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Sample records for genetic networks involved

  1. 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

  2. [China genetic counseling network (CGCN): a website on genetic counseling and genetic education].

    PubMed

    He, Min; Li, Wei

    2007-03-01

    In April 2005, with the voluntary involvement of more than 50 worldwide genetic counselors or medical geneticists, we developed a website for online genetic counseling and genetic education on common genetic disease throughout China (URL: http://www.gcnet.org.cn). This website is offering professional online genetic counseling, as well as providing information about common genetic diseases which is a resource for genetic counselors and online genetic counselees. Online genetic counseling is an alternative method to the widely accepted face-to-face counseling. The data warehouse of China Genetic Counseling Network (CGCN) will be a unique supplement to current status of Clinical Genetics and healthcare system in China.

  3. 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

  4. 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

  5. Network growth models and genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Foster, D. V.; Kauffman, S. A.; Socolar, J. E. S.

    2006-03-01

    We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks. The algorithms involve partial duplication of nodes and their links, together with the innovation of new links, allowing for the possibility that input and output links from a newly created node may have different probabilities of survival. We find some counterintuitive trends as the parameters are varied, including the broadening of the in-degree distribution when the probability for retaining input links is decreased. We also find that both the scaling of transcription factors with genome size and the measured degree distributions for genes in yeast can be reproduced by the growth algorithm if and only if a special seed is used to initiate the process.

  6. Network growth models and genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Socolar, Joshua; Foster, David; Kauffman, Stuart

    2006-03-01

    We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks. The algorithms involve partial duplication of nodes and their links, together with innovation of new links, allowing for the possibility that input and output links from a newly created node may have different probabilities of survival. We find some counterintuitive trends as parameters are varied, including the broadening of indegree distribution when the probability for retaining input links is decreased. We also find that both the scaling of transcription factors with genome size and the measured degree distributions for genes in yeast can be reproduced by the growth algorithm if and only if a special seed is used to initiate the process.

  7. 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

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

    PubMed

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

    2016-02-23

    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.

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

    PubMed Central

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

    2014-01-01

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

  10. Genetic Variations Involved in Vitamin E Status

    PubMed Central

    Borel, Patrick; Desmarchelier, Charles

    2016-01-01

    Vitamin E (VE) is the generic term for four tocopherols and four tocotrienols that exhibit the biological activity of α-tocopherol. VE status, which is usually estimated by measuring fasting blood VE concentration, is affected by numerous factors, such as dietary VE intake, VE absorption efficiency, and VE catabolism. Several of these factors are in turn modulated by genetic variations in genes encoding proteins involved in these factors. To identify these genetic variations, two strategies have been used: genome-wide association studies and candidate gene association studies. Each of these strategies has its advantages and its drawbacks, nevertheless they have allowed us to identify a list of single nucleotide polymorphisms associated with fasting blood VE concentration and α-tocopherol bioavailability. However, much work remains to be done to identify, and to replicate in different populations, all the single nucleotide polymorphisms involved, to assess the possible involvement of other kind of genetic variations, e.g., copy number variants and epigenetic modifications, in order to establish a reliable list of genetic variations that will allow us to predict the VE status of an individual by knowing their genotype in these genetic variations. Yet, the potential usefulness of this area of research is exciting with regard to personalized nutrition and for future clinical trials dedicated to assessing the biological effects of the various isoforms of VE. PMID:27983595

  11. 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)

  12. Predicting the Structure of Covert Networks using Genetic Programming, Cognitive Work Analysis and Social Network Analysis

    DTIC Science & Technology

    2009-10-01

    RTO-MP-MSG-069 15 - 1 Predicting the Structure of Covert Networks using Genetic Programming, Cognitive Work Analysis and Social Network...collation of intelligence covering types of mission, in terms of actors and goals; phase two involves the building of task models, based on Cognitive ...REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE Predicting the Structure of Covert Networks using Genetic Programming, Cognitive Work

  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.

  14. Involvement of support networks in treatment.

    PubMed

    Longabaugh, Richard

    2003-01-01

    Alcohol dependence and abuse are biopsychosocially determined and ameliorated. However, social factors involved in effecting treatment outcomes continue to be understudied as a context in which psychological and biological factors are researched and also as a treatment focus in their own right. Yet the client's social setting and relationships during and after treatment are more important forces in the recovery process than formally defined alcohol treatment interventions. Though this has been so historically, it is even more likely now in managed care environments where treatments are highly circumscribed. This suggests that treatment interventions refocus toward targeting changes in the patient's social environment. Treatments involving spouses and families of the patient's social networks it has been shown, are incrementally effective. A high priority for research is to identify the set of conditions under which the wider social network can also be effective. Early results are promising though not well understood. The next generation of network therapies will focus on testing the conditions under which various components of the patient's social network are important in affecting patient outcomes. As with other therapies, an important focus for research is on identifying the active ingredients in these treatments that bring about favorable change in patients generally or that enhance the outcomes of the subset of patients particularly amenable to network interventions (patient-treatment "matching"). Recommended priorities for future research in network interventions are identified.

  15. Genetic Network Inference Using Hierarchical Structure

    PubMed Central

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

    2016-01-01

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

  16. Functional brain networks involved in reality monitoring.

    PubMed

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

    2015-08-01

    Source monitoring refers to the recollection of variables that specify the context and conditions in which a memory episode was encoded. This process involves using the qualitative and quantitative features of a memory trace to distinguish its source. One specific class of source monitoring is reality monitoring, which involves distinguishing internally generated from externally generated information, that is, memories of imagined events from real events. The purpose of the present study was to identify functional brain networks that underlie reality monitoring, using an alternative type of source monitoring as a control condition. On the basis of previous studies on self-referential thinking, it was expected that a medial prefrontal cortex (mPFC) based network would be more active during reality monitoring than the control condition, due to the requirement to focus on a comparison of internal (self) and external (other) source information. Two functional brain networks emerged from this analysis, one reflecting increasing task-related activity, and one reflecting decreasing task-related activity. The second network was mPFC based, and was characterized by task-related deactivations in areas resembling the default-mode network; namely, the mPFC, middle temporal gyri, lateral parietal regions, and the precuneus, and these deactivations were diminished during reality monitoring relative to source monitoring, resulting in higher activity during reality monitoring. This result supports previous research suggesting that self-referential thinking involves the mPFC, but extends this to a network-level interpretation of reality monitoring. Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. Network medicine approaches to the genetics of complex diseases.

    PubMed

    Silverman, Edwin K; Loscalzo, Joseph

    2012-08-01

    Complex diseases are caused by perturbations of biological networks. Genetic analysis approaches focused on individual genetic determinants are unlikely to characterize the network architecture of complex diseases comprehensively. Network medicine, which applies systems biology and network science to complex molecular networks underlying human disease, focuses on identifying the interacting genes and proteins which lead to disease pathogenesis. The long biological path between a genetic risk variant and development of a complex disease involves a range of biochemical intermediates, including coding and non-coding RNA, proteins, and metabolites. Transcriptomics, proteomics, metabolomics, and other -omics technologies have the potential to provide insights into complex disease pathogenesis, especially if they are applied within a network biology framework. Most previous efforts to relate genetics to -omics data have focused on a single -omics platform; the next generation of complex disease genetics studies will require integration of multiple types of -omics data sets in a network context. Network medicine may also provide insight into complex disease heterogeneity, serve as the basis for new disease classifications that reflect underlying disease pathogenesis, and guide rational therapeutic and preventive strategies.

  18. Network Medicine Approaches to the Genetics of Complex Diseases

    PubMed Central

    Silverman, Edwin K.; Loscalzo, Joseph

    2013-01-01

    Complex diseases are caused by perturbations of biological networks. Genetic analysis approaches focused on individual genetic determinants are unlikely to characterize the network architecture of complex diseases comprehensively. Network medicine, which applies systems biology and network science to complex molecular networks underlying human disease, focuses on identifying the interacting genes and proteins which lead to disease pathogenesis. The long biological path between a genetic risk variant and development of a complex disease involves a range of biochemical intermediates, including coding and non-coding RNA, proteins, and metabolites. Transcriptomics, proteomics, metabolomics, and other –omics technologies have the potential to provide insights into complex disease pathogenesis, especially if they are applied within a network biology framework. Most previous efforts to relate genetics to –omics data have focused on a single –omics platform; the next generation of complex disease genetics studies will require integration of multiple types of –omics data sets in a network context. Network medicine may also provide insight into complex disease heterogeneity, serve as the basis for new disease classifications that reflect underlying disease pathogenesis, and guide rational therapeutic and preventive strategies. PMID:22935211

  19. Network analyses structure genetic diversity in independent genetic worlds

    PubMed Central

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

    2009-01-01

    DNA flows between chromosomes and mobile elements, following rules that are poorly understood. This limited knowledge is partly explained by the limits of current approaches to study the structure and evolution of genetic diversity. Network analyses of 119,381 homologous DNA families, sampled from 111 cellular genomes and from 165,529 phage, plasmid, and environmental virome sequences, offer challenging insights. Our results support a disconnected yet highly structured network of genetic diversity, revealing the existence of multiple “genetic worlds.” These divides define multiple isolated groups of DNA vehicles drawing on distinct gene pools. Mathematical studies of the centralities of these worlds’ subnetworks demonstrate that plasmids, not viruses, were key vectors of genetic exchange between bacterial chromosomes, both recently and in the past. Furthermore, network methodology introduces new ways of quantifying current sampling of genetic diversity. PMID:20007769

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

    PubMed

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

    2010-01-05

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

  1. 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.

  2. Ethics in Prevention Science Involving Genetic Testing

    PubMed Central

    Fisher, Celia B.; Harrington, Erika L.

    2013-01-01

    The Human Genome Project and rapid technological advances in genomics have begun to enrich prevention science’s contributions to understanding the role of genetic factors in the etiology, onset and escalation of mental disorders, allowing for more precise descriptions of the interplay between genetic and non-genetic influences. Understanding ethical challenges associated with the integration of genetic data into prevention science has not kept pace with the rapid increase in the collection and storage of genetic data and dissemination of research results. This article discusses ethical issues associated with (1) decisions to withhold or disclose personal genetic information to participants; (2) implications of recruitment and data collection methods that may reveal genetic information of family members; and the (3) nature and timing of informed consent. These issues are presented within the contexts of adult and pediatric research, longitudinal studies, and use of biobanks for storage of genetic materials. Recommendations for research ethics decision-making are provided. The article concludes with a section on justice and research burdens and the unique ethical responsibilities of prevention scientists to ensure the new genomic science protects the informational rights of participants, their families and communities. PMID:23354905

  3. Genetic algorithm for neural networks optimization

    NASA Astrophysics Data System (ADS)

    Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

    2004-11-01

    This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

  4. 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)

  5. Genetics studies involving Swiss needle cast.

    Treesearch

    R. Johnson; F. Temel; K. Jayawickrama

    2002-01-01

    Three studies were analyzed this year that examined genetic aspects of Swiss needle cast (SNC) tolerance . Families sampled across the Siuslaw National forest showed differences in foliage health traits, but very little of the variation could be explained by environmental or climatic conditions at the parent tree location. Five test sites of the Nehalem series of...

  6. Splitting Strategy for Simulating Genetic Regulatory Networks

    PubMed Central

    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

  7. Boolean Modelingof Genetic Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Albert, Réka

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

  8. Genetic network models: a comparative study

    NASA Astrophysics Data System (ADS)

    van Someren, Eugene P.; Wessels, Lodewyk F. A.; Reinders, Marcel J. T.

    2001-06-01

    Currently, the need arises for tools capable of unraveling the functionality of genes based on the analysis of microarray measurements. Modeling genetic interactions by means of genetic network models provides a methodology to infer functional relationships between genes. Although a wide variety of different models have been introduced so far, it remains, in general, unclear what the strengths and weaknesses of each of these approaches are and where these models overlap and differ. This paper compares different genetic modeling approaches that attempt to extract the gene regulation matrix from expression data. A taxonomy of continuous genetic network models is proposed and the following important characteristics are suggested and employed to compare the models: inferential power; predictive power; robustness; consistency; stability and computational cost. Where possible, synthetic time series data are employed to investigate some of these properties. The comparison shows that although genetic network modeling might provide valuable information regarding genetic interactions, current models show disappointing results on simple artificial problems. For now, the simplest models are favored because they generalize better, but more complex models will probably prevail once their bias is more thoroughly understood and their variance is better controlled.

  9. Genetic origins of social networks in rhesus macaques

    PubMed Central

    Brent, Lauren J. N.; Heilbronner, Sarah R.; Horvath, Julie E.; Gonzalez-Martinez, Janis; Ruiz-Lambides, Angelina; Robinson, Athy G.; Skene, J. H. Pate; Platt, Michael L.

    2013-01-01

    Sociality is believed to have evolved as a strategy for animals to cope with their environments. Yet the genetic basis of sociality remains unclear. Here we provide evidence that social network tendencies are heritable in a gregarious primate. The tendency for rhesus macaques, Macaca mulatta, to be tied affiliatively to others via connections mediated by their social partners - analogous to friends of friends in people - demonstrated additive genetic variance. Affiliative tendencies were predicted by genetic variation at two loci involved in serotonergic signalling, although this result did not withstand correction for multiple tests. Aggressive tendencies were also heritable and were related to reproductive output, a fitness proxy. Our findings suggest that, like humans, the skills and temperaments that shape the formation of multi-agent relationships have a genetic basis in nonhuman primates, and, as such, begin to fill the gaps in our understanding of the genetic basis of sociality. PMID:23304433

  10. The causes of epistasis in genetic networks.

    PubMed

    Macía, Javier; Solé, Ricard V; Elena, Santiago F

    2012-02-01

    Epistasis refers to the nonadditive interactions between genes in determining phenotypes. Considerable efforts have shown that, even for a given organism, epistasis may vary both in intensity and sign. Recent comparative studies supported that the overall sign of epistasis switches from positive to negative as the complexity of an organism increases, and it has been hypothesized that this change shall be a consequence of the underlying gene network properties. Why should this be the case? What characteristics of genetic networks determine the sign of epistasis? Here we show, by evolving genetic networks that differ in their complexity and robustness against perturbations but that perform the same tasks, that robustness increased with complexity and that epistasis was positive for small nonrobust networks but negative for large robust ones. Our results indicate that robustness and negative epistasis emerge as a consequence of the existence of redundant elements in regulatory structures of genetic networks and that the correlation between complexity and epistasis is a byproduct of such redundancy, allowing for the decoupling of epistasis from the underlying network complexity. © 2011 The Author(s). Evolution© 2011 The Society for the Study of Evolution.

  11. Probabilistic belief networks for genetic counseling.

    PubMed

    Harris, N L

    1990-05-01

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

  12. Noise in genetic and neural networks

    NASA Astrophysics Data System (ADS)

    Swain, Peter S.; Longtin, André

    2006-06-01

    Both neural and genetic networks are significantly noisy, and stochastic effects in both cases ultimately arise from molecular events. Nevertheless, a gulf exists between the two fields, with researchers in one often being unaware of similar work in the other. In this Special Issue, we focus on bridging this gap and present a collection of papers from both fields together. For each field, the networks studied range from just a single gene or neuron to endogenous networks. In this introductory article, we describe the sources of noise in both genetic and neural systems. We discuss the modeling techniques in each area and point out similarities. We hope that, by reading both sets of papers, ideas developed in one field will give insight to scientists from the other and that a common language and methodology will develop.

  13. New neuronal networks involved in ethanol reinforcement.

    PubMed

    Kiianmaa, Kalervo; Hyytiä, Petri; Samson, Herman H; Engel, Jörgen A; Svensson, Lennart; Söderpalm, Bo; Larsson, Anna; Colombo, Giancarlo; Vacca, Giovanni; Finn, Deborah A; Bachtell, Ryan K; Ryabinin, Andrey E

    2003-02-01

    This article represents the proceedings of a symposium at the 2002 ISBRA/RSA meeting in San Francisco. The organizers were Kalervo Kiianmaa and Andrey E. Ryabinin. The chairs were Kalervo Kiianmaa and Jörgen A. Engel. The presentations were (1) The role of opioidergic and dopaminergic networks in ethanol-seeking behavior, by Kalervo Kiianmaa and Petri Hyytiä; (2) Interaction between the dopamine systems in the prefrontal cortex and nucleus accumbens during ethanol self-administration, by Herman H. Samson; (3) Neurochemical and behavioral studies on ethanol and nicotine interactions, by Jörgen A. Engel, Lennart Svensson, Bo Söderpalm, and Anna Larsson; (4) Involvement of the GABA receptor in alcohol reinforcement in sP rats, by Giancarlo Colombo and Giovanni Vacca; (5) Neuroactive steroids and ethanol reinforcement, by Deborah A. Finn, and (6) Potential contribution of the urocortin system to regulation of alcohol self-administration, by Andrey E. Ryabinin and Ryan K. Bachtell.(B)

  14. 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.

  15. 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.

  16. Genetic interaction networks: better understand to better predict

    PubMed Central

    Boucher, Benjamin; Jenna, Sarah

    2013-01-01

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

  17. Genetic Network Programming with Reconstructed Individuals

    NASA Astrophysics Data System (ADS)

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

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

  18. 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.

  19. 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…

  20. Determinants of Communication Network Involvement: Connectedness and Integration.

    ERIC Educational Resources Information Center

    Monge, Peter R.; And Others

    1983-01-01

    Developed structural equation models of involvement in communication networks in organizations for communication network connectedness and for network integration. A questionnaire was administered to members of a naval training facility (N=125). Models showed acceptable goodness-of-fit for the connectedness model and excellent goodness-of-fit for…

  1. Get involved in the Young Vet Network.

    PubMed

    2016-07-02

    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. British Veterinary Association.

  2. 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

  3. Chapter 4: Genetic Identification of Fungi Involved in Wood Decay

    Treesearch

    Grant Kirker

    2014-01-01

    Wood decay is a complex process that involves contributions from molds, bacteria, decay fungi, and often insects. The first step in the accurate diagnosis of decay is identification of the causal agents, but wood decay in the strictest sense (white and brown rot) is caused by cryptic fungal species that are very difficult to identify using traditional methods. Genetic...

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

    PubMed Central

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

    2014-01-01

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

  5. 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.

  6. 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.

  7. Genetic networks: between theory and experimentation

    NASA Astrophysics Data System (ADS)

    Bottani, Samuel; Mazurie, Aurélien

    Thanks to an increasing availability of data on cell components and progress in computers and computer science, a long awaited paradigm shift is running in biology from reductionism to holistic approaches. One of the consequences is the huge development of network-related representations of cell activity and an increasing involvement of researchers from computer science, physics and mathematics in their analysis. But what are the promises of these approaches for the biologist? What is the available biological data sustaining them and is it sufficient? After a presentation of the interaction network view of the cell, we shall focus on studies on gene network structure and dynamics. Then we shall discuss the difficulties of these approaches and their theoretical and practical usefulness for the biologist.

  8. 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

  9. 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.

  10. Genetically determined phenotype covariation networks control bone strength.

    PubMed

    Jepsen, Karl J; Courtland, Hayden-William; Nadeau, Joseph H

    2010-07-01

    To identify genes affecting bone strength, we studied how genetic variants regulate components of a phenotypic covariation network that was previously shown to accurately characterize the compensatory trait interactions involved in functional adaptation during growth. Quantitative trait loci (QTLs) regulating femoral robustness, morphologic compensation, and mineralization (tissue quality) were mapped at three ages during growth using AXB/BXA Recombinant Inbred (RI) mouse strains and adult B6-i(A) Chromosome Substitution Strains (CSS). QTLs for robustness were identified on chromosomes 8, 12, 18, and 19 and confirmed at all three ages, indicating that genetic variants established robustness postnatally without further modification. A QTL for morphologic compensation, which was measured as the relationship between cortical area and body weight, was identified on chromosome 8. This QTL limited the amount of bone formed during growth and thus acted as a setpoint for diaphyseal bone mass. Additional QTLs were identified from the CSS analysis. QTLs for robustness and morphologic compensation regulated bone structure independently (ie, in a nonpleiotropic manner), indicating that each trait may be targeted separately to individualize treatments aiming to improve strength. Multiple regression analyses showed that variation in morphologic compensation and tissue quality, not bone size, determined femoral strength relative to body weight. Thus an individual inheriting slender bones will not necessarily inherit weak bones unless the individual also inherits a gene that impairs compensation. This systems genetic analysis showed that genetically determined phenotype covariation networks control bone strength, suggesting that incorporating functional adaptation into genetic analyses will advance our understanding of the genetic basis of bone strength.

  11. Network propagation: a universal amplifier of genetic associations.

    PubMed

    Cowen, Lenore; Ideker, Trey; Raphael, Benjamin J; Sharan, Roded

    2017-09-01

    Biological networks are powerful resources for the discovery of genes and genetic modules that drive disease. Fundamental to network analysis is the concept that genes underlying the same phenotype tend to interact; this principle can be used to combine and to amplify signals from individual genes. Recently, numerous bioinformatic techniques have been proposed for genetic analysis using networks, based on random walks, information diffusion and electrical resistance. These approaches have been applied successfully to identify disease genes, genetic modules and drug targets. In fact, all these approaches are variations of a unifying mathematical machinery - network propagation - suggesting that it is a powerful data transformation method of broad utility in genetic research.

  12. Security and Privacy Preservation in Human-Involved Networks

    NASA Astrophysics Data System (ADS)

    Asher, Craig; Aumasson, Jean-Philippe; Phan, Raphael C.-W.

    This paper discusses security within human-involved networks, with a focus on social networking services (SNS). We argue that more secure networks could be designed using semi-formal security models inspired from cryptography, as well as notions like that of ceremony, which exploits human-specific abilities and psychology to assist creating more secure protocols. We illustrate some of our ideas with the example of the SNS Facebook.

  13. 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.

  14. Genetic demographic networks: Mathematical model and applications.

    PubMed

    Kimmel, Marek; Wojdyła, Tomasz

    2016-10-01

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

  15. Artificial neural networks reveal efficiency in genetic value prediction.

    PubMed

    Peixoto, L A; Bhering, L L; Cruz, C D

    2015-06-18

    The objective of this study was to evaluate the efficiency of artificial neural networks (ANNs) for predicting genetic value in experiments carried out in randomized blocks. Sixteen scenarios were simulated with different values of heritability (10, 20, 30, and 40%), coefficient of variation (5 and 10%), and the number of genotypes per block (150 and 200 for validation, and 5000 for neural network training). One hundred validation populations were used in each scenario. Accuracy of ANNs was evaluated by comparing the correlation of network value with genetic value, and of phenotypic value with genetic value. Neural networks were efficient in predicting genetic value with a 0.64 to 10.3% gain compared to the phenotypic value, regardless the simulated population size, heritability, or coefficient of variation. Thus, the artificial neural network is a promising technique for predicting genetic value in balanced experiments.

  16. Information transmission in genetic regulatory networks: a review

    NASA Astrophysics Data System (ADS)

    Tkačik, Gašper; Walczak, Aleksandra M.

    2011-04-01

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

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

    PubMed

    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

    2015-05-01

    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. 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 2716 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. 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<0.006). 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. 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. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  18. Genetic and Environmental Factors Associated with Cannabis Involvement

    PubMed Central

    Bogdan, Ryan; Winstone, Jonathan MA; Agrawal, Arpana

    2016-01-01

    Approximately 50-70% of the variation in cannabis use and use disorders can be attributed to heritable factors. For cannabis use, the remaining variance can be parsed in to familial and person-specific environmental factors while for use disorders, only the latter contribute. While numerous candidate gene studies have identified the role of common variation influencing liability to cannabis involvement, replication has been elusive. To date, no genomewide association study has been sufficiently powered to identify significant loci. Despite this, studies adopting polygenic techniques and integrating genetic variation with neural phenotypes and measures of environmental risk, such as childhood adversity, are providing promising new leads. It is likely that the small effect sizes associated with variants related to cannabis involvement will only be robustly identified in substantially larger samples. Results of such large-scale efforts will provide valuable single variant targets for translational research in neurogenetic, pharmacogenetic and non-human animal models as well as polygenic risk indices that can be used to explore a host of other genetic hypotheses related to cannabis use and misuse. PMID:27642547

  19. Consumer involvement in cancer research: example from a Cancer Network.

    PubMed

    Arain, Mubashir; Pyne, Sarah; Thornton, Nigel; Palmer, Susan; Sharma, Ricky A

    2015-10-01

    The involvement of consumers and the general public in improving cancer services is an important component of health services. However, consumer involvement in cancer research is relatively unexplored. The objective of this study was to explore different ways of involving consumers in cancer research in one regional network. Thames Valley Cancer Network Consumer Research Partnership (CRP) group was formed in 2009. The group consists of consumers and professionals to help in promoting consumer involvement in Cancer Research in the Thames Valley. This study evaluated the project of consumer involvement in cancer research in the Thames Valley from March 2010 to March 2011. We used different indices to judge the level of consumer involvement: number of projects involving consumers through the group, types of projects, level of involvement (ranged from consultation on research documents to collaborating in preparing grant applications) and the methods of involving consumers in cancer research. Fifteen projects were submitted to the CRP group during the 12-month period studied. Of these, eight projects were clinical trials, three were qualitative research projects, two were patients' surveys and two were non-randomized interventional studies. Seven projects requested consumer involvement on patient information sheets for clinical trials. Of these seven applications, three also requested consumers' help in designing research questionnaires and another three requested that consumers should be involved in their project management group. In addition, four projects involved consumers in the proposal development phase and another four projects asked for advice on how to increase trial recruitment, conduct patient interviews or help with grant applications. The creation of the CRP and this audit of its activity have documented consumer involvement in cancer research in the Thames Valley. We have clearly shown that consumers can be involved in designing and managing cancer

  20. 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

  1. Introduction to focus issue: quantitative approaches to genetic networks.

    PubMed

    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

  2. Genetic control of root growth: from genes to networks

    PubMed Central

    Slovak, Radka; Ogura, Takehiko; Satbhai, Santosh B.; Ristova, Daniela; Busch, Wolfgang

    2016-01-01

    Background Roots are essential organs for higher plants. They provide the plant with nutrients and water, anchor the plant in the soil, and can serve as energy storage organs. One remarkable feature of roots is that they are able to adjust their growth to changing environments. This adjustment is possible through mechanisms that modulate a diverse set of root traits such as growth rate, diameter, growth direction and lateral root formation. The basis of these traits and their modulation are at the cellular level, where a multitude of genes and gene networks precisely regulate development in time and space and tune it to environmental conditions. Scope This review first describes the root system and then presents fundamental work that has shed light on the basic regulatory principles of root growth and development. It then considers emerging complexities and how they have been addressed using systems-biology approaches, and then describes and argues for a systems-genetics approach. For reasons of simplicity and conciseness, this review is mostly limited to work from the model plant Arabidopsis thaliana, in which much of the research in root growth regulation at the molecular level has been conducted. Conclusions While forward genetic approaches have identified key regulators and genetic pathways, systems-biology approaches have been successful in shedding light on complex biological processes, for instance molecular mechanisms involving the quantitative interaction of several molecular components, or the interaction of large numbers of genes. However, there are significant limitations in many of these methods for capturing dynamic processes, as well as relating these processes to genotypic and phenotypic variation. The emerging field of systems genetics promises to overcome some of these limitations by linking genotypes to complex phenotypic and molecular data using approaches from different fields, such as genetics, genomics, systems biology and phenomics. PMID

  3. Genetic control of root growth: from genes to networks.

    PubMed

    Slovak, Radka; Ogura, Takehiko; Satbhai, Santosh B; Ristova, Daniela; Busch, Wolfgang

    2016-01-01

    Roots are essential organs for higher plants. They provide the plant with nutrients and water, anchor the plant in the soil, and can serve as energy storage organs. One remarkable feature of roots is that they are able to adjust their growth to changing environments. This adjustment is possible through mechanisms that modulate a diverse set of root traits such as growth rate, diameter, growth direction and lateral root formation. The basis of these traits and their modulation are at the cellular level, where a multitude of genes and gene networks precisely regulate development in time and space and tune it to environmental conditions. This review first describes the root system and then presents fundamental work that has shed light on the basic regulatory principles of root growth and development. It then considers emerging complexities and how they have been addressed using systems-biology approaches, and then describes and argues for a systems-genetics approach. For reasons of simplicity and conciseness, this review is mostly limited to work from the model plant Arabidopsis thaliana, in which much of the research in root growth regulation at the molecular level has been conducted. While forward genetic approaches have identified key regulators and genetic pathways, systems-biology approaches have been successful in shedding light on complex biological processes, for instance molecular mechanisms involving the quantitative interaction of several molecular components, or the interaction of large numbers of genes. However, there are significant limitations in many of these methods for capturing dynamic processes, as well as relating these processes to genotypic and phenotypic variation. The emerging field of systems genetics promises to overcome some of these limitations by linking genotypes to complex phenotypic and molecular data using approaches from different fields, such as genetics, genomics, systems biology and phenomics. © The Author 2015. Published by

  4. Social Network Influence on Father Involvement in Childrearing.

    ERIC Educational Resources Information Center

    Riley, Dave

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

  5. [Establishment and management of medical genetic clinic based on genetic testing network.].

    PubMed

    Lee, Yong Wha; Ki, Chang Seok; Shin, Hee Bong; Choi, Tae Youn; Kim, Jong Won; Kim, Sun Hee; Lee, You Kyoung

    2006-04-01

    With the progress of the Human Genome Project, genetic testing has become widely available and useful for the confirmation and treatment planning of various conditions. Additionally, the need for genetic counseling and consultation service has been increasing. We tried to establish and manage a medical genetic clinic within the department of laboratory medicine by using a genetic testing network. An Inter-laboratory network has been organized between Soonchunhyang University Bucheon Hospital and Samsung Medical Center since January, 2005. As clinical laboratory physicians, we provide medical services ranging from genetic counseling to genetic testing. In this study we surveyed the need and demand for genetic consultation services using a questionnaire. Of the 30 cases that were requested to receive a genetic consultation, 24 were referred to the genetic clinic during the last 11 months. Of these, 18 underwent genetic tests. The request for genetic consultation came mainly from neurology, obstetrics, and pediatrics departments and the distribution of requested disease entities was very heterogeneous. Operating processes became more settled compared to the early period and specific work fields were secured in the genetic consultation services. Over 80% of the respondents replied that a medical genetic clinic was important and that public relations campaign should be continued. Establishment of a medical genetic clinic by using a genetic testing network has led to important changes that the department of laboratory medicine is most suitable for genetic testing and medical genetic consultations and laboratory physicians should be concerned in that field. A medical genetic consultation system based on extensive genetic information and knowledge could enhance opportunities for cooperation in genetic research.

  6. Optimization of multicast optical networks with genetic algorithm

    NASA Astrophysics Data System (ADS)

    Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng

    2007-11-01

    In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.

