion suppression in men. PMID:26341904

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

    PubMed Central

    Slattery, Martha L.

    2012-01-01

    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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  1. Genome-wide expression analysis of genetic networks in Neurospora crassa

    PubMed Central

    Logan, David A; Koch, Allison L; Dong, Wubei; Griffith, James; Nilsen, Roger; Case, Mary E; Schüttler, Heinz-Bernd; Arnold, Jonathan

    2007-01-01

    The products of five structural genes and two regulatory genes of the qa gene cluster of Neurospora crassa control the metabolism of quinic acid (QA) as a carbon source. A detailed genetic network model of this metabolic process has been reported. This investigation is designed to expand the current model of the QA reaction network. The ensemble method of network identification was used to model RNA profiling data on the qa gene cluster. Through microarray and cluster analysis, genome-wide identification of RNA transcripts associated with quinic acid metabolism in N. crassa is described and suggests a connection to other metabolic circuits. More than 100 genes whose products include carbon metabolism, protein degradation and modification, amino acid metabolism and ribosome synthesis appear to be connected to quinic acid metabolism. The core of the qa gene cluster network is validated with respect to RNA profiling data obtained from microarrays. PMID:17597928

  2. Fuzzy-Kohonen-clustering neural network trained by genetic algorithm and fuzzy competition learning

    NASA Astrophysics Data System (ADS)

    Xie, Weixing; Li, Wenhua; Gao, Xinbo

    1995-08-01

    Kohonen networks are well known for clustering analysis. Classical Kohonen networks for hard c-means clustering (trained by winner-take-all learning) have some severe drawbacks. Fuzzy Kohonen networks (FKCNN) for fuzzy c-means clustering are trained by fuzzy competition learning, and can get better clustering results than the classical Kohonen networks. However, both winner-take-all and fuzzy competition learning algorithms are in essence local search techniques that search for the optimum by using a hill-climbing technique. Thus, they often fail in the search for the global optimum. In this paper we combine genetic algorithms (GAs) with fuzzy competition learning to train the FKCNN. Our experimental results show that the proposed GA/FC learning algorithm has much higher probabilities of finding the global optimal solutions than either the winner-take-all or the fuzzy competition learning.

  3. Neural network and genetic algorithm technology in data mining of manufacturing quality information

    NASA Astrophysics Data System (ADS)

    Song, Limei; Qu, Xing-Hua; Ye, Shenghua

    2002-03-01

    Data Mining of Manufacturing Quality Information (MQI) is the key technology in Quality Lead Control. Of all the data mining methods, Neural Network and Genetic Algorithm is widely used for their strong advantages, such as non-linear, collateral, veracity etc. But if you singly use them, there will be some limitations preventing your research, such as convergence slowly, searching blindness etc. This paper combines their merits and use Genetic BP Algorithm in Data Mining of MQI. It has been successfully used in the key project of Natural Science Foundation of China (NSFC) - Quality Control and Zero-defect Engineering (Project No. 59735120).

  4. Genetic Co-Occurrence Network across Sequenced Microbes

    PubMed Central

    Kim, Pan-Jun; Price, Nathan D.

    2011-01-01

    The phenotype of any organism on earth is, in large part, the consequence of interplay between numerous gene products encoded in the genome, and such interplay between gene products affects the evolutionary fate of the genome itself through the resulting phenotype. In this regard, contemporary genomes can be used as molecular records that reveal associations of various genes working in their natural lifestyles. By analyzing thousands of orthologs across ∼600 bacterial species, we constructed a map of gene-gene co-occurrence across much of the sequenced biome. If genes preferentially co-occur in the same organisms, they were called herein correlogs; in the opposite case, called anti-correlogs. To quantify correlogy and anti-correlogy, we alleviated the contribution of indirect correlations between genes by adapting ideas developed for reverse engineering of transcriptional regulatory networks. Resultant correlogous associations are highly enriched for physically interacting proteins and for co-expressed transcripts, clearly differentiating a subgroup of functionally-obligatory protein interactions from conditional or transient interactions. Other biochemical and phylogenetic properties were also found to be reflected in correlogous and anti-correlogous relationships. Additionally, our study elucidates the global organization of the gene association map, in which various modules of correlogous genes are strikingly interconnected by anti-correlogous crosstalk between the modules. We then demonstrate the effectiveness of such associations along different domains of life and environmental microbial communities. These phylogenetic profiling approaches infer functional coupling of genes regardless of mechanistic details, and may be useful to guide exogenous gene import in synthetic biology. PMID:22219725

  5. 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

  6. 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. PMID:26036858

  7. 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.

  8. 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

  9. 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.

  10. Buying and Selling Stocks of Multi Brands Using Genetic Network Programming with Control Nodes

    NASA Astrophysics Data System (ADS)

    Ohkawa, Etsushi; Chen, Yan; Bao, Zhiguo; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

    A new evolutionary method named “Genetic Network Programming with control nodes, GNPcn” has been applied to determine the timing of buying or selling stocks. GNPcn represents its solutions as directed graph structures which has some useful features inherently. For example, GNPcn has an implicit memory function which memorizes the past action sequences of agents and GNPcn can re-use nodes repeatedly in the network flow, so very compact graph structures can be made. GNPcn can determine the strategy of buying and selling stocks of multi issues. The effectiveness of the proposed method is confirmed by simulations.

  11. 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.

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

    PubMed Central

    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 coregulated genes and their annotation using gene ontology analysis and cis-regulatory element discovery. The causal basis for coregulation is detected through the use of quantitative trait locus mapping. PMID:16046823

  13. [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

  14. 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

  15. Optimal Parameter for the Training of Multilayer Perceptron Neural Networks by Using Hierarchical Genetic Algorithm

    SciTech Connect

    Orozco-Monteagudo, Maykel; Taboada-Crispi, Alberto; Gutierrez-Hernandez, Liliana

    2008-11-06

    This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm.

  16. A genetic system involving superoxide causes F1 necrosis in wheat (T. aestivum L.).

    PubMed

    Khanna-Chopra, R; Dalal, M; Kumar, G P; Laloraya, M

    1998-07-30

    A genetic system in wheat is described in which F1 produced by crossing a drought tolerant cultivar C306 and high yielding cultivar WL711 exhibits leaf necrosis leading to the death of the plant. The mechanism underlying hybrid necrosis is not yet known. The hybrid exhibited a higher level of superoxide anion compared to the healthy leaves of parents at similar developmental stages. This increase in superoxide generation preceded necrotic lesion formation and displayed a gradient from the leaf tip to base. The leaf tip where necrotic lesions make their first appearance exhibited a higher level of superoxide compared to the base. Superoxide anion thus appears to play a vital role in necrosis of leaves in F1 hybrid. This genetic system can be a model system for understanding cell death in higher plants. PMID:9703992

  17. 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

  18. 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

  19. 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

  20. 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

  1. Genetic networks specifying the functional architecture of orientation domains in V1

    NASA Astrophysics Data System (ADS)

    Liedtke, Joscha; Wolf, Fred

    2015-03-01

    Although genetic information is critically important for brain development and structure, it is widely believed that neocortical functional architecture is largely shaped by activity dependent mechanisms. Here we show theoretically that mathematical models of genetic networks of principal neurons interacting by long range axonal morphogen transport can generate morphogen patterns that exactly prescribe the functional architecture of the primary visual cortex (V1) as experimentally observed. We analyze in detail an example genetic network that encodes the functional architecture of V1 by a dynamically generated morphogen pattern. We use analytical methods from weakly non-linear analysis [Cross & Hohenberg 1993] complemented by numerical simulations to obtain solutions of the model. In particular we find that the pinwheel statistics are in quantitative agreement with high precision experimental measurements [Kaschube et al. 2010]. This theory opens a novel perspective on the experimentally observed robustness of V1's architecture against radically abnormal developmental conditions such a dark rearing [White et al. 2001]. Furthermore, it provides for the first time a scheme how the pattern of a complex cortical architecture can be specified using only a small genetic bandwidth.

  2. 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

  3. 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

  4. Analysis of schizophrenia and hepatocellular carcinoma genetic network with corresponding modularity and pathways: novel insights to the immune system

    PubMed Central

    2013-01-01

    Background Schizophrenic patients show lower incidences of cancer, implicating schizophrenia may be a protective factor against cancer. To study the genetic correlation between the two diseases, a specific PPI network was constructed with candidate genes of both schizophrenia and hepatocellular carcinoma. The network, designated schizophrenia-hepatocellular carcinoma network (SHCN), was analysed and cliques were identified as potential functional modules or complexes. The findings were compared with information from pathway databases such as KEGG, Reactome, PID and ConsensusPathDB. Results The functions of mediator genes from SHCN show immune system and cell cycle regulation have important roles in the eitology mechanism of schizophrenia. For example, the over-expressing schizophrenia candidate genes, SIRPB1, SYK and LCK, are responsible for signal transduction in cytokine production; immune responses involving IL-2 and TREM-1/DAP12 pathways are relevant for the etiology mechanism of schizophrenia. Novel treatments were proposed by searching the target genes of FDA approved drugs with genes in potential protein complexes and pathways. It was found that Vitamin A, retinoid acid and a few other immune response agents modulated by RARA and LCK genes may be potential treatments for both schizophrenia and hepatocellular carcinoma. Conclusions This is the first study showing specific mediator genes in the SHCN which may suppress tumors. We also show that the schizophrenic protein interactions and modulation with cancer implicates the importance of immune system for etiology of schizophrenia. PMID:24564241

  5. Genetic pleiotropy between multiple sclerosis and schizophrenia but not bipolar disorder: differential involvement of immune-related gene loci

    PubMed Central

    Andreassen, O A; Harbo, H F; Wang, Y; Thompson, W K; Schork, A J; Mattingsdal, M; Zuber, V; Bettella, F; Ripke, S; Kelsoe, J R; Kendler, K S; O'Donovan, M C; Sklar, P; McEvoy, L K; Desikan, R S; Lie, B A; Djurovic, S; Dale, A M

    2015-01-01

    Converging evidence implicates immune abnormalities in schizophrenia (SCZ), and recent genome-wide association studies (GWAS) have identified immune-related single-nucleotide polymorphisms (SNPs) associated with SCZ. Using the conditional false discovery rate (FDR) approach, we evaluated pleiotropy in SNPs associated with SCZ (n=21 856) and multiple sclerosis (MS) (n=43 879), an inflammatory, demyelinating disease of the central nervous system. Because SCZ and bipolar disorder (BD) show substantial clinical and genetic overlap, we also investigated pleiotropy between BD (n=16 731) and MS. We found significant genetic overlap between SCZ and MS and identified 21 independent loci associated with SCZ, conditioned on association with MS. This enrichment was driven by the major histocompatibility complex (MHC). Importantly, we detected the involvement of the same human leukocyte antigen (HLA) alleles in both SCZ and MS, but with an opposite directionality of effect of associated HLA alleles (that is, MS risk alleles were associated with decreased SCZ risk). In contrast, we found no genetic overlap between BD and MS. Considered together, our findings demonstrate genetic pleiotropy between SCZ and MS and suggest that the MHC signals may differentiate SCZ from BD susceptibility. PMID:24468824

  6. Genetic pleiotropy between multiple sclerosis and schizophrenia but not bipolar disorder: differential involvement of immune-related gene loci.

    PubMed

    Andreassen, O A; Harbo, H F; Wang, Y; Thompson, W K; Schork, A J; Mattingsdal, M; Zuber, V; Bettella, F; Ripke, S; Kelsoe, J R; Kendler, K S; O'Donovan, M C; Sklar, P; McEvoy, L K; Desikan, R S; Lie, B A; Djurovic, S; Dale, A M

    2015-02-01

    Converging evidence implicates immune abnormalities in schizophrenia (SCZ), and recent genome-wide association studies (GWAS) have identified immune-related single-nucleotide polymorphisms (SNPs) associated with SCZ. Using the conditional false discovery rate (FDR) approach, we evaluated pleiotropy in SNPs associated with SCZ (n=21,856) and multiple sclerosis (MS) (n=43,879), an inflammatory, demyelinating disease of the central nervous system. Because SCZ and bipolar disorder (BD) show substantial clinical and genetic overlap, we also investigated pleiotropy between BD (n=16,731) and MS. We found significant genetic overlap between SCZ and MS and identified 21 independent loci associated with SCZ, conditioned on association with MS. This enrichment was driven by the major histocompatibility complex (MHC). Importantly, we detected the involvement of the same human leukocyte antigen (HLA) alleles in both SCZ and MS, but with an opposite directionality of effect of associated HLA alleles (that is, MS risk alleles were associated with decreased SCZ risk). In contrast, we found no genetic overlap between BD and MS. Considered together, our findings demonstrate genetic pleiotropy between SCZ and MS and suggest that the MHC signals may differentiate SCZ from BD susceptibility. PMID:24468824

  7. Genetic Analysis of the Pathogenic Molecular Sub-phenotype Interferon Alpha Identifies Multiple Novel Loci Involved in Systemic Lupus Erythematosus

    PubMed Central

    Kariuki, Silvia N.; Ghodke-Puranik, Yogita; Dorschner, Jessica M.; Chrabot, Beverly S.; Kelly, Jennifer A.; Tsao, Betty P.; Kimberly, Robert P.; Alarcón-Riquelme, Marta E.; Jacob, Chaim O.; Criswell, Lindsey A.; Sivils, Kathy L.; Langefeld, Carl D.; Harley, John B.; Skol, Andrew D.; Niewold, Timothy B.

    2014-01-01

    Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disorder characterized by inflammation of multiple organ systems and dysregulated interferon responses. SLE is both genetically and phenotypically heterogeneous, greatly reducing the power of case-control studies in SLE. Elevated circulating interferon alpha (IFN-α) is a stable, heritable trait in SLE, which has been implicated in primary disease pathogenesis. 40–50% of patients have high IFN-α, and high levels correspond with clinical differences. To study genetic heterogeneity in SLE, we performed a case-case study comparing patients with high vs. low IFN-α in over 1550 SLE cases, including GWAS and replication cohorts. In meta-analysis, the top associations in European ancestry were PRKG1 rs7897633 (PMeta=2.75 × 10−8) and PNP rs1049564 (PMeta=1.24 × 10−7). We also found evidence for cross-ancestral background associations with the ANKRD44 and PLEKHF2 loci. These loci have not been previously identified in case-control SLE genetic studies. Bioinformatic analyses implicated these loci functionally in dendritic cells and natural killer cells, both of which are involved in IFN-α production in SLE. As case-control studies of heterogeneous diseases reach a limit of feasibility with respect to subject number and detectable effect size, the study of informative pathogenic subphenotypes becomes an attractive strategy for genetic discovery in complex disease. PMID:25338677

  8. Preparation of agar nanospheres: comparison of response surface and artificial neural network modeling by a genetic algorithm approach.

    PubMed

    Zaki, Mohammad Reza; Varshosaz, Jaleh; Fathi, Milad

    2015-05-20

    Multivariate nature of drug loaded nanospheres manufacturing in term of multiplicity of involved factors makes it a time consuming and expensive process. In this study genetic algorithm (GA) and artificial neural network (ANN), two tools inspired by natural process, were employed to optimize and simulate the manufacturing process of agar nanospheres. The efficiency of GA was evaluated against the response surface methodology (RSM). The studied responses included particle size, poly dispersity index, zeta potential, drug loading and release efficiency. GA predicted greater extremum values for response factors compared to RSM. However, real values showed some deviations from predicted data. Appropriate agreement was found between ANN model predicted and real values for all five response factors with high correlation coefficients. GA was more successful than RSM in optimization and along with ANN were efficient tools in optimizing and modeling the fabrication process of drug loaded in agar nanospheres. PMID:25817674

  9. 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

  10. 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...

  11. Camera Calibration by Hybrid Hopfield Network and Self- Adaptive Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Xiang, Wen-Jiang; Zhou, Zhi-Xiong; Ge, Dong-Yuan; Zhang, Qing-Ying; Yao, Qing-He

    2012-12-01

    A new approach based on hybrid Hopfield neural network and self-adaptive genetic algorithm for camera calibration is proposed. First, a Hopfield network based on dynamics is structured according to the normal equation obtained from experiment data. The network has 11 neurons, its weights are elements of the symmetrical matrix of the normal equation and keep invariable, whose input vector is corresponding to the right term of normal equation, and its output signals are corresponding to the fitting coefficients of the camera’s projection matrix. At the same time an innovative genetic algorithm is presented to get the global optimization solution, where the cross-over probability and mutation probability are tuned self-adaptively according to the evolution speed factor in longitudinal direction and the aggregation degree factor in lateral direction, respectively. When the system comes to global equilibrium state, the camera’s projection matrix is estimated from the output vector of the Hopfield network, so the camera calibration is completed. Finally, the precision analysis is carried out, which demonstrates that, as opposed to the existing methods, such as Faugeras’s, the proposed approach has high precision, and provides a new scheme for machine vision system and precision manufacture.