  7. Quantifying and analyzing the network basis of genetic complexity.

    PubMed

    Thompson, Ethan G; Galitski, Timothy

    2012-01-01

    Genotype-to-phenotype maps exhibit complexity. This genetic complexity is mentioned frequently in the literature, but a consistent and quantitative definition is lacking. Here, we derive such a definition and investigate its consequences for model genetic systems. The definition equates genetic complexity with a surplus of genotypic diversity over phenotypic diversity. Applying this definition to ensembles of Boolean network models, we found that the in-degree distribution and the number of periodic attractors produced determine the relative complexity of different topology classes. We found evidence that networks that are difficult to control, or that exhibit a hierarchical structure, are genetically complex. We analyzed the complexity of the cell cycle network of Sacchoromyces cerevisiae and pinpointed genes and interactions that are most important for its high genetic complexity. The rigorous definition of genetic complexity is a tool for unraveling the structure and properties of genotype-to-phenotype maps by enabling the quantitative comparison of the relative complexities of different genetic systems. The definition also allows the identification of specific network elements and subnetworks that have the greatest effects on genetic complexity. Moreover, it suggests ways to engineer biological systems with desired genetic properties.

  8. Event-based cluster synchronization of coupled genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Yue, Dandan; Guan, Zhi-Hong; Li, Tao; Liao, Rui-Quan; Liu, Feng; Lai, Qiang

    2017-09-01

    In this paper, the cluster synchronization of coupled genetic regulatory networks with a directed topology is studied by using the event-based strategy and pinning control. An event-triggered condition with a threshold consisting of the neighbors' discrete states at their own event time instants and a state-independent exponential decay function is proposed. The intra-cluster states information and extra-cluster states information are involved in the threshold in different ways. By using the Lyapunov function approach and the theories of matrices and inequalities, we establish the cluster synchronization criterion. It is shown that both the avoidance of continuous transmission of information and the exclusion of the Zeno behavior are ensured under the presented triggering condition. Explicit conditions on the parameters in the threshold are obtained for synchronization. The stability criterion of a single GRN is also given under the reduced triggering condition. Numerical examples are provided to validate the theoretical results.

  9. Evolving neural networks with genetic algorithms to study the string landscape

    NASA Astrophysics Data System (ADS)

    Ruehle, Fabian

    2017-08-01

    We study possible applications of artificial neural networks to examine the string landscape. Since the field of application is rather versatile, we propose to dynamically evolve these networks via genetic algorithms. This means that we start from basic building blocks and combine them such that the neural network performs best for the application we are interested in. We study three areas in which neural networks can be applied: to classify models according to a fixed set of (physically) appealing features, to find a concrete realization for a computation for which the precise algorithm is known in principle but very tedious to actually implement, and to predict or approximate the outcome of some involved mathematical computation which performs too inefficient to apply it, e.g. in model scans within the string landscape. We present simple examples that arise in string phenomenology for all three types of problems and discuss how they can be addressed by evolving neural networks from genetic algorithms.

  10. Evolving complex dynamics in electronic models of genetic networks

    NASA Astrophysics Data System (ADS)

    Mason, Jonathan; Linsay, Paul S.; Collins, J. J.; Glass, Leon

    2004-09-01

    Ordinary differential equations are often used to model the dynamics and interactions in genetic networks. In one particularly simple class of models, the model genes control the production rates of products of other genes by a logical function, resulting in piecewise linear differential equations. In this article, we construct and analyze an electronic circuit that models this class of piecewise linear equations. This circuit combines CMOS logic and RC circuits to model the logical control of the increase and decay of protein concentrations in genetic networks. We use these electronic networks to study the evolution of limit cycle dynamics. By mutating the truth tables giving the logical functions for these networks, we evolve the networks to obtain limit cycle oscillations of desired period. We also investigate the fitness landscapes of our networks to determine the optimal mutation rate for evolution.

  11. Human Genetic Evidence for Involvement of CD137 in Atherosclerosis

    PubMed Central

    Söderström, Leif Å; Gertow, Karl; Folkersen, Lasse; Sabater-Lleal, Maria; Sundman, Eva; Sheikine, Yuri; Goel, Anuj; Baldassarre, Damiano; Humphries, Steve E; de Faire, Ulf; Watkins, Hugh; Tremoli, Elena; Veglia, Fabrizio; Hamsten, Anders; Hansson, Göran K; Olofsson, Peder S

    2014-01-01

    Atherosclerosis is an inflammatory disease and the main cause of cardiovascular disease. Inflammation promotes plaque instability and clinical disease, such as myocardial infarction, stroke and peripheral vascular disease. Subclinical atherosclerosis begins with thickening of the arterial intimal layer, and increased intima-media thickness (IMT) in the carotid artery is a widely used measurement of subclinical atherosclerosis. Activation of CD137 (tumor necrosis factor receptor super family 9) promotes inflammation and disease development in murine atherosclerosis. CD137 is expressed in human atherosclerosis, but its role is largely unknown. This study uses a genetic approach to investigate CD137 in human atherosclerotic disease. In publicly available data on genotype and gene expression from the HapMap project, the minor T allele of rs2453021, a single nucleotide polymorphism in CD137, was significantly associated with CD137 gene expression. In the PROCARDIS and Wellcome Trust Case Control Consortium (WTCCC) cohorts of 13,029 cases and controls, no significant association was detected between the minor T allele of rs2453021 and risk for coronary artery disease or myocardial infarction. However, in the IMPROVE multicenter study of 3,418 individuals, the minor T allele of rs2453021 was associated with increased IMT of the common carotid artery (CCA), as measured by ultrasonography, with presence of plaque in CCA and with increased incidence of adverse noncardiac vascular events. Taken together, this study shows that the minor T allele of rs2453021 is associated with increased IMT in the CCA and increased risk of incident noncardiac vascular events, thus providing the first human genetic evidence for involvement of CD137 in atherosclerosis. PMID:25032953

  12. A genetic algorithm for solving supply chain network design model

    NASA Astrophysics Data System (ADS)

    Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

    2013-09-01

    Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

  13. Genetic networks controlling the development of midbrain dopaminergic neurons

    PubMed Central

    Prakash, Nilima; Wurst, Wolfgang

    2006-01-01

    Recent data have substantially advanced our understanding of midbrain dopaminergic neuron development. Firstly, a Wnt1-regulated genetic network, including Otx2 and Nkx2-2, and a Shh-controlled genetic cascade, including Lmx1a, Msx1 and Nkx6-1, have been unravelled, acting in parallel or sequentially to establish a territory competent for midbrain dopaminergic precursor production at relatively early stages of neural development. Secondly, the same factors (Wnt1 and Lmx1a/Msx1) appear to regulate midbrain dopaminergic and/or neuronal fate specification in the postmitotic progeny of these precursors by controlling the expression of midbrain dopaminergic-specific and/or general proneural factors at later stages of neural development. For the first time, early inductive events have thus been linked to later differentiation processes in midbrain dopaminergic neuron development. Given the pivotal importance of this neuronal population for normal function of the human brain and its involvement in severe neurological and psychiatric disorders such as Parkinson's Disease, these advances open new prospects for potential stem cell-based therapies. We will summarize these new findings in the overall context of midbrain dopaminergic neuron development in this review. PMID:16825303

  14. Genetic regulatory network models of biological clocks: evolutionary history matters.

    PubMed

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

    2008-01-01

    We study the evolvability and dynamics of artificial genetic regulatory networks (GRNs), as active control systems, realizing simple models of biological clocks that have evolved to respond to periodic environmental stimuli of various kinds with appropriate periodic behaviors. GRN models may differ in the evolvability of expressive regulatory dynamics. A new class of artificial GRNs with an evolvable number of complex cis-regulatory control sites--each involving a finite number of inhibitory and activatory binding factors--is introduced, allowing realization of complex regulatory logic. Previous work on biological clocks in nature has noted the capacity of clocks to oscillate in the absence of environmental stimuli, putting forth several candidate explanations for their observed behavior, related to anticipation of environmental conditions, compartmentation of activities in time, and robustness to perturbations of various kinds or to unselected accidents of neutral selection. Several of these hypotheses are explored by evolving GRNs with and without (Gaussian) noise and blackout periods for environmental stimulation. Robustness to certain types of perturbation appears to account for some, but not all, dynamical properties of the evolved networks. Unselected abilities, also observed for biological clocks, include the capacity to adapt to change in wavelength of environmental stimulus and to clock resetting.

  15. Special considerations in prognostic research in cancer involving genetic polymorphisms

    PubMed Central

    2013-01-01

    Analysis of genetic polymorphisms may help identify putative prognostic markers and determine the biological basis of variable prognosis in patients. However, in contrast to other variables commonly used in the prognostic studies, there are special considerations when studying genetic polymorphisms. For example, variable inheritance patterns (recessive, dominant, codominant, and additive genetic models) need to be explored to identify the specific genotypes associated with the outcome. In addition, several characteristics of genetic polymorphisms, such as their minor allele frequency and linkage disequilibrium among multiple polymorphisms, and the population substructure of the cohort investigated need to be accounted for in the analyses. In addition, in cancer research due to the genomic differences between the tumor and non-tumor DNA, differences in the genetic information obtained using these tissues need to be carefully assessed in prognostic studies. In this article, we review these and other considerations specific to genetic polymorphism by focusing on genetic prognostic studies in cancer. PMID:23773794

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

    NASA Astrophysics Data System (ADS)

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

    2012-11-01

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

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

    PubMed

    Jung, Jae-Yoon; Wall, Dennis Paul

    2013-01-01

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

  18. Metabolome 2.0: quantitative genetics and network biology of metabolic phenotypes.

    PubMed

    Dumas, Marc-Emmanuel

    2012-10-01

    The characterization of the metabolome has rapidly evolved over two decades, from early developments in analytical chemistry to systems biology. Metabolites and small molecules are not independent; they are organized in biochemical pathways and in a wider metabolic network, which is itself dependent on various genetic and signaling networks for its regulation. Recent advances in genomics, transcriptomics, proteomics and metabolomics have been matched by the development of publicly available repositories, which have helped shaping a new generation of integrative studies using metabolite measurements in molecular epidemiology and genetic studies. Although the environment influences metabolism, the identification of the genetic determinants of metabolic phenotypes (metabotypes) was made possible by the development of metabotype quantitative trait locus (mQTL) mapping and metabolomic genome-wide association studies (mGWAS) in a rigorous statistical genetics framework, deriving associations between metabolite concentrations and genetic polymorphisms. However, given the complexity of the biomolecular events involved in the regulation of metabolic patterns, alternative network biology approaches have also been recently introduced, such as integrated metabolome and interactome mapping (iMIM). This unprecedented convergence of metabolic biochemistry, quantitative genetics and network biology already has had a strong impact on the role of the metabolome in biomedical sciences, and this review gives a foretaste of its anticipated successes in eventually delivering personalized medicine.

  19. Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis

    PubMed Central

    Goelzer, Anne; Bekkal Brikci, Fadia; Martin-Verstraete, Isabelle; Noirot, Philippe; Bessières, Philippe; Aymerich, Stéphane; Fromion, Vincent

    2008-01-01

    Background Few genome-scale models of organisms focus on the regulatory networks and none of them integrates all known levels of regulation. In particular, the regulations involving metabolite pools are often neglected. However, metabolite pools link the metabolic to the genetic network through genetic regulations, including those involving effectors of transcription factors or riboswitches. Consequently, they play pivotal roles in the global organization of the genetic and metabolic regulatory networks. Results We report the manually curated reconstruction of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis (transcriptional, translational and post-translational regulations and modulation of enzymatic activities). We provide a systematic graphic representation of regulations of each metabolic pathway based on the central role of metabolites in regulation. We show that the complex regulatory network of B. subtilis can be decomposed as sets of locally regulated modules, which are coordinated by global regulators. Conclusion This work reveals the strong involvement of metabolite pools in the general regulation of the metabolic network. Breaking the metabolic network down into modules based on the control of metabolite pools reveals the functional organization of the genetic and metabolic regulatory networks of B. subtilis. PMID:18302748

  20. Material procedure quality forecast based on genetic BP neural network

    NASA Astrophysics Data System (ADS)

    Zheng, Bao-Hua

    2017-07-01

    Material procedure quality forecast plays an important role in quality control. This paper proposes a prediction model based on genetic algorithm (GA) and back propagation (BP) neural network. It can obtain the initial weights and thresholds of optimized BP neural network with the GA global search ability. A material process quality prediction model with the optimized BP neural network is adopted to predict the error of future process to measure the accuracy of process quality. The results show that the proposed method has the advantages of high accuracy and fast convergence rate compared with BP neural network.

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

    PubMed

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

    2016-07-01

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

  2. A Full Bayesian Approach for Boolean Genetic Network Inference

    PubMed Central

    Han, Shengtong; Wong, Raymond K. W.; Lee, Thomas C. M.; Shen, Linghao; Li, Shuo-Yen R.; Fan, Xiaodan

    2014-01-01

    Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data. PMID:25551820

  3. Are genetically robust regulatory networks dynamically different from random ones?

    NASA Astrophysics Data System (ADS)

    Sevim, Volkan; Rikvold, Per Arne

    We study a genetic regulatory network model developed to demonstrate that genetic robustness can evolve through stabilizing selection for optimal phenotypes. We report preliminary results on whether such selection could result in a reorganization of the state space of the system. For the chosen parameters, the evolution moves the system slightly toward the more ordered part of the phase diagram. We also find that strong memory effects cause the Derrida annealed approximation to give erroneous predictions about the model's phase diagram.

  4. Clone size distributions in networks of genetic similarity

    NASA Astrophysics Data System (ADS)

    Hernández-García, E.; Rozenfeld, A. F.; Eguíluz, V. M.; Arnaud-Haond, S.; Duarte, C. M.

    2006-12-01

    We build networks of genetic similarity in which the nodes are organisms sampled from biological populations. The procedure is illustrated by constructing networks from genetic data of a marine clonal plant. An important feature in the networks is the presence of clone subgraphs, i.e. sets of organisms with identical genotype forming clones. As a first step to understanding the dynamics that has shaped these networks, we point up a relationship between a particular degree distribution and the clone size distribution in the populations. We construct a dynamical model for the population dynamics, focussing on the dynamics of the clones, and solve it for the required distributions. Scale free and exponentially decaying forms are obtained depending on parameter values, the first type being obtained when clonal growth is the dominant process. Average distributions are dominated by the power law behavior presented by the fastest replicating populations.

  5. Wind power prediction based on genetic neural network

    NASA Astrophysics Data System (ADS)

    Zhang, Suhan

    2017-04-01

    The scale of grid connected wind farms keeps increasing. To ensure the stability of power system operation, make a reasonable scheduling scheme and improve the competitiveness of wind farm in the electricity generation market, it's important to accurately forecast the short-term wind power. To reduce the influence of the nonlinear relationship between the disturbance factor and the wind power, the improved prediction model based on genetic algorithm and neural network method is established. To overcome the shortcomings of long training time of BP neural network and easy to fall into local minimum and improve the accuracy of the neural network, genetic algorithm is adopted to optimize the parameters and topology of neural network. The historical data is used as input to predict short-term wind power. The effectiveness and feasibility of the method is verified by the actual data of a certain wind farm as an example.

  6. MicroRNA-dependent genetic networks during neural development.

    PubMed

    Abernathy, Daniel G; Yoo, Andrew S

    2015-01-01

    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 microRNAs enriched in the brain and discuss the way that genetic networks in neural development depend on microRNAs.

  7. Genetic Networks in Mouse Retinal Ganglion Cells

    PubMed Central

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

    2016-01-01

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

  8. 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.

  9. Shared genetic data and the rights of involved people.

    PubMed

    Sellaroli, Valentina; Cucca, Francesco; Santosuosso, Amedeo

    2007-01-01

    The authors examine the two main attitudes toward genetics: Exceptionalism and Undervaluation. They firstly pose the basis of the matter from the scientific point of view and then verify how these two attitudes really work in the different fields where human genetics finds relevant applications, dealing with the questions arising from the unique characteristics of genetic data that is shared among the whole bio-group. Then some judicial cases related to the conflicts arising when genetic data are stored in repositories, whatever the aims and reasons, are presented and discussed. The matter is then considered from the criminal law perspective, in the light of the new possible implications of DNA fingerprinting in criminal investigations. Finally, some general considerations on opposing Exceptionalism/Undervaluation viewpoints and the real reason for making up new rules are presented.

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

    PubMed Central

    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

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

    PubMed Central

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

    2013-01-01

    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

  12. 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.

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

    PubMed

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

    2015-09-15

    A central goal of synthetic biology is to engineer cellular behavior by engineering synthetic gene networks for a variety of biotechnology and medical applications. The process of engineering gene networks often involves an iterative 'design-build-test' cycle, whereby the parts and connections that make up the network are built, characterized and varied until the desired network function is reached. Many advances have been made in the design and build portions of this cycle. However, the slow process of in vivo characterization of network function often limits the timescale of the testing step. Cell-free transcription-translation (TX-TL) systems offer a simple and fast alternative to performing these characterizations in cells. Here we provide an overview of a cell-free TX-TL system that utilizes the native Escherichia coli TX-TL machinery, thereby allowing a large repertoire of parts and networks to be characterized. As a way to demonstrate the utility of cell-free TX-TL, we illustrate the characterization of two genetic networks: an RNA transcriptional cascade and a protein regulated incoherent feed-forward loop. We also provide guidelines for designing TX-TL experiments to characterize new genetic networks. We end with a discussion of current and emerging applications of cell free systems. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. Converting genetic network oscillations into somite spatial patterns

    NASA Astrophysics Data System (ADS)

    Mazzitello, K. I.; Arizmendi, C. M.; Hentschel, H. G. E.

    2008-08-01

    The segmentation of vertebrate embryos, a process known as somitogenesis, depends on a complex genetic network that generates highly dynamic gene expression in an oscillatory manner. A recent proposal for the mechanism underlying these oscillations involves negative-feedback regulation at transcriptional translational levels, also known as the “delay model” [J. Lewis Curr. Biol. 13, 1398 (2003)]. In addition, in the zebrafish a longitudinal positional information signal in the form of an Fgf8 gradient constitutes a determination front that could be used to transform these coupled intracellular temporal oscillations into the observed spatial periodicity of somites. Here we consider an extension of the delay model by taking into account the interaction of the oscillation clock with the determination front. Comparison is made with the known properties of somite formation in the zebrafish embryo. We also show that the model can mimic the anomalies formed when progression of the determination wave front is perturbed and make an experimental prediction that can be used to test the model.

  15. Genetic networks in the mouse retina: Growth Associated Protein 43 and Phosphatase Tensin Homolog network

    PubMed Central

    Freeman, Natalie E.; Templeton, Justin P.; Orr, William E.; Lu, Lu; Williams, Robert W.

    2011-01-01

    Purpose The present study examines the structure and covariance of endogenous variation in gene expression across the recently expanded family of C57BL/6J (B) X DBA/2J (D) Recombinant Inbred (BXD RI) strains of mice. This work is accompanied by a highly interactive database that can be used to generate and test specific hypotheses. For example, we define the genetic network regulating growth associated protein 43 (Gap43) and phosphatase tensin homolog (Pten). Methods The Hamilton Eye Institute (HEI) Retina Database within GeneNetwork features the data analysis of 346 Illumina Sentrix BeadChip Arrays (mouse whole genome-6 version 2). Eighty strains of mice are presented, including 75 BXD RI strains, the parental strains (C57BL/6J and DBA/2J), the reciprocal crosses, and the BALB/cByJ mice. Independent biologic samples for at least two animals from each gender were obtained with a narrow age range (48 to 118 days). Total RNA was prepared followed by the production of biotinylated cRNAs, which were pipetted into the Mouse WG-6V2 arrays. The data was globally normalized with rank invariant and stabilization (2z+8). Results The HEI Retina Database is located on the GeneNetwork website. The database was used to extract unique transcriptome signatures for specific cell types in the retina (retinal pigment epithelial, amacrine, and retinal ganglion cells). Two genes associated with axonal outgrowth (Gap43 and Pten) were used to display the power of this new retina database. Bioinformatic tools located within GeneNetwork in conjunction with the HEI Retina Database were used to identify the unique signature Quantitative Trait Loci (QTLs) for Gap43 and Pten on chromosomes 1, 2, 12, 15, 16, and 19. Gap43 and Pten possess networks that are similar to ganglion cell networks that may be associated with axonal growth in the mouse retina. This network involves high correlations of transcription factors (SRY sex determining region Y-box 2 [Sox2], paired box gene 6 [Pax6], and

  16. Genetic Dissection of Acute Ethanol Responsive Gene Networks in Prefrontal Cortex: Functional and Mechanistic Implications

    PubMed Central

    Wolen, Aaron R.; Phillips, Charles A.; Langston, Michael A.; Putman, Alex H.; Vorster, Paul J.; Bruce, Nathan A.; York, Timothy P.; Williams, Robert W.; Miles, Michael F.

    2012-01-01

    Background Individual differences in initial sensitivity to ethanol are strongly related to the heritable risk of alcoholism in humans. To elucidate key molecular networks that modulate ethanol sensitivity we performed the first systems genetics analysis of ethanol-responsive gene expression in brain regions of the mesocorticolimbic reward circuit (prefrontal cortex, nucleus accumbens, and ventral midbrain) across a highly diverse family of 27 isogenic mouse strains (BXD panel) before and after treatment with ethanol. Results Acute ethanol altered the expression of ∼2,750 genes in one or more regions and 400 transcripts were jointly modulated in all three. Ethanol-responsive gene networks were extracted with a powerful graph theoretical method that efficiently summarized ethanol's effects. These networks correlated with acute behavioral responses to ethanol and other drugs of abuse. As predicted, networks were heavily populated by genes controlling synaptic transmission and neuroplasticity. Several of the most densely interconnected network hubs, including Kcnma1 and Gsk3β, are known to influence behavioral or physiological responses to ethanol, validating our overall approach. Other major hub genes like Grm3, Pten and Nrg3 represent novel targets of ethanol effects. Networks were under strong genetic control by variants that we mapped to a small number of chromosomal loci. Using a novel combination of genetic, bioinformatic and network-based approaches, we identified high priority cis-regulatory candidate genes, including Scn1b, Gria1, Sncb and Nell2. Conclusions The ethanol-responsive gene networks identified here represent a previously uncharacterized intermediate phenotype between DNA variation and ethanol sensitivity in mice. Networks involved in synaptic transmission were strongly regulated by ethanol and could contribute to behavioral plasticity seen with chronic ethanol. Our novel finding that hub genes and a small number of loci exert major influence

  17. Integration of biological networks and pathways with genetic association studies.

    PubMed

    Sun, Yan V

    2012-10-01

    Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.

  18. Hierarchical genetic networks and noncoding RNAs

    NASA Astrophysics Data System (ADS)

    Zhdanov, Vladimir P.

    2010-12-01

    In eukaryotic cells, many genes are transcribed into noncoding RNAs. Such RNAs may associate with mRNAs and inhibit their translation and facilitate degradation. To clarify what may happen in this case, we propose a kinetic model describing the effect of noncoding RNAs on a mRNA-protein network with the hierarchical three-layer architecture. For positive regulation of the layers, our model predicts either bistability with a fairly narrow hysteresis loop or a unique steady state. For negative or mixed regulation, the steady state is found to be unique.

  19. Complex and unexpected dynamics in simple genetic regulatory networks

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  20. 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…

  1. 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…

  2. 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.

  3. 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.

  4. 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

  5. Information theory and the ethylene genetic network

    PubMed Central

    González-García, José S

    2011-01-01

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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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. Copyright © 2014 Elsevier Inc. All rights reserved.

  11. 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.

  12. Electronic circuit analog of synthetic genetic networks: Revisited

    NASA Astrophysics Data System (ADS)

    Hellen, Edward H.; Kurths, Jürgen; Dana, Syamal K.

    2017-06-01

    Electronic circuits are useful tools for studying potential dynamical behaviors of synthetic genetic networks. The circuit models are complementary to numerical simulations of the networks, especially providing a framework for verification of dynamical behaviors in the presence of intrinsic and extrinsic noise of the electrical systems. Here we present an improved version of our previous design of an electronic analog of genetic networks that includes the 3-gene Repressilator and we show conversions between model parameters and real circuit component values to mimic the numerical results in experiments. Important features of the circuit design include the incorporation of chemical kinetics representing Hill function inhibition, quorum sensing coupling, and additive noise. Especially, we make a circuit design for a systematic change of initial conditions in experiment, which is critically important for studies of dynamical systems' behavior, particularly, when it shows multistability. This improved electronic analog of the synthetic genetic network allows us to extend our investigations from an isolated Repressilator to coupled Repressilators and to reveal the dynamical behavior's complexity.

  13. Fusion techniques of fuzzy systems and neural networks, and fuzzy systems and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Takagi, Hideyuki

    1993-12-01

    This paper overviews four combinations of fuzzy logic, neural networks and genetic algorithms: (1) neural networks to auto-design fuzzy systems, (2) employing fuzzy rule structure to construct structured neural networks, (3) genetic algorithms to auto-design fuzzy systems, and (4) a fuzzy knowledge-based system to control genetic parameter dynamically.

  14. Involvement of mesolimbic dopaminergic network in neuropathic pain relief by treadmill exercise

    PubMed Central

    Wakaizumi, Kenta; Kondo, Takashige; Hamada, Yusuke; Narita, Michiko; Kawabe, Rui; Narita, Hiroki; Watanabe, Moe; Kato, Shigeki; Senba, Emiko; Kobayashi, Kazuto; Yamanaka, Akihiro

    2016-01-01

    Background Exercise alleviates pain and it is a central component of treatment strategy for chronic pain in clinical setting. However, little is known about mechanism of this exercise-induced hypoalgesia. The mesolimbic dopaminergic network plays a role in positive emotions to rewards including motivation and pleasure. Pain negatively modulates these emotions, but appropriate exercise is considered to activate the dopaminergic network. We investigated possible involvement of this network as a mechanism of exercise-induced hypoalgesia. Methods In the present study, we developed a protocol of treadmill exercise, which was able to recover pain threshold under partial sciatic nerve ligation in mice, and investigated involvement of the dopaminergic reward network in exercise-induced hypoalgesia. To temporally suppress a neural activation during exercise, a genetically modified inhibitory G-protein-coupled receptor, hM4Di, was specifically expressed on dopaminergic pathway from the ventral tegmental area to the nucleus accumbens. Results The chemogenetic-specific neural suppression by Gi-DREADD system dramatically offset the effect of exercise-induced hypoalgesia in transgenic mice with hM4Di expressed on the ventral tegmental area dopamine neurons. Additionally, anti-exercise-induced hypoalgesia effect was significantly observed under the suppression of neurons projecting out of the ventral tegmental area to the nucleus accumbens as well. Conclusion Our findings suggest that the dopaminergic pathway from the ventral tegmental area to the nucleus accumbens is involved in the anti-nociception under low-intensity exercise under a neuropathic pain-like state. PMID:27909152

  15. 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

  16. 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.

  17. 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.

  18. Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver.

    PubMed

    Ziebarth, Jesse D; Cui, Yan

    2017-01-01

    The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

  19. 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.

  20. Gene Expression Network Reconstruction by Convex Feature Selection when Incorporating Genetic Perturbations

    PubMed Central

    Logsdon, Benjamin A.; Mezey, Jason

    2010-01-01

    Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL), which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1), and genes involved in endocytosis (RCY1), the spindle checkpoint (BUB2), sulfonate catabolism (JLP1), and cell-cell communication (PRM7). Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data. PMID:21152011

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

    PubMed Central

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

    2009-01-01

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

  2. 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.

  3. Rewiring of genetic networks in response to DNA damage.

    PubMed

    Bandyopadhyay, Sourav; Mehta, Monika; Kuo, Dwight; Sung, Min-Kyung; Chuang, Ryan; Jaehnig, Eric J; Bodenmiller, Bernd; Licon, Katherine; Copeland, Wilbert; Shales, Michael; Fiedler, Dorothea; Dutkowski, Janusz; Guénolé, Aude; van Attikum, Haico; Shokat, Kevan M; Kolodner, Richard D; Huh, Won-Ki; Aebersold, Ruedi; Keogh, Michael-Christopher; Krogan, Nevan J; Ideker, Trey

    2010-12-03

    Although cellular behaviors are dynamic, the networks that govern these behaviors have been mapped primarily as static snapshots. Using an approach called differential epistasis mapping, we have discovered widespread changes in genetic interaction among yeast kinases, phosphatases, and transcription factors as the cell responds to DNA damage. Differential interactions uncover many gene functions that go undetected in static conditions. They are very effective at identifying DNA repair pathways, highlighting new damage-dependent roles for the Slt2 kinase, Pph3 phosphatase, and histone variant Htz1. The data also reveal that protein complexes are generally stable in response to perturbation, but the functional relations between these complexes are substantially reorganized. Differential networks chart a new type of genetic landscape that is invaluable for mapping cellular responses to stimuli.

  4. Simulating genetic networks made easy: network construction with simple building blocks.

    PubMed

    Vercruysse, Steven; Kuiper, Martin

    2005-01-15

    We present SIM-plex, a genetic network simulator with a very intuitive interface in which a user can easily specify interactions as simple 'if-then' statements. The simulator is based on the mathematical model of Piecewise Linear Differential Equations (PLDEs). With PLDEs, genetic interactions are approximated as acting in a switch-like manner. The Java program, examples and a tutorial are available at http://www.psb.ugent.be/cbd/ {stcru,makui}@psb.ugent.be

  5. Gene network inference via structural equation modeling in genetical genomics experiments.

    PubMed

    Liu, Bing; de la Fuente, Alberto; Hoeschele, Ina

    2008-03-01

    Our goal is gene network inference in genetical genomics or systems genetics experiments. For species where sequence information is available, we first perform expression quantitative trait locus (eQTL) mapping by jointly utilizing cis-, cis-trans-, and trans-regulation. After using local structural models to identify regulator-target pairs for each eQTL, we construct an encompassing directed network (EDN) by assembling all retained regulator-target relationships. The EDN has nodes corresponding to expressed genes and eQTL and directed edges from eQTL to cis-regulated target genes, from cis-regulated genes to cis-trans-regulated target genes, from trans-regulator genes to target genes, and from trans-eQTL to target genes. For network inference within the strongly constrained search space defined by the EDN, we propose structural equation modeling (SEM), because it can model cyclic networks and the EDN indeed contains feedback relationships. On the basis of a factorization of the likelihood and the constrained search space, our SEM algorithm infers networks involving several hundred genes and eQTL. Structure inference is based on a penalized likelihood ratio and an adaptation of Occam's window model selection. The SEM algorithm was evaluated using data simulated with nonlinear ordinary differential equations and known cyclic network topologies and was applied to a real yeast data set.

  6. Genetic Networks Activated by Blast Injury to the Eye

    DTIC Science & Technology

    2014-08-01

    Major Finding: Collected retinas from 40 normal strains with 148 microarrays run. We have collected phenotypic data on corneal thickness, lOP and...pressure ( lOP ), central corneal thickness (CCT) and visual acuity. Task 2) Define the genetic networks activated by blast injury in the eye and in...retina. Accomplishments Under These Goals: Taskl: At the present time we have measured lOP and central corneal thickness on 27 strains of mice

  7. Modeling Genetic Regulatory Networks Using First-Order Probabilistic Logic

    DTIC Science & Technology

    2013-03-01

    Researchers can target specific genes or proteins in signal transduction pathways to cure a number of diseases and disorders, with applications in...medication and drug delivery. Genetic Regulatory Networks (GRNs) represent the signal transduction, or the activation and deactivation of genes , as...intelligence, prolog, gene regulation, “Raf” pathway 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 28 19a

  8. 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

  9. 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

  10. 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.