  12. 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

  13. 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

  14. Predicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks

    PubMed Central

    Dar-Odeh, Najla S; Alsmadi, Othman M; Bakri, Faris; Abu-Hammour, Zaer; Shehabi, Asem A; Al-Omiri, Mahmoud K; Abu-Hammad, Shatha M K; Al-Mashni, Hamzeh; Saeed, Mohammad B; Muqbil, Wael; Abu-Hammad, Osama A

    2010-01-01

    Objective To construct and optimize a neural network that is capable of predicting the occurrence of recurrent aphthous ulceration (RAU) based on a set of appropriate input data. Participants and methods Artificial neural networks (ANN) software employing genetic algorithms to optimize the architecture neural networks was used. Input and output data of 86 participants (predisposing factors and status of the participants with regards to recurrent aphthous ulceration) were used to construct and train the neural networks. The optimized neural networks were then tested using untrained data of a further 10 participants. Results The optimized neural network, which produced the most accurate predictions for the presence or absence of recurrent aphthous ulceration was found to employ: gender, hematological (with or without ferritin) and mycological data of the participants, frequency of tooth brushing, and consumption of vegetables and fruits. Conclusions Factors appearing to be related to recurrent aphthous ulceration and appropriate for use as input data to construct ANNs that predict recurrent aphthous ulceration were found to include the following: gender, hemoglobin, serum vitamin B12, serum ferritin, red cell folate, salivary candidal colony count, frequency of tooth brushing, and the number of fruits or vegetables consumed daily. PMID:21918622

  15. Regulators of genetic risk of breast cancer identified by integrative network analysis

    PubMed Central

    Castro, Mauro AA; de Santiago, Ines; Campbell, Thomas M; Vaughn, Courtney; Hickey, Theresa E; Ross, Edith; Tilley, Wayne D; Markowetz, Florian; Ponder, Bruce AJ; Meyer, Kerstin B

    2015-01-01

    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 between transcription factors (TFs) and putative target genes (regulons) and asked whether specific regulons are enriched for genes associated with risk loci via eQTLs. We identified 36 overlapping regulons that were enriched and formed a distinct cluster within the network, suggesting shared biology. The risk-TFs driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to ER+ luminal A/B and to ER− basal-like cancers and to different, luminal epithelial cell populations in the adult mammary gland. Our network approach provides a foundation to reveal the regulatory circuits governing breast cancer, to identify targets for intervention, and is transferable to other disease settings. PMID:26618344

  16. Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns

    PubMed Central

    Lezon, Timothy R.; Banavar, Jayanth R.; Cieplak, Marek; Maritan, Amos; Fedoroff, Nina V.

    2006-01-01

    We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that 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. PMID:17138668

  17. Genetics

    MedlinePlus

    Homozygous; 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 ...

  18. 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 ...

  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

    2015-08-01

    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

  20. 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

  1. 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. PMID:23410879

  2. Variants of SCARB1 and VDR Involved in Complex Genetic Interactions May Be Implicated in the Genetic Susceptibility to Clear Cell Renal Cell Carcinoma.

    PubMed

    Pośpiech, Ewelina; Ligęza, Janusz; Wilk, Wacław; Gołas, Aniela; Jaszczyński, Janusz; Stelmach, Andrzej; Ryś, Janusz; Blecharczyk, Aleksandra; Wojas-Pelc, Anna; Jura, Jolanta; Branicki, Wojciech

    2015-01-01

    The current data are still inconclusive in terms of a genetic component involved in the susceptibility to renal cell carcinoma. Our aim was to evaluate 40 selected candidate polymorphisms for potential association with clear cell renal cell carcinoma (ccRCC) based on independent group of 167 patients and 200 healthy controls. The obtained data were searched for independent effects of particular polymorphisms as well as haplotypes and genetic interactions. Association testing implied position rs4765623 in the SCARB1 gene (OR = 1.688, 95% CI: 1.104-2.582, P = 0.016) and a haplotype in VDR comprising positions rs739837, rs731236, rs7975232, and rs1544410 (P = 0.012) to be the risk factors in the studied population. The study detected several epistatic effects contributing to the genetic susceptibility to ccRCC. Variation in GNAS1 was implicated in a strong synergistic interaction with BIRC5. This effect was part of a model suggested by multifactor dimensionality reduction method including also a synergy between GNAS1 and SCARB1 (P = 0.036). Significance of GNAS1-SCARB1 interaction was further confirmed by logistic regression (P = 0.041), which also indicated involvement of SCARB1 in additional interaction with EPAS1 (P = 0.008) as well as revealing interactions between GNAS1 and EPAS1 (P = 0.016), GNAS1 and MC1R (P = 0.031), GNAS1 and VDR (P = 0.032), and MC1R and VDR (P = 0.035). PMID:25945350

  3. 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

  4. CCR2-V64I genetic polymorphism: a possible involvement in HER2+ breast cancer.

    PubMed

    Banin-Hirata, Bruna Karina; Losi-Guembarovski, Roberta; Oda, Julie Massayo Maeda; de Oliveira, Carlos Eduardo Coral; Campos, Clodoaldo Zago; Mazzuco, Tânia Longo; Borelli, Sueli Donizete; Ceribelli, Jesus Roberto; Watanabe, Maria Angelica Ehara

    2016-05-01

    Many tumor cells express chemokines and chemokine receptors, and these molecules can affect both tumor progression and anti-tumor immune response. Genetic polymorphisms of some chemokine receptors were found to be closely related to malignant tumors, especially in metastasis process, including breast cancer (BC). Considering this, it was investigated a possible role for CCR2-V64I (C-C chemokine receptor 2) and CCR5-Δ32 (C-C chemokine receptor 5) genetic variants in BC context. Patients were divided into subgroups according to immunohistochemical profile of estrogen (ER) and progesterone (PR) receptors and the human epidermal growth factor receptor 2 (HER2) overexpression. No significant associations were found in relation to susceptibility (CCR2-V64I: OR 1.32; 95 % CI 0.57-3.06; CCR5-∆32: OR 1.04; 95 % CI 0.60-1.81), clinical outcome (tumor size, lymph nodes commitment and/or distant metastasis, TNM staging and nuclear grade) or therapeutic response (recurrence and survival). However, it was found a significant correlation between CCR2-V64I allelic variant and HER2 immunohistochemical positive samples (p = 0.026). All in all, we demonstrate, for the first time, a positive correlation between CCR2 receptor gene polymorphism and a subgroup of BC related to poor prognosis, which deserves further investigation in larger samples for validation. PMID:25716470

  5. UNCOVERING GENETIC COMPONENTS INVOLVED IN EARLY REGULATORY IMMUNE RESPONSE DURING PRRSV INFECTION

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Our goal is to identify the most significant pathways involved in early immune responses during porcine reproductive and respiratory syndrome virus (PRRSV) infection as compared to protective vaccination. For this experiment PRRSV-naïve animals were divided into four groups: (1) pigs infected with A...

  6. Integument pattern formation involves genetic and epigenetic controls: feather arrays simulated by digital hormone models.

    PubMed

    Jiang, Ting-Xin; Widelitz, Randall B; Shen, Wei-Min; Will, Peter; Wu, Da-Yu; Lin, Chih-Min; Jung, Han-Sung; Chuong, Cheng-Ming

    2004-01-01

    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 colocalize 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

  7. Genomic and Network Patterns of Schizophrenia Genetic Variation in Human Evolutionary Accelerated Regions

    PubMed Central

    Xu, Ke; Schadt, Eric E.; Pollard, Katherine S.; Roussos, Panos; Dudley, Joel T.

    2015-01-01

    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. PMID:25681384

  8. 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.

  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. PMID:25030903

  10. Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

    PubMed

    Jafari, Seyed Ali; Jahandideh, Sepideh; Jahandideh, Mina; Asadabadi, Ebrahim Barzegari

    2015-01-01

    Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors. PMID:24304230

  11. 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.

  12. 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.

  13. 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.

  14. Genetic algorithm-based neural fuzzy decision tree for mixed scheduling in ATM networks.

    PubMed

    Lin, Chin-Teng; Chung, I-Fang; Pu, Her-Chang; Lee', Tsern-Huei; Chang, Jyh-Yeong

    2002-01-01

    Future broadband integrated services networks based on asynchronous transfer mode (ATM) technology are expected to support multiple types of multimedia information with diverse statistical characteristics and quality of service (QoS) requirements. To meet these requirements, efficient scheduling methods are important for traffic control in ATM networks. Among general scheduling schemes, the rate monotonic algorithm is simple enough to be used in high-speed networks, but does not attain the high system utilization of the deadline driven algorithm. However, the deadline driven scheme is computationally complex and hard to implement in hardware. The mixed scheduling algorithm is a combination of the rate monotonic algorithm and the deadline driven algorithm; thus it can provide most of the benefits of these two algorithms. In this paper, we use the mixed scheduling algorithm to achieve high system utilization under the hardware constraint. Because there is no analytic method for schedulability testing of mixed scheduling, we propose a genetic algorithm-based neural fuzzy decision tree (GANFDT) to realize it in a real-time environment. The GANFDT combines a GA and a neural fuzzy network into a binary classification tree. This approach also exploits the power of the classification tree. Simulation results show that the GANFDT provides an efficient way of carrying out mixed scheduling in ATM networks. PMID:18244889

  15. 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.

  16. 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

  17. 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

  18. 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

  19. 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.

  20. 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

  1. Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations

    PubMed Central

    Gupta, Chinmaya; López, José Manuel; Azencott, Robert; Bennett, Matthew R.; Josić, Krešimir; Ott, William

    2014-01-01

    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. PMID:24880267

  2. 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.

  3. Genetic variability of genes involved in DNA repair influence treatment outcome in osteosarcoma.

    PubMed

    Wang, M J; Zhu, Y; Guo, X J; Tian, Z Z

    2015-01-01

    We conducted a perspective study to investigate the role of ERCC1 (rs11615), ERCC2 (rs13181 and rs1799793), ERCC4 (rs1800067), and ERCC5 (rs17655) in NER pathway in the prognosis of osteosarcoma patients. In total, 146 osteosarcoma patients were recruited between 2008 and 2013. ERCC1 rs11615, ERCC2 rs13181 and rs1799793, ERCC4 rs1800067, and ERCC5 rs17655 gene polymorphisms were assessed by polymerase chain reaction-restriction fragment length polymorphism assay. By multivariate Cox proportional hazards models, we found that carriers of ERCC1 rs11615 TT genotype showed significantly favorable survival compared to wide-type CC genotype, and the adjusted OR (95%CI) was 0.24 (0.08-0.96). Moreover, we found that subjects with ERCC2 rs1799793 AA genotype were associated with decreased hazards of death in multivariate analysis (HR = 0.22, 95%CI = 0.12-0.93). In conclusion, our results suggest that ERCC1 rs11615 and ERCC2 rs1799793 may be useful genetic prognostic markers for osteosarcoma in a Chinese population. PMID:26436406

  4. A Genetic and Mosaic Analysis of a Locus Involved in the Anesthesia Response of Drosophila Melanogaster

    PubMed Central

    Mir, B.; Iyer, S.; Ramaswami, M.; Krishnan, K. S.

    1997-01-01

    We describe a genetic and behavioral analysis of several alleles of har38, a mutant with altered sensitivity to the general anesthetic halothane. We obtained a P-element-induced allele of har38 and generated several excision alleles by remobilizing the P element. The mutants narrow abdomen (na) and har85 are confirmed to be allelic to har38. Besides a decreased sensitivity to halothane, all mutant alleles of this locus cause a characteristic walking behavior in the absence of anesthetics. We have quantified this behavior using a geotaxis apparatus. Responses of the mutant alleles to different inhalational anesthetics were tested. The results strongly favor a multipathway model for the onset of anesthesia. Mosaic flies were tested for their response to halothane and checked for their abnormal walking behavior. The analysis suggests that both the behaviors are exhibited only by such mosaics as have the entire head of mutant origin. It is likely that this focus represents an element of a common pathway in the anesthetic response to several inhalational anesthetics but not all. This result is the first demonstration of regional specificity in the CNS of any animal for general anesthetic action. PMID:9335606

  5. 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

  6. Functional Validation of Rare Human Genetic Variants Involved in Homologous Recombination Using Saccharomyces cerevisiae

    PubMed Central

    Lee, Min-Soo; Yu, Mi; Kim, Kyoung-Yeon; Park, Geun-Hee; Kwack, KyuBum; Kim, Keun P.

    2015-01-01

    Systems for the repair of DNA double-strand breaks (DSBs) are necessary to maintain genome integrity and normal functionality of cells in all organisms. Homologous recombination (HR) plays an important role in repairing accidental and programmed DSBs in mitotic and meiotic cells, respectively. Failure to repair these DSBs causes genome instability and can induce tumorigenesis. Rad51 and Rad52 are two key proteins in homologous pairing and strand exchange during DSB-induced HR; both are highly conserved in eukaryotes. In this study, we analyzed pathogenic single nucleotide polymorphisms (SNPs) in human RAD51 and RAD52 using the Polymorphism Phenotyping (PolyPhen) and Sorting Intolerant from Tolerant (SIFT) algorithms and observed the effect of mutations in highly conserved domains of RAD51 and RAD52 on DNA damage repair in a Saccharomyces cerevisiae-based system. We identified a number of rad51 and rad52 alleles that exhibited severe DNA repair defects. The functionally inactive SNPs were located near ATPase active site of Rad51 and the DNA binding domain of Rad52. The rad51-F317I, rad52-R52W, and rad52-G107C mutations conferred hypersensitivity to methyl methane sulfonate (MMS)-induced DNA damage and were defective in HR-mediated DSB repair. Our study provides a new approach for detecting functional and loss-of-function genetic polymorphisms and for identifying causal variants in human DNA repair genes that contribute to the initiation or progression of cancer. PMID:25938495

  7. Control of genetic stability by a new heterochromatin compaction pathway involving the Tip60 histone acetyltransferase

    PubMed Central

    Grézy, Aude; Chevillard-Briet, Martine; Trouche, Didier; Escaffit, Fabrice

    2016-01-01

    Pericentric heterochromatin is a highly compacted structure required for accurate chromosome segregation in mitosis. In mammals, it relies on methylation of histone H3K9 by Suv39H enzymes, which provides a docking site for HP1 proteins, therefore mediating heterochromatin compaction. Here we show that, when this normal compaction pathway is defective, the histone acetyltransferase Tip60 is recruited to pericentric heterochromatin, where it mediates acetylation of histone H4K12. Furthermore, in such a context, depletion of Tip60 leads to derepression of satellite transcription, decompaction of pericentric heterochromatin, and defects in chromosome segregation in mitosis. Finally, we show that depletion of BRD2, a double bromodomain–containing protein that binds H4K12ac, phenocopies the Tip60 depletion with respect to heterochromatin decompaction and defects in chromosome segregation. Taking the results together, we identify a new compaction pathway of mammalian pericentric heterochromatin relying on Tip60 that might be dependent on BRD2 recruitment by H4K12 acetylation. We propose that the underexpression of Tip60 observed in many human tumors can promote genetic instability via defective pericentric heterochromatin. PMID:26700317

  8. 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

  9. 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.

  10. 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

  11. Architecture for High Speed Learning of Neural Network using Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Yoshikawa, Masaya; Terai, Hidekazu

    This paper discusses the architecture for high speed learning of Neural Network (NN) using Genetic Algorithm (GA). The proposed architecture prevents local minimum by using the GA characteristic of holding several individual populations for a population-based search and achieves high speed processing adopting dedicated hardware. To keep general purpose equal software processing, the proposed architecture can be flexible genetic operations on GA and is introduced both Sigmoid function and Heaviside function on NN. Furthermore, the proposed architecture is not optimized only the pipeline at evaluation phase on NN, but also optimized hierarchic pipelines on the whole at evolutionary phase. We have done the simulation, verification and logic synthesis using library of 0.35μm CMOS standard cell. Simulation results evaluating the proposed architecture show to achieve 22 times speed on average compared with software processing.