  11. Protein complexes predictions within protein interaction networks using genetic algorithms.

    PubMed

    Ramadan, Emad; Naef, Ahmed; Ahmed, Moataz

    2016-07-25

    Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .

  12. 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.

  13. Issues impacting genetic network reverse engineering algorithm validation using small networks.

    PubMed

    Vinh, Nguyen Xuan; Chetty, Madhu; Coppel, Ross; Wangikar, Pramod P

    2012-12-01

    Genetic network reverse engineering has been an area of intensive research within the systems biology community during the last decade. With many techniques currently available, the task of validating them and choosing the best one for a certain problem is a complex issue. Current practice has been to validate an approach on in-silico synthetic data sets, and, wherever possible, on real data sets with known ground-truth. In this study, we highlight a major issue that the validation of reverse engineering algorithms on small benchmark networks very often results in networks which are not statistically better than a randomly picked network. Another important issue highlighted is that with short time series, a small variation in the pre-processing procedure might yield large differences in the inferred networks. To demonstrate these issues, we have selected as our case study the IRMA in-vivo synthetic yeast network recently published in Cell. Using Fisher's exact test, we show that many results reported in the literature on reverse-engineering this network are not significantly better than random. The discussion is further extended to some other networks commonly used for validation purposes in the literature. The results presented in this study emphasize that studies carried out using small genetic networks are likely to be trivial, making it imperative that larger real networks be used for validating and benchmarking purposes. If smaller networks are considered, then the results should be interpreted carefully to avoid over confidence. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.

  14. Disentangling genetic and environmental risk factors for individual diseases from multiplex comorbidity networks

    PubMed Central

    Klimek, Peter; Aichberger, Silke; Thurner, Stefan

    2016-01-01

    Most disorders are caused by a combination of multiple genetic and/or environmental factors. If two diseases are caused by the same molecular mechanism, they tend to co-occur in patients. Here we provide a quantitative method to disentangle how much genetic or environmental risk factors contribute to the pathogenesis of 358 individual diseases, respectively. We pool data on genetic, pathway-based, and toxicogenomic disease-causing mechanisms with disease co-occurrence data obtained from almost two million patients. From this data we construct a multiplex network where nodes represent disorders that are connected by links that either represent phenotypic comorbidity of the patients or the involvement of a certain molecular mechanism. From the similarity of phenotypic and mechanism-based networks for each disorder we derive measure that allows us to quantify the relative importance of various molecular mechanisms for a given disease. We find that most diseases are dominated by genetic risk factors, while environmental influences prevail for disorders such as depressions, cancers, or dermatitis. Almost never we find that more than one type of mechanisms is involved in the pathogenesis of diseases. PMID:28008973

  15. Disentangling genetic and environmental risk factors for individual diseases from multiplex comorbidity networks.

    PubMed

    Klimek, Peter; Aichberger, Silke; Thurner, Stefan

    2016-12-23

    Most disorders are caused by a combination of multiple genetic and/or environmental factors. If two diseases are caused by the same molecular mechanism, they tend to co-occur in patients. Here we provide a quantitative method to disentangle how much genetic or environmental risk factors contribute to the pathogenesis of 358 individual diseases, respectively. We pool data on genetic, pathway-based, and toxicogenomic disease-causing mechanisms with disease co-occurrence data obtained from almost two million patients. From this data we construct a multiplex network where nodes represent disorders that are connected by links that either represent phenotypic comorbidity of the patients or the involvement of a certain molecular mechanism. From the similarity of phenotypic and mechanism-based networks for each disorder we derive measure that allows us to quantify the relative importance of various molecular mechanisms for a given disease. We find that most diseases are dominated by genetic risk factors, while environmental influences prevail for disorders such as depressions, cancers, or dermatitis. Almost never we find that more than one type of mechanisms is involved in the pathogenesis of diseases.

  16. Comparison and evaluation of network clustering algorithms applied to genetic interaction networks.

    PubMed

    Hou, Lin; Wang, Lin; Berg, Arthur; Qian, Minping; Zhu, Yunping; Li, Fangting; Deng, Minghua

    2012-01-01

    The goal of network clustering algorithms detect dense clusters in a network, and provide a first step towards the understanding of large scale biological networks. With numerous recent advances in biotechnologies, large-scale genetic interactions are widely available, but there is a limited understanding of which clustering algorithms may be most effective. In order to address this problem, we conducted a systematic study to compare and evaluate six clustering algorithms in analyzing genetic interaction networks, and investigated influencing factors in choosing algorithms. The algorithms considered in this comparison include hierarchical clustering, topological overlap matrix, bi-clustering, Markov clustering, Bayesian discriminant analysis based community detection, and variational Bayes approach to modularity. Both experimentally identified and synthetically constructed networks were used in this comparison. The accuracy of the algorithms is measured by the Jaccard index in comparing predicted gene modules with benchmark gene sets. The results suggest that the choice differs according to the network topology and evaluation criteria. Hierarchical clustering showed to be best at predicting protein complexes; Bayesian discriminant analysis based community detection proved best under epistatic miniarray profile (EMAP) datasets; the variational Bayes approach to modularity was noticeably better than the other algorithms in the genome-scale networks.

  17. Engineering microbial phenotypes through rewiring of genetic networks.

    PubMed

    Windram, Oliver P F; Rodrigues, Rui T L; Lee, Sangjin; Haines, Matthew; Bayer, Travis S

    2017-03-21

    The ability to program cellular behaviour is a major goal of synthetic biology, with applications in health, agriculture and chemicals production. Despite efforts to build 'orthogonal' systems, interactions between engineered genetic circuits and the endogenous regulatory network of a host cell can have a significant impact on desired functionality. We have developed a strategy to rewire the endogenous cellular regulatory network of yeast to enhance compatibility with synthetic protein and metabolite production. We found that introducing novel connections in the cellular regulatory network enabled us to increase the production of heterologous proteins and metabolites. This strategy is demonstrated in yeast strains that show significantly enhanced heterologous protein expression and higher titers of terpenoid production. Specifically, we found that the addition of transcriptional regulation between free radical induced signalling and nitrogen regulation provided robust improvement of protein production. Assessment of rewired networks revealed the importance of key topological features such as high betweenness centrality. The generation of rewired transcriptional networks, selection for specific phenotypes, and analysis of resulting library members is a powerful tool for engineering cellular behavior and may enable improved integration of heterologous protein and metabolite pathways.

  18. 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

  19. Networks of genetic loci and the scientific literature

    NASA Astrophysics Data System (ADS)

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

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

  20. 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.

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

    PubMed

    Reaves, Allison Cook; Bianchi, Diana W

    2013-05-01

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

  2. Differential Network Analysis Reveals Genetic Effects on Catalepsy Modules

    PubMed Central

    Iancu, Ovidiu D.; Oberbeck, Denesa; Darakjian, Priscila; Kawane, Sunita; Erk, Jason; McWeeney, Shannon; Hitzemann, Robert

    2013-01-01

    We performed short-term bi-directional selective breeding for haloperidol-induced catalepsy, starting from three mouse populations of increasingly complex genetic structure: an F2 intercross, a heterogeneous stock (HS) formed by crossing four inbred strains (HS4) and a heterogeneous stock (HS-CC) formed from the inbred strain founders of the Collaborative Cross (CC). All three selections were successful, with large differences in haloperidol response emerging within three generations. Using a custom differential network analysis procedure, we found that gene coexpression patterns changed significantly; importantly, a number of these changes were concordant across genetic backgrounds. In contrast, absolute gene-expression changes were modest and not concordant across genetic backgrounds, in spite of the large and similar phenotypic differences. By inferring strain contributions from the parental lines, we are able to identify significant differences in allelic content between the selected lines concurrent with large changes in transcript connectivity. Importantly, this observation implies that genetic polymorphisms can affect transcript and module connectivity without large changes in absolute expression levels. We conclude that, in this case, selective breeding acts at the subnetwork level, with the same modules but not the same transcripts affected across the three selections. PMID:23555609

  3. 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

    2015-08-19

    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.

  4. Genetic Networks Controlling Structural Outcome of Glucosinolate Activation across Development

    PubMed Central

    Wentzell, Adam M.; Boeye, Ian; Zhang, Zhiyong; Kliebenstein, Daniel J.

    2008-01-01

    Most phenotypic variation present in natural populations is under polygenic control, largely determined by genetic variation at quantitative trait loci (QTLs). These genetic loci frequently interact with the environment, development, and each other, yet the importance of these interactions on the underlying genetic architecture of quantitative traits is not well characterized. To better study how epistasis and development may influence quantitative traits, we studied genetic variation in Arabidopsis glucosinolate activation using the moderately sized Bayreuth×Shahdara recombinant inbred population, in terms of number of lines. We identified QTLs for glucosinolate activation at three different developmental stages. Numerous QTLs showed developmental dependency, as well as a large epistatic network, centered on the previously cloned large-effect glucosinolate activation QTL, ESP. Analysis of Heterogeneous Inbred Families validated seven loci and all of the QTL×DPG (days post-germination) interactions tested, but was complicated by the extensive epistasis. A comparison of transcript accumulation data within 211 of these RILs showed an extensive overlap of gene expression QTLs for structural specifiers and their homologs with the identified glucosinolate activation loci. Finally, we were able to show that two of the QTLs are the result of whole-genome duplications of a glucosinolate activation gene cluster. These data reveal complex age-dependent regulation of structural outcomes and suggest that transcriptional regulation is associated with a significant portion of the underlying ontogenic variation and epistatic interactions in glucosinolate activation. PMID:18949035

  5. 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

  6. 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.

  7. Multilevel modeling for inference of genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Ng, Shu-Kay; Wang, Kui; McLachlan, Geoffrey J.

    2005-12-01

    Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

  8. 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

  9. 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.

  10. 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/.

  11. Genetic effect on blood pressure is modulated by age: the Hypertension Genetic Epidemiology Network Study.

    PubMed

    Shi, Gang; Gu, Chi C; Kraja, Aldi T; Arnett, Donna K; Myers, Richard H; Pankow, James S; Hunt, Steven C; Rao, Dabeeru C

    2009-01-01

    Genome-wide linkage analysis was performed for systolic and diastolic blood pressures in the Hypertension Genetic Epidemiology Network. We investigated the role of gene-age interactions using a recently developed variance components method that incorporates age variation in genetic effects. Substantially improved linkage evidence, in terms of both the number of linkage peaks and their significance levels, was observed. Twenty-six linkage peaks were identified with maximum logarithm of odds scores ranging between 3.0 and 4.6, 15 of which were cross-validated by the literature. The chromosomal region 1p36 that showed the highest logarithm of odds score in our study was found to be supported by evidence from 3 studies. The new method also led to vastly improved validation across ethnic groups. Ten of the 15 supported linkage peaks were cross-validated between 2 different ethnic groups, and 2 peaks on chromosomal region 1q31 and 16p11 were validated in 3 ethnic groups. In conclusion, this investigation demonstrates that genetic effects on blood pressure vary by age. The improved genetic linkage results presented here should help to identify the specific genetic variants that explain the observed results.

  12. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease

    PubMed Central

    Zhang, Bin; Gaiteri, Chris; Bodea, Liviu-Gabriel; Wang, Zhi; McElwee, Joshua; Podtelezhnikov, Alexei A.; Zhang, Chunsheng; Xie, Tao; Tran, Linh; Dobrin, Radu; Fluder, Eugene; Clurman, Bruce; Melquist, Stacey; Narayanan, Manikandan; Suver, Christine; Shah, Hardik; Mahajan, Milind; Gillis, Tammy; Mysore, Jayalakshmi; MacDonald, Marcy E.; Lamb, John R.; Bennett, David A; Molony, Cliona; Stone, David J.; Gudnason, Vilmundur; Myers, Amanda J.; Schadt, Eric E.; Neumann, Harald; Zhu, Jun; Emilsson, Valur

    2013-01-01

    SUMMARY The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer’s disease (LOAD), we constructed gene regulatory networks in 1647 post-mortem brain tissues from LOAD patients and non-demented subjects, and demonstrate that LOAD reconfigures specific portions of the molecular interaction structure. Through an integrative network-based approach, we rank-ordered these network structures for relevance to LOAD pathology, highlighting an immune and microglia-specific module dominated by genes involved in pathogen phagocytosis, containing TYROBP as a key regulator and up-regulated in LOAD. Mouse microglia cells over-expressing intact or truncated TYROBP revealed expression changes that significantly overlapped the human brain TYROBP network. Thus the causal network structure is a useful predictor of response to gene perturbations and presents a novel framework to test models of disease mechanisms underlying LOAD. PMID:23622250

  13. 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

  14. Between "design" and "bricolage": genetic networks, levels of selection, and adaptive evolution.

    PubMed

    Wilkins, Adam S

    2007-05-15

    The extent to which "developmental constraints" in complex organisms restrict evolutionary directions remains contentious. Yet, other forms of internal constraint, which have received less attention, may also exist. It will be argued here that a set of partial constraints below the level of phenotypes, those involving genes and molecules, influences and channels the set of possible evolutionary trajectories. At the top-most organizational level there are the genetic network modules, whose operations directly underlie complex morphological traits. The properties of these network modules, however, have themselves been set by the evolutionary history of the component genes and their interactions. Characterization of the components, structures, and operational dynamics of specific genetic networks should lead to a better understanding not only of the morphological traits they underlie but of the biases that influence the directions of evolutionary change. Furthermore, such knowledge may permit assessment of the relative degrees of probability of short evolutionary trajectories, those on the microevolutionary scale. In effect, a "network perspective" may help transform evolutionary biology into a scientific enterprise with greater predictive capability than it has hitherto possessed.

  15. Versatile RNA-sensing transcriptional regulators for engineering genetic networks.

    PubMed

    Lucks, Julius B; Qi, Lei; Mutalik, Vivek K; Wang, Denise; Arkin, Adam P

    2011-05-24

    The widespread natural ability of RNA to sense small molecules and regulate genes has become an important tool for synthetic biology in applications as diverse as environmental sensing and metabolic engineering. Previous work in RNA synthetic biology has engineered RNA mechanisms that independently regulate multiple targets and integrate regulatory signals. However, intracellular regulatory networks built with these systems have required proteins to propagate regulatory signals. In this work, we remove this requirement and expand the RNA synthetic biology toolkit by engineering three unique features of the plasmid pT181 antisense-RNA-mediated transcription attenuation mechanism. First, because the antisense RNA mechanism relies on RNA-RNA interactions, we show how the specificity of the natural system can be engineered to create variants that independently regulate multiple targets in the same cell. Second, because the pT181 mechanism controls transcription, we show how independently acting variants can be configured in tandem to integrate regulatory signals and perform genetic logic. Finally, because both the input and output of the attenuator is RNA, we show how these variants can be configured to directly propagate RNA regulatory signals by constructing an RNA-meditated transcriptional cascade. The combination of these three features within a single RNA-based regulatory mechanism has the potential to simplify the design and construction of genetic networks by directly propagating signals as RNA molecules.

  16. 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.

  17. Genetic network driven control of PHBV copolymer composition.

    PubMed

    Iadevaia, Sergio; Mantzaris, Nikos V

    2006-03-09

    We developed a detailed mathematical model describing the coupling between the molecular weight distribution dynamics of poly(3-hydroxybutyrate-co-3hydroxyvalerate) (PHBV) copolymer chains with those of hydroxybutyrate (HB) and hydroxyvalerate (HV) monomer formation. Sensitivity analysis of the model revealed that both the monomer composition and the molecular weight distribution of the copolymer chains are strongly affected by the ratio between the rates at which the two-monomer units are incorporated into the chains. This ratio depends on the relative HB and HV availability, which in turn is a function of the expression levels of genes encoding enzymes that catalyze monomer formation. Regulation of gene expression was accomplished through the aid of an artificial genetic network, the patterns of expression of which can be controlled by appropriately tuning the concentration of an extracellular inducer. Extensive simulations were used to study the effects of operating conditions and parameter uncertainties on the range of achievable copolymer compositions. Since the predicted conditions fell in the range of feasible bioprocessing manipulations, it is expected that such strategy could be successfully employed. Thus, the presented model constitutes a powerful tool for designing genetic networks that can drive the formation of PHBV copolymer structures with desirable characteristics.

  18. Versatile RNA-sensing transcriptional regulators for engineering genetic networks

    PubMed Central

    Lucks, Julius B.; Qi, Lei; Mutalik, Vivek K.; Wang, Denise; Arkin, Adam P.

    2011-01-01

    The widespread natural ability of RNA to sense small molecules and regulate genes has become an important tool for synthetic biology in applications as diverse as environmental sensing and metabolic engineering. Previous work in RNA synthetic biology has engineered RNA mechanisms that independently regulate multiple targets and integrate regulatory signals. However, intracellular regulatory networks built with these systems have required proteins to propagate regulatory signals. In this work, we remove this requirement and expand the RNA synthetic biology toolkit by engineering three unique features of the plasmid pT181 antisense-RNA-mediated transcription attenuation mechanism. First, because the antisense RNA mechanism relies on RNA-RNA interactions, we show how the specificity of the natural system can be engineered to create variants that independently regulate multiple targets in the same cell. Second, because the pT181 mechanism controls transcription, we show how independently acting variants can be configured in tandem to integrate regulatory signals and perform genetic logic. Finally, because both the input and output of the attenuator is RNA, we show how these variants can be configured to directly propagate RNA regulatory signals by constructing an RNA-meditated transcriptional cascade. The combination of these three features within a single RNA-based regulatory mechanism has the potential to simplify the design and construction of genetic networks by directly propagating signals as RNA molecules. PMID:21555549

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

    PubMed Central

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

    2016-01-01

    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

  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.

  1. 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.

  2. Structure Learning of Bayesian Networks Using Dual Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Lee, Jaehun; Chung, Wooyong; Kim, Euntai

    A new structure learning approach for Bayesian networks (BNs) based on dual genetic algorithm (DGA) is proposed in this paper. An individual of the population is represented as a dual chromosome composed of two chromosomes. The first chromosome represents the ordering among the BN nodes and the second represents the conditional dependencies among the ordered BN nodes. It is rigorously shown that there is no BN structure that cannot be encoded by the proposed dual genetic encoding and the proposed encoding explores the entire solution space of the BN structures. In contrast with existing GA-based structure learning methods, the proposed method learns not only the topology of the BN nodes, but also the ordering among the BN nodes, thereby, exploring the wider solution space of a given problem than the existing method. The dual genetic operators are closed in the set of the admissible individuals. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulation.

  3. 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.

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

    PubMed Central

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

    2016-01-01

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

  5. Global asymptotic stability of Hopfield neural network involving distributed delays.

    PubMed

    Zhao, Hongyong

    2004-01-01

    In the paper, we study dynamical behaviors of Hopfield neural networks system with distributed delays. Some new criteria ensuring the existence and uniqueness, and the global asymptotic stability (GAS) of equilibrium point are derived. In the results, we do not assume that the signal propagation functions satisfy the Lipschitz condition and do not require them to be bounded, differentiable or strictly increasing. Moreover, the symmetry of the connection matrix is not also necessary. Thus, we improve some previous works of other researchers. These conditions are presented in terms of system parameters and have importance leading significance in designs and applications of the GAS for Hopfield neural networks system with distributed delays. Two examples are also worked out to demonstrate the advantages of our results.

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

    PubMed

    Mao, Lei

    2012-01-01

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

  7. 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.

  8. 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

  9. Comprehensive assessment and network analysis of the emerging genetic susceptibility landscape of prostate cancer.

    PubMed

    Hicks, Chindo; Miele, Lucio; Koganti, Tejaswi; Vijayakumar, Srinivasan

    2013-01-01

    Recent advances in high-throughput genotyping have made possible identification of genetic variants associated with increased risk of developing prostate cancer using genome-wide associations studies (GWAS). However, the broader context in which the identified genetic variants operate is poorly understood. Here we present a comprehensive assessment, network, and pathway analysis of the emerging genetic susceptibility landscape of prostate cancer. We created a comprehensive catalog of genetic variants and associated genes by mining published reports and accompanying websites hosting supplementary data on GWAS. We then performed network and pathway analysis using single nucleotide polymorphism (SNP)-containing genes to identify gene regulatory networks and pathways enriched for genetic variants. We identified multiple gene networks and pathways enriched for genetic variants including IGF-1, androgen biosynthesis and androgen signaling pathways, and the molecular mechanisms of cancer. The results provide putative functional bridges between GWAS findings and gene regulatory networks and biological pathways.

  10. The study of genetic information flux network properties in genetic algorithms

    NASA Astrophysics Data System (ADS)

    Wu, Zhengping; Xu, Qiong; Ni, Gaosheng; Yu, Gaoming

    2015-04-01

    In this paper, an empirical analysis is done on the information flux network (IFN) statistical properties of genetic algorithms (GA) and the results suggest that the node degree distribution of IFN is scale-free when there is at least some selection pressure, and it has two branches as node degree is small. Increasing crossover, decreasing the mutation rate or decreasing the selective pressure will increase the average node degree, thus leading to the decrease of scaling exponent. These studies will be helpful in understanding the combination and distribution of excellent gene segments of the population in GA evolving, and will be useful in devising an efficient GA.

  11. 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.

  12. [Networks involved in motor cognition : Physiology and pathophysiology of apraxia].

    PubMed

    Martin, M; Hermsdörfer, J; Bohlhalter, S; Weiss, P H

    2017-06-29

    Apraxia is an umbrella term for different disorders of higher motor abilities that are not explained by elementary sensorimotor deficits (e. g. paresis or ataxia). Characteristic features of apraxia that are easy to recognize in clinical practice are difficulties in pantomimed or actual use of tools as well as in imitation of meaningless gestures. Apraxia is bilateral, explaining the cognitive motor disorders and occurs frequently (but not exclusively) after left hemispheric lesions, as well as in neurodegenerative diseases, such as corticobasal syndrome and Alzheimer's disease. Apraxic deficits can seriously impair activities of daily living, which is why the appropriate diagnosis is of great relevance. At the functional anatomical level, different cognitive motor skills rely on at least partly different brain networks, namely, a ventral processing pathway for semantic components, such as tool-action associations, a ventro-dorsal pathway for sensorimotor representations of learnt motor acts, as well as a dorso-dorsal pathway for on-line motor control and, probably, imitation of meaningless gestures. While these networks partially overlap with language-relevant regions, more clear cut dissociations are found between apraxia deficits and disorders of spatial attention. In addition to behavioral interventions, noninvasive neuromodulation approaches, as well as human-computer interface assistance systems are a growing focus of interest for the treatment of apraxia.

  13. 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…

  14. 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…

  15. 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…

  16. Caco-2 cell permeability modelling: a neural network coupled genetic algorithm approach

    NASA Astrophysics Data System (ADS)

    Di Fenza, Armida; Alagona, Giuliano; Ghio, Caterina; Leonardi, Riccardo; Giolitti, Alessandro; Madami, Andrea

    2007-04-01

    The ability to cross the intestinal cell membrane is a fundamental prerequisite of a drug compound. However, the experimental measurement of such an important property is a costly and highly time consuming step of the drug development process because it is necessary to synthesize the compound first. Therefore, in silico modelling of intestinal absorption, which can be carried out at very early stages of drug design, is an appealing alternative procedure which is based mainly on multivariate statistical analysis such as partial least squares (PLS) and neural networks (NN). Our implementation of neural network models for the prediction of intestinal absorption is based on the correlation of Caco-2 cell apparent permeability ( P app) values, as a measure of intestinal absorption, to the structures of two different data sets of drug candidates. Several molecular descriptors of the compounds were calculated and the optimal subsets were selected using a genetic algorithm; therefore, the method was indicated as Genetic Algorithm-Neural Network (GA-NN). A methodology combining a genetic algorithm search with neural network analysis applied to the modelling of Caco-2 P app has never been presented before, although the two procedures have been already employed separately. Moreover, we provide new Caco-2 cell permeability measurements for more than two hundred compounds. Interestingly, the selected descriptors show to possess physico-chemical connotations which are in excellent accordance with the well known relevant molecular properties involved in the cellular membrane permeation phenomenon: hydrophilicity, hydrogen bonding propensity, hydrophobicity and molecular size. The predictive ability of the models, although rather good for a preliminary study, is somewhat affected by the poor precision of the experimental Caco-2 measurements. Finally, the generalization ability of one model was checked on an external test set not derived from the data sets used to build the models

  17. 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

  18. 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

  19. 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. © The Author (2013). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  20. 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.

  1. A Parallel Attractor Finding Algorithm Based on Boolean Satisfiability for Genetic Regulatory Networks

    PubMed Central

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

    2014-01-01

    In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures. PMID:24718686

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

    PubMed

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

    2014-01-01

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

  3. 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…

  4. 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…

  5. 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

  6. Cellular networks involved in the influenza virus life cycle

    PubMed Central

    Watanabe, Tokiko; Watanabe, Shinji; Kawaoka, Yoshihiro

    2011-01-01

    Influenza viruses cause epidemics and pandemics. Like all other viruses, influenza viruses rely on the host cellular machinery to support their life cycle. Accordingly, identification of host functions that participate in viral replication steps is of great interest to understand the mechanisms of the virus life cycle as well as to find new targets for the development of antiviral compounds. Multiple laboratories have used various approaches to explore host factors involvement in the influenza virus replication cycle. One of the most powerful approaches is an RNAi-based genome-wide screen, which has thrown new light on the search for host factors involved in virus replication. In this review, we examine the cellular genes identified to date as important for influenza virus replication in genome-wide screens, assess pathways that were repeatedly identified in these studies, and discuss how these pathways might be involved in the individual steps of influenza virus replication, ultimately leading to a comprehensive understanding of the virus life cycle. PMID:20542247

  7. Scale-dependent genetic structure of the Idaho giant salamander (Dicamptodon aterrimus) in stream networks

    Treesearch

    Lindy B. Mullen; H. Arthur Woods; Michael K. Schwartz; Adam J. Sepulveda; Winsor H. Lowe

    2010-01-01

    The network architecture of streams and rivers constrains evolutionary, demographic and ecological processes of freshwater organisms. This consistent architecture also makes stream networks useful for testing general models of population genetic structure and the scaling of gene flow. We examined genetic structure and gene flow in the facultatively paedomorphic Idaho...

  8. 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

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

    PubMed

    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.

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

    PubMed Central

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

    2011-01-01

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

  11. [Genetic mental retardation. Presentation of the GIRMOGEN network].

    PubMed

    Tejada, M I

    2006-01-07

    Mental retardation is the most frequent disability and is already quite apparent in infancy. The World Health Organisation (WHO) estimates that it affects approximately 3% of the population in industrialised countries. Among the aetiologies that cause mental retardation it would appear that 30% have a genetic origin, although in recent years the progress made in molecular genetics in relation to new mutations and new genes that can account for mental retardation advances at an incredible pace. It is for this reason that, three years ago, a group of clinicians and researchers, most of whom were working in Spain on fragile X syndrome (the most prevalent of the hereditary causes of mental retardation), decided to set up the GIRMOGEN (Genetic Mental Retardation Research Group). Most of us had noticed how many of the clinical cases that we dealt with went undiagnosed and that the exact prevalence of this disability in Spain was not known either. GIRMOGEN was funded by the Carlos III Health Institute and is made up of eight groups; a ninth group is responsible for coordinating the work. Its members are all involved in clinical studies or research into mental retardation with a genetic origin, and belong to 15 hospitals and to five universities from a total of 11 different autonomous communities in Spain. Some of its aims include gathering all the data on patients and families in a common database for epidemiological and prevalence studies; distributing genes to be studied in order to search for mutations; creating generally approved work protocols, and training professionals in this field. In this supplement, which is the result of a training course, we report all the findings we have obtained in these three years of work.

  12. 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

  13. 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

  14. 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.

  15. Non-resident Fathers’ Social Networks: The Relationship between Social Support and Father Involvement

    PubMed Central

    Castillo, Jason T.; Sarver, Christian M.

    2011-01-01

    Literature and research examining non-resident fathers’ involvement with their chidren has focused primarily on the fathers’ relationship with their child’s mother. Receiving limited attention in the literature has been the inclusion of examining non-resident fathers’ social support networks, the function of these social networks—perceived and received social support, and how these social support networks affect non-resident fathers’ involvement with their children. Using data from Wave One of the Fragile Families and Child Well-being Study, this study examined the social support networks non-resident fathers (n = 895) utilized in their involvement with their children. Results of the regression analyses indicate that non-resident fathers’ relationship with their child’s mother and perceived social support from their social networks contributed positively to their involvement with their children. Policy and practice implications are discussed. PMID:23288998

  16. Cytokine Network Involvement in Subjects Exposed to Benzene

    PubMed Central

    Gangemi, Sebastiano

    2014-01-01

    Benzene represents an ubiquitous pollutant both in the workplace and in the general environment. Health risk and stress posed by benzene have long been a concern because of the carcinogenic effects of the compound which was classified as a Group 1 carcinogen to humans and animals. There is a close correlation between leukemia, especially acute myeloid leukemia, and benzene exposure. In addition, exposure to benzene can cause harmful effects on immunological, neurological, and reproductive systems. Benzene can directly damage hematopoietic progenitor cells, which in turn could lead to apoptosis or may decrease responsiveness to cytokines and cellular adhesion molecules. Alternatively, benzene toxicity to stromal cells or mature blood cells could disrupt the regulation of hematopoiesis, including hematopoietic commitment, maturation, or mobilization, through the network of cytokines, chemokines, and adhesion molecules. Today there is mounting evidence that benzene may alter the gene expression, production, or processing of several cytokines in vitro and in vivo. The purpose of this review was to systematically analyze the published cases of cytokine effects on human benzene exposure, particularly hematotoxicity, and atopy, and on lungs. PMID:25202711

  17. Ochratoxin A carcinogenicity involves a complex network of epigenetic mechanisms.

    PubMed

    Marin-Kuan, Maricel; Cavin, Christophe; Delatour, Thierry; Schilter, Benoît

    2008-08-01

    Ochratoxin A (OTA) is a mycotoxin occurring in a wide range of food products. Because of the limitation of human epidemiological data, the safety significance of OTA in food has to rely on animal data, with renal toxicity and carcinogenicity being considered the pivotal effects. The elucidation of the mechanism of action would improve the use of experimental animal data for risk assessment. Direct genotoxicity versus epigenetic mechanisms appears to be a key question. In the present review, the increasingly documented epigenetic cellular effects of OTA and their potential toxicological relevance are discussed. The information available suggests that OTA is unlikely to act through a single, well-defined mechanism of action. Instead, it is proposed that a network of interacting epigenetic mechanisms, including protein synthesis inhibition, oxidative stress and the activation of specific cell signalling pathways, is responsible for OTA carcinogenicity. From a risk assessment perspective, it has to be noted that the mechanisms proposed above depend mainly upon gene expression and enzyme activation, and are, therefore, likely to be thresholded.

  18. Transcriptional master regulator analysis in breast cancer genetic networks.

    PubMed

    Tovar, Hugo; García-Herrera, Rodrigo; Espinal-Enríquez, Jesús; Hernández-Lemus, Enrique

    2015-12-01

    Gene regulatory networks account for the delicate mechanisms that control gene expression. Under certain circumstances, gene regulatory programs may give rise to amplification cascades. Such transcriptional cascades are events in which activation of key-responsive transcription factors called master regulators trigger a series of gene expression events. The action of transcriptional master regulators is then important for the establishment of certain programs like cell development and differentiation. However, such cascades have also been related with the onset and maintenance of cancer phenotypes. Here we present a systematic implementation of a series of algorithms aimed at the inference of a gene regulatory network and analysis of transcriptional master regulators in the context of primary breast cancer cells. Such studies were performed in a highly curated database of 880 microarray gene expression experiments on biopsy-captured tissue corresponding to primary breast cancer and healthy controls. Biological function and biochemical pathway enrichment analyses were also performed to study the role that the processes controlled - at the transcriptional level - by such master regulators may have in relation to primary breast cancer. We found that transcription factors such as AGTR2, ZNF132, TFDP3 and others are master regulators in this gene regulatory network. Sets of genes controlled by these regulators are involved in processes that are well-known hallmarks of cancer. This kind of analyses may help to understand the most upstream events in the development of phenotypes, in particular, those regarding cancer biology.

  19. [Parkinson's disease: Role of genetic and environment factors. Involvement in everyday clinical practice].

    PubMed

    Defebvre, L

    2010-10-01

    Genetics and exposure to toxins constitute the main determinants in the onset of Parkinson's disease (PD). At least, 13 loci and nine genes involved in familial and sporadic forms have been described. A significant association between occupational exposure to pesticides (especially insecticides) and PD has been confirmed recently with rare cases even being recognized as occupational disease. We develop in this paper a practical approach for such situations where a common genetic or toxic origin is suggested. Such an approach can be applied very broadly using case by case study then further analysis in a specialized center of reference in the field of genetics or occupational diseases and the environment. A pedigree needs to be drawn to evaluate a potential genetic factor with, if possible, the examination of various family members. Depending on the mode of inheritance, age of disease onset and phenotypic expression, genetic analysis will be carried out (mainly the study of parkin gene for recessive transmission and LRRK2 gene for dominant transmission). The evaluation of a toxic factor is more difficult because its direct involvement may not always be defined with certainty, the collection of information is more complex (product list, causal relationship, protection system used...). The course of action will identify the existence of a potential risk factor particularly in patients at risk (farmers, workers in a factory using heavy metals) by considering secondary specialized consultation with the occupational physician or pathology consultation work for possible development of a procedure for recognition of occupational disease.

  20. Genetic programming of polynomial harmonic networks using the discrete Fourier transform.

    PubMed

    Nikolaev, Nikolay Y; Iba, Hitoshi

    2002-10-01

    This paper presents a genetic programming system that evolves polynomial harmonic networks. These are multilayer feed-forward neural networks with polynomial activation functions. The novel hybrids assume that harmonics with non-multiple frequencies may enter as inputs the activation polynomials. The harmonics with non-multiple, irregular frequencies are derived analytically using the discrete Fourier transform. The polynomial harmonic networks have tree-structured topology which makes them especially suitable for evolutionary structural search. Empirical results show that this hybrid genetic programming system outperforms an evolutionary system manipulating polynomials, the traditional Koza-style genetic programming, and the harmonic GMDH network algorithm on processing time series.

  1. 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.

  2. Cortical Networks Involved in Memory for Temporal Order.

    PubMed

    Manelis, Anna; Popov, Vencislav; Paynter, Christopher; Walsh, Matthew; Wheeler, Mark E; Vogt, Keith M; Reder, Lynne M

    2017-03-15

    We examined the neurobiological basis of temporal resetting, an aspect of temporal order memory, using a version of the delayed-match-to-multiple-sample task. While in an fMRI scanner, participants evaluated whether an item was novel or whether it had appeared before or after a reset event that signified the start of a new block of trials. Participants responded "old" to items that were repeated within the current block and "new" to both novel items and items that had last appeared before the reset event (pseudonew items). Medial-temporal, prefrontal, and occipital regions responded to absolute novelty of the stimulus-they differentiated between novel items and previously seen items, but not between old and pseudonew items. Activation for pseudonew items in the frontopolar and parietal regions, in contrast, was intermediate between old and new items. The posterior cingulate cortex extending to precuneus was the only region that showed complete temporal resetting, and its activation reflected whether an item was new or old according to the task instructions regardless of its familiarity. There was also a significant Condition (old/pseudonew) × Familiarity (second/third presentations) interaction effect on behavioral and neural measures. For pseudonew items, greater familiarity decreased response accuracy, increased RTs, increased ACC activation, and increased functional connectivity between ACC and the left frontal pole. The reverse was observed for old items. On the basis of these results, we propose a theoretical framework in which temporal resetting relies on an episodic retrieval network that is modulated by cognitive control and conflict resolution.

  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. On the underlying assumptions of threshold Boolean networks as a model for genetic regulatory network behavior

    PubMed Central

    Tran, Van; McCall, Matthew N.; McMurray, Helene R.; Almudevar, Anthony

    2013-01-01

    Boolean networks (BoN) are relatively simple and interpretable models of gene regulatory networks. Specifying these models with fewer parameters while retaining their ability to describe complex regulatory relationships is an ongoing methodological challenge. Additionally, extending these models to incorporate variable gene decay rates, asynchronous gene response, and synergistic regulation while maintaining their Markovian nature increases the applicability of these models to genetic regulatory networks (GRN). We explore a previously-proposed class of BoNs characterized by linear threshold functions, which we refer to as threshold Boolean networks (TBN). Compared to traditional BoNs with unconstrained transition functions, these models require far fewer parameters and offer a more direct interpretation. However, the functional form of a TBN does result in a reduction in the regulatory relationships which can be modeled. We show that TBNs can be readily extended to permit self-degradation, with explicitly modeled degradation rates. We note that the introduction of variable degradation compromises the Markovian property fundamental to BoN models but show that a simple state augmentation procedure restores their Markovian nature. Next, we study the effect of assumptions regarding self-degradation on the set of possible steady states. Our findings are captured in two theorems relating self-degradation and regulatory feedback to the steady state behavior of a TBN. Finally, we explore assumptions of synchronous gene response and asynergistic regulation and show that TBNs can be easily extended to relax these assumptions. Applying our methods to the budding yeast cell-cycle network revealed that although the network is complex, its steady state is simplified by the presence of self-degradation and lack of purely positive regulatory cycles. PMID:24376454

  5. Steady-State Analysis of Genetic Regulatory Networks Modelled by Probabilistic Boolean Networks

    PubMed Central

    Gluhovsky, Ilya; Hashimoto, Ronaldo F.; Dougherty, Edward R.; Zhang, Wei

    2003-01-01

    Probabilistic Boolean networks (PBNs) have recently been introduced as a promising class of models of genetic regulatory networks. The dynamic behaviour of PBNs can be analysed in the context of Markov chains. A key goal is the determination of the steady-state (long-run) behaviour of a PBN by analysing the corresponding Markov chain. This allows one to compute the long-term influence of a gene on another gene or determine the long-term joint probabilistic behaviour of a few selected genes. Because matrix-based methods quickly become prohibitive for large sizes of networks, we propose the use of Monte Carlo methods. However, the rate of convergence to the stationary distribution becomes a central issue. We discuss several approaches for determining the number of iterations necessary to achieve convergence of the Markov chain corresponding to a PBN. Using a recently introduced method based on the theory of two-state Markov chains, we illustrate the approach on a sub-network designed from human glioma gene expression data and determine the joint steadystate probabilities for several groups of genes. PMID:18629023

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

    PubMed

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

    2016-01-01

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

  7. Genetic analysis of absB, a Streptomyces coelicolor locus involved in global antibiotic regulation.

    PubMed

    Adamidis, T; Champness, W

    1992-07-01

    The filamentous soil bacterium Streptomyces coelicolor is known to produce four antibiotics which are genetically and structurally distinct. An extensive search for antibiotic regulatory mutants led to the discovery of absB mutants, which are antibiotic deficient but sporulation proficient. Genetic analysis of the absB mutants has resulted in definition of the absB locus at 5 o'clock on the genetic map. Multiple cloned copies of the actII-ORF4 gene, an activator of synthesis of the antibiotic actinorhodin, restore actinorhodin biosynthetic capability to the absB mutants. These results are interpreted to mean that the failure of absB mutants to produce antibiotics results from decreased expression of the antibiotic genes. The absB gene is proposed to be involved in global regulation of antibiotic synthesis.

  8. 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

    2016-03-28

    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.

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

    PubMed Central

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

    2014-01-01

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

  10. 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

  11. A service-oriented architecture for integrating the modeling and formal verification of genetic regulatory networks

    PubMed Central

    2009-01-01

    Background The study of biological networks has led to the development of increasingly large and detailed models. Computer tools are essential for the simulation of the dynamical behavior of the networks from the model. However, as the size of the models grows, it becomes infeasible to manually verify the predictions against experimental data or identify interesting features in a large number of simulation traces. Formal verification based on temporal logic and model checking provides promising methods to automate and scale the analysis of the models. However, a framework that tightly integrates modeling and simulation tools with model checkers is currently missing, on both the conceptual and the implementational level. Results We have developed a generic and modular web service, based on a service-oriented architecture, for integrating the modeling and formal verification of genetic regulatory networks. The architecture has been implemented in the context of the qualitative modeling and simulation tool GNA and the model checkers NUSMV and CADP. GNA has been extended with a verification module for the specification and checking of biological properties. The verification module also allows the display and visual inspection of the verification results. Conclusions The practical use of the proposed web service is illustrated by means of a scenario involving the analysis of a qualitative model of the carbon starvation response in E. coli. The service-oriented architecture allows modelers to define the model and proceed with the specification and formal verification of the biological properties by means of a unified graphical user interface. This guarantees a transparent access to formal verification technology for modelers of genetic regulatory networks. PMID:20042075

  12. Genetic and epigenetic determinants mediate proneness of oncogene breakpoint sites for involvement in TCR translocations.

    PubMed

    Larmonie, N S D; van der Spek, A; Bogers, A J J C; van Dongen, J J M; Langerak, A W

    2014-03-01

    T-cell receptor (TCR) translocations are a genetic hallmark of T-cell acute lymphoblastic leukemia and lead to juxtaposition of oncogene and TCR loci. Oncogene loci become involved in translocations because they are accessible to the V(D)J recombination machinery. Such accessibility is predicted at cryptic recombination signal sequence (cRSS) sites ('Type 1') as well as other sites that are subject to DNA double-strand breaks (DSBs) ('Type 2') during early stages of thymocyte development. As chromatin accessibility markers have not been analyzed in the context of TCR-associated translocations, various genetic and epigenetic determinants of LMO2, TAL1 and TLX1 translocation breakpoint (BP) sites and BP cluster regions (BCRs) were examined in human thymocytes to establish DSB proneness and heterogeneity of BP site involvement in TCR translocations. Our data show that DSBs in BCRs are primarily induced in the presence of a genetic element of sequence vulnerability (cRSSs, transposable elements), whereas breaks at single BP sites lacking such elements are more likely induced by chance or perhaps because of patient-specific genetic vulnerability. Vulnerability to obtain DSBs is increased by features that determine chromatin organization, such as methylation status and nucleosome occupancy, although at different levels at different BP sites.

  13. Human loci involved in drug biotransformation: worldwide genetic variation, population structure, and pharmacogenetic implications.

    PubMed

    Maisano Delser, Pierpaolo; Fuselli, Silvia

    2013-05-01

    Understanding the role of inheritance in individual variation in drug response is the focus of pharmacogenetics (PGx). A key part of this understanding is quantifying the role of genetic ancestry in this phenotypic outcome. To provide insight into the relationship between ethnicity and drug response, this study first infers the global distribution of PGx variation and defines its structure. Second, the study evaluates if geographic population structure stems from all PGx loci in general, or if structure is caused by specific genes. Lastly, we identify the genetic variants contributing the greatest proportion of such structure. Our study describes the global genetic structure of PGx loci across the 52 populations of the Human Genome Diversity Cell-Line Panel, the most inclusive set of human populations freely available for studies on human genetic variation. By analysing genetic variation at 1,001 single nucleotide polymorphisms (SNPs) involved in biotransformation of exogenous substances, we describe the between-populations PGx variation, as well geographical groupings of diversity. In addition, with discriminant analysis of principal component (DAPC), we infer how many and which groups of populations are supported by PGx variation, and identify which SNPs actually contribute to the PGx structure between such groups. Our results show that intergenic, synonymous and non-synonymous SNPs show similar levels of genetic variation across the globe. Conversely, loci coding for Cytochrome P450s (mainly metabolizing exogenous substances) show significantly higher levels of genetic diversity between populations than the other gene categories. Overall, genetic variation at PGx loci correlates with geographic distances between populations, and the apportionment of genetic variation is similar to that observed for the rest of the genome. In other words, the pattern of PGx variation has been mainly shaped by the demographic history of our species, as in the case of most of our

  14. Scale-dependent genetic structure of the Idaho giant salamander (Dicamptodon aterrimus) in stream networks.

    PubMed

    Mullen, Lindy B; Arthur Woods, H; Schwartz, Michael K; Sepulveda, Adam J; Lowe, Winsor H

    2010-03-01

    The network architecture of streams and rivers constrains evolutionary, demographic and ecological processes of freshwater organisms. This consistent architecture also makes stream networks useful for testing general models of population genetic structure and the scaling of gene flow. We examined genetic structure and gene flow in the facultatively paedomorphic Idaho giant salamander, Dicamptodon aterrimus, in stream networks of Idaho and Montana, USA. We used microsatellite data to test population structure models by (i) examining hierarchical partitioning of genetic variation in stream networks; and (ii) testing for genetic isolation by distance along stream corridors vs. overland pathways. Replicated sampling of streams within catchments within three river basins revealed that hierarchical scale had strong effects on genetic structure and gene flow. amova identified significant structure at all hierarchical scales (among streams, among catchments, among basins), but divergence among catchments had the greatest structural influence. Isolation by distance was detected within catchments, and in-stream distance was a strong predictor of genetic divergence. Patterns of genetic divergence suggest that differentiation among streams within catchments was driven by limited migration, consistent with a stream hierarchy model of population structure. However, there was no evidence of migration among catchments within basins, or among basins, indicating that gene flow only counters the effects of genetic drift at smaller scales (within rather than among catchments). These results show the strong influence of stream networks on population structure and genetic divergence of a salamander, with contrasting effects at different hierarchical scales.

  15. 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

  16. 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

  17. High-content behavioral profiling reveals neuronal genetic network modulating Drosophila larval locomotor program.

    PubMed

    Aleman-Meza, Boanerges; Loeza-Cabrera, Mario; Peña-Ramos, Omar; Stern, Michael; Zhong, Weiwei

    2017-05-12

    Two key questions in understanding the genetic control of behaviors are: what genes are involved and how these genes interact. To answer these questions at a systems level, we conducted high-content profiling of Drosophila larval locomotor behaviors for over 100 genotypes. We studied 69 genes whose C. elegans orthologs were neuronal signalling genes with significant locomotor phenotypes, and conducted RNAi with ubiquitous, pan-neuronal, or motor-neuronal Gal4 drivers. Inactivation of 42 genes, including the nicotinic acetylcholine receptors nAChRα1 and nAChRα3, in the neurons caused significant movement defects. Bioinformatic analysis suggested 81 interactions among these genes based on phenotypic pattern similarities. Comparing the worm and fly data sets, we found that these genes were highly conserved in having neuronal expressions and locomotor phenotypes. However, the genetic interactions were not conserved for ubiquitous profiles, and may be mildly conserved for the neuronal profiles. Unexpectedly, our data also revealed a possible motor-neuronal control of body size, because inactivation of Rdl and Gαo in the motor neurons reduced the larval body size. Overall, these data established a framework for further exploring the genetic control of Drosophila larval locomotion. High content, quantitative phenotyping of larval locomotor behaviours provides a framework for system-level understanding of the gene networks underlying such behaviours.

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

    PubMed

    Halvorson, H O; Quezada, F

    1999-11-01

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

  19. Optimization of a fermentation medium using neural networks and genetic algorithms.

    PubMed

    Nagata, Yuko; Chu, Khim Hoong

    2003-11-01

    Artificial neural networks and genetic algorithms are used to model and optimize a fermentation medium for the production of the enzyme hydantoinase by Agrobacterium radiobacter. Experimental data reported in the literature were used to build two neural network models. The concentrations of four medium components served as inputs to the neural network models, and hydantoinase or cell concentration served as a single output of each model. Genetic algorithms were used to optimize the input space of the neural network models to find the optimum settings for maximum enzyme and cell production. Using this procedure, two artificial intelligence techniques have been effectively integrated to create a powerful tool for process modeling and optimization.

  20. 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.

  1. 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

  2. Effects of genetic and environmental factors on trait network predictions from quantitative trait locus data.

    PubMed

    Remington, David L

    2009-03-01

    The use of high-throughput genomic techniques to map gene expression quantitative trait loci has spurred the development of path analysis approaches for predicting functional networks linking genes and natural trait variation. The goal of this study was to test whether potentially confounding factors, including effects of common environment and genes not included in path models, affect predictions of cause-effect relationships among traits generated by QTL path analyses. Structural equation modeling (SEM) was used to test simple QTL-trait networks under different regulatory scenarios involving direct and indirect effects. SEM identified the correct models under simple scenarios, but when common-environment effects were simulated in conjunction with direct QTL effects on traits, they were poorly distinguished from indirect effects, leading to false support for indirect models. Application of SEM to loblolly pine QTL data provided support for biologically plausible a priori hypotheses of QTL mechanisms affecting height and diameter growth. However, some biologically implausible models were also well supported. The results emphasize the need to include any available functional information, including predictions for genetic and environmental correlations, to develop plausible models if biologically useful trait network predictions are to be made.

  3. Learning genetic epistasis using Bayesian network scoring criteria

    PubMed Central

    2011-01-01

    Background Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is Multifactor Dimensionality Reduction (MDR). Jiang et al. created a combinatorial epistasis learning method called BNMBL to learn Bayesian network (BN) epistatic models. They compared BNMBL to MDR using simulated data sets. Each of these data sets was generated from a model that associates two SNPs with a disease and includes 18 unrelated SNPs. For each data set, BNMBL and MDR were used to score all 2-SNP models, and BNMBL learned significantly more correct models. In real data sets, we ordinarily do not know the number of SNPs that influence phenotype. BNMBL may not perform as well if we also scored models containing more than two SNPs. Furthermore, a number of other BN scoring criteria have been developed. They may detect epistatic interactions even better than BNMBL. Although BNs are a promising tool for learning epistatic relationships from data, we cannot confidently use them in this domain until we determine which scoring criteria work best or even well when we try learning the correct model without knowledge of the number of SNPs in that model. Results We evaluated the performance of 22 BN scoring criteria using 28,000 simulated data sets and a real Alzheimer's GWAS data set. Our results were surprising in that the Bayesian scoring criterion with large values of a hyperparameter called α performed best. This score performed better than other BN scoring criteria and MDR at recall using simulated data sets, at detecting the hardest-to-detect models using simulated data sets, and at substantiating previous results using the real Alzheimer's data set. Conclusions We conclude that representing epistatic interactions using BN models

  4. Genetic Control of Chromatin States in Humans Involves Local and Distal Chromosomal Interactions

    PubMed Central

    Grubert, Fabian; Zaugg, Judith B.; Kasowski, Maya; Ursu, Oana; Spacek, Damek V.; Martin, Alicia R.; Greenside, Peyton; Srivas, Rohith; Phanstiel, Doug H.; Pekowska, Aleksandra; Heidari, Nastaran; Euskirchen, Ghia; Huber, Wolfgang; Pritchard, Jonathan K.; Bustamante, Carlos D.; Steinmetz, Lars M.; Kundaje, Anshul; Snyder, Michael

    2015-01-01

    SUMMARY Deciphering the impact of genetic variants on gene regulation is fundamental to understanding human disease. Although gene regulation often involves long-range interactions, it is unknown to what extent non-coding genetic variants influence distal molecular phenotypes. Here, we integrate chromatin profiling for three histone marks in lymphoblastoid cell lines (LCLs) from 75 sequenced individuals with LCL-specific Hi-C and ChIA-PET-based chromatin contact maps to uncover one of the largest collections of local and distal histone quantitative trait loci (hQTLs). Distal QTLs are enriched within topologically associated domains and exhibit largely concordant variation of chromatin state coordinated by proximal and distal non-coding genetic variants. Histone QTLs are enriched for common variants associated with autoimmune diseases and enable identification of putative target genes of disease-associated variants from genome-wide association studies. These analyses provide insights into how genetic variation can affect human disease phenotypes by coordinated changes in chromatin at interacting regulatory elements. PMID:26300125

  5. 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

  6. 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

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

    PubMed

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

    1999-04-01

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

  8. 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.

  9. A global genetic interaction network maps a wiring diagram of cellular function.

    PubMed

    Costanzo, Michael; VanderSluis, Benjamin; Koch, Elizabeth N; Baryshnikova, Anastasia; Pons, Carles; Tan, Guihong; Wang, Wen; Usaj, Matej; Hanchard, Julia; Lee, Susan D; Pelechano, Vicent; Styles, Erin B; Billmann, Maximilian; van Leeuwen, Jolanda; van Dyk, Nydia; Lin, Zhen-Yuan; Kuzmin, Elena; Nelson, Justin; Piotrowski, Jeff S; Srikumar, Tharan; Bahr, Sondra; Chen, Yiqun; Deshpande, Raamesh; Kurat, Christoph F; Li, Sheena C; Li, Zhijian; Usaj, Mojca Mattiazzi; Okada, Hiroki; Pascoe, Natasha; San Luis, Bryan-Joseph; Sharifpoor, Sara; Shuteriqi, Emira; Simpkins, Scott W; Snider, Jamie; Suresh, Harsha Garadi; Tan, Yizhao; Zhu, Hongwei; Malod-Dognin, Noel; Janjic, Vuk; Przulj, Natasa; Troyanskaya, Olga G; Stagljar, Igor; Xia, Tian; Ohya, Yoshikazu; Gingras, Anne-Claude; Raught, Brian; Boutros, Michael; Steinmetz, Lars M; Moore, Claire L; Rosebrock, Adam P; Caudy, Amy A; Myers, Chad L; Andrews, Brenda; Boone, Charles

    2016-09-23

    We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

  10. 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.

  11. 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

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

    PubMed

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

    2013-08-30

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

  13. 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

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

    PubMed

    Ahnert, Sebastian E

    2013-11-01

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

  15. Synthesis of PHBV block copolymers driven by an oscillatory genetic network.

    PubMed

    Iadevaia, Sergio; Mantzaris, Nikos V

    2007-02-20

    Artificial genetic networks constitute a powerful tool to achieve various biotechnological objectives. In this work, we propose the modification of an oscillatory genetic network, known as the repressilator, to drive synthesis of poly(3hydroxybutyrate-co-3hydroxyvalerate) (PHBV) block copolymer chains in recombinant Escherichia coli cells. To study the feasibility of this idea, we developed a detailed mathematical model describing the dynamics of the genetic network, which drive the formation of monomer units that are subsequently incorporated into actively growing block copolymer chains. Extensive simulation studies have shown that appropriate choice of the molecular characteristics of the network and manipulation of extracelllular conditions lead to tight control of both the micro- and macro-structures of the resulting block copolymer chains. Thus, the model can guide network design aiming at producing block copolymer structures with desirable characteristics.

  16. GENNET-Toolbox: An Evolving Genetic Algorithm for Neural Network Training

    NASA Astrophysics Data System (ADS)

    Gómez-Garay, Vicente; Irigoyen, Eloy; Artaza, Fernando

    Genetic Algorithms have been used from 1989 for both Neural Network training and design. Nevertheless, the use of a Genetic Algorithm for adjusting the Neural Network parameters can still be engaging. This work presents the study and validation of a different approach to this matter by introducing a Genetic Algorithm designed for Neural Network training. This algorithm features a mutation operator capable of working on three levels (network, neuron and layer) and with the mutation parameters encoded and evolving within each individual. We also explore the use of three types of hybridization: post-training, Lamarckian and Baldwinian. These proposes in combination with the algorithm, show for a fast and powerful tool for Neural Network training.

  17. Analysis of the enzyme network involved in cattle milk production using graph theory.

    PubMed

    Ghorbani, Sholeh; Tahmoorespur, Mojtaba; Masoudi Nejad, Ali; Nasiri, Mohammad; Asgari, Yazdan

    2015-06-01

    Understanding cattle metabolism and its relationship with milk products is important in bovine breeding. A systemic view could lead to consequences that will result in a better understanding of existing concepts. Topological indices and quantitative characterizations mostly result from the application of graph theory on biological data. In the present work, the enzyme network involved in cattle milk production was reconstructed and analyzed based on available bovine genome information using several public datasets (NCBI, Uniprot, KEGG, and Brenda). The reconstructed network consisted of 3605 reactions named by KEGG compound numbers and 646 enzymes that catalyzed the corresponding reactions. The characteristics of the directed and undirected network were analyzed using Graph Theory. The mean path length was calculated to be4.39 and 5.41 for directed and undirected networks, respectively. The top 11 hub enzymes whose abnormality could harm bovine health and reduce milk production were determined. Therefore, the aim of constructing the enzyme centric network was twofold; first to find out whether such network followed the same properties of other biological networks, and second, to find the key enzymes. The results of the present study can improve our understanding of milk production in cattle. Also, analysis of the enzyme network can help improve the modeling and simulation of biological systems and help design desired phenotypes to increase milk production quality or quantity.

  18. Specialized Genetic Recombination Systems in Bacteria: Their Involvement in Gene Expression and Evolution,

    DTIC Science & Technology

    1980-01-01

    I AA-M98 873 WALTER EED ARMY INST OF RESEARCH WASHINGTON DC F/ 6 6 /13 I SPECIALIZED GENETIC RECOMBINATION SYSTEMS IN BACTERIA? THI R IM--ETC(U) 1980 D...YEO EOT5 EIDCV7E Bacteria: Their Involvement in Gene Expression _______________ and Evolution. 6 . PERFORMING ORG. REPORT NUMBER 7. AUTHOR(.)G S...undifferentiated organisms that reproduce by asexual fission, a process characterized by the doubling of the cellular 81 2 09 11) 6 136 contents followed by

  19. New insights into the genetic networks affecting seed fatty acid concentrations in Brassica napus.

    PubMed

    Wang, Xiaodong; Long, Yan; Yin, Yongtai; Zhang, Chunyu; Gan, Lu; Liu, Liezhao; Yu, Longjiang; Meng, Jinling; Li, Maoteng

    2015-03-27

    Rapeseed (B. napus, AACC, 2n = 38) is one of the most important oil seed crops in the world, it is also one of the most common oil for production of biodiesel. Its oil is a mixture of various fatty acids and dissection of the genetic network for fatty acids biosynthesis is of great importance for improving seed quality. The genetic basis of fatty acid biosynthesis in B. napus was investigated via quantitative trail locus (QTL) analysis using a doubled haploid (DH) population with 202 lines. A total of 72 individual QTLs and a large number pairs of epistatic interactions associated with the content of 10 different fatty acids were detected. A total of 234 homologous genes of Arabidopsis thaliana that are involved in fatty acid metabolism were found within the confidence intervals (CIs) of 47 QTLs. Among them, 47 and 15 genes homologous to those of B. rapa and B. oleracea were detected, respectively. After the QTL mapping, the epistatic and the candidate gene interaction analysis, a potential regulatory pathway controlling fatty acid biosynthesis in B. napus was constructed, including 50 enzymes encoded genes and five regulatory factors (LEC1, LEC2, FUS3, WRI1 and ABI3). Subsequently, the interaction between these five regulatory factors and the genes involved in fatty acid metabolism were analyzed. In this study, a potential regulatory pathway controlling the fatty acid was constructed by QTL analysis and in silico mapping analysis. These results enriched our knowledge of QTLs for fatty acids metabolism and provided a new clue for genetic engineering fatty acids composition in B. napus.

  20. 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.

  1. Parents, Friends, and Romantic Partners: Enmeshment in Deviant Networks and Adolescent Delinquency Involvement

    PubMed Central

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

    2010-01-01

    Adolescent networks include parents, friends, and romantic partners, but research on the social learning mechanisms related to delinquency has not typically examined the characteristics of all three domains simultaneously. Employing data from the Toledo Adolescent Relationships Study (n = 957), we assess the relationship of romantic partners’ delinquency with respondents’ self-reported involvement after taking parents’ and peers’ deviance into account. Next, we explore the associations between enmeshment level (number of deviant networks), enmeshment type (specific combinations of deviant networks), and delinquency. Parents,’ peers,’ and partners’ deviance are each related to respondents’ self-reported behavior, but affiliation with a greater number of deviant networks is associated with higher delinquency involvement. Results that consider enmeshment type indicate that those with both above average romantic partner and friend delinquency report especially high levels of self-reported involvement. In all comparisons, however, adolescents with deviant romantic partners are more delinquent than those youths with more prosocial partners, regardless of friends’ and parents’ behavior. Results highlight the importance of capturing the adolescent’s entire network of affiliations, rather than viewing these in isolation, and suggest the need for additional research on romantic partner influences on delinquent behavior and other adolescent outcomes. PMID:19636751

  2. 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

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

    PubMed Central

    2010-01-01

    The first scientific meeting of the newly established European SYSGENET network took place at the Helmholtz Centre for Infection Research (HZI) in Braunschweig, April 7-9, 2010. About 50 researchers working in the field of systems genetics using mouse genetic reference populations (GRP) participated in the meeting and exchanged their results, phenotyping approaches, and data analysis tools for studying systems genetics. In addition, the future of GRP resources and phenotyping in Europe was discussed. PMID:20623354

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

    PubMed

    Schughart, Klaus

    2010-08-01

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

  5. 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

    2010-12-03

    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.

  6. 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

  7. 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

  8. 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

    2015-11-07

    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.

  9. Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map.

    PubMed

    Collins, Sean R; Miller, Kyle M; Maas, Nancy L; Roguev, Assen; Fillingham, Jeffrey; Chu, Clement S; Schuldiner, Maya; Gebbia, Marinella; Recht, Judith; Shales, Michael; Ding, Huiming; Xu, Hong; Han, Junhong; Ingvarsdottir, Kristin; Cheng, Benjamin; Andrews, Brenda; Boone, Charles; Berger, Shelley L; Hieter, Phil; Zhang, Zhiguo; Brown, Grant W; Ingles, C James; Emili, Andrew; Allis, C David; Toczyski, David P; Weissman, Jonathan S; Greenblatt, Jack F; Krogan, Nevan J

    2007-04-12

    Defining the functional relationships between proteins is critical for understanding virtually all aspects of cell biology. Large-scale identification of protein complexes has provided one important step towards this goal; however, even knowledge of the stoichiometry, affinity and lifetime of every protein-protein interaction would not reveal the functional relationships between and within such complexes. Genetic interactions can provide functional information that is largely invisible to protein-protein interaction data sets. Here we present an epistatic miniarray profile (E-MAP) consisting of quantitative pairwise measurements of the genetic interactions between 743 Saccharomyces cerevisiae genes involved in various aspects of chromosome biology (including DNA replication/repair, chromatid segregation and transcriptional regulation). This E-MAP reveals that physical interactions fall into two well-represented classes distinguished by whether or not the individual proteins act coherently to carry out a common function. Thus, genetic interaction data make it possible to dissect functionally multi-protein complexes, including Mediator, and to organize distinct protein complexes into pathways. In one pathway defined here, we show that Rtt109 is the founding member of a novel class of histone acetyltransferases responsible for Asf1-dependent acetylation of histone H3 on lysine 56. This modification, in turn, enables a ubiquitin ligase complex containing the cullin Rtt101 to ensure genomic integrity during DNA replication.