  12. Significant impact of miRNA–target gene networks on genetics of human complex traits

    PubMed Central

    Okada, Yukinori; Muramatsu, Tomoki; Suita, Naomasa; Kanai, Masahiro; Kawakami, Eiryo; Iotchkova, Valentina; Soranzo, Nicole; Inazawa, Johji; Tanaka, Toshihiro

    2016-01-01

    The impact of microRNA (miRNA) on the genetics of human complex traits, especially in the context of miRNA-target gene networks, has not been fully assessed. Here, we developed a novel analytical method, MIGWAS, to comprehensively evaluate enrichment of genome-wide association study (GWAS) signals in miRNA–target gene networks. We applied the method to the GWAS results of the 18 human complex traits from >1.75 million subjects, and identified significant enrichment in rheumatoid arthritis (RA), kidney function, and adult height (P < 0.05/18 = 0.0028, most significant enrichment in RA with P = 1.7 × 10−4). Interestingly, these results were consistent with current literature-based knowledge of the traits on miRNA obtained through the NCBI PubMed database search (adjusted P = 0.024). Our method provided a list of miRNA and target gene pairs with excess genetic association signals, part of which included drug target genes. We identified a miRNA (miR-4728-5p) that downregulates PADI2, a novel RA risk gene considered as a promising therapeutic target (rs761426, adjusted P = 2.3 × 10−9). Our study indicated the significant impact of miRNA–target gene networks on the genetics of human complex traits, and provided resources which should contribute to drug discovery and nucleic acid medicine. PMID:26927695

  13. Significant impact of miRNA-target gene networks on genetics of human complex traits.

    PubMed

    Okada, Yukinori; Muramatsu, Tomoki; Suita, Naomasa; Kanai, Masahiro; Kawakami, Eiryo; Iotchkova, Valentina; Soranzo, Nicole; Inazawa, Johji; Tanaka, Toshihiro

    2016-01-01

    The impact of microRNA (miRNA) on the genetics of human complex traits, especially in the context of miRNA-target gene networks, has not been fully assessed. Here, we developed a novel analytical method, MIGWAS, to comprehensively evaluate enrichment of genome-wide association study (GWAS) signals in miRNA-target gene networks. We applied the method to the GWAS results of the 18 human complex traits from >1.75 million subjects, and identified significant enrichment in rheumatoid arthritis (RA), kidney function, and adult height (P < 0.05/18 = 0.0028, most significant enrichment in RA with P = 1.7 × 10(-4)). Interestingly, these results were consistent with current literature-based knowledge of the traits on miRNA obtained through the NCBI PubMed database search (adjusted P = 0.024). Our method provided a list of miRNA and target gene pairs with excess genetic association signals, part of which included drug target genes. We identified a miRNA (miR-4728-5p) that downregulates PADI2, a novel RA risk gene considered as a promising therapeutic target (rs761426, adjusted P = 2.3 × 10(-9)). Our study indicated the significant impact of miRNA-target gene networks on the genetics of human complex traits, and provided resources which should contribute to drug discovery and nucleic acid medicine. PMID:26927695

  14. 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. PMID:26775432

  15. HOS1, a genetic locus involved in cold-responsive gene expression in arabidopsis.

    PubMed Central

    Ishitani, M; Xiong, L; Lee, H; Stevenson, B; Zhu, J K

    1998-01-01

    Low-temperature stress induces the expression of a variety of genes in plants. However, the signal transduction pathway(s) that activates gene expression under cold stress is poorly understood. Mutants defective in cold signaling should facilitate molecular analysis of plant responses to low temperature and eventually lead to the identification and cloning of a cold stress receptor(s) and intracellular signaling components. In this study, we characterize a plant mutant affected in its response to low temperatures. The Arabidopsis hos1-1 mutation identified by luciferase imaging causes superinduction of cold-responsive genes, such as RD29A, COR47, COR15A, KIN1, and ADH. Although these genes are also induced by abscisic acid, high salt, or polyethylene glycol in addition to cold, the hos1-1 mutation only enhances their expression under cold stress. Genetic analysis revealed that hos1-1 is a single recessive mutation in a nuclear gene. Our studies using the firefly luciferase reporter gene under the control of the cold-responsive RD29A promoter have indicated that cold-responsive genes can be induced by temperatures as high as 19 degrees C in hos1-1 plants. In contrast, wild-type plants do not express the luciferase reporter at 10 degrees C or higher. Compared with the wild type, hos1-1 plants are l ess cold hardy. Nonetheless, after 2 days of cold acclimation, hos1-1 plants acquired the same degree of freezing tolerance as did the wild type. The hos1-1 plants flowered earlier than did the wild-type plants and appeared constitutively vernalized. Taken together, our findings show that the HOS1 locus is an important negative regulator of cold signal transduction in plant cells and that it plays critical roles in controlling gene expression under cold stress, freezing tolerance, and flowering time. PMID:9668134

  16. A large-scale genetic screen in Arabidopsis to identify genes involved in pollen exine production.

    PubMed

    Dobritsa, Anna A; Geanconteri, Aliza; Shrestha, Jay; Carlson, Ann; Kooyers, Nicholas; Coerper, Daniel; Urbanczyk-Wochniak, Ewa; Bench, Bennie J; Sumner, Lloyd W; Swanson, Robert; Preuss, Daphne

    2011-10-01

    Exine, the outer plant pollen wall, has elaborate species-specific patterns, provides a protective barrier for male gametophytes, and serves as a mediator of strong and species-specific pollen-stigma adhesion. Exine is made of sporopollenin, a material remarkable for its strength, elasticity, and chemical durability. The chemical nature of sporopollenin, as well as the developmental mechanisms that govern its assembly into diverse patterns in different species, are poorly understood. Here, we describe a simple yet effective genetic screen in Arabidopsis (Arabidopsis thaliana) that was undertaken to advance our understanding of sporopollenin synthesis and exine assembly. This screen led to the recovery of mutants with a variety of defects in exine structure, including multiple mutants with novel phenotypes. Fifty-six mutants were selected for further characterization and are reported here. In 14 cases, we have mapped defects to specific genes, including four with previously demonstrated or suggested roles in exine development (MALE STERILITY2, CYP703A2, ANTHER-SPECIFIC PROTEIN6, TETRAKETIDE α-PYRONE REDUCTASE/DIHYDROFLAVONOL-4-REDUCTASE-LIKE1), and a number of genes that have not been implicated in exine production prior to this screen (among them, fatty acid ω-hydroxylase CYP704B1, putative glycosyl transferases At1g27600 and At1g33430, 4-coumarate-coenzyme A ligase 4CL3, polygalacturonase QUARTET3, novel gene At5g58100, and nucleotide-sugar transporter At5g65000). Our study illustrates that morphological screens of pollen can be extremely fruitful in identifying previously unknown exine genes and lays the foundation for biochemical, developmental, and evolutionary studies of exine production. PMID:21849515

  17. Stability of genetic regulatory networks based on switched systems and mixed time-delays.

    PubMed

    Wang, Lan; Luo, Zong-Ping; Yang, Hui-Lin; Cao, Jinde

    2016-08-01

    In this paper, the switched genetic regulatory networks (GRNs) are modeled from a real biological system, based on switched systems, noise and mixed time-delays. Global asymptotical stability for the proposed switched GRNs are studied by the Lyapunov method and the matrix inequality techniques. Some new sufficient conditions are obtained to ensure the global asymptotical stability of the proposed switched GRNs. Furthermore, the proposed LMI results are computationally efficient as it can be solved numerically with standard commercial software. Finally, an example is provided to illustrate the usefulness of the results. PMID:27326659

  18. 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.

  19. Stochastic stability of switched genetic regulatory networks with time-varying delays.

    PubMed

    Zhang, Wenbing; Tang, Yang; Wu, Xiaotai; Fang, Jian-An

    2014-09-01

    This paper investigates the exponential stability problem of switched stochastic genetic regulatory networks (GRNs) with time-varying delays. Two types of switched systems are studied respectively: one is the stochastic switched delayed GRNs with only stable subsystems and the other is the stochastic switched delayed GRNs with both stable and unstable subsystems. By using switching analysis techniques and the modified Halanay differential inequality, new criteria are developed for the exponential stability of switched stochastic GRNs with time-varying delays. Finally, an example is given to illustrate the main results. PMID:25265564

  20. 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.

  1. 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.

  2. 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.

  3. The case for an ancestral genetic system involving simple analogues of the nucleotides

    NASA Technical Reports Server (NTRS)

    Joyce, Gerald F.; Orgel, Leslie E.; Schwartz, Alan W.; Miller, Stanley L.

    1987-01-01

    The idea that the first living systems on earth were based on self-replicating RNA molecules has recently become popular as a result of the discovery of ribozymes. However, there are several major problems associated with the prebiotic synthesis of ribonucleotides. In addition, there is the newly recognized problem of enantiomeric cross-inhibition, whereby template-directed polymerization involving one enantiomer of RNA is inhibited strongly by the presence of the other enantiomer. Here, it is proposed that RNA was preceded in the evolution of life by a polymer constructed from flexible, acyclic, probably prochiral nucleotide analogues that were synthesized readily on the primitive earth. Several potentially prebiotic nucleotide analogues are considered in this context, and some of the consequences of this proposal are discussed.

  4. A mouse forward genetics screen identifies LISTERIN as an E3 ubiquitin ligase involved in neurodegeneration

    PubMed Central

    Chu, Jessie; Hong, Nancy A.; Masuda, Claudio A.; Jenkins, Brian V.; Nelms, Keats A.; Goodnow, Christopher C.; Glynne, Richard J.; Wu, Hua; Masliah, Eliezer; Joazeiro, Claudio A. P.; Kay, Steve A.

    2009-01-01

    A mouse neurological mutant, lister, was identified through a genome-wide N-ethyl-N-nitrosourea (ENU) mutagenesis screen. Homozygous lister mice exhibit profound early-onset and progressive neurological and motor dysfunction. lister encodes a RING finger protein, LISTERIN, which functions as an E3 ubiquitin ligase in vitro. Although lister is widely expressed in all tissues, motor and sensory neurons and neuronal processes in the brainstem and spinal cord are primarily affected in the mutant. Pathological signs include gliosis, dystrophic neurites, vacuolated mitochondria, and accumulation of soluble hyperphosphorylated tau. Analysis with a different lister allele generated through targeted gene trap insertion reveals LISTERIN is required for embryonic development and confirms that direct perturbation of a LISTERIN-regulated process causes neurodegeneration. The lister mouse uncovers a pathway involved in neurodegeneration and may serves as a model for understanding the molecular mechanisms underlying human neurodegenerative disorders. PMID:19196968

  5. 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

  6. 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

  7. Tau loss attenuates neuronal network hyperexcitability in mouse and Drosophila genetic models of epilepsy

    PubMed Central

    Holth, Jerrah K.; Bomben, Valerie C.; Reed, J. Graham; Inoue, Taeko; Younkin, Linda; Younkin, Steven G.; Pautler, Robia G.; Botas, Juan; Noebels, Jeffrey L.

    2013-01-01

    Neuronal network hyperexcitability underlies the pathogenesis of seizures and is a component of some degenerative neurological disorders such as Alzheimer’s disease (AD). Recently, the microtubule binding protein tau has been implicated in the regulation of network synchronization. Genetic removal of Mapt, the gene encoding tau, in AD models overexpressing amyloid-beta (Aβ) decreases hyperexcitability and normalizes the excitation/inhibition imbalance. Whether this effect of tau removal is specific to Aβ mouse models remains to be determined. Here we examined tau as an excitability modifier in the non-AD nervous system using genetic deletion of tau in mouse and Drosophila models of hyperexcitability. Kcna1−/− mice lack Kv1.1 delayed rectifier currents and exhibit severe spontaneous seizures, early lethality, and megencephaly. Young Kcna1−/− mice retained wild-type levels of Aβ, tau, and tau phospho-Thr231. Decreasing tau in Kcna1−/− mice reduced hyperexcitability and alleviated seizure-related comorbidities. Tau reduction decreased Kcna1−/− video-EEG recorded seizure frequency and duration as well as normalized Kcna1−/− hippocampal network hyperexcitability in vitro. Additionally, tau reduction increased Kcna1−/− survival and prevented megencephaly and hippocampal hypertrophy, as determined by MRI. Bang-sensitive Drosophila mutants display paralysis and seizures in response to mechanical stimulation, providing a complementary excitability assay for epistatic interactions. We found that tau reduction significantly decreased seizure sensitivity in two independent bang-sensitive mutant models, kcc and eas. Our results indicate that tau plays a general role in regulating intrinsic neuronal network hyperexcitability independently of Aβ overexpression and suggest that reducing tau function could be a viable target for therapeutic intervention in seizure disorders and antiepileptogenesis. PMID:23345237

  8. Supervised feature ranking using a genetic algorithm optimized artificial neural network.

    PubMed

    Lin, Thy-Hou; Chiu, Shih-Hau; Tsai, Keng-Chang

    2006-01-01

    A genetic algorithm optimized artificial neural network GNW has been designed to rank features for two diversified multivariate data sets. The dimensions of these data sets are 85x24 and 62x25 for 24 or 25 molecular descriptors being computed for 85 matrix metalloproteinase-1 inhibitors or 62 hepatitis C virus NS3 protease inhibitors, respectively. Each molecular descriptor computed is treated as a feature and input into an input layer node of the artificial neural network. To optimize the artificial neural network by the genetic algorithm, each interconnected weight between input and hidden or between hidden and output layer nodes is binary encoded as a 16 bits string in a chromosome, and the chromosome is evolved by crossover and mutation operations. Each input layer node and its associated weights of the trained GNW are systematically omitted once (the self-depleted weights), and the corresponding weight adjustments due to the omission are computed to keep the overall network behavior unchanged. The primary feature ranking index defined as the sum of self-depleted weights and the corresponding weight adjustments computed is found capable of separating good from bad features for some artificial data sets of known feature rankings tested. The final feature indexes used to rank the data sets are computed as a sum of the weighted frequency of each feature being ranked in a particular rank for each data set being partitioned into numerous clusters. The two data sets are also clustered by a standard K-means method and trained by a support vector machine (SVM) for feature ranking using the computed F-scores as feature ranking index. It is found that GNW outperforms the SVM method on three artificial as well as the matrix metalloproteinase-1 inhibitor data sets studied. A clear-cut separation of good from bad features is offered by the GNW but not by the SVM method for a feature pool of known feature ranking. PMID:16859292

  9. 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

  10. Mastication dyspraxia: a neurodevelopmental disorder reflecting disruption of the cerebellocerebral network involved in planned actions.

    PubMed

    Mariën, Peter; Vidts, Annelies; Van Hecke, Wim; De Surgeloose, Didier; De Belder, Frank; Parizel, Paul M; Engelborghs, Sebastiaan; De Deyn, Peter P; Verhoeven, Jo

    2013-04-01

    cerebellocerebral network is crucially important in the planning and execution of skilled actions, but also seem to show for the first time that mastication deficits may be of true apraxic origin. As a result, it is hypothesized that "mastication dyspraxia" may have to be considered as a distinct nosological entity within the group of the developmental dyspraxias following a disruption of the cerebellocerebral network involved in planned actions. PMID:23065651

  11. Genetic counselling in carriers of reciprocal chromosomal translocations involving short arm of chromosome X.

    PubMed

    Panasiuk, Barbara; Usinskiené, Ruta; Kostyk, Ewa; Rybałko, Alicja; Stasiewicz-Jarocka, Beata; Krzykwa, Bogustawa; Pieńkowska-Grela, Barbara; Kucinskas, Vaidutis; Michalova, Kyra; Midro, Alina T

    2004-01-01

    A central concept in genetic counselling is the estimation of the probability of occurrence of unbalanced progeny at birth and other unfavourable outcomes of pregnancy (miscarriages, stillbirths and early death). The estimation of the occurrence probability for individual carriers of four different X-autosome translocations with breakpoints at Xp, namely t(X;5)(p22.2;q32), t(X;6)(p11.2;q21), t(X;7)(p22.2;p11.1), and t(X;22)(p22.1;p11.1), is presented. The breakpoint positions of chromosomal translocations were interpreted using GTG, RBG and FISH-wcp. Most of these translocations were detected in women with normal phenotype, karyotyped because of repeated miscarriages and/or malformed progeny. A girl with very rare pure trisomy Xp22.1-->pter and a functional Xp disomy was ascertained in one family and her clinical picture has been described in details. It has been suggested that not fully skewed X chromosome inactivation of X-autosome translocation with breakpoint positions at Xp22 (critical segment) could influence the phenotype and risk value. Therefore, the X inactivation status was additionally evaluated by analysis of replication banding patterns using RBG technique after incorporation of BrdU. In two carriers of translocations: t(X;5)(p22.2;q32) and t(X;7)(p22.2;p11.1), late replication state of der(X) was observed in 5/100 and 10/180 analysed cells, respectively. In these both cases the breakpoint positions were clustered at the critical segment Xp22.2. In two other cases, one with the breakpoint position within [t(X;22)(p22.1;p11.1)] and one outside the critical region [t(X;6)(p11.2;q21)], fully skewed inactivation was seen. Therefore, we suggest that neither the distribution of the breakpoint positions nor fully skewed inactivation influenced the phenotype of observed t(X;A) carriers. The occurrence probabilities of the unbalanced progeny were calculated according to Stene and Stengel-Rutkowski along with application of updated available empirical data. In

  12. 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

  13. Non-coding RNAs and a layered architecture of genetic networks

    NASA Astrophysics Data System (ADS)

    Zhdanov, Vladimir

    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.

  14. 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.

  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. Genetic Evidence That the Ovo Locus Is Involved in Drosophila Germ Line Sex Determination

    PubMed Central

    Oliver, B.; Pauli, D.; Mahowald, A. P.

    1990-01-01

    Zygotically contributed ovo gene product is required for the survival of female germ cells in Drosophila melanogaster. Trans-allelic combinations of weak and dominant ovo mutations (ovo(D)) result in viable germ cells that appear to be partially transformed from female to male sexual identity. The ovo(D2) mutation is partially suppressed by many Sex-lethal alleles that affect the soma, while those that affect only the germ line fail to interact with ovo(D2). One of two loss-of-function ovo alleles is suppressed by a loss-of-function Sex-lethal allele. Because ovo mutations are germ line dependent, it is likely that ovo is suppressed by way of communication between the somatic and germ lines. A loss-of-function allele of ovo is epistatic to germ line dependent mutations in Sex-lethal. The germ line dependent sex determination mutation, sans fille, and ovo(D) mutations show a dominant synergistic interaction resulting in partial transformation of germ line sexual identity. The ovo locus appears to be involved in germ line sex determination and is linked in some manner to sex determination in the soma. PMID:2116356

  17. 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.;

  18. Estradiol rapidly modulates synaptic plasticity of hippocampal neurons: Involvement of kinase networks.

    PubMed

    Hasegawa, Yoshitaka; Hojo, Yasushi; Kojima, Hiroki; Ikeda, Muneki; Hotta, Keisuke; Sato, Rei; Ooishi, Yuuki; Yoshiya, Miyuki; Chung, Bon-Chu; Yamazaki, Takeshi; Kawato, Suguru

    2015-09-24

    Estradiol (E2) is locally synthesized within the hippocampus in addition to the gonads. Rapid modulation of hippocampal synaptic plasticity by E2 is essential for synaptic regulation. Molecular mechanisms of modulation through synaptic estrogen receptor (ER) and its downstream signaling, however, have been still unknown. We investigated induction of LTP by the presence of E2 upon weak theta burst stimulation (weak-TBS) in CA1 region of adult male hippocampus. Since only weak-TBS did not induce full-LTP, weak-TBS was sub-threshold stimulation. We observed LTP induction by the presence of E2, after incubation of hippocampal slices with 10nM E2 for 30 min, upon weak-TBS. This E2-induced LTP was blocked by ICI, an ER antagonist. This E2-LTP induction was inhibited by blocking Erk MAPK, PKA, PKC, PI3K, NR2B and CaMKII, individually, suggesting that Erk MAPK, PKA, PKC, PI3K and CaMKII may be involved in downstream signaling for activation of NMDA receptors. Interestingly, dihydrotestosterone suppressed the E2-LTP. We also investigated rapid changes of dendritic spines (=postsynapses) in response to E2, using hippocampal slices from adult male rats. We found 1nM E2 increased the density of spines by approximately 1.3-fold within 2h by imaging Lucifer Yellow-injected CA1 pyramidal neurons. The E2-induced spine increase was blocked by ICI. The increase in spines was suppressed by blocking PI3K, Erk MAPK, p38 MAPK, PKA, PKC, LIMK, CaMKII or calcineurin, individually. On the other hand, blocking JNK did not inhibit the E2-induced spine increase. Taken together, these results suggest that E2 rapidly induced LTP and also increased the spine density through kinase networks that are driven by synaptic ER. This article is part of a Special Issue entitled SI: Brain and Memory. PMID:25595055

  19. 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

  20. 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

  1. 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

  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. 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.

  4. Identification of muscarinic receptor subtypes involved in catecholamine secretion in adrenal medullary chromaffin cells by genetic deletion

    PubMed Central

    Harada, Keita; Matsuoka, Hidetada; Miyata, Hironori; Matsui, Minoru; Inoue, Masumi

    2015-01-01

    Background and Purpose Activation of muscarinic receptors results in catecholamine secretion in adrenal chromaffin cells in many mammals, and muscarinic receptors partly mediate synaptic transmission from the splanchnic nerve, at least in guinea pigs. To elucidate the physiological functions of muscarinic receptors in chromaffin cells, it is necessary to identify the muscarinic receptor subtypes involved in excitation. Experimental Approach To identify muscarinic receptors, pharmacological tools and strains of mice where one or several muscarinic receptor subtypes were genetically deleted were used. Cellular responses to muscarinic stimulation in isolated chromaffin cells were studied with the patch clamp technique and amperometry. Key Results Muscarinic M1, M4 and M5 receptors were immunologically detected in mouse chromaffin cells, and these receptors disappeared after the appropriate gene deletion. Mouse cells secreted catecholamines in response to muscarinic agonists, angiotensin II and a decrease in external pH. Genetic deletion of M1, but not M3, M4 or M5, receptors in mice abolished secretion in response to muscarine, but not to other stimuli. The muscarine-induced secretion was suppressed by MT7, a snake peptide toxin specific for M1 receptors. Similarly, muscarine failed to induce an inward current in the presence of MT7 in mouse and rat chromaffin cells. The binding affinity of VU0255035 for the inhibition of muscarine-induced currents agreed with that for the M1 receptor. Conclusions and Implications Based upon the effects of genetic deletion of muscarinic receptors and MT7, it is concluded that the M1 receptor alone is responsible for muscarine-induced catecholamine secretion. PMID:25393049

  5. Genetic Evidence for Possible Involvement of the Calcium Channel Gene CACNA1A in Autism Pathogenesis in Chinese Han Population

    PubMed Central

    Yue, Weihua; Jia, Meixiang; Yu, Hao; Lu, Tianlan; Wu, Zhiliu; Ruan, Yanyan; Wang, Lifang; Zhang, Dai

    2015-01-01

    Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders. Recent studies suggested that calcium channel genes might be involved in the genetic etiology of ASD. CACNA1A, encoding an alpha-1 subunit of voltage-gated calcium channel, has been reported to play an important role in neural development. Previous study detected that a single nucleotide polymorphism (SNP) in CACNA1A confers risk to ASD in Central European population. However, the genetic relationship between autism and CACNA1A in Chinese Han population remains unclear. To explore the association of CACNA1A with autism, we performed a family-based association study. First, we carried out a family-based association test between twelve tagged SNPs and autism in 239 trios. To further confirm the association, the sample size was expanded to 553 trios by recruiting 314 additional trios. In a total of 553 trios, we identified association of rs7249246 and rs12609735 with autism though this would not survive after Bonferroni correction. Our findings suggest that CACNA1A might play a role in the etiology of autism. PMID:26566276

  6. Genetic Evidence for Possible Involvement of the Calcium Channel Gene CACNA1A in Autism Pathogenesis in Chinese Han Population.

    PubMed

    Li, Jun; You, Yang; Yue, Weihua; Jia, Meixiang; Yu, Hao; Lu, Tianlan; Wu, Zhiliu; Ruan, Yanyan; Wang, Lifang; Zhang, Dai

    2015-01-01

    Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders. Recent studies suggested that calcium channel genes might be involved in the genetic etiology of ASD. CACNA1A, encoding an alpha-1 subunit of voltage-gated calcium channel, has been reported to play an important role in neural development. Previous study detected that a single nucleotide polymorphism (SNP) in CACNA1A confers risk to ASD in Central European population. However, the genetic relationship between autism and CACNA1A in Chinese Han population remains unclear. To explore the association of CACNA1A with autism, we performed a family-based association study. First, we carried out a family-based association test between twelve tagged SNPs and autism in 239 trios. To further confirm the association, the sample size was expanded to 553 trios by recruiting 314 additional trios. In a total of 553 trios, we identified association of rs7249246 and rs12609735 with autism though this would not survive after Bonferroni correction. Our findings suggest that CACNA1A might play a role in the etiology of autism. PMID:26566276

  7. 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.

  8. 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...

  9. 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.

  10. The Transmission of Gun and Other Weapon-Involved Violence Within Social Networks.

    PubMed

    Tracy, Melissa; Braga, Anthony A; Papachristos, Andrew V

    2016-01-01

    Fatal and nonfatal injuries resulting from gun violence remain a persistent problem in the United States. The available research suggests that gun violence diffuses among people and across places through social relationships. Understanding the relationship between gun violence within social networks and individual gun violence risk is critical in preventing the spread of gun violence within populations. This systematic review examines the existing scientific evidence on the transmission of gun and other weapon-related violence in household, intimate partner, peer, and co-offending networks. Our review identified 16 studies published between 1996 and 2015 that suggest that exposure to a victim or perpetrator of violence in one's interpersonal relationships and social networks increases the risk of individual victimization and perpetration. Formal network analyses find high concentrations of gun violence in small networks and that exposure to gun violence in one's networks is highly correlated with one's own probability of being a gunshot victim. Physical violence by parents and weapon use by intimate partners also increase risk for victimization and perpetration. Additional work is needed to better characterize the mechanisms through which network exposures increase individual risk for violence and to evaluate interventions aimed at disrupting the spread of gun and other weapon violence in high-risk social networks. PMID:26733492

  11. 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.

  12. 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

  13. 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

  14. Environmentally-Modulated Changes in Fluorescence Distribution in Cells with Oscillatory Genetic Network Dynamics

    PubMed Central

    Portle, Stephanie; Iadevaia, Sergio; San, Ka-Yiu; Bennett, George N.; Mantzaris, Nikos

    2009-01-01

    We investigated the distribution of green fluorescent protein (GFP) expression levels in a population of E. coli cells expressing an artificial genetic regulatory network, known as the “repressilator” (Elowitz and Leibler, 2000), which exhibits oscillations at the single-cell level. A series of shake flask experiments were performed and analyzed using flow cytometry to test how cell populations carrying this system could be controlled extracellularly using the inducers anhydrotetracycline (aTc) and isopropyl-β-D-thiogalactopyranoside (IPTG). With variation of [aTc], it exhibits bi-threshold behavior, such that the entire culture reaches one of three steady states at a quasi-time invariant “reference state.” Also, there is significant hysteresis. Transiently, the middle state shows damping oscillations, while the low and high states show a stable steady state. The addition of IPTG serves to fine-tune the characteristics of the aTc-only expression, lowering the average and CV of the distributions, and possibly perturbing the network to a different state. However, in modeling this system, the multiplicity and bi-threshold behavior are not theoretically possible according to the designed interactions. In order to explain this discrepancy, we hypothesize that one or more of the repressors has a significant nonspecific interaction with a promoter that does not contain its operator site. The new modeling results incorporating these extra interactions qualitatively match our experimental findings. After constructing plasmids to test these hypotheses, we discover that at least four of these interactions exist, which can create the low and high states and multiplicity seen experimentally. This genetic architecture has flexibility in its behavior that has not been demonstrated before, and the combination of experiment and modeling enlightened our understanding of the molecular interactions driving the network's behavior, leading us to discover the significance of

  15. 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

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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...

  1. Tinnitus and hyperacusis involve hyperactivity and enhanced connectivity in auditory-limbic-arousal-cerebellar network.

    PubMed

    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. PMID:25962854

  2. Optimization of geometry and modeling parameters of artificial neural networks using genetic algorithms

    NASA Astrophysics Data System (ADS)

    Sahoo, G. B.

    2007-12-01

    In recent years, artificial neural networks (ANNs) appear to be viable alternative to models that use phenomenological hypotheses (i.e. knowledge based models) for cases (1) the available data are not detailed and sufficient for using a process based model and (2) the detailed complex physics of the system is partially understood. ANNs have been widely used in many fields such as chemical and environmental engineering, hydrology, and water resources applications for optimum prediction of system parameters and variables. However, in most cases, parameters and system variables were forecasted employing suboptimal ANNs. The geometry and modeling parameters of an artificial neural network (ANN) and the training dataset have significant effects on its predictive performance efficiency. The combination of ANN modeling parameter and geometry arranged in the modeling domain (i.e. lower and upper bounds of each modeling parameter and geometry) is large enough (i.e. greater than 100000) that it is difficult to examine all cases using trial and error approach for the selection of an optimum set. Thus, one could easily end up with finding a set of suboptimal values. This study presents the use of genetic algorithms (GAs) to search for the optimal geometry and values of modeling parameters of a multilayer feedforward backpropagation neural network (BPNN) and a radial basis function network (RBFN). The predictive performance efficiency of the GA and ANN combination is examined using two datasets derived from the same population for training. It is illustrated that (1) the GA optimized ANN outperforms to the ANN using a trial and error approach, and (2) ANN predictive performance and geometry depend on the number of samples and the characteristics of samples included in the training dataset.

  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. 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

    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

  5. Identification of Protein Networks Involved in the Disease Course of Experimental Autoimmune Encephalomyelitis, an Animal Model of Multiple Sclerosis

    PubMed Central

    Plaisance, Stéphane; Baeten, Kurt; Hendriks, Jerome J. A.; Leprince, Pierre; Dumont, Debora; Robben, Johan; Brône, Bert; Stinissen, Piet; Noben, Jean-Paul; Hellings, Niels

    2012-01-01

    A more detailed insight into disease mechanisms of multiple sclerosis (MS) is crucial for the development of new and more effective therapies. MS is a chronic inflammatory autoimmune disease of the central nervous system. The aim of this study is to identify novel disease associated proteins involved in the development of inflammatory brain lesions, to help unravel underlying disease processes. Brainstem proteins were obtained from rats with MBP induced acute experimental autoimmune encephalomyelitis (EAE), a well characterized disease model of MS. Samples were collected at different time points: just before onset of symptoms, at the top of the disease and following recovery. To analyze changes in the brainstem proteome during the disease course, a quantitative proteomics study was performed using two-dimensional difference in-gel electrophoresis (2D-DIGE) followed by mass spectrometry. We identified 75 unique proteins in 92 spots with a significant abundance difference between the experimental groups. To find disease-related networks, these regulated proteins were mapped to existing biological networks by Ingenuity Pathway Analysis (IPA). The analysis revealed that 70% of these proteins have been described to take part in neurological disease. Furthermore, some focus networks were created by IPA. These networks suggest an integrated regulation of the identified proteins with the addition of some putative regulators. Post-synaptic density protein 95 (DLG4), a key player in neuronal signalling and calcium-activated potassium channel alpha 1 (KCNMA1), involved in neurotransmitter release, are 2 putative regulators connecting 64% of the identified proteins. Functional blocking of the KCNMA1 in macrophages was able to alter myelin phagocytosis, a disease mechanism highly involved in EAE and MS pathology. Quantitative analysis of differentially expressed brainstem proteins in an animal model of MS is a first step to identify disease-associated proteins and networks that

  6. Cold header machine process monitoring using a genetic algorithm designed neural network approach

    NASA Astrophysics Data System (ADS)

    dos Reis, Henrique L. M.; Voegele, Aaron C.; Cook, David B.

    1999-12-01

    In cold heading manufacturing processes, complete or partial fracture of the punch-pin leads to production of out-of-tolerance parts. A process monitoring system has been developed to assure that out-of-tolerance parts do not contaminate the batch of acceptable parts. A four-channel data acquisition system was assembled to collect and store the acoustic signal generated during the manufacturing process. A genetic algorithm was designed to select the smallest subset of waveform features necessary to develop a robust artificial neural network that could differentiate among the various cold head machine conditions, including complete or partial failure of the punch pin. The developed monitoring system is able to terminate production within seconds of punch pin failure using only four waveform features.

  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. Coexpression Network Analysis in Abdominal and Gluteal Adipose Tissue Reveals Regulatory Genetic Loci for Metabolic Syndrome and Related Phenotypes

    PubMed Central

    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 (DABD-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

  10. 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…

  11. Optimization of LPDC Process Parameters Using the Combination of Artificial Neural Network and Genetic Algorithm Method

    NASA Astrophysics Data System (ADS)

    Zhang, Liqiang; Li, Luoxing; Wang, Shiuping; Zhu, Biwu

    2012-04-01

    In this article, the low-pressure die-cast (LPDC) process parameters of aluminum alloy thin-walled component with permanent mold are optimized using a combining artificial neural network and genetic algorithm (ANN/GA) method. In this method, an ANN model combining learning vector quantization (LVQ) and back-propagation (BP) algorithm is proposed to map the complex relationship between process conditions and quality indexes of LPDC. The genetic algorithm is employed to optimize the process parameters with the fitness function based on the trained ANN model. Then, by applying the optimized parameters, a thin-walled component with 300 mm in length, 100 mm in width, and 1.5 mm in thickness is successfully prepared and no obvious defects such as shrinkage, gas porosity, distortion, and crack were found in the component. The results indicate that the combining ANN/GA method is an effective tool for the process optimization of LPDC, and they also provide valuable reference on choosing the right process parameters for LPDC thin-walled aluminum alloy casting.

  12. 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

  13. 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

  14. 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.

  15. Multi-input CRISPR/Cas genetic circuits that interface host regulatory networks.

    PubMed

    Nielsen, Alec A K; 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. Enhanced antibiotic production by Streptomyces sindenensis using artificial neural networks coupled with genetic algorithm and Nelder-Mead downhill simplex.

    PubMed

    Tripathi, C K M; Khan, Mahvish; Praveen, Vandana; Khan, Saif; Srivastava, Akanksha

    2012-07-01

    Antibiotic production with Streptomyces sindenensis MTCC 8122 was optimized under submerged fermentation conditions by artificial neural network (ANN) coupled with genetic algorithm (GA) and Nelder-Mead downhill simplex (NMDS). Feed forward back-propagation ANN was trained to establish the mathematical relationship among the medium components and length of incubation period for achieving maximum antibiotic yield. The optimization strategy involved growing the culture with varying concentrations of various medium components for different incubation periods. Under non-optimized condition, antibiotic production was found to be 95 microgram/ml, which nearly doubled (176 microgram/ml) with the ANN-GA optimization. ANN-NMDS optimization was found to be more efficacious, and maximum antibiotic production (197 microgram/ml) was obtained by cultivating the cells with (g/l) fructose 2.7602, MgSO4 1.2369, (NH4)2PO4 0.2742, DL-threonine 3.069%, and soyabean meal 1.952%, for 9.8531 days of incubation, which was roughly 12% higher than the yield obtained by ANN coupled with GA under the same conditions. PMID:22580313

  17. Multiplexed visualization of dynamic signaling networks using genetically encoded fluorescent protein-based biosensors

    PubMed Central

    Depry, Charlene; Mehta, Sohum; Zhang, Jin

    2012-01-01

    Cells rely on a complex, interconnected network of signaling pathways to sense and interpret changes in their extracellular environment. The development of genetically encoded fluorescent protein (FP)-based biosensors has made it possible for researchers to directly observe and characterize the spatiotemporal dynamics of these intracellular signaling pathways in living cells. However, detailed information regarding the precise temporal and spatial relationships between intersecting pathways is often lost when individual signaling events are monitored in isolation. As the development of biosensor technology continues to advance, it is becoming increasingly feasible to image multiple FP-based biosensors concurrently, permitting greater insights into the intricate coordination of intracellular signaling networks by enabling parallel monitoring of distinct signaling events within the same cell. In this review, we discuss several strategies for multiplexed imaging of FP-based biosensors, while also underscoring some of the challenges associated with these techniques and highlighting additional avenues that could lead to further improvements in parallel monitoring of intracellular signaling events. PMID:23138230

  18. 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.