  10. 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…

  11. 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, followed by…

  12. Involvement or Isolation? The Social Networks of Children with Autism in Regular Classrooms

    ERIC Educational Resources Information Center

    Chamberlain, Brandt; Kasari, Connie; Rotheram-Fuller, Erin

    2007-01-01

    Including children with autism in regular classrooms has become prevalent; yet some evidence suggests such placements could increase the risk of isolation and rejection. In this study, we used social network methods to explore the involvement of children with autism in typical classrooms. Participants were 398 children (196 boys) in regular 2nd…

  13. 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, followed by…

  14. 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…

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

    ERIC Educational Resources Information Center

    Umoh, Ukeme Ekpedeme; Etuk, Etuk Nssien

    2016-01-01

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

  16. 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.

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

    PubMed

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

    2013-01-01

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

  18. Genomic signal processing: from matrix algebra to genetic networks.

    PubMed

    Alter, Orly

    2007-01-01

    DNA microarrays make it possible, for the first time, to record the complete genomic signals that guide the progression of cellular processes. Future discovery in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment, and drug development. This chapter reviews the first data-driven models that were created from these genome-scale data, through adaptations and generalizations of mathematical frameworks from matrix algebra that have proven successful in describing the physical world, in such diverse areas as mechanics and perception: the singular value decomposition model, the generalized singular value decomposition model comparative model, and the pseudoinverse projection integrative model. These models provide mathematical descriptions of the genetic networks that generate and sense the measured data, where the mathematical variables and operations represent biological reality. The variables, patterns uncovered in the data, correlate with activities of cellular elements such as regulators or transcription factors that drive the measured signals and cellular states where these elements are active. The operations, such as data reconstruction, rotation, and classification in subspaces of selected patterns, simulate experimental observation of only the cellular programs that these patterns represent. These models are illustrated in the analyses of RNA expression data from yeast and human during their cell cycle programs and DNA-binding data from yeast cell cycle transcription factors and replication initiation proteins. Two alternative pictures of RNA expression oscillations during the cell cycle that emerge from these analyses, which parallel well-known designs of physical oscillators, convey the capacity of the models to elucidate the design principles of cellular systems, as well as guide the design of

  19. 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

  20. Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling

    PubMed Central

    Tegnér, Jesper; Yeung, M. K. Stephen; Hasty, Jeff; Collins, James J.

    2003-01-01

    While the fundamental building blocks of biology are being tabulated by the various genome projects, microarray technology is setting the stage for the task of deducing the connectivity of large-scale gene networks. We show how the perturbation of carefully chosen genes in a microarray experiment can be used in conjunction with a reverse engineering algorithm to reveal the architecture of an underlying gene regulatory network. Our iterative scheme identifies the network topology by analyzing the steady-state changes in gene expression resulting from the systematic perturbation of a particular node in the network. We highlight the validity of our reverse engineering approach through the successful deduction of the topology of a linear in numero gene network and a recently reported model for the segmentation polarity network in Drosophila melanogaster. Our method may prove useful in identifying and validating specific drug targets and in deconvolving the effects of chemical compounds. PMID:12730377

  1. 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

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

    PubMed

    Fogelmark, Karl; Peterson, Carsten; Troein, Carl

    2016-01-01

    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. 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. 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.

  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. 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

  5. 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.

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

    PubMed Central

    Hether, Tyler D.; Hohenlohe, Paul A.

    2013-01-01

    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

  7. Using attribute grammars for the genetic selection of back-propagation networks for character recognition

    NASA Astrophysics Data System (ADS)

    Browse, Roger A.; Hussain, Talib S.; Smillie, Matthew B.

    1999-03-01

    Determining exactly which neural network architecture, with which parameters, will provide the best solution to a classification task is often based upon the intuitions and experience of the implementers of neural network solutions. The research presented in this paper is centered on the development of automated methods for the selection of appropriate networks, as applied to character recognition. The Network Generating Attribute Grammar Encoding system is a compact and general method for the specification of commonly accepted network architectures that can be easily expanded to include novel architectures, or that can be easily restricted to a small subset of some known architecture. Within this system, the context-free component of the attribute grammar specifies a class of basic architectures by using the non-terminals to represent network, layers and component structures. The inherited and synthesized attributes indicate the connections necessary to develop a functioning network from any parse tree that is generated from the grammar. The attribute grammar encoding is particularly conducive to the use of genetic algorithms as a strategy for searching the space of possible networks. The resultant parse trees are used as the genetic code, permitting a variety of different genetic manipulations. We apply this approach in the generation of backpropagation networks for recognition of characters from a set consisting of 20,000 examples of 26 letters.

  8. Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem

    NASA Astrophysics Data System (ADS)

    Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

    2017-08-01

    Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.

  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 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

  10. Genetic algorithms for the design of looped irrigation water distribution networks

    NASA Astrophysics Data System (ADS)

    Reca, Juan; MartíNez, Juan

    2006-05-01

    A new computer model called Genetic Algorithm Pipe Network Optimization Model (GENOME) has been developed with the aim of optimizing the design of new looped irrigation water distribution networks. The model is based on a genetic algorithm method, although relevant modifications and improvements have been implemented to adapt the model to this specific problem. It makes use of the robust network solver EPANET. The model has been tested and validated by applying it to the least cost optimization of several benchmark networks reported in the literature. The results obtained with GENOME have been compared with those found in previous works, obtaining the same results as the best published in the literature to date. Once the model was validated, the optimization of a real complex irrigation network has been carried out to evaluate the potential of the genetic algorithm for the optimal design of large-scale networks. Although satisfactory results have been obtained, some adjustments would be desirable to improve the performance of genetic algorithms when the complexity of the network requires it.

  11. Genetic dissection of a regionally differentiated network for exploratory behavior in Drosophila larvae.

    PubMed

    Berni, Jimena

    2015-05-18

    An efficient strategy to explore the environment for available resources involves the execution of random walks where straight line locomotion alternates with changes of direction. This strategy is highly conserved in the animal kingdom, from zooplankton to human hunter-gatherers. Drosophila larvae execute a routine of this kind, performing straight line crawling interrupted at intervals by pause turns that halt crawling and redirect the trajectory of movement. The execution of this routine depends solely on the activity of networks located in the thoracic and abdominal segments of the nervous system, while descending input from the brain serves to modify it in a context-dependent fashion. I used a genetic method to investigate the location and function of the circuitry required for the different elements of exploratory crawling. By using the Slit-Robo axon guidance pathway to target neuronal midline crossing defects selectively to particular regions of the thoracic and abdominal networks, it has been possible to define at least three functions required for the performance of the exploratory routine: (1) symmetrical outputs in thoracic and abdominal segments that generate the crawls; (2) asymmetrical output that is uniquely initiated in the thoracic segments and generates the turns; and (3) an intermittent interruption to crawling that determines the time-dependent transition between crawls and turns.

  12. Genetic Dissection of a Regionally Differentiated Network for Exploratory Behavior in Drosophila Larvae

    PubMed Central

    Berni, Jimena

    2015-01-01

    Summary An efficient strategy to explore the environment for available resources involves the execution of random walks where straight line locomotion alternates with changes of direction. This strategy is highly conserved in the animal kingdom, from zooplankton to human hunter-gatherers [1–8]. Drosophila larvae execute a routine of this kind, performing straight line crawling interrupted at intervals by pause turns that halt crawling and redirect the trajectory of movement [9–11]. The execution of this routine depends solely on the activity of networks located in the thoracic and abdominal segments of the nervous system, while descending input from the brain serves to modify it in a context-dependent fashion [9]. I used a genetic method to investigate the location and function of the circuitry required for the different elements of exploratory crawling. By using the Slit-Robo axon guidance pathway to target neuronal midline crossing defects selectively to particular regions of the thoracic and abdominal networks, it has been possible to define at least three functions required for the performance of the exploratory routine: (1) symmetrical outputs in thoracic and abdominal segments that generate the crawls; (2) asymmetrical output that is uniquely initiated in the thoracic segments and generates the turns; and (3) an intermittent interruption to crawling that determines the time-dependent transition between crawls and turns. PMID:25959962

  13. 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.

  14. Social and Genetic Networks of HIV-1 Transmission in New York City

    PubMed Central

    Wertheim, Joel O.; Kosakovsky Pond, Sergei L.; Forgione, Lisa A.; Mehta, Sanjay R.; Murrell, Ben; Shah, Sharmila; Smith, Davey M.; Scheffler, Konrad; Torian, Lucia V.

    2017-01-01

    Background Sexually transmitted infections spread across contact networks. Partner elicitation and notification are commonly used public health tools to identify, notify, and offer testing to persons linked in these contact networks. For HIV-1, a rapidly evolving pathogen with low per-contact transmission rates, viral genetic sequences are an additional source of data that can be used to infer or refine transmission networks. Methods and Findings The New York City Department of Health and Mental Hygiene interviews individuals newly diagnosed with HIV and elicits names of sexual and injection drug using partners. By law, the Department of Health also receives HIV sequences when these individuals enter healthcare and their physicians order resistance testing. Our study used both HIV sequence and partner naming data from 1342 HIV-infected persons in New York City between 2006 and 2012 to infer and compare sexual/drug-use named partner and genetic transmission networks. Using these networks, we determined a range of genetic distance thresholds suitable for identifying potential transmission partners. In 48% of cases, named partners were infected with genetically closely related viruses, compatible with but not necessarily representing or implying, direct transmission. Partner pairs linked through the genetic similarity of their HIV sequences were also linked by naming in 53% of cases. Persons who reported high-risk heterosexual contact were more likely to name at least one partner with a genetically similar virus than those reporting their risk as injection drug use or men who have sex with men. Conclusions We analyzed an unprecedentedly large and detailed partner tracing and HIV sequence dataset and determined an empirically justified range of genetic distance thresholds for identifying potential transmission partners. We conclude that genetic linkage provides more reliable evidence for identifying potential transmission partners than partner naming, highlighting the

  15. 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.

  16. Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks.

    PubMed

    Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang

    2011-05-01

    A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.

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

    PubMed

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

    2015-09-01

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

  18. Combining epidemiological and genetic networks signifies the importance of early treatment in HIV-1 transmission.

    PubMed

    Zarrabi, Narges; Prosperi, Mattia; Belleman, Robert G; Colafigli, Manuela; De Luca, Andrea; Sloot, Peter M A

    2012-01-01

    Inferring disease transmission networks is important in epidemiology in order to understand and prevent the spread of infectious diseases. Reconstruction of the infection transmission networks requires insight into viral genome data as well as social interactions. For the HIV-1 epidemic, current research either uses genetic information of patients' virus to infer the past infection events or uses statistics of sexual interactions to model the network structure of viral spreading. Methods for a reliable reconstruction of HIV-1 transmission dynamics, taking into account both molecular and societal data are still lacking. The aim of this study is to combine information from both genetic and epidemiological scales to characterize and analyse a transmission network of the HIV-1 epidemic in central Italy.We introduce a novel filter-reduction method to build a network of HIV infected patients based on their social and treatment information. The network is then combined with a genetic network, to infer a hypothetical infection transmission network. We apply this method to a cohort study of HIV-1 infected patients in central Italy and find that patients who are highly connected in the network have longer untreated infection periods. We also find that the network structures for homosexual males and heterosexual populations are heterogeneous, consisting of a majority of 'peripheral nodes' that have only a few sexual interactions and a minority of 'hub nodes' that have many sexual interactions. Inferring HIV-1 transmission networks using this novel combined approach reveals remarkable correlations between high out-degree individuals and longer untreated infection periods. These findings signify the importance of early treatment and support the potential benefit of wide population screening, management of early diagnoses and anticipated antiretroviral treatment to prevent viral transmission and spread. The approach presented here for reconstructing HIV-1 transmission networks

  19. 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

  20. Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function.

    PubMed

    Braun, Urs; Schäfer, Axel; Bassett, Danielle S; Rausch, Franziska; Schweiger, Janina I; Bilek, Edda; Erk, Susanne; Romanczuk-Seiferth, Nina; Grimm, Oliver; Geiger, Lena S; Haddad, Leila; Otto, Kristina; Mohnke, Sebastian; Heinz, Andreas; Zink, Mathias; Walter, Henrik; Schwarz, Emanuel; Meyer-Lindenberg, Andreas; Tost, Heike

    2016-11-01

    Schizophrenia is increasingly recognized as a disorder of distributed neural dynamics, but the molecular and genetic contributions are poorly understood. Recent work highlights a role for altered N-methyl-d-aspartate (NMDA) receptor signaling and related impairments in the excitation-inhibitory balance and synchrony of large-scale neural networks. Here, we combined a pharmacological intervention with novel techniques from dynamic network neuroscience applied to functional magnetic resonance imaging (fMRI) to identify alterations in the dynamic reconfiguration of brain networks related to schizophrenia genetic risk and NMDA receptor hypofunction. We quantified "network flexibility," a measure of the dynamic reconfiguration of the community structure of time-variant brain networks during working memory performance. Comparing 28 patients with schizophrenia, 37 unaffected first-degree relatives, and 139 healthy controls, we detected significant differences in network flexibility [F(2,196) = 6.541, P = 0.002] in a pattern consistent with the assumed genetic risk load of the groups (highest for patients, intermediate for relatives, and lowest for controls). In an observer-blinded, placebo-controlled, randomized, cross-over pharmacological challenge study in 37 healthy controls, we further detected a significant increase in network flexibility as a result of NMDA receptor antagonism with 120 mg dextromethorphan [F(1,34) = 5.291, P = 0.028]. Our results identify a potential dynamic network intermediate phenotype related to the genetic liability for schizophrenia that manifests as altered reconfiguration of brain networks during working memory. The phenotype appears to be influenced by NMDA receptor antagonism, consistent with a critical role for glutamate in the temporal coordination of neural networks and the pathophysiology of schizophrenia.

  1. Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function

    PubMed Central

    Braun, Urs; Schäfer, Axel; Rausch, Franziska; Schweiger, Janina I.; Bilek, Edda; Erk, Susanne; Romanczuk-Seiferth, Nina; Grimm, Oliver; Geiger, Lena S.; Haddad, Leila; Otto, Kristina; Mohnke, Sebastian; Heinz, Andreas; Zink, Mathias; Walter, Henrik; Schwarz, Emanuel; Meyer-Lindenberg, Andreas; Tost, Heike

    2016-01-01

    Schizophrenia is increasingly recognized as a disorder of distributed neural dynamics, but the molecular and genetic contributions are poorly understood. Recent work highlights a role for altered N-methyl-d-aspartate (NMDA) receptor signaling and related impairments in the excitation–inhibitory balance and synchrony of large-scale neural networks. Here, we combined a pharmacological intervention with novel techniques from dynamic network neuroscience applied to functional magnetic resonance imaging (fMRI) to identify alterations in the dynamic reconfiguration of brain networks related to schizophrenia genetic risk and NMDA receptor hypofunction. We quantified “network flexibility,” a measure of the dynamic reconfiguration of the community structure of time-variant brain networks during working memory performance. Comparing 28 patients with schizophrenia, 37 unaffected first-degree relatives, and 139 healthy controls, we detected significant differences in network flexibility [F(2,196) = 6.541, P = 0.002] in a pattern consistent with the assumed genetic risk load of the groups (highest for patients, intermediate for relatives, and lowest for controls). In an observer-blinded, placebo-controlled, randomized, cross-over pharmacological challenge study in 37 healthy controls, we further detected a significant increase in network flexibility as a result of NMDA receptor antagonism with 120 mg dextromethorphan [F(1,34) = 5.291, P = 0.028]. Our results identify a potential dynamic network intermediate phenotype related to the genetic liability for schizophrenia that manifests as altered reconfiguration of brain networks during working memory. The phenotype appears to be influenced by NMDA receptor antagonism, consistent with a critical role for glutamate in the temporal coordination of neural networks and the pathophysiology of schizophrenia. PMID:27791105

  2. 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

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

    PubMed

    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

  4. Achromobacter xylosoxidans: an emerging pathogen carrying different elements involved in horizontal genetic transfer.

    PubMed

    Traglia, German Matías; Almuzara, Marisa; Merkier, Andrea Karina; Adams, Christina; Galanternik, Laura; Vay, Carlos; Centrón, Daniela; Ramírez, María Soledad

    2012-12-01

    In the last few years, numerous cases of multidrug-resistant Achromobacter xylosoxidans infections have been documented in immunocompromised and cystic fibrosis patients. To gain insights into the molecular mechanisms and mobile elements related to multidrug resistance in this bacterium, we studied 24 non-epidemiological A. xylosoxidans clinical isolates from Argentina. Specific primers for plasmids, transposons, insertion sequences, bla(ampC), intI1, and intI2 genes were used in PCR reactions. The obtained results showed the presence of wide host range IncP plasmids in ten isolates and a high dispersion of class 1 integrons (n = 10) and class 2 integrons (n = 3). Four arrays in the variable region (vr) of class 1 integrons were identified carrying different gene cassettes as the aminoglycoside resistance aac(6')-Ib and aadA1, the trimethoprim resistance dfrA1 and dfrA16, and the β-lactamase bla(OXA-2). In only one of the class 2 integrons, a vr was amplified that includes sat2-aadA1. The bla(ampC) gene was found in all isolates, confirming its ubiquitous nature. Our results show that A. xylosoxidans clinical isolates contain a rich variety of genetic elements commonly associated with resistance genes and their dissemination. This supports the hypothesis that A. xylosoxidans is becoming a reservoir of horizontal genetic transfer elements commonly involved in spreading antibiotic resistance.

  5. 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

  6. Curve-based multivariate distance matrix regression analysis: application to genetic association analyses involving repeated measures

    PubMed Central

    Salem, Rany M.; O'Connor, Daniel T.

    2010-01-01

    Most, if not all, human phenotypes exhibit a temporal, dosage-dependent, or age effect. Despite this fact, it is rare that data are collected over time or in sequence in relevant studies of the determinants of these phenotypes. The costs and organizational sophistication necessary to collect repeated measurements or longitudinal data for a given phenotype are clearly impediments to this, but greater efforts in this area are needed if insights into human phenotypic expression are to be obtained. Appropriate data analysis methods for genetic association studies involving repeated or longitudinal measures are also needed. We consider the use of longitudinal profiles obtained from fitted functions on repeated data collections from a set of individuals whose similarities are contrasted between sets of individuals with different genotypes to test hypotheses about genetic influences on time-dependent phenotype expression. The proposed approach can accommodate uncertainty of the fitted functions, as well as weighting factors across the time points, and is easily extended to a wide variety of complex analysis settings. We showcase the proposed approach with data from a clinical study investigating human blood vessel response to tyramine. We also compare the proposed approach with standard analytic procedures and investigate its robustness and power via simulation studies. The proposed approach is found to be quite flexible and performs either as well or better than traditional statistical methods. PMID:20423962

  7. 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.

  8. 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.

  9. 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.

  10. 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

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

    PubMed

    Wolf, Jochen B W; Trillmich, Fritz

    2008-09-22

    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.

  12. Establishment of apoptotic regulatory network for genetic markers of colorectal cancer.

    PubMed

    Hao, Yibin; Shan, Guoyong; Nan, Kejun

    2017-03-01

    Our purpose is to screen out genetic markers applicable to early diagnosis for colorectal cancer and to establish apoptotic regulatory network model for colorectal cancer, thereby providing theoretical evidence and targeted therapy for early diagnosis of colorectal cancer. Taking databases including CNKI, VIP, Wanfang data, Pub Med, and MEDLINE as main sources of literature retrieval, literatures associated with genetic markers applied to early diagnosis of colorectal cancer were searched to perform comprehensive and quantitative analysis by Meta analysis, hence screening genetic markers used in early diagnosis of colorectal cancer. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were employed to establish apoptotic regulatory network model based on screened genetic markers, and then verification experiment was conducted. Through Meta analysis, seven genetic markers were screened out, including WWOX, K-ras, COX-2, p53, APC, DCC and PTEN, among which DCC shows highest diagnostic efficiency. GO analysis of genetic markers found that six genetic markers played role in biological process, molecular function and cellular component. It was indicated in apoptotic regulatory network built by KEGG analysis and verification experiment that WWOX could promote tumor cell apoptotic in colorectal cancer and elevate expression level of p53. The apoptotic regulatory model of colorectal cancer established in this study provides clinically theoretical evidence and targeted therapy for early diagnosis of colorectal cancer.

  13. On the application of the genetic algorithm to the predictability problems involving "on-off" switches

    NASA Astrophysics Data System (ADS)

    Zheng, Q.

    2011-12-01

    On the application of the genetic algorithm to the predictability problems involving "on-off" switches ZHENG Qin(1,2), DAI Yi(1), ZHANG Lu(1)and LU Xiaoqing(1) (1)Institute of Science, PLA University of Science and Technology, Nanjing 211101, China; (2)State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China Abstract The lower bound of maximum predictable time can be formulated into a constrained nonlinear optimization problem, and the traditional solution to this problem is the filtering method and the conditional nonlinear optimal perturbation (CNOP) method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named as the ADJ-CNOP, hereinafter. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the "on-off" switch problem, which affects tremendously the effectiveness of the conventional gradient descent algorithm based on the adjoint method. This paper attempts to apply a genetic algorithm (GA) to the CNOP method, named as the GA-CNOP, to solve the predictability problems involving the "on-off" switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results are taken as benchmarks and a series of comparisons between the ADJ-CNOP and the GA-CNOP are performed. It is revealed that the GA-CNOP can always figure out the accurate lower bound of maximum predictable time even in discontinuous cases, while the ADJ-CNOP, owing to the effect of "on-off" switches, often yields the incorrect lower bound of maximum predictable time. This would suggest that in non-smooth cases, using a GA to solve the predictability problems is more effective than using the conventional

  14. Genetic variants in Alzheimer disease – molecular and brain network approaches

    PubMed Central

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

    2016-01-01

    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 for AD. However, due to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extracting 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 effect of LOAD-associated genetic variants. We then discuss emerging combinations of omic data types in multiscale models, which provide a more comprehensive representation of the effect of LOAD-associated genetic variants at multiple biophysical scales. Further, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models. PMID:27282653

  15. 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

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

    PubMed

    Moreland-Russell, Sarah; Carothers, Bobbi J

    2015-09-08

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

  17. 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.

  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.

  19. A Drosophila screen identifies neurofibromatosis-1 genetic modifiers involved in systemic and synaptic growth

    PubMed Central

    Walker, James A; Bernards, André

    2014-01-01

    Neurofibromatosis type 1 (NF1) is caused by loss of a negative regulator of Ras oncoproteins. Unknown genetic modifiers have been implicated in NF1’s characteristic variability. Drosophila melanogaster dNf1 phenotypes include cognitive deficits and reduced growth, both of which resemble human symptoms. We recently reported results of a screen for dominant modifiers of dNf1 growth. Suppressors include the dAlk tyrosine kinase and its activating ligand, two other genes involved in Ras/ERK signal transduction, the synaptic scaffold Dap160 and the CCKLR-17D1 drosulfakinin receptor. Additional modifiers include several genes involved in cAMP/PKA signaling. Providing mechanistic insights, dAlk, jeb, and CCKLR-17D1 also suppress a dNf1 synaptic overgrowth defect, and increasing cAMP/PKA signaling in the neuroendocrine ring gland rescued the dNf1 growth deficiency. Finally, among the several suppressors identified in our screen, we specifically implicate ALK as a potential therapeutic target by showing that NF1-regulated ALK/RAS/ERK signaling is conserved in human cells. PMID:25054093

  20. Expression-based network biology identifies immune-related functional modules involved in plant defense.

    PubMed

    Tully, Joel P; Hill, Aubrey E; Ahmed, Hadia M R; Whitley, Ryan; Skjellum, Anthony; Mukhtar, M Shahid

    2014-06-03

    Plants respond to diverse environmental cues including microbial perturbations by coordinated regulation of thousands of genes. These intricate transcriptional regulatory interactions depend on the recognition of specific promoter sequences by regulatory transcription factors. The combinatorial and cooperative action of multiple transcription factors defines a regulatory network that enables plant cells to respond to distinct biological signals. The identification of immune-related modules in large-scale transcriptional regulatory networks can reveal the mechanisms by which exposure to a pathogen elicits a precise phenotypic immune response. We have generated a large-scale immune co-expression network using a comprehensive set of Arabidopsis thaliana (hereafter Arabidopsis) transcriptomic data, which consists of a wide spectrum of immune responses to pathogens or pathogen-mimicking stimuli treatments. We employed both linear and non-linear models to generate Arabidopsis immune co-expression regulatory (AICR) network. We computed network topological properties and ascertained that this newly constructed immune network is densely connected, possesses hubs, exhibits high modularity, and displays hallmarks of a "real" biological network. We partitioned the network and identified 156 novel modules related to immune functions. Gene Ontology (GO) enrichment analyses provided insight into the key biological processes involved in determining finely tuned immune responses. We also developed novel software called OCCEAN (One Click Cis-regulatory Elements ANalysis) to discover statistically enriched promoter elements in the upstream regulatory regions of Arabidopsis at a whole genome level. We demonstrated that OCCEAN exhibits higher precision than the existing promoter element discovery tools. In light of known and newly discovered cis-regulatory elements, we evaluated biological significance of two key immune-related functional modules and proposed mechanism(s) to explain

  1. 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.

  2. Autocatalytic genetic networks modeled by piecewise-deterministic Markov processes.

    PubMed

    Zeiser, Stefan; Franz, Uwe; Liebscher, Volkmar

    2010-02-01

    In the present work we propose an alternative approach to model autocatalytic networks, called piecewise-deterministic Markov processes. These were originally introduced by Davis in 1984. Such a model allows for random transitions between the active and inactive state of a gene, whereas subsequent transcription and translation processes are modeled in a deterministic manner. We consider three types of autoregulated networks, each based on a positive feedback loop. It is shown that if the densities of the stationary distributions exist, they are the solutions of a system of equations for a one-dimensional correlated random walk. These stationary distributions are determined analytically. Further, the distributions are analyzed for different simulation periods and different initial concentration values by numerical means. We show that, depending on the network structure, beside a binary response also a graded response is observable.

  3. Diversification in the genetic architecture of gene expression and transcriptional networks in organ differentiation of Populus.

    PubMed

    Drost, Derek R; Benedict, Catherine I; Berg, Arthur; Novaes, Evandro; Novaes, Carolina R D B; Yu, Qibin; Dervinis, Christopher; Maia, Jessica M; Yap, John; Miles, Brianna; Kirst, Matias

    2010-05-04

    A fundamental goal of systems biology is to identify genetic elements that contribute to complex phenotypes and to understand how they interact in networks predictive of system response to genetic variation. Few studies in plants have developed such networks, and none have examined their conservation among functionally specialized organs. Here we used genetical genomics in an interspecific hybrid population of the model hardwood plant Populus to uncover transcriptional networks in xylem, leaves, and roots. Pleiotropic eQTL hotspots were detected and used to construct coexpression networks a posteriori, for which regulators were predicted based on cis-acting expression regulation. Networks were shown to be enriched for groups of genes that function in biologically coherent processes and for cis-acting promoter motifs with known roles in regulating common groups of genes. When contrasted among xylem, leaves, and roots, transcriptional networks were frequently conserved in composition, but almost invariably regulated by different loci. Similarly, the genetic architecture of gene expression regulation is highly diversified among plant organs, with less than one-third of genes with eQTL detected in two organs being regulated by the same locus. However, colocalization in eQTL position increases to 50% when they are detected in all three organs, suggesting conservation in the genetic regulation is a function of ubiquitous expression. Genes conserved in their genetic regulation among all organs are primarily cis regulated (approximately 92%), whereas genes with eQTL in only one organ are largely trans regulated. Trans-acting regulation may therefore be the primary driver of differentiation in function between plant organs.

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

    PubMed

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

    2016-04-01

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

  5. 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.

  6. 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.

  7. A New Sparse Simplex Model for Brain Anatomical and Genetic Network Analysis

    PubMed Central

    Huang, Heng; Yan, Jingwen; Nie, Feiping; Huang, Jin; Cai, Weidong; Saykin, Andrew J.; Shen, Li

    2013-01-01

    The Allen Brain Atlas (ABA) database provides comprehensive 3D atlas of gene expression in the adult mouse brain for studying the spatial expression patterns in the mammalian central nervous system. It is computationally challenging to construct the accurate anatomical and genetic networks using the ABA 4D data. In this paper, we propose a novel sparse simplex model to accurately construct the brain anatomical and genetic networks, which are important to reveal the brain spatial expression patterns. Our new approach addresses the shift-invariant and parameter tuning problems, which are notorious in the existing network analysis methods, such that the proposed model is more suitable for solving practical biomedical problems. We validate our new model using the 4D ABA data, and the network construction results show the superior performance of the proposed sparse simplex model. PMID:24579193

  8. Multistability and bifurcation Analysis of Inhibitory Coupled Cyclic Genetic Regulatory Networks with Delays.

    PubMed

    Ling, Guang; Guan, Zhi-Hong; Hu, Bin; Lai, Qiang; Wu, Yonghong

    2017-02-14

    Many biological systems have the conspicuous property to present more than one stable state and diverse rhythmic behaviors. A closed relationship between these complex dynamic behaviors and cyclic genetic structures has been witnessed by pioneering works. In this paper, a typical structure of inhibitory coupled cyclic genetic networks is introduced to further enlighten this mechanism of stability and biological rhythms of living cells. The coupled networks consist two identical cyclic genetic subnetworks, which inhibit each other directly. Each subnetwork can be regarded as a genetic unit at the cellular level. Multiple time delays, including both internal delays and coupling delays, are considered. The existence of positive equilibriums for this kind of coupled systems is proved, and the stability for each equilibrium are analyzed without or with delays. It is shown that the coupled networks with positive cyclic genetic units have a ability to show multistability, while the coupled networks with negative units may present a series of Hopf bifurcations with the variation of time delays. Several numerical simulations are made to prove our results.

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

    PubMed Central

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

    2012-01-01

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

  10. 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

  11. Identification of a neuronal transcription factor network involved in medulloblastoma development.

    PubMed

    Lastowska, Maria; Al-Afghani, Hani; Al-Balool, Haya H; Sheth, Harsh; Mercer, Emma; Coxhead, Jonathan M; Redfern, Chris P F; Peters, Heiko; Burt, Alastair D; Santibanez-Koref, Mauro; Bacon, Chris M; Chesler, Louis; Rust, Alistair G; Adams, David J; Williamson, Daniel; Clifford, Steven C; Jackson, Michael S

    2013-07-11

    Medulloblastomas, the most frequent malignant brain tumours affecting children, comprise at least 4 distinct clinicogenetic subgroups. Aberrant sonic hedgehog (SHH) signalling is observed in approximately 25% of tumours and defines one subgroup. Although alterations in SHH pathway genes (e.g. PTCH1, SUFU) are observed in many of these tumours, high throughput genomic analyses have identified few other recurring mutations. Here, we have mutagenised the Ptch+/- murine tumour model using the Sleeping Beauty transposon system to identify additional genes and pathways involved in SHH subgroup medulloblastoma development. Mutagenesis significantly increased medulloblastoma frequency and identified 17 candidate cancer genes, including orthologs of genes somatically mutated (PTEN, CREBBP) or associated with poor outcome (PTEN, MYT1L) in the human disease. Strikingly, these candidate genes were enriched for transcription factors (p=2x10-5), the majority of which (6/7; Crebbp, Myt1L, Nfia, Nfib, Tead1 and Tgif2) were linked within a single regulatory network enriched for genes associated with a differentiated neuronal phenotype. Furthermore, activity of this network varied significantly between the human subgroups, was associated with metastatic disease, and predicted poor survival specifically within the SHH subgroup of tumours. Igf2, previously implicated in medulloblastoma, was the most differentially expressed gene in murine tumours with network perturbation, and network activity in both mouse and human tumours was characterised by enrichment for multiple gene-sets indicating increased cell proliferation, IGF signalling, MYC target upregulation, and decreased neuronal differentiation. Collectively, our data support a model of medulloblastoma development in SB-mutagenised Ptch+/- mice which involves disruption of a novel transcription factor network leading to Igf2 upregulation, proliferation of GNPs, and tumour formation. Moreover, our results identify rational

  12. Informatic parcellation of the network involved in the computation of subjective value

    PubMed Central

    Rangel, Antonio

    2014-01-01

    Understanding how the brain computes value is a basic question in neuroscience. Although individual studies have driven this progress, meta-analyses provide an opportunity to test hypotheses that require large collections of data. We carry out a meta-analysis of a large set of functional magnetic resonance imaging studies of value computation to address several key questions. First, what is the full set of brain areas that reliably correlate with stimulus values when they need to be computed? Second, is this set of areas organized into dissociable functional networks? Third, is a distinct network of regions involved in the computation of stimulus values at decision and outcome? Finally, are different brain areas involved in the computation of stimulus values for different reward modalities? Our results demonstrate the centrality of ventromedial prefrontal cortex (VMPFC), ventral striatum and posterior cingulate cortex (PCC) in the computation of value across tasks, reward modalities and stages of the decision-making process. We also find evidence of distinct subnetworks of co-activation within VMPFC, one involving central VMPFC and dorsal PCC and another involving more anterior VMPFC, left angular gyrus and ventral PCC. Finally, we identify a posterior-to-anterior gradient of value representations corresponding to concrete-to-abstract rewards. PMID:23887811

  13. Information propagation within the Genetic Network of Saccharomyces cerevisiae.

    PubMed

    Chowdhury, Sharif; Lloyd-Price, Jason; Smolander, Olli-Pekka; Baici, Wayne C V; Hughes, Timothy R; Yli-Harja, Olli; Chua, Gordon; Ribeiro, Andre S

    2010-10-26

    A gene network's capacity to process information, so as to bind past events to future actions, depends on its structure and logic. From previous and new microarray measurements in Saccharomyces cerevisiae following gene deletions and overexpressions, we identify a core gene regulatory network (GRN) of functional interactions between 328 genes and the transfer functions of each gene. Inferred connections are verified by gene enrichment. We find that this core network has a generalized clustering coefficient that is much higher than chance. The inferred Boolean transfer functions have a mean p-bias of 0.41, and thus similar amounts of activation and repression interactions. However, the distribution of p-biases differs significantly from what is expected by chance that, along with the high mean connectivity, is found to cause the core GRN of S. cerevisiae's to have an overall sensitivity similar to critical Boolean networks. In agreement, we find that the amount of information propagated between nodes in finite time series is much higher in the inferred core GRN of S. cerevisiae than what is expected by chance. We suggest that S. cerevisiae is likely to have evolved a core GRN with enhanced information propagation among its genes.

  14. 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

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

    PubMed

    2015-11-01

    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. We describe the rich genetic and phenotypic information available in this consortium comprising heart, kidney, liver, and lung transplant cohorts. 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. 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.

  16. 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

  17. River network architecture, genetic effective size and distributional patterns predict differences in genetic structure across species in a dryland stream fish community.

    PubMed

    Pilger, Tyler J; Gido, Keith B; Propst, David L; Whitney, James E; Turner, Thomas F

    2017-05-01

    Dendritic ecological network (DEN) architecture can be a strong predictor of spatial genetic patterns in theoretical and simulation studies. Yet, interspecific differences in dispersal capabilities and distribution within the network may equally affect species' genetic structuring. We characterized patterns of genetic variation from up to ten microsatellite loci for nine numerically dominant members of the upper Gila River fish community, New Mexico, USA. Using comparative landscape genetics, we evaluated the role of network architecture for structuring populations within species (pairwise FST ) while explicitly accounting for intraspecific demographic influences on effective population size (Ne ). Five species exhibited patterns of connectivity and/or genetic diversity gradients that were predicted by network structure. These species were generally considered to be small-bodied or habitat specialists. Spatial variation of Ne was a strong predictor of pairwise FST for two species, suggesting patterns of connectivity may also be influenced by genetic drift independent of network properties. Finally, two study species exhibited genetic patterns that were unexplained by network properties and appeared to be related to nonequilibrium processes. Properties of DENs shape community-wide genetic structure but effects are modified by intrinsic traits and nonequilibrium processes. Further theoretical development of the DEN framework should account for such cases. © 2017 John Wiley & Sons Ltd.

  18. Separate cortical networks involved in music perception: preliminary functional MRI evidence for modularity of music processing.

    PubMed

    Schmithorst, Vincent J

    2005-04-01

    Music perception is a quite complex cognitive task, involving the perception and integration of various elements including melody, harmony, pitch, rhythm, and timbre. A preliminary functional MRI investigation of music perception was performed, using a simplified passive listening task. Group independent component analysis (ICA) was used to separate out various components involved in music processing, as the hemodynamic responses are not known a priori. Various components consistent with auditory processing, expressive language, syntactic processing, and visual association were found. The results are discussed in light of various hypotheses regarding modularity of music processing and its overlap with language processing. The results suggest that, while some networks overlap with ones used for language processing, music processing may involve its own domain-specific processing subsystems.

  19. Yeast Augmented Network Analysis (YANA): a new systems approach to identify therapeutic targets for human genetic diseases

    PubMed Central

    Wiley, David J.; Juan, Ilona; Le, Hao; Cai, Xiaodong; Baumbach, Lisa; Beattie, Christine; D'Urso, Gennaro

    2014-01-01

    Genetic interaction networks that underlie most human diseases are highly complex and poorly defined. Better-defined networks will allow identification of a greater number of therapeutic targets. Here we introduce our Yeast Augmented Network Analysis (YANA) approach and test it with the X-linked spinal muscular atrophy (SMA) disease gene UBA1. First, we express UBA1 and a mutant variant in fission yeast and use high-throughput methods to identify fission yeast genetic modifiers of UBA1. Second, we analyze available protein-protein interaction network databases in both fission yeast and human to construct UBA1 genetic networks. Third, from these networks we identified potential therapeutic targets for SMA. Finally, we validate one of these targets in a vertebrate (zebrafish) SMA model. This study demonstrates the power of combining synthetic and chemical genetics with a simple model system to identify human disease gene networks that can be exploited for treating human diseases. PMID:25075304

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

    PubMed

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

    2015-10-01

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

  1. Exact and heuristic methods for network completion for time-varying genetic networks.

    PubMed

    Nakajima, Natsu; Akutsu, Tatsuya

    2014-01-01

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

  2. Pin1: Intimate involvement with the regulatory protein kinase networks in the global phosphorylation landscape.

    PubMed

    Litchfield, David W; Shilton, Brian H; Brandl, Christopher J; Gyenis, Laszlo

    2015-10-01

    Protein phosphorylation is a universal regulatory mechanism that involves an extensive network of protein kinases. The discovery of the phosphorylation-dependent peptidyl-prolyl isomerase Pin1 added an additional layer of complexity to these regulatory networks. We have evaluated interactions between Pin1 and the regulatory kinome and proline-dependent phosphoproteome taking into consideration findings from targeted studies as well as data that has emerged from systematic phosphoproteomic workflows and from curated protein interaction databases. The relationship between Pin1 and the regulatory protein kinase networks is not restricted simply to the recognition of proteins that are substrates for proline-directed kinases. In this respect, Pin1 itself is phosphorylated in cells by protein kinases that modulate its functional properties. Furthermore, the phosphorylation-dependent targets of Pin1 include a number of protein kinases as well as other enzymes such as phosphatases and regulatory subunits of kinases that modulate the actions of protein kinases. As a result of its interactions with numerous protein kinases and their substrates, as well as itself being a target for phosphorylation, Pin1 has an intricate relationship with the regulatory protein kinase and phosphoproteomic networks that orchestrate complex cellular processes and respond to environmental cues. This article is part of a Special Issue entitled Proline-directed Foldases: Cell Signaling Catalysts and Drug Targets. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. 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

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

    PubMed

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

    2010-11-13

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

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

    PubMed

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

    2016-09-27

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

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

    PubMed Central

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

    2016-01-01

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

  7. 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.

  8. Genetic network and gene set enrichment analysis to identify biomarkers related to cigarette smoking and lung cancer.

    PubMed

    Fang, Xiaocong; Netzer, Michael; Baumgartner, Christian; Bai, Chunxue; Wang, Xiangdong

    2013-02-01

    Cigarette smoking is the most demonstrated risk factor for the development of lung cancer, while the related genetic mechanisms are still unclear. The preprocessed microarray expression dataset was downloaded from Gene Expression Omnibus database. Samples were classified according to the disease state, stage and smoking state. A new computational strategy was applied for the identification and biological interpretation of new candidate genes in lung cancer and smoking by coupling a network-based approach with gene set enrichment analysis. Network analysis was performed by pair-wise comparison according to the disease states (tumor or normal), smoking states (current smokers or nonsmokers or former smokers), or the disease stage (stages I-IV). The most activated metabolic pathways were identified by gene set enrichment analysis. Panels of top ranked gene candidates in smoking or cancer development were identified, including genes involved in cell proliferation and drug metabolism like cytochrome P450 and WW domain containing transcription regulator 1. Semaphorin 5A and protein phosphatase 1F are the common genes represented as major hubs in both the smoking and cancer related network. Six pathways, e.g. cell cycle, DNA replication, RNA transport, protein processing in endoplasmic reticulum, vascular smooth muscle contraction and endocytosis were commonly involved in smoking and lung cancer when comparing the top ten selected pathways. New approach of bioinformatics for biomarker identification and validation can probe into deep genetic relationships between cigarette smoking and lung cancer. Our studies indicate that disease-specific network biomarkers, interaction between genes/proteins, or cross-talking of pathways provide more specific values for the development of precision therapies for lung. Copyright © 2012 Elsevier Ltd. All rights reserved.

  9. Analysis of functional networks involved in motor execution and motor imagery using combined hierarchical clustering analysis and independent component analysis.

    PubMed

    Wang, Yuqing; Chen, Huafu; Gong, Qiyong; Shen, Shan; Gao, Qing

    2010-06-01

    Cognitive experiments involving motor execution (ME) and motor imagery (MI) have been intensively studied using functional magnetic resonance imaging (fMRI). However, the functional networks of a multitask paradigm which include ME and MI were not widely explored. In this article, we aimed to investigate the functional networks involved in MI and ME using a method combining the hierarchical clustering analysis (HCA) and the independent component analysis (ICA). Ten right-handed subjects were recruited to participate a multitask experiment with conditions such as visual cue, MI, ME and rest. The results showed that four activation clusters were found including parts of the visual network, ME network, the MI network and parts of the resting state network. Furthermore, the integration among these functional networks was also revealed. The findings further demonstrated that the combined HCA with ICA approach was an effective method to analyze the fMRI data of multitasks. Copyright 2010 Elsevier Inc. All rights reserved.

  10. 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.

  11. A systematic genetic screen for genes involved in sensing inorganic phosphate availability in Saccharomyces cerevisiae

    DOE PAGES

    Choi, Joonhyuk; Rajagopal, Abbhirami; Xu, Yi -Fan; ...

    2017-05-17

    Saccharomyces cerevisiae responds to changes in extracellular inorganic phosphate (Pi) availability by regulating the activity of the phosphate-responsive (PHO) signaling pathway, enabling cells to maintain intracellular levels of the essential nutrient Pi. Pi-limitation induces upregulation of inositol heptakisphosphate (IP7) synthesized by the inositol hexakisphosphate kinase Vip1, triggering inhibition of the Pho80/Pho85 cyclin-cyclin dependent kinase (CDK) complex by the CDK inhibitor Pho81, which upregulates the PHO regulon through the CDK target and transcription factor Pho4. To identify genes that are involved in signaling upstream of the Pho80/Pho85/Pho81 complex and how they interact with each other to regulate the PHO pathway, wemore » performed genome-wide screens with the synthetic genetic array method. We identified more than 300 mutants with defects in signaling upstream of the Pho80/Pho85/Pho81 complex, including AAH1, which encodes an adenine deaminase that negatively regulates the PHO pathway in a Vip1-dependent manner. Moreover, we showed that even in the absence of VIP1, the PHO pathway can be activated under prolonged periods of Pi starvation, suggesting complexity in the mechanisms by which the PHO pathway is regulated.« less

  12. 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

    2016-05-03

    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.

  13. 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

  14. 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

  15. 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

  16. Genetic polymorphisms of molecules involved in host immune response to dengue virus infection.

    PubMed

    Fang, Xin; Hu, Zhen; Shang, Weilong; Zhu, Junmin; Xu, Chuanshan; Rao, Xiancai

    2012-11-01

    The dengue virus (DENV) belongs to the flavivirus family. Each of the four distinct serotypes of this virus is capable of causing human disease, especially in tropical and subtropical areas. The majority of people infected with DENV manifest asymptomatic or dengue fever with flu-like self-limited symptoms. However, a small portion of patients emerge with severe manifestations referred to as dengue hemorrhagic fever, which has a high mortality rate if not treated promptly. The host immune system, which plays important roles throughout the whole process of DENV infection, has been confirmed to have double-edged effects on DENV infection. Recently, much attention has been paid to the genetic heterogeneity of molecules involved in the host immune response to DENV infection. This heterogeneity has been proved to be the determining factor for DENV disease orientation. The present review discusses the primary functions and single nucleotide polymorphisms of some critical molecules in the human DENV immunological defense, especially the polymorphism locus associated with the DENV pathogenesis and disease susceptibility. © 2012 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

  17. A genetic algorithm for the arrival probability in the stochastic networks.

    PubMed

    Shirdel, Gholam H; Abdolhosseinzadeh, Mohsen

    2016-01-01

    A genetic algorithm is presented to find the arrival probability in a directed acyclic network with stochastic parameters, that gives more reliability of transmission flow in delay sensitive networks. Some sub-networks are extracted from the original network, and a connection is established between the original source node and the original destination node by randomly selecting some local source and the local destination nodes. The connections are sorted according to their arrival probabilities and the best established connection is determined with the maximum arrival probability. There is an established discrete time Markov chain in the network. The arrival probability to a given destination node from a given source node in the network is defined as the multi-step transition probability of the absorbtion in the final state of the established Markov chain. The proposed method is applicable on large stochastic networks, where the previous methods were not. The effectiveness of the proposed method is illustrated by some numerical results with perfect fitness values of the proposed genetic algorithm.

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

    NASA Astrophysics Data System (ADS)

    Sahoo, Sasmita; Jha, Madan K.

    2017-03-01

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

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

    PubMed

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

    2012-10-01

    When grown on solid substrates, different microorganisms often form colonies with very specific morphologies. Whereas the pioneers of microbiology often used colony morphology to discriminate between species and strains, the phenomenon has not received much attention recently. In this study, we use a genome-wide assay in the model yeast Saccharomyces cerevisiae to identify all genes that affect colony morphology. We show that several major signalling cascades, including the MAPK, TORC, SNF1 and RIM101 pathways play a role, indicating that morphological changes are a reaction to changing environments. Other genes that affect colony morphology are involved in protein sorting and epigenetic regulation. Interestingly, the screen reveals only few genes that are likely to play a direct role in establishing colony morphology, with one notable example being FLO11, a gene encoding a cell-surface adhesin that has already been implicated in colony morphology, biofilm formation, and invasive and pseudohyphal growth. Using a series of modified promoters for fine-tuning FLO11 expression, we confirm the central role of Flo11 and show that differences in FLO11 expression result in distinct colony morphologies. Together, our results provide a first comprehensive look at the complex genetic network that underlies the diversity in the morphologies of yeast colonies. © 2012 Blackwell Publishing Ltd.

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

    PubMed Central

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

    2012-01-01

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

  1. Investigating the mechanism of hepatocellular carcinoma progression by constructing genetic and epigenetic networks using NGS data identification and big database mining method.

    PubMed

    Li, Cheng-Wei; Chang, Ping-Yao; Chen, Bor-Sen

    2016-11-29

    The mechanisms leading to the development and progression of hepatocellular carcinoma (HCC) are complicated and regulated genetically and epigenetically. The recent advancement in high-throughput sequencing has facilitated investigations into the role of genetic and epigenetic regulations in hepatocarcinogenesis. Therefore, we used systems biology and big database mining to construct genetic and epigenetic networks (GENs) using the information about mRNA, miRNA, and methylation profiles of HCC patients. Our approach involves analyzing gene regulatory networks (GRNs), protein-protein networks (PPINs), and epigenetic networks at different stages of hepatocarcinogenesis. The core GENs, influencing each stage of HCC, were extracted via principal network projection (PNP). The pathways during different stages of HCC were compared. We observed that extracellular signals were further transduced to transcription factors (TFs), resulting in the aberrant regulation of their target genes, in turn inducing mechanisms that are responsible for HCC progression, including cell proliferation, anti-apoptosis, aberrant cell cycle, cell survival, and metastasis. We also selected potential multiple drugs specific to prominent epigenetic network markers of each stage of HCC: lestaurtinib, dinaciclib, and perifosine against the NTRK2, MYC, and AKT1 markers influencing HCC progression from stage I to stage II; celecoxib, axitinib, and vinblastine against the DDIT3, PDGFB, and JUN markers influencing HCC progression from stage II to stage III; and atiprimod, celastrol, and bortezomib against STAT3, IL1B, and NFKB1 markers influencing HCC progression from stage III to stage IV.

  2. Investigating the mechanism of hepatocellular carcinoma progression by constructing genetic and epigenetic networks using NGS data identification and big database mining method

    PubMed Central

    Li, Cheng-Wei; Chang, Ping-Yao; Chen, Bor-Sen

    2016-01-01

    The mechanisms leading to the development and progression of hepatocellular carcinoma (HCC) are complicated and regulated genetically and epigenetically. The recent advancement in high-throughput sequencing has facilitated investigations into the role of genetic and epigenetic regulations in hepatocarcinogenesis. Therefore, we used systems biology and big database mining to construct genetic and epigenetic networks (GENs) using the information about mRNA, miRNA, and methylation profiles of HCC patients. Our approach involves analyzing gene regulatory networks (GRNs), protein-protein networks (PPINs), and epigenetic networks at different stages of hepatocarcinogenesis. The core GENs, influencing each stage of HCC, were extracted via principal network projection (PNP). The pathways during different stages of HCC were compared. We observed that extracellular signals were further transduced to transcription factors (TFs), resulting in the aberrant regulation of their target genes, in turn inducing mechanisms that are responsible for HCC progression, including cell proliferation, anti-apoptosis, aberrant cell cycle, cell survival, and metastasis. We also selected potential multiple drugs specific to prominent epigenetic network markers of each stage of HCC: lestaurtinib, dinaciclib, and perifosine against the NTRK2, MYC, and AKT1 markers influencing HCC progression from stage I to stage II; celecoxib, axitinib, and vinblastine against the DDIT3, PDGFB, and JUN markers influencing HCC progression from stage II to stage III; and atiprimod, celastrol, and bortezomib against STAT3, IL1B, and NFKB1 markers influencing HCC progression from stage III to stage IV. PMID:27821810

  3. Exponential stability of discrete-time genetic regulatory networks with delays.

    PubMed

    Cao, Jinde; Ren, Fengli

    2008-03-01

    Discrete-time versions of the continuous-time genetic regulatory networks (GRNs) with SUM regulatory functions are formulated and studied in this letter. Sufficient conditions are derived to ensure the global exponential stability of the discrete-time GRNs with delays. An illustrative example is given to demonstrate the effectiveness of the obtained results.

  4. 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…

  5. 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…

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

    USDA-ARS?s Scientific Manuscript database

    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...

  7. Sensory-Motor Networks Involved in Speech Production and Motor Control: An fMRI Study

    PubMed Central

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

    2015-01-01

    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

  8. 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. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. 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.

  10. 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

  11. 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.

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

    PubMed

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

    2012-07-26

    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.

  13. 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

  14. 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

  15. Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

    NASA Astrophysics Data System (ADS)

    Shao, Yuxiang; Chen, Qing; Wei, Zhenhua

    Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.

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

    PubMed

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

    2009-09-01

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

  17. 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.

  18. 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.

  19. A lateralized functional auditory network is involved in anuran sexual selection.

    PubMed

    Xue, Fei; Fang, Guangzhan; Yue, Xizi; Zhao, Ermi; Brauth, Steven E; Tang, Yezhong

    2016-12-01

    Right ear advantage (REA) exists in many land vertebrates in which the right ear and left hemisphere preferentially process conspecific acoustic stimuli such as those related to sexual selection. Although ecological and neural mechanisms for sexual selection have been widely studied, the brain networks involved are still poorly understood. In this study we used multi-channel electroencephalographic data in combination with Granger causal connectivity analysis to demonstrate, for the first time, that auditory neural network interconnecting the left and right midbrain and forebrain function asymmetrically in the Emei music frog (Babina daunchina), an anuran species which exhibits REA. The results showed the network was lateralized. Ascending connections between the mesencephalon and telencephalon were stronger in the left side while descending ones were stronger in the right, which matched with the REA in this species and implied that inhibition from the forebrainmay induce REA partly. Connections from the telencephalon to ipsilateral mesencephalon in response to white noise were the highest in the non-reproductive stage while those to advertisement calls were the highest in reproductive stage, implying the attention resources and living strategy shift when entered the reproductive season. Finally, these connection changes were sexually dimorphic, revealing sex differences in reproductive roles.

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed Central

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

    2016-01-01

    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

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

    PubMed

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

    2014-10-01

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

  3. Identification of biochemical networks by S-tree based genetic programming.

    PubMed

    Cho, Dong-Yeon; Cho, Kwang-Hyun; Zhang, Byoung-Tak

    2006-07-01

    Most previous approaches to model biochemical networks have focused either on the characterization of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviors of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system. We propose a new representation named S-trees for both the structural and dynamical modeling of a biochemical network within a unified scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse primitives of a biochemical network in an efficient way. We evaluate our algorithm on the dynamic profiles of an artificial genetic network. In 20 trials for four settings, we obtain the true structure and their relative squared errors are <5% regardless of releasing constraints about structural sparseness. In addition, we confirm that the proposed algorithm is robust within +/-10% noise ratio. Furthermore, the proposed approach ensures a reasonable estimate of a real yeast fermentation pathway. The comparatively less important connections with non-zero parameters can be detected even though their orders are below 10(-2). To demonstrate the usefulness of the proposed algorithm for real experimental biological data, we provide an additional example on the transcriptional network of SOS response to DNA damage in Escherichia

  4. Reactivation of L1 retrotransposon by benzo(a)pyrene involves complex genetic and epigenetic regulation.

    PubMed

    Teneng, Ivo; Montoya-Durango, Diego E; Quertermous, James L; Lacy, Mary E; Ramos, Kenneth S

    2011-03-01

    Benzo(a)pyrene (BaP), is an environmental pollutant present in tobacco smoke and a byproduct of fossil fuel combustion which likely contributes to the tumorigenic processes in human cancers including lung and esophageal. Long Interspersed Nuclear Element-1 (LINE-1) or L1 is a mobile element within the mammalian genome that propagates via a "copy-and-paste" mechanism using reverse transcriptase and RNA intermediates. L1 is strongly expressed during early embryogenesis and then silenced as cells initiate differentiation programming. Although the complex transcriptional control mechanisms of L1 are not well understood, L1 reactivation has been described in several human cancers and following exposure of mouse or human cells to BaP. In this study we investigated the molecular mechanisms and epigenetic events that regulate L1 reactivation following BaP exposure. We show that challenge of HeLa cells with BaP induces early enrichment of the transcriptionally-active chromatin markers histone H3 trimethylated at lysine 4 (H3K4Me3) and histone H3 acetylated at lysine 9 (H3K9Ac), and reduces association of DNA methyltransferase-1 (DNMT1) with the L1 promoter. These changes are followed by proteasome-dependent decreases in cellular DNMT1 expression and sustained reduction of cytosine methylation within the L1 promoter CpG island. Pharmacological inhibition of the proteasome signaling pathway with the inhibitor MG132 blocks degradation of DNMT1 and alters BaP-mediated histone epigenetic modifications. We conclude that genetic reactivation of L1 by BaP involves an ordered cascade of epigenetic events that begin with nucleosomal histone modifications and is completed with alterations in DNMT1 recruitment to the L1 promoter and reduced DNA methylation of CpG islands.

  5. Reactivation of L1 retrotransposon by benzo(a)pyrene involves complex genetic and epigenetic regulation

    PubMed Central

    Quertermous, James L; Lacy, Mary E

    2011-01-01

    Benzo(a)pyrene (BaP), is an environmental pollutant present in tobacco smoke and a byproduct of fossil fuel combustion that likely contributes to the tumorigenic processes in human cancers including lung and esophageal. Long Interspersed Nuclear Element-1 (LINE-1) or L1 is a mobile element within the mammalian genome that propagates via a “copy-and-paste” mechanism using reverse transcriptase and RNA intermediates. L1 is strongly expressed during early embryogenesis and then silenced as cells initiate differentiation programming. Although the complex transcriptional control mechanisms of L1 are not well understood, L1 reactivation has been described in several human cancers and following exposure of mouse or human cells to BaP. In this study we investigated the molecular mechanisms and epigenetic events that regulate L1 reactivation following BaP exposure. We show that challenge of HeLa cells with BaP induces early enrichment of the transcriptionally-active chromatin markers histone H3 trimethylated at lysine 4 (H3K4Me3) and histone H3 acetylated at lysine 9 (H3K9Ac), and reduces association of DNA methyltransferase-1 (DNMT1) with the L1 promoter. These changes are followed by proteasome-dependent decreases in cellular DNMT1 expression and sustained reduction of cytosine methylation within the L1 promoter CpG island. Pharmacological inhibition of the proteasome signaling pathway with the inhibitor MG132 blocks degradation of DNMT1 and alters BaP-mediated histone epigenetic modifications. We conclude that genetic reactivation of L1 by BaP involves an ordered cascade of epigenetic events that begin with nucleosomal histone modifications and is completed with alterations in DNMT1 recruitment to the L1 promoter and reduced DNA methylation of CpG islands. PMID:21150308

  6. Context-sensitive network-based disease genetics prediction and its implications in drug discovery.

    PubMed

    Chen, Yang; Xu, Rong

    2017-04-01

    Disease phenotype networks play an important role in computational approaches to identifying new disease-gene associations. Current disease phenotype networks often model disease relationships based on pairwise similarities, therefore ignore the specific context on how two diseases are connected. In this study, we propose a new strategy to model disease associations using context-sensitive networks (CSNs). We developed a CSN-based phenome-driven approach for disease genetics prediction, and investigated the translational potential of the predicted genes in drug discovery. We constructed CSNs by directly connecting diseases with associated phenotypes. Here, we constructed two CSNs using different data sources; the two networks contain 26 790 and 13 822 nodes respectively. We integrated the CSNs with a genetic functional relationship network and predicted disease genes using a network-based ranking algorithm. For comparison, we built Similarity-Based disease Networks (SBN) using the same disease phenotype data. In a de novo cross validation for 3324 diseases, the CSN-based approach significantly increased the average rank from top 12.6 to top 8.8% for all tested genes comparing with the SBN-based approach ( pgenetics prediction comparing with SBNs and provide leads for potential drug targets. nlp.case.edu/public/data/. rxx@case.edu.

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2011-01-01

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

  9. 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…

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

    ERIC Educational Resources Information Center

    Beaver, Kevin M.

    2011-01-01

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

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

    PubMed Central

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

    2009-01-01

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

  12. A hybrid of genetic algorithm and particle swarm optimization for recurrent network design.

    PubMed

    Juang, Chia-Feng

    2004-04-01

    An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority.

  13. Network-Informed Gene Ranking Tackles Genetic Heterogeneity in Exome-Sequencing Studies of Monogenic Disease.

    PubMed

    Dand, Nick; Schulz, Reiner; Weale, Michael E; Southgate, Laura; Oakey, Rebecca J; Simpson, Michael A; Schlitt, Thomas

    2015-12-01

    Genetic heterogeneity presents a significant challenge for the identification of monogenic disease genes. Whole-exome sequencing generates a large number of candidate disease-causing variants and typical analyses rely on deleterious variants being observed in the same gene across several unrelated affected individuals. This is less likely to occur for genetically heterogeneous diseases, making more advanced analysis methods necessary. To address this need, we present HetRank, a flexible gene-ranking method that incorporates interaction network data. We first show that different genes underlying the same monogenic disease are frequently connected in protein interaction networks. This motivates the central premise of HetRank: those genes carrying potentially pathogenic variants and whose network neighbors do so in other affected individuals are strong candidates for follow-up study. By simulating 1,000 exome sequencing studies (20,000 exomes in total), we model varying degrees of genetic heterogeneity and show that HetRank consistently prioritizes more disease-causing genes than existing analysis methods. We also demonstrate a proof-of-principle application of the method to prioritize genes causing Adams-Oliver syndrome, a genetically heterogeneous rare disease. An implementation of HetRank in R is available via the Website http://sourceforge.net/p/hetrank/.

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

    PubMed

    Yoon, Yourim; Kim, Yong-Hyuk

    2013-10-01

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

  15. 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.

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

    PubMed

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

    2015-01-01

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

  17. 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

  18. A methodology for pseudo-genetic stochastic modeling of discrete fracture networks

    NASA Astrophysics Data System (ADS)

    Bonneau, François; Henrion, Vincent; Caumon, Guillaume; Renard, Philippe; Sausse, Judith

    2013-07-01

    Stochastic simulation of fracture systems is an interesting approach to build a set of dense and complex networks. However, discrete fracture models made of planar fractures generally fail to reproduce the complexity of natural networks, both in terms of geometry and connectivity. In this study a pseudo-genetic method is developed to generate stochastic fracture models that are consistent with patterns observed on outcrops and fracture growth principles. The main idea is to simulate evolving fracture networks through geometric proxies by iteratively growing 3D fractures. The algorithm defines heuristic rules in order to mimic the mechanics of fracture initiation, propagation, interaction and termination. The growth process enhances the production of linking structure and impacts the connectivity of fracture networks. A sensitivity study is performed on synthetic examples. The method produces unbiased fracture dip and strike statistics and qualitatively reproduces the fracture density map. The fracture length distribution law is underestimated because of the early stop in fracture growth after intersection.