  19. 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.

  20. Shared signaling networks active in B cells isolated from genetically distinct mouse models of lupus

    PubMed Central

    Wu, Tianfu; Qin, Xiangmei; Kurepa, Zoran; Kumar, Kirthi Raman; Liu, Kui; Kanta, Hasna; Zhou, Xin J.; Satterthwaite, Anne B.; Davis, Laurie S.; Mohan, Chandra

    2007-01-01

    Though B cells play key roles in lupus pathogenesis, the molecular circuitry and its dysregulation in these cells as disease evolves remain poorly understood. To address this, a comprehensive scan of multiple signaling axes using multiplexed Western blotting was undertaken in several different murine lupus strains. PI3K/AKT/mTOR (mTOR, mammalian target of rapamycin), MEK1/Erk1/2, p38, NF-κB, multiple Bcl-2 family members, and cell-cycle molecules were observed to be hyperexpressed in lupus B cells in an age-dependent and lupus susceptibility gene–dose–dependent manner. Therapeutic targeting of the AKT/mTOR axis using a rapamycin (sirolimus) derivative ameliorated the serological, cellular, and pathological phenotypes associated with lupus. Surprisingly, the targeting of this axis was associated with the crippling of several other signaling axes. These studies reveal that lupus pathogenesis is contingent upon the activation of an elaborate network of signaling cascades that is shared among genetically distinct mouse models and raise hope that targeting pivotal nodes in these networks may offer therapeutic benefit. PMID:17641780

  1. Use of genetic algorithms and neural networks to optimize well locations and reduce well requirements

    SciTech Connect

    Johnson, V.M.; Rogers, L.L.

    1994-09-01

    A goal common to both the environmental and petroleum industries is the reduction of costs and/or enhancement of profits by the optimal placement of extraction/production and injection wells. Formal optimization techniques facilitate this goal by searching among the potentially infinite number of possible well patterns for ones that best meet engineering and economic objectives. However, if a flow and transport model or reservoir simulator is being used to evaluate the effectiveness of each network of wells, the computational resources required to apply most optimization techniques to real field problems become prohibitively expensive. This paper describes a new approach to field-scale, nonlinear optimization of well patterns that is intended to make such searches tractable on conventional computer equipment. Artificial neural networks (ANNs) are trained to predict selected information that would normally be calculated by the simulator. The ANNs are then embedded in a variant of the genetic algorithm (GA), which drives the search for increasingly effective well patterns and uses the ANNs, rather than the original simulator, to evaluate the effectiveness of each pattern. Once the search is complete, the ANNs are reused in sensitivity studies to give additional information on the performance of individual or clusters of wells.

  2. 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

  3. Robust control of uncertain nonlinear switched genetic regulatory networks with time delays: A redesign approach.

    PubMed

    Moradi, Hojjatullah; Majd, Vahid Johari

    2016-05-01

    In this paper, the problem of robust stability of nonlinear genetic regulatory networks (GRNs) is investigated. The developed method is an integral sliding mode control based redesign for a class of perturbed dissipative switched GRNs with time delays. The control law is redesigned by modifying the dissipativity-based control law that was designed for the unperturbed GRNs with time delays. The switched GRNs are switched from one mode to another based on time, state, etc. Although, the active subsystem is known in any instance, but the switching law and the transition probabilities are not known. The model for each mode is considered affine with matched and unmatched perturbations. The redesigned control law forces the GRN to always remain on the sliding surface and the dissipativity is maintained from the initial time in the presence of the norm-bounded perturbations. The global stability of the perturbed GRNs is maintained if the unperturbed model is globally dissipative. The designed control law for the perturbed GRNs guarantees robust exponential or asymptotic stability of the closed-loop network depending on the type of stability of the unperturbed model. The results are applied to a nonlinear switched GRN, and its convergence to the origin is verified by simulation. PMID:26924600

  4. 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,…

  5. 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

  6. 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

  7. 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. PMID:23818133

  8. 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

  9. 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. PMID:26001771

  10. Biochemical and genetic characterization of Hmi1p, a yeast DNA helicase involved in the maintenance of mitochondrial DNA.

    PubMed

    Monroe, Danny S; Leitzel, Adelaide K; Klein, Hannah L; Matson, Steven W

    2005-12-01

    The HMI1 gene encodes a DNA helicase that localizes to the mitochondria and is required for maintenance of the mitochondrial DNA (mtDNA) genome of Saccharomyces cerevisiae. Identified based on its homology with E. coli uvrD, the HMI1 gene product, Hmi1p, has been presumed to be involved in the replication of the 80 kb linear S. cerevisiae mtDNA genome. Here we report the purification of Hmi1p to apparent homogeneity and provide a characterization of the helicase reaction and the ATPase reaction with regard to NTP preference, divalent cation preference and the stimulatory effects of different nucleic acids on Hmi1p-catalysed ATPase activity. Genetic complementation assays indicate that mitochondrial localization of Hmi1p is essential for its role in mtDNA metabolism. The helicase activity, however, is not essential. Point mutants that lack ATPase/helicase activity partially complement a strain lacking Hmi1p. We suggest several possible roles for Hmi1p in mtDNA metabolism. PMID:16358299

  11. 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

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

    PubMed

    Liang, Yulan; Kelemen, Arpad

    2016-08-01

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

  13. Automatic Compilation from High-Level Biologically-Oriented Programming Language to Genetic Regulatory Networks

    PubMed Central

    Beal, Jacob; Lu, Ting; Weiss, Ron

    2011-01-01

    Background The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. Methodology/Principal Findings To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes () and latency of the optimized engineered gene networks. Conclusions/Significance Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems. PMID:21850228

  14. 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.

  15. A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

    PubMed Central

    Tahmasebi, Pejman; Hezarkhani, Ardeshir

    2012-01-01

    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. PMID:25540468

  16. 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.

  17. 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). PMID:22533413

  18. [Sporulation or competence development? A genetic regulatory network model of cell-fate determination in Bacillus subtilis].

    PubMed

    Lu, Zhenghui; Zhou, Yuling; Zhang, Xiaozhou; Zhang, Guimin

    2015-11-01

    Bacillus subtilis is a generally recognized as safe (GRAS) strain that has been widely used in industries including fodder, food, and biological control. In addition, B. subtilis expression system also plays a significant role in the production of industrial enzymes. However, its application is limited by its low sporulation frequency and transformation efficiency. Immense studies have been done on interpreting the molecular mechanisms of sporulation and competence development, whereas only few of them were focused on improving sporulation frequency and transformation efficiency of B. subtilis by genetic modification. The main challenge is that sporulation and competence development, as the two major developmental events in the stationary phase of B. subtilis, are regulated by the complicated intracellular genetic regulatory systems. In addition, mutual regulatory mechanisms also exist in these two developmental events. With the development of genetic and metabolic engineering, constructing genetic regulatory networks is currently one of the most attractive research fields, together with the genetic information of cell growth, metabolism, and development, to guide the industrial application. In this review, the mechanisms of sporulation and competence development of B. subtilis, their interactions, and the genetic regulation of cell growth were interpreted. In addition, the roles of these regulatory networks in guiding basic and applied research of B. subtilis and its related species were discussed. PMID:26939438

  19. Proteomic and biochemical analyses show a functional network of proteins involved in antioxidant defense of the Arabidopsis anp2anp3 double mutant.

    PubMed

    Takáč, Tomáš; Šamajová, Olga; Vadovič, Pavol; Pechan, Tibor; Košútová, Petra; Ovečka, Miroslav; Husičková, Alexandra; Komis, George; Šamaj, Jozef

    2014-12-01

    Disentanglement of functional complexity associated with plant mitogen-activated protein kinase (MAPK) signaling has benefited from transcriptomic, proteomic, phosphoproteomic, and genetic studies. Published transcriptomic analysis of a double homozygous recessive anp2anp3 mutant of two MAPK kinase kinase (MAPKKK) genes called Arabidopsis thaliana Homologues of Nucleus- and Phragmoplast-localized Kinase 2 (ANP2) and 3 (ANP3) showed the upregulation of stress-related genes. In this study, a comparative proteomic analysis of anp2anp3 mutant against its respective Wassilevskaja ecotype (Ws) wild type background is provided. Such differential proteomic analysis revealed overabundance of core enzymes such as FeSOD1, MnSOD, DHAR1, and FeSOD1-associated regulatory protein CPN20, which are involved in the detoxification of reactive oxygen species in the anp2anp3 mutant. The proteomic results were validated at the level of single protein abundance by Western blot analyses and by quantitative biochemical determination of antioxidant enzymatic activities. Finally, the functional network of proteins involved in antioxidant defense in the anp2anp3 mutant was physiologically linked with the increased resistance of mutant seedlings against paraquat treatment. PMID:25325904

  20. Fine-scale population structure and riverscape genetics of brook trout (Salvelinus fontinalis) distributed continuously along headwater channel networks

    USGS Publications Warehouse

    Kanno, Yoichiro; Vokoun, Jason C.; Letcher, Benjamin H.

    2011-01-01

    Linear and heterogeneous habitat makes headwater stream networks an ideal ecosystem in which to test the influence of environmental factors on spatial genetic patterns of obligatory aquatic species. We investigated fine-scale population structure and influence of stream habitat on individual-level genetic differentiation in brook trout (Salvelinus fontinalis) by genotyping eight microsatellite loci in 740 individuals in two headwater channel networks (7.7 and 4.4 km) in Connecticut, USA. A weak but statistically significant isolation-by-distance pattern was common in both sites. In the field, many tagged individuals were recaptured in the same 50-m reaches within a single field season (summer to fall). One study site was characterized with a hierarchical population structure, where seasonal barriers (natural falls of 1.5–2.5 m in height during summer base-flow condition) greatly reduced gene flow and perceptible spatial patterns emerged because of the presence of tributaries, each with a group of genetically distinguishable individuals. Genetic differentiation increased when pairs of individuals were separated by high stream gradient (steep channel slope) or warm stream temperature in this site, although the evidence of their influence was equivocal. In a second site, evidence for genetic clusters was weak at best, but genetic differentiation between individuals was positively correlated with number of tributary confluences. We concluded that the population-level movement of brook trout was limited in the study headwater stream networks, resulting in the fine-scale population structure (genetic clusters and clines) even at distances of a few kilometres, and gene flow was mitigated by ‘riverscape’ variables, particularly by physical barriers, waterway distance (i.e. isolation-by-distance) and the presence of tributaries.

  1. 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

  2. Genetic architecture of wood properties based on association analysis and co-expression networks in white spruce.

    PubMed

    Lamara, Mebarek; Raherison, Elie; Lenz, Patrick; Beaulieu, Jean; Bousquet, Jean; MacKay, John

    2016-04-01

    Association studies are widely utilized to analyze complex traits but their ability to disclose genetic architectures is often limited by statistical constraints, and functional insights are usually minimal in nonmodel organisms like forest trees. We developed an approach to integrate association mapping results with co-expression networks. We tested single nucleotide polymorphisms (SNPs) in 2652 candidate genes for statistical associations with wood density, stiffness, microfibril angle and ring width in a population of 1694 white spruce trees (Picea glauca). Associations mapping identified 229-292 genes per wood trait using a statistical significance level of P < 0.05 to maximize discovery. Over-representation of genes associated for nearly all traits was found in a xylem preferential co-expression group developed in independent experiments. A xylem co-expression network was reconstructed with 180 wood associated genes and several known MYB and NAC regulators were identified as network hubs. The network revealed a link between the gene PgNAC8, wood stiffness and microfibril angle, as well as considerable within-season variation for both genetic control of wood traits and gene expression. Trait associations were distributed throughout the network suggesting complex interactions and pleiotropic effects. Our findings indicate that integration of association mapping and co-expression networks enhances our understanding of complex wood traits. PMID:26619072

  3. The rural-urban effect on spatial genetic structure of type II Toxoplasma gondii strains involved in human congenital toxoplasmosis, France, 2002-2009.

    PubMed

    Ajzenberg, Daniel; Collinet, Frédéric; Aubert, Dominique; Villena, Isabelle; Dardé, Marie-Laure; Devillard, Sébastien

    2015-12-01

    Congenital toxoplasmosis involves Toxoplasma gondii type II strains in 95% of cases in France. We used spatial principal component analysis (sPCA) and 15 microsatellite markers to investigate the spatial genetic structure of type II strains involved in 240 cases of congenital toxoplasmosis in France from 2002 through 2009. Mailing addresses of patients were geo-referenced a posteriori in decimal degrees and categorized into urban or rural areas of residence. No spatial genetic structure was found for type II strains that infected mothers who were living in urban areas, but a global spatial genetic structure was found for those that infected mothers who were living in a rural environment. Our results suggest that sources of infection by T. gondii are different in rural and urban areas in France, and advocate for targeted messages in the prevention of toxoplasmosis according to the type of residence of susceptible people. PMID:26305624

  4. MP-GeneticSynth: inferring biological network regulations from time series.

    PubMed

    Castellini, Alberto; Paltrinieri, Daniele; Manca, Vincenzo

    2015-03-01

    MP-GeneticSynth is a Java tool for discovering the logic and regulation mechanisms responsible for observed biological dynamics in terms of finite difference recurrent equations. The software makes use of: (i) metabolic P systems as a modeling framework, (ii) an evolutionary approach to discover flux regulation functions as linear combinations of given primitive functions, (iii) a suitable reformulation of the least squares method to estimate function parameters considering simultaneously all the reactions involved in complex dynamics. The tool is available as a plugin for the virtual laboratory MetaPlab. It has graphical and interactive interfaces for data preparation, a priori knowledge integration, and flux regulator analysis. Availability and implementation: Source code, binaries, documentation (including quick start guide and videos) and case studies are freely available at http://mplab.sci.univr.it/plugins/mpgs/index.html. PMID:25344496

  5. Genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The genus Capsicum represents one of several well characterized Solanaceous genera. A wealth of classical and molecular genetics research is available for the genus. Information gleaned from its cultivated relatives, tomato and potato, provide further insight for basic and applied studies. Early ...

  6. Genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Maintaining genetic variation in wild populations of Arctic organisms is fundamental to the long-term persistence of high latitude biodiversity. Variability is important because it provides options for species to respond to changing environmental conditions and novel challenges such as emerging path...

  7. Network Biomarkers of Bladder Cancer Based on a Genome-Wide Genetic and Epigenetic Network Derived from Next-Generation Sequencing Data

    PubMed Central

    Li, Cheng-Wei

    2016-01-01

    Epigenetic and microRNA (miRNA) regulation are associated with carcinogenesis and the development of cancer. By using the available omics data, including those from next-generation sequencing (NGS), genome-wide methylation profiling, candidate integrated genetic and epigenetic network (IGEN) analysis, and drug response genome-wide microarray analysis, we constructed an IGEN system based on three coupling regression models that characterize protein-protein interaction networks (PPINs), gene regulatory networks (GRNs), miRNA regulatory networks (MRNs), and epigenetic regulatory networks (ERNs). By applying system identification method and principal genome-wide network projection (PGNP) to IGEN analysis, we identified the core network biomarkers to investigate bladder carcinogenic mechanisms and design multiple drug combinations for treating bladder cancer with minimal side-effects. The progression of DNA repair and cell proliferation in stage 1 bladder cancer ultimately results not only in the derepression of miR-200a and miR-200b but also in the regulation of the TNF pathway to metastasis-related genes or proteins, cell proliferation, and DNA repair in stage 4 bladder cancer. We designed a multiple drug combination comprising gefitinib, estradiol, yohimbine, and fulvestrant for treating stage 1 bladder cancer with minimal side-effects, and another multiple drug combination comprising gefitinib, estradiol, chlorpromazine, and LY294002 for treating stage 4 bladder cancer with minimal side-effects. PMID:27034531

  8. Network Biomarkers of Bladder Cancer Based on a Genome-Wide Genetic and Epigenetic Network Derived from Next-Generation Sequencing Data.

    PubMed

    Li, Cheng-Wei; Chen, Bor-Sen

    2016-01-01

    Epigenetic and microRNA (miRNA) regulation are associated with carcinogenesis and the development of cancer. By using the available omics data, including those from next-generation sequencing (NGS), genome-wide methylation profiling, candidate integrated genetic and epigenetic network (IGEN) analysis, and drug response genome-wide microarray analysis, we constructed an IGEN system based on three coupling regression models that characterize protein-protein interaction networks (PPINs), gene regulatory networks (GRNs), miRNA regulatory networks (MRNs), and epigenetic regulatory networks (ERNs). By applying system identification method and principal genome-wide network projection (PGNP) to IGEN analysis, we identified the core network biomarkers to investigate bladder carcinogenic mechanisms and design multiple drug combinations for treating bladder cancer with minimal side-effects. The progression of DNA repair and cell proliferation in stage 1 bladder cancer ultimately results not only in the derepression of miR-200a and miR-200b but also in the regulation of the TNF pathway to metastasis-related genes or proteins, cell proliferation, and DNA repair in stage 4 bladder cancer. We designed a multiple drug combination comprising gefitinib, estradiol, yohimbine, and fulvestrant for treating stage 1 bladder cancer with minimal side-effects, and another multiple drug combination comprising gefitinib, estradiol, chlorpromazine, and LY294002 for treating stage 4 bladder cancer with minimal side-effects. PMID:27034531

  9. Optimisation of groundwater level monitoring networks using geostatistical modelling based on the Spartan family variogram and a genetic algorithm method

    NASA Astrophysics Data System (ADS)

    Parasyris, Antonios E.; Spanoudaki, Katerina; Kampanis, Nikolaos A.