  19. Exploitation of genetic interaction network topology for the prediction of epistatic behavior.

    PubMed

    Alanis-Lobato, Gregorio; Cannistraci, Carlo Vittorio; Ravasi, Timothy

    2013-10-01

    Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks. We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks. Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab.

  20. Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean.

    PubMed

    Fang, Chao; Ma, Yanming; Wu, Shiwen; Liu, Zhi; Wang, Zheng; Yang, Rui; Hu, Guanghui; Zhou, Zhengkui; Yu, Hong; Zhang, Min; Pan, Yi; Zhou, Guoan; Ren, Haixiang; Du, Weiguang; Yan, Hongrui; Wang, Yanping; Han, Dezhi; Shen, Yanting; Liu, Shulin; Liu, Tengfei; Zhang, Jixiang; Qin, Hao; Yuan, Jia; Yuan, Xiaohui; Kong, Fanjiang; Liu, Baohui; Li, Jiayang; Zhang, Zhiwu; Wang, Guodong; Zhu, Baoge; Tian, Zhixi

    2017-08-24

    Soybean (Glycine max [L.] Merr.) is one of the most important oil and protein crops. Ever-increasing soybean consumption necessitates the improvement of varieties for more efficient production. However, both correlations among different traits and genetic interactions among genes that affect a single trait pose a challenge to soybean breeding. To understand the genetic networks underlying phenotypic correlations, we collected 809 soybean accessions worldwide and phenotyped them for two years at three locations for 84 agronomic traits. Genome-wide association studies identified 245 significant genetic loci, among which 95 genetically interacted with other loci. We determined that 14 oil synthesis-related genes are responsible for fatty acid accumulation in soybean and function in line with an additive model. Network analyses demonstrated that 51 traits could be linked through the linkage disequilibrium of 115 associated loci and these links reflect phenotypic correlations. We revealed that 23 loci, including the known Dt1, E2, E1, Ln, Dt2, Fan, and Fap loci, as well as 16 undefined associated loci, have pleiotropic effects on different traits. This study provides insights into the genetic correlation among complex traits and will facilitate future soybean functional studies and breeding through molecular design.

  1. 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. © 2015 John Wiley & Sons Ltd.

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

    PubMed Central

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

    2009-01-01

    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 upregulated following exposure to rotting senita tissue while Cyp4D10 was highly upregulated 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, though 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

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

    PubMed

    Borate, Bhavesh; Baxevanis, Andreas D

    2009-09-01

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

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

    PubMed

    Baxevanis, Andreas D

    2012-04-01

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

  5. An Improved Integral Inequality to Stability Analysis of Genetic Regulatory Networks With Interval Time-Varying Delays.

    PubMed

    Zhang, Xian; Wu, Ligang; Cui, Shaochun

    2015-01-01

    This paper focuses on stability analysis for a class of genetic regulatory networks with interval time-varying delays. An improved integral inequality concerning on double-integral items is first established. Then, we use the improved integral inequality to deal with the resultant double-integral items in the derivative of the involved Lyapunov-Krasovskii functional. As a result, a delay-range-dependent and delay-rate-dependent asymptotical stability criterion is established for genetic regulatory networks with differential time-varying delays. Furthermore, it is theoretically proven that the stability criterion proposed here is less conservative than the corresponding one in [Neurocomputing, 2012, 93: 19-26]. Based on the obtained result, another stability criterion is given under the case that the information of the derivatives of delays is unknown. Finally, the effectiveness of the approach proposed in this paper is illustrated by a pair of numerical examples which give the comparisons of stability criteria proposed in this paper and some literature.

  6. 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.

  7. A target coverage scheduling scheme based on genetic algorithms in directional sensor networks.

    PubMed

    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.

  8. A Systems Approach Implicates a Brain Mitochondrial Oxidative Homeostasis Co-expression Network in Genetic Vulnerability to Alcohol Withdrawal

    PubMed Central

    Walter, Nicole A. R.; Denmark, DeAunne L.; Kozell, Laura B.; Buck, Kari J.

    2017-01-01

    Genetic factors significantly affect vulnerability to alcohol dependence (alcoholism). We previously identified quantitative trait loci on distal mouse chromosome 1 with large effects on predisposition to alcohol physiological dependence and associated withdrawal following both chronic and acute alcohol exposure in mice (Alcdp1 and Alcw1, respectively). We fine-mapped these loci to a 1.1–1.7 Mb interval syntenic with human 1q23.2-23.3. Alcw1/Alcdp1 interval genes show remarkable genetic variation among mice derived from the C57BL/6J and DBA/2J strains, the two most widely studied genetic animal models for alcohol-related traits. Here, we report the creation of a novel recombinant Alcw1/Alcdp1 congenic model (R2) in which the Alcw1/Alcdp1 interval from a donor C57BL/6J strain is introgressed onto a uniform, inbred DBA/2J genetic background. As expected, R2 mice demonstrate significantly less severe alcohol withdrawal compared to wild-type littermates. Additionally, comparing R2 and background strain animals, as well as reciprocal congenic (R8) and appropriate background strain animals, we assessed Alcw1/Alcdp1 dependent brain gene expression using microarray and quantitative PCR analyses. To our knowledge this includes the first Weighted Gene Co-expression Network Analysis using reciprocal congenic models. Importantly, this allows detection of co-expression patterns limited to one or common to both genetic backgrounds with high or low predisposition to alcohol withdrawal severity. The gene expression patterns (modules) in common contain genes related to oxidative phosphorylation, building upon human and animal model studies that implicate involvement of oxidative phosphorylation in alcohol use disorders (AUDs). Finally, we demonstrate that administration of N-acetylcysteine, an FDA-approved antioxidant, significantly reduces symptoms of alcohol withdrawal (convulsions) in mice, thus validating a phenotypic role for this network. Taken together, these studies

  9. Genetic, molecular and physiological mechanisms involved in human obesity: Society for Endocrinology Medal Lecture 2012.

    PubMed

    Farooqi, Sadaf I

    2015-01-01

    The health consequences of obesity represent one of the major public health challenges of our time. Whilst the role of environmental drivers such as reduced physical activity and increased food intake is widely acknowledged, the importance of biological factors which influence individual variation in weight is less readily recognised. Considerable evidence suggests that genetic factors influence a person's weight in a given environment and that these genetic influences are more potent at the extremes of the body mass index (BMI) distribution. The discovery that genetic disruption of certain pathways can lead to severe obesity has informed our current understanding of how body weight is regulated by brain circuits that regulate appetite and energy expenditure. These studies provide a framework for investigating patients and ultimately may guide the development of more rational-targeted therapies for genetically susceptible individuals with severe obesity. © 2014 The Author. Clinical Endocrinology Published by John Wiley & Sons Ltd.

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

    PubMed

    Ye, Qian; Cui, Baotong

    2010-06-01

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

  11. 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.

  12. Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling

    NASA Astrophysics Data System (ADS)

    Gardner, Timothy S.; di Bernardo, Diego; Lorenz, David; Collins, James J.

    2003-07-01

    The complexity of cellular gene, protein, and metabolite networks can hinder attempts to elucidate their structure and function. To address this problem, we used systematic transcriptional perturbations to construct a first-order model of regulatory interactions in a nine-gene subnetwork of the SOS pathway in Escherichia coli. The model correctly identified the major regulatory genes and the transcriptional targets of mitomycin C activity in the subnetwork. This approach, which is experimentally and computationally scalable, provides a framework for elucidating the functional properties of genetic networks and identifying molecular targets of pharmacological compounds.

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

    PubMed

    Chua, Gordon

    2013-12-01

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

  14. 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.

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

    PubMed

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

    2013-01-01

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

  16. Prediction of microRNAs involved in immune system diseases through network based features.

    PubMed

    Prabahar, Archana; Natarajan, Jeyakumar

    2017-01-01

    MicroRNAs are a class of small non-coding regulatory RNA molecules that modulate the expression of several genes at post-transcriptional level and play a vital role in disease pathogenesis. Recent research shows that a range of miRNAs are involved in the regulation of immunity and its deregulation results in immune mediated diseases such as cancer, inflammation and autoimmune diseases. Computational discovery of these immune miRNAs using a set of specific features is highly desirable. In the current investigation, we present a SVM based classification system which uses a set of novel network based topological and motif features in addition to the baseline sequential and structural features to predict immune specific miRNAs from other non-immune miRNAs. The classifier was trained and tested on a balanced set of equal number of positive and negative examples to show the discriminative power of our network features. Experimental results show that our approach achieves an accuracy of 90.2% and outperforms the classification accuracy of 63.2% reported using the traditional miRNA sequential and structural features. The proposed classifier was further validated with two immune disease sub-class datasets related to multiple sclerosis microarray data and psoriasis RNA-seq data with higher accuracy. These results indicate that our classifier which uses network and motif features along with sequential and structural features will lead to significant improvement in classifying immune miRNAs and hence can be applied to identify other specific classes of miRNAs as an extensible miRNA classification system.

  17. Automatic reconstruction of molecular and genetic networks from discrete time series data.

    PubMed

    Durzinsky, Markus; Wagler, Annegret; Weismantel, Robert; Marwan, Wolfgang

    2008-09-01

    We apply a mathematical algorithm which processes discrete time series data to generate a complete list of Petri net structures containing the minimal number of nodes required to reproduce the data set. The completeness of the list as guaranteed by a mathematical proof allows to define a minimal set of experiments required to discriminate between alternative network structures. This in principle allows to prove all possible minimal network structures by disproving all alternative candidate structures. The dynamic behaviour of the networks in terms of a switching rule for the transitions of the Petri net is part of the result. In addition to network reconstruction, the algorithm can be used to determine how many yet undetected components at least must be involved in a certain process. The algorithm also reveals all alternative structural modifications of a network that are required to generate a predefined behaviour.

  18. A functional network involved in the recycling of nucleocytoplasmic pre-60S factors

    PubMed Central

    Lebreton, Alice; Saveanu, Cosmin; Decourty, Laurence; Rain, Jean-Christophe; Jacquier, Alain; Fromont-Racine, Micheline

    2006-01-01

    Eukaryotic pre-ribosomes go through cytoplasmic maturation steps before entering translation. The nucleocytoplasmic proteins participating in these late stages of maturation are reimported to the nucleus. In this study, we describe a functional network focused on Rei1/Ybr267w, a strictly cytoplasmic pre-60S factor indirectly involved in nuclear 27S pre-ribosomal RNA processing. In the absence of Rei1, the nuclear import of at least three other pre-60S factors is impaired. The accumulation in the cytoplasm of a small complex formed by the association of Arx1 with a novel factor, Alb1/Yjl122w, inhibits the release of the putative antiassociation factor Tif6 from the premature large ribosomal subunits and its recycling to the nucleus. We propose a model in which Rei1 is a key factor for the coordinated dissociation and recycling of the last pre-60S factors before newly synthesized large ribosomal subunits enter translation. PMID:16651379

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

    PubMed Central

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

    2014-01-01

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

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

    DOE PAGES

    Baldwin, Nicole E.; Chesler, Elissa J.; Kirov, Stefan; ...

    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

  1. Maximizing influence in a social network: Improved results using a genetic algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, Kaiqi; Du, Haifeng; Feldman, Marcus W.

    2017-07-01

    The influence maximization problem focuses on finding a small subset of nodes in a social network that maximizes the spread of influence. While the greedy algorithm and some improvements to it have been applied to solve this problem, the long solution time remains a problem. Stochastic optimization algorithms, such as simulated annealing, are other choices for solving this problem, but they often become trapped in local optima. We propose a genetic algorithm to solve the influence maximization problem. Through multi-population competition, using this algorithm we achieve an optimal result while maintaining diversity of the solution. We tested our method with actual networks, and our genetic algorithm performed slightly worse than the greedy algorithm but better than other algorithms.

  2. Exploring the Link between Genetic Relatedness r and Social Contact Structure k in Animal Social Networks.

    PubMed

    Wolf, Jochen B W; Traulsen, Arne; James, Richard

    2011-01-01

    Our understanding of how cooperation can arise in a population of selfish individuals has been greatly advanced by theory. More than one approach has been used to explore the effect of population structure. Inclusive fitness theory uses genetic relatedness r to express the role of population structure. Evolutionary graph theory models the evolution of cooperation on network structures and focuses on the number of interacting partners k as a quantity of interest. Here we use empirical data from a hierarchically structured animal contact network to examine the interplay between independent, measurable proxies for these key parameters. We find strong inverse correlations between estimates of r and k over three levels of social organization, suggesting that genetic relatedness and social contact structure capture similar structural information in a real population.

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

    PubMed

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

    2017-02-01

    Ovarian folliculogenesis corresponds to the development of follicles leading to either ovulation or degeneration, this latter process being called atresia. Even if atresia involves apoptosis, its mechanism is not well understood. The objective of this study was to analyze global gene expression in pig granulosa cells of ovarian follicles during atresia. The transcriptome analysis was performed on a 9,216 cDNA microarray to identify gene networks and candidate genes involved in pig ovarian follicular atresia. We found 1,684 significantly regulated genes to be differentially regulated between small healthy follicles and small atretic follicles. Among them, 287 genes had a fold-change higher than two between the two follicle groups. Eleven genes (DKK3, GADD45A, CAMTA2, CCDC80, DAPK2, ECSIT, MSMB, NUPR1, RUNX2, SAMD4A, and ZNF628) having a fold-change higher than five between groups could likely serve as markers of follicular atresia. Moreover, automatic confrontation of deregulated genes with literature data highlighted 93 genes as regulatory candidates of pig granulosa cell atresia. Among these genes known to be inhibitors of apoptosis, stimulators of apoptosis, or tumor suppressors INHBB, HNF4, CLU, different interleukins (IL5, IL24), TNF-associated receptor (TNFR1), and cytochrome-c oxidase (COX) were suggested as playing an important role in porcine atresia. The present study also enlists key upstream regulators in follicle atresia based on our results and on a literature review. The novel gene candidates and gene networks identified in the current study lead to a better understanding of the molecular regulation of ovarian follicular atresia. Copyright © 2017 the American Physiological Society.

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

    NASA Astrophysics Data System (ADS)

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

    2001-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2002-01-01

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

  6. 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.

  7. Co-expression network analysis and genetic algorithms for gene prioritization in preeclampsia.

    PubMed

    Tejera, Eduardo; Bernardes, João; Rebelo, Irene

    2013-11-12

    In this study, we explored the gene prioritization in preeclampsia, combining co-expression network analysis and genetic algorithms optimization approaches. We analysed five public projects obtaining 1,146 significant genes after cross-platform and processing of 81 and 149 microarrays in preeclamptic and normal conditions, respectively. After co-expression network construction, modular and node analysis were performed using several approaches. Moreover, genetic algorithms were also applied in combination with the nearest neighbour and discriminant analysis classification methods. Significant differences were found in the genes connectivity distribution, both in normal and preeclampsia conditions pointing to the need and importance of examining connectivity alongside expression for prioritization. We discuss the global as well as intra-modular connectivity for hubs detection and also the utility of genetic algorithms in combination with the network information. FLT1, LEP, INHA and ENG genes were identified according to the literature, however, we also found other genes as FLNB, INHBA, NDRG1 and LYN highly significant but underexplored during normal pregnancy or preeclampsia. Weighted genes co-expression network analysis reveals a similar distribution along the modules detected both in normal and preeclampsia conditions. However, major differences were obtained by analysing the nodes connectivity. All models obtained by genetic algorithm procedures were consistent with a correct classification, higher than 90%, restricting to 30 variables in both classification methods applied.Combining the two methods we identified well known genes related to preeclampsia, but also lead us to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which may have to be validated experimentally.

  8. Systematic proteomic analysis of human hepotacellular carcinoma cells reveals molecular pathways and networks involved in metastasis.

    PubMed

    Yu, Yanyan; Shen, Huali; Yu, Hongxiu; Zhong, Fan; Zhang, Yang; Zhang, Chen; Zhao, Jian; Li, Hong; Chen, Jie; Liu, Yinkun; Yang, Pengyuan

    2011-06-01

    Systematic proteomic studying of the mechanism of hepatocellular carcinoma (HCC) metastasis remains challenging. We performed comparative proteomic and pathway analysis of four human metastatic HCC cell lines to identify metastasis-associated proteins. These HCC cell lines had a similar genetic background but with an increasing potential of metastasis. Using a combination of two dimensional electrophoresis (2-DE) and MALDI-TOF mass spectrometry, a total of 125 proteins and their post-translational modification forms or isoforms were found to be differentially expressed in the cell lines. Among them, 29 were gradually up-regulated whereas 17 were down-regulated with increasing metastatic potential. Instead of a traditional single-gene readout, global bioinformatics analysis was carried out, which revealed that the glycolysis pathway was the most significantly enriched pathway. The heat shock proteins (HSPs) centered and NF-kappaB centered networks were also enriched in the result, which may imply the key function of inflaming on metastasis. Meanwhile, knockdown of HDGF, an up-regulated protein and a target of NF-kappaB, induced cell apoptosis in the metastatic HCC cells. This work provides a demonstration that a combination of bioinformatics and comparative proteomics can help in finding out potential biomarkers associated with HCC metastasis on the level of pathways.

  9. Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn.

    PubMed

    Patra, Tarak K; Meenakshisundaram, Venkatesh; Hung, Jui-Hsiang; Simmons, David S

    2017-02-13

    Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting properties and performance beyond those previously achieved, machine learning approaches are frequently limited by two shortcomings. First, because they are intrinsically interpolative, they are 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 large pre-existing data sets, which are frequently unavailable and prohibitively expensive to produce. Here we describe a new strategy, the neural-network-biased genetic algorithm (NBGA), for combining genetic algorithms, machine learning, and high-throughput computation or experiment to discover materials with extremal properties in the absence of pre-existing data. Within this strategy, predictions from a progressively constructed artificial neural network are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct simulation or experiment. In effect, this strategy gives the evolutionary algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process. We test this algorithm against several standard optimization problems and polymer design problems and demonstrate that it matches and typically exceeds the efficiency and reproducibility of standard approaches including a direct-evaluation genetic algorithm and a neural-network-evaluated genetic algorithm. The success of this algorithm in a range of test problems indicates that the NBGA provides a robust strategy for employing informatics-accelerated high-throughput methods to accelerate materials design in the absence of pre-existing data.

  10. Mathematical methods for inferring regulatory networks interactions: application to genetic regulation.

    PubMed

    Aracena, J; Demongeot, J

    2004-01-01

    This paper deals with the problem of reconstruction of the intergenic interaction graph from the raw data of genetic co-expression coming with new technologies of bio-arrays (DMA-arrays, protein-arrays, etc.). These new imaging devices in general only give information about the asymptotical part (fixed configurations of co-expression or limit cycles of such configurations) of the dynamical evolution of the regulatory networks (genetic and/or proteic) underlying the functioning of living systems. Extracting the casual structure and interaction coefficients of a gene interaction network from the observed configurations is a complex problem. But if all the fixed configurations are supposedly observed and if they are factorizable into two or more subsets of values, then the interaction graph possesses as many connected components as the number of factors and the solution is obtained in polynomial time. This new result allows us for example to partly solve the topology of the genetic regulatory network ruling the flowering in Arabidopsis thaliana .

  11. Scale-free properties of information flux networks in genetic algorithms

    NASA Astrophysics Data System (ADS)

    Wu, Jieyu; Shao, Xinyu; Li, Jinhang; Huang, Gang

    2012-02-01

    In this study, we present empirical analysis of statistical properties of mating networks in genetic algorithms (GAs). Under the framework of GAs, we study a class of interaction network model-information flux network (IFN), which describes the information flow among generations during evolution process. The IFNs are found to be scale-free when the selection operator uses a preferential strategy rather than a random. The topology structure of IFN is remarkably affected by operations used in genetic algorithms. The experimental results suggest that the scaling exponent of the power-law degree distribution is shown to decrease when crossover rate increases, but increase when mutation rate increases, and the reason may be that high crossover rate leads to more edges that are shared between nodes and high mutation rate leads to many individuals in a generation possessing low fitness. The magnitude of the out-degree exponent is always more than the in-degree exponent for the systems tested. These results may provide a new viewpoint with which to view GAs and guide the dissemination process of genetic information throughout a population.

  12. Statistical epistasis networks reduce the computational complexity of searching three-locus genetic models.

    PubMed

    Hu, Ting; Andrew, Angeline S; Karagas, Margaret R; Moore, Jason H

    2013-01-01

    The rapid development of sequencing technologies makes thousands to millions of genetic attributes available for testing associations with various biological traits. Searching this enormous high-dimensional data space imposes a great computational challenge in genome-wide association studies. We introduce a network-based approach to supervise the search for three-locus models of disease susceptibility. Such statistical epistasis networks (SEN) are built using strong pairwise epistatic interactions and provide a global interaction map to search for higher-order interactions by prioritizing genetic attributes clustered together in the networks. Applying this approach to a population-based bladder cancer dataset, we found a high susceptibility three-way model of genetic variations in DNA repair and immune regulation pathways, which holds great potential for studying the etiology of bladder cancer with further biological validations. We demonstrate that our SEN-supervised search is able to find a small subset of three-locus models with significantly high associations at a substantially reduced computational cost.

  13. Systematic interactome mapping and genetic perturbation analysis of a C. elegans TGF-beta signaling network.

    PubMed

    Tewari, Muneesh; Hu, Patrick J; Ahn, Jin Sook; Ayivi-Guedehoussou, Nono; Vidalain, Pierre-Olivier; Li, Siming; Milstein, Stuart; Armstrong, Chris M; Boxem, Mike; Butler, Maurice D; Busiguina, Svetlana; Rual, Jean-François; Ibarrola, Nieves; Chaklos, Sabrina T; Bertin, Nicolas; Vaglio, Philippe; Edgley, Mark L; King, Kevin V; Albert, Patrice S; Vandenhaute, Jean; Pandey, Akhilesh; Riddle, Donald L; Ruvkun, Gary; Vidal, Marc

    2004-02-27

    To initiate a system-level analysis of C. elegans DAF-7/TGF-beta signaling, we combined interactome mapping with single and double genetic perturbations. Yeast two-hybrid (Y2H) screens starting with known DAF-7/TGF-beta pathway components defined a network of 71 interactions among 59 proteins. Coaffinity purification (co-AP) assays in mammalian cells confirmed the overall quality of this network. Systematic perturbations of the network using RNAi, both in wild-type and daf-7/TGF-beta pathway mutant animals, identified nine DAF-7/TGF-beta signaling modifiers, seven of which are conserved in humans. We show that one of these has functional homology to human SNO/SKI oncoproteins and that mutations at the corresponding genetic locus daf-5 confer defects in DAF-7/TGF-beta signaling. Our results reveal substantial molecular complexity in DAF-7/TGF-beta signal transduction. Integrating interactome maps with systematic genetic perturbations may be useful for developing a systems biology approach to this and other signaling modules.

  14. 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

  15. 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

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

    PubMed

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

    2015-11-11

    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. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular

  17. A Bayesian network coding scheme for annotating biomedical information presented to genetic counseling clients.

    PubMed

    Green, Nancy

    2005-04-01

    We developed a Bayesian network coding scheme for annotating biomedical content in layperson-oriented clinical genetics documents. The coding scheme supports the representation of probabilistic and causal relationships among concepts in this domain, at a high enough level of abstraction to capture commonalities among genetic processes and their relationship to health. We are using the coding scheme to annotate a corpus of genetic counseling patient letters as part of the requirements analysis and knowledge acquisition phase of a natural language generation project. This paper describes the coding scheme and presents an evaluation of intercoder reliability for its tag set. In addition to giving examples of use of the coding scheme for analysis of discourse and linguistic features in this genre, we suggest other uses for it in analysis of layperson-oriented text and dialogue in medical communication.

  18. Singapore Cancer Network (SCAN) Guidelines for Referral for Genetic Evaluation of Common Hereditary Cancer Syndromes.

    PubMed

    2015-10-01

    The SCAN cancer genetics workgroup aimed to develop Singapore Cancer Network (SCAN) clinical practice guidelines for referral for genetic evaluation of common hereditary cancer syndromes. The workgroup utilised a modified ADAPTE process to calibrate high quality international evidence-based clinical practice guidelines to our local setting. To formulate referral guidelines for the 3 most commonly encountered hereditary cancer syndromes to guide healthcare providers in Singapore who care for cancer patients and/or their family members, 7, 5, and 3 sets of international guidelines respectively for hereditary breast and ovarian cancer (HBOC) syndrome, Lynch syndrome (LS), and familial adenomatous polyposis (FAP) were evaluated. For each syndrome, the most applicable one was selected, with modifications made such that they would be appropriate to the local context. These adapted guidelines form the SCAN Guidelines 2015 for referral for genetic evaluation of common hereditary cancer syndromes.

  19. Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors.

    PubMed

    Kemmeren, Patrick; Sameith, Katrin; van de Pasch, Loes A L; Benschop, Joris J; Lenstra, Tineke L; Margaritis, Thanasis; O'Duibhir, Eoghan; Apweiler, Eva; van Wageningen, Sake; Ko, Cheuk W; van Heesch, Sebastiaan; Kashani, Mehdi M; Ampatziadis-Michailidis, Giannis; Brok, Mariel O; Brabers, Nathalie A C H; Miles, Anthony J; Bouwmeester, Diane; van Hooff, Sander R; van Bakel, Harm; Sluiters, Erik; Bakker, Linda V; Snel, Berend; Lijnzaad, Philip; van Leenen, Dik; Groot Koerkamp, Marian J A; Holstege, Frank C P

    2014-04-24

    To understand regulatory systems, it would be useful to uniformly determine how different components contribute to the expression of all other genes. We therefore monitored mRNA expression genome-wide, for individual deletions of one-quarter of yeast genes, focusing on (putative) regulators. The resulting genetic perturbation signatures reflect many different properties. These include the architecture of protein complexes and pathways, identification of expression changes compatible with viability, and the varying responsiveness to genetic perturbation. The data are assembled into a genetic perturbation network that shows different connectivities for different classes of regulators. Four feed-forward loop (FFL) types are overrepresented, including incoherent type 2 FFLs that likely represent feedback. Systematic transcription factor classification shows a surprisingly high abundance of gene-specific repressors, suggesting that yeast chromatin is not as generally restrictive to transcription as is often assumed. The data set is useful for studying individual genes and for discovering properties of an entire regulatory system.

  20. Environmentally induced changes in correlated responses to selection reveal variable pleiotropy across a complex genetic network.

    PubMed

    Sikkink, Kristin L; Reynolds, Rose M; Cresko, William A; Phillips, Patrick C

    2015-05-01

    Selection in novel environments can lead to a coordinated evolutionary response across a suite of characters. Environmental conditions can also potentially induce changes in the genetic architecture of complex traits, which in turn could alter the pattern of the multivariate response to selection. We describe a factorial selection experiment using the nematode Caenorhabditis remanei in which two different stress-related phenotypes (heat and oxidative stress resistance) were selected under three different environmental conditions. The pattern of covariation in the evolutionary response between phenotypes or across environments differed depending on the environment in which selection occurred, including asymmetrical responses to selection in some cases. These results indicate that variation in pleiotropy across the stress response network is highly sensitive to the external environment. Our findings highlight the complexity of the interaction between genes and environment that influences the ability of organisms to acclimate to novel environments. They also make clear the need to identify the underlying genetic basis of genetic correlations in order understand how patterns of pleiotropy are distributed across complex genetic networks.

  1. Genetic variation among 82 pharmacogenes: The PGRNseq data from the eMERGE network

    PubMed Central

    Bush, WS; Crosslin, DR; Owusu‐Obeng, A; Wallace, J; Almoguera, B; Basford, MA; Bielinski, SJ; Carrell, DS; Connolly, JJ; Crawford, D; Doheny, KF; Gallego, CJ; Gordon, AS; Keating, B; Kirby, J; Kitchner, T; Manzi, S; Mejia, AR; Pan, V; Perry, CL; Peterson, JF; Prows, CA; Ralston, J; Scott, SA; Scrol, A; Smith, M; Stallings, SC; Veldhuizen, T; Wolf, W; Volpi, S; Wiley, K; Li, R; Manolio, T; Bottinger, E; Brilliant, MH; Carey, D; Chisholm, RL; Chute, CG; Haines, JL; Hakonarson, H; Harley, JB; Holm, IA; Kullo, IJ; Jarvik, GP; Larson, EB; McCarty, CA; Williams, MS; Denny, JC; Rasmussen‐Torvik, LJ; Roden, DM

    2016-01-01

    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

  2. ENVIRONMENTALLY INDUCED CHANGES IN CORRELATED RESPONSES TO SELECTION REVEAL VARIABLE PLEIOTROPY ACROSS A COMPLEX GENETIC NETWORK

    PubMed Central

    Sikkink, Kristin L.; Reynolds, Rose M.; Cresko, William A.; Phillips, Patrick C.

    2017-01-01

    Selection in novel environments can lead to a coordinated evolutionary response across a suite of characters. Environmental conditions can also potentially induce changes in the genetic architecture of complex traits, which in turn could alter the pattern of the multivariate response to selection. We describe a factorial selection experiment using the nematode Caenorhabditis remanei in which two different stress-related phenotypes (heat and oxidative stress resistance) were selected under three different environmental conditions. The pattern of covariation in the evolutionary response between phenotypes or across environments differed depending on the environment in which selection occurred, including asymmetrical responses to selection in some cases. These results indicate that variation in pleiotropy across the stress response network is highly sensitive to the external environment. Our findings highlight the complexity of the interaction between genes and environment that influences the ability of organisms to acclimate to novel environments. They also make clear the need to identify the underlying genetic basis of genetic correlations in order understand how patterns of pleiotropy are distributed across complex genetic networks. PMID:25809411

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

    PubMed

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

    2004-03-19

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

  4. 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.

  5. Strain Dependent Genetic Networks for Antibiotic-Sensitivity in a Bacterial Pathogen with a Large Pan-Genome

    PubMed Central

    van Opijnen, Tim; Bento, José

    2016-01-01

    The interaction between an antibiotic and bacterium is not merely restricted to the drug and its direct target, rather antibiotic induced stress seems to resonate through the bacterium, creating selective pressures that drive the emergence of adaptive mutations not only in the direct target, but in genes involved in many different fundamental processes as well. Surprisingly, it has been shown that adaptive mutations do not necessarily have the same effect in all species, indicating that the genetic background influences how phenotypes are manifested. However, to what extent the genetic background affects the manner in which a bacterium experiences antibiotic stress, and how this stress is processed is unclear. Here we employ the genome-wide tool Tn-Seq to construct daptomycin-sensitivity profiles for two strains of the bacterial pathogen Streptococcus pneumoniae. Remarkably, over half of the genes that are important for dealing with antibiotic-induced stress in one strain are dispensable in another. By confirming over 100 genotype-phenotype relationships, probing potassium-loss, employing genetic interaction mapping as well as temporal gene-expression experiments we reveal genome-wide conditionally important/essential genes, we discover roles for genes with unknown function, and uncover parts of the antibiotic’s mode-of-action. Moreover, by mapping the underlying genomic network for two query genes we encounter little conservation in network connectivity between strains as well as profound differences in regulatory relationships. Our approach uniquely enables genome-wide fitness comparisons across strains, facilitating the discovery that antibiotic responses are complex events that can vary widely between strains, which suggests that in some cases the emergence of resistance could be strain specific and at least for species with a large pan-genome less predictable. PMID:27607357

  6. Bioelectric gene and reaction networks: computational modelling of genetic, biochemical and bioelectrical dynamics in pattern regulation.