    2016-04-01

    Groundwater level monitoring networks provide essential information for water resources management, especially in areas with significant groundwater exploitation for agricultural and domestic use. Given the high maintenance costs of these networks, development of tools, which can be used by regulators for efficient network design is essential. In this work, a monitoring network optimisation tool is presented. The network optimisation tool couples geostatistical modelling based on the Spartan family variogram with a genetic algorithm method and is applied to Mires basin in Crete, Greece, an area of high socioeconomic and agricultural interest, which suffers from groundwater overexploitation leading to a dramatic decrease of groundwater levels. The purpose of the optimisation tool is to determine which wells to exclude from the monitoring network because they add little or no beneficial information to groundwater level mapping of the area. Unlike previous relevant investigations, the network optimisation tool presented here uses Ordinary Kriging with the recently-established non-differentiable Spartan variogram for groundwater level mapping, which, based on a previous geostatistical study in the area leads to optimal groundwater level mapping. Seventy boreholes operate in the area for groundwater abstraction and water level monitoring. The Spartan variogram gives overall the most accurate groundwater level estimates followed closely by the power-law model. The geostatistical model is coupled to an integer genetic algorithm method programmed in MATLAB 2015a. The algorithm is used to find the set of wells whose removal leads to the minimum error between the original water level mapping using all the available wells in the network and the groundwater level mapping using the reduced well network (error is defined as the 2-norm of the difference between the original mapping matrix with 70 wells and the mapping matrix of the reduced well network). The solution to the

  10. Characteristics of Transcriptional Activity in Nonlinear Dynamics of Genetic Regulatory Networks

    PubMed Central

    Rosenfeld, Simon

    2009-01-01

    Microarray measurements of mRNA abundances is a standard tool for evaluation of transcriptional activity in functional genomics. The methodology underlying these measurements assumes existence of a direct link between transcription levels, that is, gene-specific mRNA copy numbers present in the cell, and transcription rates, that is, the numbers of gene-specific mRNA molecules synthesized per unit of time. In this paper, the question of whether or not such a tight interdependence may exist is examined in the context of nonlinear dynamics of genetic regulatory networks. Using the equations of chemical kinetics, a model has been constructed that is capable of explicitly taking into consideration nonlinear interactions between the genes through the teamwork of transcription factors. Jacobian analysis of stability has shown that steady state equilibrium is impossible in such systems. However, phase space compressibility is found to be negative, thus suggesting that asymptotic stability may exist and assume either the form of limit cycle or of a chaotic attractor. It is argued that in rapidly fluctuating or chaotic systems, direct evaluation of transcription rates through transcription levels is highly problematic. It is also noted that even if a hypothetical steady state did exist, the knowledge of transcription levels alone would not be sufficient for the evaluation of transcription rates; an additional set of parameters, namely the mRNA decay rates, would be required. An overall conclusion of the work is that the measurements of mRNA abundances are not truly representative of the functionality of genes and structural fidelity of the genetic codes. PMID:20054406

  11. A UV–Induced Genetic Network Links the RSC Complex to Nucleotide Excision Repair and Shows Dose-Dependent Rewiring

    PubMed Central

    Srivas, Rohith; Costelloe, Thomas; Carvunis, Anne-Ruxandra; Sarkar, Sovan; Malta, Erik; Sun, Su Ming; Pool, Marijke; Licon, Katherine; van Welsem, Tibor; van Leeuwen, Fred; McHugh, Peter J.; van Attikum, Haico; Ideker, Trey

    2014-01-01

    SUMMARY Efficient repair of UV-induced DNA damage requires the precise coordination of nucleotide excision repair (NER) with numerous other biological processes. To map this crosstalk, we generated a differential genetic interaction map centered on quantitative growth measurements of >45,000 double mutants before and after different doses of UV radiation. Integration of genetic data with physical interaction networks identified a global map of 89 UV-induced functional interactions amongst 62 protein complexes, including a number of links between the RSC complex and several NER factors. We show that RSC is recruited to both silenced and transcribed loci following UV damage where it facilitates efficient repair by promoting nucleosome remodeling. Finally, a comparison of the response to high versus low levels of UV shows that the degree of genetic rewiring correlates with dose of UV and reveals a network of dose-specific interactions. This study makes available a large resource of UV-induced interactions, and it illustrates a methodology for identifying dose-dependent interactions based on quantitative shifts in genetic networks. PMID:24360959

  12. Genuine genetic redundancy in maleylacetate-reductase-encoding genes involved in degradation of haloaromatic compounds by Cupriavidus necator JMP134.

    PubMed

    Pérez-Pantoja, Danilo; Donoso, Raúl A; Sánchez, Miguel A; González, Bernardo

    2009-11-01

    Maleylacetate reductases (MAR) are required for biodegradation of several substituted aromatic compounds. To date, the functionality of two MAR-encoding genes (tfdF(I) and tfdF(II)) has been reported in Cupriavidus necator JMP134(pJP4), a known degrader of aromatic compounds. These two genes are located in tfd gene clusters involved in the turnover of 2,4-dichlorophenoxyacetate (2,4-D) and 3-chlorobenzoate (3-CB). The C. necator JMP134 genome comprises at least three other genes that putatively encode MAR (tcpD, hqoD and hxqD), but confirmation of their functionality and their role in the catabolism of haloaromatic compounds has not been assessed. RT-PCR expression analyses of C. necator JMP134 cells exposed to 2,4-D, 3-CB, 2,4,6-trichlorophenol (2,4,6-TCP) or 4-fluorobenzoate (4-FB) showed that tfdF(I) and tfdF(II) are induced by haloaromatics channelled to halocatechols as intermediates. In contrast, 2,4,6-TCP only induces tcpD, and any haloaromatic compounds tested did not induce hxqD and hqoD. However, the tcpD, hxqD and hqoD gene products showed MAR activity in cell extracts and provided the MAR function for 2,4-D catabolism when heterologously expressed in MAR-lacking strains. Growth tests for mutants of the five MAR-encoding genes in strain JMP134 showed that none of these genes is essential for degradation of the tested compounds. However, the role of tfdF(I)/tfdF(II) and tcpD genes in the expression of MAR activity during catabolism of 2,4-D and 2,4,6-TCP, respectively, was confirmed by enzyme activity tests in mutants. These results reveal a striking example of genetic redundancy in the degradation of aromatic compounds. PMID:19684066

  13. Genetic mechanisms involved in the evolution of the cephalopod camera eye revealed by transcriptomic and developmental studies

    PubMed Central

    2011-01-01

    Background Coleoid cephalopods (squids and octopuses) have evolved a camera eye, the structure of which is very similar to that found in vertebrates and which is considered a classic example of convergent evolution. Other molluscs, however, possess mirror, pin-hole, or compound eyes, all of which differ from the camera eye in the degree of complexity of the eye structures and neurons participating in the visual circuit. Therefore, genes expressed in the cephalopod eye after divergence from the common molluscan ancestor could be involved in eye evolution through association with the acquisition of new structural components. To clarify the genetic mechanisms that contributed to the evolution of the cephalopod camera eye, we applied comprehensive transcriptomic analysis and conducted developmental validation of candidate genes involved in coleoid cephalopod eye evolution. Results We compared gene expression in the eyes of 6 molluscan (3 cephalopod and 3 non-cephalopod) species and selected 5,707 genes as cephalopod camera eye-specific candidate genes on the basis of homology searches against 3 molluscan species without camera eyes. First, we confirmed the expression of these 5,707 genes in the cephalopod camera eye formation processes by developmental array analysis. Second, using molecular evolutionary (dN/dS) analysis to detect positive selection in the cephalopod lineage, we identified 156 of these genes in which functions appeared to have changed after the divergence of cephalopods from the molluscan ancestor and which contributed to structural and functional diversification. Third, we selected 1,571 genes, expressed in the camera eyes of both cephalopods and vertebrates, which could have independently acquired a function related to eye development at the expression level. Finally, as experimental validation, we identified three functionally novel cephalopod camera eye genes related to optic lobe formation in cephalopods by in situ hybridization analysis of

  14. Modulation of monoamine oxidase (MAO) expression in neuropsychiatric disorders: genetic and environmental factors involved in type A MAO expression.

    PubMed

    Naoi, Makoto; Riederer, Peter; Maruyama, Wakako

    2016-02-01

    -A activity may increase the levels of serotonin and norepinephrine, resulting in disturbed neurotransmitter system development and behavior. This review discusses genetic and environmental factors involved in the regulation of MAO-A expression, in the contexts of neuropsychiatric function and of the regulation of neuronal survival and death. PMID:25604428

  15. Neonatal Odor-Shock Conditioning Alters the Neural Network Involved in Odor Fear Learning at Adulthood

    ERIC Educational Resources Information Center

    Sevelinges, Yannick; Sullivan, Regina M.; Messaoudi, Belkacem; Mouly, Anne-Marie

    2008-01-01

    Adult learning and memory functions are strongly dependent on neonatal experiences. We recently showed that neonatal odor-shock learning attenuates later life odor fear conditioning and amygdala activity. In the present work we investigated whether changes observed in adults can also be observed in other structures normally involved, namely…

  16. Involvement of the mentalizing network in social and non-social high construal

    PubMed Central

    Ma, Ning; Steen, Johan; Van Overwalle, Frank

    2014-01-01

    The dorsomedial prefrontal cortex (dmPFC) is consistently involved in tasks requiring the processing of mental states, and much rarer so by tasks that do not involve mental state inferences. We hypothesized that the dmPFC might be more generally involved in high construal of stimuli, defined as the formation of concepts or ideas by omitting non-essential features of stimuli, irrespective of their social or non-social nature. In an fMRI study, we presented pictures of a person engaged in everyday activities (social stimuli) or of objects (non-social stimuli) and induced a higher level of construal by instructing participants to generate personality traits of the person or categories to which the objects belonged. This was contrasted against a lower level task where participants had to describe these same pictures visually. As predicted, we found strong involvement of the dmPFC in high construal, with substantial overlap across social and non-social stimuli, including shared activation in the vmPFC/OFC, parahippocampal, fusiform and angular gyrus, precuneus, posterior cingulate and right cerebellum. PMID:23552077

  17. Social Disparity of Family Involvement in Hong Kong: Effect of Family Resources and Family Network

    ERIC Educational Resources Information Center

    Ho, Esther Sui-Chu

    2006-01-01

    Using data from Programme for International Student Assessment (PISA) developed by the Organisation for Economic Co-operation and Development (OECD), this study examines the social disparity of family involvement. A total of 4,405 students from 140 Hong Kong secondary schools participated in the first cycle of PISA study identifying four types of…

  18. Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data

    PubMed Central

    2014-01-01

    Background Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information. Methods Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome. Results Better cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings. Conclusions This study shows that if prediction accuracy is the objective, the GA

  19. Neural networks involved in learning lexical-semantic and syntactic information in a second language

    PubMed Central

    Mueller, Jutta L.; Rueschemeyer, Shirley-Ann; Ono, Kentaro; Sugiura, Motoaki; Sadato, Norihiro; Nakamura, Akinori

    2014-01-01

    The present study used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of language acquisition in a realistic learning environment. Japanese native speakers were trained in a miniature version of German prior to fMRI scanning. During scanning they listened to (1) familiar sentences, (2) sentences including a novel sentence structure, and (3) sentences containing a novel word while visual context provided referential information. Learning-related decreases of brain activation over time were found in a mainly left-hemispheric network comprising classical frontal and temporal language areas as well as parietal and subcortical regions and were largely overlapping for novel words and the novel sentence structure in initial stages of learning. Differences occurred at later stages of learning during which content-specific activation patterns in prefrontal, parietal and temporal cortices emerged. The results are taken as evidence for a domain-general network supporting the initial stages of language learning which dynamically adapts as learners become proficient. PMID:25400602

  20. Design of Polymer Networks Involving a Photoinduced Electronic Transmission Circuit toward Artificial Photosynthesis.

    PubMed

    Okeyoshi, Kosuke; Kawamura, Ryuzo; Yoshida, Ryo; Osada, Yoshihito

    2016-01-19

    Many strategies have been explored to achieve artificial photosynthesis utilizing mediums such as liposomes and supramolecules. Because the photochemical reaction is composed of multiple functional molecules, the surrounding microenvironment is expected to be rationally integrated as observed during photosynthesis in chloroplasts. In this study, photoinduced electronic transmission surrounding the microenvironment of Ru(bpy)3(2+) in a polymer network was investigated using poly(N-isopropylacrylamide-co-Ru(bpy)3), poly(acrylamide-co-Ru(bpy)3), and Ru(bpy)3-conjugated microtubules. Photoinduced energy conversion was evaluated by investigating the effects of (i) Ru(bpy)3(2+) immobilization, (ii) polymer type, (iii) thermal energy, and (iv) cross-linking. The microenvironment surrounding copolymerized Ru(bpy)3(2+) in poly(N-isopropylacrylamide) suppressed quenching and had a higher radiative process energy than others. This finding is related to the nonradiative process, i.e., photoinduced H2 generation with significantly higher overall quantum efficiency (13%) than for the bulk solution. We envision that useful molecules will be generated by photoinduced electronic transmission in polymer networks, resulting in the development of a wide range of biomimetic functions with applications for a sustainable society. PMID:26735211

  1. EDENetworks: a user-friendly software to build and analyse networks in biogeography, ecology and population genetics.

    PubMed

    Kivelä, Mikko; Arnaud-Haond, Sophie; Saramäki, Jari

    2015-01-01

    The recent application of graph-based network theory analysis to biogeography, community ecology and population genetics has created a need for user-friendly software, which would allow a wider accessibility to and adaptation of these methods. EDENetworks aims to fill this void by providing an easy-to-use interface for the whole analysis pipeline of ecological and evolutionary networks starting from matrices of species distributions, genotypes, bacterial OTUs or populations characterized genetically. The user can choose between several different ecological distance metrics, such as Bray-Curtis or Sorensen distance, or population genetic metrics such as FST or Goldstein distances, to turn the raw data into a distance/dissimilarity matrix. This matrix is then transformed into a network by manual or automatic thresholding based on percolation theory or by building the minimum spanning tree. The networks can be visualized along with auxiliary data and analysed with various metrics such as degree, clustering coefficient, assortativity and betweenness centrality. The statistical significance of the results can be estimated either by resampling the original biological data or by null models based on permutations of the data. PMID:24902875

  2. Integrative microRNA-gene expression network analysis in genetic hypercalciuric stone-forming rat kidney

    PubMed Central

    Lu, Yuchao; Qin, Baolong; Hu, Henglong; Zhang, Jiaqiao; Wang, Yufeng; Wang, Qing

    2016-01-01

    Background. MicroRNAs (miRNAs) influence a variety of biological functions by regulating gene expression post-transcriptionally. Aberrant miRNA expression has been associated with many human diseases. Urolithiasis is a common disease, and idiopathic hypercalciuria (IH) is an important risk factor for calcium urolithiasis. However, miRNA expression patterns and their biological functions in urolithiasis remain unknown. Methods and Results. A multi-step approach combining microarray miRNA and mRNA expression profile and bioinformatics analysis was adopted to analyze dysregulated miRNAs and genes in genetic hypercalciuric stone-forming (GHS) rat kidneys, using normal Sprague-Dawley (SD) rats as controls. We identified 2418 mRNAs and 19 miRNAs as significantly differentially expressed, over 700 gene ontology (GO) terms and 83 KEGG pathways that were significantly enriched in GHS rats. In addition, we constructed an miRNA-gene network that suggested that rno-miR-674-5p, rno-miR-672-5p, rno-miR-138-5p and rno-miR-21-3p may play important roles in the regulatory network. Furthermore, signal-net analysis suggested that NF-kappa B likely plays a crucial role in hypercalciuria urolithiasis. Conclusions. This study presents a global view of mRNA and miRNA expression in GHS rat kidneys, and suggests that miRNAs may be important in the regulation of hypercalciuria. The data provide valuable insights for future research, which should aim at validating the role of the genes featured here in the pathophysiology of hypercalciuria. PMID:27069814

  3. Brain network involved in visual processing of movement stimuli used in upper limb robotic training: an fMRI study

    PubMed Central

    2012-01-01

    Background The potential of robot-mediated therapy and virtual reality in neurorehabilitation is becoming of increasing importance. However, there is limited information, using neuroimaging, on the neural networks involved in training with these technologies. This study was intended to detect the brain network involved in the visual processing of movement during robotic training. The main aim was to investigate the existence of a common cerebral network able to assimilate biological (human upper limb) and non-biological (abstract object) movements, hence testing the suitability of the visual non-biological feedback provided by the InMotion2 Robot. Methods A visual functional Magnetic Resonance Imaging (fMRI) task was administered to 22 healthy subjects. The task required observation and retrieval of motor gestures and of the visual feedback used in robotic training. Functional activations of both biological and non-biological movements were examined to identify areas activated in both conditions, along with differential activity in upper limb vs. abstract object trials. Control of response was also tested by administering trials with congruent and incongruent reaching movements. Results The observation of upper limb and abstract object movements elicited similar patterns of activations according to a caudo-rostral pathway for the visual processing of movements (including specific areas of the occipital, temporal, parietal, and frontal lobes). Similarly, overlapping activations were found for the subsequent retrieval of the observed movement. Furthermore, activations of frontal cortical areas were associated with congruent trials more than with the incongruent ones. Conclusions This study identified the neural pathway associated with visual processing of movement stimuli used in upper limb robot-mediated training and investigated the brain’s ability to assimilate abstract object movements with human motor gestures. In both conditions, activations were elicited in

  4. Design of a Broadband Electrical Impedance Matching Network for Piezoelectric Ultrasound Transducers Based on a Genetic Algorithm

    PubMed Central

    An, Jianfei; Song, Kezhu; Zhang, Shuangxi; Yang, Junfeng; Cao, Ping

    2014-01-01

    An improved method based on a genetic algorithm (GA) is developed to design a broadband electrical impedance matching network for piezoelectric ultrasound transducer. A key feature of the new method is that it can optimize both the topology of the matching network and perform optimization on the components. The main idea of this method is to find the optimal matching network in a set of candidate topologies. Some successful experiences of classical algorithms are absorbed to limit the size of the set of candidate topologies and greatly simplify the calculation process. Both binary-coded GA and real-coded GA are used for topology optimization and components optimization, respectively. Some calculation strategies, such as elitist strategy and clearing niche method, are adopted to make sure that the algorithm can converge to the global optimal result. Simulation and experimental results prove that matching networks with better performance might be achieved by this improved method. PMID:24743156

  5. Wiring a plant: genetic networks for phloem formation in Arabidopsis thaliana roots.

    PubMed

    Rodriguez-Villalon, Antia

    2016-04-01

    45 I. 45 II. 46 III. 46 IV. 47 V. 48 VI. 48 49 References 49 SUMMARY: In plants, phloem conduits form a specialized vascular network mediating the exchange of nutrients and signaling molecules between distantly separated organs. To become effective transport elements, protophloem cells undergo a rather unique, differentiation program that involves nucleus degradation, organelle rearrangement and cell wall thickening. Yet, protophloem sieve elements remain alive because their essential metabolic functions are supported by their neighboring companion cells. In spite of the importance of the phloem, the molecular mechanisms orchestrating protophloem specification and differentiation remain still poorly understood. In this review, I provide a summary of recent discoveries regarding morphogenetic events that determine phloem formation, and also a discussion of the systemic effects on root architecture derived from impaired protophloem differentiation programs. PMID:26171671

  6. Immunity factor contributes to altered brain functional networks in individuals at risk for Alzheimer's disease: Neuroimaging-genetic evidence.

    PubMed

    Bai, Feng; Shi, Yongmei; Yuan, Yonggui; Xie, Chunming; Zhang, Zhijun

    2016-08-01

    Clusterin (CLU) is recognized as a secreted protein that is related to the processes of inflammation and immunity in the pathogenesis of Alzheimer's disease (AD). The effects of the risk variant of the C allele at the rs11136000 locus of the CLU gene are associated with variations in the brain structure and function. However, the relationship of the CLU-C allele to architectural disruptions in resting-state networks in amnestic mild cognitive impairment (aMCI) subjects (i.e., individuals with elevated risk of AD) remains relatively unknown. Using resting-state functional magnetic resonance imaging and an imaging genetic approach, this study investigated whether individual brain functional networks, i.e., the default mode network (DMN) and the task-positive network, were modulated by the CLU-C allele (rs11136000) in 50 elderly participants, including 26 aMCI subjects and 24 healthy controls. CLU-by-aMCI interactions were associated with the information-bridging regions between resting-state networks rather than with the DMN itself, especially in cortical midline regions. Interestingly, the complex communications between resting-state networks were enhanced in aMCI subjects with the CLU rs11136000 CC genotype and were modulated by the degree of memory impairment, suggesting a reconstructed balance of the resting-state networks in these individuals with an elevated risk of AD. The neuroimaging-genetic evidence indicates that immunity factors may contribute to alterations in brain functional networks in aMCI. These findings add to the evidence that the CLU gene may represent a potential therapeutic target for slowing disease progression in AD. PMID:26899953

  7. Trading Rules on Stock Markets Using Genetic Network Programming with Reinforcement Learning and Importance Index

    NASA Astrophysics Data System (ADS)

    Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki

    Genetic Network Programming (GNP) is an evolutionary computation which represents its solutions using graph structures. Since GNP can create quite compact programs and has an implicit memory function, it has been clarified that GNP works well especially in dynamic environments. In addition, a study on creating trading rules on stock markets using GNP with Importance Index (GNP-IMX) has been done. IMX is a new element which is a criterion for decision making. In this paper, we combined GNP-IMX with Actor-Critic (GNP-IMX&AC) and create trading rules on stock markets. Evolution-based methods evolve their programs after enough period of time because they must calculate fitness values, however reinforcement learning can change programs during the period, therefore the trading rules can be created efficiently. In the simulation, the proposed method is trained using the stock prices of 10 brands in 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. The simulation results show that the proposed method can obtain larger profits than GNP-IMX without AC and Buy&Hold.

  8. Pile-up correction by Genetic Algorithm and Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Kafaee, M.; Saramad, S.

    2009-08-01

    Pile-up distortion is a common problem for high counting rates radiation spectroscopy in many fields such as industrial, nuclear and medical applications. It is possible to reduce pulse pile-up using hardware-based pile-up rejections. However, this phenomenon may not be eliminated completely by this approach and the spectrum distortion caused by pile-up rejection can be increased as well. In addition, inaccurate correction or rejection of pile-up artifacts in applications such as energy dispersive X-ray (EDX) spectrometers can lead to losses of counts, will give poor quantitative results and even false element identification. Therefore, it is highly desirable to use software-based models to predict and correct any recognized pile-up signals in data acquisition systems. The present paper describes two new intelligent approaches for pile-up correction; the Genetic Algorithm (GA) and Artificial Neural Networks (ANNs). The validation and testing results of these new methods have been compared, which shows excellent agreement with the measured data with 60Co source and NaI detector. The Monte Carlo simulation of these new intelligent algorithms also shows their advantages over hardware-based pulse pile-up rejection methods.

  9. Genetic programming-based approach to elucidate biochemical interaction networks from data.

    PubMed

    Kandpal, Manoj; Kalyan, Chakravarthy Mynampati; Samavedham, Lakshminarayanan

    2013-02-01

    Biochemical systems are characterised by cyclic/reversible reciprocal actions, non-linear interactions and a mixed relationship structures (linear and non-linear; static and dynamic). Deciphering the architecture of such systems using measured data to provide quantitative information regarding the nature of relationships that exist between the measured variables is a challenging proposition. Causality detection is one of the methodologies that are applied to elucidate biochemical networks from such data. Autoregressive-based modelling approach such as granger causality, partial directed coherence, directed transfer function and canonical variate analysis have been applied on different systems for deciphering such interactions, but with limited success. In this study, the authors propose a genetic programming-based causality detection (GPCD) methodology which blends evolutionary computation-based procedures along with parameter estimation methods to derive a mathematical model of the system. Application of the GPCD methodology on five data sets that contained the different challenges mentioned above indicated that GPCD performs better than the other methods in uncovering the exact structure with less false positives. On a glycolysis data set, GPCD was able to fill the 'interaction gaps' which were missed by other methods. PMID:23848052

  10. Surface Roughness Optimization of Polyamide-6/Nanoclay Nanocomposites Using Artificial Neural Network: Genetic Algorithm Approach

    PubMed Central

    Moghri, Mehdi; Omidi, Mostafa; Farahnakian, Masoud

    2014-01-01

    During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite. PMID:24578636

  11. Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques

    NASA Astrophysics Data System (ADS)

    Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat

    2016-05-01

    The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models' accuracy was also investigated. Including periodicity component in models' inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.

  12. Case Study on Optimal Routing in Logistics Network by Priority-based Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Xiaoguang; Lin, Lin; Gen, Mitsuo; Shiota, Mitsushige

    Recently, research on logistics caught more and more attention. One of the important issues on logistics system is to find optimal delivery routes with the least cost for products delivery. Numerous models have been developed for that reason. However, due to the diversity and complexity of practical problem, the existing models are usually not very satisfying to find the solution efficiently and convinently. In this paper, we treat a real-world logistics case with a company named ABC Co. ltd., in Kitakyusyu Japan. Firstly, based on the natures of this conveyance routing problem, as an extension of transportation problem (TP) and fixed charge transportation problem (fcTP) we formulate the problem as a minimum cost flow (MCF) model. Due to the complexity of fcTP, we proposed a priority-based genetic algorithm (pGA) approach to find the most acceptable solution to this problem. In this pGA approach, a two-stage path decoding method is adopted to develop delivery paths from a chromosome. We also apply the pGA approach to this problem, and compare our results with the current logistics network situation, and calculate the improvement of logistics cost to help the management to make decisions. Finally, in order to check the effectiveness of the proposed method, the results acquired are compared with those come from the two methods/ software, such as LINDO and CPLEX.

  13. Interpreting Microarray Data to Build Models of Microbial Genetic Regulation Networks

    SciTech Connect

    Sokhansanj, B; Garnham, J B; Fitch, J P

    2002-01-23

    Microarrays and DNA chips are an efficient, high-throughput technology for measuring temporal changes in the expression of message RNA (mRNA) from thousands of genes (often the entire genome of an organism) in a single experiment. A crucial drawback of microarray experiments is that results are inherently qualitative: data are generally neither quantitatively repeatable, nor may microarray spot intensities be calibrated to in vivo mRNA concentrations. Nevertheless, microarrays represent by the far the cheapest and fastest way to obtain information about a cells global genetic regulatory networks. Besides poor signal characteristics, the massive number of data produced by microarray experiments poses challenges for visualization, interpretation and model building. Towards initial model development, we have developed a Java tool for visualizing the spatial organization of gene expression in bacteria. We are also developing an approach to inferring and testing qualitative fuzzy logic models of gene regulation using microarray data. Because we are developing and testing qualitative hypotheses that do not require quantitative precision, our statistical evaluation of experimental data is limited to checking for validity and consistency. Our goals are to maximize the impact of inexpensive microarray technology, bearing in mind that biological models and hypotheses are typically qualitative.

  14. Convergence and divergence of genetic and modular networks between diabetes and breast cancer

    PubMed Central

    Zhang, Xiaoxu; Zhang, Yingying; Yu, Yanan; Liu, Jun; Yuan, Ye; Zhao, Yijun; Li, Haixia; Wang, Jie; Wang, Zhong

    2015-01-01

    Diabetes mellitus (DM) and breast cancer (BC) can simultaneously occur in the same patient populations, but the molecular relationship between them remains unknown. In this study, we constructed genetic networks and used modularized analysis approaches to investigate the multi-dimensional characteristics of two diseases and one disease subtype. A text search engine (Agilent Literature Search 2.71) and MCODE software were applied to validate potential subnetworks and to divide the modules, respectively. A total of 793 DM-related genes, 386 type 2 diabetes (T2DM) genes and 873 BC-related genes were identified from the Online Mendelian Inheritance in Man database. For DM and BC, a total of 99 overlapping genes, 9 modules, 29 biological processes and 7 pathways were identified. Meanwhile, for T2DM and BC, 56 overlapping genes, 5 modules, 20 biological processes and 12 pathways were identified. Based on the Gene Ontology functional enrichment analysis of the top 10 non-overlapping modules of the two diseases, 10 biological functions and 5 pathways overlapped between them. The glycosphingolipid and lysosome pathways verified molecular mechanisms of cell death related to both DM and BC. We also identified new biological functions of dopamine receptors and four signalling pathways (Parkinson's disease, Alzheimer's disease, Huntington's disease and long-term depression) related to both diseases; these warrant further investigation. Our results illustrate the landscape of the novel molecular substructures between DM and BC, which may support a new model for complex disease classification and rational therapies for multiple diseases. PMID:25752479

  15. Production of Engineered Fabrics Using Artificial Neural Network-Genetic Algorithm Hybrid Model

    NASA Astrophysics Data System (ADS)

    Mitra, Ashis; Majumdar, Prabal Kumar; Banerjee, Debamalya

    2015-10-01

    The process of fabric engineering which is generally practised in most of the textile mills is very complicated, repetitive, tedious and time consuming. To eliminate this trial and error approach, a new approach of fabric engineering has been attempted in this work. Data sets of construction parameters [comprising of ends per inch, picks per inch, warp count and weft count] and three fabric properties (namely drape coefficient, air permeability and thermal resistance) of 25 handloom cotton fabrics have been used. The weights and biases of three artificial neural network (ANN) models developed for the prediction of drape coefficient, air permeability and thermal resistance were used to formulate the fitness or objective function and constraints of the optimization problem. The optimization problem was solved using genetic algorithm (GA). In both the fabrics which were attempted for engineering, the target and simulated fabric properties were very close. The GA was able to search the optimum set of fabric construction parameters with reasonably good accuracy except in case of EPI. However, the overall result is encouraging and can be improved further by using larger data sets of handloom fabrics by hybrid ANN-GA model.

  16. Elevator Group Supervisory Control System Using Genetic Network Programming with Macro Nodes and Reinforcement Learning

    NASA Astrophysics Data System (ADS)

    Zhou, Jin; Yu, Lu; Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Markon, Sandor

    Elevator Group Supervisory Control System (EGSCS) is a very large scale stochastic dynamic optimization problem. Due to its vast state space, significant uncertainty and numerous resource constraints such as finite car capacities and registered hall/car calls, it is hard to manage EGSCS using conventional control methods. Recently, many solutions for EGSCS using Artificial Intelligence (AI) technologies have been reported. Genetic Network Programming (GNP), which is proposed as a new evolutionary computation method several years ago, is also proved to be efficient when applied to EGSCS problem. In this paper, we propose an extended algorithm for EGSCS by introducing Reinforcement Learning (RL) into GNP framework, and an improvement of the EGSCS' performances is expected since the efficiency of GNP with RL has been clarified in some other studies like tile-world problem. Simulation tests using traffic flows in a typical office building have been made, and the results show an actual improvement of the EGSCS' performances comparing to the algorithms using original GNP and conventional control methods. Furthermore, as a further study, an importance weight optimization algorithm is employed based on GNP with RL and its efficiency is also verified with the better performances.

  17. The Mosaic Ancestry of the Drosophila Genetic Reference Panel and the D. melanogaster Reference Genome Reveals a Network of Epistatic Fitness Interactions

    PubMed Central

    Pool, John E.

    2015-01-01

    North American populations of Drosophila melanogaster derive from both European and African source populations, but despite their importance for genetic research, patterns of ancestry along their genomes are largely undocumented. Here, I infer geographic ancestry along genomes of the Drosophila Genetic Reference Panel (DGRP) and the D. melanogaster reference genome, which may have implications for reference alignment, association mapping, and population genomic studies in Drosophila. Overall, the proportion of African ancestry was estimated to be 20% for the DGRP and 9% for the reference genome. Combining my estimate of admixture timing with historical records, I provide the first estimate of natural generation time for this species (approximately 15 generations per year). Ancestry levels were found to vary strikingly across the genome, with less African introgression on the X chromosome, in regions of high recombination, and at genes involved in specific processes (e.g., circadian rhythm). An important role for natural selection during the admixture process was further supported by evidence that many unlinked pairs of loci showed a deficiency of Africa–Europe allele combinations between them. Numerous epistatic fitness interactions may therefore exist between African and European genotypes, leading to ongoing selection against incompatible variants. By focusing on hubs in this network of fitness interactions, I identified a set of interacting loci that include genes with roles in sensation and neuropeptide/hormone reception. These findings suggest that admixed D. melanogaster samples could become an important study system for the genetics of early-stage isolation between populations. PMID:26354524

  18. The involvement of the fronto-parietal brain network in oculomotor sequence learning using fMRI.

    PubMed

    Gonzalez, Claudia C; Billington, Jac; Burke, Melanie R

    2016-07-01

    The basis of motor learning involves decomposing complete actions into a series of predictive individual components that form the whole. The present fMRI study investigated the areas of the human brain important for oculomotor short-term learning, by using a novel sequence learning paradigm that is equivalent in visual and temporal properties for both saccades and pursuit, enabling more direct comparisons between the oculomotor subsystems. In contrast with previous studies that have implemented a series of discrete ramps to observe predictive behaviour as evidence for learning, we presented a continuous sequence of interlinked components that better represents sequences of actions. We implemented both a classic univariate fMRI analysis, followed by a further multivariate pattern analysis (MVPA) within a priori regions of interest, to investigate oculomotor sequence learning in the brain and to determine whether these mechanisms overlap in pursuit and saccades as part of a higher order learning network. This study has uniquely identified an equivalent frontal-parietal network (dorsolateral prefrontal cortex, frontal eye fields and posterior parietal cortex) in both saccades and pursuit sequence learning. In addition, this is the first study to investigate oculomotor sequence learning during fMRI brain imaging, and makes significant contributions to understanding the role of the dorsal networks in motor learning. PMID:27157884

  19. CBL controls a tyrosine kinase network involving AXL, SYK and LYN in nilotinib-resistant chronic myeloid leukaemia.

    PubMed

    Gioia, Romain; Trégoat, Claire; Dumas, Pierre-Yves; Lagarde, Valérie; Prouzet-Mauléon, Valérie; Desplat, Vanessa; Sirvent, Audrey; Praloran, Vincent; Lippert, Eric; Villacreces, Arnaud; Leconet, Wilhem; Robert, Bruno; Vigon, Isabelle; Roche, Serge; Mahon, François-Xavier; Pasquet, Jean-Max

    2015-09-01

    A tyrosine kinase network composed of the TAM receptor AXL and the cytoplasmic kinases LYN and SYK is involved in nilotinib-resistance of chronic myeloid leukaemia (CML) cells. Here, we show that the E3-ubiquitin ligase CBL down-regulation occurring during prolonged drug treatment plays a critical role in this process. Depletion of CBL in K562 cells increases AXL and LYN protein levels, promoting cell resistance to nilotinib. Conversely, forced expression of CBL in nilotinib-resistant K562 cells (K562-rn) dramatically reduces AXL and LYN expression and resensitizes K562-rn cells to nilotinib. A similar mechanism was found to operate in primary CML CD34(+) cells. Mechanistically, the E3-ligase CBL counteracts AXL/SYK signalling, promoting LYN transcription by controlling AXL protein stability. Surprisingly, the role of AXL in resistance was independent of its ligand GAS6 binding and its TK activity, in accordance with a scaffold activity for this receptor being involved in this cellular process. Collectively, our results demonstrate a pivotal role for CBL in the control of a tyrosine kinase network mediating resistance to nilotinib treatment in CML cells. PMID:25965880

  20. Inference of S-system models of genetic networks by solving one-dimensional function optimization problems.

    PubMed

    Kimura, S; Araki, D; Matsumura, K; Okada-Hatakeyama, M

    2012-02-01

    Voit and Almeida have proposed the decoupling approach as a method for inferring the S-system models of genetic networks. The decoupling approach defines the inference of a genetic network as a problem requiring the solutions of sets of algebraic equations. The computation can be accomplished in a very short time, as the approach estimates S-system parameters without solving any of the differential equations. Yet the defined algebraic equations are non-linear, which sometimes prevents us from finding reasonable S-system parameters. In this study, we propose a new technique to overcome this drawback of the decoupling approach. This technique transforms the problem of solving each set of algebraic equations into a one-dimensional function optimization problem. The computation can still be accomplished in a relatively short time, as the problem is transformed by solving a linear programming problem. We confirm the effectiveness of the proposed approach through numerical experiments. PMID:22155075

  1. Model-based action planning involves cortico-cerebellar and basal ganglia networks.

    PubMed

    Fermin, Alan S R; Yoshida, Takehiko; Yoshimoto, Junichiro; Ito, Makoto; Tanaka, Saori C; Doya, Kenji

    2016-01-01

    Humans can select actions by learning, planning, or retrieving motor memories. Reinforcement Learning (RL) associates these processes with three major classes of strategies for action selection: exploratory RL learns state-action values by exploration, model-based RL uses internal models to simulate future states reached by hypothetical actions, and motor-memory RL selects past successful state-action mapping. In order to investigate the neural substrates that implement these strategies, we conducted a functional magnetic resonance imaging (fMRI) experiment while humans performed a sequential action selection task under conditions that promoted the use of a specific RL strategy. The ventromedial prefrontal cortex and ventral striatum increased activity in the exploratory condition; the dorsolateral prefrontal cortex, dorsomedial striatum, and lateral cerebellum in the model-based condition; and the supplementary motor area, putamen, and anterior cerebellum in the motor-memory condition. These findings suggest that a distinct prefrontal-basal ganglia and cerebellar network implements the model-based RL action selection strategy. PMID:27539554

  2. Estradiol rapidly modulates spinogenesis in hippocampal dentate gyrus: Involvement of kinase networks.

    PubMed

    Hojo, Yasushi; Munetomo, Arisa; Mukai, Hideo; Ikeda, Muneki; Sato, Rei; Hatanaka, Yusuke; Murakami, Gen; Komatsuzaki, Yoshimasa; Kimoto, Tetsuya; Kawato, Suguru

    2015-08-01

    This article is part of a Special Issue "Estradiol and cognition". Estradiol (E2) is locally synthesized within the hippocampus and the gonads. Rapid modulation of hippocampal synaptic plasticity by E2 is essential for synaptic regulation. The molecular mechanisms of modulation through the synaptic estrogen receptor (ER) and its downstream signaling, however, are largely unknown in the dentate gyrus (DG). We investigated the E2-induced modulation of dendritic spines in male adult rat hippocampal slices by imaging Lucifer Yellow-injected DG granule cells. Treatments with 1 nM E2 increased the density of spines by approximately 1.4-fold within 2h. Spine head diameter analysis showed that the density of middle-head spines (0.4-0.5 μm) was significantly increased. The E2-induced spine density increase was suppressed by blocking Erk MAPK, PKA, PKC and LIMK. These suppressive effects by kinase inhibitors are not non-specific ones because the GSK-3β antagonist did not inhibit E2-induced spine increase. The ER antagonist ICI 182,780 also blocked the E2-induced spine increase. Taken together, these results suggest that E2 rapidly increases the density of spines through kinase networks that are driven by synaptic ER. PMID:26122288

  3. Identification of Crowding Stress Tolerance Co-Expression Networks Involved in Sweet Corn Yield.

    PubMed

    Choe, Eunsoo; Drnevich, Jenny; Williams, Martin M

    2016-01-01

    Tolerance to crowding stress has played a crucial role in improving agronomic productivity in field corn; however, commercial sweet corn hybrids vary greatly in crowding stress tolerance. The objectives were to 1) explore transcriptional changes among sweet corn hybrids with differential yield under crowding stress, 2) identify relationships between phenotypic responses and gene expression patterns, and 3) identify groups of genes associated with yield and crowding stress tolerance. Under conditions of crowding stress, three high-yielding and three low-yielding sweet corn hybrids were grouped for transcriptional and phenotypic analyses. Transcriptional analyses identified from 372 to 859 common differentially expressed genes (DEGs) for each hybrid. Large gene expression pattern variation among hybrids and only 26 common DEGs across all hybrid comparisons were identified, suggesting each hybrid has a unique response to crowding stress. Over-represented biological functions of DEGs also differed among hybrids. Strong correlation was observed between: 1) modules with up-regulation in high-yielding hybrids and yield traits, and 2) modules with up-regulation in low-yielding hybrids and plant/ear traits. Modules linked with yield traits may be important crowding stress response mechanisms influencing crop yield. Functional analysis of the modules and common DEGs identified candidate crowding stress tolerant processes in photosynthesis, glycolysis, cell wall, carbohydrate/nitrogen metabolic process, chromatin, and transcription regulation. Moreover, these biological functions were greatly inter-connected, indicating the importance of improving the mechanisms as a network. PMID:26796516

  4. Identification of Crowding Stress Tolerance Co-Expression Networks Involved in Sweet Corn Yield

    PubMed Central

    Choe, Eunsoo; Drnevich, Jenny; Williams, Martin M.

    2016-01-01

    Tolerance to crowding stress has played a crucial role in improving agronomic productivity in field corn; however, commercial sweet corn hybrids vary greatly in crowding stress tolerance. The objectives were to 1) explore transcriptional changes among sweet corn hybrids with differential yield under crowding stress, 2) identify relationships between phenotypic responses and gene expression patterns, and 3) identify groups of genes associated with yield and crowding stress tolerance. Under conditions of crowding stress, three high-yielding and three low-yielding sweet corn hybrids were grouped for transcriptional and phenotypic analyses. Transcriptional analyses identified from 372 to 859 common differentially expressed genes (DEGs) for each hybrid. Large gene expression pattern variation among hybrids and only 26 common DEGs across all hybrid comparisons were identified, suggesting each hybrid has a unique response to crowding stress. Over-represented biological functions of DEGs also differed among hybrids. Strong correlation was observed between: 1) modules with up-regulation in high-yielding hybrids and yield traits, and 2) modules with up-regulation in low-yielding hybrids and plant/ear traits. Modules linked with yield traits may be important crowding stress response mechanisms influencing crop yield. Functional analysis of the modules and common DEGs identified candidate crowding stress tolerant processes in photosynthesis, glycolysis, cell wall, carbohydrate/nitrogen metabolic process, chromatin, and transcription regulation. Moreover, these biological functions were greatly inter-connected, indicating the importance of improving the mechanisms as a network. PMID:26796516

  5. Model-based action planning involves cortico-cerebellar and basal ganglia networks

    PubMed Central

    Fermin, Alan S. R.; Yoshida, Takehiko; Yoshimoto, Junichiro; Ito, Makoto; Tanaka, Saori C.; Doya, Kenji

    2016-01-01

    Humans can select actions by learning, planning, or retrieving motor memories. Reinforcement Learning (RL) associates these processes with three major classes of strategies for action selection: exploratory RL learns state-action values by exploration, model-based RL uses internal models to simulate future states reached by hypothetical actions, and motor-memory RL selects past successful state-action mapping. In order to investigate the neural substrates that implement these strategies, we conducted a functional magnetic resonance imaging (fMRI) experiment while humans performed a sequential action selection task under conditions that promoted the use of a specific RL strategy. The ventromedial prefrontal cortex and ventral striatum increased activity in the exploratory condition; the dorsolateral prefrontal cortex, dorsomedial striatum, and lateral cerebellum in the model-based condition; and the supplementary motor area, putamen, and anterior cerebellum in the motor-memory condition. These findings suggest that a distinct prefrontal-basal ganglia and cerebellar network implements the model-based RL action selection strategy. PMID:27539554

  6. Hormonal networks involved in apical hook development in darkness and their response to light

    PubMed Central

    Mazzella, Maria A.; Casal, Jorge J.; Muschietti, Jorge P.; Fox, Ana R.

    2013-01-01

    In darkness, the dicot seedlings produce an apical hook as result of differential cell division and extension at opposite sides of the hypocotyl. This hook protects the apical meristem from mechanical damage during seedling emergence from the soil. In darkness, gibberellins act via the DELLA-PIF (PHYTOCHROME INTERACTING FACTORs) pathway, and ethylene acts via the EIN3/EIL1 (ETHYLENE INSENSITIVE 3/EIN3 like 1)-HLS1 (HOOKLESS 1) pathway to control the asymmetric accumulation of auxin required for apical hook formation and maintenance. These core pathways form a network with multiple points of connection. Light perception by phytochromes and cryptochromes reduces the activity of PIFs and (COP1) CONSTITUTIVE PHOTOMORPHOGENIC 1—both required for hook formation in darkness—, lowers the levels of gibberellins, and triggers hook opening as a component of the switch between heterotrophic and photoautotrophic development. Apical hook opening is thus a suitable model to study the convergence of endogenous and exogenous signals on the control of cell division and cell growth. PMID:24616725

  7. The Prediction of Key Cytoskeleton Components Involved in Glomerular Diseases Based on a Protein-Protein Interaction Network

    PubMed Central

    Ju, Wenjun; Li, Xuejuan; Li, Shao; Ding, Jie

    2016-01-01

    Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet), which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN) induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN) transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and prediction, GCNet

  8. Rational Extension of the Ribosome Biogenesis Pathway Using Network-Guided Genetics

    PubMed Central

    Li, Zhihua; Lee, Insuk; Moradi, Emily; Hung, Nai-Jung; Johnson, Arlen W.; Marcotte, Edward M.

    2009-01-01

    Biogenesis of ribosomes is an essential cellular process conserved across all eukaryotes and is known to require >170 genes for the assembly, modification, and trafficking of ribosome components through multiple cellular compartments. Despite intensive study, this pathway likely involves many additional genes. Here, we employ network-guided genetics—an approach for associating candidate genes with biological processes that capitalizes on recent advances in functional genomic and proteomic studies—to computationally identify additional ribosomal biogenesis genes. We experimentally evaluated >100 candidate yeast genes in a battery of assays, confirming involvement of at least 15 new genes, including previously uncharacterized genes (YDL063C, YIL091C, YOR287C, YOR006C/TSR3, YOL022C/TSR4). We associate the new genes with specific aspects of ribosomal subunit maturation, ribosomal particle association, and ribosomal subunit nuclear export, and we identify genes specifically required for the processing of 5S, 7S, 20S, 27S, and 35S rRNAs. These results reveal new connections between ribosome biogenesis and mRNA splicing and add >10% new genes—most with human orthologs—to the biogenesis pathway, significantly extending our understanding of a universally conserved eukaryotic process. PMID:19806183

  9. A Systems Genetics Approach Identifies Gene Regulatory Networks Associated with Fatty Acid Composition in Brassica rapa Seed.

    PubMed

    Basnet, Ram Kumar; Del Carpio, Dunia Pino; Xiao, Dong; Bucher, Johan; Jin, Mina; Boyle, Kerry; Fobert, Pierre; Visser, Richard G F; Maliepaard, Chris; Bonnema, Guusje

    2016-01-01

    Fatty acids in seeds affect seed germination and seedling vigor, and fatty acid composition determines the quality of seed oil. In this study, quantitative trait locus (QTL) mapping of fatty acid and transcript abundance was integrated with gene network analysis to unravel the genetic regulation of seed fatty acid composition in a Brassica rapa doubled haploid population from a cross between a yellow sarson oil type and a black-seeded pak choi. The distribution of major QTLs for fatty acids showed a relationship with the fatty acid types: linkage group A03 for monounsaturated fatty acids, A04 for saturated fatty acids, and A05 for polyunsaturated fatty acids. Using a genetical genomics approach, expression quantitative trait locus (eQTL) hotspots were found at major fatty acid QTLs on linkage groups A03, A04, A05, and A09. An eQTL-guided gene coexpression network of lipid metabolism-related genes showed major hubs at the genes BrPLA2-ALPHA, BrWD-40, a number of seed storage protein genes, and the transcription factor BrMD-2, suggesting essential roles for these genes in lipid metabolism. Three subnetworks were extracted for the economically important and most abundant fatty acids erucic, oleic, linoleic, and linolenic acids. Network analysis, combined with comparison of the genome positions of cis- or trans-eQTLs with fatty acid QTLs, allowed the identification of candidate genes for genetic regulation of these fatty acids. The generated insights in the genetic architecture of fatty acid composition and the underlying complex gene regulatory networks in B. rapa seeds are discussed. PMID:26518343

  10. Discriminative gene co-expression network analysis uncovers novel modules involved in the formation of phosphate deficiency-induced root hairs in Arabidopsis.

    PubMed

    Salazar-Henao, Jorge E; Lin, Wen-Dar; Schmidt, Wolfgang

    2016-01-01

    Cell fate and differentiation in the Arabidopsis root epidermis are genetically defined but remain plastic to environmental signals such as limited availability of inorganic phosphate (Pi). Root hairs of Pi-deficient plants are more frequent and longer than those of plants grown under Pi-replete conditions. To dissect genes involved in Pi deficiency-induced root hair morphogenesis, we constructed a co-expression network of Pi-responsive genes against a customized database that was assembled from experiments in which differentially expressed genes that encode proteins with validated functions in root hair development were over-represented. To further filter out less relevant genes, we combined this procedure with a search for common cis-regulatory elements in the promoters of the selected genes. In addition to well-described players and processes such as auxin signalling and modifications of primary cell walls, we discovered several novel aspects in the biology of root hairs induced by Pi deficiency, including cell cycle control, putative plastid-to-nucleus signalling, pathogen defence, reprogramming of cell wall-related carbohydrate metabolism, and chromatin remodelling. This approach allows the discovery of novel of aspects of a biological process from transcriptional profiles with high sensitivity and accuracy. PMID:27220366

  11. Discriminative gene co-expression network analysis uncovers novel modules involved in the formation of phosphate deficiency-induced root hairs in Arabidopsis

    PubMed Central

    Salazar-Henao, Jorge E.; Lin, Wen-Dar; Schmidt, Wolfgang

    2016-01-01

    Cell fate and differentiation in the Arabidopsis root epidermis are genetically defined but remain plastic to environmental signals such as limited availability of inorganic phosphate (Pi). Root hairs of Pi-deficient plants are more frequent and longer than those of plants grown under Pi-replete conditions. To dissect genes involved in Pi deficiency-induced root hair morphogenesis, we constructed a co-expression network of Pi-responsive genes against a customized database that was assembled from experiments in which differentially expressed genes that encode proteins with validated functions in root hair development were over-represented. To further filter out less relevant genes, we combined this procedure with a search for common cis-regulatory elements in the promoters of the selected genes. In addition to well-described players and processes such as auxin signalling and modifications of primary cell walls, we discovered several novel aspects in the biology of root hairs induced by Pi deficiency, including cell cycle control, putative plastid-to-nucleus signalling, pathogen defence, reprogramming of cell wall-related carbohydrate metabolism, and chromatin remodelling. This approach allows the discovery of novel of aspects of a biological process from transcriptional profiles with high sensitivity and accuracy. PMID:27220366

  12. Mining to find the lipid interaction networks involved in Ovarian Cancers

    PubMed Central

    Kanagasabai, Rajaraman; Narasimhan, Kothandaraman; Low, Hong-Sang; Ang, Wee Tiong; Fernandis, Aaron Z.; Wenk, Markus R.; Choolani, Mahesh A.; Baker, Christopher J. O.

    2009-01-01

    The role of lipids in cancer during the genesis, progression and subsequent metastasis stages is increasingly discussed in the scie