    PubMed

    Pietak, Alexis; Levin, Michael

    2017-09-01

    Gene regulatory networks (GRNs) describe interactions between gene products and transcription factors that control gene expression. In combination with reaction-diffusion models, GRNs have enhanced comprehension of biological pattern formation. However, although it is well known that biological systems exploit an interplay of genetic and physical mechanisms, instructive factors such as transmembrane potential (Vmem) have not been integrated into full GRN models. Here we extend regulatory networks to include bioelectric signalling, developing a novel synthesis: the bioelectricity-integrated gene and reaction (BIGR) network. Using in silico simulations, we highlight the capacity for Vmem to alter steady-state concentrations of key signalling molecules inside and out of cells. We characterize fundamental feedbacks where Vmem both controls, and is in turn regulated by, biochemical signals and thereby demonstrate Vmem homeostatic control, Vmem memory and Vmem controlled state switching. BIGR networks demonstrating hysteresis are identified as a mechanisms through which more complex patterns of stable Vmem spots and stripes, along with correlated concentration patterns, can spontaneously emerge. As further proof of principle, we present and analyse a BIGR network model that mechanistically explains key aspects of the remarkable regenerative powers of creatures such as planarian flatworms. The functional properties of BIGR networks generate the first testable, quantitative hypotheses for biophysical mechanisms underlying the stability and adaptive regulation of anatomical bioelectric pattern. © 2017 The Author(s).

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

    PubMed

    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

    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. 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. 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. 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.

  8. Identification and reconstitution of genetic regulatory networks for improved microbial tolerance to isooctane.

    PubMed

    Kang, Aram; Chang, Matthew Wook

    2012-04-01

    Microbial tolerance to hydrocarbons has been studied in an effort to improve the productivity of biochemical processes and to enhance the efficiency of hydrocarbon bioremediation. Despite these studies, few attempts have been made to design rational strategies to improve microbial tolerance to hydrocarbons. Herein, we present an engineering framework that enables us to harness our understanding of genetic regulatory networks to improve hydrocarbon tolerance. In this study, isooctane was used as a representative hydrocarbon due to its use in petroleum refining and in biochemical processes. To increase isooctane tolerance, we first identified essential transcriptional determinants and genetic regulatory networks underlying cellular responses to isooctane in Escherichia coli using genome-wide microarray analysis. Based on functional transcriptome and bioinformatics analysis, a range of combinations of transcription factors whose activity was predictably perturbed by isooctane were knocked out and overexpressed to reconstitute the regulatory networks. We demonstrated that the reconstitution of the regulatory networks led to a significant improvement in isooctane tolerance, and especially, engineered E. coli strains lacking and overexpressing some of the perturbed transcription factors showed 3- to 5-fold improvement. This microbe with high tolerance to isooctane can be harnessed for biochemical processes, fuel oil bioremediation and metabolic engineering for biofuel production. Furthermore, we envision that the engineering framework employed to improve the tolerance in this study can be exploited for developing other microbes with desired phenotypes.

  9. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

    PubMed Central

    Ramadan Suleiman, Ahmed; Nehdi, Moncef L.

    2017-01-01

    This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials. PMID:28772495

  10. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm-Artificial Neural Network.

    PubMed

    Ramadan Suleiman, Ahmed; Nehdi, Moncef L

    2017-02-07

    This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm-artificial neural network (GA-ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA-ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials.

  11. 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

  12. 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.

  13. Gene network analysis shows immune-signaling and ERK1/2 as novel genetic markers for multiple addiction phenotypes: alcohol, smoking and opioid addiction.

    PubMed

    Reyes-Gibby, Cielito C; Yuan, Christine; Wang, Jian; Yeung, Sai-Ching J; Shete, Sanjay

    2015-06-05

    Addictions to alcohol and tobacco, known risk factors for cancer, are complex heritable disorders. Addictive behaviors have a bidirectional relationship with pain. We hypothesize that the associations between alcohol, smoking, and opioid addiction observed in cancer patients have a genetic basis. Therefore, using bioinformatics tools, we explored the underlying genetic basis and identified new candidate genes and common biological pathways for smoking, alcohol, and opioid addiction. Literature search showed 56 genes associated with alcohol, smoking and opioid addiction. Using Core Analysis function in Ingenuity Pathway Analysis software, we found that ERK1/2 was strongly interconnected across all three addiction networks. Genes involved in immune signaling pathways were shown across all three networks. Connect function from IPA My Pathway toolbox showed that DRD2 is the gene common to both the list of genetic variations associated with all three addiction phenotypes and the components of the brain neuronal signaling network involved in substance addiction. The top canonical pathways associated with the 56 genes were: 1) calcium signaling, 2) GPCR signaling, 3) cAMP-mediated signaling, 4) GABA receptor signaling, and 5) G-alpha i signaling. Cancer patients are often prescribed opioids for cancer pain thus increasing their risk for opioid abuse and addiction. Our findings provide candidate genes and biological pathways underlying addiction phenotypes, which may be future targets for treatment of addiction. Further study of the variations of the candidate genes could allow physicians to make more informed decisions when treating cancer pain with opioid analgesics.

  14. Brain network reorganization differs in response to stress in rats genetically predisposed to depression and stress-resilient rats.

    PubMed

    Gass, N; Becker, R; Schwarz, A J; Weber-Fahr, W; Clemm von Hohenberg, C; Vollmayr, B; Sartorius, A

    2016-12-06

    Treatment-resistant depression (TRD) remains a pressing clinical problem. Optimizing treatment requires better definition of the specificity of the involved brain circuits. The rat strain bred for negative cognitive state (NC) represents a genetic animal model of TRD with high face, construct and predictive validity. Vice versa, the positive cognitive state (PC) strain represents a stress-resilient phenotype. Although NC rats show depressive-like behavior, some symptoms such as anhedonia require an external trigger, i.e. a stressful event, which is similar to humans when stressful event induces a depressive episode in genetically predisposed individuals (gene-environment interaction). We aimed to distinguish neurobiological predisposition from the depressogenic pathology at the level of brain-network reorganization. For this purpose, resting-state functional magnetic resonance imaging time series were acquired at 9.4 Tesla scanner in NC (N=11) and PC (N=7) rats before and after stressful event. We used a graph theory analytical approach to calculate the brain-network global and local properties. There was no difference in the global characteristics between the strains. At the local level, the response in the risk strain was characterized with an increased internodal role and reduced local clustering and efficiency of the anterior cingulate cortex (ACC) and prelimbic cortex compared to the stress-resilient strain. We suggest that the increased internodal role of these prefrontal regions could be due to the enhancement of some of their long-range connections, given their connectivity with the amygdala and other default-mode-like network hubs, which could create a bias to attend to negative information characteristic for depression.

  15. Brain network reorganization differs in response to stress in rats genetically predisposed to depression and stress-resilient rats

    PubMed Central

    Gass, N; Becker, R; Schwarz, A J; Weber-Fahr, W; Clemm von Hohenberg, C; Vollmayr, B; Sartorius, A

    2016-01-01

    Treatment-resistant depression (TRD) remains a pressing clinical problem. Optimizing treatment requires better definition of the specificity of the involved brain circuits. The rat strain bred for negative cognitive state (NC) represents a genetic animal model of TRD with high face, construct and predictive validity. Vice versa, the positive cognitive state (PC) strain represents a stress-resilient phenotype. Although NC rats show depressive-like behavior, some symptoms such as anhedonia require an external trigger, i.e. a stressful event, which is similar to humans when stressful event induces a depressive episode in genetically predisposed individuals (gene–environment interaction). We aimed to distinguish neurobiological predisposition from the depressogenic pathology at the level of brain-network reorganization. For this purpose, resting-state functional magnetic resonance imaging time series were acquired at 9.4 Tesla scanner in NC (N=11) and PC (N=7) rats before and after stressful event. We used a graph theory analytical approach to calculate the brain-network global and local properties. There was no difference in the global characteristics between the strains. At the local level, the response in the risk strain was characterized with an increased internodal role and reduced local clustering and efficiency of the anterior cingulate cortex (ACC) and prelimbic cortex compared to the stress-resilient strain. We suggest that the increased internodal role of these prefrontal regions could be due to the enhancement of some of their long-range connections, given their connectivity with the amygdala and other default-mode-like network hubs, which could create a bias to attend to negative information characteristic for depression. PMID:27922640

  16. 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

  17. 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...

  18. 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...

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

    PubMed Central

    Wilcke, Arndt; Müller, Bent; Schaadt, Gesa; Kirsten, Holger; Boltze, Johannes; Angela, h c; Friederici, D; Emmrich, Frank; Brauer, Jens; Wilcke, Arndt; Neef, Nicole; Boltze, Johannes; Skeide, Michael; Kirsten, Holger; Schaadt, Gesa; Müller, Bent; Kraft, Indra; Czepezauer, Ivonne; Bobovnikov, Nadin

    2016-01-01

    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

  20. 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.

  1. Genetic variation in genes involved in folate and drug metabolism in a south Indian population

    PubMed Central

    Rai, Padmalatha S; Murali, T. S; Vasudevan, T. G; Prasada, Shama K.; Bhagavath, Ashok Kumar; Pai, Pranita; Gopinath, P. M.; Satyamoorthy, K.

    2011-01-01

    BACKGROUND: Genetic variations represented as single nucleotide polymorphisms (SNPs) vary across the world population. This genetic polymorphism (such as SNPs) plays an important role in pharmacogenomics. SNPs that affects cellular metabolism, by altering the enzyme activity, have an important role in therapeutic outcome. Allele frequencies in number of clinically relevant SNPs within south Indian populations are not yet known. Hence, we genotyped randomly selected unrelated south Indian subjects from different locations of south India representing the heterogeneous ethnic background of the population. MATERIALS AND METHODS: Common variants of MTHFD1, TYMS, SHMT1, MTR, MTRR, CBS and SULT1A1 gene polymorphisms were screened from healthy unrelated south Indian volunteers. Genotypes were determined using RFLP analysis of polymerase chain reaction-amplified products and confirmed by DNA sequencing. Chi-square test was performed to test for deviation from the Hardy-Weinberg equilibrium for each locus. RESULTS: Gene allele frequency for several polymorphisms in our study differed significantly between the populations of other nations reported for several of the SNPs. These results demonstrate that the populations in different geographic regions may have widely varying genetic allele frequencies for clinically relevant SNPs. CONCLUSION: The present study reports, for the first time, the frequency distribution of MTHFD1, TYMS, SHMT1, MTR, MTRR, CBS and SULTIA1 gene polymorphisms in a south Indian population. Population-specific genetic polymorphism studies will help in practicing pharmacogenomic principles in the clinics. PMID:21747588

  2. 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

  3. Involvement of mesolimbic dopaminergic network in neuropathic pain relief by treadmill exercise: A study for specific neural control with Gi-DREADD in mice.

    PubMed

    Wakaizumi, Kenta; Kondo, Takashige; Hamada, Yusuke; Narita, Michiko; Kawabe, Rui; Narita, Hiroki; Watanabe, Moe; Kato, Shigeki; Senba, Emiko; Kobayashi, Kazuto; Kuzumaki, Naoko; Yamanaka, Akihiro; Morisaki, Hiroshi; Narita, Minoru

    2016-01-01

    Exercise alleviates pain and it is a central component of treatment strategy for chronic pain in clinical setting. However, little is known about mechanism of this exercise-induced hypoalgesia. The mesolimbic dopaminergic network plays a role in positive emotions to rewards including motivation and pleasure. Pain negatively modulates these emotions, but appropriate exercise is considered to activate the dopaminergic network. We investigated possible involvement of this network as a mechanism of exercise-induced hypoalgesia. In the present study, we developed a protocol of treadmill exercise, which was able to recover pain threshold under partial sciatic nerve ligation in mice, and investigated involvement of the dopaminergic reward network in exercise-induced hypoalgesia. To temporally suppress a neural activation during exercise, a genetically modified inhibitory G-protein-coupled receptor, hM4Di, was specifically expressed on dopaminergic pathway from the ventral tegmental area to the nucleus accumbens. The chemogenetic-specific neural suppression by Gi-DREADD system dramatically offset the effect of exercise-induced hypoalgesia in transgenic mice with hM4Di expressed on the ventral tegmental area dopamine neurons. Additionally, anti-exercise-induced hypoalgesia effect was significantly observed under the suppression of neurons projecting out of the ventral tegmental area to the nucleus accumbens as well. Our findings suggest that the dopaminergic pathway from the ventral tegmental area to the nucleus accumbens is involved in the anti-nociception under low-intensity exercise under a neuropathic pain-like state. © The Author(s) 2016.

  4. A genetic network model of cellular responses to lithium treatment and cocaine abuse in bipolar disorder.

    PubMed

    McEachin, Richard C; Chen, Haiming; Sartor, Maureen A; Saccone, Scott F; Keller, Benjamin J; Prossin, Alan R; Cavalcoli, James D; McInnis, Melvin G

    2010-11-19

    Lithium is an effective treatment for Bipolar Disorder (BD) and significantly reduces suicide risk, though the molecular basis of lithium's effectiveness is not well understood. We seek to improve our understanding of this effectiveness by posing hypotheses based on new experimental data as well as published data, testing these hypotheses in silico, and posing new hypotheses for validation in future studies. We initially hypothesized a gene-by-environment interaction where lithium, acting as an environmental influence, impacts signal transduction pathways leading to differential expression of genes important in the etiology of BD mania. Using microarray and rt-QPCR assays, we identified candidate genes that are differentially expressed with lithium treatment. We used a systems biology approach to identify interactions among these candidate genes and develop a network of genes that interact with the differentially expressed candidates. Notably, we also identified cocaine as having a potential influence on the network, consistent with the observed high rate of comorbidity for BD and cocaine abuse. The resulting network represents a novel hypothesis on how multiple genetic influences on bipolar disorder are impacted by both lithium treatment and cocaine use. Testing this network for association with BD and related phenotypes, we find that it is significantly over-represented for genes that participate in signal transduction, consistent with our hypothesized-gene-by environment interaction. In addition, it models related pharmacogenomic, psychiatric, and chemical dependence phenotypes. We offer a network model of gene-by-environment interaction associated with lithium's effectiveness in treating BD mania, as well as the observed high rate of comorbidity of BD and cocaine abuse. We identified drug targets within this network that represent immediate candidates for therapeutic drug testing. Posing novel hypotheses for validation in future work, we prioritized SNPs near

  5. Preliminary evidence of genetic determinants of adiponectin response to fenofibrate in the Genetics of Lipid Lowering Drugs and Diet Network.

    PubMed

    Aslibekyan, S; An, P; Frazier-Wood, A C; Kabagambe, E K; Irvin, M R; Straka, R J; Tiwari, H K; Tsai, M Y; Hopkins, P N; Borecki, I B; Ordovas, J M; Arnett, D K

    2013-10-01

    Adiponectin is an adipose-secreted protein that has been linked to changes in insulin sensitivity, high-density lipoprotein cholesterol levels, and inflammatory patterns. Although fenofibrate therapy can raise adiponectin levels, treatment response is heterogeneous and heritable, suggesting a role for genetic mediators. This is the first genome-wide association study of fenofibrate effects on circulating adiponectin. Plasma adiponectin was measured in participants of the Genetics of Lipid Lowering Drugs and Diet Network (n = 793) before and after a 3-week daily treatment with 160 mg of fenofibrate. Associations between variants on the Affymetrix Genome-Wide Human SNP Array 6.0 and adiponectin were assessed using mixed linear models, adjusted for age, sex, site, and family. We observed a statistically significant (P = 5 × 10⁻⁸) association between rs2384207 in 12q24, a region previously linked to several metabolic traits, and the fenofibrate-induced change in circulating adiponectin. Additionally, our genome-wide analysis of baseline adiponectin levels replicated the previously reported association with CDH13 and suggested novel associations with markers near the PCK1, ZBP1, TMEM18, and SCUBE1 genes. The findings from the single marker tests were corroborated in gene-based analyses. Biological pathway analyses suggested a borderline significant association between the EGF receptor signaling pathway and baseline adiponectin levels. We present preliminary evidence linking several biologically relevant genetic variants to adiponectin levels at baseline and in response to fenofibrate therapy. Our findings provide support for fine-mapping of the 12q24 region to investigate the shared biological mechanisms underlying levels of circulating adiponectin and susceptibility to metabolic disease. Copyright © 2012 Elsevier B.V. All rights reserved.

  6. International cooperation and networking in genetic health care provision: issues arising from the genetic services plan for the Emilia-Romagna region, Italy.

    PubMed

    Calzolari, E; Baroncini, A

    2005-01-01

    The aims of this report are to describe the genetic plan for Emilia-Romagna, a region in Italy, and to contribute to the international exchange of information on developing and applying policy frameworks to provide high-quality and comprehensive genetic health care in the publicly funded health systems. At the present time there is no national policy for genetic medicine in Italy, and only two regions, Emilia-Romagna and Liguria, have formally agreed to a strategic plan for health care in genetics. The current provision of genetic services in Emilia-Romagna is described focusing on the intra- and inter-organizational linkages to ensure a comprehensive system of coordinated activities. Strengths and implementation areas are highlighted. Points that must be solved within the regional or national context are the definition of the level of assistance required in genetic medicine, the formal professional recognition of the genetic counselor and the adjustment of the billing mechanisms to the complexities of clinical genetic services. Issues that need to be addressed at a wider level include full assessment of genetic tests before their introduction into clinical practice, networking to provide tests for the rarest genetic diseases, consensus on fundamental terminology and clinical and administrative data sets to promote a cohesive framework for the flow of information throughout the health care systems with respect to genetics.

  7. VAN3 ARF-GAP-mediated vesicle transport is involved in leaf vascular network formation.

    PubMed

    Koizumi, Koji; Naramoto, Satoshi; Sawa, Shinichiro; Yahara, Natsuko; Ueda, Takashi; Nakano, Akihiko; Sugiyama, Munetaka; Fukuda, Hiroo

    2005-04-01

    Within the leaf of an angiosperm, the vascular system is constructed in a complex network pattern called venation. The formation of this vein pattern has been widely studied as a paradigm of tissue pattern formation in plants. To elucidate the molecular mechanism controlling the vein patterning process, we previously isolated Arabidopsis mutants van1 to van7, which show a discontinuous vein pattern. Here we report the phenotypic analysis of the van3 mutant in relation to auxin signaling and polar transport, and the molecular characterization of the VAN3 gene and protein. Double mutant analyses with pin1, emb30-7/gn and mp, and physiological analyses using the auxin-inducible marker DR5::GUS and an auxin transport inhibitor indicated that VAN3 may be involved in auxin signal transduction, but not in polar auxin transport. Positional cloning identified VAN3 as a gene that encodes an adenosine diphosphate (ADP)-ribosylation factor-guanosine triphosphatase (GTPase) activating protein (ARF-GAP). It resembles animal ACAPs and contains four domains: a BAR (BIN/amphiphysin/RVS) domain, a pleckstrin homology (PH) domain, an ARF-GAP domain and an ankyrin (ANK)-repeat domain. Recombinant VAN3 protein showed GTPase-activating activity and a specific affinity for phosphatidylinositols. This protein can self-associate through the N-terminal BAR domain in the yeast two-hybrid system. Subcellular localization analysis by double staining for Venus-tagged VAN3 and several green-fluorescent-protein-tagged intracellular markers indicated that VAN3 is located in a subpopulation of the trans-Golgi network (TGN). Our results indicate that the expression of this gene is induced by auxin and positively regulated by VAN3 itself, and that a specific ACAP type of ARF-GAP functions in vein pattern formation by regulating auxin signaling via a TGN-mediated vesicle transport system.

  8. Target mimics: an embedded layer of microRNA-involved gene regulatory networks in plants

    PubMed Central

    2012-01-01

    Background MicroRNAs (miRNAs) play an essential role in gene regulation in plants. At the same time, the expression of miRNA genes is also tightly controlled. Recently, a novel mechanism called “target mimicry” was discovered, providing another layer for modulating miRNA activities. However, except for the artificial target mimics manipulated for functional studies on certain miRNA genes, only one example, IPS1 (Induced by Phosphate Starvation 1)—miR399 was experimentally confirmed in planta. To date, few analyses for comprehensive identification of natural target mimics have been performed in plants. Thus, limited evidences are available to provide detailed information for interrogating the questionable issue whether target mimicry was widespread in planta, and implicated in certain biological processes. Results In this study, genome-wide computational prediction of endogenous miRNA mimics was performed in Arabidopsis and rice, and dozens of target mimics were identified. In contrast to a recent report, the densities of target mimic sites were found to be much higher within the untranslated regions (UTRs) when compared to those within the coding sequences (CDSs) in both plants. Some novel sequence characteristics were observed for the miRNAs that were potentially regulated by the target mimics. GO (Gene Ontology) term enrichment analysis revealed some functional insights into the predicted mimics. After degradome sequencing data-based identification of miRNA targets, the regulatory networks constituted by target mimics, miRNAs and their downstream targets were constructed, and some intriguing subnetworks were further exploited. Conclusions These results together suggest that target mimicry may be widely implicated in regulating miRNA activities in planta, and we hope this study could expand the current understanding of miRNA-involved regulatory networks. PMID:22613869

  9. The Congenital Heart Disease Genetic Network Study: rationale, design, and early results.

    PubMed

    Gelb, Bruce; Brueckner, Martina; Chung, Wendy; Goldmuntz, Elizabeth; Kaltman, Jonathan; Kaski, Juan Pablo; Kim, Richard; Kline, Jennie; Mercer-Rosa, Laura; Porter, George; Roberts, Amy; Rosenberg, Ellen; Seiden, Howard; Seidman, Christine; Sleeper, Lynn; Tennstedt, Sharon; Kaltman, Jonathan; Schramm, Charlene; Burns, Kristin; Pearson, Gail; Rosenberg, Ellen

    2013-02-15

    Congenital heart defects (CHD) are the leading cause of infant mortality among birth defects, and later morbidities and premature mortality remain problematic. Although genetic factors contribute significantly to cause CHD, specific genetic lesions are unknown for most patients. The National Heart, Lung, and Blood Institute-funded Pediatric Cardiac Genomics Consortium established the Congenital Heart Disease Genetic Network Study to investigate relationships between genetic factors, clinical features, and outcomes in CHD. The Pediatric Cardiac Genomics Consortium comprises 6 main and 4 satellite sites at which subjects are recruited, and medical data and biospecimens (blood, saliva, cardiovascular tissue) are collected. Core infrastructure includes an administrative/data-coordinating center, biorepository, data hub, and core laboratories (genotyping, whole-exome sequencing, candidate gene evaluation, and variant confirmation). Eligibility includes all forms of CHD. Annual follow-up is obtained for probands <1-year-old. Parents are enrolled whenever available. Enrollment from December 2010 to June 2012 comprised 3772 probands. One or both parents were enrolled for 72% of probands. Proband median age is 5.5 years. The one third enrolled at age <1 year are contacted annually for follow-up information. The distribution of CHD favors more complex lesions. Approximately, 11% of probands have a genetic diagnosis. Adequate DNA is available from 97% and 91% of blood and saliva samples, respectively. Genomic analyses of probands with heterotaxy, atrial septal defects, conotruncal, and left ventricular outflow tract obstructive lesions are underway. The scientific community's use of Pediatric Cardiac Genomics Consortium resources is welcome.

  10. Genetic Architecture and Molecular Networks Underlying Leaf Thickness in Desert-Adapted Tomato Solanum pennellii.

    PubMed

    Coneva, Viktoriya; Frank, Margaret H; Balaguer, Maria A de Luis; Li, Mao; Sozzani, Rosangela; Chitwood, Daniel H

    2017-09-01

    Thicker leaves allow plants to grow in water-limited conditions. However, our understanding of the genetic underpinnings of this highly functional leaf shape trait is poor. We used a custom-built confocal profilometer to directly measure leaf thickness in a set of introgression lines (ILs) derived from the desert tomato Solanum pennellii and identified quantitative trait loci. We report evidence of a complex genetic architecture of this trait and roles for both genetic and environmental factors. Several ILs with thick leaves have dramatically elongated palisade mesophyll cells and, in some cases, increased leaf ploidy. We characterized the thick IL2-5 and IL4-3 in detail and found increased mesophyll cell size and leaf ploidy levels, suggesting that endoreduplication underpins leaf thickness in tomato. Next, we queried the transcriptomes and inferred dynamic Bayesian networks of gene expression across early leaf ontogeny in these lines to compare the molecular networks that pattern leaf thickness. We show that thick ILs share S. pennellii-like expression profiles for putative regulators of cell shape and meristem determinacy as well as a general signature of cell cycle-related gene expression. However, our network data suggest that leaf thickness in these two lines is patterned at least partially by distinct mechanisms. Consistent with this hypothesis, double homozygote lines combining introgression segments from these two ILs show additive phenotypes, including thick leaves, higher ploidy levels, and larger palisade mesophyll cells. Collectively, these data establish a framework of genetic, anatomical, and molecular mechanisms that pattern leaf thickness in desert-adapted tomato. © 2017 American Society of Plant Biologists. All Rights Reserved.

  11. Genetic and epigenetic changes involving (retro)transposons in animal hybrids and polyploids.

    PubMed

    Arkhipova, I R; Rodriguez, F

    2013-01-01

    Transposable elements (TEs) are discrete genetic units that have the ability to change their location within chromosomal DNA, and constitute a major and rapidly evolving component of eukaryotic genomes. They can be subdivided into 2 distinct types: retrotransposons, which use an RNA intermediate for transposition, and DNA transposons, which move only as DNA. Rapid advances in genome sequencing significantly improved our understanding of TE roles in genome shaping and restructuring, and studies of transcriptomes and epigenomes shed light on the previously unknown molecular mechanisms underlying genetic and epigenetic TE controls. Knowledge of these control systems may be important for better understanding of reticulate evolution and speciation in the context of bringing different genomes together by hybridization and perturbing the established regulatory balance by ploidy changes. See also sister article focusing on plants by Bento et al. in this themed issue.

  12. Genomic analysis of clonal eosinophils by CGH arrays reveals new genetic regions involved in chronic eosinophilia.

    PubMed

    Arefi, Maryam; Robledo, Cristina; Peñarrubia, María J; García de Coca, Alfonso; Cordero, Miguel; Hernández-Rivas, Jesús M; García, Juan Luis

    2014-11-01

    To assess the presence of genetic imbalances in patients with myeloproliferative neoplasms (MPNs), 38 patients with chronic eosinophilia were studied by array comparative genomic hybridization (aCGH): seven had chronic myelogenous leukaemia (CML), BCR-ABL1 positive, nine patients had myeloproliferative neoplasia Ph- (MPN-Ph-), three had a myeloid neoplasm associated with a PDGFRA rearrangement, and the remaining two cases were Lymphoproliferative T neoplasms associated with eosinophilia. In addition, 17 patients had a secondary eosinophilia and were used as controls. Eosinophilic enrichment was carried out in all cases. Genomic imbalances were found in 76% of all MPN patients. Losses on 20q were the most frequent genetic abnormality in MPNs (32%), affected the three types of MPN studied. This study also found losses at 11q13.3 in 26% of patients with MPN-Ph- and in 19p13.11 in two of the three patients with an MPN associated with a PDGFRA rearrangement. In addition, 29% of patients with CML had losses on 8q24. In summary, aCGH revealed clonality in eosinophils in most MPNs, suggesting that it could be a useful technique for defining clonality in these diseases. The presence of genetic losses in new regions could provide new insights into the knowledge of these MPN associated with eosinophilia. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  13. Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

    PubMed

    Barton, Alan J; Valdés, Julio J; Orchard, Robert

    2009-01-01

    Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

  14. 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.

  15. TL1A/DR3 axis involvement in the inflammatory cytokine network during pulmonary sarcoidosis.

    PubMed

    Facco, M; Cabrelle, A; Calabrese, F; Teramo, A; Cinetto, F; Carraro, S; Martini, V; Calzetti, F; Tamassia, N; Cassatella, M A; Semenzato, G; Agostini, C

    2015-01-01

    TNF-like ligand 1A (TL1A), a recently recognized member of the TNF superfamily, and its death domain receptor 3 (DR3), firstly identified for their relevant role in T lymphocyte homeostasis, are now well-known mediators of several immune-inflammatory diseases, ranging from rheumatoid arthritis to inflammatory bowel diseases to psoriasis, whereas no data are available on their involvement in sarcoidosis, a multisystemic granulomatous disease where a deregulated T helper (Th)1/Th17 response takes place. In this study, by flow cytometry, real-time PCR, confocal microscopy and immunohistochemistry analyses, TL1A and DR3 were investigated in the pulmonary cells and the peripheral blood of 43 patients affected by sarcoidosis in different phases of the disease (29 patients with active sarcoidosis, 14 with the inactive form) and in 8 control subjects. Our results demonstrated a significant higher expression, both at protein and mRNA levels, of TL1A and DR3 in pulmonary T cells and alveolar macrophages of patients with active sarcoidosis as compared to patients with the inactive form of the disease and to controls. In patients with sarcoidosis TL1A was strongly more expressed in the lung than the blood, i.e., at the site of the involved organ. Additionally, zymography assays showed that TL1A is able to increase the production of matrix metalloproteinase 9 by sarcoid alveolar macrophages characterized, in patients with the active form of the disease, by reduced mRNA levels of the tissue inhibitor of metalloproteinase (TIMP)-1. These data suggest that TL1A/DR3 interactions are part of the extended and complex immune-inflammatory network that characterizes sarcoidosis during its active phase and may contribute to the pathogenesis and to the progression of the disease.

  16. 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.

  17. Molecular stripping, targets and decoys as modulators of oscillations in the NF-κB/IκBα/DNA genetic network

    PubMed Central

    Wang, Zhipeng; Wolynes, Peter G.

    2016-01-01

    Eukaryotic transcription factors in the NF-κB family are central components of an extensive genetic network that activates cellular responses to inflammation and to a host of other external stressors. This network consists of feedback loops that involve the inhibitor IκBα, numerous downstream functional targets, and still more numerous binding sites that do not appear to be directly functional. Under steady stimulation, the regulatory network of NF-κB becomes oscillatory, and temporal patterns of NF-κB pulses appear to govern the patterns of downstream gene expression needed for immune response. Understanding how the information from external stress passes to oscillatory signals and is then ultimately relayed to gene expression is a general issue in systems biology. Recently, in vitro kinetic experiments as well as molecular simulations suggest that active stripping of NF-κB by IκBα from its binding sites can modify the traditional systems biology view of NF-κB/IκBα gene circuits. In this work, we revise the commonly adopted minimal model of the NF-κB regulatory network to account for the presence of the large number of binding sites for NF-κB along with dissociation from these sites that may proceed either by passive unbinding or by active molecular stripping. We identify regimes where the kinetics of target and decoy unbinding and molecular stripping enter a dynamic tug of war that may either compensate each other or amplify nuclear NF-κB activity, leading to distinct oscillatory patterns. Our finding that decoys and stripping play a key role in shaping the NF-κB oscillations suggests strategies to control NF-κB responses by introducing artificial decoys therapeutically. PMID:27683001

  18. 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.

  19. Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural