WorldWideScience

Sample records for genetic regulation networks

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

    Science.gov (United States)

    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).

  2. Genome-level analysis of genetic regulation of liver gene expression networks

    Energy Technology Data Exchange (ETDEWEB)

    Gatti, Daniel [University of North Carolina, Chapel Hill; Maki, Akira [University of North Carolina, Chapel Hill; Chesler, Elissa J [ORNL; Kirova, Roumyana [Oak Ridge National Laboratory (ORNL); Kosyk, Oksana [University of North Carolina, Chapel Hill; Lu, Lu [University of Tennessee Health Science Center, Memphis; Manly, Kenneth [University of Tennessee Health Science Center, Memphis; Matthews, Douglas B. [University of Memphis; Qu, Yanhua [University of Tennessee Health Science Center, Memphis; Williams, Robert [University of Tennessee Health Science Center, Memphis; Perkins, Andy [University of Tennessee, Knoxville (UTK); Langston, Michael A [University of Tennessee, Knoxville (UTK); Threadgill, David [University of North Carolina, Chapel Hill; Rusyn, Ivan [University of North Carolina, Chapel Hill

    2007-01-01

    Liver is the primary site for metabolism of nutrients, drugs and chemical agents. While metabolic pathways are complex and tightly regulated, genetic variation among individuals, reflected in variation in gene expression levels, introduces complexity into research on liver disease. This study aimed to dissect genetic networks that control liver gene expression by combining largescale quantitative mRNA expression analysis with genetic mapping in a reference population of BXD recombinant inbred mouse strains for which extensive SNP, haplotype and phenotypic data is publicly available. We profiled gene expression in livers of naive mice of both sexes from C57BL/6J, DBA/2J, B6D2F1, and 37 BXD strains using Agilent oligonucleotide microarrays. This data was used to map quantitative trait loci (QTLs) responsible for variation in expression of about 19,000 transcripts. We identified polymorphic cis- and trans-acting loci, including several loci that control expression of large numbers of genes in liver, by comparing the physical transcript position with the location of the controlling QTL. The data is available through a public web-based resource (www.genenetwork.org) that allows custom data mining, identification of co-regulated transcripts and correlated phenotypes, cross-tissue and -species comparisons, as well as testing of a broad array of hypotheses.

  3. Interpreting microarray data to build models of microbial genetic regulation networks

    Science.gov (United States)

    Sokhansanj, Bahrad A.; Garnham, Janine B.; Fitch, J. Patrick

    2002-06-01

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

  4. Inference of the genetic network regulating lateral root initiation in Arabidopsis thaliana.

    Science.gov (United States)

    Muraro, Daniele; Voβ, Ute; Wilson, Michael; Bennett, Malcolm; Byrne, Helen; De Smet, Ive; Hodgman, Charlie; King, John

    2013-01-01

    Regulation of gene expression is crucial for organism growth, and it is one of the challenges in systems biology to reconstruct the underlying regulatory biological networks from transcriptomic data. The formation of lateral roots in Arabidopsis thaliana is stimulated by a cascade of regulators of which only the interactions of its initial elements have been identified. Using simulated gene expression data with known network topology, we compare the performance of inference algorithms, based on different approaches, for which ready-to-use software is available. We show that their performance improves with the network size and the inclusion of mutants. We then analyze two sets of genes, whose activity is likely to be relevant to lateral root initiation in Arabidopsis, and assess causality of their regulatory interactions by integrating sequence analysis with the intersection of the results of the best performing methods on time series and mutants. The methods applied capture known interactions between genes that are candidate regulators at early stages of development. The network inferred from genes significantly expressed during lateral root formation exhibits distinct scale free, small world and hierarchical properties and the nodes with a high out-degree may warrant further investigation.

  5. Inference of the Genetic Network Regulating Lateral Root Initiation in Arabidopsis thaliana

    KAUST Repository

    Muraro, D.

    2013-01-01

    Regulation of gene expression is crucial for organism growth, and it is one of the challenges in systems biology to reconstruct the underlying regulatory biological networks from transcriptomic data. The formation of lateral roots in Arabidopsis thaliana is stimulated by a cascade of regulators of which only the interactions of its initial elements have been identified. Using simulated gene expression data with known network topology, we compare the performance of inference algorithms, based on different approaches, for which ready-to-use software is available. We show that their performance improves with the network size and the inclusion of mutants. We then analyze two sets of genes, whose activity is likely to be relevant to lateral root initiation in Arabidopsis, and assess causality of their regulatory interactions by integrating sequence analysis with the intersection of the results of the best performing methods on time series and mutants. The methods applied capture known interactions between genes that are candidate regulators at early stages of development. The network inferred from genes significantly expressed during lateral root formation exhibits distinct scale free, small world and hierarchical properties and the nodes with a high out-degree may warrant further investigation. © 2004-2012 IEEE.

  6. Genetic and systems level analysis of Drosophila sticky/citron kinase and dFmr1 mutants reveals common regulation of genetic networks

    Directory of Open Access Journals (Sweden)

    Zarnescu Daniela C

    2008-11-01

    Full Text Available Abstract Background In Drosophila, the genes sticky and dFmr1 have both been shown to regulate cytoskeletal dynamics and chromatin structure. These genes also genetically interact with Argonaute family microRNA regulators. Furthermore, in mammalian systems, both genes have been implicated in neuronal development. Given these genetic and functional similarities, we tested Drosophila sticky and dFmr1 for a genetic interaction and measured whole genome expression in both mutants to assess similarities in gene regulation. Results We found that sticky mutations can dominantly suppress a dFmr1 gain-of-function phenotype in the developing eye, while phenotypes produced by RNAi knock-down of sticky were enhanced by dFmr1 RNAi and a dFmr1 loss-of-function mutation. We also identified a large number of transcripts that were misexpressed in both mutants suggesting that sticky and dFmr1 gene products similarly regulate gene expression. By integrating gene expression data with a protein-protein interaction network, we found that mutations in sticky and dFmr1 resulted in misexpression of common gene networks, and consequently predicted additional specific phenotypes previously not known to be associated with either gene. Further phenotypic analyses validated these predictions. Conclusion These findings establish a functional link between two previously unrelated genes. Microarray analysis indicates that sticky and dFmr1 are both required for regulation of many developmental genes in a variety of cell types. The diversity of transcripts regulated by these two genes suggests a clear cause of the pleiotropy that sticky and dFmr1 mutants display and provides many novel, testable hypotheses about the functions of these genes. As both of these genes are implicated in the development and function of the mammalian brain, these results have relevance to human health as well as to understanding more general biological processes.

  7. Dynamics Analysis and Prediction of Genetic Regulation in Glycerol Metabolic Network via Structural Kinetic Modelling

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    Jianxiong Ye

    2015-01-01

    Full Text Available Glycerol can be biologically converted to 1,3-propanediol (1,3-PD by Klebsiella pneumoniae. In the synthesis pathway of 1,3-PD, the accumulation of an intermediary metabolite 3-hydroxypropionaldehyde (3-HPA would cause an irreversible cessation of the dynamic system. Genetic manipulation on the key enzymes which control the formation rate and consumption rate of 3-HPA would decrease the accumulation of 3-HPA, resulting in nonlinear regulation on the dynamic system. The interest of this work is to focus on analyzing the influence of 3-HPA inhibition on the stability of the dynamic system. Due to the lack of intracellular knowledge, structural kinetic modelling is applied. On the basis of statistical account of the dynamical capabilities of the system in the parameter space, we conclude that, under weak or no inhibition to the reaction of 3-HPA consumption, the system is much easier to obtain a stable state, whereas strong inhibition to its formation is in favor of stabilizing the system. In addition, the existence of Hopf bifurcation in this system is also verified. The obtained results are helpful for deeply understanding the metabolic and genetic regulations of glycerol fermentation by Klebsiella pneumoniae.

  8. Regulation of Genetic Tests

    Science.gov (United States)

    Skip to main content Regulation of Genetic Tests Enter Search Term(s): Español Research Funding An Overview Bioinformatics Current Grants Education and Training Funding Extramural Research News Features Funding Divisions ...

  9. Differential Regulation of Cryptic Genetic Variation Shapes the Genetic Interactome Underlying Complex Traits.

    Science.gov (United States)

    Yadav, Anupama; Dhole, Kaustubh; Sinha, Himanshu

    2016-12-01

    Cryptic genetic variation (CGV) refers to genetic variants whose effects are buffered in most conditions but manifest phenotypically upon specific genetic and environmental perturbations. Despite having a central role in adaptation, contribution of CGV to regulation of quantitative traits is unclear. Instead, a relatively simplistic architecture of additive genetic loci is known to regulate phenotypic variation in most traits. In this paper, we investigate the regulation of CGV and its implication on the genetic architecture of quantitative traits at a genome-wide level. We use a previously published dataset of biparental recombinant population of Saccharomyces cerevisiae phenotyped in 34 diverse environments to perform single locus, two-locus, and covariance mapping. We identify loci that have independent additive effects as well as those which regulate the phenotypic manifestation of other genetic variants (variance QTL). We find that whereas additive genetic variance is predominant, a higher order genetic interaction network regulates variation in certain environments. Despite containing pleiotropic loci, with effects across environments, these genetic networks are highly environment specific. CGV is buffered under most allelic combinations of these networks and perturbed only in rare combinations resulting in high phenotypic variance. The presence of such environment specific genetic networks is the underlying cause of abundant gene–environment interactions. We demonstrate that overlaying identified molecular networks on such genetic networks can identify potential candidate genes and underlying mechanisms regulating phenotypic variation. Such an integrated approach applied to human disease datasets has the potential to improve the ability to predict disease predisposition and identify specific therapeutic targets.

  10. Stochastic dynamics of genetic broadcasting networks

    Science.gov (United States)

    Potoyan, Davit A.; Wolynes, Peter G.

    2017-11-01

    The complex genetic programs of eukaryotic cells are often regulated by key transcription factors occupying or clearing out of a large number of genomic locations. Orchestrating the residence times of these factors is therefore important for the well organized functioning of a large network. The classic models of genetic switches sidestep this timing issue by assuming the binding of transcription factors to be governed entirely by thermodynamic protein-DNA affinities. Here we show that relying on passive thermodynamics and random release times can lead to a "time-scale crisis" for master genes that broadcast their signals to a large number of binding sites. We demonstrate that this time-scale crisis for clearance in a large broadcasting network can be resolved by actively regulating residence times through molecular stripping. We illustrate these ideas by studying a model of the stochastic dynamics of the genetic network of the central eukaryotic master regulator NFκ B which broadcasts its signals to many downstream genes that regulate immune response, apoptosis, etc.

  11. The population genetics of cooperative gene regulation

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    Stewart Alexander J

    2012-09-01

    Full Text Available Abstract Background Changes in gene regulatory networks drive the evolution of phenotypic diversity both within and between species. Rewiring of transcriptional networks is achieved either by changes to transcription factor binding sites or by changes to the physical interactions among transcription factor proteins. It has been suggested that the evolution of cooperative binding among factors can facilitate the adaptive rewiring of a regulatory network. Results We use a population-genetic model to explore when cooperative binding of transcription factors is favored by evolution, and what effects cooperativity then has on the adaptive re-writing of regulatory networks. We consider a pair of transcription factors that regulate multiple targets and overlap in the sets of target genes they regulate. We show that, under stabilising selection, cooperative binding between the transcription factors is favoured provided the amount of overlap between their target genes exceeds a threshold. The value of this threshold depends on several population-genetic factors: strength of selection on binding sites, cost of pleiotropy associated with protein-protein interactions, rates of mutation and population size. Once it is established, we find that cooperative binding of transcription factors significantly accelerates the adaptive rewiring of transcriptional networks under positive selection. We compare our qualitative predictions to systematic data on Saccharomyces cerevisiae transcription factors, their binding sites, and their protein-protein interactions. Conclusions Our study reveals a rich set of evolutionary dynamics driven by a tradeoff between the beneficial effects of cooperative binding at targets shared by a pair of factors, and the detrimental effects of cooperative binding for non-shared targets. We find that cooperative regulation will evolve when transcription factors share a sufficient proportion of their target genes. These findings help to

  12. Network Regulation and Support Schemes

    DEFF Research Database (Denmark)

    Ropenus, Stephanie; Schröder, Sascha Thorsten; Jacobsen, Henrik

    2009-01-01

    -in tariffs to market-based quota systems, and network regulation approaches, comprising rate-of-return and incentive regulation. National regulation and the vertical structure of the electricity sector shape the incentives of market agents, notably of distributed generators and network operators....... This article seeks to investigate the interactions between the policy dimensions of support schemes and network regulation and how they affect the deployment of distributed generation. Firstly, a conceptual analysis examines how the incentives of the different market agents are affected. In particular......At present, there exists no explicit European policy framework on distributed generation. Various Directives encompass distributed generation; inherently, their implementation is to the discretion of the Member States. The latter have adopted different kinds of support schemes, ranging from feed...

  13. Information transmission in genetic regulatory networks: a review

    CERN Document Server

    Walczak, Aleksandra M

    2011-01-01

    Genetic regulatory networks enable cells to respond to the 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 network's inputs and its 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 to understand recent work. We then discuss the functional complexity of gene regulation which arrises from the molecular nature of the regulatory interactions. We end by reviewing som...

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

    Science.gov (United States)

    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

  15. Genetic Algorithm Optimized Neural Networks Ensemble as ...

    African Journals Online (AJOL)

    NJD

    Genetic Algorithm Optimized Neural Networks Ensemble as. Calibration Model for Simultaneous Spectrophotometric. Estimation of Atenolol and Losartan Potassium in Tablets. Dondeti Satyanarayana*, Kamarajan Kannan and Rajappan Manavalan. Department of Pharmacy, Annamalai University, Annamalainagar, Tamil ...

  16. Genetic algorithm for neural networks optimization

    Science.gov (United States)

    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«.

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

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    Mjolsness, Eric

    2000-01-01

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

  18. Assessing the molecular genetics of attention networks

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    Pfaff Donald W

    2002-10-01

    Full Text Available Abstract Background Current efforts to study the genetic underpinnings of higher brain functions have been lacking appropriate phenotypes to describe cognition. One of the problems is that many cognitive concepts for which there is a single word (e.g. attention have been shown to be related to several anatomical networks. Recently, we have developed an Attention Network Test (ANT that provides a separate measure for each of three anatomically defined attention networks. Results In this study we have measured the efficiency of neural networks related to aspects of attention using the ANT in a population of 200 adult subjects. We then examined genetic polymorphisms in four candidate genes (DRD4, DAT, COMT and MAOA that have been shown to contribute to the risk of developing various psychiatric disorders where attention is disrupted. We find modest associations of several polymorphisms with the efficiency of executive attention but not with overall performance measures such as reaction time. Conclusions These results suggest that genetic variation may underlie inter-subject variation in the efficiency of executive attention. This study also shows that genetic influences on executive attention may be specific to certain anatomical networks rather than affecting performance in a global or non-specific manner. Lastly, this study further validates the ANT as an endophenotypic assay suitable for assessing how genes influence certain anatomical networks that may be disrupted in various psychiatric disorders.

  19. Structural systems identification of genetic regulatory networks.

    Science.gov (United States)

    Xiong, Hao; Choe, Yoonsuck

    2008-02-15

    Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit. In this report, we extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles. We applied our method to synthetic data and to SOS data and showed that our method significantly outperforms the regular EM without structural constraints. The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his website, http://www.che.udel.edu/systems/people/zak.

  20. Mining protein networks for synthetic genetic interactions

    Directory of Open Access Journals (Sweden)

    Zhao Shan

    2008-10-01

    Full Text Available Abstract Background The local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/lethal gene pairs have been extensively identified. Results We design a support vector machine system that uses graph-theoretic properties of two proteins in a protein interaction network as input features for prediction of synthetic sick/lethal interactions. The system is trained on interacting and non-interacting gene pairs culled from large scale genetic screens as well as literature-curated data. We find that the method is capable of predicting synthetic genetic interactions with sensitivity and specificity both exceeding 85%. We further find that the prediction performance is reasonably robust with respect to errors in the protein interaction network and with respect to changes in the features of test datasets. Using the prediction system, we carried out novel predictions of synthetic sick/lethal gene pairs at a genome-wide scale. These pairs appear to have functional properties that are similar to those that characterize the known synthetic lethal gene pairs. Conclusion Our analysis shows that protein interaction networks can be used to predict synthetic lethal interactions with accuracies on par with or exceeding that of other computational methods that use a variety of input features, including functional annotations. This indicates that protein

  1. Genetics-genomics competencies and nursing regulation.

    Science.gov (United States)

    Kirk, Maggie; Calzone, Kathleen; Arimori, Naoko; Tonkin, Emma

    2011-06-01

    The aim of this article is to explore the interaction between the integration of genetics-genomics competencies into nursing curricula and regulatory standards. By taking a global perspective of activity in this field, we aim to develop a framework that can inform strategic planning in relation to international genetics-genomics and nursing education. We focus our exploration around a small-scale international survey on the progress, achievements, and critical success factors of 10 countries in relation to the integration of genetics-genomics into nursing education, with exemplars from three of those countries. Analysis of the data generated 10 themes, each with several subthemes that play a critical role in the development of genetics-genomics in nursing education and practice. The themes were organized into three overarching themes: nursing in genetics, genetics in nursing, and recognition and support. Genetics-genomics competence is not fully integrated into nursing education at an appropriate level in any country, nor was it reflected robustly in current standards for registration and licensure. Strong leadership from the specialist genetics community plays a critical role in defining genetics-genomics competence but the engagement of nursing professionals at senior levels in both government and regulatory institutions is essential if nurses are to be active participants in the innovations offered by genomic healthcare. Safe and effective nursing practice must incorporate the needs of those with, at risk for, or susceptible to genetic-genomic conditions, as well as those who might benefit from the application of genomic technologies in the diagnosis and management of common conditions such as cancer and heart disease. The scope of such practice can be articulated though competence statements. Professional regulation defines the standard of competence that practicing nurses should demonstrate at initial registration and licensure. No claim to original US

  2. Gene Regulation Networks for Modeling Drosophila Development

    Science.gov (United States)

    Mjolsness, E.

    1999-01-01

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

  3. GKIN: a tool for drawing genetic networks

    Directory of Open Access Journals (Sweden)

    Jonathan Arnold

    2012-03-01

    Full Text Available We present GKIN, a simulator and a comprehensive graphical interface where one can draw the model specification of reactions between hypothesized molecular participants in a gene regulatory and biochemical reaction network (or genetic network for short. The solver is written in C++ in a nearly platform independentmanner to simulate large ensembles of models, which can run on PCs, Macintoshes, and UNIX machines, and its graphical user interface is written in Java which can run as a standalone or WebStart application. The drawing capability for rendering a network significantly enhances the ease of use of other reaction network simulators, such as KINSOLVER (Aleman-Meza et al., 2009 and enforces a correct semantic specification of the network. In a usability study with novice users, drawing the network with GKIN was preferred and faster in comparison with entry with a dialog-box guided interface in COPASI (Hoops, et al., 2006 with no difference in error rates between GKIN and COPASI in specifying the network. GKIN is freely available at http://faculty.cs.wit.edu/~ldeligia/PROJECTS/GKIN/.

  4. The fallacies of network neutrality regulation

    OpenAIRE

    Knieps, Günter; Zenhäusern, Patrick

    2008-01-01

    In this paper, historical functionalities of the traditional Internet are contrasted with today's Internet functionalities of the 'smart' Internet architecture. It is shown that network neutrality regulation prohibiting congestion management and traffic quality differentiation is contrary to economically founded allocation mechanisms. By access regulation of local loop bottleneck components the transfer of market power from the telecommunications infrastructure into the complementary Internet...

  5. A candidate multimodal functional genetic network for thermal adaptation

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    Katharina C. Wollenberg Valero

    2014-09-01

    Full Text Available 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

  6. Character Recognition Using Genetically Trained Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    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

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

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    Aymerich Stéphane

    2008-02-01

    Full Text Available Abstract 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.

  8. Training product unit neural networks with genetic algorithms

    Science.gov (United States)

    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.

  9. Genetic dissection of the Gpnmb network in the eye.

    Science.gov (United States)

    Lu, Hong; Wang, Xusheng; Pullen, Matthew; Guan, Huaijin; Chen, Hui; Sahu, Shwetapadma; Zhang, Bing; Chen, Hao; Williams, Robert W; Geisert, Eldon E; Lu, Lu; Jablonski, Monica M

    2011-06-13

    To use a systematic genetics approach to investigate the regulation of Gpnmb, a gene that contributes to pigmentary dispersion syndrome (PDS) and pigmentary glaucoma (PG) in the DBA/2J (D2) mouse. Global patterns of gene expression were studied in whole eyes of a large family of BXD mouse strains (n = 67) generated by crossing the PDS- and PG-prone parent (DBA/2J) with a resistant strain (C57BL/6J). Quantitative trait locus (eQTL) mapping methods and gene set analysis were used to evaluate Gpnmb coexpression networks in wild-type and mutant cohorts. The level of Gpnmb expression was associated with a highly significant cis-eQTL at the location of the gene itself. This autocontrol of Gpnmb is likely to be a direct consequence of the known premature stop codon in exon 4. Both gene ontology and coexpression network analyses demonstrated that the mutation in Gpnmb radically modified the set of genes with which Gpnmb expression is correlated. The covariates of wild-type Gpnmb are involved in biological processes including melanin synthesis and cell migration, whereas the covariates of mutant Gpnmb are involved in the biological processes of posttranslational modification, stress activation, and sensory processing. These results demonstrated that a systematic genetics approach provides a powerful tool for constructing coexpression networks that define the biological process categories within which similarly regulated genes function. The authors showed that the R150X mutation in Gpnmb dramatically modified its list of genetic covariates, which may explain the associated ocular pathology.

  10. Noncommutative Biology: Sequential Regulation of Complex Networks.

    Directory of Open Access Journals (Sweden)

    William Letsou

    2016-08-01

    Full Text Available Single-cell variability in gene expression is important for generating distinct cell types, but it is unclear how cells use the same set of regulatory molecules to specifically control similarly regulated genes. While combinatorial binding of transcription factors at promoters has been proposed as a solution for cell-type specific gene expression, we found that such models resulted in substantial information bottlenecks. We sought to understand the consequences of adopting sequential logic wherein the time-ordering of factors informs the final outcome. We showed that with noncommutative control, it is possible to independently control targets that would otherwise be activated simultaneously using combinatorial logic. Consequently, sequential logic overcomes the information bottleneck inherent in complex networks. We derived scaling laws for two noncommutative models of regulation, motivated by phosphorylation/neural networks and chromosome folding, respectively, and showed that they scale super-exponentially in the number of regulators. We also showed that specificity in control is robust to the loss of a regulator. Lastly, we connected these theoretical results to real biological networks that demonstrate specificity in the context of promiscuity. These results show that achieving a desired outcome often necessitates roundabout steps.

  11. HAND2 targets define a network of transcriptional regulators that compartmentalize the early limb bud mesenchyme

    NARCIS (Netherlands)

    Osterwalder, Marco; Speziale, Dario; Shoukry, Malak; Mohan, Rajiv; Ivanek, Robert; Kohler, Manuel; Beisel, Christian; Wen, Xiaohui; Scales, Suzie J.; Christoffels, Vincent M.; Visel, Axel; Lopez-Rios, Javier; Zeller, Rolf

    2014-01-01

    The genetic networks that govern vertebrate development are well studied, but how the interactions of trans-acting factors with cis-regulatory modules (CRMs) are integrated into spatiotemporal regulation of gene expression is not clear. The transcriptional regulator HAND2 is required during limb,

  12. A Model of Genetic Variation in Human Social Networks

    CERN Document Server

    Fowler, James H; Christakis, Nicholas A

    2008-01-01

    Social networks influence the evolution of cooperation and they exhibit strikingly systematic patterns across a wide range of human contexts. Both of these facts suggest that variation in the topological attributes of human social networks might have a genetic basis. While genetic variation accounts for a significant portion of the variation in many complex social behaviors, the heritability of egocentric social network attributes is unknown. Here we show that three of these attributes (in-degree, transitivity, and centrality) are heritable. We then develop a "mirror network" method to test extant network models and show that none accounts for observed genetic variation in human social networks. We propose an alternative "attract and introduce" model that generates significant heritability as well as other important network features, and we show that this model with two simple forms of heterogeneity is well suited to the modeling of real social networks in humans. These results suggest that natural selection ...

  13. On the attenuation and amplification of molecular noise in genetic regulatory networks

    Directory of Open Access Journals (Sweden)

    Wang Yu-Chao

    2006-02-01

    Full Text Available Abstract Background Noise has many important roles in cellular genetic regulatory functions at the nanomolar scale. At present, no good theory exists for identifying all possible mechanisms of genetic regulatory networks to attenuate the molecular noise to achieve regulatory ability or to amplify the molecular noise to randomize outcomes to the advantage of diversity. Therefore, the noise filtering of genetic regulatory network is an important topic for gene networks under intrinsic fluctuation and extrinsic noise. Results Based on stochastic dynamic regulation equation, the intrinsic fluctuation in reaction rates is modeled as a state-dependent stochastic process, which will influence the stability of gene regulatory network, especially, with low concentrations of reacting species. Then the mechanisms of genetic regulatory network to attenuate or amplify extrinsic fluctuation are revealed from the nonlinear stochastic filtering point of view. Furthermore, a simple measure of attenuation level or amplification level of extrinsic noise for genetic regulatory networks is also introduced by nonlinear robust filtering method. Based on the global linearization scheme, a convenient method is introduced to measure noise attenuation or amplification for each gene of the nonlinear stochastic regulatory network by solving a set of filtering problems, which correspond to a set of linearized stochastic regulatory networks. Finally, by the proposed methods, several simulation examples of genetic regulatory networks are given to measure their robust stability under intrinsic fluctuations, and to estimate the genes' attenuation and amplification levels under extrinsic noises. Conclusion In this study, a stochastic nonlinear dynamic model is developed for genetic regulatory networks under intrinsic fluctuation and extrinsic noise. By the method we proposed, we could determine the robust stability under intrinsic fluctuations and identify the genes that are

  14. Protecting Biodiversity: National Laws Regulating Access to Genetic ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    2000-01-01

    Protecting Biodiversity: National Laws Regulating Access to Genetic Resources in the Americas. Book cover Protecting Biodiversity: National Laws Regulating Access to Genetic Resources in the Americas. Editor(s):. Susan P. Bass and Manuel Ruiz Muller. Publisher(s):. IDRC. January 1, 2000. ISBN: Out of print. 120 pages.

  15. Coherent organization in gene regulation: a study on six networks

    Science.gov (United States)

    Aral, Neşe; Kabakçıoğlu, Alkan

    2016-04-01

    Structural and dynamical fingerprints of evolutionary optimization in biological networks are still unclear. Here we analyze the dynamics of genetic regulatory networks responsible for the regulation of cell cycle and cell differentiation in three organisms or cell types each, and show that they follow a version of Hebb's rule which we have termed coherence. More precisely, we find that simultaneously expressed genes with a common target are less likely to act antagonistically at the attractors of the regulatory dynamics. We then investigate the dependence of coherence on structural parameters, such as the mean number of inputs per node and the activatory/repressory interaction ratio, as well as on dynamically determined quantities, such as the basin size and the number of expressed genes.

  16. Genetic control of functional brain network efficiency in children

    NARCIS (Netherlands)

    Heuvel, M.P.; van Soelen, I.L.C.; Stam, C.J.; Kahn, R.S.; Boomsma, D.I.; Hulshoff Pol, H.E.

    2013-01-01

    The human brain is a complex network of interconnected brain regions. In adulthood, the brain's network was recently found to be under genetic influence. However, the extent to which genes influence the functional brain network early in development is not yet known. We report on the heritability of

  17. Adaptive genetic variation, stress and glucose regulation.

    Science.gov (United States)

    Oriel, Roxanne C; Wiley, Christopher D; Dewey, Michael J; Vrana, Paul B

    2008-01-01

    Elevated glucose levels in the presence of insulin are indicative of type 2 diabetes and the more inclusive metabolic syndrome. Alleles conferring susceptibility to these and other common conditions may be adaptations to past environments. It is possible that other mammals exhibiting environmental diversity harbor similar variants; therefore, we assessed glucose regulation in two species of deer mice (Peromyscus), a diverse endemic North American group. The prairie deer mouse, P. maniculatus bairdii (BW), and the Oldfield mouse, P. polionotus subgriseus (PO) differ in sexual dimorphism, behavior and habitat. PO animals exhibit better regulatory ability than BW animals, particularly among males, although both species display equivalent insulin levels/responses and non-fasted glucose levels. Hybrid males exhibit a PO glucose challenge response and subsequent analysis of consomic animals implicates Y chromosome variation as the genetic cause. Two pieces of evidence indicate that the male glucose regulatory differences are mediated by stress response: (1) fasting and handling alone account for most of the variation; (2) an inhibitor of glucocorticoid (GC) stress hormone synthesis eliminates these differences. PO males have GC levels that are twice those of BW males, indicating the presence of alleles that attenuate the GC response. We hypothesize that the interspecific physiological and behavioral differences are interrelated and that similar human variants exist.

  18. Self-Regulated Inquiry with Networked Resources

    Directory of Open Access Journals (Sweden)

    John C. Nesbit

    2003-10-01

    Full Text Available Abstract. In the context of continued growth in the accessibility of information through the internet, recent advances in theories of self-regulated learning present an opportunity to reexamine how learners work with networked resources in constructivist approaches such as problem-based learning, project-based learning, and collaborative problem solving. We present a Resource Inquiry model consisting of five stages: (1 Set resource inquiry goals, (2 Plan for resource study, (3 Search and select resources, (4 Study and assess new knowledge, and (5 Critique and recommend resources. Our model informs designers of online tools about how to support learners' cognitive and metacognitive strategies when learning activities involve interacting with networked resources.

  19. State Observer Design for Delayed Genetic Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Li-Ping Tian

    2014-01-01

    Full Text Available Genetic regulatory networks are dynamic systems which describe the interactions among gene products (mRNAs and proteins. The internal states of a genetic regulatory network consist of the concentrations of mRNA and proteins involved in it, which are very helpful in understanding its dynamic behaviors. However, because of some limitations such as experiment techniques, not all internal states of genetic regulatory network can be effectively measured. Therefore it becomes an important issue to estimate the unmeasured states via the available measurements. In this study, we design a state observer to estimate the states of genetic regulatory networks with time delays from available measurements. Furthermore, based on linear matrix inequality (LMI approach, a criterion is established to guarantee that the dynamic of estimation error is globally asymptotically stable. A gene repressillatory network is employed to illustrate the effectiveness of our design approach.

  20. Solving Hub Network Problem Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Mursyid Hasan Basri

    2012-01-01

    Full Text Available This paper addresses a network problem that described as follows. There are n ports that interact, and p of those will be designated as hubs. All hubs are fully interconnected. Each spoke will be allocated to only one of available hubs. Direct connection between two spokes is allowed only if they are allocated to the same hub. The latter is a distinct characteristic that differs it from pure hub-and-spoke system. In case of pure hub-and-spoke system, direct connection between two spokes is not allowed. The problem is where to locate hub ports and to which hub a spoke should be allocated so that total transportation cost is minimum. In the first model, there are some additional aspects are taken into consideration in order to achieve a better representation of the problem. The first, weekly service should be accomplished. Secondly, various vessel types should be considered. The last, a concept of inter-hub discount factor is introduced. Regarding the last aspect, it represents cost reduction factor at hub ports due to economies of scale. In practice, it is common that the cost rate for inter-hub movement is less than the cost rate for movement between hub and origin/destination. In this first model, inter-hub discount factor is assumed independent with amount of flows on inter-hub links (denoted as flow-independent discount policy. The results indicated that the patterns of enlargement of container ship size, to some degree, are similar with those in Kurokawa study. However, with regard to hub locations, the results have not represented the real practice. In the proposed model, unsatisfactory result on hub locations is addressed. One aspect that could possibly be improved to find better hub locations is inter-hub discount factor. Then inter-hub discount factor is assumed to depend on amount of inter-hub flows (denoted as flow-dependent discount policy. There are two discount functions examined in this paper. Both functions are characterized by

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

    Directory of Open Access Journals (Sweden)

    Ethan G Thompson

    Full Text Available 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.

  2. Smart Business Networks Design and Business Genetics

    NARCIS (Netherlands)

    L-F. Pau (Louis-François)

    2006-01-01

    textabstractWith the emergence of smart business networks, agile networks, etc. as important research areas in management, for all the attractiveness of these concepts, a major issue remains around their design and the selection rules. While smart business networks should provide advantages due to

  3. Genetic and epigenetic regulation of intestinal fibrosis.

    Science.gov (United States)

    Li, Chao; Kuemmerle, John F

    2016-08-01

    Crohn's disease affects those individuals with polygenic risk factors. The identified risk loci indicate that the genetic architecture of Crohn's disease involves both innate and adaptive immunity and the response to the intestinal environment including the microbiome. Genetic risk alone, however, predicts only 25% of disease, indicating that other factors, including the intestinal environment, can shape the epigenome and also confer heritable risk to patients. Patients with Crohn's disease can have purely inflammatory disease, penetrating disease or fibrostenosis. Analysis of the genetic risk combined with epigenetic marks of Crohn's disease and other disease associated with organ fibrosis reveals common events are affecting the genes and pathways key to development of fibrosis. This review will focus on what is known about the mechanisms by which genetic and epigenetic risk factors determine development of fibrosis in Crohn's disease and contrast that with other fibrotic conditions.

  4. District Heating Network Design and Configuration Optimization with Genetic Algorithm

    DEFF Research Database (Denmark)

    Li, Hongwei; Svendsen, Svend

    2013-01-01

    and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding...

  5. Robust state estimation for stochastic genetic regulatory networks

    Science.gov (United States)

    Liang, Jinling; Lam, James

    2010-01-01

    In this article, the state estimation problem is investigated for genetic regulatory networks (GRNs) with parameter uncertainties and stochastic disturbances. To account for the unavoidable modelling errors and parameter fluctuations, the network parameters are assumed to be time-varying but norm-bounded. Furthermore, scalar multiplicative white noises are introduced into both the translation process and the feedback regulation process in order to reflect the inherent intracellular and extracellular noise perturbations. The purpose of the addressed problem is to design a linear state estimator that can estimate the true concentration of the mRNA and the protein of the uncertain GRNs. By resorting to the Lyapunov-Krasovskii functional method combined with the linear matrix inequality (LMI) technique, sufficient conditions are first established for ensuring the stochastic stability of the dynamics of the estimation error, and the estimator gains are then designed in terms of the solutions to some LMIs that can be easily solved by using the standard numerical software. A three-node GRN is presented to show the effectiveness of the proposed design procedures.

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

    Directory of Open Access Journals (Sweden)

    Neszt Michael

    2008-07-01

    Full Text Available Abstract 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.

  7. Genetic and Epigenetic Regulation of Aortic Aneurysms

    Directory of Open Access Journals (Sweden)

    Ha Won Kim

    2017-01-01

    Full Text Available Aneurysms are characterized by structural deterioration of the vascular wall leading to progressive dilatation and, potentially, rupture of the aorta. While aortic aneurysms often remain clinically silent, the morbidity and mortality associated with aneurysm expansion and rupture are considerable. Over 13,000 deaths annually in the United States are attributable to aortic aneurysm rupture with less than 1 in 3 persons with aortic aneurysm rupture surviving to surgical intervention. Environmental and epidemiologic risk factors including smoking, male gender, hypertension, older age, dyslipidemia, atherosclerosis, and family history are highly associated with abdominal aortic aneurysms, while heritable genetic mutations are commonly associated with aneurysms of the thoracic aorta. Similar to other forms of cardiovascular disease, family history, genetic variation, and heritable mutations modify the risk of aortic aneurysm formation and provide mechanistic insight into the pathogenesis of human aortic aneurysms. This review will examine the relationship between heritable genetic and epigenetic influences on thoracic and abdominal aortic aneurysm formation and rupture.

  8. Genetic and Epigenetic Regulation of Aortic Aneurysms.

    Science.gov (United States)

    Kim, Ha Won; Stansfield, Brian K

    2017-01-01

    Aneurysms are characterized by structural deterioration of the vascular wall leading to progressive dilatation and, potentially, rupture of the aorta. While aortic aneurysms often remain clinically silent, the morbidity and mortality associated with aneurysm expansion and rupture are considerable. Over 13,000 deaths annually in the United States are attributable to aortic aneurysm rupture with less than 1 in 3 persons with aortic aneurysm rupture surviving to surgical intervention. Environmental and epidemiologic risk factors including smoking, male gender, hypertension, older age, dyslipidemia, atherosclerosis, and family history are highly associated with abdominal aortic aneurysms, while heritable genetic mutations are commonly associated with aneurysms of the thoracic aorta. Similar to other forms of cardiovascular disease, family history, genetic variation, and heritable mutations modify the risk of aortic aneurysm formation and provide mechanistic insight into the pathogenesis of human aortic aneurysms. This review will examine the relationship between heritable genetic and epigenetic influences on thoracic and abdominal aortic aneurysm formation and rupture.

  9. A guide to integrating transcriptional regulatory and metabolic networks using PROM (probabilistic regulation of metabolism).

    Science.gov (United States)

    Simeonidis, Evangelos; Chandrasekaran, Sriram; Price, Nathan D

    2013-01-01

    The integration of transcriptional regulatory and metabolic networks is a crucial step in the process of predicting metabolic behaviors that emerge from either genetic or environmental changes. Here, we present a guide to PROM (probabilistic regulation of metabolism), an automated method for the construction and simulation of integrated metabolic and transcriptional regulatory networks that enables large-scale phenotypic predictions for a wide range of model organisms.

  10. Genetic Algorithm Optimized Neural Networks Ensemble as ...

    African Journals Online (AJOL)

    Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous spectrophotometric multicomponent analysis are suggested, with a study on the estimation of the components of an antihypertensive combination, namely, atenolol and losartan potassium.

  11. Bayesian neural networks for detecting epistasis in genetic association studies.

    Science.gov (United States)

    Beam, Andrew L; Motsinger-Reif, Alison; Doyle, Jon

    2014-11-21

    Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships. The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets.

  12. Angiogenesis: the genetic regulation of vascular development

    NARCIS (Netherlands)

    R.A. Haasdijk (Remco Anton)

    2014-01-01

    markdownabstract__Abstract__ For centuries, many scientists are fascinated by the organisation of the vascular network. The Greek philosopher and polymath Aristotle (384 BC) was one of the first man who described the vasculature. He wrote: “the system of blood vessels in the body may

  13. Seeded Bayesian Networks: Constructing genetic networks from microarray data

    Directory of Open Access Journals (Sweden)

    Quackenbush John

    2008-07-01

    Full Text Available Abstract Background DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes – often represented as networks – in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results. Results Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data. Conclusion The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.

  14. [Genetic regulation of plant shoot stem cells].

    Science.gov (United States)

    Al'bert, E V; Ezhova, T A

    2013-02-01

    This article describes the main features of plant stem cells and summarizes the results of studies of the genetic control of stem cell maintenance in the apical meristem of the shoot. It is demonstrated that the WUS-CLV gene system plays a key role in the maintenance of shoot apical stem cells and the formation of adventitious buds and somatic embryos. Unconventional concepts of plant stem cells are considered.

  15. Empirical multiscale networks of cellular regulation.

    Directory of Open Access Journals (Sweden)

    Benjamin de Bivort

    2007-10-01

    Full Text Available Grouping genes by similarity of expression across multiple cellular conditions enables the identification of cellular modules. The known functions of genes enable the characterization of the aggregate biological functions of these modules. In this paper, we use a high-throughput approach to identify the effective mutual regulatory interactions between modules composed of mouse genes from the Alliance for Cell Signaling (AfCS murine B-lymphocyte database which tracks the response of approximately 15,000 genes following chemokine perturbation. This analysis reveals principles of cellular organization that we discuss along four conceptual axes. (1 Regulatory implications: the derived collection of influences between any two modules quantifies intuitive as well as unexpected regulatory interactions. (2 Behavior across scales: trends across global networks of varying resolution (composed of various numbers of modules reveal principles of assembly of high-level behaviors from smaller components. (3 Temporal behavior: tracking the mutual module influences over different time intervals provides features of regulation dynamics such as duration, persistence, and periodicity. (4 Gene Ontology correspondence: the association of modules to known biological roles of individual genes describes the organization of functions within coexpressed modules of various sizes. We present key specific results in each of these four areas, as well as derive general principles of cellular organization. At the coarsest scale, the entire transcriptional network contains five divisions: two divisions devoted to ATP production/biosynthesis and DNA replication that activate all other divisions, an "extracellular interaction" division that represses all other divisions, and two divisions (proliferation/differentiation and membrane infrastructure that activate and repress other divisions in specific ways consistent with cell cycle control.

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

    Science.gov (United States)

    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.

  17. Adaptive genetic variation, stress and glucose regulation

    OpenAIRE

    Oriel, Roxanne C.; Wiley, Christopher D.; Dewey, Michael J.; Vrana, Paul B.

    2008-01-01

    Elevated glucose levels in the presence of insulin are indicative of type 2 diabetes and the more inclusive metabolic syndrome. Alleles conferring susceptibility to these and other common conditions may be adaptations to past environments. It is possible that other mammals exhibiting environmental diversity harbor similar variants; therefore, we assessed glucose regulation in two species of deer mice (Peromyscus), a diverse endemic North American group. The prairie deer mouse, P. maniculatus ...

  18. Access Network Selection Based on Fuzzy Logic and Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Mohammed Alkhawlani

    2008-01-01

    Full Text Available In the next generation of heterogeneous wireless networks (HWNs, a large number of different radio access technologies (RATs will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS provisioning. This paper proposes a general scheme to solve the access network selection (ANS problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL and genetic algorithms (GAs have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection.

  19. District Heating Network Design and Configuration Optimization with Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Hongwei Li

    2013-12-01

    Full Text Available In this paper, the configuration of a district heating network which connects from the heating plant to the end users is optimized. Each end user in the network represents a building block. The connections between the heat generation plant and the end users are represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding and is optimized in terms of the net present cost. The optimization results indicates that the optimal DH network configuration is determined by multiple factors such as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding the pressure and temperature limitation, as well as the corresponding network heat loss.

  20. A full bayesian approach for boolean genetic network inference.

    Directory of Open Access Journals (Sweden)

    Shengtong Han

    Full Text Available 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.

  1. Genetics of the ceramide/sphingosine-1-phosphate rheostat in blood pressure regulation and hypertension

    Directory of Open Access Journals (Sweden)

    Eugen-Olsen Jesper

    2011-05-01

    Full Text Available Abstract Background Several attempts to decipher the genetics of hypertension of unknown causes have been made including large-scale genome-wide association analysis (GWA, but only a few genes have been identified. Unsolved heterogeneity of the regulation of blood pressure and the shortcomings of the prevailing monogenic approach to capture genetic effects in a polygenic condition are the main reasons for the modest results. The level of the blood pressure is the consequence of the genotypic state of the presumably vast network of genes involved in regulating the vascular tonus and hence the blood pressure. Recently it has been suggested that components of the sphingolipid metabolism pathways may be of importance in vascular physiology. The basic metabolic network of sphingolipids has been established, but the influence of genetic variations on the blood pressure is not known. In the approach presented here the impact of genetic variations in the sphingolipid metabolism is elucidated by a two-step procedure. First, the physiological heterogeneity of the blood pressure is resolved by a latent class/structural equation modelling to obtain homogenous subpopulations. Second, the genetic effects of the sphingolipid metabolism with focus on de novo synthesis of ceramide are analysed. The model does not assume a particular genetic model, but assumes that genes operate in networks. Results The stratification of the study population revealed that (at least 14 distinct subpopulations are present with different propensity to develop hypertension. Main effects of genes in the de novo synthesis of ceramides were rare (0.14% of all possible. However, epistasis was highly significant and prevalent amounting to approximately 70% of all possible two-gene interactions. The phenotypic variance explained by the ceramide synthesis network were substantial in 4 of the subpopulations amounting to more than 50% in the subpopulation in which all subjects were

  2. Ripple-Spreading Network Model Optimization by Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xiao-Bing Hu

    2013-01-01

    Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.

  3. Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Davide Caputo

    2010-01-01

    Full Text Available In recent years, wireless sensor networks have been attracting considerable research attention for a wide range of applications, but they still present significant network communication challenges, involving essentially the use of large numbers of resource-constrained nodes operating unattended and exposed to potential local failures. In order to maximize the network lifespan, in this paper, genetical swarm optimization (GSO is applied, a class of hybrid evolutionary techniques developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches; particle swarm optimization (PSO and genetic algorithms (GA. This procedure is here implemented to optimize the communication energy consumption in a wireless network by selecting the optimal multihop routing schemes, with a suitable hybridization of different routing criteria, confirming itself as a flexible and useful tool for engineering applications.

  4. Genetic Regulation of Transcriptional Variation in Natural Arabidopsis thaliana Accessions

    OpenAIRE

    Yanjun Zan; Xia Shen; Forsberg, Simon K. G.; Örjan Carlborg

    2016-01-01

    An increased knowledge of the genetic regulation of expression in Arabidopsis thaliana is likely to provide important insights about the basis of the plant’s extensive phenotypic variation. Here, we reanalyzed two publicly available datasets with genome-wide data on genetic and transcript variation in large collections of natural A. thaliana accessions. Transcripts from more than half of all genes were detected in the leaves of all accessions, and from nearly all annotated genes in at least o...

  5. Genetic Algorithms in Wireless Networking: Techniques, Applications, and Issues

    OpenAIRE

    Mehboob, Usama; Qadir, Junaid; Ali, Salman; Vasilakos, Athanasios

    2014-01-01

    In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorit...

  6. Phenotypic plasticity in Drosophila pigmentation caused by temperature sensitivity of a chromatin regulator network.

    Directory of Open Access Journals (Sweden)

    Jean-Michel Gibert

    2007-02-01

    Full Text Available Phenotypic plasticity is the ability of a genotype to produce contrasting phenotypes in different environments. Although many examples have been described, the responsible mechanisms are poorly understood. In particular, it is not clear how phenotypic plasticity is related to buffering, the maintenance of a constant phenotype against genetic or environmental variation. We investigate here the genetic basis of a particularly well described plastic phenotype: the abdominal pigmentation in female Drosophila melanogaster. Cold temperature induces a dark pigmentation, in particular in posterior segments, while higher temperature has the opposite effect. We show that the homeotic gene Abdominal-B (Abd-B has a major role in the plasticity of pigmentation in the abdomen. Abd-B plays opposite roles on melanin production through the regulation of several pigmentation enzymes. This makes the control of pigmentation very unstable in the posterior abdomen, and we show that the relative spatio-temporal expression of limiting pigmentation enzymes in this region of the body is thermosensitive. Temperature acts on melanin production by modulating a chromatin regulator network, interacting genetically with the transcription factor bric-à-brac (bab, a target of Abd-B and Hsp83, encoding the chaperone Hsp90. Genetic disruption of this chromatin regulator network increases the effect of temperature and the instability of the pigmentation pattern in the posterior abdomen. Colocalizations on polytene chromosomes suggest that BAB and these chromatin regulators cooperate in the regulation of many targets, including several pigmentation enzymes. We show that they are also involved in sex comb development in males and that genetic destabilization of this network is also strongly modulated by temperature for this phenotype. Thus, we propose that phenotypic plasticity of pigmentation is a side effect reflecting a global impact of temperature on epigenetic mechanisms

  7. Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex: functional and mechanistic implications.

    Directory of Open Access Journals (Sweden)

    Aaron R Wolen

    Full Text Available 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.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.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 over the ethanol

  8. eQTL Networks Reveal Complex Genetic Architecture in the Immature Soybean Seed

    Directory of Open Access Journals (Sweden)

    Yung-Tsi Bolon

    2014-03-01

    Full Text Available The complex network of regulatory factors and interactions involved in transcriptional regulation within the seed is not well understood. To evaluate gene expression regulation in the immature seed, we utilized a genetical genomics approach on a soybean [ (L. Merr.] recombinant inbred line (RIL population and produced a genome-wide expression quantitative trait loci (eQTL dataset. The validity of the dataset was confirmed by mapping the eQTL hotspot for flavonoid biosynthesis-related genes to a region containing repeats of chalcone synthase (CHS genes known to correspond to the soybean inhibitor locus that regulates seed color. We then identified eQTL for genes with seed-specific expression and discovered striking eQTL hotspots at distinct genomic intervals on chromosomes (Chr 20, 7, and 13. The main eQTL hotspot for transcriptional regulation of fatty acid biosynthesis genes also coincided with regulation of oleosin genes. Transcriptional upregulation of genesets from eQTL with opposite allelic effects were also found. Gene–eQTL networks were constructed and candidate regulatory genes were identified from these three key loci specific to seed expression and enriched in genes involved in seed oil accumulation. Our data provides new insight into the complex nature of gene networks in the immature soybean seed and the genetic architecture that contributes to seed development.

  9. GeneNetwork: A Toolbox for Systems Genetics.

    Science.gov (United States)

    Mulligan, Megan K; Mozhui, Khyobeni; Prins, Pjotr; Williams, Robert W

    2017-01-01

    The goal of systems genetics is to understand the impact of genetic variation across all levels of biological organization, from mRNAs, proteins, and metabolites, to higher-order physiological and behavioral traits. This approach requires the accumulation and integration of many types of data, and also requires the use of many types of statistical tools to extract relevant patterns of covariation and causal relations as a function of genetics, environment, stage, and treatment. In this protocol we explain how to use the GeneNetwork web service, a powerful and free online resource for systems genetics. We provide workflows and methods to navigate massive multiscalar data sets and we explain how to use an extensive systems genetics toolkit for analysis and synthesis. Finally, we provide two detailed case studies that take advantage of human and mouse cohorts to evaluate linkage between gene variants, addiction, and aging.

  10. Sparse time series chain graphical models for reconstructing genetic networks

    NARCIS (Netherlands)

    Abegaz, Fentaw; Wit, Ernst

    We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic networks from gene expression data parametrized by a precision matrix and autoregressive coefficient matrix. We consider the time steps as blocks or chains. The proposed approach explores patterns of

  11. GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE

    Directory of Open Access Journals (Sweden)

    Ashish Jain

    2012-07-01

    Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.

  12. A network characteristic that correlates environmental and genetic robustness.

    Directory of Open Access Journals (Sweden)

    Zeina Shreif

    2014-02-01

    Full Text Available As scientific advances in perturbing biological systems and technological advances in data acquisition allow the large-scale quantitative analysis of biological function, the robustness of organisms to both transient environmental stresses and inter-generational genetic changes is a fundamental impediment to the identifiability of mathematical models of these functions. An approach to overcoming this impediment is to reduce the space of possible models to take into account both types of robustness. However, the relationship between the two is still controversial. This work uncovers a network characteristic, transient responsiveness, for a specific function that correlates environmental imperturbability and genetic robustness. We test this characteristic extensively for dynamic networks of ordinary differential equations ranging up to 30 interacting nodes and find that there is a power-law relating environmental imperturbability and genetic robustness that tends to linearity as the number of nodes increases. Using our methods, we refine the classification of known 3-node motifs in terms of their environmental and genetic robustness. We demonstrate our approach by applying it to the chemotaxis signaling network. In particular, we investigate plausible models for the role of CheV protein in biochemical adaptation via a phosphorylation pathway, testing modifications that could improve the robustness of the system to environmental and/or genetic perturbation.

  13. Genetic variants regulating expression levels and isoform diversity during embryogenesis.

    Science.gov (United States)

    Cannavò, Enrico; Koelling, Nils; Harnett, Dermot; Garfield, David; Casale, Francesco P; Ciglar, Lucia; Gustafson, Hilary E; Viales, Rebecca R; Marco-Ferreres, Raquel; Degner, Jacob F; Zhao, Bingqing; Stegle, Oliver; Birney, Ewan; Furlong, Eileen E M

    2017-01-19

    Embryonic development is driven by tightly regulated patterns of gene expression, despite extensive genetic variation among individuals. Studies of expression quantitative trait loci (eQTL) indicate that genetic variation frequently alters gene expression in cell-culture models and differentiated tissues. However, the extent and types of genetic variation impacting embryonic gene expression, and their interactions with developmental programs, remain largely unknown. Here we assessed the effect of genetic variation on transcriptional (expression levels) and post-transcriptional (3' RNA processing) regulation across multiple stages of metazoan development, using 80 inbred Drosophila wild isolates, identifying thousands of developmental-stage-specific and shared QTL. Given the small blocks of linkage disequilibrium in Drosophila, we obtain near base-pair resolution, resolving causal mutations in developmental enhancers, validated transcription-factor-binding sites and RNA motifs. This fine-grain mapping uncovered extensive allelic interactions within enhancers that have opposite effects, thereby buffering their impact on enhancer activity. QTL affecting 3' RNA processing identify new functional motifs leading to transcript isoform diversity and changes in the lengths of 3' untranslated regions. These results highlight how developmental stage influences the effects of genetic variation and uncover multiple mechanisms that regulate and buffer expression variation during embryogenesis.

  14. Regulation of Genetically Modified Organisms in the European Union

    NARCIS (Netherlands)

    Grossman, M.R.; Bryan Endres, A.

    2000-01-01

    To be successful, laws that regulate genetically modified organisms (GMOs) must help society decide rationally when to pause and when to proceed in adopting new biotechnological developments. In the context of European Union (EU) institutions and lawmaking procedures, this article examines European

  15. Scheduling maintenance of electrical power transmission networks using genetic programming

    Energy Technology Data Exchange (ETDEWEB)

    Langdon, W.B.; Treleaven, P.C.

    1997-12-31

    The National Grid Company plc is responsible for the maintenance of the high voltage electricity transmission network in England and Wales. It must plan maintenance so as to minimise costs taking into account: (i) location and size of demand, (ii) generator capacities and availabilities, (iii) electricity carrying capacity of the remainder of the network, i.e. that part not undergoing maintenance. Previous work showed the combination of a genetic algorithm using an order or permutation chromosome combined with hand coded ``greedy`` optimisers can readily produce an optimal schedule for a four node test problem [10]. Following this the same GA has been used to find low cost schedules for the South Wales region of the UK high voltage power network. This paper describes the evolution of the best known schedule for the base South Wales problem using genetic programming starting from the hand coded heuristics used with the GA. (Author)

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    Koçer, Sabri; Canal, M Rahmi

    2011-08-01

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

  18. Electronic Circuit Analog of Synthetic Genetic Networks: Revisited

    CERN Document Server

    Hellen, Edward H

    2016-01-01

    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 ...

  19. Evolving neural networks using a genetic algorithm for heartbeat classification.

    Science.gov (United States)

    Sekkal, Mansouria; Chikh, Mohamed Amine; Settouti, Nesma

    2011-07-01

    This study investigates the effectiveness of a genetic algorithm (GA) evolved neural network (NN) classifier and its application to the classification of premature ventricular contraction (PVC) beats. As there is no standard procedure to determine the network structure for complicated cases, generally the design of the NN would be dependent on the user's experience. To prevent this problem, we propose a neural classifier that uses a GA for the determination of optimal connections between neurons for better recognition. The MIT-BIH arrhythmia database is employed to evaluate its accuracy. First, the topology of the NN was determined using the trial and error method. Second, the genetic operators were carefully designed to optimize the neural network structure. Performance and accuracy of the two techniques are presented and compared. Copyright © 2011 Informa UK, Ltd.

  20. Information theory and the ethylene genetic network.

    Science.gov (United States)

    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

  1. Ensemble learning of genetic networks from time-series expression data.

    Science.gov (United States)

    Nam, Dougu; Yoon, Sung Ho; Kim, Jihyun F

    2007-12-01

    Inferring genetic networks from time-series expression data has been a great deal of interest. In most cases, however, the number of genes exceeds that of data points which, in principle, makes it impossible to recover the underlying networks. To address the dimensionality problem, we apply the subset selection method to a linear system of difference equations. Previous approaches assign the single most likely combination of regulators to each target gene, which often causes over-fitting of the small number of data. Here, we propose a new algorithm, named LEARNe, which merges the predictions from all the combinations of regulators that have a certain level of likelihood. LEARNe provides more accurate and robust predictions than previous methods for the structure of genetic networks under the linear system model. We tested LEARNe for reconstructing the SOS regulatory network of Escherichia coli and the cell cycle regulatory network of yeast from real experimental data, where LEARNe also exhibited better performances than previous methods. The MATLAB codes are available upon request from the authors.

  2. Predicting genetic interactions with random walks on biological networks

    Directory of Open Access Journals (Sweden)

    Singh Ambuj K

    2009-01-01

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

  3. Genetic Regulation of Phenotypic Plasticity and Canalisation in Yeast Growth.

    Science.gov (United States)

    Yadav, Anupama; Dhole, Kaustubh; Sinha, Himanshu

    2016-01-01

    The ability of a genotype to show diverse phenotypes in different environments is called phenotypic plasticity. Phenotypic plasticity helps populations to evade extinctions in novel environments, facilitates adaptation and fuels evolution. However, most studies focus on understanding the genetic basis of phenotypic regulation in specific environments. As a result, while it's evolutionary relevance is well established, genetic mechanisms regulating phenotypic plasticity and their overlap with the environment specific regulators is not well understood. Saccharomyces cerevisiae is highly sensitive to the environment, which acts as not just external stimulus but also as signalling cue for this unicellular, sessile organism. We used a previously published dataset of a biparental yeast population grown in 34 diverse environments and mapped genetic loci regulating variation in phenotypic plasticity, plasticity QTL, and compared them with environment-specific QTL. Plasticity QTL is one whose one allele exhibits high plasticity whereas the other shows a relatively canalised behaviour. We mapped phenotypic plasticity using two parameters-environmental variance, an environmental order-independent parameter and reaction norm (slope), an environmental order-dependent parameter. Our results show a partial overlap between pleiotropic QTL and plasticity QTL such that while some plasticity QTL are also pleiotropic, others have a significant effect on phenotypic plasticity without being significant in any environment independently. Furthermore, while some plasticity QTL are revealed only in specific environmental orders, we identify large effect plasticity QTL, which are order-independent such that whatever the order of the environments, one allele is always plastic and the other is canalised. Finally, we show that the environments can be divided into two categories based on the phenotypic diversity of the population within them and the two categories have differential regulators of

  4. Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets.

    Directory of Open Access Journals (Sweden)

    Zhidong Tu

    Full Text Available Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6 and diabetes-susceptible (BTBR mouse strains made genetically obese by the Leptin(ob/ob mutation (Lep(ob. High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein-protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

  5. Patterns of genetic connectivity in invertebrates of temperate MPA networks

    Directory of Open Access Journals (Sweden)

    Patricia Marti-Puig

    2013-11-01

    Full Text Available Temperate reefs are among the most threatened marine habitats due to impacts caused by high density of human settlements, coastal development, pollution, fisheries and tourism. Networks of marine protected areas (MPAs are an important tool for ensuring long-term health and conservation of ecological processes in the marine environment. Design of the MPA network has to be based on deep understanding of spatial patterns of species distribution, and on the make-up of connectivity among populations. Most benthic invertebrates are sessile and/or sedentary in the adult phase, and their dispersal relies mainly on the gametes and/or larval behaviours. Genetic markers allow us to quantify gene flow and structuring among populations, and to infer patterns of genetic connectivity. Based on the information available in the peer reviewed literature on genetic connectivity in benthic invertebrates of temperate MPAs, we provide a comment about the gaps and the needs. Moreover, we propose a rationale to plan and optimise future studies on this topic. A conceptual framework for planning effective studies on genetic connectivity in an MPAs network is provided, including general recommendations on sampling design, key species and molecular markers to use.

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

    Science.gov (United States)

    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.

  7. Genetics of aplasia cutis reveal novel regulators of skin morphogenesis.

    Science.gov (United States)

    Marneros, Alexander G

    2015-03-01

    The molecular mechanisms that control skin morphogenesis are complex and only incompletely understood. Aplasia cutis manifests with localized skin defects at birth and is a feature in various syndromes. Identifying the genes that cause these genetic skin conditions provides the opportunity to define novel regulators of skin morphogenesis. Recently, human genetic approaches have led to the identification of aplasia cutis-causing mutations in genes that have previously not been implicated to have an important role in skin biology. These findings reveal novel molecular mechanisms that are involved in skin formation during development.

  8. Critical dynamics in genetic regulatory networks: examples from four kingdoms.

    Directory of Open Access Journals (Sweden)

    Enrique Balleza

    2008-06-01

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

  9. Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M. [Escuela Politecnica Superior, Departamento de Electrotecnia y Electronica, Avda. Menendez Pidal s/n, Cordoba (Spain); Martinez B, M. R.; Vega C, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Calle Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas (Mexico); Gallego D, E.; Lorente F, A. [Universidad Politecnica de Madrid, Departamento de Ingenieria Nuclear, ETSI Industriales, C. Jose Gutierrez Abascal 2, 28006 Madrid (Spain); Mendez V, R.; Los Arcos M, J. M.; Guerrero A, J. E., E-mail: morvymm@yahoo.com.m [CIEMAT, Laboratorio de Metrologia de Radiaciones Ionizantes, Avda. Complutense 22, 28040 Madrid (Spain)

    2011-02-15

    With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

  10. Output Regulation of Large-Scale Hydraulic Networks

    NARCIS (Netherlands)

    De Persis, C.; Jensen, T.N.; Ortega, R.; Wisniewski, R.

    The problem of output regulation for a class of hydraulic networks found in district heating systems is addressed in this brief. The results show that global asymptotic and semiglobal exponential output regulation is achievable using a set of decentralized proportional-integral controllers. The fact

  11. Co-regulation of pluripotency and genetic integrity at the genomic level

    Directory of Open Access Journals (Sweden)

    Daniel J. Cooper

    2014-11-01

    Full Text Available The Disposable Soma Theory holds that genetic integrity will be maintained at more pristine levels in germ cells than in somatic cells because of the unique role germ cells play in perpetuating the species. We tested the hypothesis that the same concept applies to pluripotent cells compared to differentiated cells. Analyses of transcriptome and cistrome databases, along with canonical pathway analysis and chromatin immunoprecipitation confirmed differential expression of DNA repair and cell death genes in embryonic stem cells and induced pluripotent stem cells relative to fibroblasts, and predicted extensive direct and indirect interactions between the pluripotency and genetic integrity gene networks in pluripotent cells. These data suggest that enhanced maintenance of genetic integrity is fundamentally linked to the epigenetic state of pluripotency at the genomic level. In addition, these findings demonstrate how a small number of key pluripotency factors can regulate large numbers of downstream genes in a pathway-specific manner.

  12. District Heating Network Design and Configuration Optimization with Genetic Algorithm

    DEFF Research Database (Denmark)

    Li, Hongwei; Svendsen, Svend

    2011-01-01

    the heating plant location is allowed to vary. The connection between the heat generation plant and the end users can be represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal DH distribution pipeline configuration, the genetic algorithm...... which handles the mixed integer nonlinear programming problem was chosen. The network configuration was represented through binary and integer encoding and was optimized in terms of the net present cost (NPC). The optimization results indicated that the optimal DH network configuration is determined...

  13. Inference of Vohradsky's models of genetic networks by solving two-dimensional function optimization problems.

    Directory of Open Access Journals (Sweden)

    Shuhei Kimura

    Full Text Available The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský's model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský's model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský's models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations.

  14. The genetic regulation of the terminating phase of liver regeneration

    DEFF Research Database (Denmark)

    Nygård, Ingvild E.; Mortensen, Kim E.; Hedegaard, Jakob

    2012-01-01

    Background After partial hepatectomy (PHx), the liver regeneration process terminates when the normal liver-mass/body-weight ratio of 2.5% has been re-established. To investigate the genetic regulation of the terminating phase of liver regeneration, we performed a 60% PHx in a porcine model. Liver...... biopsies were taken at the time of resection, after three weeks and upon termination the sixth week. Gene expression profiles were obtained using porcine oligonucleotide microarrays. Our study reveals the interactions between genes regulating the cell cycle, apoptosis and angiogenesis, and the role...

  15. A Systems genetics approach identifies gene regulatory networks associated with fatty acid composition in brassica rapa seed

    NARCIS (Netherlands)

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

    2016-01-01

    Fatty acids in seeds affect seed germination and seedling vigor, and fatty acid composition determines the quality of seed oil. In this study, quantitative trait locus (QTL) mapping of fatty acid and transcript abundance was integrated with gene network analysis to unravel the genetic regulation

  16. Regulating innovative crop technologies in Canada: the case of regulating genetically modified crops.

    Science.gov (United States)

    Smyth, Stuart; McHughen, Alan

    2008-04-01

    The advent of genetically modified crops in the late 1980s triggered a regulatory response to the relatively new field of plant genetic engineering. Over a 7-year period, a new regulatory framework was created, based on scientific principles that focused on risk mitigation. The process was transparent and deliberately sought the input of those involved in crop development from non-governmental organizations, industry, academia and federal research laboratories. The resulting regulations have now been in place for over a decade, and the resilience of the risk-mitigating regulations is evident as there has been no documented case of damage to either environment or human health.

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

    Science.gov (United States)

    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

  18. Genetic Regulation of Transcriptional Variation in Natural Arabidopsis thaliana Accessions

    Directory of Open Access Journals (Sweden)

    Yanjun Zan

    2016-08-01

    Full Text Available An increased knowledge of the genetic regulation of expression in Arabidopsis thaliana is likely to provide important insights about the basis of the plant’s extensive phenotypic variation. Here, we reanalyzed two publicly available datasets with genome-wide data on genetic and transcript variation in large collections of natural A. thaliana accessions. Transcripts from more than half of all genes were detected in the leaves of all accessions, and from nearly all annotated genes in at least one accession. Thousands of genes had high transcript levels in some accessions, but no transcripts at all in others, and this pattern was correlated with the genome-wide genotype. In total, 2669 eQTL were mapped in the largest population, and 717 of them were replicated in the other population. A total of 646 cis-eQTL-regulated genes that lacked detectable transcripts in some accessions was found, and for 159 of these we identified one, or several, common structural variants in the populations that were shown to be likely contributors to the lack of detectable RNA transcripts for these genes. This study thus provides new insights into the overall genetic regulation of global gene expression diversity in the leaf of natural A. thaliana accessions. Further, it also shows that strong cis-acting polymorphisms, many of which are likely to be structural variations, make important contributions to the transcriptional variation in the worldwide A. thaliana population.

  19. Revealing gene regulation and association through biological networks

    Science.gov (United States)

    This review had first summarized traditional methods used by plant breeders for genetic improvement, such as QTL analysis and transcriptomic analysis. With accumulating data, we can draw a network that comprises all possible links between members of a community, including protein–protein interaction...

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

    Science.gov (United States)

    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 .

  1. Bayesian network structure learning using chaos hybrid genetic algorithm

    Science.gov (United States)

    Shen, Jiajie; Lin, Feng; Sun, Wei; Chang, KC

    2012-06-01

    A new Bayesian network (BN) learning method using a hybrid algorithm and chaos theory is proposed. The principles of mutation and crossover in genetic algorithm and the cloud-based adaptive inertia weight were incorporated into the proposed simple particle swarm optimization (sPSO) algorithm to achieve better diversity, and improve the convergence speed. By means of ergodicity and randomicity of chaos algorithm, the initial network structure population is generated by using chaotic mapping with uniform search under structure constraints. When the algorithm converges to a local minimal, a chaotic searching is started to skip the local minima and to identify a potentially better network structure. The experiment results show that this algorithm can be effectively used for BN structure learning.

  2. Exploring regulation in tissues with eQTL networks.

    Science.gov (United States)

    Fagny, Maud; Paulson, Joseph N; Kuijjer, Marieke L; Sonawane, Abhijeet R; Chen, Cho-Yi; Lopes-Ramos, Camila M; Glass, Kimberly; Quackenbush, John; Platig, John

    2017-09-12

    Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.

  3. The genetic regulation of transcription in human endometrial tissue.

    Science.gov (United States)

    Fung, Jenny N; Girling, Jane E; Lukowski, Samuel W; Sapkota, Yadav; Wallace, Leanne; Holdsworth-Carson, Sarah J; Henders, Anjali K; Healey, Martin; Rogers, Peter A W; Powell, Joseph E; Montgomery, Grant W

    2017-04-01

    Do genetic effects regulate gene expression in human endometrium? This study demonstrated strong genetic effects on endometrial gene expression and some evidence for genetic regulation of gene expression in a menstrual cycle stage-specific manner. Genetic effects on expression levels for many genes are tissue specific. Endometrial gene expression varies across menstrual cycle stages and between individuals, but there are limited data on genetic control of expression in endometrium. We analysed genome-wide genotype and gene expression data to map cis expression quantitative trait loci (eQTL) in endometrium. We recruited 123 women of European ancestry. DNA samples from blood were genotyped on Illumina HumanCoreExome chips. Total RNA was extracted from endometrial tissues. Whole-transcriptome profiles were characterized using Illumina Human HT-12 v4.0 Expression Beadchips. We performed eQTL mapping with ~8 000 000 genotyped and imputed single nucleotide polymorphisms (SNPs) and 12 329 genes. We identified a total of 18 595 cis SNP-probe associations at a study-wide level of significance (P endometrial tissue were rs4902335 for CHURC1 (P = 1.05 × 10-32) and rs147253019 for ZP3 (P = 8.22 × 10-30). We further performed a context-specific eQTL analysis to investigate if genetic effects on gene expression regulation act in a menstrual cycle-specific manner. Interestingly, five cis-eQTLs were identified with a significant stage-by-genotype interaction. The strongest stage interaction was the eQTL for C10ORF33 (PYROXD2) with SNP rs2296438 (P = 2.0 × 10-4), where we observe a 2-fold difference in the average expression levels of heterozygous samples depending on the stage of the menstrual cycle. The summary eQTL results are publicly available to browse or download. A limitation of the present study was the relatively modest sample size. It was not powered to identify trans-eQTLs and larger sample sizes will also be needed to provide better power to detect cis-eQTLs and

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

    Science.gov (United States)

    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.

  5. Regulation of burstiness by network-driven activation

    CERN Document Server

    García-Pérez, Guillermo; Serrano, M Ángeles

    2014-01-01

    We prove that complex networks of interactions have the capacity to regulate and buffer unpredictable fluctuations in production events. We show that non-bursty network-driven activation dynamics can effectively regulate the level of burstiness in the production of nodes, which can be enhanced or reduced. Burstiness can be induced even when the endogenous inter-event time distribution of nodes' production is non-bursty. We found that hubs tend to be less controllable than low degree nodes, which are more susceptible to the networked regulatory effects. Our results have important implications for the analysis and engineering of bursty activity in a range of systems, from telecommunication networks to transcription and translation of genes into proteins in cells.

  6. Fuzzy Naive Bayesian for constructing regulated network with weights.

    Science.gov (United States)

    Zhou, Xi Y; Tian, Xue W; Lim, Joon S

    2015-01-01

    In the data mining field, classification is a very crucial technology, and the Bayesian classifier has been one of the hotspots in classification research area. However, assumptions of Naive Bayesian and Tree Augmented Naive Bayesian (TAN) are unfair to attribute relations. Therefore, this paper proposes a new algorithm named Fuzzy Naive Bayesian (FNB) using neural network with weighted membership function (NEWFM) to extract regulated relations and weights. Then, we can use regulated relations and weights to construct a regulated network. Finally, we will classify the heart and Haberman datasets by the FNB network to compare with experiments of Naive Bayesian and TAN. The experiment results show that the FNB has a higher classification rate than Naive Bayesian and TAN.

  7. Local and global responses in complex gene regulation networks

    Science.gov (United States)

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

    2009-04-01

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

  8. Measuring the regulation of keratin filament network dynamics

    OpenAIRE

    Moch, Marcin; Herberich, Gerlind; Aach, Til; Leube, Rudolf E.; Windoffer, Reinhard

    2013-01-01

    The organization of the keratin intermediate filament cytoskeleton is closely linked to epithelial function. To study keratin network plasticity and its regulation at different levels, tools are needed to localize and measure local network dynamics. In this paper, we present image analysis methods designed to determine the speed and direction of keratin filament motion and to identify locations of keratin filament polymerization and depolymerization at subcellular resolution. Using these meth...

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

    Directory of Open Access Journals (Sweden)

    Anna R. Docherty

    2015-08-01

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

  10. Application of Wireless Sensor Networks for Indoor Temperature Regulation

    DEFF Research Database (Denmark)

    Stojkoska, Biljana Risteska; Popovska Avramova, Andrijana; Chatzimisios, Periklis

    2014-01-01

    Wireless sensor networks take a major part in our everyday lives by enhancing systems for home automation, healthcare, temperature control, energy consumption monitoring, and so forth. In this paper we focus on a system used for temperature regulation for residential, educational, industrial......, and commercial premises, and so forth. We propose a framework for indoor temperature regulation and optimization using wireless sensor networks based on ZigBee platform. This paper considers architectural design of the system, as well as implementation guidelines. The proposed system favors methods that provide...

  11. Controversy over genetically modified organisms: the governing laws and regulations.

    Science.gov (United States)

    Keatley, K L

    2000-01-01

    Genetically Modified Organisms (GMOs) are increasingly becoming a topic of controversy in the U.S. and abroad. The public is questioning their safety and wanting the products labeled as genetically modified. There are other concerns from some of the scientific world and some government officials and organizations such as the Food & Agricultural Organization (FAO) that question whether adequate research has been done to qualify GMOs as safe for long-term use. Of particular concern are the allergenic properties, a GMO may impart, possible transfer effects of antibiotic resistance (given that antibiotic resistant marker genes are used for many GMOs), the expression of previously unexpressed traits, and the drift of pollen from genetically modified crops. It has also been noted that the laws and regulations governing the biotechnology world are outdated, are not comprehensive, and span too many agencies. The primary agencies currently regulating biotechnology are the U.S. Department of Agriculture (USDA), the Food and Drug Administration (FDA), and the Environmental Protection Agency (EPA).

  12. Genetic-epigenetic interaction modulates μ-opioid receptor regulation.

    Science.gov (United States)

    Oertel, Bruno G; Doehring, Alexandra; Roskam, Bianca; Kettner, Mattias; Hackmann, Nadja; Ferreirós, Nerea; Schmidt, Peter H; Lötsch, Jörn

    2012-11-01

    Genetic and epigenetic mechanisms play important roles in protein expression, although at different levels. Genetic variations can alter CpG sites and thus influence the epigenetic regulation of mRNA expression, providing an increasingly recognized mechanism of functional consequences of genetic polymorphisms. One of those genetic effects is the association of reduced μ-opioid receptor expression with the functional genetic variant N40D (OPRM1 118A>G, rs1799971) that causes an amino acid exchange in the extracellular terminal of the μ-opioid receptor. We report that the nucleotide exchange at gene position +118 introduces a new CpG-methylation site into the OPRM1 DNA at position +117. This leads to an enhanced methylation of the OPRM1 DNA at this site and downstream. This epigenetic mechanism impedes μ-opioid receptor upregulation in brain tissue of Caucasian chronic opiate addicts, assessed postmortem. While in wild-type subjects, a reduced signalling efficiency associated with chronic heroin exposure was compensated by an increased receptor density, this upregulation was absent in carriers of the 118G receptor variant due to a diminished OPRM1 mRNA transcription. Thus, the OPRM1 118A>G SNP variant not only reduces µ-opioid receptor signalling efficiency, but, by a genetic-epigenetic interaction, reduces opioid receptor expression and therefore, depletes the opioid system of a compensatory reaction to chronic exposure. This demonstrates that a change in the genotype can cause a change in the epigenotype with major functional consequences.

  13. Nitrogen deprivation promotes Populus root growth through global transcriptome reprogramming and activation of hierarchical genetic networks.

    Science.gov (United States)

    Wei, Hairong; Yordanov, Yordan S; Georgieva, Tatyana; Li, Xiang; Busov, Victor

    2013-10-01

    We show a distinct and previously poorly characterized response of poplar (Populus tremula × Populus alba) roots to low nitrogen (LN), which involves activation of root growth and significant transcriptome reprogramming. Analysis of the temporal patterns of enriched ontologies among the differentially expressed genes revealed an ordered assembly of functionally cohesive biological events that aligned well with growth and morphological responses. A core set of 28 biological processes was significantly enriched across the whole studied period and 21 of these were also enriched in the roots of Arabidopsis thaliana during the LN response. More than half (15) of the 28 processes belong to gene ontology (GO) terms associated with signaling and signal transduction pathways, suggesting the presence of conserved signaling mechanisms triggered by LN. A reconstruction of genetic regulatory network analysis revealed a sub-network centered on a PtaNAC1 (P. tremula × alba NAM, ATAF, CUC 1) transcription factor. PtaNAC1 root-specific up-regulation increased root biomass and significantly changed the expression of the connected hub genes specifically under LN. Our results provide evidence that the root response to LN involves hierarchically structured genetic networks centered on key regulatory factors. Targeting these factors via genetic engineering or breeding approaches can allow dynamic adjustment of root architecture in response to variable nitrogen availabilities in the soil. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

  14. The regulation of direct-to-consumer genetic tests.

    Science.gov (United States)

    Kaye, Jane

    2008-10-15

    The past year has been marked by the emergence of several companies, such as 23andMe, deCODEME, Navigenics and Knome, offering tests using genome-wide technology direct to consumers over the internet. On the basis of the published research findings of GWAS and other studies, these companies will calculate an individual's risk to a number of common diseases, without the necessity of going through a medical practitioner. One of the significant challenges of direct-to-consumer testing is that it shifts the control of genetic testing from the clinical domain and medical professionals into the hands of consumers. No longer is genetic testing being carried out solely for medical reasons, by specialists in clinical genetics. Testing is now being used to empower consumers and can be used 'to shed new light on your distant ancestors, your close family and most of all, yourself' (23andMe). Such information can be shared with family and friends for 'fun', as part of the new 'recreational genomics'. Direct-to-consumer testing challenges many of the assumptions that underpin current practice surrounding genetic tests while at the same time exposing the deficiencies in the current regulatory frameworks to regulate this area. The purpose of this paper is to explore some of these issues, at a time when the science and the law are changing rapidly.

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

    Science.gov (United States)

    2013-03-01

    that model GRNs from real data. PRISM, a probabilistic learning framework based on B- prolog , was used to program the Bayesian networks. Instead of...intelligence, prolog , gene regulation, “Raf” pathway 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 28 19a...probabilistic logic paradigm. PRISM is a probabilistic logical framework based on B- prolog the language extends the Horn clauses to include random variables

  16. A Network of Local and Redundant Gene Regulation Governs Arabidopsis Seed Maturation

    Science.gov (United States)

    To, Alexandra; Valon, Christiane; Savino, Gil; Guilleminot, Jocelyne; Devic, Martine; Giraudat, Jérôme; Parcy, François

    2006-01-01

    In Arabidopsis thaliana, four major regulators (ABSCISIC ACID INSENSITIVE3 [ABI3], FUSCA3 [FUS3], LEAFY COTYLEDON1 [LEC1], and LEC2) control most aspects of seed maturation, such as accumulation of storage compounds, cotyledon identity, acquisition of desiccation tolerance, and dormancy. The molecular basis for complex genetic interactions among these regulators is poorly understood. By analyzing ABI3 and FUS3 expression in various single, double, and triple maturation mutants, we have identified multiple regulatory links among all four genes. We found that one of the major roles of LEC2 was to upregulate FUS3 and ABI3. The lec2 mutation is responsible for a dramatic decrease in ABI3 and FUS3 expression, and most lec2 phenotypes can be rescued by ABI3 or FUS3 constitutive expression. In addition, ABI3 and FUS3 positively regulate themselves and each other, thereby forming feedback loops essential for their sustained and uniform expression in the embryo. Finally, LEC1 also positively regulates ABI3 and FUS3 in the cotyledons. Most of the genetic controls discovered were found to be local and redundant, explaining why they had previously been overlooked. This works establishes a genetic framework for seed maturation, organizing the key regulators of this process into a hierarchical network. In addition, it offers a molecular explanation for the puzzling variable features of lec2 mutant embryos. PMID:16731585

  17. Computational Genetic Regulatory Networks Evolvable, Self-organizing Systems

    CERN Document Server

    Knabe, Johannes F

    2013-01-01

    Genetic Regulatory Networks (GRNs) in biological organisms are primary engines for cells to enact their engagements with environments, via incessant, continually active coupling. In differentiated multicellular organisms, tremendous complexity has arisen in the course of evolution of life on earth. Engineering and science have so far achieved no working system that can compare with this complexity, depth and scope of organization. Abstracting the dynamics of genetic regulatory control to a computational framework in which artificial GRNs in artificial simulated cells differentiate while connected in a changing topology, it is possible to apply Darwinian evolution in silico to study the capacity of such developmental/differentiated GRNs to evolve. In this volume an evolutionary GRN paradigm is investigated for its evolvability and robustness in models of biological clocks, in simple differentiated multicellularity, and in evolving artificial developing 'organisms' which grow and express an ontogeny starting fr...

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

    Science.gov (United States)

    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. Copyright © 2013 Wiley Periodicals, Inc.

  19. Multilayer Optimization of Heterogeneous Networks Using Grammatical Genetic Programming.

    Science.gov (United States)

    Fenton, Michael; Lynch, David; Kucera, Stepan; Claussen, Holger; O'Neill, Michael

    2017-09-01

    Heterogeneous cellular networks are composed of macro cells (MCs) and small cells (SCs) in which all cells occupy the same bandwidth. Provision has been made under the third generation partnership project-long term evolution framework for enhanced intercell interference coordination (eICIC) between cell tiers. Expanding on previous works, this paper instruments grammatical genetic programming to evolve control heuristics for heterogeneous networks. Three aspects of the eICIC framework are addressed including setting SC powers and selection biases, MC duty cycles, and scheduling of user equipments (UEs) at SCs. The evolved heuristics yield minimum downlink rates three times higher than a baseline method, and twice that of a state-of-the-art benchmark. Furthermore, a greater number of UEs receive transmissions under the proposed scheme than in either the baseline or benchmark cases.

  20. High Performance Data mining by Genetic Neural Network

    Directory of Open Access Journals (Sweden)

    Dadmehr Rahbari

    2013-10-01

    Full Text Available Data mining in computer science is the process of discovering interesting and useful patterns and relationships in large volumes of data. Most methods for mining problems is based on artificial intelligence algorithms. Neural network optimization based on three basic parameters topology, weights and the learning rate is a powerful method. We introduce optimal method for solving this problem. In this paper genetic algorithm with mutation and crossover operators change the network structure and optimized that. Dataset used for our work is stroke disease with twenty features that optimized number of that achieved by new hybrid algorithm. Result of this work is very well incomparison with other similar method. Low present of error show that our method is our new approach to efficient, high-performance data mining problems is introduced.

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

    Science.gov (United States)

    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

  2. Wireless Sensor Networks Framework for Indoor Temperature Regulation

    DEFF Research Database (Denmark)

    Stojkoska, Biljana; Popovska Avramova, Andrijana

    2013-01-01

    Wireless Sensor Networks take a major part in our everyday lives by enhancing systems for home automation, health-care, temperature control, energy consumption monitoring etc. In this paper we focus on a system used for temperature regulation for homes, educational, industrial, commercial premises...

  3. Laboratory experiments on the regulation of European network industries

    NARCIS (Netherlands)

    Henze, B.

    2016-01-01

    The main objective of this thesis is to use economic laboratory experiments in order to evaluate the performance of regulatory schemes and market designs in addressing challenges encountered in the regulation of European network industries. Chapter 2 assesses whether regulatory holidays and Long

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

    Science.gov (United States)

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

    2008-04-11

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

  5. Robust cooperative output regulation of heterogeneous Lur'e networks

    NARCIS (Netherlands)

    Zhang, Fan; Trentelman, Harry L.; Scherpen, Jacquelien M. A.

    2017-01-01

    In this paper, we study robust cooperative output regulation problems for a directed network of Lur'e systems that consist of a nominal linear dynamics with an unknown static nonlinearity around it through negative feedback. We assume that the linear part of each agent is identical, but the

  6. Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

    Directory of Open Access Journals (Sweden)

    Xiaodong Cai

    Full Text Available Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL, for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL based scheme, and the QTL-directed dependency graph (QDG method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.

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

    Science.gov (United States)

    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. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Uncovering transcriptional regulation of metabolism by using metabolic network topology

    DEFF Research Database (Denmark)

    Patil, Kiran Raosaheb; Nielsen, Jens

    2005-01-01

    therefore developed an algorithm that is based on hypothesis-driven data analysis to uncover the transcriptional regulatory architecture of metabolic networks. By using information on the metabolic network topology from genome-scale metabolic reconstruction, we show that it is possible to reveal patterns...... or environmental perturbations. We find that cells respond to perturbations by changing the expression pattern of several genes involved in the specific part(s) of the metabolism in which a perturbation is introduced. These changes then are propagated through the metabolic network because of the highly connected......Cellular response to genetic and environmental perturbations is often reflected and/or mediated through changes in the metabolism, because the latter plays a key role in providing Gibbs free energy and precursors for biosynthesis. Such metabolic changes are often exerted through transcriptional...

  9. Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling

    Science.gov (United States)

    Abrahart, R. J.

    2004-05-01

    This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and

  10. Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction

    Science.gov (United States)

    Yeger-Lotem, Esti; Sattath, Shmuel; Kashtan, Nadav; Itzkovitz, Shalev; Milo, Ron; Pinter, Ron Y.; Alon, Uri; Margalit, Hanah

    2004-04-01

    Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.

  11. 75 FR 8299 - Draft Environmental Impact Statement; Determination of Regulated Status of Alfalfa Genetically...

    Science.gov (United States)

    2010-02-24

    ... Regulated Status of Alfalfa Genetically Engineered for Tolerance to the Herbicide Glyphosate AGENCY: Animal... and Forage Genetics International alfalfa lines designated as events J101 and J163 as regulated... determination on the status of the Monsanto Company and Forage Genetics International alfalfa lines designated...

  12. DMPD: The Lps locus: genetic regulation of host responses to bacteriallipopolysaccharide. [Dynamic Macrophage Pathway CSML Database

    Lifescience Database Archive (English)

    Full Text Available 10669111 The Lps locus: genetic regulation of host responses to bacteriallipopolysa...ccharide. Qureshi ST, Gros P, Malo D. Inflamm Res. 1999 Dec;48(12):613-20. (.png) (.svg) (.html) (.csml) Show The Lps locus: genetic... regulation of host responses to bacteriallipopolysaccharide. PubmedID 10669111 Title The Lps locus: genetic

  13. The Congenital Heart Disease Genetic Network Study: Cohort description.

    Science.gov (United States)

    Hoang, Thanh T; Goldmuntz, Elizabeth; Roberts, Amy E; Chung, Wendy K; Kline, Jennie K; Deanfield, John E; Giardini, Alessandro; Aleman, Adolfo; Gelb, Bruce D; Mac Neal, Meghan; Porter, George A; Kim, Richard; Brueckner, Martina; Lifton, Richard P; Edman, Sharon; Woyciechowski, Stacy; Mitchell, Laura E; Agopian, A J

    2018-01-01

    The Pediatric Cardiac Genomics Consortium (PCGC) designed the Congenital Heart Disease Genetic Network Study to provide phenotype and genotype data for a large congenital heart defects (CHDs) cohort. This article describes the PCGC cohort, overall and by major types of CHDs (e.g., conotruncal defects) and subtypes of conotrucal heart defects (e.g., tetralogy of Fallot) and left ventricular outflow tract obstructions (e.g., hypoplastic left heart syndrome). Cases with CHDs were recruited through ten sites, 2010-2014. Information on cases (N = 9,727) and their parents was collected through interviews and medical record abstraction. Four case characteristics, eleven parental characteristics, and thirteen parent-reported neurodevelopment outcomes were summarized using counts and frequencies and compared across CHD types and subtypes. Eleven percent of cases had a genetic diagnosis. Among cases without a genetic diagnosis, the majority had conotruncal heart defects (40%) or left ventricular outflow tract obstruction (21%). Across CHD types, there were significant differences (ptypes and subtypes, provides a reference work for investigators who are interested in collaborating with or using publically available resources from the PCGC.

  14. Intracellular spatial localization regulated by the microtubule network.

    Directory of Open Access Journals (Sweden)

    Jing Chen

    Full Text Available The commonly recognized mechanisms for spatial regulation inside the cell are membrane-bounded compartmentalization and biochemical association with subcellular organelles. We use computational modeling to investigate another spatial regulation mechanism mediated by the microtubule network in the cell. Our results demonstrate that the mitotic spindle can impose strong sequestration and concentration effects on molecules with binding affinity for microtubules, especially dynein-directed cargoes. The model can recapitulate the essence of three experimental observations on distinct microtubule network morphologies: the sequestration of germ plasm components by the mitotic spindles in the Drosophila syncytial embryo, the asymmetric cell division initiated by the time delay in centrosome maturation in the Drosophila neuroblast, and the diffusional block between neighboring energids in the Drosophila syncytial embryo. Our model thus suggests that the cell cycle-dependent changes in the microtubule network are critical for achieving different spatial regulation effects. The microtubule network provides a spatially extensive docking platform for molecules and gives rise to a "structured cytoplasm", in contrast to a free and fluid environment.

  15. Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation

    National Research Council Canada - National Science Library

    Zhu, Jun; Sova, Pavel; Xu, Qiuwei; Dombek, Kenneth M; Xu, Ethan Y; Vu, Heather; Tu, Zhidong; Brem, Rachel B; Bumgarner, Roger E; Schadt, Eric E

    2012-01-01

    Cells employ multiple levels of regulation, including transcriptional and translational regulation, that drive core biological processes and enable cells to respond to genetic and environmental changes...

  16. Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network.

    Science.gov (United States)

    Li, Ruowang; Dudek, Scott M; Kim, Dokyoon; Hall, Molly A; Bradford, Yuki; Peissig, Peggy L; Brilliant, Murray H; Linneman, James G; McCarty, Catherine A; Bao, Le; Ritchie, Marylyn D

    2016-01-01

    The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing. For this study, we present a new algorithm, Grammatical Evolution Bayesian Network (GEBN) that utilizes Bayesian Networks to identify interactions in the data, and at the same time, uses an evolutionary algorithm to reduce the computational cost associated with network optimization. GEBN excelled in simulation studies where the data contained main effects and interaction effects. We also applied GEBN to a Type 2 diabetes (T2D) dataset obtained from the Marshfield Personalized Medicine Research Project (PMRP). We were able to identify genetic interactions for T2D cases and controls and use information from those interactions to classify T2D samples. We obtained an average testing area under the curve (AUC) of 86.8 %. We also identified several interacting genes such as INADL and LPP that are known to be associated with T2D. Developing the computational tools to explore genetic associations beyond main

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

    Energy Technology Data Exchange (ETDEWEB)

    Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)

    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.

  18. Genetic control of lithium sensitivity and regulation of inositol biosynthetic genes.

    Directory of Open Access Journals (Sweden)

    Jason King

    2010-06-01

    Full Text Available Lithium (Li(+ is a common treatment for bipolar mood disorder, a major psychiatric illness with a lifetime prevalence of more than 1%. Risk of bipolar disorder is heavily influenced by genetic predisposition, but is a complex genetic trait and, to date, genetic studies have provided little insight into its molecular origins. An alternative approach is to investigate the genetics of Li(+ sensitivity. Using the social amoeba Dictyostelium, we previously identified prolyl oligopeptidase (PO as a modulator of Li(+ sensitivity. In a link to the clinic, PO enzyme activity is altered in bipolar disorder patients. Further studies demonstrated that PO is a negative regulator of inositol(1,4,5trisphosphate (IP(3 synthesis, a Li(+ sensitive intracellular signal. However, it was unclear how PO could influence either Li(+ sensitivity or risk of bipolar disorder. Here we show that in both Dictyostelium and cultured human cells PO acts via Multiple Inositol Polyphosphate Phosphatase (Mipp1 to control gene expression. This reveals a novel, gene regulatory network that modulates inositol metabolism and Li(+ sensitivity. Among its targets is the inositol monophosphatase gene IMPA2, which has also been associated with risk of bipolar disorder in some family studies, and our observations offer a cellular signalling pathway in which PO activity and IMPA2 gene expression converge.

  19. Wise regulates bone deposition through genetic interactions with Lrp5.

    Directory of Open Access Journals (Sweden)

    Debra L Ellies

    Full Text Available In this study using genetic approaches in mouse we demonstrate that the secreted protein Wise plays essential roles in regulating early bone formation through its ability to modulate Wnt signaling via interactions with the Lrp5 co-receptor. In Wise-/- mutant mice we find an increase in the rate of osteoblast proliferation and a transient increase in bone mineral density. This change in proliferation is dependent upon Lrp5, as Wise;Lrp5 double mutants have normal bone mass. This suggests that Wise serves as a negative modulator of Wnt signaling in active osteoblasts. Wise and the closely related protein Sclerostin (Sost are expressed in osteoblast cells during temporally distinct early and late phases in a manner consistent with the temporal onset of their respective increased bone density phenotypes. These data suggest that Wise and Sost may have common roles in regulating bone development through their ability to control the balance of Wnt signaling. We find that Wise is also required to potentiate proliferation in chondrocytes, serving as a potential positive modulator of Wnt activity. Our analyses demonstrate that Wise plays a key role in processes that control the number of osteoblasts and chondrocytes during bone homeostasis and provide important insight into mechanisms regulating the Wnt pathway during skeletal development.

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

    Directory of Open Access Journals (Sweden)

    Nuno D Pires

    2016-01-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Novel transcriptional networks regulated by CLOCK in human neurons.

    Science.gov (United States)

    Fontenot, Miles R; Berto, Stefano; Liu, Yuxiang; Werthmann, Gordon; Douglas, Connor; Usui, Noriyoshi; Gleason, Kelly; Tamminga, Carol A; Takahashi, Joseph S; Konopka, Genevieve

    2017-11-01

    The molecular mechanisms underlying human brain evolution are not fully understood; however, previous work suggested that expression of the transcription factor CLOCK in the human cortex might be relevant to human cognition and disease. In this study, we investigated this novel transcriptional role for CLOCK in human neurons by performing chromatin immunoprecipitation sequencing for endogenous CLOCK in adult neocortices and RNA sequencing following CLOCK knockdown in differentiated human neurons in vitro. These data suggested that CLOCK regulates the expression of genes involved in neuronal migration, and a functional assay showed that CLOCK knockdown increased neuronal migratory distance. Furthermore, dysregulation of CLOCK disrupts coexpressed networks of genes implicated in neuropsychiatric disorders, and the expression of these networks is driven by hub genes with human-specific patterns of expression. These data support a role for CLOCK-regulated transcriptional cascades involved in human brain evolution and function. © 2017 Fontenot et al.; Published by Cold Spring Harbor Laboratory Press.

  4. Carotenoids in staple cereals: Metabolism, regulation, and genetic manipulation

    Directory of Open Access Journals (Sweden)

    shengnan zhai

    2016-08-01

    Full Text Available Carotenoids play a critical role in animal and human health. Animals and humans are unable to synthesize carotenoids de novo, and therefore rely upon diet as sources of these compounds. However, major staple cereals often contain only small amounts of carotenoids in their grain. Consequently, there is considerable interest in genetic manipulation of carotenoid content in cereal grain. In this review, we focus on carotenoid metabolism and regulation in non-green plant tissues, as well as genetic manipulation in staple cereals such as rice, maize, and wheat. Significant progress has been made in three aspects: (1 seven carotenogenes play vital roles in carotenoid regulation in non-green plant tissues, including DXS (1-deoxyxylulose-5-phosphate synthase influencing isoprenoid precursor supply, PSY (phytoene synthase, LCYB (β-cyclase and LCYE (ε-cyclase controlling biosynthesis, HYDB (1-hydroxy-2-methyl-2-(E-butenyl 4-diphosphate reductase and CCDs (carotenoid cleavage dioxygenases responsible for degradation, and OR (orange conditioning sequestration sink; (2 pro-vitamin A-biofortified crops, such as rice and maize, were developed by either metabolic engineering or marker-assisted breeding; (3 QTLs for carotenoid content on chromosomes 3B, 7A, and 7B were consistently identified, eight carotenogenes including 23 loci were detected, and ten gene-specific markers for carotenoid accumulation were developed and applied in wheat improvement. A comprehensive and deeper understanding of the regulatory mechanisms of carotenoid metabolism in crops will be benefitical in improving our precision in improving carotenoid contents. Genomic selection and gene editing are emerging as transformative technologies for vitamin A biofortification.

  5. Genetically modified organisms in light of domestic and world regulations

    Directory of Open Access Journals (Sweden)

    Nikolić Zorica

    2007-01-01

    Full Text Available At the same time as development and registration of new genetic modification of plant species have intensified, the number of countries in which they are grown has also increased considerably. Genetically modified crops were grown in 22 countries in 2006, six of which were in European Union. Protocol on Biosafety, known as Cartagena protocol was adopted at the international level in February, 2000. Presence, but not growing of GMO in food is allowed in many countries, while in some others labeling of food origination from GMO is obligatory. Labeling is obligatory in European Union, Australia, New Zealand, Japan, Norway, Switzerland and some others. In our country the Law on GMO and sub-law acts were conceived according to EU regulative. The terms for limited use, production, trade of GMO and GMO products have been prescribed. Validation and standardization of GMO testing methods are now being implemented. It is expected that the analytical GMO methods will soon be harmonized at the international level. .

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

    Science.gov (United States)

    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.

  7. Sirtuins as regulators of the yeast metabolic network

    Directory of Open Access Journals (Sweden)

    Markus eRalser

    2012-03-01

    Full Text Available There is growing evidence that the metabolic network is an integral regulator of cellularphysiology. Dynamic changes in metabolite concentrations, metabolic flux, or networktopology act as reporters of biological or environmental signals, and are required for the cellto trigger an appropriate biological reaction. Changes in the metabolic network are recognizedby specific sensory macromolecules and translated into a transcriptional or translationalresponse. The protein family of sirtuins, discovered more than 30 years ago as regulators ofsilent chromatin, seems to fulfill the role of a metabolic sensor during aging and conditions ofcaloric restriction. NAD+/NADH interconverting metabolic enzymes glyceraldehyde-3-phosphate dehydrogenase and alcohol dehydrogenase, as well as enzymes involved inNAD(H, synthesis provide or deprive NAD+ in close proximity to Sir2. This influence sirtuinactivity, and facilitates a dynamic response of the metabolic network to changes inmetabolism with effects on physiology and aging. The molecular network downstream Sir2,however, is complex. In just two orders, Sir2’s metabolism-related interactions span half ofthe yeast proteome, and are connected with virtually every physiological process. Thus,although it is fundamental to analyze single molecular mechanisms, it is at the same timecrucial to consider this genome-scale complexity when correlating single molecular eventswith phenotypes such as aging, cell growth, or stress resistance.

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

    Science.gov (United States)

    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...

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

    Science.gov (United States)

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

    2016-05-01

    Gibberellins (GAs) regulate a wide range of important processes in plant growth and development, including photosynthesis. However, the mechanism by which GAs regulate photosynthesis remains to be understood. Here, we used multi-gene association to investigate the effect of genes in the GA-responsive pathway, as constructed by RNA sequencing, on photosynthesis, growth, and wood property traits, in a population of 435 Populus tomentosa By analyzing changes in the transcriptome following GA treatment, we identified many key photosynthetic genes, in agreement with the observed increase in measurements of photosynthesis. Regulatory motif enrichment analysis revealed that 37 differentially expressed genes related to photosynthesis shared two essential GA-related cis-regulatory elements, the GA response element and the pyrimidine box. Thus, we constructed a GA-responsive pathway consisting of 47 genes involved in regulating photosynthesis, including GID1, RGA, GID2, MYBGa, and 37 photosynthetic differentially expressed genes. Single nucleotide polymorphism (SNP)-based association analysis showed that 142 SNPs, representing 40 candidate genes in this pathway, were significantly associated with photosynthesis, growth, and wood property traits. Epistasis analysis uncovered interactions between 310 SNP-SNP pairs from 37 genes in this pathway, revealing possible genetic interactions. Moreover, a structural gene-gene matrix based on a time-course of transcript abundances provided a better understanding of the multi-gene pathway affecting photosynthesis. The results imply a functional role for these genes in mediating photosynthesis, growth, and wood properties, demonstrating the potential of combining transcriptome-based regulatory pathway construction and genetic association approaches to detect the complex genetic networks underlying quantitative traits. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights

  10. Transcriptional regulation of the carbohydrate utilization network in Thermotoga maritima

    Directory of Open Access Journals (Sweden)

    Dmitry A Rodionov

    2013-08-01

    Full Text Available Hyperthermophilic bacteria from the Thermotogales lineage can produce hydrogen by fermenting a wide range of carbohydrates. Previous experimental studies identified a large fraction of genes committed to carbohydrate degradation and utilization in the model bacterium Thermotoga maritima. Knowledge of these genes enabled comprehensive reconstruction of biochemical pathways comprising the carbohydrate utilization network. However, transcriptional factors (TFs and regulatory mechanisms driving this network remained largely unknown. Here, we used an integrated approach based on comparative analysis of genomic and transcriptomic data for the reconstruction of the carbohydrate utilization regulatory networks in 11 Thermotogales genomes. We identified DNA-binding motifs and regulons for 19 orthologous TFs in the Thermotogales. The inferred regulatory network in T. maritima contains 181 genes encoding TFs, sugar catabolic enzymes and ABC-family transporters. In contrast to many previously described bacteria, a transcriptional regulation strategy of Thermotoga does not employ global regulatory factors. The reconstructed regulatory network in T. maritima was validated by gene expression profiling on a panel of mono- and disaccharides and by in vitro DNA-binding assays. The observed upregulation of genes involved in catabolism of pectin, trehalose, cellobiose, arabinose, rhamnose, xylose, glucose, galactose, and ribose showed a strong correlation with the UxaR, TreR, BglR, CelR, AraR, RhaR, XylR, GluR, GalR, and RbsR regulons. Ultimately, this study elucidated the transcriptional regulatory network and mechanisms controlling expression of carbohydrate utilization genes in T. maritima. In addition to improving the functional annotations of associated transporters and catabolic enzymes, this research provides novel insights into the evolution of regulatory networks in Thermotogales.

  11. Genetic Algorithm for Restricted Maximum k-Satisfiability in the Hopfield Network

    Directory of Open Access Journals (Sweden)

    Mohd Shareduwan Bin Mohd Kasihmuddin

    2016-12-01

    Full Text Available The restricted Maximum k-Satisfiability MAX- kSAT is an enhanced Boolean satisfiability counterpart that has attracted numerous amount of research. Genetic algorithm has been the prominent optimization heuristic algorithm to solve constraint optimization problem. The core motivation of this paper is to introduce Hopfield network incorporated with genetic algorithm in solving MAX-kSAT problem. Genetic algorithm will be integrated with Hopfield network as a single network. The proposed method will be compared with the conventional Hopfield network. The results demonstrate that Hopfield network with genetic algorithm outperforms conventional Hopfield networks. Furthermore, the outcome had provided a solid evidence of the robustness of our proposed algorithms to be used in other satisfiability problem.

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

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2006-04-01

    Full Text Available 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.

  13. DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models.

    Directory of Open Access Journals (Sweden)

    Alex Greenfield

    2010-10-01

    Full Text Available Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge.We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations.Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations. Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/.

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

    Science.gov (United States)

    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

  15. A co-association network analysis of the genetic determination of pig conformation, growth and fatness.

    Directory of Open Access Journals (Sweden)

    Anna Puig-Oliveras

    Full Text Available 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.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.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.

  16. Genetic alterations of m6A regulators predict poorer survival in acute myeloid leukemia.

    Science.gov (United States)

    Kwok, Chau-To; Marshall, Amy D; Rasko, John E J; Wong, Justin J L

    2017-02-02

    Methylation of N6 adenosine (m6A) is known to be important for diverse biological processes including gene expression control, translation of protein, and messenger RNA (mRNA) splicing. However, its role in the development of human cancers is poorly understood. By analyzing datasets from the Cancer Genome Atlas Research Network (TCGA) acute myeloid leukemia (AML) study, we discover that mutations and/or copy number variations of m6A regulatory genes are strongly associated with the presence of TP53 mutations in AML patients. Further, our analyses reveal that alterations in m6A regulatory genes confer a worse survival in AML. Our work indicates that genetic alterations of m6A regulatory genes may cooperate with TP53 and/or its regulator/downstream targets in the pathogenesis and/or maintenance of AML.

  17. Genetic alterations of m6A regulators predict poorer survival in acute myeloid leukemia

    Directory of Open Access Journals (Sweden)

    Chau-To Kwok

    2017-02-01

    Full Text Available Abstract Methylation of N6 adenosine (m6A is known to be important for diverse biological processes including gene expression control, translation of protein, and messenger RNA (mRNA splicing. However, its role in the development of human cancers is poorly understood. By analyzing datasets from the Cancer Genome Atlas Research Network (TCGA acute myeloid leukemia (AML study, we discover that mutations and/or copy number variations of m6A regulatory genes are strongly associated with the presence of TP53 mutations in AML patients. Further, our analyses reveal that alterations in m6A regulatory genes confer a worse survival in AML. Our work indicates that genetic alterations of m6A regulatory genes may cooperate with TP53 and/or its regulator/downstream targets in the pathogenesis and/or maintenance of AML.

  18. Design of Supply Chain Networks with Supply Disruptions using Genetic Algorithm

    OpenAIRE

    Taha, Raghda; Abdallah, Khaled; Sadek, Yomma; El-Kharbotly, Amin; Afia, Nahid

    2014-01-01

    The design of supply chain networks subject to disruptions is tackled. A genetic algorithm with the objective of minimizing the design cost and regret cost is developed to achieve a reliable supply chain network. The improvement of supply chain network reliability is measured against the supply chain cost.

  19. Mechatronic Hydraulic Drive with Regulator, Based on Artificial Neural Network

    Science.gov (United States)

    Burennikov, Y.; Kozlov, L.; Pyliavets, V.; Piontkevych, O.

    2017-06-01

    Mechatronic hydraulic drives, based on variable pump, proportional hydraulics and controllers find wide application in technological machines and testing equipment. Mechatronic hydraulic drives provide necessary parameters of actuating elements motion with the possibility of their correction in case of external loads change. This enables to improve the quality of working operations, increase the capacity of machines. The scheme of mechatronic hydraulic drive, based on the pump, hydraulic cylinder, proportional valve with electrohydraulic control and programmable controller is suggested. Algorithm for the control of mechatronic hydraulic drive to provide necessary pressure change law in hydraulic cylinder is developed. For the realization of control algorithm in the controller artificial neural networks are used. Mathematical model of mechatronic hydraulic drive, enabling to create the training base for adjustment of artificial neural networks of the regulator is developed.

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

    Science.gov (United States)

    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

  1. Improved reactor regulating system logical architecture using genetic algorithm

    Directory of Open Access Journals (Sweden)

    Hyo-Sub Shim

    2017-12-01

    Full Text Available An improved Reactor Regulating System (RRS logic architecture, which is combined with genetic algorithm (GA, is implemented in this work. It is devised to provide an optimal solution to the current RRS. The current system works desirably and has contributed to safe and stable nuclear power plant operation. However, during the ascent and descent section of the reactor power, the RRS output reveals a relatively high steady-state error, and the output also carries a considerable level of overshoot. In an attempt to consolidate conservatism and minimize the error, this work proposes to apply GA to RRS and suggests reconfiguring the system. Prior to the use of GA, reverse engineering is implemented to build a Simulink-based RRS model. Reengineering is followed to produce a newly configured RRS to generate an output that has a reduced steady-state error and diminished overshoot level. A full-scope APR1400 simulator is used to examine the dynamic behaviors of RRS and to build the RRS Simulink model.

  2. The Sleeping Beauty transposable element: evolution, regulation and genetic applications.

    Science.gov (United States)

    Ivics, Zoltán; Kaufman, Christopher D; Zayed, Hatem; Miskey, Csaba; Walisko, Oliver; Izsvák, Zsuzsanna

    2004-01-01

    Members of the Tc1/mariner superfamily of transposable elements isolated from vertebrate species are inactive due to the accumulation of mutations. A representative of a subfamily of fish elements estimated to be last active > 10 million years ago has been reconstructed, and named Sleeping Beauty(SB). This element opened up new avenues for studies on DNA transposition in vertebrates, and for the development of transposon tools for genetic manipulation in important model species and in humans. Multiple transposase binding sites within the terminal inverted repeats, a transpositional enhancer sequence, unequal affinity of the transposase to the binding sites and the activity of the cellular HMGB1 protein all contribute to a highly regulated assembly of SB synaptic complexes, which is likely a requirement for the subsequent catalytic steps. Host proteins involved in double-strand DNA break repair are limiting factors of SB transposition in mammalian cells, underscoring evolutionary, structural and functional links between DNA transposition, retroviral integration and V(D)J recombination. SB catalyzes efficient cut-and-paste transposition in a wide range of vertebrate cells in tissue culture, and in somatic tissues as well as the germline of the mouse and zebrafish in vivo, indicating its usefulness as a vector for transgenesis and insertional mutagenesis.

  3. myGRN: a database and visualisation system for the storage and analysis of developmental genetic regulatory networks

    Directory of Open Access Journals (Sweden)

    Bacha Jamil

    2009-06-01

    Full Text Available Abstract Background Biological processes are regulated by complex interactions between transcription factors and signalling molecules, collectively described as Genetic Regulatory Networks (GRNs. The characterisation of these networks to reveal regulatory mechanisms is a long-term goal of many laboratories. However compiling, visualising and interacting with such networks is non-trivial. Current tools and databases typically focus on GRNs within simple, single celled organisms. However, data is available within the literature describing regulatory interactions in multi-cellular organisms, although not in any systematic form. This is particularly true within the field of developmental biology, where regulatory interactions should also be tagged with information about the time and anatomical location of development in which they occur. Description We have developed myGRN (http://www.myGRN.org, a web application for storing and interrogating interaction data, with an emphasis on developmental processes. Users can submit interaction and gene expression data, either curated from published sources or derived from their own unpublished data. All interactions associated with publications are publicly visible, and unpublished interactions can only be shared between collaborating labs prior to publication. Users can group interactions into discrete networks based on specific biological processes. Various filters allow dynamic production of network diagrams based on a range of information including tissue location, developmental stage or basic topology. Individual networks can be viewed using myGRV, a tool focused on displaying developmental networks, or exported in a range of formats compatible with third party tools. Networks can also be analysed for the presence of common network motifs. We demonstrate the capabilities of myGRN using a network of zebrafish interactions integrated with expression data from the zebrafish database, ZFIN. Conclusion Here we

  4. Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks.

    Science.gov (United States)

    Chen, Chi-Kan

    2017-07-26

    The identification of genetic regulatory networks (GRNs) provides insights into complex cellular processes. A class of recurrent neural networks (RNNs) captures the dynamics of GRN. Algorithms combining the RNN and machine learning schemes were proposed to reconstruct small-scale GRNs using gene expression time series. We present new GRN reconstruction methods with neural networks. The RNN is extended to a class of recurrent multilayer perceptrons (RMLPs) with latent nodes. Our methods contain two steps: the edge rank assignment step and the network construction step. The former assigns ranks to all possible edges by a recursive procedure based on the estimated weights of wires of RNN/RMLP (RE RNN /RE RMLP ), and the latter constructs a network consisting of top-ranked edges under which the optimized RNN simulates the gene expression time series. The particle swarm optimization (PSO) is applied to optimize the parameters of RNNs and RMLPs in a two-step algorithm. The proposed RE RNN -RNN and RE RMLP -RNN algorithms are tested on synthetic and experimental gene expression time series of small GRNs of about 10 genes. The experimental time series are from the studies of yeast cell cycle regulated genes and E. coli DNA repair genes. The unstable estimation of RNN using experimental time series having limited data points can lead to fairly arbitrary predicted GRNs. Our methods incorporate RNN and RMLP into a two-step structure learning procedure. Results show that the RE RMLP using the RMLP with a suitable number of latent nodes to reduce the parameter dimension often result in more accurate edge ranks than the RE RNN using the regularized RNN on short simulated time series. Combining by a weighted majority voting rule the networks derived by the RE RMLP -RNN using different numbers of latent nodes in step one to infer the GRN, the method performs consistently and outperforms published algorithms for GRN reconstruction on most benchmark time series. The framework of two

  5. Specific gene-regulation networks during the pre-implantation development of the pig embryo as revealed by deep sequencing

    OpenAIRE

    Cao, Suying; Han, Jianyong; Wu, Jun; Li, Qiuyan; Liu, Shichao; Zhang, Wei; Pei, Yangli; Ruan, Xiaoan; Liu, Zhonghua; Wang, Xumin; Lim, Bing; Li, Ning

    2014-01-01

    Background Because few studies exist to describe the unique molecular network regulation behind pig pre-implantation embryonic development (PED), genetic engineering in the pig embryo is limited. Also, this lack of research has hindered derivation and application of porcine embryonic stem cells and porcine induced pluripotent stem cells (iPSCs). Results We identified and analyzed the genome wide transcriptomes of pig in vivo-derived and somatic cell nuclear transferred (SCNT) as well as mouse...

  6. Genetics and evolution of MIXTA genes regulating cotton lint fiber development.

    Science.gov (United States)

    Wu, Huaitong; Tian, Yue; Wan, Qun; Fang, Lei; Guan, Xueying; Chen, Jiedan; Hu, Yan; Ye, Wenxue; Zhang, Hua; Guo, Wangzhen; Chen, Xiaoya; Zhang, Tianzhen

    2017-10-16

    Cotton, with cellulose-enriched mature fibers, is the largest source of natural textiles. Through a map-based cloning strategy, we isolated an industrially important lint fiber development gene (Li3 ) that encodes an MYB-MIXTA-like transcription factor (MML) on chromosome D12 (GhMML4_D12). Virus-induced gene silencing or decreasing the expression of the GhMML4_D12 gene in n2 NSM plants resulted in a significant reduction in epidermal cell prominence and lint fiber production. GhMML4_D12 is arranged in tandem with GhMML3, another MIXTA gene responsible for fuzz fiber development. These two very closely related MIXTA genes direct fiber initiation production in two specialized cell forms: lint and fuzz fibers. They may control the same metabolic pathways in different cell types. The MIXTAs expanded in Malvaceae during their evolution and produced a Malvaceae-specific family that regulates epidermal cell differentiation, different from the gene family that regulates leaf hair trichome development. Cotton has developed a unique transcriptional regulatory network for fiber development. Characterization of target genes regulating fiber production has provided insights into the molecular mechanisms underlying cotton fiber development and has allowed the use of genetic engineering to increase lint yield by inducing more epidermal cells to develop into lint rather than fuzz fibers. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

  7. Epidemic Modelling by Ripple-Spreading Network and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Jian-Qin Liao

    2013-01-01

    Full Text Available Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals’ physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results.

  8. Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

    Science.gov (United States)

    Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

    2000-01-01

    Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

  9. Pre-clinical drug prioritization via prognosis-guided genetic interaction networks.

    Directory of Open Access Journals (Sweden)

    Jianghui Xiong

    Full Text Available The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call 'synergistic outcome determination' (SOD, a concept similar to 'Synthetic Lethality'. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function. In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies.

  10. Genetic and epigenetic regulation of YKL-40 in childhood.

    Science.gov (United States)

    Guerra, Stefano; Melén, Erik; Sunyer, Jordi; Xu, Cheng-Jian; Lavi, Iris; Benet, Marta; Bustamante, Mariona; Carsin, Anne-Elie; Dobaño, Carlota; Guxens, Mònica; Tischer, Christina; Vrijheid, Martine; Kull, Inger; Bergström, Anna; Kumar, Ashish; Söderhäll, Cilla; Gehring, Ulrike; Dijkstra, Dorieke J; van der Vlies, Pieter; Wickman, Magnus; Bousquet, Jean; Postma, Dirkje S; Anto, Josep M; Koppelman, Gerard H

    2017-07-21

    Circulating levels of the chitinase-like protein YKL-40 are influenced by genetic variation in its encoding gene (chitinase 3-like 1 [CHI3L1]) and are increased in patients with several diseases, including asthma. Epigenetic regulation of circulating YKL-40 early in life is unknown. We sought to determine (1) whether methylation levels at CHI3L1 CpG sites mediate the association of CHI3L1 single nucleotide polymorphisms (SNPs) with YKL-40 levels in the blood and (2) whether these biomarkers (CHI3L1 SNPs, methylation profiles, and YKL-40 levels) are associated with asthma in early childhood. We used data from up to 2405 participants from the Spanish Infancia y Medio Ambiente; the Swedish Barn/Children, Allergy, Milieu, Stockholm, Epidemiological survey; and the Dutch Prevention and Incidence of Asthma and Mite Allergy birth cohorts. Associations between 68 CHI3L1 SNPs, methylation levels at 14 CHI3L1 CpG sites in whole-blood DNA, and circulating YKL-40 levels at 4 years of age were tested by using correlation analysis, multivariable regression, and mediation analysis. Each of these biomarkers was also tested for association with asthma at 4 years of age by using multivariable logistic regression. YKL-40 levels were significantly associated with 7 SNPs and with methylation at 5 CpG sites. Consistent associations between these 7 SNPs (particularly rs10399931 and rs4950928) and 5 CpG sites were observed. Alleles linked to lower YKL-40 levels were associated with higher methylation levels. Participants with high YKL-40 levels (defined as the highest YKL-40 tertile) had increased odds for asthma compared with subjects with low YKL-40 levels (meta-analyzed adjusted odds ratio, 1.90 [95% CI, 1.08-3.36]). In contrast, neither SNPs nor methylation levels at CpG sites in CHI3L1 were associated with asthma. The effects of CHI3L1 genetic variation on circulating YKL-40 levels are partly mediated by methylation profiles. In our study YKL-40 levels, but not CHI3L1 SNPs or

  11. Gene regulation and genetics in neurochemistry, past to future.

    Science.gov (United States)

    Barger, Steven W

    2016-10-01

    Ask any neuroscientist to name the most profound discoveries in the field in the past 60 years, and at or near the top of the list will be a phenomenon or technique related to genes and their expression. Indeed, our understanding of genetics and gene regulation has ushered in whole new systems of knowledge and new empirical approaches, many of which could not have even been imagined prior to the molecular biology boon of recent decades. Neurochemistry, in the classic sense, intersects with these concepts in the manifestation of neuropeptides, obviously dependent upon the central dogma (the established rules by which DNA sequence is eventually converted into protein primary structure) not only for their conformation but also for their levels and locales of expression. But, expanding these considerations to non-peptide neurotransmitters illustrates how gene regulatory events impact neurochemistry in a much broader sense, extending beyond the neurochemicals that translate electrical signals into chemical ones in the synapse, to also include every aspect of neural development, structure, function, and pathology. From the beginning, the mutability - yet relative stability - of genes and their expression patterns were recognized as potential substrates for some of the most intriguing phenomena in neurobiology - those instances of plasticity required for learning and memory. Near-heretical speculation was offered in the idea that perhaps the very sequence of the genome was altered to encode memories. A fascinating component of the intervening progress includes evidence that the central dogma is not nearly as rigid and consistent as we once thought. And this mutability extends to the potential to manipulate that code for both experimental and clinical purposes. Astonishing progress has been made in the molecular biology of neurochemistry during the 60 years since this journal debuted. Many of the gains in conceptual understanding have been driven by methodological

  12. Thermodynamic analysis of regulation in metabolic networks using constraint-based modeling

    Directory of Open Access Journals (Sweden)

    Mahadevan Radhakrishnan

    2010-05-01

    Full Text Available Abstract Background Geobacter sulfurreducens is a member of the Geobacter species, which are capable of oxidation of organic waste coupled to the reduction of heavy metals and electrode with applications in bioremediation and bioenergy generation. While the metabolism of this organism has been studied through the development of a stoichiometry based genome-scale metabolic model, the associated regulatory network has not yet been well studied. In this manuscript, we report on the implementation of a thermodynamics based metabolic flux model for Geobacter sulfurreducens. We use this updated model to identify reactions that are subject to regulatory control in the metabolic network of G. sulfurreducens using thermodynamic variability analysis. Findings As a first step, we have validated the regulatory sites and bottleneck reactions predicted by the thermodynamic flux analysis in E. coli by evaluating the expression ranges of the corresponding genes. We then identified ten reactions in the metabolic network of G. sulfurreducens that are predicted to be candidates for regulation. We then compared the free energy ranges for these reactions with the corresponding gene expression fold changes under conditions of different environmental and genetic perturbations and show that the model predictions of regulation are consistent with data. In addition, we also identify reactions that operate close to equilibrium and show that the experimentally determined exchange coefficient (a measure of reversibility is significant for these reactions. Conclusions Application of the thermodynamic constraints resulted in identification of potential bottleneck reactions not only from the central metabolism but also from the nucleotide and amino acid subsystems, thereby showing the highly coupled nature of the thermodynamic constraints. In addition, thermodynamic variability analysis serves as a valuable tool in estimating the ranges of ΔrG' of every reaction in the model

  13. Forward Genetic Approaches for Elucidation of Novel Regulators of Lyme Arthritis Severity

    Directory of Open Access Journals (Sweden)

    Kenneth K.C. Bramwell

    2014-06-01

    Full Text Available Patients experiencing natural infection with Borrelia burgdorferi display a spectrum of associated symptoms and severity, strongly implicating the impact of genetically determined host factors in the pathogenesis of Lyme disease. Herein, we provide a summary of the host genetic factors that have been demonstrated to influence the severity and chronicity of Lyme arthritis symptoms, and a review of the resources available, current progress, and added value of a forward genetic approach for identification of novel genetic regulators.

  14. Maximizing lifetime of wireless sensor networks using genetic approach

    DEFF Research Database (Denmark)

    Wagh, Sanjeev; Prasad, Ramjee

    2014-01-01

    The wireless sensor networks are designed to install the smart network applications or network for emergency solutions, where human interaction is not possible. The nodes in wireless sensor networks have to self organize as per the users requirements through monitoring environments. As the sensor...

  15. Transmission Expansion Planning Considering Network Adequacy and Investment Cost Limitation using Genetic Algorithm

    OpenAIRE

    M. Mahdavi; E. Mahdavi

    2011-01-01

    In this research, STNEP is being studied considering network adequacy and limitation of investment cost by decimal codification genetic algorithm (DCGA). The goal is obtaining the maximum of network adequacy with lowest expansion cost for a specific investment. Finally, the proposed idea is applied to the Garvers 6-bus network. The results show that considering the network adequacy for solution of STNEP problem is caused that among of expansion plans for a determined investment, configuration...

  16. Genetic Optimization of Neural Networks for Person Recognition based on the Iris

    Directory of Open Access Journals (Sweden)

    Oscar Castillo

    2012-06-01

    Full Text Available This paper describes the application of modular neural network architectures for person recognition using the human iris images as a biometric measure. The iris database was obtained from the Institute of Automation of the Academy of Sciences China (CASIA. We show simulation results with the modular neural network approach, its optimization using genetic algorithms, and the integration with different methods, such as: the gating network method, type-1 fuzzy integration and optimized fuzzy integration using genetic algorithms. Simulation results show a good identification rate using fuzzy integrators and the best structure found by the genetic algorithm.

  17. Regulating Intracellular Calcium in Plants: From Molecular Genetics to Physiology

    Energy Technology Data Exchange (ETDEWEB)

    Heven Sze

    2008-06-22

    To grow, develop, adapt, and reproduce, plants have evolved mechanisms to regulate the uptake, translocation and sorting of calcium ions into different cells and subcellular compartments. Yet how plants accomplish this remarkable feat is still poorly understood. The spatial and temporal changes in intracellular [Ca2+] during growth and during responses to hormonal and environmental stimuli indicate that Ca2+ influx and efflux transporters are diverse and tightly regulated in plants. The specific goals were to determine the biological roles of multiple Ca pumps (ECAs) in the model plant Arabidopsis thaliana. We had pioneered the use of K616 yeast strain to functionally express plant Ca pumps, and demonstrated two distinct types of Ca pumps in plants (Sze et al., 2000. Annu Rev Plant Biol. 51,433). ACA2 represented one type that was auto-inhibited by the N-terminal region and stimulated by calmodulin. ECA1 represented another type that was not sensitive to calmodulin and phylogenetically distinct from ACAs. The goal to determine the biological roles of multiple ECA-type Ca pumps in Arabidopsis has been accomplished. Although we demonstrated ECA1 was a Ca pump by functional expression in yeast, the in vivo roles of ECAs was unclear. A few highlights are described. ECA1 and/or ECA4 are Ca/Mn pumps localized to the ER and are highly expressed in all cell types. Using homozygous T-DNA insertional mutants of eca1, we demonstrated that the ER-bound ECA1 supports growth and confers tolerance of plants growing on medium low in Ca or containing toxic levels of Mn. This is the first genetic study to determine the in vivo function of a Ca pump in plants. A phylogenetically distinct ECA3 is also a Ca/Mn pump that is localized to endosome, such as post-Golgi compartments. Although it is expressed at lower levels than ECA1, eca3 mutants are impaired in Ca-dependent root growth and in pollen tube elongation. Increased secretion of wall proteins in mutants suggests that Ca and Mn

  18. Inference of Vohradský's models of genetic networks by solving two-dimensional function optimization problems.

    Science.gov (United States)

    Kimura, Shuhei; Sato, Masanao; Okada-Hatakeyama, Mariko

    2013-01-01

    The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský's model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský's model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský's models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations.

  19. Aflatoxin regulations in a network of global maize trade.

    Directory of Open Access Journals (Sweden)

    Felicia Wu

    Full Text Available Worldwide, food supplies often contain unavoidable contaminants, many of which adversely affect health and hence are subject to regulations of maximum tolerable levels in food. These regulations differ from nation to nation, and may affect patterns of food trade. We soughtto determine whether there is an association between nations' food safety regulations and global food trade patterns, with implications for public health and policymaking. We developed a network model of maize trade around the world. From maize import/export data for 217 nations from 2000-2009, we calculated basic statistics on volumes of trade; then examined how regulations of aflatoxin, a common contaminant of maize, are similar or different between pairs of nations engaging in significant amounts of maize trade. Globally, market segregation appears to occur among clusters of nations. The United States is at the center of one cluster; European countries make up another cluster with hardly any maize trade with the US; and Argentina, Brazil, and China export maize all over the world. Pairs of nations trading large amounts of maize have very similar aflatoxin regulations: nations with strict standards tend to trade maize with each other, while nations with more relaxed standards tend to trade maize with each other. Rarely among the top pairs of maize-trading nations do total aflatoxin standards (standards based on the sum of the levels of aflatoxins B(1, B(2, G(1, and G(2 differ by more than 5 µg/kg. These results suggest that, globally, separate maize trading communities emerge; and nations tend to trade with other nations that have very similar food safety standards.

  20. A Systems Genetics Approach Identifies Gene Regulatory Networks Associated with Fatty Acid Composition in Brassica rapa Seed1

    Science.gov (United States)

    Xiao, Dong; Bucher, Johan; Jin, Mina; Boyle, Kerry; Fobert, Pierre; Maliepaard, Chris

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

    Fatty acids in seeds affect seed germination and seedling vigor, and fatty acid composition determines the quality of seed oil. In this study, quantitative trait locus (QTL) mapping of fatty acid and transcript abundance was integrated with gene network analysis to unravel the genetic regulation of seed fatty acid composition in a Brassica rapa doubled haploid population from a cross between a yellow sarson oil type and a black-seeded pak choi. The distribution of major QTLs for fatty acids showed a relationship with the fatty acid types: linkage group A03 for monounsaturated fatty acids, A04 for saturated fatty acids, and A05 for polyunsaturated fatty acids. Using a genetical genomics approach, expression quantitative trait locus (eQTL) hotspots were found at major fatty acid QTLs on linkage groups A03, A04, A05, and A09. An eQTL-guided gene coexpression network of lipid metabolism-related genes showed major hubs at the genes BrPLA2-ALPHA, BrWD-40, a number of seed storage protein genes, and the transcription factor BrMD-2, suggesting essential roles for these genes in lipid metabolism. Three subnetworks were extracted for the economically important and most abundant fatty acids erucic, oleic, linoleic, and linolenic acids. Network analysis, combined with comparison of the genome positions of cis- or trans-eQTLs with fatty acid QTLs, allowed the identification of candidate genes for genetic regulation of these fatty acids. The generated insights in the genetic architecture of fatty acid composition and the underlying complex gene regulatory networks in B. rapa seeds are discussed. © 2016 American Society of Plant Biologists. All Rights Reserved.

  2. Phospho-regulation of the Neurospora crassa septation initiation network.

    Science.gov (United States)

    Heilig, Yvonne; Schmitt, Kerstin; Seiler, Stephan

    2013-01-01

    Proper cell division is essential for growth and development of uni- and multicellular organisms. The fungal septation initiation network (SIN) functions as kinase cascade that connects cell cycle progression with the initiation of cytokinesis. Miss-regulation of the homologous Hippo pathway in animals results in excessive cell proliferation and formation of tumors, underscoring the conservation of both pathways. How SIN proteins interact and transmit signals through the cascade is only beginning to be understood. Moreover, our understanding of septum formation and its regulation in filamentous fungi, which represent the vast majority of the fungal kingdom, is highly fragmentary. We determined that a tripartite kinase cascade, consisting of CDC-7, SID-1 and DBF-2, together with their regulatory subunits CDC-14 and MOB-1, is important for septum formation in the model mold Neurospora crassa. DBF-2 activity and septum formation requires auto-phosphorylation at Ser499 within the activation segment and phosphorylation of Thr671 in the hydrophobic motif by SID-1. Moreover, SID-1-stimulated DBF-2 activity is further enhanced by CDC-7, supporting a stepwise activation mechanism of the tripartite SIN kinase cascade in fungi. However, in contrast to the situation described for unicellular yeasts, the localization of the entire SIN cascade to spindle pole bodies is constitutive and cell cycle independent. Moreover, all SIN proteins except CDC-7 form cortical rings prior to septum initiation and localize to constricting septa. Thus, SIN localization and activity regulation significantly differs in unicellular versus syncytial ascomycete fungi.

  3. 76 FR 5780 - Determination of Regulated Status of Alfalfa Genetically Engineered for Tolerance to the...

    Science.gov (United States)

    2011-02-02

    ... movement, or release into the environment) of organisms and products altered or produced through genetic... connection with its determination. The regulations in 7 CFR part 340, ``Introduction of Organisms and... engineered (GE) organisms and products are considered ``regulated articles.'' The regulations in Sec. 340.6(a...

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

    Science.gov (United States)

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

    2016-04-01

    Association studies are widely utilized to analyze complex traits but their ability to disclose genetic architectures is often limited by statistical constraints, and functional insights are usually minimal in nonmodel organisms like forest trees. We developed an approach to integrate association mapping results with co-expression networks. We tested single nucleotide polymorphisms (SNPs) in 2652 candidate genes for statistical associations with wood density, stiffness, microfibril angle and ring width in a population of 1694 white spruce trees (Picea glauca). Associations mapping identified 229-292 genes per wood trait using a statistical significance level of P wood associated genes and several known MYB and NAC regulators were identified as network hubs. The network revealed a link between the gene PgNAC8, wood stiffness and microfibril angle, as well as considerable within-season variation for both genetic control of wood traits and gene expression. Trait associations were distributed throughout the network suggesting complex interactions and pleiotropic effects. Our findings indicate that integration of association mapping and co-expression networks enhances our understanding of complex wood traits. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

  5. Ensembles of Bayesian-regularized genetic neural networks for modeling of acetylcholinesterase inhibition by huprines.

    Science.gov (United States)

    Fernández, Michael; Caballero, Julio

    2006-10-01

    Acetylcholinesterase inhibition was modeled for a set of huprines using ensembles of Bayesian-regularized Genetic Neural Networks. In the Bayesian-regularized Genetic Neural Network approach the Bayesian regularization avoids overfitted regressions and the genetic algorithm allows exploring a wide pool of three-dimensional descriptors. The predictive capacity of our selected model was evaluated by averaging multiple validation sets generated as members of neural network ensembles. When 60 members are assembled, the neural network ensemble provides a reliable measure of training and test set R(2)-values of 0.945 and 0.850 respectively. In other respects, the ability of the nonlinear selected genetic algorithm space for differentiate the data were evidenced when total data set was well distributed in a Kohonen self-organizing map. The analysis of the self-organizing map zones allows establishing the main structural features differentiated by our vectorial space.

  6. Calibration of parameters of water supply network model using genetic algorithm

    National Research Council Canada - National Science Library

    Tomasz Boczar; Norbert Adamikiewicz; Włodzimierz Stanisławski

    2017-01-01

    ...: the pressure on the node and volume flow in the network section. The first calibration method regards to application of the genetic algorithm, which is a build in plugin - “Epanet Calibrator...

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

    Science.gov (United States)

    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.

  8. Reconstruction of switching thresholds in piecewise-affine models of genetic regulatory networks

    OpenAIRE

    Drulhe, Samuel; Ferrari-Trecate, Giancarlo; De Jong, Hidde; Viari, Alain

    2006-01-01

    http://dx.doi.org/10.1007/11730637_16; Recent advances of experimental techniques in biology have led to the production of enormous amounts of data on the dynamics of genetic regulatory networks. In this paper, we present an approach for the identification of PieceWise-Affine (PWA) models of genetic regulatory networks from experimental data, focusing on the reconstruction of switching thresholds associated with regulatory interactions. In particular, our method takes into account geometric c...

  9. Dentate gyrus network dysfunctions precede the symptomatic phase in a genetic mouse model of seizures

    Directory of Open Access Journals (Sweden)

    Oana eToader

    2013-08-01

    Full Text Available Neuronal circuit disturbances that lead to hyperexcitability in the cortico-hippocampal network are one of the landmarks of temporal lobe epilepsy. The dentate gyrus (DG network plays an important role in regulating the excitability of the entire hippocampus by filtering and integrating information received via the perforant path. Here, we investigated possible epileptogenic abnormalities in the function of the DG neuronal network in the Synapsin II (Syn II knockout mouse (Syn II-/-, a genetic mouse model of epilepsy. Syn II is a presynaptic protein whose deletion in mice reproducibly leads to generalized seizures starting at the age of two months. We made use of a high-resolution microelectrode array (4096 electrodes and patch-clamp recordings, and found that in acute hippocampal slices of young pre-symptomatic (3-6 weeks-old Syn II-/- mice excitatory synaptic output of the mossy fibers is reduced. Moreover, we showed that the main excitatory neurons present in the polymorphic layer of the DG, hilar mossy cells, display a reduced excitability. We also provide evidence of a predominantly inhibitory regulatory output from mossy cells to granule cells, through feed-forward inhibition, and show that the excitatory-inhibitory ratio is increased in both pre-symptomatic and symptomatic Syn II-/- mice. These results support the key role of the hilar mossy neurons in maintaining the normal excitability of the hippocampal network and show that the late epileptic phenotype of the Syn II-/- mice is preceded by neuronal circuitry dysfunctions. Our data provide new insights into the mechanisms of epileptogenesis in the Syn II-/- mice and open the possibility for early diagnosis and therapeutic interventions.

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

    Science.gov (United States)

    Fierst, Janna L; Phillips, Patrick C

    2015-01-01

    In 1994 and 1996, Andreas Wagner introduced a novel model in two papers addressing the evolution of genetic regulatory networks. This work, and a suite of papers that followed using similar models, helped integrate network thinking into biology and motivate research focused on the evolution of genetic networks. The Wagner network has its mathematical roots in the Ising model, a statistical physics model describing the activity of atoms on a lattice, and in neural networks. These models have given rise to two branches of applications, one in physics and biology and one in artificial intelligence and machine learning. Here, we review development along these branches, outline similarities and differences between biological models of genetic regulatory circuits and neural circuits models used in machine learning, and identify ways in which these models can provide novel insights into biological systems. © 2014 Wiley Periodicals, Inc.

  11. Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration.

    Science.gov (United States)

    Arneson, Douglas; Bhattacharya, Anindya; Shu, Le; Mäkinen, Ville-Petteri; Yang, Xia

    2016-09-09

    Human diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development. To make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server ( http://mergeomics. idre.ucla.edu/ ). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use. Our Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators

  12. Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

    Directory of Open Access Journals (Sweden)

    Yeh Cheng-Yu

    2009-12-01

    Full Text Available Abstract Background Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. Results To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2 regulated by RUNX1 and STAT3 is correlated to the pathological stage

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

    Science.gov (United States)

    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

  14. DMPD: Genetic regulation of macrophage priming/activation: the Lsh gene story. [Dynamic Macrophage Pathway CSML Database

    Lifescience Database Archive (English)

    Full Text Available 1757110 Genetic regulation of macrophage priming/activation: the Lsh gene story. Bl... (.svg) (.html) (.csml) Show Genetic regulation of macrophage priming/activation: the Lsh gene story. Pubmed...ID 1757110 Title Genetic regulation of macrophage priming/activation: the Lsh gene stor

  15. The role of genetic sex in affect regulation and expression of GABA-related genes across species

    Directory of Open Access Journals (Sweden)

    Marianne eSeney

    2013-09-01

    Full Text Available Although circulating hormones and inhibitory gamma-amino butyric acid (GABA-related factors are known to affect mood, considerable knowledge gaps persist for biological mechanisms underlying the female bias in mood disorders. Here, we combine human and mouse studies to investigate sexual dimorphism in the GABA system in the context of major depressive disorder (MDD and then use a genetic model to dissect the role of sex-related factors in GABA-related gene expression and anxiety-/depressive-like behaviors in mice. First, using meta-analysis of gene array data in human postmortem brain (N = 51 MDD subjects, 50 controls, we show that the previously-reported down-regulation in MDD of somatostatin (SST, a marker of a GABA neuron subtype, is significantly greater in women with MDD. Second, using gene co-expression network analysis in control human subjects (N = 214; 2 frontal cortex regions and expression quantitative trait loci mapping (N = 170 subjects, we show that expression of SST and the GABA-synthesizing enzymes glutamate decarboxylase 67 (GAD67 and GAD65 are tightly co-regulated and influenced by X-chromosome genetic polymorphisms. Third, using a rodent genetic model (Four Core Genotypes (FCG mice, in which genetic and gonadal sex are artificially dissociated (N ≥ 12/group, we show that genetic sex (i.e. X/Y chromosome influences both gene expression (lower Sst, Gad67, Gad65 in XY mice and anxiety-like behaviors (higher in XY mice. This suggests that in an intact male animal, the observed behavior represents the outcomes of male genetic sex increasing and male-like testosterone decreasing anxiety-like behaviors. Gonadal sex was the only factor influencing depressive-like behavior (gonadal males < gonadal females. Collectively, these combined human and mouse studies provide mechanistic insight into sexual dimorphism in mood disorders, and specifically demonstrate an unexpected role for XY genetic sex on GABA-related genes and anxiety

  16. Deterministic and stochastic population-level simulations of an artificial lac operon genetic network

    Directory of Open Access Journals (Sweden)

    Zygourakis Kyriacos

    2011-07-01

    Full Text Available Abstract Background The lac operon genetic switch is considered as a paradigm of genetic regulation. This system has a positive feedback loop due to the LacY permease boosting its own production by the facilitated transport of inducer into the cell and the subsequent de-repression of the lac operon genes. Previously, we have investigated the effect of stochasticity in an artificial lac operon network at the single cell level by comparing corresponding deterministic and stochastic kinetic models. Results This work focuses on the dynamics of cell populations by incorporating the above kinetic scheme into two Monte Carlo (MC simulation frameworks. The first MC framework assumes stochastic reaction occurrence, accounts for stochastic DNA duplication, division and partitioning and tracks all daughter cells to obtain the statistics of the entire cell population. In order to better understand how stochastic effects shape cell population distributions, we develop a second framework that assumes deterministic reaction dynamics. By comparing the predictions of the two frameworks, we conclude that stochasticity can create or destroy bimodality, and may enhance phenotypic heterogeneity. Conclusions Our results show how various sources of stochasticity act in synergy with the positive feedback architecture, thereby shaping the behavior at the cell population level. Further, the insights obtained from the present study allow us to construct simpler and less computationally intensive models that can closely approximate the dynamics of heterogeneous cell populations.

  17. Optimization of the Compensation of a Meshed MV Network by a Modified Genetic Algorithm

    DEFF Research Database (Denmark)

    Nielsen, Hans; Paar, M.; Toman, P.

    2007-01-01

    The article discusses the utilization of a modified genetic algorithm (GA) for the optimization of the shunt compensation in meshed and radial MV distribution networks. The algorithm looks for minimum costs of the network power losses and minimum capital and operating costs of applied capacitors...

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

    Science.gov (United States)

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

    2016-04-01

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

  19. Identification of yeast transcriptional regulation networks using multivariate random forests.

    Directory of Open Access Journals (Sweden)

    Yuanyuan Xiao

    2009-06-01

    Full Text Available The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays to sequence features residing in gene promoters (as derived from DNA motif data and transcription factor binding to gene promoters (as derived from tiling microarrays. We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression measures. An analysis of the multivariate random forest output reveals complex regulatory networks, which consist of cohesive, condition-dependent regulatory cliques. Each regulatory clique features homogeneous gene expression profiles and common motifs or synergistic motif groups. We apply our method to several yeast physiological processes: cell cycle, sporulation, and various stress conditions. Our technique displays excellent performance with regard to identifying known regulatory motifs, including high order interactions. In addition, we present evidence of the existence of an alternative MCB-binding pathway, which we confirm using data from two independent cell cycle studies and two other physioloigical processes. Finally, we have uncovered elaborate transcription regulation refinement mechanisms involving PAC and mRRPE motifs that govern essential rRNA processing. These include intriguing instances of differing motif dosages and differing combinatorial motif control that promote regulatory specificity in rRNA metabolism under differing physiological processes.

  20. Global transcriptome and gene regulation network for secondary metabolite biosynthesis of tea plant (Camellia sinensis).

    Science.gov (United States)

    Li, Chun-Fang; Zhu, Yan; Yu, Yao; Zhao, Qiong-Yi; Wang, Sheng-Jun; Wang, Xin-Chao; Yao, Ming-Zhe; Luo, Da; Li, Xuan; Chen, Liang; Yang, Ya-Jun

    2015-07-29

    . The gene network responsible for the regulation of the secondary metabolic pathways was analyzed. Our work elucidated the possible cross talk in gene regulation between the secondary metabolite biosynthetic pathways in C. sinensis. The results increase our understanding of how secondary metabolic pathways are regulated during plant development and growth cycles, and help pave the way for genetic selection and engineering for germplasm improvement.

  1. POWER AWARE ROUTING PROTOCOLS FOR MOBILE ADHOC NETWORKS MANETS USING MODIFIED GENETIC ALGORITHM

    OpenAIRE

    Priya Mudgal *, Dushyant Singh **

    2016-01-01

    Mobile adhoc networks MANETs are very popular networks which are having many applications in science and engineering. MANETs are very dynamic networks which does not have any infrastructure for their operation. Routing in MANETs is an area of research for many authors in recent years. Devices in MANETs are battery operated so routing protocols must be power aware which consumes less battery of nodes in transferring data. Genetic algorithm (GA) is a very common optimizing algorithm which can m...

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

    Science.gov (United States)

    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. Copyright © 2015 by the Genetics Society of America.

  3. Genetic regulation of growth from birth to 18 years of age

    DEFF Research Database (Denmark)

    Silventoinen, Karri; Pietiläinen, Kirsi H; Tynelius, Per

    2008-01-01

    Growth is a complex process, and only little is known on the genetic regulation of it. We analyzed the effect of genetic and environmental factors on growth in a longitudinal Swedish cohort of 231 monozygotic and 144 dizygotic twin pairs born 1973-1979 with length or height measured annually from...

  4. Genetic identification of a network of factors that functionally interact with the nucleosome remodeling ATPase ISWI.

    Directory of Open Access Journals (Sweden)

    Giosalba Burgio

    2008-06-01

    Full Text Available Nucleosome remodeling and covalent modifications of histones play fundamental roles in chromatin structure and function. However, much remains to be learned about how the action of ATP-dependent chromatin remodeling factors and histone-modifying enzymes is coordinated to modulate chromatin organization and transcription. The evolutionarily conserved ATP-dependent chromatin-remodeling factor ISWI plays essential roles in chromosome organization, DNA replication, and transcription regulation. To gain insight into regulation and mechanism of action of ISWI, we conducted an unbiased genetic screen to identify factors with which it interacts in vivo. We found that ISWI interacts with a network of factors that escaped detection in previous biochemical analyses, including the Sin3A gene. The Sin3A protein and the histone deacetylase Rpd3 are part of a conserved histone deacetylase complex involved in transcriptional repression. ISWI and the Sin3A/Rpd3 complex co-localize at specific chromosome domains. Loss of ISWI activity causes a reduction in the binding of the Sin3A/Rpd3 complex to chromatin. Biochemical analysis showed that the ISWI physically interacts with the histone deacetylase activity of the Sin3A/Rpd3 complex. Consistent with these findings, the acetylation of histone H4 is altered when ISWI activity is perturbed in vivo. These findings suggest that ISWI associates with the Sin3A/Rpd3 complex to support its function in vivo.

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

    Science.gov (United States)

    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.

  6. Cumulative-Genetic Plasticity, Parenting and Adolescent Self-Regulation

    Science.gov (United States)

    Belsky, Jay; Beaver, Kevin M.

    2011-01-01

    Background: The capacity to control or regulate one's emotions, cognitions and behavior is central to competent functioning, with limitations in these abilities associated with developmental problems. Parenting appears to influence such self-regulation. Here the differential-susceptibility hypothesis is tested that the more putative "plasticity…

  7. Influence Maximization in Social Networks with Genetic Algorithms

    NARCIS (Netherlands)

    Bucur, Doina; Iacca, Giovanni; Squillero, Giovanni; Burelli, Paolo

    We live in a world of social networks. Our everyday choices are often influenced by social interactions. Word of mouth, meme diffusion on the Internet, and viral marketing are all examples of how social networks can affect our behaviour. In many practical applications, it is of great interest to

  8. Molecular Network Analysis Suggests Aberrant CREB-Mediated Gene Regulation in the Alzheimer Disease Hippocampus

    Directory of Open Access Journals (Sweden)

    Jun-ichi Satoh

    2009-01-01

    Full Text Available The pathogenesis of Alzheimer disease (AD involves the complex interaction between genetic and environmental factors affecting multiple cellular pathways. Recent advances in systems biology provide a system-level understanding of AD by elucidating the genome-wide molecular interactions. By using KeyMolnet, a bioinformatics tool for analyzing molecular interactions on the curated knowledgebase, we characterized molecular network of 2,883 all stages of AD-related genes (ADGs and 559 incipient AD-related genes (IADGs identified by global gene expression profiling of the hippocampal CA1 region of AD brains in terms of significant clinical and pathological correlations (Blalock et al., Proc Natl Acad Sci USA 101: 2173-2178, 2004. By the common upstream search, KeyMolnet identified cAMP-response element-binding protein (CREB as the principal transcription factor exhibiting the most significant relevance to molecular networks of both ADGs and IADGs. The CREB-regulated transcriptional network included upregulated and downregulated sets of ADGs and IADGs, suggesting an involvement of generalized deregulation of the CREB signaling pathway in the pathophysiology of AD, beginning at the early stage of the disease. To verify the in silico observations in vivo, we conducted immunohistochemical studies of 11 AD and 13 age-matched control brains by using anti-phoshorylated CREB (pCREB antibody. An abnormal accumulation of pCREB imunoreactivity was identified in granules of granulovacuolar degeneration (GVD in the hippocampal neurons of AD brains. These observations suggest that aberrant CREB-mediated gene regulation serves as a molecular biomarker of AD-related pathological processes, and support the hypothesis that sequestration of pCREB in GVD granules is in part responsible for deregulation of CREB-mediated gene expression in AD hippocampus.

  9. Definition of genetic and pathogenic mechanisms regulating neuroinflammation

    OpenAIRE

    Beyeen, Amennai Daniel

    2010-01-01

    Although complex inflammatory diseases affect 5% of the population we still do not understand fully the underlying disease triggers and mechanisms. This partly explains why current treatments are not curative but only modify disease. These diseases arise from combined genetic, environmental and unknown effects. In this thesis, I have focused on ...

  10. Molecular and genetic regulation of tree branch orientation

    Science.gov (United States)

    The ability to genetically manipulate tree form can significantly benefit orchard and tree plantation management by enabling higher density plantings, mechanized harvesting, and reduce both chemical use and costly manual labor. Using both Prunus persica and Arabidopsis thaliana, we identified an an...

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

    Science.gov (United States)

    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.

  12. Mondo/ChREBP-Mlx-regulated transcriptional network is essential for dietary sugar tolerance in Drosophila.

    Directory of Open Access Journals (Sweden)

    Essi Havula

    2013-04-01

    Full Text Available Sugars are important nutrients for many animals, but are also proposed to contribute to overnutrition-derived metabolic diseases in humans. Understanding the genetic factors governing dietary sugar tolerance therefore has profound biological and medical significance. Paralogous Mondo transcription factors ChREBP and MondoA, with their common binding partner Mlx, are key sensors of intracellular glucose flux in mammals. Here we report analysis of the in vivo function of Drosophila melanogaster Mlx and its binding partner Mondo (ChREBP in respect to tolerance to dietary sugars. Larvae lacking mlx or having reduced mondo expression show strikingly reduced survival on a diet with moderate or high levels of sucrose, glucose, and fructose. mlx null mutants display widespread changes in lipid and phospholipid profiles, signs of amino acid catabolism, as well as strongly elevated circulating glucose levels. Systematic loss-of-function analysis of Mlx target genes reveals that circulating glucose levels and dietary sugar tolerance can be genetically uncoupled: Krüppel-like transcription factor Cabut and carbonyl detoxifying enzyme Aldehyde dehydrogenase type III are essential for dietary sugar tolerance, but display no influence on circulating glucose levels. On the other hand, Phosphofructokinase 2, a regulator of the glycolysis pathway, is needed for both dietary sugar tolerance and maintenance of circulating glucose homeostasis. Furthermore, we show evidence that fatty acid synthesis, which is a highly conserved Mondo-Mlx-regulated process, does not promote dietary sugar tolerance. In contrast, survival of larvae with reduced fatty acid synthase expression is sugar-dependent. Our data demonstrate that the transcriptional network regulated by Mondo-Mlx is a critical determinant of the healthful dietary spectrum allowing Drosophila to exploit sugar-rich nutrient sources.

  13. Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

    This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additio...... space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model.......This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures...

  14. Acoustic Performance of Exhaust Muffler based Genetic Algorithms and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Wang Xiao Li

    2013-07-01

    Full Text Available The noise level was one of the important indicators as a measure of the quality and performance of the diesel engine, exhaust noise in diesel engines machine noise accounted for an important proportion of installed performance exhaust mufflerwas an effective way to control exhaust noise. This article using orthogonal test program was to the muffler structure parameters as input to the sound pressure level and diesel fuel each output artificial neural network (BP network learning sample. Matlab artificial neural network toolbox to complete the training of the network, and better noise performance and fuel consumption rate performance muffler internal structure parameters combination was obtained through genetic algorithm gifted collaborative validation of artificial neural networks and genetic algorithms to optimize application exhaust muffler design is entirely feasible

  15. 75 FR 1585 - Draft Environmental Impact Statement; Determination of Regulated Status of Alfalfa Genetically...

    Science.gov (United States)

    2010-01-12

    ...,'' regulate, among other things, the introduction (importation, interstate movement, or release into the environment) of organisms and products altered or produced through genetic engineering that are plant pests or... INFORMATION: The regulations in 7 CFR part 340, ``Introduction of Organisms and Products Altered or Produced...

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

    Directory of Open Access Journals (Sweden)

    Narges Zarrabi

    Full Text Available 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

  17. ARN: Analysis and Visualization System for Adipogenic Regulation Network Information.

    Science.gov (United States)

    Huang, Yan; Wang, Li; Zan, Lin-Sen

    2016-12-16

    Adipogenesis is the process of cell differentiation through which preadipocytes become adipocytes. Lots of research is currently ongoing to identify genes, including their gene products and microRNAs, that correlate with fat cell development. However, information fragmentation hampers the identification of key regulatory genes and pathways. Here, we present a database of literature-curated adipogenesis-related regulatory interactions, designated the Adipogenesis Regulation Network (ARN, http://210.27.80.93/arn/), which currently contains 3101 nodes (genes and microRNAs), 1863 regulatory interactions, and 33,969 expression records associated with adipogenesis, based on 1619 papers. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 37,000 PubMed abstracts. Additionally, we further determined 13,103 possible node relationships by searching miRGate, BioGRID, PAZAR and TRRUST. ARN also has several useful features: i) regulatory map information; ii) tests to examine the impact of a query node on adipogenesis; iii) tests for the interactions and modes of a query node; iv) prediction of interactions of a query node; and v) analysis of experimental data or the construction of hypotheses related to adipogenesis. In summary, ARN can store, retrieve and analyze adipogenesis-related information as well as support ongoing adipogenesis research and contribute to the discovery of key regulatory genes and pathways.

  18. Optimization procedures for establishing reserve networks for biodiversity conservation taking into account population genetic structure

    Directory of Open Access Journals (Sweden)

    José Alexandre Felizola Diniz Filho

    2006-01-01

    Full Text Available Conservation genetics has been focused on the ecological and evolutionary persistence of targets (species or other intraspecific units, especially when dealing with narrow-ranged species, and no generalized solution regarding the problem of where to concentrate conservation efforts for multiple genetic targets has yet been achieved. Broadly distributed and abundant species allow the identification of evolutionary significant units, management units, phylogeographical units or other spatial patterns in genetic variability, including those generated by effects of habitat fragmentation caused by human activities. However, these genetic units are rarely considered as priority conservation targets in regional conservation planning procedures. In this paper, we discuss a theoretical framework in which target persistence and genetic representation of targets defined using multiple genetic criteria can be explicitly incorporated into the broad-scale reserve network models used to optimize biodiversity conservation based on multiple species data. When genetic variation can be considered discrete in geographical space, the solution is straightforward, and each spatial unit must be considered as a distinct target. But methods for dealing with continuous genetic variation in space are not trivial and optimization procedures must still be developed. We present a simple heuristic and sequential algorithm to deal with this problem by combining multiple networks of local populations of multiple species in which minimum separation distance between conserved populations is a function of spatial autocorrelation patterns of genetic variability within each species.

  19. A genetic algorithm solution for the operation of green LTE networks with energy and environment considerations

    KAUST Repository

    Ghazzai, Hakim

    2012-01-01

    The Base Station (BS) sleeping strategy has become a well-known technique to achieve energy savings in cellular networks by switching off redundant BSs mainly for lightly loaded networks. Besides, the exploitation of renewable energies, as additional power sources in smart grids, becomes a real challenge to network operators to reduce power costs. In this paper, we propose a method based on genetic algorithms that decreases the energy consumption of a Long-Term Evolution (LTE) cellular network by not only shutting down underutilized BSs but also by optimizing the amounts of energy procured from the smart grid without affecting the desired Quality of Service. © 2012 Springer-Verlag.

  20. An integrative analysis of gene expression and molecular interaction data to identify dys-regulated sub-networks in inflammatory bowel disease.

    Science.gov (United States)

    Muraro, Daniele; Simmons, Alison

    2016-01-19

    Inflammatory bowel disease (IBD) consists of two main disease-subtypes, Crohn's disease (CD) and ulcerative colitis (UC); these subtypes share overlapping genetic and clinical features. Genome-wide microarray data enable unbiased documentation of alterations in gene expression that may be disease-specific. As genetic diseases are believed to be caused by genetic alterations affecting the function of signalling pathways, module-centric optimisation algorithms, whose aim is to identify sub-networks that are dys-regulated in disease, are emerging as promising approaches. In order to account for the topological structure of molecular interaction networks, we developed an optimisation algorithm that integrates databases of known molecular interactions with gene expression data; such integration enables identification of differentially regulated network modules. We verified the performance of our algorithm by testing it on simulated networks; we then applied the same method to study experimental data derived from microarray analysis of CD and UC biopsies and human interactome databases. This analysis allowed the extraction of dys-regulated subnetworks under different experimental conditions (inflamed and uninflamed tissues in CD and UC). Optimisation was performed to highlight differentially expressed network modules that may be common or specific to the disease subtype. We show that the selected subnetworks include genes and pathways of known relevance for IBD; in particular, the solutions found highlight cross-talk among enriched pathways, mainly the JAK/STAT signalling pathway and the EGF receptor signalling pathway. In addition, integration of gene expression with molecular interaction data highlights nodes that, although not being differentially expressed, interact with differentially expressed nodes and are part of pathways that are relevant to IBD. The method proposed here may help identifying dys-regulated sub-networks that are common in different diseases and

  1. Sensitization to cockroach allergen: immune regulation and genetic determinants.

    Science.gov (United States)

    Gao, Peisong

    2012-01-01

    Asthma is a major public health concern. Cockroach allergen exposure and cockroach allergic sensitization could contribute to the higher prevalence of asthma. However, the underlying immune mechanism and the genetic etiology remain unclear. Recent advances have demonstrated that several receptors (PAR-2, TLRs, CLRs) and their pathways mediate antigen uptake from the environment and induce allergies by signaling T cells to activate an inappropriate immune response. Cockroach-derived protease can disturb airway epithelial integrity via PAR-2 and leads to an increased penetration of cockroach allergen, resulting in activation of innate immune cells (e.g., DCs) via binding to either TLRs or CLRs. The activated DCs can direct cells of the adaptive immune system to facilitate promotion of Th2 cell response and subsequently increase risk of sensitization. Mannose receptor (MR), as a CLR, has been shown to mediate Bla g2 (purified cockroach allergen) uptake by DCs and to determine allergen-induced T cell polarization. Additionally, genetic factors may play an important role in conferring the susceptibility to cockroach sensitization. Several genes have been associated with cockroach sensitization and related phenotypes (HLA-D, TSLP, IL-12A, MBL2). In this review, we have focused on studies on the cockroach allergen induced immunologic responses and genetic basis for cockroach sensitization.

  2. Sensitization to Cockroach Allergen: Immune Regulation and Genetic Determinants

    Directory of Open Access Journals (Sweden)

    Peisong Gao

    2012-01-01

    Full Text Available Asthma is a major public health concern. Cockroach allergen exposure and cockroach allergic sensitization could contribute to the higher prevalence of asthma. However, the underlying immune mechanism and the genetic etiology remain unclear. Recent advances have demonstrated that several receptors (PAR-2, TLRs, CLRs and their pathways mediate antigen uptake from the environment and induce allergies by signaling T cells to activate an inappropriate immune response. Cockroach-derived protease can disturb airway epithelial integrity via PAR-2 and leads to an increased penetration of cockroach allergen, resulting in activation of innate immune cells (e.g., DCs via binding to either TLRs or CLRs. The activated DCs can direct cells of the adaptive immune system to facilitate promotion of Th2 cell response and subsequently increase risk of sensitization. Mannose receptor (MR, as a CLR, has been shown to mediate Bla g2 (purified cockroach allergen uptake by DCs and to determine allergen-induced T cell polarization. Additionally, genetic factors may play an important role in conferring the susceptibility to cockroach sensitization. Several genes have been associated with cockroach sensitization and related phenotypes (HLA-D, TSLP, IL-12A, MBL2. In this review, we have focused on studies on the cockroach allergen induced immunologic responses and genetic basis for cockroach sensitization.

  3. LICORN: learning cooperative regulation networks from gene expression data

    National Research Council Canada - National Science Library

    Elati, Mohamed; Neuvial, Pierre; Bolotin-Fukuhara, Monique; Barillot, Emmanuel; Radvanyi, François; Rouveirol, Céline

    2007-01-01

    .... The goal is to identify, for each gene expressed in a particular cellular context, the regulators affecting its transcription, and the co-ordination of several regulators in specific types of regulation...

  4. Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks

    Science.gov (United States)

    Aguiar, Manuela A. D.; Dias, Ana Paula S.; Ferreira, Flora

    2017-01-01

    We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks.

  5. Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks.

    Science.gov (United States)

    Aguiar, Manuela A D; Dias, Ana Paula S; Ferreira, Flora

    2017-01-01

    We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks.

  6. Genetic Approaches to Hypothalamic-Pituitary-Adrenal Axis Regulation

    Science.gov (United States)

    Arnett, Melinda G; Muglia, Lisa M; Laryea, Gloria; Muglia, Louis J

    2016-01-01

    The normal function of the hypothalamic-pituitary-adrenal (HPA) axis, and resultant glucocorticoid (GC) secretion, is essential for human health. Disruption of GC regulation is associated with pathologic, psychological, and physiological disease states such as depression, post-traumatic stress disorder, hypertension, diabetes, and osteopenia, among others. As such, understanding the mechanisms by which HPA output is tightly regulated in its responses to environmental stressors and circadian cues has been an active area of investigation for decades. Over the last 20 years, however, advances in gene targeting and genome modification in rodent models have allowed the detailed dissection of roles for key molecular mediators and brain regions responsible for this control in vivo to emerge. Here, we summarize work done to elucidate the function of critical neuropeptide systems, GC-signaling targets, and inflammation-associated pathways in HPA axis regulation and behavior, and highlight areas for future investigation. PMID:26189452

  7. Optimality principles in the regulation of metabolic networks

    NARCIS (Netherlands)

    Berkhout, J.; Bruggeman, F.J.; Teusink, B.

    2012-01-01

    One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks

  8. Genetic regulation of the intercellular adhesion locus in staphylococci

    Directory of Open Access Journals (Sweden)

    David R Cue

    2012-03-01

    Full Text Available The formation of biofilms by Staphylococcus aureus and Staphylococcus epidermidis is an important aspect of many staphylococcal infections, most notably endocarditis, osteomyelitis and infections associated with indwelling medical devices. The major constituents of S. aureus biofilms are polysaccharides, such as poly N-acetyl glucosamine (PIA/PNAG, cell surface and secreted bacterial proteins, and extracellular DNA. The exact composition of biofilms often varies considerably between different strains of staphylococci and between different sites of infection by the same strain. PIA/PNAG is synthesized by the products of 4 genes, icaADBC, that are encoded in a single operon. A fifth gene, icaR, is a negative regulator of icaADBC. Expression of icaADBC is tightly regulated, but can often be induced in vitro by growing staphylococci in the presence of high salt, high glucose or ethanol. Regulation of icaADBC is complex and numerous regulatory factors have been implicated in control of icaADBC. Many of these are well known global transcriptional regulatory factors like SarA and sigmaB, whereas other regulators, such as IcaR, seem to affect expression of relatively few genes. Here, we will attempt to summarize how various regulatory factors affect the production of PIA/PNAG in staphylococci.

  9. Genetic and epigenetic regulation of CCR5 transcription

    NARCIS (Netherlands)

    Wierda, R.J.; van den Elsen, P.J.

    2012-01-01

    The chemokine receptor CCR5 regulates trafficking of immune cells of the lymphoid and the myeloid lineage (such as monocytes, macrophages and immature dendritic cells) and microglia. Because of this, there is an increasing recognition of the important role of CCR5 in the pathology of (neuro-)

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  12. Multi-species genetic connectivity in a terrestrial habitat network.

    Science.gov (United States)

    Marrotte, Robby R; Bowman, Jeff; Brown, Michael G C; Cordes, Chad; Morris, Kimberley Y; Prentice, Melanie B; Wilson, Paul J

    2017-01-01

    Habitat fragmentation reduces genetic connectivity for multiple species, yet conservation efforts tend to rely heavily on single-species connectivity estimates to inform land-use planning. Such conservation activities may benefit from multi-species connectivity estimates, which provide a simple and practical means to mitigate the effects of habitat fragmentation for a larger number of species. To test the validity of a multi-species connectivity model, we used neutral microsatellite genetic datasets of Canada lynx (Lynx canadensis), American marten (Martes americana), fisher (Pekania pennanti), and southern flying squirrel (Glaucomys volans) to evaluate multi-species genetic connectivity across Ontario, Canada. We used linear models to compare node-based estimates of genetic connectivity for each species to point-based estimates of landscape connectivity (current density) derived from circuit theory. To our knowledge, we are the first to evaluate current density as a measure of genetic connectivity. Our results depended on landscape context: habitat amount was more important than current density in explaining multi-species genetic connectivity in the northern part of our study area, where habitat was abundant and fragmentation was low. In the south however, where fragmentation was prevalent, genetic connectivity was correlated with current density. Contrary to our expectations however, locations with a high probability of movement as reflected by high current density were negatively associated with gene flow. Subsequent analyses of circuit theory outputs showed that high current density was also associated with high effective resistance, underscoring that the presence of pinch points is not necessarily indicative of gene flow. Overall, our study appears to provide support for the hypothesis that landscape pattern is important when habitat amount is low. We also conclude that while current density is proportional to the probability of movement per unit area, this

  13. Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data.

    Science.gov (United States)

    Fu, Changhe; Deng, Su; Jin, Guangxu; Wang, Xinxin; Yu, Zu-Guo

    2017-09-21

    Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously

  14. Use of genetic algorithms for encoding efficient neural network architectures: neurocomputer implementation

    Science.gov (United States)

    James, Jason; Dagli, Cihan H.

    1995-04-01

    In this study an attempt is being made to encode the architecture of a neural network in a chromosome string for evolving robust, fast-learning, minimal neural network architectures through genetic algorithms. Various attributes affecting the learning of the network are represented as genes. The performance of the networks is used as the fitness value. Neural network architecture design concepts are initially demonstrated using a backpropagation architecture with the standard data set of Rosenberg and Sejnowski for text to speech conversion on Adaptive Solutions Inc.'s CNAPS Neuro-Computer. The architectures obtained are compared with the one reported in the literature for the standard data set used. The study concludes by providing some insights regarding the architecture encoding for other artificial neural network paradigms.

  15. A Network of HMG-box Transcription Factors Regulates Sexual Cycle in the Fungus Podospora anserina

    Science.gov (United States)

    Ait Benkhali, Jinane; Coppin, Evelyne; Brun, Sylvain; Peraza-Reyes, Leonardo; Martin, Tom; Dixelius, Christina; Lazar, Noureddine; van Tilbeurgh, Herman; Debuchy, Robert

    2013-01-01

    High-mobility group (HMG) B proteins are eukaryotic DNA-binding proteins characterized by the HMG-box functional motif. These transcription factors play a pivotal role in global genomic functions and in the control of genes involved in specific developmental or metabolic pathways. The filamentous ascomycete Podospora anserina contains 12 HMG-box genes. Of these, four have been previously characterized; three are mating-type genes that control fertilization and development of the fruit-body, whereas the last one encodes a factor involved in mitochondrial DNA stability. Systematic deletion analysis of the eight remaining uncharacterized HMG-box genes indicated that none were essential for viability, but that seven were involved in the sexual cycle. Two HMG-box genes display striking features. PaHMG5, an ortholog of SpSte11 from Schizosaccharomyces pombe, is a pivotal activator of mating-type genes in P. anserina, whereas PaHMG9 is a repressor of several phenomena specific to the stationary phase, most notably hyphal anastomoses. Transcriptional analyses of HMG-box genes in HMG-box deletion strains indicated that PaHMG5 is at the hub of a network of several HMG-box factors that regulate mating-type genes and mating-type target genes. Genetic analyses revealed that this network also controls fertility genes that are not regulated by mating-type transcription factors. This study points to the critical role of HMG-box members in sexual reproduction in fungi, as 11 out of 12 members were involved in the sexual cycle in P. anserina. PaHMG5 and SpSte11 are conserved transcriptional regulators of mating-type genes, although P. anserina and S. pombe diverged 550 million years ago. Two HMG-box genes, SOX9 and its upstream regulator SRY, also play an important role in sex determination in mammals. The P. anserina and S. pombe mating-type genes and their upstream regulatory factor form a module of HMG-box genes analogous to the SRY/SOX9 module, revealing a commonality of sex

  16. MetaNetwork : A computational protocol for the genetic study of metabolic networks

    NARCIS (Netherlands)

    Fu, Jingyuan; Swertz, Morris A.; Keurentjes, Joost J. B.; Jansen, Ritsert C.

    2007-01-01

    We here describe the MetaNetwork protocol to reconstruct metabolic networks using metabolite abundance data from segregating populations. MetaNetwork maps metabolite quantitative trait loci (mQTLs) underlying variation in metabolite abundance in individuals of a segregating population using a

  17. MetaNetwork: a computational protocol for the genetic study of metabolic networks

    NARCIS (Netherlands)

    Fu, J.; Swertz, M.A.; Keurentjes, J.J.B.; Jansen, R.C.

    2007-01-01

    We here describe the MetaNetwork protocol to reconstruct metabolic networks using metabolite abundance data from segregating populations. MetaNetwork maps metabolite quantitative trait loci (mQTLs) underlying variation in metabolite abundance in individuals of a segregating population using a

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  20. Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

    . Additionally, weather disturbances such as solar heat gain can be anticipated and compensated for, while taking into account the slow dynamics of the floor. Together with a genetic algorithm, they provide a way to search for optimal future set-point sequences, when convexity and continuity in the solution......This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures...

  1. Phelan-McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports.

    Science.gov (United States)

    Kothari, Cartik; Wack, Maxime; Hassen-Khodja, Claire; Finan, Sean; Savova, Guergana; O'Boyle, Megan; Bliss, Geraldine; Cornell, Andria; Horn, Elizabeth J; Davis, Rebecca; Jacobs, Jacquelyn; Kohane, Isaac; Avillach, Paul

    2017-09-01

    The heterogeneity of patient phenotype data are an impediment to the research into the origins and progression of neuropsychiatric disorders. This difficulty is compounded in the case of rare disorders such as Phelan-McDermid Syndrome (PMS) by the paucity of patient clinical data. PMS is a rare syndromic genetic cause of autism and intellectual deficiency. In this paper, we describe the Phelan-McDermid Syndrome Data Network (PMS_DN), a platform that facilitates research into phenotype-genotype correlation and progression of PMS by: a) integrating knowledge of patient phenotypes extracted from Patient Reported Outcomes (PRO) data and clinical notes-two heterogeneous, underutilized sources of knowledge about patient phenotypes-with curated genetic information from the same patient cohort and b) making this integrated knowledge, along with a suite of statistical tools, available free of charge to authorized investigators on a Web portal https://pmsdn.hms.harvard.edu. PMS_DN is a Patient Centric Outcomes Research Initiative (PCORI) where patients and their families are involved in all aspects of the management of patient data in driving research into PMS. To foster collaborative research, PMS_DN also makes patient aggregates from this knowledge available to authorized investigators using distributed research networks such as the PCORnet PopMedNet. PMS_DN is hosted on a scalable cloud based environment and complies with all patient data privacy regulations. As of October 31, 2016, PMS_DN integrates high-quality knowledge extracted from the clinical notes of 112 patients and curated genetic reports of 176 patients with preprocessed PRO data from 415 patients. © 2017 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

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

    Science.gov (United States)

    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.

  3. Optimality principles in the regulation of metabolic networks.

    Science.gov (United States)

    Berkhout, Jan; Bruggeman, Frank J; Teusink, Bas

    2012-08-29

    One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular "task" of the network-its function-should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.

  4. Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.

    Science.gov (United States)

    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.

  5. Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.

    Directory of Open Access Journals (Sweden)

    Matan Sorek

    Full Text Available 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.

  6. Network statistics of genetically-driven gene co-expression modules in mouse crosses

    Directory of Open Access Journals (Sweden)

    Marie-Pier eScott-Boyer

    2013-12-01

    Full Text Available In biology, networks are used in different contexts as ways to represent relationships between entities, such as for instance interactions between genes, proteins or metabolites. Despite progress in the analysis of such networks and their potential to better understand the collective impact of genes on complex traits, one remaining challenge is to establish the biologic validity of gene co-expression networks and to determine what governs their organization. We used WGCNA to construct and analyze seven gene expression datasets from several tissues of mouse recombinant inbred strains (RIS. For six out of the 7 networks, we found that linkage to module QTLs (mQTLs could be established for 29.3% of gene co-expression modules detected in the several mouse RIS. For about 74.6% of such genetically-linked modules, the mQTL was on the same chromosome as the one contributing most genes to the module, with genes originating from that chromosome showing higher connectivity than other genes in the modules. Such modules (that we considered as genetically-driven had network statistic properties (density, centralization and heterogeneity that set them apart from other modules in the network. Altogether, a sizeable portion of gene co-expression modules detected in mouse RIS panels had genetic determinants as their main organizing principle. In addition to providing a biologic interpretation validation for these modules, these genetic determinants imparted on them particular properties that set them apart from other modules in the network, to the point that they can be predicted to a large extent on the basis of their network statistics.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    Martin, Alexander; Ochagavia, Maria E; Rabasa, Laya C; Miranda, Jamilet; Fernandez-de-Cossio, Jorge; Bringas, Ricardo

    2010-02-17

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

  9. Genetic Evaluation of Children with Global Developmental Delay—Current Status of Network Systems in Taiwan

    Directory of Open Access Journals (Sweden)

    Yong-Lin Foo

    2015-08-01

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

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

    Science.gov (United States)

    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.

  11. The state of genetically modified crop regulation in Canada

    Science.gov (United States)

    Smyth, Stuart J

    2014-01-01

    Genetically modified (GM) crops were first commercialized in Canada in 1995 and the 2014 crop represents the 20th year of successful production. Prior to the first commercialization of GM crops, Canada reviewed its existing science-based regulatory framework and adapted the existing framework to allow for risk assessments on the new technology to be undertaken in a timely and efficient manner. The result has been the rapid and widespread adoption of GM varieties of canola, corn and soybeans. The first decade of GM crop production precipitated 2 landmark legal cases relating to patent infringement and economic liability, while the second decade witnessed increased political efforts to have GM crops labeled in Canada as well as significant challenges from the low level comingling of GM crops with non-GM commodities. This article reviews the 20 y of GM crop production in Canada from a social science perspective that includes intellectual property, consumer acceptance and low level presence. PMID:25437238

  12. The state of genetically modified crop regulation in Canada.

    Science.gov (United States)

    Smyth, Stuart J

    2014-07-03

    Genetically modified (GM) crops were first commercialized in Canada in 1995 and the 2014 crop represents the 20th year of successful production. Prior to the first commercialization of GM crops, Canada reviewed its existing science-based regulatory framework and adapted the existing framework to allow for risk assessments on the new technology to be undertaken in a timely and efficient manner. The result has been the rapid and widespread adoption of GM varieties of canola, corn and soybeans. The first decade of GM crop production precipitated 2 landmark legal cases relating to patent infringement and economic liability, while the second decade witnessed increased political efforts to have GM crops labeled in Canada as well as significant challenges from the low level comingling of GM crops with non-GM commodities. This article reviews the 20 y of GM crop production in Canada from a social science perspective that includes intellectual property, consumer acceptance and low level presence.

  13. Sex-biased genetic effects on gene regulation in humans

    Science.gov (United States)

    Dimas, Antigone S.; Nica, Alexandra C.; Montgomery, Stephen B.; Stranger, Barbara E.; Raj, Towfique; Buil, Alfonso; Giger, Thomas; Lappalainen, Tuuli; Gutierrez-Arcelus, Maria; McCarthy, Mark I.; Dermitzakis, Emmanouil T.

    2012-01-01

    Human regulatory variation, reported as expression quantitative trait loci (eQTLs), contributes to differences between populations and tissues. The contribution of eQTLs to differences between sexes, however, has not been investigated to date. Here we explore regulatory variation in females and males and demonstrate that 12%–15% of autosomal eQTLs function in a sex-biased manner. We show that genes possessing sex-biased eQTLs are expressed at similar levels across the sexes and highlight cases of genes controlling sexually dimorphic and shared traits that are under the control of distinct regulatory elements in females and males. This study illustrates that sex provides important context that can modify the effects of functional genetic variants. PMID:22960374

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

    Science.gov (United States)

    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 F ST ) while explicitly accounting for intraspecific demographic influences on effective population size (N e ). 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 N e was a strong predictor of pairwise F ST 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.

  15. Optimality Principles in the Regulation of Metabolic Networks

    Directory of Open Access Journals (Sweden)

    Jan Berkhout

    2012-08-01

    Full Text Available One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular “task” of the network—its function—should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.

  16. Harnessing mobile genetic elements to explore gene regulation.

    Science.gov (United States)

    Shakes, Leighcraft A; Wolf, Hope M; Norford, Derek C; Grant, Delores J; Chatterjee, Pradeep K

    2014-01-01

    Sequences that regulate expression of a gene in cis but are located at large distances along the DNA from the gene, as found with most developmentally regulated genes in higher vertebrates, are difficult to identify if those sequences are not conserved across species. Mutating suspected gene-regulatory sequences to alter expression then becomes a hit-or-miss affair. The relaxed specificity of transposon insertions offers an opportunity to develop alternate strategies, to scan in an unbiased manner, pieces of chromosomal DNA cloned in BACs for transcription enhancing elements. This article illustrates how insertions of Tn10 with enhancer-traps into BAC DNA containing the gene, and its germ-line expression in zebrafish, have identified distal regulatory elements functionally. Transposition of Tn10 first introduces the enhancer-trap with a loxP site randomly into BAC DNA. Cre-recombination between the inserted loxP and the loxP endogenous to a BAC-end positions the enhancer-trap to the newly created truncated end of BAC DNA. The procedure generates a library of integration-ready enhancer-trap BACs with progressive truncations from an end in a single experiment. Individual enhancer-trap BACs from the library can be evaluated functionally in zebrafish or mice. Furthermore, the ability to readily alter sequences in a small transposon plasmid containing a regulatory domain of the gene allows re-introduction of altered parts of a BAC back into itself. It serves as a useful strategy to functionally dissect multiple discontinuous regulatory domains of a gene quickly. These methodologies have been successfully used in identifying novel regulatory domains of the Amyloid Precursor Protein (appb) gene in zebrafish, and provided important clues for regulation of the gene in humans.

  17. Optimizing information flow in small genetic networks. IV. Spatial coupling

    Science.gov (United States)

    Sokolowski, Thomas R.; Tkačik, Gašper

    2015-06-01

    We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the input) by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively different regulatory strategy emerges where individual cells respond to the input in a nearly steplike fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models.

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

    Science.gov (United States)

    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.

  19. Genetics and Insurance in Australia: Concerns around a Self-Regulated Industry.

    Science.gov (United States)

    Newson, Ainsley J; Tiller, Jane; Keogh, Louise A; Otlowski, Margaret; Lacaze, Paul

    2017-01-01

    Regulating the use of genetic information in insurance is an issue of ongoing international debate. In Australia, providers of life and other mutually rated insurance products can request applicants to disclose all results from any genetic test. Insurers can then use this information to adjust premiums and make policy decisions. The Australian Financial Services Council (FSC; an industry body) developed and maintains the relevant industry standard, which was updated in late 2016. Aims/Objective: To review the 2016 FSC Standard in light of relevant research and determine the legitimacy of the Australian regulatory environment regarding use of genetic information by insurers. We identified five concerns arising from the 2016 FSC Standard: (1) use of results obtained from research; (2) the requirement for an applicant to disclose whether they are "considering" a genetic test; (3) failure to account for genome sequencing and other technology developments; (4) limited evidence regarding adverse selection; and (5) the inappropriateness of industry self-regulation. Industry self-regulation of the use of genetic information by life insurers, combined with a lack of government oversight, is inappropriate and threatens to impede the progress of genomic medicine in Australia. At this critical time, Australia requires closer government oversight of the use of genetic information in insurance. © 2017 S. Karger AG, Basel.

  20. Genetic Regulation of Maternal Oxytocin Response and Its Influences on Maternal Behavior

    Directory of Open Access Journals (Sweden)

    Divya Mehta

    2016-01-01

    Full Text Available We interrogated the genetic modulation of maternal oxytocin response and its association with maternal behavior using genetic risk scores within the oxytocin receptor (OXTR gene. We identified a novel SNP, rs968389, to be significantly associated with maternal oxytocin response after a challenging mother-infant interaction task (Still Face Paradigm and maternal separation anxiety from the infant. Performing a multiallelic analysis across OXTR by calculating a cumulative genetic risk score revealed a significant gene-by-environment (G×E interaction, with OXTR genetic risk score interacting with adult separation anxiety to modulate levels of maternal sensitivity. Mothers with higher OXTR genetic risk score and adult separation anxiety showed significantly reduced levels of maternal sensitivity during free play with the infant. The same G×E interaction was also observed for the extended OXTR cumulative genetic risk score that included rs968389. Moreover, the extended cumulative OXTR genetic risk score itself was found to be significantly associated with maternal separation anxiety as it specifically relates to the infant. Our results suggest a complex montage of individual and synergistic genetic mediators of maternal behavior. These findings add to specific knowledge about genetic regulation of maternal oxytocin response in relation to maternal adjustment and infant bonding through the first few months of life.

  1. Genetic Regulation of Virulence and Antibiotic Resistance in Acinetobacter baumannii

    Science.gov (United States)

    Kröger, Carsten; Kary, Stefani C.; Schauer, Kristina; Cameron, Andrew D. S.

    2016-01-01

    Multidrug resistant microorganisms are forecast to become the single biggest challenge to medical care in the 21st century. Over the last decades, members of the genus Acinetobacter have emerged as bacterial opportunistic pathogens, in particular as challenging nosocomial pathogens because of the rapid evolution of antimicrobial resistances. Although we lack fundamental biological insight into virulence mechanisms, an increasing number of researchers are working to identify virulence factors and to study antibiotic resistance. Here, we review current knowledge regarding the regulation of virulence genes and antibiotic resistance in Acinetobacter baumannii. A survey of the two-component systems AdeRS, BaeSR, GacSA and PmrAB explains how each contributes to antibiotic resistance and virulence gene expression, while BfmRS regulates cell envelope structures important for pathogen persistence. A. baumannii uses the transcription factors Fur and Zur to sense iron or zinc depletion and upregulate genes for metal scavenging as a critical survival tool in an animal host. Quorum sensing, nucleoid-associated proteins, and non-classical transcription factors such as AtfA and small regulatory RNAs are discussed in the context of virulence and antibiotic resistance. PMID:28036056

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

    Science.gov (United States)

    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.

  3. DMPD: Toll-like receptor (TLR)-based networks regulate neutrophilic inflammation inrespiratory disease. [Dynamic Macrophage Pathway CSML Database

    Lifescience Database Archive (English)

    Full Text Available 18031251 Toll-like receptor (TLR)-based networks regulate neutrophilic inflammation...l) (.csml) Show Toll-like receptor (TLR)-based networks regulate neutrophilic inflammation inrespiratory dis...ease. PubmedID 18031251 Title Toll-like receptor (TLR)-based networks regulate neutrophilic inflammation

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

    NARCIS (Netherlands)

    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

  5. Co-regulation of metabolic genes is better explained by flux coupling than by network distance.

    Directory of Open Access Journals (Sweden)

    Richard A Notebaart

    2008-01-01

    Full Text Available To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

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

    Science.gov (United States)

    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.

  7. Incentive-Based Voltage Regulation in Distribution Networks

    Energy Technology Data Exchange (ETDEWEB)

    Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Baker, Kyri A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhou, Xinyang [University of Colorado; Chen, Lijun [University of Colorado

    2017-07-03

    This paper considers distribution networks fea- turing distributed energy resources, and designs incentive-based mechanisms that allow the network operator and end-customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Two different network-customer coordination mechanisms that require different amounts of information shared between the network operator and end-customers are developed to identify a solution of a well-defined social-welfare maximization prob- lem. Notably, the signals broadcast by the network operator assume the connotation of prices/incentives that induce the end- customers to adjust the generated/consumed powers in order to avoid the violation of the voltage constraints. Stability of the proposed schemes is analytically established and numerically corroborated.

  8. Incentive-Based Voltage Regulation in Distribution Networks: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Xinyang; Chen, Lijun; Dall' Anese, Emiliano; Baker, Kyri

    2017-03-03

    This paper considers distribution networks fea- turing distributed energy resources, and designs incentive-based mechanisms that allow the network operator and end-customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Two different network-customer coordination mechanisms that require different amounts of information shared between the network operator and end-customers are developed to identify a solution of a well-defined social-welfare maximization prob- lem. Notably, the signals broadcast by the network operator assume the connotation of prices/incentives that induce the end- customers to adjust the generated/consumed powers in order to avoid the violation of the voltage constraints. Stability of the proposed schemes is analytically established and numerically corroborated.

  9. Commercializing genetically modified crops under EU regulations: objectives and barriers.

    Science.gov (United States)

    Raybould, Alan; Poppy, Guy M

    2012-01-01

    Agriculture faces serious problems in feeding 9 billion people by 2050: production must be increased and ecosystem services maintained under conditions for growing crops that are predicted to worsen in many parts of the world. A proposed solution is sustainable intensification of agriculture, whereby yields are increased on land that is currently cultivated, so sparing land to deliver other ecosystem services. Genetically modified (GM) crops are already contributing to sustainable intensification through higher yields and lower environmental impacts, and have potential to deliver further significant improvements. Despite their widespread successful use elsewhere, the European Union (EU) has been slow to introduce GM crops: decisions on applications to import GM commodities are lengthy, and decision-making on applications to cultivate GM crops has virtually ceased. Delayed import approvals result in economic losses, particularly in the EU itself as a result of higher commodity prices. Failure to grant cultivation approvals costs EU farmers opportunities to reduce inputs, and results in loss of agricultural research and development from the EU to countries such as the United States and China. Delayed decision-making in the EU ostensibly results from scientific uncertainty about the effects of using GM crops; however, scientific uncertainty may be a means to justify a political decision to restrict cultivation of GM crops in the EU. The problems associated with delayed decision-making will not improve until there is clarity about the EU's agricultural policy objectives, and whether the use of GM crops will be permitted to contribute to achieving those objectives.

  10. Genetic regulation of glucoraphanin accumulation in Beneforté broccoli.

    Science.gov (United States)

    Traka, Maria H; Saha, Shikha; Huseby, Stine; Kopriva, Stanislav; Walley, Peter G; Barker, Guy C; Moore, Jonathan; Mero, Gene; van den Bosch, Frans; Constant, Howard; Kelly, Leo; Schepers, Hans; Boddupalli, Sekhar; Mithen, Richard F

    2013-06-01

    · Diets rich in broccoli (Brassica oleracea var italica) have been associated with maintenance of cardiovascular health and reduction in risk of cancer. These health benefits have been attributed to glucoraphanin that specifically accumulates in broccoli. The development of broccoli with enhanced concentrations of glucoraphanin may deliver greater health benefits. · Three high-glucoraphanin F1 broccoli hybrids were developed in independent programmes through genome introgression from the wild species Brassica villosa. Glucoraphanin and other metabolites were quantified in experimental field trials. Global SNP analyses quantified the differential extent of B. villosa introgression · The high-glucoraphanin broccoli hybrids contained 2.5-3 times the glucoraphanin content of standard hybrids due to enhanced sulphate assimilation and modifications in sulphur partitioning between sulphur-containing metabolites. All of the high-glucoraphanin hybrids possessed an introgressed B. villosa segment which contained a B. villosa Myb28 allele. Myb28 expression was increased in all of the high-glucoraphanin hybrids. Two high-glucoraphanin hybrids have been commercialised as Beneforté broccoli. · The study illustrates the translation of research on glucosinolate genetics from Arabidopsis to broccoli, the use of wild Brassica species to develop cultivars with potential consumer benefits, and the development of cultivars with contrasting concentrations of glucoraphanin for use in blinded human intervention studies. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

  11. Peroxisomes sense and respond to environmental cues by regulating ROS and RNS signalling networks.

    Science.gov (United States)

    Sandalio, L M; Romero-Puertas, M C

    2015-09-01

    Peroxisomes are highly dynamic, metabolically active organelles that used to be regarded as a sink for H2O2 generated in different organelles. However, peroxisomes are now considered to have a more complex function, containing different metabolic pathways, and they are an important source of reactive oxygen species (ROS), nitric oxide (NO) and reactive nitrogen species (RNS). Over-accumulation of ROS and RNS can give rise oxidative and nitrosative stress, but when produced at low concentrations they can act as signalling molecules. This review focuses on the production of ROS and RNS in peroxisomes and their regulation by antioxidants. ROS production is associated with metabolic pathways such as photorespiration and fatty acid β-oxidation, and disturbances in any of these processes can be perceived by the cell as an alarm that triggers defence responses. Genetic and pharmacological studies have shown that photorespiratory H2O2 can affect nuclear gene expression, regulating the response to pathogen infection and light intensity. Proteomic studies have shown that peroxisomal proteins are targets for oxidative modification, S-nitrosylation and nitration and have highlighted the importance of these modifications in regulating peroxisomal metabolism and signalling networks. The morphology, size, number and speed of movement of peroxisomes can also change in response to oxidative stress, meaning that an ROS/redox receptor is required. Information available on the production and detection of NO/RNS in peroxisomes is more limited. Peroxisomal homeostasis is critical for maintaining the cellular redox balance and is regulated by ROS, peroxisomal proteases and autophagic processes. Peroxisomes play a key role in many aspects of plant development and acclimation to stress conditions. These organelles can sense ROS/redox changes in the cell and thus trigger rapid and specific responses to environmental cues involving changes in peroxisomal dynamics as well as ROS- and NO

  12. Recombination networks as genetic markers in a human variation study of the Old World.

    NARCIS (Netherlands)

    Javed, A.; Mele, M.; Pybus, M.; Zalloua, P.; Haber, M.; Comas, D.; Netea, M.G.; Balanovsky, O.; Balanovska, E.; Jin, L.; Yang, Y.; Arunkumar, G.; Pitchappan, R.; Bertranpetit, J.; Calafell, F.; Parida, L.

    2012-01-01

    We have analyzed human genetic diversity in 33 Old World populations including 23 populations obtained through Genographic Project studies. A set of 1,536 SNPs in five X chromosome regions were genotyped in 1,288 individuals (mostly males). We use a novel analysis employing subARG network

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

    Science.gov (United States)

    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…

  14. Feature extraction of osteoporosis risk factors using artificial neural networks and genetic algorithms.

    Science.gov (United States)

    Anastassopoulos, George; Adamopoulos, Adam; Galiatsatos, Dimitrios; Drosos, Georgios

    2013-01-01

    A hybrid model that consists of an Artificial Neural Network and Genetic Algorithm is used in order to select the most significant osteoporosis risk factors. The results indicated that just 8 to 10 parameters, out of a total 34, are essential in order of high performance to be achieved.

  15. A hybrid Genetic and Simulated Annealing Algorithm for Chordal Ring implementation in large-scale networks

    DEFF Research Database (Denmark)

    Riaz, M. Tahir; Gutierrez Lopez, Jose Manuel; Pedersen, Jens Myrup

    2011-01-01

    of the networks. There have been many use of evolutionary algorithms to solve the problems which are in combinatory complexity nature, and extremely hard to solve by exact approaches. Both Genetic and Simulated annealing algorithms are similar in using controlled stochastic method to search the solution...

  16. Investigating the Relationship between Topology and Evolution in a Dynamic Nematode Odor Genetic Network

    Directory of Open Access Journals (Sweden)

    David A. Fitzpatrick

    2012-01-01

    Full Text Available The relationship between biological network architectures and evolution is unclear. Within the phylum nematoda olfaction represents a critical survival tool. For nematodes, olfaction contributes to multiple processes including the finding of food, hosts, and reproductive partners, making developmental decisions, and evading predators. Here we examine a dynamic nematode odor genetic network to investigate how divergence, diversity, and contribution are shaped by network topology. Our findings describe connectivity frameworks and characteristics that correlate with molecular evolution and contribution across the olfactory network. Our data helps guide the development of a robust evolutionary description of the nematode odor network that may eventually aid in the prediction of interactive and functional qualities of novel nodes.

  17. Finding Robust Adaptation Gene Regulatory Networks Using Multi-Objective Genetic Algorithm.

    Science.gov (United States)

    Ren, Hai-Peng; Huang, Xiao-Na; Hao, Jia-Xuan

    2016-01-01

    Robust adaptation plays a key role in gene regulatory networks, and it is thought to be an important attribute for the organic or cells to survive in fluctuating conditions. In this paper, a simplified three-node enzyme network is modeled by the Michaelis-Menten rate equations for all possible topologies, and a family of topologies and the corresponding parameter sets of the network with satisfactory adaptation are obtained using the multi-objective genetic algorithm. The proposed approach improves the computation efficiency significantly as compared to the time consuming exhaustive searching method. This approach provides a systemic way for searching the feasible topologies and the corresponding parameter sets to make the gene regulatory networks have robust adaptation. The proposed methodology, owing to its universality and simplicity, can be used to address more complex issues in biological networks.

  18. Genetic regulation of recurrent spontaneous abortion in humans

    Directory of Open Access Journals (Sweden)

    Daniel Vaiman

    2015-02-01

    Full Text Available Recurrent pregnancy loss, defined as a pregnancy failure occurring before 24 weeks of gestation more than two or three times according to most definitions, is a fertility defect encountered in 1-5% of the patients. This defect is of course of multifactorial origin. Among the possible origins of recurrent pregnancy loss are uterine structural defaults, defective ploidy control of the embryo, defective immunological dialog between the embryo (or the fetus and the uterus sometimes in relation with immunological disorders (such as autoimmune diseases, thrombophilia, and free radical metabolism imbalance. Numerous studies attempted to correlate variants of genes supposed to be intervening in the different facets of the early maternal-fetal or maternal-embryonic dialog, and eventually modify the outcome of fertilization, leading to success or failure of post-implantation development. The objective of the present review is to portray the major genes and gene polymorphisms studied for their putative association with recurrent pregnancy loss. Most of these genes have been studied as candidate genes for which strong biological arguments were put forward as to their putative involvement in recurrent pregnancy loss. They were mostly studied by genetic analysis, often in various populations of different ethnic origins, throughout the world. Some of these studies were available only in English as abstracts and were nevertheless used if the information was given with enough detail. With the space being too short to depict all the available literature, different major pathways releva nt to the scientific question are presented without any attempt to hide the fact that discordant views often aroused for a given gene.

  19. Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis

    Science.gov (United States)

    Hasmi, Laila; Drukker, Marjan; Guloksuz, Sinan; Menne-Lothmann, Claudia; Decoster, Jeroen; van Winkel, Ruud; Collip, Dina; Delespaul, Philippe; De Hert, Marc; Derom, Catherine; Thiery, Evert; Jacobs, Nele; Rutten, Bart P. F.; Wichers, Marieke; van Os, Jim

    2017-01-01

    Background: The network analysis of intensive time series data collected using the Experience Sampling Method (ESM) may provide vital information in gaining insight into the link between emotion regulation and vulnerability to psychopathology. The aim of this study was to apply the network approach to investigate whether genetic liability (GL) to psychopathology and childhood trauma (CT) are associated with the network structure of the emotions “cheerful,” “insecure,” “relaxed,” “anxious,” “irritated,” and “down”—collected using the ESM method. Methods: Using data from a population-based sample of twin pairs and siblings (704 individuals), we examined whether momentary emotion network structures differed across strata of CT and GL. GL was determined empirically using the level of psychopathology in monozygotic and dizygotic co-twins. Network models were generated using multilevel time-lagged regression analysis and were compared across three strata (low, medium, and high) of CT and GL, respectively. Permutations were utilized to calculate p values and compare regressions coefficients, density, and centrality indices. Regression coefficients were presented as connections, while variables represented the nodes in the network. Results: In comparison to the low GL stratum, the high GL stratum had significantly denser overall (p = 0.018) and negative affect network density (p emotions. The present finding partially replicates an earlier analysis, suggesting it may be instructive to model negative emotional dynamics as a function of genetic influence. PMID:29163289

  20. Temporal ChIP-on-chip reveals Biniou as a universal regulator of the visceral muscle transcriptional network.

    Science.gov (United States)

    Jakobsen, Janus S; Braun, Martina; Astorga, Jeanette; Gustafson, E Hilary; Sandmann, Thomas; Karzynski, Michal; Carlsson, Peter; Furlong, Eileen E M

    2007-10-01

    Smooth muscle plays a prominent role in many fundamental processes and diseases, yet our understanding of the transcriptional network regulating its development is very limited. The FoxF transcription factors are essential for visceral smooth muscle development in diverse species, although their direct regulatory role remains elusive. We present a transcriptional map of Biniou (a FoxF transcription factor) and Bagpipe (an Nkx factor) activity, as a first step to deciphering the developmental program regulating Drosophila visceral muscle development. A time course of chromatin immunoprecipitatation followed by microarray analysis (ChIP-on-chip) experiments and expression profiling of mutant embryos reveal a dynamic map of in vivo bound enhancers and direct target genes. While Biniou is broadly expressed, it regulates enhancers driving temporally and spatially restricted expression. In vivo reporter assays indicate that the timing of Biniou binding is a key trigger for the time span of enhancer activity. Although bagpipe and biniou mutants phenocopy each other, their regulatory potential is quite different. This network architecture was not apparent from genetic studies, and highlights Biniou as a universal regulator in all visceral muscle, regardless of its developmental origin or subsequent function. The regulatory connection of a number of Biniou target genes is conserved in mice, suggesting an ancient wiring of this developmental program.

  1. Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations.

    Directory of Open Access Journals (Sweden)

    Benjamin A Logsdon

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  3. Expression Profiling of Human Genetic and Protein Interaction Networks in Type 1 Diabetes

    DEFF Research Database (Denmark)

    Brunak, Søren; Bergholdt, R; Brorsson, C

    2009-01-01

    Proteins contributing to a complex disease are often members of the same functional pathways. Elucidation of such pathways may provide increased knowledge about functional mechanisms underlying disease. By combining genetic interactions in Type 1 Diabetes (T1D) with protein interaction data we have...... stress, regulation of transcription and apoptosis. To understand biological systems, integration of genetic and functional information is necessary, and the current study has used this approach to improve understanding of T1D and the underlying biological mechanisms....

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

    Science.gov (United States)

    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.

  5. 75 FR 19241 - Financial Crimes Enforcement Network; Amendment to the Bank Secrecy Act Regulations; Defining...

    Science.gov (United States)

    2010-04-14

    ... 31 CFR Part 103 RIN 1506-AA93 Financial Crimes Enforcement Network; Amendment to the Bank Secrecy Act Regulations; Defining Mutual Funds as Financial Institutions. AGENCY: Financial Crimes Enforcement Network... systems currently in place, the size of the mutual fund complex, and how the mutual fund shares are...

  6. Output regulation of large-scale hydraulic networks with minimal steady state power consumption

    NARCIS (Netherlands)

    Jensen, Tom Nørgaard; Wisniewski, Rafał; De Persis, Claudio; Kallesøe, Carsten Skovmose

    2014-01-01

    An industrial case study involving a large-scale hydraulic network is examined. The hydraulic network underlies a district heating system, with an arbitrary number of end-users. The problem of output regulation is addressed along with a optimization criterion for the control. The fact that the

  7. Normative and legal regulation of state testing, registration of plant varieties based on genetically modified organisms

    Directory of Open Access Journals (Sweden)

    М. О. Собова

    2012-02-01

    Full Text Available The review of legal regulation of state testing, registration of plant varieties based on genetically modified organisms in Ukraine and the European Union. Defined problems, namely the problem of harmonization of legislation, the actual Ukraine's lack of specific legislation in the area of biosafety for GMO handling, lack of clear procedures for registration of GMOs and others.

  8. Genetics of the ceramide/sphingosine-1-phosphate rheostat in blood pressure regulation and hypertension

    DEFF Research Database (Denmark)

    Fenger, Mogens; Linneberg, Allan; Jørgensen, Torben

    2011-01-01

    Several attempts to decipher the genetics of hypertension of unknown causes have been made including large-scale genome-wide association analysis (GWA), but only a few genes have been identified. Unsolved heterogeneity of the regulation of blood pressure and the shortcomings of the prevailing mon...

  9. Genetic architecture and phenotypic plasticity of thermally-regulated traits in an eruptive species, Dendroctonus ponderosae

    Science.gov (United States)

    Barbara J. Bentz; Ryan B. Bracewell; Karen E. Mock; Michael E. Pfrender

    2011-01-01

    Phenotypic plasticity in thermally-regulated traits enables close tracking of changing environmental conditions, and can thereby enhance the potential for rapid population increase, a hallmark of outbreak insect species. In a changing climate, exposure to conditions that exceed the capacity of existing phenotypic plasticity may occur. Combining information on genetic...

  10. Modelling genetic regulation of growth and form in a branching sponge

    NARCIS (Netherlands)

    Kaandorp, J.A.; Blom, J.G.; Verhoef, J.; Filatov, M.; Postma, M.; Müller, W.E.G.

    2008-01-01

    We present a mathematical model of the genetic regulation controlling skeletogenesis and the influence of the physical environment on a branching sponge with accretive growth (e.g. Haliclona oculata or Lubomirskia baikalensis). From previous work, it is known that high concentrations of silicate

  11. Modelling genetic regulation of growth and form in a branching sponge

    NARCIS (Netherlands)

    J.A. Kaandorp (Jaap); J.G. Blom (Joke); J. Verhoef; M. Filatov; M. Postma; W.E.G. Müller (Werner)

    2008-01-01

    htmlabstractWe present a mathematical model of the genetic regulation controlling skeletogenesis and the influence of the physical environment on a branching sponge with accretive growth (e.g. Haliclona oculata or Lubomirskia baikalensis). From previous work, it is known that high concentrations of

  12. Genetic Networks Activated by Blast Injury to the Eye

    Science.gov (United States)

    2013-08-01

    can deliver a 50-psi blast without muscle tears or death to the animal. In the process of modifying the blast to the eye we began to look for good...severity of blast-induced ocular pathologies, using a set of 60 BXD RI mouse strains and map the genomic loci that regulate the response of the eye to...this test from our characterization of phenotypic changes in the present study. Progress: We are measuring ocular phenotypes in the BXD RI strains

  13. Systems genetics reveals key genetic elements of drought induced gene regulation in diploid potato

    NARCIS (Netherlands)

    Muijen, van Dennis; Kumari, Anitha; Maliepaard, Chris; Visser, Richard G.F.; Linden, van der Gerard

    2016-01-01

    In plants, tolerance to drought stress is a result of numerous minor effect loci in which transcriptional regulation contributes significantly to the observed phenotypes. Under severe drought conditions, a major expression quantitative trait loci hotspot was identified on chromosome five in

  14. Designing a neural network for closed thermosyphon with nanofluid using a genetic algorithm

    Directory of Open Access Journals (Sweden)

    H. Salehi

    2011-03-01

    Full Text Available Heat transfer of a silver/water nanofluid in a two-phase closed thermosyphon that is thermally enhanced by magnetic field has been predicted by an optimized artificial Neural Network. Artificial neural network is a technique with flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data. A multi-layer perception neural network was used to estimate the thermal efficiency and resistance of a thermosyphon during application of a magnetic field and using nanoparticles in the water as the working fluid. The magnetic field strength, volume fraction of nanofluid in water and inlet power were used as input parameters and the thermal efficiency and thermal resistance were used as output parameters. The results were compared with experimental data and it was found that the thermal efficiency and resistance estimated by the multi-layer perception neural network are accurate. The GA-ANN (Genetic Algorithm-Artificial Neural network predicts the thermosyphon behavior correctly within the given range of the training data. In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm, has been used to predict collection output of a closed thermosyphon.

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

    Science.gov (United States)

    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 ( pdisease using CSNs, and demonstrated that the top-ranked genes are highly relevant to PD pathologenesis. We pin-pointed a top-ranked drug target gene for PD, and found its association with neurodegeneration supported by literature. In summary, CSNs lead to significantly improve the disease genetics prediction comparing with SBNs and provide leads for potential drug targets. nlp.case.edu/public/data/. rxx@case.edu.

  16. An Analysis of Social Seed Network and Its Contribution to On-Farm Conservation of Crop Genetic Diversity in Nepal

    Directory of Open Access Journals (Sweden)

    Diwakar Poudel

    2015-01-01

    Full Text Available Social seed systems are important for the maintenance of crop genetic diversity on farm. This is governed by local and informal system in the community through a farmers’ network. This paper analyses these local seed systems through application of social network analysis tools and mappings and examines the network member and its stability over space and time in a small rice farming community in Nepal. NetDraw software is used for data analysis and network mapping. We found that the dynamic network structure had key role in provisioning of traditional varieties and maintaining of crop genetic diversity on farm. We identify and ascertain the key network members, constituted either as nodal or bridging (connector farmers, occupying central position in the network who promote seed flow of local crop diversity, thus strengthening crop genetic resource diversity on farm.

  17. Why Control Activity? Evolutionary Selection Pressures Affecting the Development of Physical Activity Genetic and Biological Regulation

    Directory of Open Access Journals (Sweden)

    J. Timothy Lightfoot

    2013-01-01

    Full Text Available The literature strongly suggests that daily physical activity is genetically and biologically regulated. Potential identities of the responsible mechanisms are unclear, but little has been written concerning the possible evolutionary selection pressures leading to the development of genetic/biological controls of physical activity. Given the weak relationship between exercise endurance and activity levels and the differential genomic locations associated with the regulation of endurance and activity, it is probable that regulation of endurance and activity evolved separately. This hypothesis paper considers energy expenditures and duration of activity in hunter/gatherers, pretechnology farmers, and modern Western societies and considers the potential of each to selectively influence the development of activity regulation. Food availability is also considered given the known linkage of caloric restriction on physical activity as well as early data relating food oversupply to physical inactivity. Elucidating the selection pressures responsible for the genetic/biological control of activity will allow further consideration of these pressures on activity in today’s society, especially the linkages between food and activity. Further, current food abundance is removing the cues for activity that were present for the first 40,000 years of human evolution, and thus future research should investigate the effects of this abundance upon the mechanisms regulating activity.

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

    Science.gov (United States)

    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.

  19. Output Regulation of Large-Scale Hydraulic Networks with Minimal Steady State Power Consumption

    DEFF Research Database (Denmark)

    Jensen, Tom Nørgaard; Wisniewski, Rafal; De Persis, Claudio

    2014-01-01

    An industrial case study involving a large-scale hydraulic network is examined. The hydraulic network underlies a district heating system, with an arbitrary number of end-users. The problem of output regulation is addressed along with a optimization criterion for the control. The fact...... that the system is overactuated is exploited for minimizing the steady state electrical power consumption of the pumps in the system, while output regulation is maintained. The proposed control actions are decentralized in order to make changes in the structure of the hydraulic network easy to implement....

  20. Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis.

    Directory of Open Access Journals (Sweden)

    Brett A McKinney

    2009-03-01

    Full Text Available Evidence from human genetic studies of several disorders suggests that interactions between alleles at multiple genes play an important role in influencing phenotypic expression. Analytical methods for identifying Mendelian disease genes are not appropriate when applied to common multigenic diseases, because such methods investigate association with the phenotype only one genetic locus at a time. New strategies are needed that can capture the spectrum of genetic effects, from Mendelian to multifactorial epistasis. Random Forests (RF and Relief-F are two powerful machine-learning methods that have been studied as filters for genetic case-control data due to their ability to account for the context of alleles at multiple genes when scoring the relevance of individual genetic variants to the phenotype. However, when variants interact strongly, the independence assumption of RF in the tree node-splitting criterion leads to diminished importance scores for relevant variants. Relief-F, on the other hand, was designed to detect strong interactions but is sensitive to large backgrounds of variants that are irrelevant to classification of the phenotype, which is an acute problem in genome-wide association studies. To overcome the weaknesses of these data mining approaches, we develop Evaporative Cooling (EC feature selection, a flexible machine learning method that can integrate multiple importance scores while removing irrelevant genetic variants. To characterize detailed interactions, we construct a genetic-association interaction network (GAIN, whose edges quantify the synergy between variants with respect to the phenotype. We use simulation analysis to show that EC is able to identify a wide range of interaction effects in genetic association data. We apply the EC filter to a smallpox vaccine cohort study of single nucleotide polymorphisms (SNPs and infer a GAIN for a collection of SNPs associated with adverse events. Our results suggest an important

  1. Environmental noise, genetic diversity and the evolution of evolvability and robustness in model gene networks.

    Directory of Open Access Journals (Sweden)

    Christopher F Steiner

    Full Text Available The ability of organisms to adapt and persist in the face of environmental change is accepted as a fundamental feature of natural systems. More contentious is whether the capacity of organisms to adapt (or "evolvability" can itself evolve and the mechanisms underlying such responses. Using model gene networks, I provide evidence that evolvability emerges more readily when populations experience positively autocorrelated environmental noise (red noise compared to populations in stable or randomly varying (white noise environments. Evolvability was correlated with increasing genetic robustness to effects on network viability and decreasing robustness to effects on phenotypic expression; populations whose networks displayed greater viability robustness and lower phenotypic robustness produced more additive genetic variation and adapted more rapidly in novel environments. Patterns of selection for robustness varied antagonistically with epistatic effects of mutations on viability and phenotypic expression, suggesting that trade-offs between these properties may constrain their evolutionary responses. Evolution of evolvability and robustness was stronger in sexual populations compared to asexual populations indicating that enhanced genetic variation under fluctuating selection combined with recombination load is a primary driver of the emergence of evolvability. These results provide insight into the mechanisms potentially underlying rapid adaptation as well as the environmental conditions that drive the evolution of genetic interactions.

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

    Science.gov (United States)

    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/. © 2015 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    José-Fernando Camacho-Vallejo

    Full Text Available 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.

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

    Science.gov (United States)

    Shekarchizadeh, Hajar; Tikani, Reza; Kadivar, Mahdi

    2014-09-01

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

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

    KAUST Repository

    Alanis Lobato, Gregorio

    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. © 2013 Elsevier Inc.

  7. Volitional regulation of emotions produces distributed alterations in connectivity between visual, attention control, and default networks.

    Science.gov (United States)

    Sripada, Chandra; Angstadt, Michael; Kessler, Daniel; Phan, K Luan; Liberzon, Israel; Evans, Gary W; Welsh, Robert C; Kim, Pilyoung; Swain, James E

    2014-04-01

    The ability to volitionally regulate emotions is critical to health and well-being. While patterns of neural activation during emotion regulation have been well characterized, patterns of connectivity between regions remain less explored. It is increasingly recognized that the human brain is organized into large-scale intrinsic connectivity networks (ICNs) whose interrelationships are altered in characteristic ways during psychological tasks. In this fMRI study of 54 healthy individuals, we investigated alterations in connectivity within and between ICNs produced by the emotion regulation strategy of reappraisal. In order to gain a comprehensive picture of connectivity changes, we utilized connectomic psychophysiological interactions (PPI), a whole-brain generalization of standard single-seed PPI methods. In particular, we quantified PPI connectivity pair-wise across 837 ROIs placed throughout the cortex. We found that compared to maintaining one's emotional responses, engaging in reappraisal produced robust and distributed alterations in functional connections involving visual, dorsal attention, frontoparietal, and default networks. Visual network in particular increased connectivity with multiple ICNs including dorsal attention and default networks. We interpret these findings in terms of the role of these networks in mediating critical constituent processes in emotion regulation, including visual processing, stimulus salience, attention control, and interpretation and contextualization of stimuli. Our results add a new network perspective to our understanding of the neural underpinnings of emotion regulation, and highlight that connectomic methods can play a valuable role in comprehensively investigating modulation of connectivity across task conditions. Copyright © 2013 Elsevier Inc. All rights reserved.

  8. Level architecture in genetic regulatory networks and the role of microRNAs

    Science.gov (United States)

    Schwarz, J. M.

    2008-03-01

    It is well known that genes that code for proteins regulate the expression of each other through protein-mediated interactions. With the discovery of microRNAs^1 (miRNAs), it has been conjectured that there are many such regulatory miRNAs in the cell that are never transcribed into proteins but are important for regulation and, hence, could explain the nature of the non-coding (or junk) DNA.^2 Furthermore, miRNAs are highly conserved molecules. So, just as genes that code for proteins form regulatory networks, we conjecture that miRNAs form a higher-level regulatory network amongst themselves as mediated by the genes-coding-for-proteins regulatory network to form a complex organism. We investigate this conjecture within the framework of random Boolean networks where the two-level architecture is modelled via two coupled random Boolean networks with one network taking precedence over the other for various input/output values. Aspects of the evolution of the lower-level network will also be addressed. ^1 D. P. Bartel, Cell 116, 281 (2004). ^2 J. S. Mattick, Sci. Amer. 291, 60 (2004).

  9. Output Regulation of Large-Scale Hydraulic Networks

    DEFF Research Database (Denmark)

    De Persis, Claudio; Jensen, Tom Nørgaard; Ortega, Romeo

    2014-01-01

    . The fact that the result is global and independent of the number of end users has the consequence that structural changes such as end-user addition and removal can be made in the network while maintaining the stability properties of the system. Furthermore, the decentralized nature of the control...

  10. an improved voltage regulation of a distribution network using facts

    African Journals Online (AJOL)

    OMEJE CO

    2013-07-02

    Jul 2, 2013 ... trouble free, since the TCR achieves its fundamental frequency steady-state operating point at the .... injected by the generator at bus K. PLK and. QLK represent the active and reactive powers drawn by ..... energy efficiency of the power distribution networks. Figure 10: Comparative plot of the bus voltage ...

  11. An Improved Voltage Regulation of a Distribution Network Using ...

    African Journals Online (AJOL)

    The Newton-Raphson Load flow equation modeling was a veritable tool applied in this analysis to determine the convergence points for the voltage magnitude, power (load) angle, power losses along the lines, sending end and receiving end power values at the various buses that make up the thirteen bus network.

  12. Enzymatic regulation of functional vascular networks using gelatin hydrogels

    Science.gov (United States)

    Chuang, Chia-Hui; Lin, Ruei-Zeng; Tien, Han-Wen; Chu, Ya-Chun; Li, Yen-Cheng; Melero-Martin, Juan M.; Chen, Ying-Chieh

    2015-01-01

    To manufacture tissue engineering-based functional tissues, scaffold materials that can be sufficiently vascularized to mimic the functionality and complexity of native tissues are needed. Currently, vascular network bioengineering is largely carried out using natural hydrogels as embedding scaffolds, but most natural hydrogels have poor mechanical stability and durability, factors that critically limit their widespread use. In this study, we examined the suitability of gelatin-phenolic hydroxyl (gelatin-Ph) hydrogels that can be enzymatically crosslinked, allowing tuning of the storage modulus and the proteolytic degradation rate, for use as injectable hydrogels to support the human progenitor cell-based formation of a stable and mature vascular network. Porcine gelatin-Ph hydrogels were found to be cytocompatible with human blood-derived endothelial colony-forming cells and white adipose tissue-derived mesenchymal stem cells, resulting in >87% viability, and cell proliferation and spreading could be modulated by using hydrogels with different proteolytic degradability and stiffness. In addition, gelatin was extracted from mouse dermis and murine gelatin-Ph hydrogels were prepared. Importantly, implantation of human cell-laden porcine or murine gelatin-Ph hydrogels into immunodeficient mice resulted in the rapid formation of functional anastomoses between the bioengineered human vascular network and the mouse vasculature. Furthermore, the degree of enzymatic crosslinking of the gelatin-Ph hydrogels could be used to modulate cell behavior and the extent of vascular network formation in vivo. Our report details a technique for the synthesis of gelatin-Ph hydrogels from allogeneic or xenogeneic dermal skin and suggests that these hydrogels can be used for biomedical applications that require the formation of microvascular networks, including the development of complex engineered tissues. PMID:25749296

  13. Abnormalities in the structural covariance of emotion regulation networks in major depressive disorder.

    Science.gov (United States)

    Wu, Huawang; Sun, Hui; Wang, Chao; Yu, Lin; Li, Yilan; Peng, Hongjun; Lu, Xiaobing; Hu, Qingmao; Ning, Yuping; Jiang, Tianzi; Xu, Jinping; Wang, Jiaojian

    2017-01-01

    Major depressive disorder (MDD) is a common psychiatric disorder that is characterized by cognitive deficits and affective symptoms. To date, an increasing number of neuroimaging studies have focused on emotion regulation and have consistently shown that emotion dysregulation is one of the central features and underlying mechanisms of MDD. Although gray matter morphological abnormalities in regions within emotion regulation networks have been identified in MDD, the interactions and relationships between these gray matter structures remain largely unknown. Thus, in this study, we adopted a structural covariance method based on gray matter volume to investigate the brain morphological abnormalities within the emotion regulation networks in a large cohort of 65 MDD patients and 65 age- and gender-matched healthy controls. A permutation test with p emotion dysregulation is an underlying mechanism of MDD by revealing disrupted structural covariance patterns in the emotion regulation network. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Robustness in Regulatory Interaction Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic and Genetic

    Directory of Open Access Journals (Sweden)

    Sylvain Sené

    2009-10-01

    Full Text Available Regulatory interaction networks are often studied on their dynamical side (existence of attractors, study of their stability. We focus here also on their robustness, that is their ability to offer the same spatiotemporal patterns and to resist to external perturbations such as losses of nodes or edges in the networks interactions architecture, changes in their environmental boundary conditions as well as changes in the update schedule (or updating mode of the states of their elements (e.g., if these elements are genes, their synchronous coexpression mode versus their sequential expression. We define the generic notions of boundary, core, and critical vertex or edge of the underlying interaction graph of the regulatory network, whose disappearance causes dramatic changes in the number and nature of attractors (e.g., passage from a bistable behaviour to a unique periodic regime or in the range of their basins of stability. The dynamic transition of states will be presented in the framework of threshold Boolean automata rules. A panorama of applications at different levels will be given: brain and plant morphogenesis, bulbar cardio-respiratory regulation, glycolytic/oxidative metabolic coupling, and eventually cell cycle and feather morphogenesis genetic control.

  15. Calibration of parameters of water supply network model using genetic algorithm

    Directory of Open Access Journals (Sweden)

    Boczar Tomasz

    2017-01-01

    Full Text Available Computer simulation models of water supply networks are commonly applied in the water industry. As part of the research works, results of which are presented in the paper, OFF-LINE and ON-LINE calibration of water supply network model parameters using two methods was carried out and compared. The network skeleton was developed in the Epanet software. For optimization two types of dependent variables were subjected: the pressure on the node and volume flow in the network section. The first calibration method regards to application of the genetic algorithm, which is a build in plugin - “Epanet Calibrator”. The second method was related to the use of function ga, which is implemented in the MATLAB toolbox Genetic Algorithm and Direct Search. The possibilities of application of these algorithms to solve the issue of optimizing the parameters of the created model of water supply network in both cases: OFF-LINE and ON-LINE calibration was examined. An analysis of the effectiveness of the considered algorithms for different values of configuration parameters was performed. Based on the achieved results it was stated that application of the ga algorithm gives higher correlation of the calibrated values to the empirical data.

  16. Calibration of parameters of water supply network model using genetic algorithm

    Science.gov (United States)

    Boczar, Tomasz; Adamikiewicz, Norbert; Stanisławski, Włodzimierz

    2017-10-01

    Computer simulation models of water supply networks are commonly applied in the water industry. As part of the research works, results of which are presented in the paper, OFF-LINE and ON-LINE calibration of water supply network model parameters using two methods was carried out and compared. The network skeleton was developed in the Epanet software. For optimization two types of dependent variables were subjected: the pressure on the node and volume flow in the network section. The first calibration method regards to application of the genetic algorithm, which is a build in plugin - "Epanet Calibrator". The second method was related to the use of function ga, which is implemented in the MATLAB toolbox Genetic Algorithm and Direct Search. The possibilities of application of these algorithms to solve the issue of optimizing the parameters of the created model of water supply network in both cases: OFF-LINE and ON-LINE calibration was examined. An analysis of the effectiveness of the considered algorithms for different values of configuration parameters was performed. Based on the achieved results it was stated that application of the ga algorithm gives higher correlation of the calibrated values to the empirical data.

  17. Real time selective harmonic minimization for multilevel inverters using genetic algorithm and artifical neural network angle generation

    Energy Technology Data Exchange (ETDEWEB)

    Filho, Faete J [ORNL; Tolbert, Leon M [ORNL; Ozpineci, Burak [ORNL

    2012-01-01

    The work developed here proposes a methodology for calculating switching angles for varying DC sources in a multilevel cascaded H-bridges converter. In this approach the required fundamental is achieved, the lower harmonics are minimized, and the system can be implemented in real time with low memory requirements. Genetic algorithm (GA) is the stochastic search method to find the solution for the set of equations where the input voltages are the known variables and the switching angles are the unknown variables. With the dataset generated by GA, an artificial neural network (ANN) is trained to store the solutions without excessive memory storage requirements. This trained ANN then senses the voltage of each cell and produces the switching angles in order to regulate the fundamental at 120 V and eliminate or minimize the low order harmonics while operating in real time.

  18. Stability of Uncertain Impulsive Stochastic Genetic Regulatory Networks with Time-Varying Delay in the Leakage Term

    Directory of Open Access Journals (Sweden)

    Jun Li

    2014-01-01

    Full Text Available This paper is concerned with the stability problem for a class of uncertain impulsive stochastic genetic regulatory networks (UISGRNs with time-varying delays both in the leakage term and in the regulator function. By constructing a suitable Lyapunov-Krasovskii functional which uses the information on the lower bound of the delay sufficiently, a delay-dependent stability criterion is derived for the proposed UISGRNs model by using the free-weighting matrices method and convex combination technique. The conditions obtained here are expressed in terms of LMIs whose feasibility can be checked easily by MATLAB LMI control toolbox. In addition, three numerical examples are given to justify the obtained stability results.

  19. A Selfish Constraint Satisfaction Genetic Algorithms for Planning a Long-Distance Transportation Network

    Science.gov (United States)

    Onoyama, Takashi; Maekawa, Takuya; Kubota, Sen; Tsuruta, Setuso; Komoda, Norihisa

    To build a cooperative logistics network covering multiple enterprises, a planning method that can build a long-distance transportation network is required. Many strict constraints are imposed on this type of problem. To solve these strict-constraint problems, a selfish constraint satisfaction genetic algorithm (GA) is proposed. In this GA, each gene of an individual satisfies only its constraint selfishly, disregarding the constraints of other genes in the same individuals. Moreover, a constraint pre-checking method is also applied to improve the GA convergence speed. The experimental result shows the proposed method can obtain an accurate solution in a practical response time.

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

    Science.gov (United States)

    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.

  1. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Vinay Chandwani

    2014-01-01

    Full Text Available Artificial neural networks (ANNs have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance.

  2. Genetic and environmental influences on individual differences in emotion regulation and its relation to working memory in toddlerhood.

    Science.gov (United States)

    Wang, Manjie; Saudino, Kimberly J

    2013-12-01

    This is the first study to explore genetic and environmental contributions to individual differences in emotion regulation in toddlers, and the first to examine the genetic and environmental etiology underlying the association between emotion regulation and working memory. In a sample of 304 same-sex twin pairs (140 MZ, 164 DZ) at age 3, emotion regulation was assessed using the Behavior Rating Scale of the Bayley Scales of Infant Development (BRS; Bayley, 1993), and working memory was measured by the visually cued recall (VCR) task (Zelazo, Jacques, Burack, & Frye, 2002) and several memory tasks from the Mental Scale of the BSID. Based on model-fitting analyses, both emotion regulation and working memory were significantly influenced by genetic and nonshared environmental factors. Shared environmental effects were significant for working memory, but not for emotion regulation. Only genetic factors significantly contributed to the covariation between emotion regulation and working memory.

  3. Network Centrality Analysis in Fungi Reveals Complex Regulation of Lost and Gained Genes.

    Science.gov (United States)

    Coulombe-Huntington, Jasmin; Xia, Yu

    2017-01-01

    Gene gain and loss shape both proteomes and the networks they form. The increasing availability of closely related sequenced genomes and of genome-wide network data should enable a better understanding of the evolutionary forces driving gene gain, gene loss and evolutionary network rewiring. Using orthology mappings across 23 ascomycete fungi genomes, we identified proteins that were lost, gained or universally conserved across the tree, enabling us to compare genes across all stages of their life-cycle. Based on a collection of genome-wide network and gene expression datasets from baker's yeast, as well as a few from fission yeast, we found that gene loss is more strongly associated with network and expression features of closely related species than that of distant species, consistent with the evolutionary modulation of gene loss propensity through network rewiring. We also discovered that lost and gained genes, as compared to universally conserved "core" genes, have more regulators, more complex expression patterns and are much more likely to encode for transcription factors. Finally, we found that the relative rate of network integration of new genes into the different types of networks agrees with experimentally measured rates of network rewiring. This systems-level view of the life-cycle of eukaryotic genes suggests that the gain and loss of genes is tightly coupled to the gain and loss of network interactions, that lineage-specific adaptations drive regulatory complexity and that the relative rates of integration of new genes are consistent with network rewiring rates.

  4. Strategical incoherence regulates cooperation in social dilemmas on multiplex networks

    CERN Document Server

    Matamalas, Joan T; Gómez, Sergio; Arenas, Alex

    2015-01-01

    Cooperation is a very common, yet not fully-understood phenomenon in natural and human systems. The introduction of a network within the population is known to affect the outcome of cooperative dynamics, allowing for the survival of cooperation in adverse scenarios. Recently, the introduction of multiplex networks has yet again modified the expectations for the outcome of the Prisoner's Dilemma game, compared to the monoplex case. However, much remains unstudied regarding other social dilemmas on multiplex, as well as the unexplored microscopic underpinnings of it. In this paper, we systematically study the evolution of cooperation in all four games in the $T-S$ plane on multiplex. More importantly, we find some remarkable and previously unknown features in the microscopic organization of the strategies, that are responsible for the important differences between cooperative dynamics in monoplex and multiplex. Specifically, we find that in the stationary state, there are individuals that play the same strategy...

  5. Modeling the Drosophila gene cluster regulation network for muscle development.

    Science.gov (United States)

    Haye, Alexandre; Albert, Jaroslav; Rooman, Marianne

    2014-01-01

    The development of accurate and reliable dynamical modeling procedures that describe the time evolution of gene expression levels is a prerequisite to understanding and controlling the transcription process. We focused on data from DNA microarray time series for 20 Drosophila genes involved in muscle development during the embryonic stage. Genes with similar expression profiles were clustered on the basis of a translation-invariant and scale-invariant distance measure. The time evolution of these clusters was modeled using coupled differential equations. Three model structures involving a transcription term and a degradation term were tested. The parameters were identified in successive steps: network construction, parameter optimization, and parameter reduction. The solutions were evaluated on the basis of the data reproduction and the number of parameters, as well as on two biology-based requirements: the robustness with respect to parameter variations and the values of the expression levels not being unrealistically large upon extrapolation in time. Various solutions were obtained that satisfied all our evaluation criteria. The regulatory networks inferred from these solutions were compared with experimental data. The best solution has half of the experimental connections, which compares favorably with previous approaches. Biasing the network toward the experimental connections led to the identification of a model that is only slightly less good on the basis of the evaluation criteria. The non-uniqueness of the solutions and the variable agreement with experimental connections were discussed in the context of the different hypotheses underlying this type of approach.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  8. Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Jianyong Liu

    2015-01-01

    Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

  9. A Neural Network: Family Competition Genetic Algorithm and Its Applications in Electromagnetic Optimization

    Directory of Open Access Journals (Sweden)

    P.-Y. Chen

    2009-01-01

    Full Text Available This study proposes a neural network-family competition genetic algorithm (NN-FCGA for solving the electromagnetic (EM optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are comparable to those of the FCGA, but reducing a large amount of computation time and a well-trained NN model that can serve as a nonlinear approximator was developed during the optimization process of the NN-FCGA.

  10. Genetic algorithm pruning of probabilistic neural networks in medical disease estimation.

    Science.gov (United States)

    Mantzaris, Dimitrios; Anastassopoulos, George; Adamopoulos, Adam

    2011-10-01

    A hybrid model consisting of an Artificial Neural Network (ANN) and a Genetic Algorithm procedure for diagnostic risk factors selection in Medicine is proposed in this paper. A medical disease prediction may be viewed as a pattern classification problem based on a set of clinical and laboratory parameters. Probabilistic Neural Network models were assessed in terms of their classification accuracy concerning medical disease prediction. A Genetic Algorithm search was performed to examine potential redundancy in the diagnostic factors. This search led to a pruned ANN architecture, minimizing the number of diagnostic factors used during the training phase and therefore minimizing the number of nodes in the ANN input and hidden layer as well as the Mean Square Error of the trained ANN at the testing phase. As a conclusion, a number of diagnostic factors in a patient's data record can be omitted without loss of fidelity in the diagnosis procedure. Copyright © 2011 Elsevier Ltd. All rights reserved.

  11. NHLBI family blood pressure program: methodology and recruitment in the HyperGEN network. Hypertension genetic epidemiology network.

    Science.gov (United States)

    Williams, R R; Rao, D C; Ellison, R C; Arnett, D K; Heiss, G; Oberman, A; Eckfeldt, J H; Leppert, M F; Province, M A; Mockrin, S C; Hunt, S C

    2000-08-01

    Hypertension is a common precursor of serious disorders including stroke, myocardial infarction, congestive heart failure, and renal failure in whites and to a greater extent in African Americans. Large genetic-epidemiological studies of hypertension are needed to gain information that will improve future methods for diagnosis, treatment, and prevention of hypertension, a major contributor to cardiovascular morbidity and mortality. We report successful implementation of a new structure of research collaboration involving four NHLBI "Networks," coordinated under the Family Blood Pressure Program. The Hypertension Genetic Epidemiology Network (HyperGEN) involves scientists from six universities and the NHLBI who seek to identify and characterize genes promoting hypertension. Blood samples and clinical data were projected to be collected from a sample of 2244 hypertensive siblings diagnosed before age 60 from 960 sibships (half African-American) with two or more affected persons. Nonparametric sibship linkage analysis of over one million genotype determinations (20 candidate loci and 387 anonymous marker loci) was projected to have sufficient power for detecting genetic loci promoting hypertension. For loci showing evidence for linkage in this study and for loci reported linked or associated with hypertension by other groups, genotypes are compared in hypertensive cases versus population-based controls to identify or confirm genetic variants associated with hypertension. For some of these genetic variants associated with hypertension, detailed physiological and biochemical characterization of untreated adult offspring carriers versus non-carriers may help elucidate the pathophysiological mechanisms that promote hypertension. The projected sample size of 2244 hypertensive participants was surpassed, as 2407 hypertensive individuals (1262 African-Americans and 1145 whites) from 917 sibships were examined. Detailed consent forms were designed to offer participants

  12. Genetic analysis of the regulation of TCH gene expression, Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Braam, Janet

    2008-10-28

    investigation of CML24 function and regulation led to the finding that CML24 has a critical role in nitric oxide regulation. Distinct tilling mutant alleles demonstrated that CML24 can act as a switch in the response to day length perception. Because of potential redundancy with the related CML23 gene, CML23 T-DNA insertion mutants were identified and characterized. Together, CML23 and CML24 impact the autonomous regulatory pathway of the transition to flowering. Nitric oxide levels are elevated in cml23/cml24 double mutants. Therefore, CML23 and CML24 are potential calcium sensors regulate nitric oxide accumulation. In collaboration with Drs. McCann and Carpita, fourier transform infrared spectroscopy (FTIR) was used to assess, verify and classify wall architectural changes that occur as a result of single XTH insertion mutations. Thirty-four homozygous mutant lines of Arabidopsis representing 21 members of the xyloglucan endotransglucosylase/hydrolase gene family provided a set of mutants to characterize. Kohonen networks classified cell wall architectures of xth mutant lines and previously characterized cell wall mutants. The xth mutants were found to have chemical changes in their cell walls not detectable as phenotypic growth and development changes, consistent with the existence of feed-back loops that modify wall composition in response to a life-long deficiency of a cell wall enzyme. To gain insight into the potential physiological relevance of the distinct members of the XTH family, GUS reporter fusion genes were constructed, and plants expressing these transgenes were characterized to reveal spatial and temporal patterns of expression. In addition, Genevestigator sources were mined for comprehensive and comparative XTH expression regulation analysis. These data revealed that the Arabidopsis XTHs are likely expressed in every developmental stage from seed germination through flowering. All organs showed XTH::GUS expression and most, if not all, are found to express

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

    Science.gov (United States)

    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.

  14. Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms

    OpenAIRE

    Casillas, Myrna V.; Puig, Vicenc; Garza-Castanon, Luis E.; Rosich, Albert

    2013-01-01

    This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results ...

  15. Efficient synthesis of heat exchanger networks combining heuristic approaches with a genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Brandt, Christopher; Fieg, Georg [Hamburg University of Technology, Institute of Process and Plant Engineering, Hamburg (Germany); Luo, Xing [Helmut Schmidt University, Institute of Thermodynamics, Hamburg (Germany); University of Shanghai for Science and Technology, Institute of Thermal Engineering, Shanghai (China)

    2011-08-15

    In this work an innovative method for heat exchanger network (HEN) synthesis is introduced and examined. It combines a genetic algorithm (GA) with a heuristic based optimization procedure. The novel algorithm removes appearing heat load loops from the HEN structures when profitable, throughout the evolution. Two examples were examined with the new HEN synthesis method and for both better results were obtained. Thus, a positive effect of heuristic based optimization methods on the HEN synthesis with GA could be located. (orig.)

  16. Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations

    Directory of Open Access Journals (Sweden)

    Bhavnani Suresh K

    2010-11-01

    Full Text Available Abstract Background In a recent study, two-dimensional (2D network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D network layouts, displayed through 2D and 3D viewing methods. Findings The 3D network layout (displayed through the 3D viewing method revealed that genes implicated in many diseases (non-specific genes tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. Conclusions The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks.

  17. Robustness and backbone motif of a cancer network regulated by miR-17-92 cluster during the G1/S transition.

    Directory of Open Access Journals (Sweden)

    Lijian Yang

    Full Text Available Based on interactions among transcription factors, oncogenes, tumor suppressors and microRNAs, a Boolean model of cancer network regulated by miR-17-92 cluster is constructed, and the network is associated with the control of G1/S transition in the mammalian cell cycle. The robustness properties of this regulatory network are investigated by virtue of the Boolean network theory. It is found that, during G1/S transition in the cell cycle process, the regulatory networks are robustly constructed, and the robustness property is largely preserved with respect to small perturbations to the network. By using the unique process-based approach, the structure of this network is analyzed. It is shown that the network can be decomposed into a backbone motif which provides the main biological functions, and a remaining motif which makes the regulatory system more stable. The critical role of miR-17-92 in suppressing the G1/S cell cycle checkpoint and increasing the uncontrolled proliferation of the cancer cells by targeting a genetic network of interacting proteins is displayed with our model.

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

    Science.gov (United States)

    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.

  19. Molecular regulation and genetic improvement of seed oil content in Brassica napus L.

    Directory of Open Access Journals (Sweden)

    Wei HUA,Jing LIU,Hanzhong WANG

    2016-09-01

    Full Text Available As an important oil crop and a potential bioenergy crop, Brassica napus L. is becoming a model plant for basic research on seed lipid biosynthesis as well as seed oil content, which has always been the key breeding objective. In this review, we present current progress in understanding of the regulation of oil content in B. napus, including genetics, biosynthesis pathway, transcriptional regulation, maternal effects and QTL analysis. Furthermore, the history of breeding for high oil content in B. napus is summarized and the progress in breeding ultra-high oil content lines is described. Finally, prospects for breeding high oil content B. napus cultivars are outlined.

  20. Classroom Activities to Engage Students and Promote Critical Thinking about Genetic Regulation of Bacterial Quorum Sensing

    Directory of Open Access Journals (Sweden)

    Kimberly Aebli

    2016-05-01

    Full Text Available We developed an interactive activity to mimic bacterial quorum sensing, and a classroom worksheet to promote critical thinking about genetic regulation of the lux operon. The interactive quorum sensing activity engages students and provides a direct visualization of how population density functions to influence light production in bacteria. The worksheet activity consists of practice problems that require students to apply basic knowledge of the lux operon in order to make predictions about genetic complementation experiments, and students must evaluate how genetic mutations in the lux operon affect gene expression and overall phenotype. The worksheet promotes critical thinking and problem solving skills, and emphasizes the roles of diffusible signaling molecules, regulatory proteins, and structural proteins in quorum sensing.

  1. Policies and regulations in Mexico with regard to genetic technology and food security

    OpenAIRE

    Colmenarez Ortiz, Claudia; Ortiz Garcia, Sol

    2016-01-01

    In 1988 the first application for field trials of a GMO was formally received in Mexico. Since then a Biosafety law, few bylaws and national official standards were enacted in order to regulate the safe use of GMOs and to evaluate, control and avoid adverse effects to human health and the environment. The Law on Biosafety of Genetically Modified Organisms was enacted in 2005 in order to comply with international obligations derived from the Cartagena Protocol on Biosafety signed by Mexico in ...

  2. Classroom Activities to Engage Students and Promote Critical Thinking about Genetic Regulation of Bacterial Quorum Sensing

    OpenAIRE

    Kimberly Aebli; Elizabeth Hutchison

    2016-01-01

    We developed an interactive activity to mimic bacterial quorum sensing, and a classroom worksheet to promote critical thinking about genetic regulation of the lux operon. The interactive quorum sensing activity engages students and provides a direct visualization of how population density functions to influence light production in bacteria. The worksheet activity consists of practice problems that require students to apply basic knowledge of the lux operon in order to make predictions about g...

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

    Science.gov (United States)

    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.

  4. A genetic strategy to measure circulating Drosophila insulin reveals genes regulating insulin production and secretion.

    Directory of Open Access Journals (Sweden)

    Sangbin Park

    2014-08-01

    Full Text Available Insulin is a major regulator of metabolism in metazoans, including the fruit fly Drosophila melanogaster. Genome-wide association studies (GWAS suggest a genetic basis for reductions of both insulin sensitivity and insulin secretion, phenotypes commonly observed in humans with type 2 diabetes mellitus (T2DM. To identify molecular functions of genes linked to T2DM risk, we developed a genetic tool to measure insulin-like peptide 2 (Ilp2 levels in Drosophila, a model organism with superb experimental genetics. Our system permitted sensitive quantification of circulating Ilp2, including measures of Ilp2 dynamics during fasting and re-feeding, and demonstration of adaptive Ilp2 secretion in response to insulin receptor haploinsufficiency. Tissue specific dissection of this reduced insulin signaling phenotype revealed a critical role for insulin signaling in specific peripheral tissues. Knockdown of the Drosophila orthologues of human T2DM risk genes, including GLIS3 and BCL11A, revealed roles of these Drosophila genes in Ilp2 production or secretion. Discovery of Drosophila mechanisms and regulators controlling in vivo insulin dynamics should accelerate functional dissection of diabetes genetics.

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

    Science.gov (United States)

    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

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

    Directory of Open Access Journals (Sweden)

    Ahmed Ramadan Suleiman

    2017-02-01

    Full Text Available 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.

  7. STUDY OF SENSITIVITY OF THE PARAMETERS OF A GENETIC ALGORITHM FOR DESIGN OF WATER DISTRIBUTION NETWORKS

    Directory of Open Access Journals (Sweden)

    Pedro L. Iglesias

    2007-12-01

    Full Text Available The Genetic Algorithms (GAs are a technique of optimization used for water distribution networks design. This work has been made with a modified pseudo genetic algorithm (PGA, whose main variation with a classical GA is a change in the codification of the chromosomes, which is made of numerical form instead of the binary codification. This variation entails a series of special characteristics in the codification and in the definition of the operations of mutation and crossover. Initially, the work displays the results of the PGA on a water network studied in the literature. The results show the kindness of the method. Also is made a statistical analysis of the obtained solutions. This analysis allows verifying the values of mutation and crossing probability more suitable for the proposed method. Finally, in the study of the analyzed water supply networks the concept of reliability in introduced. This concept is essential to understand the validity of the obtained results. The second part, starting with values optimized for the probability of crossing and mutation, the influence of the population size is analyzed in the final solutions on the network of Hanoi, widely studied in the bibliography. The aim is to find the most suitable configuration of the problem, so that good solutions are obtained in the less time.

  8. Genetic Algorithm-Based Artificial Neural Network for Voltage Stability Assessment

    Directory of Open Access Journals (Sweden)

    Garima Singh

    2011-01-01

    Full Text Available With the emerging trend of restructuring in the electric power industry, many transmission lines have been forced to operate at almost their full capacities worldwide. Due to this, more incidents of voltage instability and collapse are being observed throughout the world leading to major system breakdowns. To avoid these undesirable incidents, a fast and accurate estimation of voltage stability margin is required. In this paper, genetic algorithm based back propagation neural network (GABPNN has been proposed for voltage stability margin estimation which is an indication of the power system's proximity to voltage collapse. The proposed approach utilizes a hybrid algorithm that integrates genetic algorithm and the back propagation neural network. The proposed algorithm aims to combine the capacity of GAs in avoiding local minima and at the same time fast execution of the BP algorithm. Input features for GABPNN are selected on the basis of angular distance-based clustering technique. The performance of the proposed GABPNN approach has been compared with the most commonly used gradient based BP neural network by estimating the voltage stability margin at different loading conditions in 6-bus and IEEE 30-bus system. GA based neural network learns faster, at the same time it provides more accurate voltage stability margin estimation as compared to that based on BP algorithm. It is found to be suitable for online applications in energy management systems.

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

    Science.gov (United States)

    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.

  10. Modeling the cooling performance of vortex tube using a genetic algorithm-based artificial neural network

    Directory of Open Access Journals (Sweden)

    Pouraria Hassan

    2016-01-01

    Full Text Available In this study, artificial neural networks (ANNs have been used to model the effects of four important parameters consist of the ratio of the length to diameter(L/D, the ratio of the cold outlet diameter to the tube diameter(d/D, inlet pressure(P, and cold mass fraction (Y on the cooling performance of counter flow vortex tube. In this approach, experimental data have been used to train and validate the neural network model with MATLAB software. Also, genetic algorithm (GA has been used to find the optimal network architecture. In this model, temperature drop at the cold outlet has been considered as the cooling performance of the vortex tube. Based on experimental data, cooling performance of the vortex tube has been predicted by four inlet parameters (L/D, d/D, P, Y. The results of this study indicate that the genetic algorithm-based artificial neural network model is capable of predicting the cooling performance of vortex tube in a wide operating range and with satisfactory precision.

  11. Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling

    Energy Technology Data Exchange (ETDEWEB)

    Afshar, Mohammad [Islamic Azad University, Kharg (Iran, Islamic Republic of); Gholami, Amin [Petroleum University of Technology, Abadan (Iran, Islamic Republic of); Asoodeh, Mojtaba [Islamic Azad University, Birjand (Iran, Islamic Republic of)

    2014-03-15

    Bubble point pressure is a critical pressure-volume-temperature (PVT) property of reservoir fluid, which plays an important role in almost all tasks involved in reservoir and production engineering. We developed two sophisticated models to estimate bubble point pressure from gas specific gravity, oil gravity, solution gas oil ratio, and reservoir temperature. Neural network and adaptive neuro-fuzzy inference system are powerful tools for extracting the underlying dependency of a set of input/output data. However, the mentioned tools are in danger of sticking in local minima. The present study went further by optimizing fuzzy logic and neural network models using the genetic algorithm in charge of eliminating the risk of being exposed to local minima. This strategy is capable of significantly improving the accuracy of both neural network and fuzzy logic models. The proposed methodology was successfully applied to a dataset of 153 PVT data points. Results showed that the genetic algorithm can serve the neural network and neuro-fuzzy models from local minima trapping, which might occur through back-propagation algorithm.

  12. Public utilities in networks: competition perspectives and new regulations; Services publics en reseau: perspectives de concurrence et nouvelles regulations

    Energy Technology Data Exchange (ETDEWEB)

    Bergougnoux, J

    2000-07-01

    This report makes first a status about the historical specificities, the present day situation and the perspectives of evolution of public utilities in networks with respect to the European directive of 1996 and to the 4 sectors of electricity, gas, railway transport and postal service. Then, it wonders about the new institutions and regulation procedures to implement to conciliate the public utility mission with the honest competition. (J.S.)

  13. Systems glycobiology: biochemical reaction networks regulating glycan structure and function.

    Science.gov (United States)

    Neelamegham, Sriram; Liu, Gang

    2011-12-01

    There is a growing use of bioinformatics based methods in the field of Glycobiology. These have been used largely to curate glycan structures, organize array-based experimental data and display existing knowledge of glycosylation-related pathways in silico. Although the cataloging of vast amounts of data is beneficial, it is often a challenge to gain meaningful mechanistic insight from this exercise alone. The development of specific analysis tools to query the database is necessary. If these queries can integrate existing knowledge of glycobiology, new insights may be gained. Such queries that couple biochemical knowledge and mathematics have been developed in the field of Systems Biology. The current review summarizes the current state of the art in the application of computational modeling in the field of Glycobiology. It provides (i) an overview of experimental and online resources that can be used to construct glycosylation reaction networks, (ii) mathematical methods to formulate the problem including a description of ordinary differential equation and logic-based reaction networks, (iii) optimization techniques that can be applied to fit experimental data for the purpose of model reconstruction and for evaluating unknown model parameters, (iv) post-simulation analysis methods that yield experimentally testable hypotheses and (v) a summary of available software tools that can be used by non-specialists to perform many of the above functions. © The Author 2011. Published by Oxford University Press. All rights reserved.

  14. Systems glycobiology: biochemical reaction networks regulating glycan structure and function

    Science.gov (United States)

    Neelamegham, Sriram; Liu, Gang

    2011-01-01

    There is a growing use of bioinformatics based methods in the field of Glycobiology. These have been used largely to curate glycan structures, organize array-based experimental data and display existing knowledge of glycosylation-related pathways in silico. Although the cataloging of vast amounts of data is beneficial, it is often a challenge to gain meaningful mechanistic insight from this exercise alone. The development of specific analysis tools to query the database is necessary. If these queries can integrate existing knowledge of glycobiology, new insights may be gained. Such queries that couple biochemical knowledge and mathematics have been developed in the field of Systems Biology. The current review summarizes the current state of the art in the application of computational modeling in the field of Glycobiology. It provides (i) an overview of experimental and online resources that can be used to construct glycosylation reaction networks, (ii) mathematical methods to formulate the problem including a description of ordinary differential equation and logic-based reaction networks, (iii) optimization techniques that can be applied to fit experimental data for the purpose of model reconstruction and for evaluating unknown model parameters, (iv) post-simulation analysis methods that yield experimentally testable hypotheses and (v) a summary of available software tools that can be used by non-specialists to perform many of the above functions. PMID:21436236

  15. That 70s show: regulation, evolution and development beyond molecular genetics.

    Science.gov (United States)

    Suárez-Díaz Edna; García-Deister Vivette

    2015-01-01

    This paper argues that the "long 1970s" (1969-1983) is an important though often overlooked period in the development of a rich landscape in the research of metabolism, development, and evolution. The period is marked by: shrinking public funding of basic science, shifting research agendas in molecular biology, the incorporation of new phenomena and experimental tools from previous biological research at the molecular level, and the development of recombinant DNA techniques. Research was reoriented towards eukaryotic cells and development, and in particular towards "giant" RNA processing and transcription. We will here focus on three different models of developmental regulation published in that period: the two models of eukaryotic genetic regulation at the transcriptional level that were developed by Georgii P. Georgiev on the one hand, and by Roy Britten and Eric Davidson on the other; and the model of genetic sufficiency and evolution of regulatory genes proposed by Emile Zuckerkandl. These three bases illustrate the range of exploratory hypotheses that characterised the challenging landscape of gene regulation in the 1970s, a period that in hindsight can be labelled as transitional, between the biology at the laboratory bench of the preceding period, and the biology of genetic engineering and intensive data-driven research that followed.

  16. MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers.

    Science.gov (United States)

    Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier; Lecompte, Odile

    2017-06-16

    The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user's specific interests and provides an efficient way to share information with collaborators. Furthermore, the user's behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends.

  17. Genetic and epigenetic regulation of human lincRNA gene expression.

    Science.gov (United States)

    Popadin, Konstantin; Gutierrez-Arcelus, Maria; Dermitzakis, Emmanouil T; Antonarakis, Stylianos E

    2013-12-05

    Large intergenic noncoding RNAs (lincRNAs) are still poorly functionally characterized. We analyzed the genetic and epigenetic regulation of human lincRNA expression in the GenCord collection by using three cell types from 195 unrelated European individuals. We detected a considerable number of cis expression quantitative trait loci (cis-eQTLs) and demonstrated that the genetic regulation of lincRNA expression is independent of the regulation of neighboring protein-coding genes. lincRNAs have relatively more cis-eQTLs than do equally expressed protein-coding genes with the same exon number. lincRNA cis-eQTLs are located closer to transcription start sites (TSSs) and their effect sizes are higher than cis-eQTLs found for protein-coding genes, suggesting that lincRNA expression levels are less constrained than that of protein-coding genes. Additionally, lincRNA cis-eQTLs can influence the expression level of nearby protein-coding genes and thus could be considered as QTLs for enhancer activity. Enrichment of expressed lincRNA promoters in enhancer marks provides an additional argument for the involvement of lincRNAs in the regulation of transcription in cis. By investigating the epigenetic regulation of lincRNAs, we observed both positive and negative correlations between DNA methylation and gene expression (expression quantitative trait methylation [eQTMs]), as expected, and found that the landscapes of passive and active roles of DNA methylation in gene regulation are similar to protein-coding genes. However, lincRNA eQTMs are located closer to TSSs than are protein-coding gene eQTMs. These similarities and differences in genetic and epigenetic regulation between lincRNAs and protein-coding genes contribute to the elucidation of potential functions of lincRNAs. Copyright © 2013 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

  18. Pacemaker neuron and network oscillations depend on a neuromodulator-regulated linear current

    Directory of Open Access Journals (Sweden)

    Shunbing Zhao

    2010-05-01

    Full Text Available Linear leak currents have been implicated in the regulation of neuronal excitability, generation of neuronal and network oscillations, and network state transitions. Yet, few studies have directly tested the dependence of network oscillations on leak currents or explored the role of leak currents on network activity. In the oscillatory pyloric network of decapod crustaceans neuromodulatory inputs are necessary for pacemaker activity. A large subset of neuromodulators is known to activate a single voltage-gated inward current IMI, which has been shown to regulate the rhythmic activity of the network and its pacemaker neurons. Using the dynamic clamp technique, we show that the crucial component of IMI for the generation of oscillatory activity is only a close-to-linear portion of the current-voltage relationship. The nature of this conductance is such that the presence or the absence of neuromodulators effectively regulates the amount of leak current and the input resistance in the pacemaker neurons. When deprived of neuromodulatory inputs, pyloric oscillations are disrupted; yet, a linear reduction of the total conductance in a single neuron within the pacemaker group recovers not only the pacemaker activity in that neuron, but also leads to a recovery of oscillations in the entire pyloric network. The recovered activity produces proper frequency and phasing that is similar to that induced by neuromodulators. These results show that the passive properties of pacemaker neurons can significantly affect their capacity to generate and regulate the oscillatory activity of an entire network, and that this feature is exploited by neuromodulatory inputs.

  19. Modelling phagosomal lipid networks that regulate actin assembly

    Directory of Open Access Journals (Sweden)

    Schwarz Roland

    2008-12-01

    Full Text Available Abstract Background When purified phagosomes are incubated in the presence of actin under appropriate conditions, microfilaments start growing from the membrane in a process that is affected by ATP and the lipid composition of the membrane. Isolated phagosomes are metabolically active organelles that contain enzymes and metabolites necessary for lipid interconversion. Hence, addition of ATP, lipids, and actin to the system alter the steady-state composition of the phagosomal membrane at the same time that the actin nucleation is initiated. Our aim was to model all these processes in parallel. Results We compiled detailed experimental data on the effects of different lipids and ATP on actin nucleation and we investigated experimentally lipid interconversion and ATP metabolism in phagosomes by using suitable radioactive compounds. In a first step, a complex lipid network interconnected by chemical reactions catalyzed by known enzymes was modelled in COPASI (Complex Pathway Simulator. However, several lines of experimental evidence indicated that only the phosphatidylinositol branch of the network was active, an observation that dramatically reduced the number of parameters in the model. The results also indicated that a lipid network-independent ATP-consuming activity should be included in the model. When this activity was introduced, the set of differential equations satisfactorily reproduced the experimental data. On the other hand, a molecular mechanism connecting membrane lipids, ATP, and the actin nucleation process is still missing. We therefore adopted a phenomenological (black-box approach to represent the empirical observations. We proposed that lipids and ATP influence the dynamic interconversion between active and inactive actin nucleation sites. With this simple model, all the experimental data were satisfactorily fitted with a single positive parameter per lipid and ATP. Conclusion By establishing an active 'dialogue' between an

  20. Molecular signaling networks in regulation of immunity and disease

    DEFF Research Database (Denmark)

    Laursen, Janne Marie; Jensen, Stina Rikke; Sørensen, Morten

    The gut microbiota, host tissues, and the immune system form a complex network where extensive crosstalk and molecular interactions substantially impact the overall state of the system. Concomitantly, modulation of host immune function is recurrently a result of the interaction of complex......), plays a crucial role in shaping the nature of the adaptive/memorybased immune response after encountering inflammatory compounds. In the gut, the DC is continuously exposed to microbial and dietary components that are recognized by its innate pattern recognition receptors, and the phenotype developed...... and dynamic microbial communities with the immune cell compartment in the gut, and therefore the interaction between components from different gut bacteria can efficiently shape the phenotype of the immune response. A specialized antigenpresenting cell present at mucosal surfaces, the dendritic cell (DC...

  1. Bi-directional astrocytic regulation of neuronal activity within a network

    Science.gov (United States)

    Gordleeva, S. Yu; Stasenko, S. V.; Semyanov, A. V.; Dityatev, A. E.; Kazantsev, V. B.

    2012-01-01

    The concept of a tripartite synapse holds that astrocytes can affect both the pre- and post-synaptic compartments through the Ca2+-dependent release of gliotransmitters. Because astrocytic Ca2+ transients usually last for a few seconds, we assumed that astrocytic regulation of synaptic transmission may also occur on the scale of seconds. Here, we considered the basic physiological functions of tripartite synapses and investigated astrocytic regulation at the level of neural network activity. The firing dynamics of individual neurons in a spontaneous firing network was described by the Hodgkin–Huxley model. The neurons received excitatory synaptic input driven by the Poisson spike train with variable frequency. The mean field concentration of the released neurotransmitter was used to describe the presynaptic dynamics. The amplitudes of the excitatory postsynaptic currents (PSCs) obeyed the gamma distribution law. In our model, astrocytes depressed the presynaptic release and enhanced the PSCs. As a result, low frequency synaptic input was suppressed while high frequency input was amplified. The analysis of the neuron spiking frequency as an indicator of network activity revealed that tripartite synaptic transmission dramatically changed the local network operation compared to bipartite synapses. Specifically, the astrocytes supported homeostatic regulation of the network activity by increasing or decreasing firing of the neurons. Thus, the astrocyte activation may modulate a transition of neural network into bistable regime of activity with two stable firing levels and spontaneous transitions between them. PMID:23129997

  2. Effective feature selection of clinical and genetic to predict warfarin dose using artificial neural network

    Directory of Open Access Journals (Sweden)

    Mohammad Karim Sohrabi

    2016-03-01

    Full Text Available Background: Warfarin is one of the most common oral anticoagulant, which role is to prevent the clots. The dose of this medicine is very important because changes can be dangerous for patients. Diagnosis is difficult for physicians because increase and decrease in use of warfarin is so dangerous for patients. Identifying the clinical and genetic features involved in determining dose could be useful to predict using data mining techniques. The aim of this paper is to provide a convenient way to select the clinical and genetic features to determine the dose of warfarin using artificial neural networks (ANN and evaluate it in order to predict the dose patients. Methods: This experimental study, was investigate from April to May 2014 on 552 patients in Tehran Heart Center Hospital (THC candidates for warfarin anticoagulant therapy within the international normalized ratio (INR therapeutic target. Factors affecting the dose include clinical characteristics and genetic extracted, and different methods of feature selection based on genetic algorithm and particle swarm optimization (PSO and evaluation function neural networks in MATLAB (MathWorks, MA, USA, were performed. Results: Between algorithms used, particle swarm optimization algorithm accuracy was more appropriate, for the mean square error (MSE, root mean square error (RMSE and mean absolute error (MAE were 0.0262, 0.1621 and 0.1164, respectively. Conclusion: In this article, the most important characteristics were identified using methods of feature selection and the stable dose had been predicted based on artificial neural networks. The output is acceptable and with less features, it is possible to achieve the prediction warfarin dose accurately. Since the prescribed dose for the patients is important, the output of the obtained model can be used as a decision support system.

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

    Science.gov (United States)

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

    2016-01-01

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

  4. Genetic and environmental influences on emotion regulation: A twin study of cognitive reappraisal and expressive suppression.

    Science.gov (United States)

    McRae, Kateri; Rhee, Soo Hyun; Gatt, Justine M; Godinez, Detre; Williams, Leanne M; Gross, James J

    2017-08-01

    Previous studies have established that personality traits related to emotionality are moderately heritable. However, the relative heritability of the strategies people use to regulate emotions is unknown. The present study compared the magnitude of additive genetic, shared environmental, and nonshared environmental influences on 2 commonly used emotion regulation strategies: cognitive reappraisal and expressive suppression. In 743 twin pairs (1,486 twins), we replicated previous estimates of heritability of neuroticism (a2 = .41). Furthermore, cognitive reappraisal was significantly less heritable and more influenced by nonshared environment (a2 = .20; e2 = .80) than either neuroticism or suppression (a2 = .35; e2 = .65), another emotion regulation strategy. Finally, Cholesky decomposition modeling suggested that while there were common genetic and environmental influences on neuroticism, reappraisal and suppression, there were also significant nonshared environmental influences common between reappraisal and adaptive emotional functioning after controlling for neuroticism and suppression. These findings highlight that different aspects of emotional processing, even the use of different emotion regulation strategies, are differentially heritable. The importance of the nonshared environmental influences specific to reappraisal and adaptive emotional functioning speaks to the potential impact of social context, social partners, and psychosocial interventions on reappraisal habits. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  5. Practices and ethical concerns regarding preimplantation diagnosis. Who regulates preimplantation genetic diagnosis in Brazil?

    Directory of Open Access Journals (Sweden)

    B.B. Damian

    2015-01-01

    Full Text Available Preimplantation genetic diagnosis (PGD was originally developed to diagnose embryo-related genetic abnormalities for couples who present a high risk of a specific inherited disorder. Because this technology involves embryo selection, the medical, bioethical, and legal implications of the technique have been debated, particularly when it is used to select features that are not related to serious diseases. Although several initiatives have attempted to achieve regulatory harmonization, the diversity of healthcare services available and the presence of cultural differences have hampered attempts to achieve this goal. Thus, in different countries, the provision of PGD and regulatory frameworks reflect the perceptions of scientific groups, legislators, and society regarding this technology. In Brazil, several texts have been analyzed by the National Congress to regulate the use of assisted reproduction technologies. Legislative debates, however, are not conclusive, and limited information has been published on how PGD is specifically regulated. The country requires the development of new regulatory standards to ensure adequate access to this technology and to guarantee its safe practice. This study examined official documents published on PGD regulation in Brazil and demonstrated how little direct oversight of PGD currently exists. It provides relevant information to encourage reflection on a particular regulation model in a Brazilian context, and should serve as part of the basis to enable further reform of the clinical practice of PGD in the country.

  6. The alliance between genetic biobanks and patient organisations: the experience of the telethon network of genetic biobanks

    Directory of Open Access Journals (Sweden)

    Chiara Baldo

    2016-10-01

    Full Text Available Abstract Background Rare diseases (RDs are often neglected because they affect a small percentage of the population (6–8 %, which makes research and development of new therapies challenging processes. Easy access to high-quality samples and associated clinical data is therefore a key prerequisite for biomedical research. In this context, Genetic Biobanks are critical to developing basic, translational and clinical research on RDs. The Telethon Network of Genetic Biobanks (TNGB is aware of the importance of biobanking as a service for patients and has started a dialogue with RD-Patient Organisations via promotion of dedicated meetings and round-tables, as well as by including their representatives on the TNGB Advisory Board. This has enabled the active involvement of POs in drafting biobank policies and procedures, including those concerning ethical issues. Here, we report on our experience with RD-Patient Organisations who have requested the services of existing biobanks belonging to TNGB and describe how these relationships were established, formalised and maintained. Results The process of patient engagement has proven to be successful both for lay members, who increased their understanding of the complex processes of biobanking, and for professionals, who gained awareness of the needs and expectations of the people involved. This collaboration has resulted in a real interest on the part of Patient Organisations in the biobanking service, which has led to 13 written agreements designed to formalise this process. These agreements enabled the centralisation of rare genetic disease biospecimens and their related data, thus making them available to the scientific community. Conclusions The TNGB experience has proven to be an example of good practice with regard to patient engagement in biobanking and may serve as a model of collaboration between disease-oriented Biobanks and Patient Organisations. Such collaboration serves to enhance awareness

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

    Science.gov (United States)

    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

  8. Connexin 43-Mediated Astroglial Metabolic Networks Contribute to the Regulation of the Sleep-Wake Cycle.

    Science.gov (United States)

    Clasadonte, Jerome; Scemes, Eliana; Wang, Zhongya; Boison, Detlev; Haydon, Philip G

    2017-09-13

    Astrocytes produce and supply metabolic substrates to neurons through gap junction-mediated astroglial networks. However, the role of astroglial metabolic networks in behavior is unclear. Here, we demonstrate that perturbation of astroglial networks impairs the sleep-wake cycle. Using a conditional Cre-Lox system in mice, we show that knockout of the gap junction subunit connexin 43 in astrocytes throughout the brain causes excessive sleepiness and fragmented wakefulness during the nocturnal active phase. This astrocyte-specific genetic manipulation silenced the wake-promoting orexin neurons located in the lateral hypothalamic area (LHA) by impairing glucose and lactate trafficking through astrocytic networks. This global wakefulness instability was mimicked with viral delivery of Cre recombinase to astrocytes in the LHA and rescued by in vivo injections of lactate. Our findings propose a novel regulatory mechanism critical for maintaining normal daily cycle of wakefulness and involving astrocyte-neuron metabolic interactions. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Analyzing fixed points of intracellular regulation networks with interrelated feedback topology

    Directory of Open Access Journals (Sweden)

    Radde Nicole

    2012-06-01

    Full Text Available Abstract Background Modeling the dynamics of intracellular regulation networks by systems of ordinary differential equations has become a standard method in systems biology, and it has been shown that the behavior of these networks is often tightly connected to the network topology. We have recently introduced the circuit-breaking algorithm, a method that uses the network topology to construct a one-dimensional circuit-characteristic of the system. It was shown that this characteristic can be used for an efficient calculation of the system’s fixed points. Results Here we extend previous work and show several connections between the circuit-characteristic and the stability of fixed points. In particular, we derive a sufficient condition on the characteristic for a fixed point to be unstable for certain graph structures and demonstrate that the characteristic does not contain the information to decide whether a fixed point is asymptotically stable. All statements are illustrated on biological network models. Conclusions Single feedback circuits and their role for complex dynamic behavior of biological networks have extensively been investigated, but a transfer of most of these concepts to more complex topologies is difficult. In this context, our algorithm is a powerful new approach for the analysis of regulation networks that goes beyond single isolated feedback circuits.

  10. Determinants of human adipose tissue gene expression: impact of diet, sex, metabolic status, and cis genetic regulation.

    Directory of Open Access Journals (Sweden)

    Nathalie Viguerie

    2012-09-01

    Full Text Available Weight control diets favorably affect parameters of the metabolic syndrome and delay the onset of diabetic complications. The adaptations occurring in adipose tissue (AT are likely to have a profound impact on the whole body response as AT is a key target of dietary intervention. Identification of environmental and individual factors controlling AT adaptation is therefore essential. Here, expression of 271 transcripts, selected for regulation according to obesity and weight changes, was determined in 515 individuals before, after 8-week low-calorie diet-induced weight loss, and after 26-week ad libitum weight maintenance diets. For 175 genes, opposite regulation was observed during calorie restriction and weight maintenance phases, independently of variations in body weight. Metabolism and immunity genes showed inverse profiles. During the dietary intervention, network-based analyses revealed strong interconnection between expression of genes involved in de novo lipogenesis and components of the metabolic syndrome. Sex had a marked influence on AT expression of 88 transcripts, which persisted during the entire dietary intervention and after control for fat mass. In women, the influence of body mass index on expression of a subset of genes persisted during the dietary intervention. Twenty-two genes revealed a metabolic syndrome signature common to men and women. Genetic control of AT gene expression by cis signals was observed for 46 genes. Dietary intervention, sex, and cis genetic variants independently controlled AT gene expression. These analyses help understanding the relative importance of environmental and individual factors that control the expression of human AT genes and therefore may foster strategies aimed at improving AT function in metabolic diseases.

  11. Statistical analysis of the spatial distribution of operons in the transcriptional regulation network of Escherichia coli

    OpenAIRE

    Warren, P. B.; Wolde, P.R. ten

    2003-01-01

    We have performed a statistical analysis of the spatial distribution of operons in the transcriptional regulation network of Escherichia coli. The analysis reveals that operons that regulate each other and operons that are coregulated tend to lie next to each other on the genome. Moreover, these pairs of operons tend to be transcribed in diverging directions. This spatial arrangement of operons allows the upstream regulatory regions to interfere with each other. This affords additional regula...

  12. The effect of alcohol on the differential expression of cluster of differentiation 14 gene, associated pathways, and genetic network.

    Directory of Open Access Journals (Sweden)

    Diana X Zhou

    Full Text Available Alcohol consumption affects human health in part by compromising the immune system. In this study, we examined the expression of the Cd14 (cluster of differentiation 14 gene, which is involved in the immune system through a proinflammatory cascade. Expression was evaluated in BXD mice treated with saline or acute 1.8 g/kg i.p. ethanol (12.5% v/v. Hippocampal gene expression data were generated to examine differential expression and to perform systems genetics analyses. The Cd14 gene expression showed significant changes among the BXD strains after ethanol treatment, and eQTL mapping revealed that Cd14 is a cis-regulated gene. We also identified eighteen ethanol-related phenotypes correlated with Cd14 expression related to either ethanol responses or ethanol consumption. Pathway analysis was performed to identify possible biological pathways involved in the response to ethanol and Cd14. We also constructed a genetic network for Cd14 using the top 20 correlated genes and present several genes possibly involved in Cd14 and ethanol responses based on differential gene expression. In conclusion, we found Cd14, along with several other genes and pathways, to be involved in ethanol responses in the hippocampus, such as increased susceptibility to lipopolysaccharides and neuroinflammation.

  13. Detecting Cyber-Attacks on Wireless Mobile Networks Using Multicriterion Fuzzy Classifier with Genetic Attribute Selection

    Directory of Open Access Journals (Sweden)

    El-Sayed M. El-Alfy

    2015-01-01

    Full Text Available With the proliferation of wireless and mobile network infrastructures and capabilities, a wide range of exploitable vulnerabilities emerges due to the use of multivendor and multidomain cross-network services for signaling and transport of Internet- and wireless-based data. Consequently, the rates and types of cyber-attacks have grown considerably and current security countermeasures for protecting information and communication may be no longer sufficient. In this paper, we investigate a novel methodology based on multicriterion decision making and fuzzy classification that can provide a viable second-line of defense for mitigating cyber-attacks. The proposed approach has the advantage of dealing with various types and sizes of attributes related to network traffic such as basic packet headers, content, and time. To increase the effectiveness and construct optimal models, we augmented the proposed approach with a genetic attribute selection strategy. This allows efficient and simpler models which can be replicated at various network components to cooperatively detect and report malicious behaviors. Using three datasets covering a variety of network attacks, the performance enhancements due to the proposed approach are manifested in terms of detection errors and model construction times.

  14. A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks

    Directory of Open Access Journals (Sweden)

    Ruchi Sachan

    2016-01-01

    Full Text Available The exponential growth in data traffic due to the modernization of smart devices has resulted in the need for a high-capacity wireless network in the future. To successfully deploy 5G network, it must be capable of handling the growth in the data traffic. The increasing amount of traffic volume puts excessive stress on the important factors of the resource allocation methods such as scalability and throughput. In this paper, we define a network planning as an optimization problem with the decision variables such as transmission power and transmitter (BS location in 5G networks. The decision variables lent themselves to interesting implementation using several heuristic approaches, such as differential evolution (DE algorithm and Real-coded Genetic Algorithm (RGA. The key contribution of this paper is that we modified RGA-based method to find the optimal configuration of BSs not only by just offering an optimal coverage of underutilized BSs but also by optimizing the amounts of power consumption. A comparison is also carried out to evaluate the performance of the conventional approach of DE and standard RGA with our modified RGA approach. The experimental results showed that our modified RGA can find the optimal configuration of 5G/LTE network planning problems, which is better performed than DE and standard RGA.

  15. An improved localization algorithm based on genetic algorithm in wireless sensor networks.

    Science.gov (United States)

    Peng, Bo; Li, Lei

    2015-04-01

    Wireless sensor network (WSN) are widely used in many applications. A WSN is a wireless decentralized structure network comprised of nodes, which autonomously set up a network. The node localization that is to be aware of position of the node in the network is an essential part of many sensor network operations and applications. The existing localization algorithms can be classified into two categories: range-based and range-free. The range-based localization algorithm has requirements on hardware, thus is expensive to be implemented in practice. The range-free localization algorithm reduces the hardware cost. Because of the hardware limitations of WSN devices, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is a typical range-free localization algorithm utilizing hop-distance estimation. In this paper, we propose an improved DV-Hop algorithm based on genetic algorithm. Simulation results show that our proposed algorithm improves the localization accuracy compared with previous algorithms.

  16. MicroRNA networks regulate development of brown adipocytes.

    Science.gov (United States)

    Trajkovski, Mirko; Lodish, Harvey

    2013-09-01

    Brown adipose tissue (BAT) is specialized for heat generation and energy expenditure as a defense against cold and obesity; in both humans and mice increased amounts of BAT are associated with a lean phenotype and resistance to development of the metabolic syndrome and its complications. Here we summarize recent research showing that several BAT-expressed microRNAs (miRNAs) play important roles in regulating differentiation and metabolism of brown and beige adipocytes; we discuss the key mRNA targets downregulated by these miRNAs and show how these miRNAs affect directly or indirectly transcription factors important for BAT development. We suggest that these miRNAs could be part of novel therapeutics to increase BAT in humans. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Genetic-linkage mapping of complex hereditary disorders to a whole-genome molecular-interaction network

    OpenAIRE

    Iossifov, Ivan; Zheng, Tian; Baron, Miron; Gilliam, T. Conrad; Rzhetsky, Andrey

    2008-01-01

    Common hereditary neurodevelopmental disorders such as autism, bipolar disorder, and schizophrenia are most likely both genetically multifactorial and heterogeneous. Because of these characteristics traditional methods for genetic analysis fail when applied to such diseases. To address the problem we propose a novel probabilistic framework that combines the standard genetic linkage formalism with whole-genome molecular-interaction data to predict pathways or networks of interacting genes that...

  18. Craving Facebook? Behavioral addiction to online social networking and its association with emotion regulation deficits.

    Science.gov (United States)

    Hormes, Julia M; Kearns, Brianna; Timko, C Alix

    2014-12-01

    To assess disordered online social networking use via modified diagnostic criteria for substance dependence, and to examine its association with difficulties with emotion regulation and substance use. Cross-sectional survey study targeting undergraduate students. Associations between disordered online social networking use, internet addiction, deficits in emotion regulation and alcohol use problems were examined using univariate and multivariate analyses of covariance. A large University in the Northeastern United States. Undergraduate students (n = 253, 62.8% female, 60.9% white, age mean = 19.68, standard deviation = 2.85), largely representative of the target population. The response rate was 100%. Disordered online social networking use, determined via modified measures of alcohol abuse and dependence, including DSM-IV-TR diagnostic criteria for alcohol dependence, the Penn Alcohol Craving Scale and the Cut-down, Annoyed, Guilt, Eye-opener (CAGE) screen, along with the Young Internet Addiction Test, Alcohol Use Disorders Identification Test, Acceptance and Action Questionnaire-II, White Bear Suppression Inventory and Difficulties in Emotion Regulation Scale. Disordered online social networking use was present in 9.7% [n = 23; 95% confidence interval (5.9, 13.4)] of the sample surveyed, and significantly and positively associated with scores on the Young Internet Addiction Test (P social networking sites is potentially addictive. Modified measures of substance abuse and dependence are suitable in assessing disordered online social networking use. Disordered online social networking use seems to arise as part of a cluster of symptoms of poor emotion regulation skills and heightened susceptibility to both substance and non-substance addiction. © 2014 Society for the Study of Addiction.

  19. Regulatory feedback loops bridge the human gene regulatory network and regulate carcinogenesis.

    Science.gov (United States)

    Chen, Yun-Ru; Huang, Hsuan-Cheng; Lin, Chen-Ching

    2017-11-29

    The development of disease involves a systematic disturbance inside cells and is associated with changes in the interactions or regulations among genes forming biological networks. The bridges inside a network are critical in shortening the distances between nodes. We observed that, inside the human gene regulatory network, one strongly connected core bridged the whole network. Other regulations outside the core formed a weakly connected component surrounding the core like a peripheral structure. Furthermore, the regulatory feedback loops (FBLs) inside the core compose an interface-like structure between the core and periphery. We then denoted the regulatory FBLs as the interface core. Notably, both the cancer-associated and essential biomolecules and regulations were significantly overrepresented in the interface core. These results implied that the interface core is not only critical for the network structure but central in cellular systems. Furthermore, the enrichment of the cancer-associated and essential regulations in the interface core might be attributed to its bridgeness in the network. More importantly, we identified one regulatory FBL between HNF4A and NR2F2 that possesses the highest bridgeness in the interface core. Further investigation suggested that the disturbance of the HNF4A-NR2F2 FBL might protect tumor cells from apoptotic processes. Our results emphasize the relevance of the regulatory network properties to cellular systems and might reveal a critical role of the interface core in cancer. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  20. Stimulus-Specific Transcriptional Regulation Within the p53 Network

    Science.gov (United States)

    Donner, Aaron Joseph; Hoover, Jennifer Michelle; Szostek, Stephanie Aspen; Espinosa, Joaquín Maximiliano

    2010-01-01

    The p53 transcriptional network is composed of hundreds of effector genes involved in varied stress-response pathways, including cell cycle arrest and apoptosis. It is not clear how distinct p53 target genes are differentially activated to trigger stress-specific biological responses. We analyzed the p53 transcriptional program upon activation by two DNA-damaging agents, UVC and doxorubicin, versus the non-genotoxic molecule Nutlin-3. In colorectal cancer cells, UVC triggers apoptosis, doxorubicin induces transient cell cycle arrest followed by apoptosis, and Nutlin-3 leads to cell cycle arrest with no significant apoptosis. Quantitative gene expression analysis allowed us to group p53 target genes into three main classes according to their activation profiles in each scenario. The CDK-inhibitor p21 was classified as a Class I gene, being significantly activated under cell cycle arrest conditions (i.e., doxorubicin and Nutlin-3) but not during UVC-induced apoptosis. Chromatin immunoprecipitation analysis of the p21 locus indicates that the level of p53-dependent transcription is determined by the effects of stimulus-specific transcriptional coregulators acting downstream of p53 binding and histone acetylation. In particular, our analysis indicates that the subunits of the CDK-module of the human Mediator complex function as stimulus-specific positive coregulators of p21 transcription. PMID:17957141

  1. Dopamine Autoreceptor Regulation of a Hypothalamic Dopaminergic Network

    Directory of Open Access Journals (Sweden)

    Stefanos Stagkourakis

    2016-04-01

    Full Text Available How autoreceptors contribute to maintaining a stable output of rhythmically active neuronal circuits is poorly understood. Here, we examine this issue in a dopamine population, spontaneously oscillating hypothalamic rat (TIDA neurons, that underlie neuroendocrine control of reproduction and neuroleptic side effects. Activation of dopamine receptors of the type 2 family (D2Rs at the cell-body level slowed TIDA oscillations through two mechanisms. First, they prolonged the depolarizing phase through a combination of presynaptic increases in inhibition and postsynaptic hyperpolarization. Second, they extended the discharge phase through presynaptic attenuation of calcium currents and decreased synaptic inhibition. Dopamine reuptake blockade similarly reconfigured the oscillation, indicating that ambient somatodendritic transmitter concentration determines electrical behavior. In the absence of D2R feedback, however, discharge was abolished by depolarization block. These results indicate the existence of an ultra-short feedback loop whereby neuroendocrine dopamine neurons tune network behavior to echoes of their own activity, reflected in ambient somatodendritic dopamine, and also suggest a mechanism for antipsychotic side effects.

  2. Weighted Interaction SNP Hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data.

    Science.gov (United States)

    Kogelman, Lisette J A; Kadarmideen, Haja N

    2014-01-01

    High-throughput genotype (HTG) data has been used primarily in genome-wide association (GWA) studies; however, GWA results explain only a limited part of the complete genetic variation of traits. In systems genetics, network approaches have been shown to be able to identify pathways and their underlying causal genes to unravel the biological and genetic background of complex diseases and traits, e.g., the Weighted Gene Co-expression Network Analysis (WGCNA) method based on microarray gene expression data. The main objective of this study was to develop a scale-free weighted genetic interaction network method using whole genome HTG data in order to detect biologically relevant pathways and potential genetic biomarkers for complex diseases and traits. We developed the Weighted Interaction SNP Hub (WISH) network method that uses HTG data to detect genome-wide interactions between single nucleotide polymorphism (SNPs) and its relationship with complex traits. Data dimensionality reduction was achieved by selecting SNPs based on its: 1) degree of genome-wide significance and 2) degree of genetic variation in a population. Network construction was based on pairwise Pearson's correlation between SNP genotypes or the epistatic interaction effect between SNP pairs. To identify modules the Topological Overlap Measure (TOM) was calculated, reflecting the degree of overlap in shared neighbours between SNP pairs. Modules, clusters of highly interconnected SNPs, were defined using a tree-cutting algorithm on the SNP dendrogram created from the dissimilarity TOM (1-TOM). Modules were selected for functional annotation based on their association with the trait of interest, defined by the Genome-wide Module Association Test (GMAT). We successfully tested the established WISH network method using simulated and real SNP interaction data and GWA study results for carcass weight in a pig resource population; this resulted in detecting modules and key functional and biological pathways

  3. Pathway and network-based strategies to translate genetic discoveries into effective therapies.

    Science.gov (United States)

    Greene, Casey S; Voight, Benjamin F

    2016-10-01

    One way to design a drug is to attempt to phenocopy a genetic variant that is known to have the desired effect. In general, drugs that are supported by genetic associations progress further in the development pipeline. However, the number of associations that are candidates for development into drugs is limited because many associations are in non-coding regions or difficult to target genes. Approaches that overlay information from pathway databases or biological networks can expand the potential target list. In cases where the initial variant is not targetable or there is no variant with the desired effect, this may reveal new means to target a disease. In this review, we discuss recent examples in the domain of pathway and network-based drug repositioning from genetic associations. We highlight important caveats and challenges for the field, and we discuss opportunities for further development. © The Author (2016). Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Najla S Dar-Odeh

    2010-05-01

    Full Text Available Najla S Dar-Odeh1, Othman M Alsmadi2, Faris Bakri3, Zaer Abu-Hammour2, Asem A Shehabi3, Mahmoud K Al-Omiri1, Shatha M K Abu-Hammad4, Hamzeh Al-Mashni4, Mohammad B Saeed4, Wael Muqbil4, Osama A Abu-Hammad1 1Faculty of Dentistry, 2Faculty of Engineering and Technology, 3Faculty of Medicine, University of Jordan, Amman, Jordan; 4Dental Department, University of Jordan Hospital, Amman, JordanObjective: To construct and optimize a neural network that is capable of predicting the occurrence of recurrent aphthous ulceration (RAU based on a set of appropriate input data.Participants and methods: Artificial neural networks (ANN software employing genetic algorithms to optimize the architecture neural networks was used. Input and output data of 86 participants (predisposing factors and status of the participants with regards to recurrent aphthous ulceration were used to construct and train the neural networks. The optimized neural networks were then tested using untrained data of a further 10 participants.Results: The optimized neural network, which produced the most accurate predictions for the presence or absence of recurrent aphthous ulceration was found to employ: gender, hematological (with or without ferritin and mycological data of the participants, frequency of tooth brushing, and consumption of vegetables and fruits.Conclusions: Factors appearing to be related to recurrent aphthous ulceration and appropriate for use as input data to construct ANNs that predict recurrent aphthous ulceration were found to include the following: gender, hemoglobin, serum vitamin B12, serum ferritin, red cell folate, salivary candidal colony count, frequency of tooth brushing, and the number of fruits or vegetables consumed daily.Keywords: artifical neural networks, recurrent, aphthous ulceration, ulcer

  6. Modeling delay in genetic networks: from delay birth-death processes to delay stochastic differential equations.

    Science.gov (United States)

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

    2014-05-28

    Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here, we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove a quantitative bound on the error between the pathwise realizations of these two processes. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemical Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the oscillatory behavior in negative feedback circuits, cross-correlations between nodes in a network, and spatial and temporal information in two commonly studied motifs of metastability in biochemical systems. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay.

  7. Dosage suppression genetic interaction networks enhance functional wiring diagrams of the cell.

    Science.gov (United States)

    Magtanong, Leslie; Ho, Cheuk Hei; Barker, Sarah L; Jiao, Wei; Baryshnikova, Anastasia; Bahr, Sondra; Smith, Andrew M; Heisler, Lawrence E; Choy, John S; Kuzmin, Elena; Andrusiak, Kerry; Kobylianski, Anna; Li, Zhijian; Costanzo, Michael; Basrai, Munira A; Giaever, Guri; Nislow, Corey; Andrews, Brenda; Boone, Charles

    2011-05-15

    Dosage suppression is a genetic interaction in which overproduction of one gene rescues a mutant phenotype of another gene. Although dosage suppression is known to map functional connections among genes, the extent to which it might illuminate global cellular functions is unclear. Here we analyze a network of interactions linking dosage suppressors to 437 essential genes in yeast. For 424 genes, we curated interactions from the literature. Analyses revealed that many dosage suppression interactions occur between functionally related genes and that the majority do not overlap with other types of genetic or physical interactions. To confirm the generality of these network properties, we experimentally identified dosage suppressors for 29 genes from pooled populations of temperature-sensitive mutant cells transformed with a high-copy molecular-barcoded open reading frame library, MoBY-ORF 2.0. We classified 87% of the 1,640 total interactions into four general types of suppression mechanisms, which provided insight into their relative frequencies. This work suggests that integrating the results of dosage suppression studies with other interaction networks could generate insights into the functional wiring diagram of a cell.

  8. A software tool to model genetic regulatory networks. Applications to the modeling of threshold phenomena and of spatial patterning in Drosophila.

    Science.gov (United States)

    Dilão, Rui; Muraro, Daniele

    2010-05-27

    We present a general methodology in order to build mathematical models of genetic regulatory networks. This approach is based on the mass action law and on the Jacob and Monod operon model. The mathematical models are built symbolically by the Mathematica software package GeneticNetworks. This package accepts as input the interaction graphs of the transcriptional activators and repressors of a biological process and, as output, gives the mathematical model in the form of a system of ordinary differential equations. All the relevant biological parameters are chosen automatically by the software. Within this framework, we show that concentration dependent threshold effects in biology emerge from the catalytic properties of genes and its associated conservation laws. We apply this methodology to the segment patterning in Drosophila early development and we calibrate the genetic transcriptional network responsible for the patterning of the gap gene proteins Hunchback and Knirps, along the antero-posterior axis of the Drosophila embryo. In this approach, the zygotically produced proteins Hunchback and Knirps do not diffuse along the antero-posterior axis of the embryo of Drosophila, developing a spatial pattern due to concentration dependent thresholds. This shows that patterning at the gap genes stage can be explained by the concentration gradients along the embryo of the transcriptional regulators.

  9. A software tool to model genetic regulatory networks. Applications to the modeling of threshold phenomena and of spatial patterning in Drosophila.

    Directory of Open Access Journals (Sweden)

    Rui Dilão

    Full Text Available We present a general methodology in order to build mathematical models of genetic regulatory networks. This approach is based on the mass action law and on the Jacob and Monod operon model. The mathematical models are built symbolically by the Mathematica software package GeneticNetworks. This package accepts as input the interaction graphs of the transcriptional activators and repressors of a biological process and, as output, gives the mathematical model in the form of a system of ordinary differential equations. All the relevant biological parameters are chosen automatically by the software. Within this framework, we show that concentration dependent threshold effects in biology emerge from the catalytic properties of genes and its associated conservation laws. We apply this methodology to the segment patterning in Drosophila early development and we calibrate the genetic transcriptional network responsible for the patterning of the gap gene proteins Hunchback and Knirps, along the antero-posterior axis of the Drosophila embryo. In this approach, the zygotically produced proteins Hunchback and Knirps do not diffuse along the antero-posterior axis of the embryo of Drosophila, developing a spatial pattern due to concentration dependent thresholds. This shows that patterning at the gap genes stage can be explained by the concentration gradients along the embryo of the transcriptional regulators.

  10. Mouse genetics suggests cell-context dependency for Myc-regulated metabolic enzymes during tumorigenesis.

    Science.gov (United States)

    Nilsson, Lisa M; Forshell, Tacha Zi Plym; Rimpi, Sara; Kreutzer, Christiane; Pretsch, Walter; Bornkamm, Georg W; Nilsson, Jonas A

    2012-01-01

    c-Myc (hereafter called Myc) belongs to a family of transcription factors that regulates cell growth, cell proliferation, and differentiation. Myc initiates the transcription of a large cast of genes involved in cell growth by stimulating metabolism and protein synthesis. Some of these, like those involved in glycolysis, may be part of the Warburg effect, which is defined as increased glucose uptake and lactate production in the presence of adequate oxygen supply. In this study, we have taken a mouse-genetics approach to challenge the role of select Myc-regulated metabolic enzymes in tumorigenesis in vivo. By breeding λ-Myc transgenic mice, Apc(Min) mice, and p53 knockout mice with mouse models carrying inactivating alleles of Lactate dehydrogenase A (Ldha), 3-Phosphoglycerate dehydrogenase (Phgdh) and Serine hydroxymethyltransferase 1 (Shmt1), we obtained offspring that were monitored for tumor development. Very surprisingly, we found that these genes are dispensable for tumorigenesis in these genetic settings. However, experiments in fibroblasts and colon carcinoma cells expressing oncogenic Ras show that these cells are sensitive to Ldha knockdown. Our genetic models reveal cell context dependency and a remarkable ability of tumor cells to adapt to alterations in critical metabolic pathways. Thus, to achieve clinical success, it will be of importance to correctly stratify patients and to find synthetic lethal combinations of inhibitors targeting metabolic enzymes.

  11. Genetic variants of methyl metabolizing enzymes and epigenetic regulators: Associations with promoter CpG island hypermethylation in colorectal cancer

    NARCIS (Netherlands)

    Vogel, S. de; Wouters, K.A.D.; Gottschalk, R.W.H.; Schooten, F.J. van; Goeij, A.F.P.M. de; Bruïne, A.P. de; Goldbohm, R.A.; Brandt, P.A. van den; Weijenberg, M.P.; Engeland, M. van

    2009-01-01

    Aberrant DNA methylation affects carcinogenesis of colorectal cancer. Folate metabolizing enzymes may influence the bioavailability of methyl groups, whereas DNA and histone methyltransferases are involved in epigenetic regulation of gene expression. We studied associations of genetic variants of

  12. Genetic-and-Epigenetic Interspecies Networks for Cross-Talk Mechanisms in Human Macrophages and Dendritic Cells during MTB Infection.

    Science.gov (United States)

    Li, Cheng-Wei; Lee, Yun-Lin; Chen, Bor-Sen

    2016-01-01

    Tuberculosis is caused by Mycobacterium tuberculosis ( Mtb ) infection. Mtb is one of the oldest human pathogens, and evolves mechanisms implied in human evolution. The lungs are the first organ exposed to aerosol-transmitted Mtb during gaseous exchange. Therefore, the guards of the immune system in the lungs, such as macrophages (Mϕs) and dendritic cells (DCs), are the most important defense against Mtb infection. There have been several studies discussing the functions of Mϕs and DCs during Mtb infection, but the genome-wide pathways and networks are still incomplete. Furthermore, the immune response induced by Mϕs and DCs varies. Therefore, we analyzed the cross-talk genome-wide genetic-and-epigenetic interspecies networks (GWGEINs) between Mϕs vs. Mtb and DCs vs. Mtb to determine the varying mechanisms of both the host and pathogen as it relates to Mϕs and DCs during early Mtb infection. First, we performed database mining to construct candidate cross-talk GWGEIN between human cells and Mtb . Then we constructed dynamic models to characterize the molecular mechanisms, including intraspecies gene/microRNA (miRNA) regulation networks (GRNs), intraspecies protein-protein interaction networks (PPINs), and the interspecies PPIN of the cross-talk GWGEIN. We applied a system identification method and a system order detection scheme to dynamic models to identify the real cross-talk GWGEINs using the microarray data of Mϕs, DCs and Mtb . After identifying the real cross-talk GWGEINs, the principal network projection (PNP) method was employed to construct host-pathogen core networks (HPCNs) between Mϕs vs. Mtb and DCs vs. Mtb during infection process. Thus, we investigated the underlying cross-talk mechanisms between the host and the pathogen to determine how the pathogen counteracts host defense mechanisms in Mϕs and DCs during Mtb H37Rv early infection. Based on our findings, we propose Rv1675c as a potential drug target because of its important defensive role

  13. Genetic-and-epigenetic Interspecies Networks for Cross-talk Mechanisms in Human Macrophages and Dendritic Cells During MTB Infection

    Directory of Open Access Journals (Sweden)

    Cheng-Wei Li

    2016-10-01

    Full Text Available Tuberculosis is caused by Mycobacterium tuberculosis (Mtb infection. Mtb is one of the oldest human pathogens, and evolves mechanisms implied in human evolution. The lungs are the first organ exposed to aerosol-transmitted Mtb during gaseous exchange. Therefore, the guards of the immune system in the lungs, such as macrophages (Mϕs and dendritic cells (DCs, are the most important defense against Mtb infection. There have been several studies discussing the functions of Mϕs and DCs during Mtb infection, but the genome-wide pathways and networks are still incomplete. Furthermore, the immune response induced by Mϕs and DCs varies. Therefore, we analyzed the cross-talk genome-wide genetic-and-epigenetic interspecies networks (GWGEINs between Mϕs vs. Mtb and DCs vs. Mtb to determine the varying mechanisms of both the host and pathogen as it relates to Mϕs and DCs during early Mtb infection.First, we performed database mining to construct candidate cross-talk GWGEIN between human cells and Mtb. Then we constructed dynamic models to characterize the molecular mechanisms, including intraspecies gene/microRNA (miRNA regulation networks (GRNs, intraspecies protein-protein interaction networks (PPINs, and the interspecies PPIN of the cross-talk GWGEIN. We applied a system identification method and a system order detection scheme to dynamic models to identify the real cross-talk GWGEINs using the microarray data of Mϕs, DCs and Mtb.After identifying the real cross-talk GWGEINs, the principal network projection (PNP method was employed to construct host-pathogen core networks (HPCNs between Mϕs vs. Mtb and DCs vs. Mtb during infection process. Thus, we investigated the underlying cross-talk mechanisms between the host and the pathogen to determine how the pathogen counteracts host defense mechanisms in Mϕs and DCs during Mtb H37Rv early infection. Based on our findings, we propose Rv1675c as a potential drug target because of its important defensive

  14. The regulation of social recognition, social communication and aggression: vasopressin in the social behavior neural network.

    Science.gov (United States)

    Albers, H Elliott

    2012-03-01

    Neuropeptides in the arginine vasotocin/arginine vasopressin (AVT/AVP) family play a major role in the regulation of social behavior by their actions in the brain. In mammals, AVP is found within a circuit of recriprocally connected limbic structures that form the social behavior neural network. This review examines the role played by AVP within this network in controlling social processes that are critical for the formation and maintenance of social relationships: social recognition, social communication and aggression. Studies in a number of mammalian species indicate that AVP and AVP V1a receptors are ideally suited to regulate the expression of social processes because of their plasticity in response to factors that influence social behavior. The pattern of AVP innervation and V1a receptors across the social behavior neural network may determine the potential range and intensity of social responses that individuals display in different social situations. Although fundamental information on how social behavior is wired in the brain is still lacking, it is clear that different social behaviors can be influenced by the actions of AVP in the same region of the network and that AVP can act within multiple regions of this network to regulate the expression of individual social behaviors. The existing data suggest that AVP can influence social behavior by modulating the interpretation of sensory information, by influencing decision making and by triggering complex motor outputs. This article is part of a Special Issue entitled Oxytocin, Vasopressin, and Social Behavior. Copyright © 2011 Elsevier Inc. All rights reserved.

  15. Sensory-related neural activity regulates the structure of vascular networks in the cerebral cortex

    Science.gov (United States)

    Lacoste, Baptiste; Comin, Cesar H.; Ben-Zvi, Ayal; Kaeser, Pascal S.; Xu, Xiaoyin; Costa, Luciano da F.; Gu, Chenghua

    2014-01-01

    SUMMARY Neurovascular interactions are essential for proper brain function. While the effect of neural activity on cerebral blood flow has been extensively studied, whether neural activity influences vascular patterning remains elusive. Here, we demonstrate that neural activity promotes the formation of vascular networks in the early postnatal mouse barrel cortex. Using a combination of genetics, imaging, and computational tools to allow simultaneous analysis of neuronal and vascular components, we found that vascular density and branching were decreased in the barrel cortex when sensory input was reduced by either a complete deafferentation, a genetic impairment of neurotransmitter release at thalamocortical synapses, or a selective reduction of sensory-related neural activity by whisker plucking. In contrast, enhancement of neural activity by whisker stimulation led to an increase in vascular density and branching. The finding that neural activity is necessary and sufficient to trigger alterations of vascular networks reveals a novel feature of neurovascular interactions. PMID:25155955

  16. Sensory-related neural activity regulates the structure of vascular networks in the cerebral cortex.

    Science.gov (United States)

    Lacoste, Baptiste; Comin, Cesar H; Ben-Zvi, Ayal; Kaeser, Pascal S; Xu, Xiaoyin; Costa, Luciano da F; Gu, Chenghua

    2014-09-03

    Neurovascular interactions are essential for proper brain function. While the effect of neural activity on cerebral blood flow has been extensively studied, whether or not neural activity influences vascular patterning remains elusive. Here, we demonstrate that neural activity promotes the formation of vascular networks in the early postnatal mouse barrel cortex. Using a combination of genetics, imaging, and computational tools to allow simultaneous analysis of neuronal and vascular components, we found that vascular density and branching were decreased in the barrel cortex when sensory input was reduced by either a complete deafferentation, a genetic impairment of neurotransmitter release at thalamocortical synapses, or a selective reduction of sensory-related neural activity by whisker plucking. In contrast, enhancement of neural activity by whisker stimulation led to an increase in vascular density and branching. The finding that neural activity is necessary and sufficient to trigger alterations of vascular networks reveals an important feature of neurovascular interactions. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. Calibration and validation of a genetic regulatory network model describing the production of the protein Hunchback in Drosophila early development.

    Science.gov (United States)

    Dilão, Rui; Muraro, Daniele

    2010-01-01

    We fit the parameters of a differential equations model describing the production of gap-gene proteins Hunchback and Knirps along the antero-posterior axis of the embryo of Drosophila. As initial data for the differential equations model, we take the antero-posterior distribution of the proteins Bicoid, Hunchback and Tailless at the beginning of cleavage cycle 14. We calibrate and validate the model with experimental data using single- and multi-objective evolutionary optimization techniques. In the multi-objective optimization technique, we compute the associated Pareto fronts. We analyze the cross regulation mechanism between the gap-genes protein pair Hunchback-Knirps and we show that the posterior distribution of Hunchback follow the experimental data if Hunchback is negatively regulated by the Huckebein protein. This approach enables to us predict the posterior localization on the embryo of the protein Huckebein, and to validate with the experimental data the genetic regulatory network responsible for the antero-posterior distribution of the gap-gene protein Hunchback. We discuss the importance of Pareto multi-objective optimization techniques in the calibration and validation of biological models. 2010 Académie des sciences. Published by Elsevier SAS. All rights reserved.

  18. Genetic structure and regulation of isoprene synthase in Poplar (Populus spp.).

    Science.gov (United States)

    Vickers, Claudia E; Possell, Malcolm; Nicholas Hewitt, C; Mullineaux, Philip M

    2010-07-01

    Isoprene is a volatile 5-carbon hydrocarbon derived from the chloroplastic methylerythritol 2-C-methyl-D: -erythritol 4-phosphate isoprenoid pathway. In plants, isoprene emission is controlled by the enzyme isoprene synthase; however, there is still relatively little known about the genetics and regulation of this enzyme. Isoprene synthase gene structure was analysed in three poplar species. It was found that genes encoding stromal isoprene synthase exist as a small gene family, the members of which encode virtually identical proteins and are differentially regulated. Accumulation of isoprene synthase protein is developmentally regulated, but does not differ between sun and shade leaves and does not increase when heat stress is applied. Our data suggest that, in mature leaves, isoprene emission rates are primarily determined by substrate (dimethylallyl diphosphate, DMADP) availability. In immature leaves, where isoprene synthase levels are variable, emission levels are also influenced by the amount of isoprene synthase protein. No thylakoid isoforms could be identified in Populus alba or in Salix babylonica. Together, these data show that control of isoprene emission at the genetic level is far more complicated than previously assumed.

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

    Science.gov (United States)

    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.

  20. Exploring the genetics underlying autoimmune diseases with network analysis and link prediction

    KAUST Repository

    Alanis Lobato, Gregorio

    2014-02-01

    Ever since the first Genome Wide Association Study (GWAS) was carried out we have seen an important number of discoveries of biological and clinical relevance. However, there are some scientists that consider that these research outcomes and their utility are far from what was expected from this experimental design. We instead believe that the thousands of genetic variants associated with complex disorders by means of GWASs are an extremely valuable source of information that needs to be mined in a different way. Based on this philosophy, we followed a holistic perspective to analyze GWAS data and explored the structural properties of the network representation of one of these datasets with the aim to advance our understanding of the genetic intricacies underlying autoimmune human diseases. The simplicity, computational efficiency and precision of the tools proposed in this paper represent a new means to address GWAS data and contribute to the better exploitation of these rich sources of information. © 2014 IEEE.

  1. Characterization, genetic regulation and production of cyanobacterial exopolysaccharides and its applicability for heavy metal removal.

    Science.gov (United States)

    Bhunia, Biswanath; Prasad Uday, Uma Shankar; Oinam, Gunapati; Mondal, Abhijit; Bandyopadhyay, Tarun Kanti; Tiwari, Onkar Nath

    2018-01-01

    Cyanobacteria are uniquely suited for the development of sustainable bioproduction platforms but are currently underutilized due to lack of genetic tools. Exopolysaccharide (EPS) is of significant biotechnological importance due to their technological application in various industries. It has been found that most of the research works are focused on isolation and characterization of new exopolysaccharides from microbial sources. The exopolysaccharides from cyanobacteria have been poorly explored despite their original structural features associated with specific biological and physicochemical properties. However, it could increase in a near future through the use of inexpensive cyanobacterial platform as well as available information on the structural data and specific properties of these biopolymers. This review covers genetic regulation for production of exopolysaccharide, analytical strategies for their characterization, evaluation of structure property relationship and design of extraction protocol from cyanobacterial biomass. In addition applications of exopolysaccharide for removal of heavy metal from wastewater are critically reviewed. Copyright © 2017. Published by Elsevier Ltd.

  2. Genetic and Environmental Regulation on Longitudinal Change of Metabolic Phenotypes in Danish and Chinese Adult Twins

    DEFF Research Database (Denmark)

    Li, Shuxia; Kyvik, Kirsten Ohm; Pang, Zengchang

    2016-01-01

    OBJECTIVE: The rate of change in metabolic phenotypes can be highly indicative of metabolic disorders and disorder-related modifications. We analyzed data from longitudinal twin studies on multiple metabolic phenotypes in Danish and Chinese twins representing two populations of distinct ethnic, c...... differential patterns of genetic and common environmental regulation on changes over time in metabolic phenotypes across the two samples.......OBJECTIVE: The rate of change in metabolic phenotypes can be highly indicative of metabolic disorders and disorder-related modifications. We analyzed data from longitudinal twin studies on multiple metabolic phenotypes in Danish and Chinese twins representing two populations of distinct ethnic...... environmental contribution to blood pressure but no genetic contribution to longitudinal change in body mass traits. CONCLUSION: Our results emphasize the major contribution of unique environment to the observed intra-individual variation in all metabolic phenotypes in both samples, and meanwhile reveal...

  3. Regulations governing veterinary medicinal products containing genetically modified organisms in the European community.

    Science.gov (United States)

    Moulin, G

    2005-04-01

    This paper describes particular aspects of the marketing of veterinary medicinal products (VMPs) that contain or consist of genetically modified micro-organisms (GMMs) or genetically modified organisms (GMOs). The regulatory requirements and the procedures applied in the European Union for each phase (pre-marketing, authorisation process, and post-authorisation labelling and monitoring) are explained. In most cases VMPs are subject to both pharmaceutical and GMO regulations. In the early stages of the process, before applications for marketing authorisation are submitted, the assessment of clinical trials and experiments in contained areas is principally the responsibility of national authorities. However, the marketing of all VMPs containing or consisting of GMOs must be authorised at European level, although the national authorities are informed and involved in the assessment process.

  4. Rules for processing genetic data for research purposes in view of the new EU General Data Protection Regulation.

    Science.gov (United States)

    Shabani, Mahsa; Borry, Pascal

    2017-11-29

    Genetic data contain sensitive health and non-health-related information about the individuals and their family members. Therefore, adopting adequate privacy safeguards is paramount when processing genetic data for research or clinical purposes. One of the major legal instruments for personal data protection in the EU is the new General Data Protection Regulation (GDPR), which has entered into force in May 2016 and repealed the Directive 95/46/EC, with an ultimate goal of enhancing effectiveness and harmonization of personal data protection in the EU. This paper explores the major provisions of the new Regulation with regard to processing genetic data, and assesses the influence of such provisions on reinforcing the legal safeguards when sharing genetic data for research purposes. The new Regulation attempts to elucidate the scope of personal data, by recognizing pseudonymized data as personal (identifiable) data, and including genetic data in the catalog of special categories of data (sensitive data). Moreover, a set of new rules is laid out in the Regulation for processing personal data under the scientific research exemption. For instance, further use of genetic data for scientific research purposes, without obtaining additional consent will be allowed, if the specific conditions is met. The new Regulation has already fueled concerns among various stakeholders, owing to the challenges that may emerge when implementing the Regulation across the countries. Notably, the provided definition for pseudonymized data has been criticized because it leaves too much room for interpretations, and it might undermine the harmonization of the data protection across the countries.

  5. GABAergic synapse properties may explain genetic variation in hippocampal network oscillations in mice

    Directory of Open Access Journals (Sweden)

    Tim S Heistek

    2010-06-01

    Full Text Available Cognitive ability and the properties of brain oscillation are highly heritable in humans. Genetic variation underlying oscillatory activity might give rise to differences in cognition and behavior. How genetic diversity translates into altered properties of oscillations and synchronization of neuronal activity is unknown. To address this issue, we investigated cellular and synaptic mechanisms of hippocampal fast network oscillations in eight genetically distinct inbred mouse strains. The frequency of carbachol-induced oscillations differed substantially between mouse strains. Since GABAergic inhibition sets oscillation frequency, we studied the properties of inhibitory synaptic inputs (IPSCs received by CA3 and CA1 pyramidal cells of three mouse strains that showed the highest, lowest and intermediate frequencies of oscillations. In CA3 pyramidal cells, the frequency of rhythmic IPSC input showed the same strain differences as the frequency of field oscillations. Furthermore, IPSC decay times in both CA1 and CA3 pyramidal cells were faster in mouse strains with higher oscillation frequencies than in mouse strains with lower oscillation frequency, suggesting that differences in GABAA-receptor subunit composition exist between these strains. Indeed, gene expression of GABAA-receptor β2 (Gabrb2 and β3 (Gabrb2 subunits was higher in mouse strains with faster decay kinetics compared with mouse strains with slower decay kinetics. Hippocampal pyramidal neurons in mouse strains with higher oscillation frequencies and faster decay kinetics fired action potential at higher frequencies. These data indicate that differences in genetic background may result in different GABAA-receptor subunit expression, which affects the rhythm of pyramidal neuron firing and fast network activity through GABA synapse kinetics.

  6. Comparison between genetic algorithm and self organizing map to detect botnet network traffic

    Science.gov (United States)

    Yugandhara Prabhakar, Shinde; Parganiha, Pratishtha; Madhu Viswanatham, V.; Nirmala, M.

    2017-11-01

    In Cyber Security world the botnet attacks are increasing. To detect botnet is a challenging task. Botnet is a group of computers connected in a coordinated fashion to do malicious activities. Many techniques have been developed and used to detect and prevent botnet traffic and the attacks. In this paper, a comparative study is done on Genetic Algorithm (GA) and Self Organizing Map (SOM) to detect the botnet network traffic. Both are soft computing techniques and used in this paper as data analytics system. GA is based on natural evolution process and SOM is an Artificial Neural Network type, uses unsupervised learning techniques. SOM uses neurons and classifies the data according to the neurons. Sample of KDD99 dataset is used as input to GA and SOM.

  7. Optimal groundwater remediation using artificial neural networks and the genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Rogers, Leah L. [Stanford Univ., CA (United States)

    1992-08-01

    An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ``recycle`` or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models.

  8. Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers.

    Science.gov (United States)

    Taheri, Mahboobeh; Mohebbi, Ali

    2008-08-30

    In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.

  9. NEURAL NETWORKS, FUZZY LOGIC AND GENETIC ALGORITHMS: APPLICATIONS AND POSSIBILITIES IN FINANCE AND ACCOUNTING

    Directory of Open Access Journals (Sweden)

    José Alonso Borba

    2010-04-01

    Full Text Available There are problems in Finance and Accounting that can not be easily solved by means of traditional techniques (e.g. bankruptcy prediction and strategies for investing in common stock. In these situations, it is possible to use methods of Artificial Intelligence. This paper analyzes empirical works published in international journals between 2000 and 2007 that present studies about the application of Neural Networks, Fuzzy Logic and Genetic Algorithms to problems in Finance and Accounting. The objective is to identify and quantify the relationships established between the available techniques and the problems studied by the researchers. Analyzing 258 papers, it was noticed that the most used technique is the Artificial Neural Network. The most researched applications are from the field of Finance, especially those related to stock exchanges (forecasting of common stock and indices prices.

  10. A Genetic Algorithm-based Antenna Selection Approach for Large-but-Finite MIMO Networks

    KAUST Repository

    Makki, Behrooz

    2016-12-29

    We study the performance of antenna selectionbased multiple-input-multiple-output (MIMO) networks with large but finite number of transmit antennas and receivers. Considering the continuous and bursty communication scenarios with different users’ data request probabilities, we develop an efficient antenna selection scheme using genetic algorithms (GA). As demonstrated, the proposed algorithm is generic in the sense that it can be used in the cases with different objective functions, precoding methods, levels of available channel state information and channel models. Our results show that the proposed GAbased algorithm reaches (almost) the same throughput as the exhaustive search-based optimal approach, with substantially less implementation complexity.

  11. Noise-aided Logic in an Electronic Analog of Synthetic Genetic Networks

    CERN Document Server

    Hellen, Edward H; Kurths, Jurgen; Sinha, Sudeshna

    2012-01-01

    We report the experimental verification of noise-enhanced logic behaviour in an electronic analog of a synthetic genetic network, composed of two repressors and two constructive promoters. We observe good agreement between circuit measurements and numerical prediction, with the circuit allowing for robust logic operations in an optimal window of noise. Namely, the input-output characteristics of a logic gate is reproduced faithfully under moderate noise, which is a manifestation of the phenomenon known as Logical Stochastic Resonance. Interestingly, the two dynamical variables in the system yield complementary logic behaviour simultaneously, indicating strong potential for parallel processing.

  12. Optimization of China Crude Oil Transportation Network with Genetic Ant Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Yao Wang

    2015-08-01

    Full Text Available Taking into consideration both shipping and pipeline transport, this paper first analysed the risk factors for different modes of crude oil import transportation. Then, based on the minimum of both transportation cost and overall risk, a multi-objective programming model was established to optimize the transportation network of crude oil import, and the genetic algorithm and ant colony algorithm were employed to solve the problem. The optimized result shows that VLCC (Very Large Crude Carrier is superior in long distance sea transportation, whereas pipeline transport is more secure than sea transport. Finally, this paper provides related safeguard suggestions on crude oil import transportation.

  13. Noise-aided logic in an electronic analog of synthetic genetic networks.

    Science.gov (United States)

    Hellen, Edward H; Dana, Syamal K; Kurths, Jürgen; Kehler, Elizabeth; Sinha, Sudeshna

    2013-01-01

    We report the experimental verification of noise-enhanced logic behaviour in an electronic analog of a synthetic genetic network, composed of two repressors and two constitutive promoters. We observe good agreement between circuit measurements and numerical prediction, with the circuit allowing for robust logic operations in an optimal window of noise. Namely, the input-output characteristics of a logic gate is reproduced faithfully under moderate noise, which is a manifestation of the phenomenon known as Logical Stochastic Resonance. The two dynamical variables in the system yield complementary logic behaviour simultaneously. The system is easily morphed from AND/NAND to OR/NOR logic.

  14. Satellite image processing for precision agriculture and agroindustry using convolutional neural network and genetic algorithm

    Science.gov (United States)

    Firdaus; Arkeman, Y.; Buono, A.; Hermadi, I.

    2017-01-01

    Translating satellite imagery to a useful data for decision making during this time are usually done manually by human. In this research, we are going to translate satellite imagery by using artificial intelligence method specifically using convolutional neural network and genetic algorithm to become a useful data for decision making, especially for precision agriculture and agroindustry. In this research, we are focused on how to made a sustainable land use planning with 3 objectives. The first is maximizing economic factor. Second is minimizing CO2 emission and the last is minimizing land degradation. Results show that by using artificial intelligence method, can produced a good pareto optimum solutions in a short time.

  15. A Kinase-Phosphatase Network that Regulates Kinetochore-Microtubule Attachments and the SAC.

    Science.gov (United States)

    Vallardi, Giulia; Cordeiro, Marilia Henriques; Saurin, Adrian Thomas

    2017-01-01

    The KMN network (for KNL1, MIS12 and NDC80 complexes) is a hub for signalling at the outer kinetochore. It integrates the activities of two kinases (MPS1 and Aurora B) and two phosphatases (PP1 and PP2A-B56) to regulate kinetochore-microtubule attachments and the spindle assembly checkpoint (SAC). We will first discuss each of these enzymes separately, to describe how they are regulated at kinetochores and why this is important for their primary function in controlling either microtubule attachments or the SAC. We will then discuss why inhibiting any one of them individually produces secondary effects on all the others. This cross-talk may help to explain why all enzymes have been linked to both processes, even though the direct evidence suggests they each control only one. This chapter therefore describes how a network of kinases and phosphatases work together to regulate two key mitotic processes.

  16. Genetic and Epigenetic Regulation of TOX3 Expression in Breast Cancer.

    Directory of Open Access Journals (Sweden)

    Yoo-Jeong Han

    Full Text Available Genome wide association studies (GWAS have identified low penetrance and high frequency single nucleotide polymorphisms (SNPs that contribute to genetic susceptibility of breast cancer. The SNPs at 16q12, close to the TOX3 and CASC16 genes, represent one of the susceptibility loci identified by GWAS, showing strong evidence for breast cancer association across various populations. To examine molecular mechanisms of TOX3 regulation in breast cancer, we investigated both genetic and epigenetic factors using cell lines and datasets derived from primary breast tumors available through The Cancer Genome Atlas (TCGA. TOX3 expression is highly up-regulated in luminal subtype tumors compared to normal breast tissues or basal-like tumors. Expression quantitative trait loci (eQTL analyses revealed significant associations of rs3803662 and rs4784227 genotypes with TOX3 expression in breast tumors. Bisulfite sequencing of four CpG islands in the TOX3 promoter showed a clear difference between luminal and basal-like cancer cell lines. 5-Aza-2'-deoxycytidine treatment of a basal-like cancer cell line increased expression of TOX3. TCGA dataset verified significantly lower levels of methylation of the promoter in luminal breast tumors with an inverse correlation between methylation and expression of TOX3. Methylation QTL (mQTL analyses showed a weak or no correlation of rs3803662 or rs4784227 with TOX3 promoter methylation in breast tumors, indicating an independent relationship between the genetic and epigenetic events. These data suggest a complex system of TOX3 regulation in breast tumors, driven by germline variants and somatic epigenetic modifications in a subtype specific manner.

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

    Science.gov (United States)

    Monteiro, Pedro T; Dumas, Estelle; Besson, Bruno; Mateescu, Radu; Page, Michel; Freitas, Ana T; de Jong, Hidde

    2009-12-30

    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. 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. 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.

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

    Directory of Open Access Journals (Sweden)

    Page Michel

    2009-12-01

    Full Text Available Abstract 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.

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

    Science.gov (United States)

    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

  20. On a vector space representation in genetic algorithms for sensor scheduling in wireless sensor networks.

    Science.gov (United States)

    Martins, F V C; Carrano, E G; Wanner, E F; Takahashi, R H C; Mateus, G R; Nakamura, F G

    2014-01-01

    Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system's dynamics. To the authors' knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances.

  1. Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

    Science.gov (United States)

    Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

    1998-01-01

    Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

  2. Semi-global regulation of output synchronization for heterogeneous networks of non-introspective, invertible agents subject to actuator saturation

    NARCIS (Netherlands)

    Yang, Tao; Stoorvogel, Antonie Arij; Grip, H°avard Fjær; Saberi, Ali

    2014-01-01

    In this paper, we consider the semi-global regulation of output synchronization problem for heterogeneous networks of invertible linear agents subject to actuator saturation. That is, we regulate the output of each agent according to an a priori specified reference model. The network communication

  3. Genetic background specific hypoxia resistance in rat is correlated with balanced activation of a cross-chromosomal genetic network centering on physiological homeostasis

    Directory of Open Access Journals (Sweden)

    Lei eMao

    2012-10-01

    Full Text Available 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 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 consomic rat strains under normoxia and two-weeks of hypoxia, and cross-compared them to the parental strains. The analyses showed that SS-9BN and SS-18BN represent the most hypoxia resistant CS strains with phenotype similar to BN, whereas SS-6BN and SS-YBN segregated to the direction of SS. A meta-analysis on the transcriptomic profiles of these consomic 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

  4. Regulation of ovulation rate in mammals: contribution of sheep genetic models

    Directory of Open Access Journals (Sweden)

    Persani Luca

    2006-04-01

    Full Text Available Abstract Ovarian folliculogenesis in mammals from the constitution of primordial follicles up to ovulation is a reasonably well understood mechanism. Nevertheless, underlying mechanisms that determine the number of ovulating follicles were enigmatic until the identification of the fecundity genes affecting ovulation rate in sheep, bone morphogenetic protein-15 (BMP-15, growth and differentiation factor-9 (GDF-9 and BMP receptor-1B (BMPR-1B. In this review, we focus on the use of these sheep genetic models for understanding the role of the BMP system as an intra-ovarian regulator of follicular growth and maturation, and finally, ovulation rate.

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

    Science.gov (United States)

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

    2016-01-01

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

  6. Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients: Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predicts Poorer Glycemic Control

    Directory of Open Access Journals (Sweden)

    Caroline A. Brorsson

    2016-01-01

    Full Text Available Genome-wide association studies (GWAS have identified over 40 type 1 diabetes risk loci. The clinical impact of these loci on β-cell function during disease progression is unknown. We aimed at testing whether a genetic risk score could predict glycemic control and residual β-cell function in type 1 diabetes (T1D. As gene expression may represent an intermediate phenotype between genetic variation and disease, we hypothesized that genes within T1D loci which are expressed in islets and transcriptionally regulated by proinflammatory cytokines would be the best predictors of disease progression. Two-thirds of 46 GWAS candidate genes examined were expressed in human islets, and 11 of these significantly changed expression levels following exposure to proinflammatory cytokines (IL-1β + IFNγ + TNFα for 48 h. Using the GWAS single nucleotide polymorphisms (SNPs from each locus, we constructed a genetic risk score based on the cumulative number of risk alleles carried in children with newly diagnosed T1D. With each additional risk allele carried, HbA1c levels increased significantly within first year after diagnosis. Network and gene ontology (GO analyses revealed that several of the 11 candidate genes have overlapping biological functions and interact in a common network. Our results may help predict disease progression in newly diagnosed children with T1D which can be exploited for optimizing treatment.

  7. When do adaptive plasticity and genetic evolution prevent extinction of a density-regulated population?

    Science.gov (United States)

    Chevin, Luis-Miguel; Lande, Russell

    2010-04-01

    We study the dynamics of evolutionary recovery after an abrupt environmental shift in a density-regulated population with evolving plasticity. Maladaptation to the new environment initially causes the population to decline, until adaptive phenotypic plasticity and genetic evolution restore positive population growth rate. We assume that selection on a quantitative trait is density-independent and that the initial cost of plasticity is much lower than the benefit of the initial plastic response. The initial partially adaptive plasticity reduces the effective magnitude of the environmental shift, whereas evolution of plasticity increases the rate of adaptation. Both effects greatly facilitate population persistence. In contrast, density dependence of population growth always hinders persistence. With theta-logistic population regulation, a lower value of theta produces a faster initial population decline and a higher extinction risk.

  8. Designing water distribution networks using genetic algorithms; Diseno de redes de distribucion de agua mediante algoritmos geneticos

    Energy Technology Data Exchange (ETDEWEB)

    Matias Sanchez, A.

    1999-08-01

    A proposal is put forward for calculating the economic dimension of water distribution networks using the heuristic technique of genetic algorithms. Genetic algorithms are based on the binary modification of potential solutions and their operation by means of selection, crossing and mutation so that the most economically viable individuals survive and the most expensive disappear. This method obviates the need to start off with a solution that satisfies the hydraulic requirements. If the network is not suitable, its costs will rise as a result of economic penalties and this will prevent it from being selected. Use of this approach allows the study of both discrete and continuous decision variables and it can be applied equally to new networks, the expansion of existing networks or piping renovation. An example is given to show the algorithm`s search capacity as well as its potential in regard to more complex networks. (Author) 11 refs.

  9. Becoming popular: Interpersonal emotion regulation predicts relationship formation in real life social networks

    Directory of Open Access Journals (Sweden)

    Karen eNiven

    2015-09-01

    Full Text Available Building relationships is crucial for satisfaction and success, especially when entering new social contexts. In the present paper, we investigate whether attempting to improve others’ feelings helps people to make connections in new networks. In Study 1, a social network study following new networks of people for a twelve-week period indicated that use of interpersonal emotion regulation (IER strategies predicted growth in popularity, as indicated by other network members’ reports of spending time with the person, in work and non-work interactions. In Study 2, linguistic analysis of the tweets from over 8000 Twitter users from formation of their accounts revealed that use of IER predicted greater popularity in terms of the number of followers gained. However, not all types of IER had positive effects. Behavioral IER strategies (which use behavior to reassure or comfort in order to regulate affect were associated with greater popularity, while cognitive strategies (which change a person’s thoughts about his or her situation or feelings in order to regulate affect were negatively associated with popularity. Our findings have implications for our understanding of how new relationships are formed, highlighting the important the role played by intentional emotion regulatory processes.

  10. NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

    Science.gov (United States)

    Asoodeh, Mojtaba; Bagheripour, Parisa; Gholami, Amin

    2015-06-01

    Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.

  11. GTRF: a game theory approach for regulating node behavior in real-time wireless sensor networks.

    Science.gov (United States)

    Lin, Chi; Wu, Guowei; Pirozmand, Poria

    2015-06-04

    The selfish behaviors of nodes (or selfish nodes) cause packet loss, network congestion or even void regions in real-time wireless sensor networks, which greatly decrease the network performance. Previous methods have focused on detecting selfish nodes or avoiding selfish behavior, but little attention has been paid to regulating selfish behavior. In this paper, a Game Theory-based Real-time & Fault-tolerant (GTRF) routing protocol is proposed. GTRF is composed of two stages. In the first stage, a game theory model named VA is developed to regulate nodes' behaviors and meanwhile balance energy cost. In the second stage, a jumping transmission method is adopted, which ensures that real-time packets can be successfully delivered to the sink before a specific deadline. We prove that GTRF theoretically meets real-time requirements with low energy cost. Finally, extensive simulations are conducted to demonstrate the performance of our scheme. Simulation results show that GTRF not only balances the energy cost of the network, but also prolongs network lifetime.

  12. QSAR modelling of integrin antagonists using enhanced Bayesian regularised genetic neural networks.

    Science.gov (United States)

    Jalali-Heravi, M; Mani-Varnosfaderani, A

    2011-06-01

    Bayesian regularised genetic neural network (BRGNN) has been used for modelling the inhibition activity of 141 biphenylalanine derivatives as integrin antagonists. Three local pattern search (PS) methods, simulated annealing and threshold acceptance were combined with BRGNN in the form of a hybrid genetic algorithm (HGA). The results obtained revealed that PS is a suitable method for improving the ability of BRGNN to break out from the local minima. The proposed HGA technique is able to retrieve important variables from complex systems and nonlinear search spaces for optimisation. Two models with 8-3-1 artificial neural network (ANN) architectures were developed for describing α₄β₇ and α₄β₁ modulatory activities of integrin antagonists. Monte Carlo cross-validation was performed to validate the models and Q₂ values of 0.75 and 0.74 were obtained for α₄β₇ and α₄β₁ inhibitory activities, respectively. The scrambling technique was used for sensitivity analysis of descriptors appearing in ANN models. Frequencies of repetition and sensitivity analysis of molecular descriptors revealed that 3D-Morse descriptors are influential factors for describing α₄β₇ inhibitory activity, while in the case of α₄β₁ inhibitory activity, the Randic shape index, the lowest eigenvalue of the Burden matrix and the number of rotatable bonds are important parameters.

  13. Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Manuel Fogue

    2018-01-01

    Full Text Available Vehicular networks make use of the Roadside Units (RSUs to enhance the communication capabilities of the vehicles in order to forward control messages and/or to provide Internet access to vehicles, drivers and passengers. Unfortunately, within vehicular networks, the wireless signal propagation is mostly affected by buildings and other obstacles (e.g., urban fixtures, in particular when considering the IEEE 802.11p standard. Therefore, a crowded RSU deployment may be required to ensure vehicular communications within urban environments. Furthermore, some applications, notably those applications related to safety, require a fast and reliable warning data transmission to the emergency services and traffic authorities. However, communication is not always possible in vehicular environments due to the lack of connectivity even employing multiple hops. To overcome the signal propagation problem and delayed warning notification time issues, an effective, smart, cost-effective and all-purpose RSU deployment policy should be put into place. In this paper, we propose the genetic algorithm for roadside unit deployment (GARSUD system, which uses a genetic algorithm that is capable of automatically providing an RSU deployment suitable for any given road map layout. Our simulation results show that GARSUD is able to reduce the warning notification time (the time required to inform emergency authorities in traffic danger situations and to improve vehicular communication capabilities within different density scenarios and complexity layouts.

  14. iMASKO: A Genetic Algorithm Based Optimization Framework for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Nanhao Zhu

    2013-10-01

    Full Text Available In this paper we present the design and implementation of a generic GA-based optimization framework iMASKO (iNL@MATLAB Genetic Algorithm-based Sensor NetworK Optimizer to optimize the performance metrics of wireless sensor networks. Due to the global search property of genetic algorithms, the framework is able to automatically and quickly fine tune hundreds of possible solutions for the given task to find the best suitable tradeoff. We test and evaluate the framework by using it to explore a SystemC-based simulation process to tune the configuration of the unslotted CSMA/CA algorithm of IEEE 802.15.4, aiming to discover the most available tradeoff solutions for the required performance metrics. In particular, in the test cases different sensor node platforms are under investigation. A weighted sum based cost function is used to measure the optimization effectiveness and capability of the framework. In the meantime, another experiment is performed to test the framework’s optimization characteristic in multi-scenario and multi-objectives conditions.

  15. A FTH1 gene:pseudogene:microRNA network regulates tumorigenesis in prostate cancer.

    Science.gov (United States)

    Chan, Jia Jia; Kwok, Zhi Hao; Chew, Xiao Hong; Zhang, Bin; Liu, Chao; Soong, Tuck Wah; Yang, Henry; Tay, Yvonne

    2017-12-12

    Non-coding RNAs play a vital role in diverse cellular processes. Pseudogenes, which are non-coding homologs of protein-coding genes, were once considered non-functional evolutional relics. However, recent studies have shown that pseudogene transcripts can regulate their parental transcripts by sequestering shared microRNAs (miRNAs), thus acting as competing endogenous RNAs (ceRNAs). In this study, we utilize an unbiased screen to identify the ferritin heavy chain 1 (FTH1) transcript and multiple FTH1 pseudogenes as targets of several oncogenic miRNAs in prostate cancer (PCa). We characterize the critical role of this FTH1 gene:pseudogene:miRNA network in regulating tumorigenesis in PCa, whereby oncogenic miRNAs downregulate the expression of FTH1 and its pseudogenes to drive oncogenesis. We further show that impairing miRNA binding and subsequent ceRNA crosstalk completely rescues the slow growth phenotype in vitro and in vivo. Our results also demonstrate the reciprocal regulation between the pseudogenes and intracellular iron levels, which are crucial for multiple physiological and pathophysiological processes. In summary, we describe an extensive gene:pseudogene network comprising multiple miRNAs and multiple pseudogenes derived from a single parental gene. The network could be regulated through multiple mechanisms to modulate iron storage in various signaling pathways, the deregulation of which results in PCa development and progression. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  16. Transcriptional Network Analysis Identifies BACH1 as a Master Regulator of Breast Cancer Bone Metastasis

    Science.gov (United States)

    Liang, Yajun; Wu, Heng; Lei, Rong; Chong, Robert A.; Wei, Yong; Lu, Xin; Tagkopoulos, Ilias; Kung, Sun-Yuan; Yang, Qifeng; Hu, Guohong; Kang, Yibin

    2012-01-01

    The application of functional genomic analysis of breast cancer metastasis has led to the identification of a growing number of organ-specific metastasis genes, which often function in concert to facilitate different steps of the metastatic cascade. However, the gene regulatory network that controls the expression of these metastasis genes remains largely unknown. Here, we demonstrate a computational approach for the deconvolution of transcriptional networks to discover master regulators of breast cancer bone metastasis. Several known regulators of breast cancer bone metastasis such as Smad4 and HIF1 were identified in our analysis. Experimental validation of the networks revealed BACH1, a basic leucine zipper transcription factor, as the common regulator of several functional metastasis genes, including MMP1 and CXCR4. Ectopic expression of BACH1 enhanced the malignance of breast cancer cells, and conversely, BACH1 knockdown significantly reduced bone metastasis. The expression of BACH1 and its target genes was linked to the higher risk of breast cancer recurrence in patients. This study established BACH1 as the master regulator of breast cancer bone metastasis and provided a paradigm to identify molecular determinants in complex pathological processes. PMID:22875853

  17. Mediating Role of the Reward Network in the Relationship between the Dopamine Multilocus Genetic Profile and Depression

    Directory of Open Access Journals (Sweden)

    Liang Gong

    2017-09-01

    Full Text Available Multiple genetic loci in the dopamine (DA pathway have been associated with depression symptoms in patients with major depressive disorder (MDD. However, the neural mechanisms underlying the polygenic effects of the DA pathway on depression remain unclear. We used an imaging genetic approach to investigate the polygenic effects of the DA pathway on the reward network in MDD. Fifty-three patients and 37 cognitively normal (CN subjects were recruited and underwent resting-state functional magnetic resonance imaging (R-fMRI scans. Multivariate linear regression analysis was employed to measure the effects of disease and multilocus genetic profile scores (MGPS on the reward network, which was constructed using the nucleus accumbens (NAc functional connectivity (NAFC network. DA-MGPS was widely associated within the NAFC network, mainly in the inferior frontal cortex, insula, hypothalamus, superior temporal gyrus, and occipital cortex. The pattern of DA-MGPS effects on the fronto-striatal pathway differed in MDD patients compared with CN subjects. More importantly, NAc-putamen connectivity mediates the association between DA MGPS and anxious depression traits in MDD patients. Our findings suggest that the DA multilocus genetic profile makes a considerable contribution to the reward network and anxious depression in MDD patients. These results expand our understanding of the pathophysiology of polygenic effects underlying brain network abnormalities in MDD.

  18. Mediating Role of the Reward Network in the Relationship between the Dopamine Multilocus Genetic Profile and Depression

    Science.gov (United States)

    Gong, Liang; He, Cancan; Yin, Yingying; Wang, Hui; Ye, Qing; Bai, Feng; Yuan, Yonggui; Zhang, Haisan; Lv, Luxian; Zhang, Hongxing; Zhang, Zhijun; Xie, Chunming

    2017-01-01

    Multiple genetic loci in the dopamine (DA) pathway have been associated with depression symptoms in patients with major depressive disorder (MDD). However, the neural mechanisms underlying the polygenic effects of the DA pathway on depression remain unclear. We used an imaging genetic approach to investigate the polygenic effects of the DA pathway on the reward network in MDD. Fifty-three patients and 37 cognitively normal (CN) subjects were recruited and underwent resting-state functional magnetic resonance imaging (R-fMRI) scans. Multivariate linear regression analysis was employed to measure the effects of disease and multilocus genetic profile scores (MGPS) on the reward network, which was constructed using the nucleus accumbens (NAc) functional connectivity (NAFC) network. DA-MGPS was widely associated within the NAFC network, mainly in the inferior frontal cortex, insula, hypothalamus, superior temporal gyrus, and occipital cortex. The pattern of DA-MGPS effects on the fronto-striatal pathway differed in MDD patients compared with CN subjects. More importantly, NAc-putamen connectivity mediates the association between DA MGPS and anxious depression traits in MDD patients. Our findings suggest that the DA multilocus genetic profile makes a considerable contribution to the reward network and anxious depression in MDD patients. These results expand our understanding of the pathophysiology of polygenic effects underlying brain network abnormalities in MDD. PMID:28959185

  19. Genetic networking of the Bemisia tabaci cryptic species complex reveals pattern of biological invasions.

    Directory of Open Access Journals (Sweden)

    Paul De Barro

    Full Text Available 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

  20. Molecular and Genetic Analysis of Hormone-Regulated Differential Cell Elongation in Arabidopsis

    Energy Technology Data Exchange (ETDEWEB)

    Ecker, Joseph R.

    2002-12-03

    The authors have utilized the response of Arabidopsis seedlings to the plant hormone ethylene to identify new genes involved in the regulation of ethylene biosynthesis, perception, signal transduction and differential cell growth. In building a genetic framework for the action of these genes, they developed a molecular model that has facilitated the understanding of the molecular requirements of ethylene for cell elongation processes. The ethylene response pathway in Arabidopsis appears to be primarily linear and is defined by the genes: ETR1, ETR2, ERS1, ERS2, EIN4, CTR1, EIN2, EIN3, EIN5 EIN6, and EIN. Downstream branches identified by the HLS1, EIR1, and AUX1 genes involve interactions with other hormonal (auxin) signals in the process of differential cell elongation in the hypocotyl hook. Cloning and characterization of HLS1 and three HLS1-LIKE genes in the laboratory has been supported under this award. HLS1 is required for differential elongation of cells in the hypocotyl and may act in the establishment of hormone gradients. Also during the award period, they have identified and begun preliminary characterization of two genes that genetically act upstream of the ethylene receptors. ETO1 and RAN1 encode negative regulators of ethylene biosynthesis and signaling respectively. Progress on the analysis of these genes along with HOOKLESS1 is described.

  1. Molecular and Genetic Analysis of Hormone-Regulated Differential Cell Elongation in Arabidopsis

    Energy Technology Data Exchange (ETDEWEB)

    Ecker, Joseph R.

    2005-09-15

    We have utilized the response of Arabidopsis seedlings to the plant hormone ethylene to identify new genes involved in the regulation of ethylene biosynthesis, perception, signal transduction and differential cell growth. In building a genetic framework for the action of these genes, we have developed a molecular model that has facilitated our understanding of the molecular requirements of ethylene for cell elongation processes. The ethylene response pathway in Arabidopsis appears to be primarily linear and is defined by the genes: ETR1, ETR2, ERS1, ERS2, EIN4, CTR1, EIN2, EIN3, EIN5, EIN6, and EIN. Downstream branches identified by the HLS1, EIR1, and AUX1 genes involve interactions with other hormonal (auxin) signals in the process of differential cell elongation in the hypocotyl hook. Cloning and characterization of HLS1 (and three HLL genes) and ETO1 (and ETOL genes) in my laboratory has been supported under this award. HLS1 is required for differential elongation of cells in the hypocotyl and may act in the establishment of hormone gradients. Also during the previous period, we have identified and characterized a gene that genetically acts upstream of the ethylene receptors. ETO1 encodes negative regulators of ethylene biosynthesis.

  2. Genetic regulation of allolysis in response to sub-lethal antibiotic stress in Streptococcus pneumoniae

    Directory of Open Access Journals (Sweden)

    MANISHA DASH

    2014-11-01

    Full Text Available Dash M, Dash HR, Das S. 2014. Genetic regulation of allolysis in response to sub-lethal antibiotic stress in Streptococcus pneumoniae. Nusantara Bioscience 6: 111-117. Allolysis is the phenomenon of cell lysis induced by other cells of the same species. Gram-positive bacterium Streptococcus pneumoniae, a major human pathogen exhibits competence induced allolysis that increases the genetic recombination and enhances the virulence. During allolysis, a group of non-competent bacterial cells are lysed by another group of competent cells in the same culture. This process is regulated by com operon as well as bacteriocin. In this study, allolysis was induced in Streptococcus pneumoniae MTCC655 by sub-lethal dose of antibiotic (chloramphenicol and the mechanism of allolysis has been deduced by amplification of lytA, lytC and cbpD genes in the bacterium. The strain was found to be resistant to a number of antibiotics including amoxicillin, cefpodoxime, erythromycin and vancomycin. The early onset of allolysis induction from 7-9 h under normal conditions to 2-3 h by sub-lethal dose of chloramphenicol was observed.

  3. Network analysis of microRNAs and their regulation in human ovarian cancer

    KAUST Repository

    Schmeier, Sebastian

    2011-11-03

    Background: MicroRNAs (miRNAs) are small non-coding RNA molecules that repress the translation of messenger RNAs (mRNAs) or degrade mRNAs. These functions of miRNAs allow them to control key cellular processes such as development, differentiation and apoptosis, and they have also been implicated in several cancers such as leukaemia, lung, pancreatic and ovarian cancer (OC). Unfortunately, the specific machinery of miRNA regulation, involving transcription factors (TFs) and transcription co-factors (TcoFs), is not well understood. In the present study we focus on computationally deciphering the underlying network of miRNAs, their targets, and their control mechanisms that have an influence on OC development.Results: We analysed experimentally verified data from multiple sources that describe miRNA influence on diseases, miRNA targeting of mRNAs, and on protein-protein interactions, and combined this data with ab initio transcription factor binding site predictions within miRNA promoter regions. From these analyses, we derived a network that describes the influence of miRNAs and their regulation in human OC. We developed a methodology to analyse the network in order to find the nodes that have the largest potential of influencing the network\\'s behaviour (network hubs). We further show the potentially most influential miRNAs, TFs and TcoFs, showing subnetworks illustrating the involved mechanisms as well as regulatory miRNA network motifs in OC. We find an enrichment of miRNA targeted OC genes in the highly relevant pathways cell cycle regulation and apoptosis.Conclusions: We combined several sources of interaction and association data to analyse and place miRNAs within regulatory pathways that influence human OC. These results represent the first comprehensive miRNA regulatory network analysis for human OC. This suggests that miRNAs and their regulation may play a major role in OC and that further directed research in this area is of utmost importance to enhance

  4. Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease.

    Directory of Open Access Journals (Sweden)

    Jiny Nair

    Full Text Available Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD. Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting Genes and visualized using Cytoscape v 2.8.3. Based on betweenness centrality (BC and node degree as key topological parameters, we identified interleukin-6 (IL-6, vascular endothelial growth factor A (VEGFA, interleukin-1 beta (IL-1B, tumor necrosis factor (TNF and prostaglandin-endoperoxide synthase 2 (PTGS2 as hub nodes. The backbone network constructed with these five hub genes showed 111 nodes connected via 348 edges, with IL-6 having the largest degree and highest BC. Nuclear factor kappa B1 (NFKB1, signal transducer and activator of transcription 3 (STAT3 and JUN were identified as the three core transcription factors from the regulatory network derived using MatInspector. For the purpose of validation of the hub genes, 97 test networks were constructed, which revealed the accuracy of the backbone network to be 0.7763 while the frequency of the hub nodes remained largely unaltered. Pathway enrichment analysis with ClueGO, KEGG and REACTOME showed significant enrichment of six validated CAD pathways - smooth muscle cell proliferation, acute-phase response, calcidiol 1-monooxygenase activity, toll-like receptor signaling, NOD-like receptor signaling and adipocytokine signaling pathways. Experimental verification of the above findings in 64 cases and 64 controls showed increased expression of the five candidate genes and the three transcription factors in the cases relative to the controls (p<0.05. Thus, analysis of complex networks aid in the

  5. Specific gene-regulation networks during the pre-implantation development of the pig embryo as revealed by deep sequencing.

    Science.gov (United States)

    Cao, Suying; Han, Jianyong; Wu, Jun; Li, Qiuyan; Liu, Shichao; Zhang, Wei; Pei, Yangli; Ruan, Xiaoan; Liu, Zhonghua; Wang, Xumin; Lim, Bing; Li, Ning

    2014-01-03

    Because few studies exist to describe the unique molecular network regulation behind pig pre-implantation embryonic development (PED), genetic engineering in the pig embryo is limited. Also, this lack of research has hindered derivation and application of porcine embryonic stem cells and porcine induced pluripotent stem cells (iPSCs). We identified and analyzed the genome wide transcriptomes of pig in vivo-derived and somatic cell nuclear transferred (SCNT) as well as mouse in vivo-derived pre-implantation embryos at different stages using mRNA deep sequencing. Comparison of the pig embryonic transcriptomes with those of mouse and human pre-implantation embryos revealed unique gene expression patterns during pig PED. Pig zygotic genome activation was confirmed to occur at the 4-cell stage via genome-wide gene expression analysis. This activation was delayed to the 8-cell stage in SCNT embryos. Specific gene expression analysis of the putative inner cell mass (ICM) and the trophectoderm (TE) revealed that pig and mouse pre-implantation embryos share regulatory networks during the first lineage segregation and primitive endoderm differentiation, but not during ectoderm commitment. Also, fatty acid metabolism appears to be a unique characteristic of pig pre-implantation embryonic development. In addition, the global gene expression patterns in the pig SCNT embryos were different from those in in vivo-derived pig embryos. Our results provide a resource for pluripotent stem cell engineering and for understanding pig development.

  6. Genetic factors regulating lung vasculature and immune cell functions associate with resistance to pneumococcal infection.

    Directory of Open Access Journals (Sweden)

    Magda S Jonczyk

    Full Text Available Streptococcus pneumoniae is an important human pathogen responsible for high mortality and morbidity worldwide. The susceptibility to pneumococcal infections is controlled by as yet unknown genetic factors. To elucidate these factors could help to develop new medical treatments and tools to identify those most at risk. In recent years genome wide association studies (GWAS in mice and humans have proved successful in identification of causal genes involved in many complex diseases for example diabetes, systemic lupus or cholesterol metabolism. In this study a GWAS approach was used to map genetic loci associated with susceptibility to pneumococcal infection in 26 inbred mouse strains. As a result four candidate QTLs were identified on chromosomes 7, 13, 18 and 19. Interestingly, the QTL on chromosome 7 was located within S. pneumoniae resistance QTL (Spir1 identified previously in a linkage study of BALB/cOlaHsd and CBA/CaOlaHsd F2 intercrosses. We showed that only a limited number of genes encoded within the QTLs carried phenotype-associated polymorphisms (22 genes out of several hundred located within the QTLs. These candidate genes are known to regulate TGFβ signalling, smooth muscle and immune cells functions. Interestingly, our pulmonary histopathology and gene expression data demonstrated, lung vasculature plays an important role in resistance to pneumococcal infection. Therefore we concluded that the cumulative effect of these candidate genes on vasculature and immune cells functions as contributory factors in the observed differences in susceptibility to pneumococcal infection. We also propose that TGFβ-mediated regulation of fibroblast differentiation plays an important role in development of invasive pneumococcal disease. Gene expression data submitted to the NCBI Gene Expression Omnibus Accession No: GSE49533 SNP data submitted to NCBI dbSNP Short Genetic Variation http://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewTable.cgi?handle=MUSPNEUMONIA.

  7. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus

    Science.gov (United States)

    Graham, Deborah S Cunninghame; Pinder, Christopher L; Tombleson, Philip; Behrens, Timothy W; Martín, Javier; Fairfax, Benjamin P; Knight, Julian C; Chen, Lingyan; Replogle, Joseph; Syvänen, Ann-Christine; Rönnblom, Lars; Graham, Robert R; Wither, Joan E; Rioux, John D; Alarcón-Riquelme, Marta E; Vyse, Timothy J

    2015-01-01

    Systemic lupus erythematosus (SLE; OMIM 152700) is a genetically complex autoimmune disease characterized by loss of immune tolerance to nuclear and cell surface antigens. Previous genome-wide association studies (GWAS) had modest sample sizes, reducing their scope and reliability. Our study comprised 7,219 cases and 15,991 controls of European ancestry: a new GWAS, meta-analysis with a published GWAS and a replication study. We have mapped 43 susceptibility loci, including 10 novel associations. Assisted by dense genome coverage, imputation provided evidence for missense variants underpinning associations in eight genes. Other likely causal genes were established by examining associated alleles for cis-acting eQTL effects in a range of ex vivo immune cells. We found an over-representation (n=16) of transcription factors among SLE susceptibility genes. This supports the view that aberrantly regulated gene expression networks in multiple cell types in both the innate and adaptive immune response contribute to the risk of developing SLE. PMID:26502338

  8. Neural networks, fuzzy logic and genetic algorithms: applications and possibilities in finance and accounting/ Redes neurais, logica nebulosa e algoritmos geneticos: aplicacoes e possibilidades em financas e contabilidade

    National Research Council Canada - National Science Library

    Wuerges, Artur Filipe Ewald; Borba, Jose Alonso

    2010-01-01

    .... This paper analyzes empirical works published in international journals between 2000 and 2007 that present studies about the application of Neural Networks, Fuzzy Logic and Genetic Algorithms to...

  9. Modeling the Normal and Neoplastic Cell Cycle with 'Realistic Boolean Genetic Networks': Their Application for Understanding Carcinogenesis and Assessing Therapeutic Strategies

    Science.gov (United States)

    Szallasi, Zoltan; Liang, Shoudan

    2000-01-01

    In this paper we show how Boolean genetic networks could be used to address complex problems in cancer biology. First, we describe a general strategy to generate Boolean genetic networks that incorporate all relevant biochemical and physiological parameters and cover all of their regulatory interactions in a deterministic manner. Second, we introduce 'realistic Boolean genetic networks' that produce time series measurements very similar to those detected in actual biological systems. Third, we outline a series of essential questions related to cancer biology and cancer therapy that could be addressed by the use of 'realistic Boolean genetic network' modeling.

  10. Optimizationof neural network architecture using genetic programming improvesdetection and modeling of gene-gene interactions in studies of humandiseases

    Directory of Open Access Journals (Sweden)

    Hahn Lance W

    2003-07-01

    Full Text Available Abstract Background Appropriate definitionof neural network architecture prior to data analysis is crucialfor successful data mining. This can be challenging when the underlyingmodel of the data is unknown. The goal of this study was to determinewhether optimizing neural network architecture using genetic programmingas a machine learning strategy would improve the ability of neural networksto model and detect nonlinear interactions among genes in studiesof common human diseases. Results Using simulateddata, we show that a genetic programming optimized neural network approachis able to model gene-gene interactions as well as a traditionalback propagation neural network. Furthermore, the genetic programmingoptimized neural network is better than the traditional back propagationneural network approach in terms of predictive ability and powerto detect gene-gene interactions when non-functional polymorphismsare present. Conclusion This study suggeststhat a machine learning strategy for optimizing neural network architecturemay be preferable to traditional trial-and-error approaches forthe identification and characterization of gene-gene interactionsin common, complex human diseases.

  11. The research of emergency managers’ risk attitude based on genetic neural network

    Science.gov (United States)

    Pan, Chen; Jia, Chuanliang; Su, Jie; Chen, Junlin

    2017-08-01

    Since the 21st century, the sudden emergencies occurred around the world. The decision which emergency managers made in the emergency environment is actually a risk decision-making. And decision-makers will be affected by its risk attitude in the risk decision-making. The paper puts forward a kind of forecasting method of risk attitudes of emergency managers, which are influenced by various factors such as their own gender, age, training experiences, treatment experiences, actual average accuracy and self-confidence and so on. Based on the questionnaire results from Kunming and Wuhan, the above six factors were chosen as the input variables of the BP neural network model and the risk attitude values as the output variable. Genetic algorithm was used to optimize the weights and thresholds of the neural network and establish BP Neural network prediction model of emergency managers. The results of risk attitude value prediction show that the GA-BP is a more effective and accurate method to predict emergency managers’ risk attitude, which can provide the reference for the risk managers’ risk attitude prediction and more efficient management.

  12. Thermomechanical processing optimization for 304 austenitic stainless steel using artificial neural network and genetic algorithm

    Science.gov (United States)

    Feng, Wen; Yang, Sen

    2016-12-01

    Thermomechanical processing has an important effect on the grain boundary character distribution. To obtain the optimal thermomechanical processing parameters is the key of grain boundary engineering. In this study, genetic algorithm (GA) based on artificial neural network model was proposed to optimize the thermomechanical processing parameters. In this model, a back-propagation neural network (BPNN) was established to map the relationship between thermomechanical processing parameters and the fraction of low-Σ CSL boundaries, and GA integrated with BPNN (BPNN/GA) was applied to optimize the thermomechanical processing parameters. The validation of the optimal thermomechanical processing parameters was verified by an experiment. Moreover, the microstructures and the intergranular corrosion resistance of the base material (BM) and the materials produced by the optimal thermomechanical processing parameters (termed as the GBEM) were studied. Compared to the BM specimen, the fraction of low-Σ CSL boundaries was increased from 56.8 to 77.9% and the random boundary network was interrupted by the low-Σ CSL boundaries, and the intergranular corrosion resistance was improved in the GBEM specimen. The results indicated that the BPNN/GA model was an effective and reliable means for the thermomechanical processing parameters optimization, which resulted in improving the intergranular corrosion resistance in 304 austenitic stainless steel.

  13. Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks.

    Science.gov (United States)

    Beal, Jacob; Lu, Ting; Weiss, Ron

    2011-01-01

    The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes (~ 50%) and latency of the optimized engineered gene networks. Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.

  14. A drug-sensitive genetic network masks fungi from the immune system.

    Directory of Open Access Journals (Sweden)

    2006-04-01

    Full Text Available Fungal pathogens can be recognized by the immune system via their beta-glucan, a potent proinflammatory molecule that is present at high levels but is predominantly buried beneath a mannoprotein coat and invisible to the host. To investigate the nature and significance of "masking" this molecule, we characterized the mechanism of masking and consequences of unmasking for immune recognition. We found that the underlying beta-glucan in the cell wall of Candida albicans is unmasked by subinhibitory doses of the antifungal drug caspofungin, causing the exposed fungi to elicit a stronger immune response. Using a library of bakers' yeast (Saccharomyces cerevisiae mutants, we uncovered a conserved genetic network that is required for concealing beta-glucan from the immune system and limiting the host response. Perturbation of parts of this network in the pathogen C. albicans caused unmasking of its beta-glucan, leading to increased beta-glucan receptor-dependent elicitation of key proinflammatory cytokines from primary mouse macrophages. By creating an anti-inflammatory barrier to mask beta-glucan, opportunistic fungi may promote commensal colonization and have an increased propensity for causing disease. Targeting the widely conserved gene network required for creating and maintaining this barrier may lead to novel broad-spectrum antimycotics.

  15. Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method.

    Science.gov (United States)

    Dorier, Julien; Crespo, Isaac; Niknejad, Anne; Liechti, Robin; Ebeling, Martin; Xenarios, Ioannis

    2016-10-06

    Prior knowledge networks (PKNs) provide a framework for the development of computational biological models, including Boolean models of regulatory networks which are the focus of this work. PKNs are created by a painstaking process of literature curation, and generally describe all relevant regulatory interactions identified using a variety of experimental conditions and systems, such as specific cell types or tissues. Certain of these regulatory interactions may not occur in all biological contexts of interest, and their presence may dramatically change the dynamical behaviour of the resulting computational model, hindering the elucidation of the underlying mechanisms and reducing the usefulness of model predictions. Methods are therefore required to generate optimized contextual network models from generic PKNs. We developed a new approach to generate and optimize Boolean networks, based on a given PKN. Using a genetic algorithm, a model network is built as a sub-network of the PKN and trained against experimental data to reproduce the experimentally observed behaviour in terms of attractors and the transitions that occur between them under specific perturbations. The resulting model network is therefore contextualized to the experimental conditions and constitutes a dynamical Boolean model closer to the observed biological process used to train the model than the original PKN. Such a model can then be interrogated to simulate response under perturbation, to detect stable states and their properties, to get insights into the underlying mechanisms and to generate new testable hypotheses. Generic PKNs attempt to synthesize knowledge of all interactions occurring in a biological process of interest, irrespective of the specific biological context. This limits their usefulness as a basis for the development of context-specific, predictive dynamical Boolean models. The optimization method presented in this article produces specific, contextualized models from generic

  16. [Back-propagation neural network and genetic algorithm for multi-objective optimization of extraction technology of Cortex Fraxini].

    Science.gov (United States)

    Yang, Ming; Yu, Min-ying; Shi, Xiu-feng; Teng, Yan-ping

    2008-11-01

    To introduce Back-propagation (BP) neural network and genetic algorithm for multi-objective optimization of extraction technology of Cortex Fraxini. BP neural network was established and optimized with uniform design. Genetic algotithm was used for multi-objective optimization of extraction technology of cortex fraxini. the optimization of extraction was as follows: extraction temperature was 99 degrees C, concentration of EtOH was 50%, liquid-solid ratio was 7, extraction time was 94 min. The proportional error between predictive value and practical measured value was just -1.16% and -5.14%. Back-propagation neural network and genetic algorithm for multi-objective optimization of extraction technology of cortex fraxini is advisable.

  17. Genetic evidence for the coordinated regulation of collagen fibrillogenesis in the cornea by decorin and biglycan.

    Science.gov (United States)

    Zhang, Guiyun; Chen, Shoujun; Goldoni, Silvia; Calder, Bennett W; Simpson, Holly C; Owens, Rick T; McQuillan, David J; Young, Marian F; Iozzo, Renato V; Birk, David E

    2009-03-27

    Decorin and biglycan are class I small leucine-rich proteoglycans (SLRPs) involved in regulation of collagen fibril and matrix assembly. We hypothesize that tissue-specific matrix assembly, such as in the cornea, requires a coordinate regulation involving multiple SLRPs. To this end, we investigated the expression of decorin and biglycan in the cornea of mice deficient in either SLRP gene and in double-mutant mice. Decorin and biglycan exhibited overlapping spatial expression patterns throughout the corneal stroma with differential temporal expression. Whereas decorin was expressed at relatively high levels in all developmental stages, biglycan expression was high early, decreased during development, and was present at very low levels in the mature cornea. Ultrastructural analyses demonstrated comparable fibril structure in the decorin- and biglycan-null corneas compared with wild-type controls. We found a compensatory up-regulation of biglycan gene expression in the decorin-deficient mice, but not the reverse. Notably, the corneas of compound decorin/biglycan-null mice showed severe disruption in fibril structure and organization, especially affecting the posterior corneal regions, corroborating the idea that biglycan compensates for the loss of decorin. Fibrillogenesis assays using recombinant decorin and biglycan confirmed a functional compensation, with both having similar effects at high SLRP/collagen ratios. However, at low ratios decorin was a more efficient regulator. The use of proteoglycan or protein core yielded comparable results. These findings provide firm genetic evidence for an interaction of decorin and biglycan during corneal development and further suggest that decorin has a primary role in regulating fibril assembly, a function that can be fine-tuned by biglycan during early development.

  18. Cooperative Adaptive Output Regulation for Second-Order Nonlinear Multiagent Systems With Jointly Connected Switching Networks.

    Science.gov (United States)

    Liu, Wei; Huang, Jie

    2017-01-11

    This paper studies the cooperative global robust output regulation problem for a class of heterogeneous second-order nonlinear uncertain multiagent systems with jointly connected switching networks. The main contributions consist of the following three aspects. First, we generalize the result of the adaptive distributed observer from undirected jointly connected switching networks to directed jointly connected switching networks. Second, by performing a new coordinate and input transformation, we convert our problem into the cooperative global robust stabilization problem of a more complex augmented system via the distributed internal model principle. Third, we solve the stabilization problem by a distributed state feedback control law. Our result is illustrated by the leader-following consensus problem for a group of Van der Pol oscillators.

  19. Deducing corticotropin-releasing hormone receptor type 1 signaling networks from gene expression data by usage of genetic algorithms and graphical Gaussian models.

    Science.gov (United States)

    Trümbach, Dietrich; Graf, Cornelia; Pütz, Benno; Kühne, Claudia; Panhuysen, Marcus; Weber, Peter; Holsboer, Florian; Wurst, Wolfgang; Welzl, Gerhard; Deussing, Jan M

    2010-11-19

    Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis is a hallmark of complex and multifactorial psychiatric diseases such as anxiety and mood disorders. About 50-60% of patients with major depression show HPA axis dysfunction, i.e. hyperactivity and impaired negative feedback regulation. The neuropeptide corticotropin-releasing hormone (CRH) and its receptor type 1 (CRHR1) are key regulators of this neuroendocrine stress axis. Therefore, we analyzed CRH/CRHR1-dependent gene expression data obtained from the pituitary corticotrope cell line AtT-20, a well-established in vitro model for CRHR1-mediated signal transduction. To extract significantly regulated genes from a genome-wide microarray data set and to deduce underlying CRHR1-dependent signaling networks, we combined supervised and unsupervised algorithms. We present an efficient variable selection strategy by consecutively applying univariate as well as multivariate methods followed by graphical models. First, feature preselection was used to exclude genes not differentially regulated over time from the dataset. For multivariate variable selection a maximum likelihood (MLHD) discriminant function within GALGO, an R package based on a genetic algorithm (GA), was chosen. The topmost genes representing major nodes in the expression network were ranked to find highly separating candidate genes. By using groups of five genes (chromosome size) in the discriminant function and repeating the genetic algorithm separately four times we found eleven genes occurring at least in three of the top ranked result lists of the four repetitions. In addition, we compared the results of GA/MLHD with the alternative optimization algorithms greedy selection and simulated annealing as well as with the state-of-the-art method random forest. In every case we obtained a clear overlap of the selected genes independently confirming the results of MLHD in combination with a genetic algorithm. With two unsupervised algorithms

  20. Deducing corticotropin-releasing hormone receptor type 1 signaling networks from gene expression data by usage of genetic algorithms and graphical Gaussian models

    Directory of Open Access Journals (Sweden)

    Holsboer Florian

    2010-11-01

    Full Text Available Abstract Background Dysregulation of the hypothalamic-pituitary-adrenal (HPA axis is a hallmark of complex and multifactorial psychiatric diseases such as anxiety and mood disorders. About 50-60% of patients with major depression show HPA axis dysfunction, i.e. hyperactivity and impaired negative feedback regulation. The neuropeptide corticotropin-releasing hormone (CRH and its receptor type 1 (CRHR1 are key regulators of this neuroendocrine stress axis. Therefore, we analyzed CRH/CRHR1-dependent gene expression data obtained from the pituitary corticotrope cell line AtT-20, a well-established in vitro model for CRHR1-mediated signal transduction. To extract significantly regulated genes from a genome-wide microarray data set and to deduce underlying CRHR1-dependent signaling networks, we combined supervised and unsupervised algorithms. Results We present an efficient variable selection strategy by consecutively applying univariate as well as multivariate methods followed by graphical models. First, feature preselection was used to exclude genes not differentially regulated over time from the dataset. For multivariate variable selection a maximum likelihood (MLHD discriminant function within GALGO, an R package based on a genetic algorithm (GA, was chosen. The topmost genes representing major nodes in the expression network were ranked to find highly separating candidate genes. By using groups of five genes (chromosome size in the discriminant function and repeating the genetic algorithm separately four times we found eleven genes occurring at least in three of the top ranked result lists of the four repetitions. In addition, we compared the results of GA/MLHD with the alternative optimization algorithms greedy selection and simulated annealing as well as with the state-of-the-art method random forest. In every case we obtained a clear overlap of the selected genes independently confirming the results of MLHD in combination with a genetic

  1. Nonlinear software sensor for monitoring genetic regulation processes with noise and modeling errors

    Science.gov (United States)

    Ibarra-Junquera, V.; Torres, L. A.; Rosu, H. C.; Argüello, G.; Collado-Vides, J.

    2005-07-01

    Nonlinear control techniques by means of a software sensor that are commonly used in chemical engineering could be also applied to genetic regulation processes. We provide here a realistic formulation of this procedure by introducing an additive white Gaussian noise, which is usually found in experimental data. Besides, we include model errors, meaning that we assume we do not know the nonlinear regulation function of the process. In order to illustrate this procedure, we employ the Goodwin dynamics of the concentrations [B. C. Goodwin, Temporal Oscillations in Cells (Academic, New York, 1963)] in the simple form recently applied to single gene systems and some operon cases [H. De Jong, J. Comput. Biol. 9, 67 (2002)], which involves the dynamics of the mRNA, given protein and metabolite concentrations. Further, we present results for a three gene case in coregulated sets of transcription units as they occur in prokaryotes. However, instead of considering their full dynamics, we use only the data of the metabolites and a designed software sensor. We also show, more generally, that it is possible to rebuild the complete set of nonmeasured concentrations despite the uncertainties in the regulation function or, even more, in the case of not knowing the mRNA dynamics. In addition, the rebuilding of concentrations is not affected by the perturbation due to the additive white Gaussian noise and also we managed to filter the noisy output of the biological system.

  2. Genetic and epigenetic mechanisms of regulation, chronology and dynamics of spermatogenesis of mammals

    Directory of Open Access Journals (Sweden)

    L. F. Kurilo

    2015-01-01

    Full Text Available Genetic and epigenetic mechanisms of spermatogenesis – long process with many stages regulation are discussed. DNA code is the entirety of hereditary information, epigenetic mechanisms of gene regulation act without altering primary nucleotide sequences. Epigenetic regulation is a complex process, in which components of different groups of epigenetic modifications (non-coding RNAs, DNA methylation and histone modification work together. Mistakes in any of the components of the process may cause impaired spermatogenesis and/or infertility, and may cause epigenetic diseases. Nowadays 90 imprinted genes and loci on 13 chromosomes are revealed. More then 10 human diseases involving genomic imprinting are known (Angelman syndrome, Prader–Willi syndrome, Russell–Silver syndrome, Beckwith–Wiedemann syndrome etc.. DNA methylation is essential for normal development and is associated with a number of key processes including animal growth and development, transcription, DNA replication and reparation, cell differentiation, genomic imprinting, X-chromosome inactivation, suppression of repetitive elements and carcinogenesis. 

  3. Genetic and epigenetic mechanisms of regulation, chronology and dynamics of spermatogenesis of mammals

    Directory of Open Access Journals (Sweden)

    L. F. Kurilo

    2015-04-01

    Full Text Available Genetic and epigenetic mechanisms of spermatogenesis – long process with many stages regulation are discussed. DNA code is the entirety of hereditary information, epigenetic mechanisms of gene regulation act without altering primary nucleotide sequences. Epigenetic regulation is a complex process, in which components of different groups of epigenetic modifications (non-coding RNAs, DNA methylation and histone modification work together. Mistakes in any of the components of the process may cause impaired spermatogenesis and/or infertility, and may cause epigenetic diseases. Nowadays 90 imprinted genes and loci on 13 chromosomes are revealed. More then 10 human diseases involving genomic imprinting are known (Angelman syndrome, Prader–Willi syndrome, Russell–Silver syndrome, Beckwith–Wiedemann syndrome etc.. DNA methylation is essential for normal development and is associated with a number of key processes including animal growth and development, transcription, DNA replication and reparation, cell differentiation, genomic imprinting, X-chromosome inactivation, suppression of repetitive elements and carcinogenesis. 

  4. Genetic regulation of CNC expression in the pharnygeal primordia ofDrosophila blastoderm embryos.

    Science.gov (United States)

    Mohler, Jym

    1993-04-01

    Genetic controls regulating the establishment of the pharyngeal primordia in the anterior region of the Drosophila embryo were investigated through the analysis of the expression of thecnc gene, which is continuously expressed specificially in three pharyngeal segments. The spatial regulation ofcnc gene transcription was analyzed by in situ hybridization of CNC transcript-specific probes to embryos mutant for other cephalic patterning genes. The anterior domain of CNC expression (corresponding to the labral segment primordium) was found to be activated bybicoid andtorso maternal pathways, independently of known zygotic gap genes, and sequentially constricted to its final size by repression from neighboring region-specific genes. Control of the posterior domain (corresponding to the intercalary and mandibular segment primordia) involved combinatorial regulation by zygotic gap genes: activation by thebtd gap gene and repression from theotd gap gene anteriorly and thesna gene ventrally. Surprisingly, the posterior domain was shifted relative to the segmentation plan in mutants of theems gap gene. These regulatory controls establishing the limits of CNC expression in the pharyngeal primordia suggest that one mechanism for patterning within the anterior terminal region may involve direct activation of region-specific gene(s) by maternal factors over a relatively broad domain followed by constriction of that domain by repression from adjacently activated zygotic genes.

  5. MicroRNA-regulated networks: the perfect storm for classical molecular biology, the ideal scenario for systems biology.

    Science.gov (United States)

    Vera, Julio; Lai, Xin; Schmitz, Ulf; Wolkenhauer, Olaf

    2013-01-01

    MicroRNAs (miRNAs) are involved in many regulatory pathways some of which are complex networks enriched in regulatory motifs like positive or negative feedback loops or coherent and incoherent feedforward loops. Their complexity makes the understanding of their regulation difficult and the interpretation of experimental data cumbersome. In this book chapter we claim that systems biology is the appropriate approach to investigate the regulation of these miRNA-regulated networks. Systems biology is an interdisciplinary approach by which biomedical questions on biochemical networks are addressed by integrating experiments with mathematical modelling and simulation. We here introduce the foundations of the systems biology approach, the basic theoretical and computational tools used to perform model-based analyses of miRNA-regulated networks and review the scientific literature in systems biology of miRNA regulation, with a focus on cancer.

  6. A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology.

    Science.gov (United States)

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

    Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view.

  7. Deployment strategy for battery energy storage system in distribution network based on voltage violation regulation

    Science.gov (United States)

    Wu, H.; Zhou, L.; Xu, T.; Fang, W. L.; He, W. G.; Liu, H. M.

    2017-11-01

    In order to improve the situation of voltage violation caused by the grid-connection of photovoltaic (PV) system in a distribution network, a bi-level programming model is proposed for battery energy storage system (BESS) deployment. The objective function of inner level programming is to minimize voltage violation, with the power of PV and BESS as the variables. The objective function of outer level programming is to minimize the comprehensive function originated from inner layer programming and all the BESS operating parameters, with the capacity and rated power of BESS as the variables. The differential evolution (DE) algorithm is applied to solve the model. Based on distribution network operation scenarios with photovoltaic generation under multiple alternative output modes, the simulation results of IEEE 33-bus system prove that the deployment strategy of BESS proposed in this paper is well adapted to voltage violation regulation invariable distribution network operation scenarios. It contributes to regulating voltage violation in distribution network, as well as to improve the utilization of PV systems.

  8. Molecular stripping in the NF-κB/IκB/DNA genetic regulatory network.

    Science.gov (United States)

    Potoyan, Davit A; Zheng, Weihua; Komives, Elizabeth A; Wolynes, Peter G

    2016-01-05

    Genetic switches based on the [Formula: see text] system are master regulators of an array of cellular responses. Recent kinetic experiments have shown that [Formula: see text] can actively remove NF-κB bound to its genetic sites via a process called "molecular stripping." This allows the [Formula: see text] switch to function under kinetic control rather than the thermodynamic control contemplated in the traditional models of gene switches. Using molecular dynamics simulations of coarse-grained predictive energy landscape models for the constituent proteins by themselves and interacting with the DNA we explore the functional motions of the transcription factor [Formula: see text] and its various binary and ternary complexes with DNA and the inhibitor IκB. These studies show that the function of the [Formula: see text] genetic switch is realized via an allosteric mechanism. Molecular stripping occurs through the activation of a domain twist mode by the binding of [Formula: see text] that occurs through conformational selection. Free energy calculations for DNA binding show that the binding of [Formula: see text] not only results in a significant decrease of the affinity of the transcription factor for the DNA but also kinetically speeds DNA release. Projections of the free energy onto various reaction coordinates reveal the structural details of the stripping pathways.

  9. Preservation Analysis of Macrophage Gene Coexpression Between Human and Mouse Identifies PARK2 as a Genetically Controlled Master Regulator of Oxidative Phosphorylation in Humans

    Directory of Open Access Journals (Sweden)

    Veronica Codoni

    2016-10-01

    Full Text Available Macrophages are key players involved in numerous pathophysiological pathways and an in-depth characterization of their gene regulatory networks can help in better understanding how their dysfunction may impact on human diseases. We here conducted a cross-species network analysis of macrophage gene expression data between human and mouse to identify conserved networks across both species, and assessed whether such networks could reveal new disease-associated regulatory mechanisms. From a sample of 684 individuals processed for genome-wide macrophage gene expression profiling, we identified 27 groups of coexpressed genes (modules. Six modules were found preserved (P < 10−4 in macrophages from 86 mice of the Hybrid Mouse Diversity Panel. One of these modules was significantly [false discovery rate (FDR = 8.9 × 10−11] enriched for genes belonging to the oxidative phosphorylation (OXPHOS pathway. This pathway was also found significantly (FDR < 10−4 enriched in susceptibility genes for Alzheimer, Parkinson, and Huntington diseases. We further conducted an expression quantitative trait loci analysis to identify SNP that could regulate macrophage OXPHOS gene expression in humans. This analysis identified the PARK2 rs192804963 as a trans-acting variant influencing (minimal P-value = 4.3 × 10−8 the expression of most OXPHOS genes in humans. Further experimental work demonstrated that PARK2 knockdown expression was associated with increased OXPHOS gene expression in THP1 human macrophages. This work provided strong new evidence that PARK2 participates to the regulatory networks associated with oxidative phosphorylation and suggested that PARK2 genetic variations could act as a trans regulator of OXPHOS gene macrophage expression in humans.

  10. Quantitative analysis of cefalexin based on artificial neural networks combined with modified genetic algorithm using short near-infrared spectroscopy.

    Science.gov (United States)

    Huan, Yanfu; Feng, Guodong; Wang, Bin; Ren, Yulin; Fei, Qiang

    2013-05-15

    In this paper, a novel chemometric method was developed for rapid, accurate, and quantitative analysis of cefalexin in samples. The experiments were carried out by using the short near-infrared spectroscopy coupled with artificial neural networks. In order to enhancing the predictive ability of artificial neural networks model, a modified genetic algorithm was used to select fixed number of wavelength. Copyright © 2013 Elsevier B.V. All rights reserved.

  11. The decision to germinate is regulated by divergent molecular networks in spores and seeds

    DEFF Research Database (Denmark)

    Vesty, Eleanor F.; Saidi, Younousse; Moody, Laura A.

    2016-01-01

    Dispersal is a key step in land plant life cycles, usually via formation of spores or seeds. Regulation of spore- or seed-germination allows control over the timing of transition from one generation to the next, enabling plant dispersal. A combination of environmental and genetic factors determines...... when seed germination occurs. Endogenous hormones mediate this decision in response to the environment. Less is known about how spore germination is controlled in earlier-evolving nonseed plants. Here, we present an in-depth analysis of the environmental and hormonal regulation of spore germination...... in the model bryophyte Physcomitrella patens (Aphanoregma patens). Our data suggest that the environmental signals regulating germination are conserved, but also that downstream hormone integration pathways mediating these responses in seeds were acquired after the evolution of the bryophyte lineage. Moreover...

  12. Differential RNA-seq, Multi-Network Analysis and Metabolic Regulation Analysis of Kluyveromyces marxianus Reveals a Compartmentalised Response to Xylose.

    Directory of Open Access Journals (Sweden)

    Du Toit W P Schabort

    Full Text Available We investigated the transcriptomic response of a new strain of the yeast Kluyveromyces marxianus, in glucose and xylose media using RNA-seq. The data were explored in a number of innovative ways using a variety of networks types, pathway maps, enrichment statistics, reporter metabolites and a flux simulation model, revealing different aspects of the genome-scale response in an integrative systems biology manner. The importance of the subcellular localisation in the transcriptomic response is emphasised here, revealing new insights. As was previously reported by others using a rich medium, we show that peroxisomal fatty acid catabolism was dramatically up-regulated in a defined xylose mineral medium without fatty acids, along with mechanisms to activate fatty acids and transfer products of β-oxidation to the mitochondria. Notably, we observed a strong up-regulation of the 2-methylcitrate pathway, supporting capacity for odd-chain fatty acid catabolism. Next we asked which pathways would respond to the additional requirement for NADPH for xylose utilisation, and rationalised the unexpected results using simulations with Flux Balance Analysis. On a fundamental level, we investigated the contribution of the hierarchical and metabolic regulation levels to the regulation of metabolic fluxes. Metabolic regulation analysis suggested that genetic level regulation plays a major role in regulating metabolic fluxes in adaptation to xylose, even for the high capacity reactions, which is unexpected. In addition, isozyme switching may play an important role in re-routing of metabolic fluxes in subcellular compartments in K. marxianus.

  13. Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis.

    Science.gov (United States)

    Hasmi, Laila; Drukker, Marjan; Guloksuz, Sinan; Menne-Lothmann, Claudia; Decoster, Jeroen; van Winkel, Ruud; Collip, Dina; Delespaul, Philippe; De Hert, Marc; Derom, Catherine; Thiery, Evert; Jacobs, Nele; Rutten, Bart P F; Wichers, Marieke; van Os, Jim

    2017-01-01

    Background: The network analysis of intensive time series data collected using the Experience Sampling Method (ESM) may provide vital information in gaining insight into the link between emotion regulation and vulnerability to psychopathology. The aim of this study was to apply the network approach to investigate whether genetic liability (GL) to psychopathology and childhood trauma (CT) are associated with the network structure of the emotions "cheerful," "insecure," "relaxed," "anxious," "irritated," and "down"-collected using the ESM method. Methods: Using data from a population-based sample of twin pairs and siblings (704 individuals), we examined whether momentary emotion network structures differed across strata of CT and GL. GL was determined empirically using the level of psychopathology in monozygotic and dizygotic co-twins. Network models were generated using multilevel time-lagged regression analysis and were compared across three strata (low, medium, and high) of CT and GL, respectively. Permutations were utilized to calculate p values and compare regressions coefficients, density, and centrality indices. Regression coefficients were presented as connections, while variables represented the nodes in the network. Results: In comparison to the low GL stratum, the high GL stratum had significantly denser overall (p = 0.018) and negative affect network density (p < 0.001). The medium GL stratum also showed a directionally similar (in-between high and low GL strata) but statistically inconclusive association with network density. In contrast to GL, the results of the CT analysis were less conclusive, with increased positive affect density (p = 0.021) and overall density (p = 0.042) in the high CT stratum compared to the medium CT stratum but not to the low CT stratum. The individual node comparisons across strata of GL and CT yielded only very few significant results, after adjusting for multiple testing. Conclusions: The present findings demonstrate that

  14. Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis

    Directory of Open Access Journals (Sweden)

    Laila Hasmi

    2017-11-01

    Full Text Available Background: The network analysis of intensive time series data collected using the Experience Sampling Method (ESM may provide vital information in gaining insight into the link between emotion regulation and vulnerability to psychopathology. The aim of this study was to apply the network approach to investigate whether genetic liability (GL to psychopathology and childhood trauma (CT are associated with the network structure of the emotions “cheerful,” “insecure,” “relaxed,” “anxious,” “irritated,” and “down”—collected using the ESM method.Methods: Using data from a population-based sample of twin pairs and siblings (704 individuals, we examined whether momentary emotion network structures differed across strata of CT and GL. GL was determined empirically using the level of psychopathology in monozygotic and dizygotic co-twins. Network models were generated using multilevel time-lagged regression analysis and were compared across three strata (low, medium, and high of CT and GL, respectively. Permutations were utilized to calculate p values and compare regressions coefficients, density, and centrality indices. Regression coefficients were presented as connections, while variables represented the nodes in the network.Results: In comparison to the low GL stratum, the high GL stratum had significantly denser overall (p = 0.018 and negative affect network density (p < 0.001. The medium GL stratum also showed a directionally similar (in-between high and low GL strata but statistically inconclusive association with network density. In contrast to GL, the results of the CT analysis were less conclusive, with increased positive affect density (p = 0.021 and overall density (p = 0.042 in the high CT stratum compared to the medium CT stratum but not to the low CT stratum. The individual node comparisons across strata of GL and CT yielded only very few significant results, after adjusting for multiple testing.Conclusions: The

  15. Hybrid artificial neural network genetic algorithm technique for modeling chemical oxygen demand removal in anoxic/oxic process.

    Science.gov (United States)

    Ma, Yongwen; Huang, Mingzhi; Wan, Jinquan; Hu, Kang; Wang, Yan; Zhang, Huiping

    2011-01-01

    In this paper, a hybrid artificial neural network (ANN) - genetic algorithm (GA) numerical technique was successfully developed to deal with complicated problems that cannot be solved by conventional solutions. ANNs and Gas were used to model and simulate the process of removing chemical oxygen demand (COD) in an anoxic/oxic system. The minimization of the error function with respect to the network parameters (weights and biases) has been considered as training of the network. Real-coded genetic algorithm was used to train the network in an unsupervised manner. Meanwhile the important process parameters, such as the influent COD (COD(in)), reflux ratio (R(r)), carbon-nitrogen ratio (C/N) and the effluent COD (COD(out)) were considered. The result shows that compared with the performance of ANN model, the performance of the GA-ANN (genetic algorithm - artificial neural network) network was found to be more impressive. Using ANN, the mean absolute percentage error (MAPE), mean squared error (MSE) and correlation coefficient (R) were 9.33×10(-4), 2.82 and 0.98596, respectively; while for the GA-ANN, they were converged to be 4.18×10(-4), 1.12 and 0.99476, respectively.

  16. Systems Genetic Analyses Highlight a TGFβ-FOXO3 Dependent Striatal Astrocyte Network Conserved across Species and Associated with Stress, Sleep, and Huntington's Disease.

    Directory of Open Access Journals (Sweden)

    Joseph R Scarpa

    2016-07-01

    Full Text Available Recent systems-based analyses have demonstrated that sleep and stress traits emerge from shared genetic and transcriptional networks, and clinical work has elucidated the emergence of sleep dysfunction and stress susceptibility as early symptoms of Huntington's disease. Understanding the biological bases of these early non-motor symptoms may reveal therapeutic targets that prevent disease onset or slow disease progression, but the molecular mechanisms underlying this complex clinical presentation remain largely unknown. In the present work, we specifically examine the relationship between these psychiatric traits and Huntington's disease (HD by identifying striatal transcriptional networks shared by HD, stress, and sleep phenotypes. First, we utilize a systems-based approach to examine a large publicly available human transcriptomic dataset for HD (GSE3790 from GEO in a novel way. We use weighted gene coexpression network analysis and differential connectivity analyses to identify transcriptional networks dysregulated in HD, and we use an unbiased ranking scheme that leverages both gene- and network-level information to identify a novel astrocyte-specific network as most relevant to HD caudate. We validate this result in an independent HD cohort. Next, we computationally predict FOXO3 as a regulator of this network, and use multiple publicly available in vitro and in vivo experimental datasets to validate that this astrocyte HD network is downstream of a signaling pathway important in adult neurogenesis (TGFβ-FOXO3. We also map this HD-relevant caudate subnetwork to striatal transcriptional networks in a large (n = 100 chronically stressed (B6xA/JF2 mouse population that has been extensively phenotyped (328 stress- and sleep-related measurements, and we show that this striatal astrocyte network is correlated to sleep and stress traits, many of which are known to be altered in HD cohorts. We identify causal regulators of this network through

  17. Predicting network modules of cell cycle regulators using relative protein abundance statistics.

    Science.gov (United States)

    Oguz, Cihan; Watson, Layne T; Baumann, William T; Tyson, John J

    2017-02-28

    Parameter estimation in systems biology is typically done by enforcing experimental observations through an objective function as the parameter space of a model is explored by numerical simulations. Past studies have shown that one usually finds a set of "feasible" parameter vectors that fit the available experimental data equally well, and that these alternative vectors can make different predictions under novel experimental conditions. In this study, we characterize the feasible region of a complex model of the budding yeast cell cycle under a large set of discrete experimental constraints in order to test whether the statistical features of relative protein abundance predictions are influenced by the topology of the cell cycle regulatory network. Using differential evolution, we generate an ensemble of feasible parameter vectors that reproduce the phenotypes (viable or inviable) of wild-type yeast cells and 110 mutant strains. We use this ensemble to predict the phenotypes of 129 mutant strains for which experimental data is not available. We identify 86 novel mutants that are predicted to be viable and then rank the cell cycle proteins in terms of their contributions to cumulative variability of relative protein abundance predictions. Proteins involved in "regulation of cell size" and "regulation of G1/S transition" contribute most to predictive variability, whereas proteins involved in "positive regulation of transcription involved in exit from mitosis," "mitotic spindle assembly checkpoint" and "negative regulation of cyclin-dependent protein kinase by cyclin degradation" contribute the least. These results suggest that the statistics of these predictions may be generating patterns specific to individual network modules (START, S/G2/M, and EXIT). To test this hypothesis, we develop random forest models for predicting the network modules of cell cycle regulators using relative abundance statistics as model inputs. Predictive performance is assessed by the

  18. Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM).

    Science.gov (United States)

    Fernandez, Michael; Caballero, Julio; Fernandez, Leyden; Sarai, Akinori

    2011-02-01

    Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.

  19. An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network

    Directory of Open Access Journals (Sweden)

    Kai Hu

    2013-01-01

    Full Text Available A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA to decide weights in a back propagation neural network (BPN. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.

  20. Prediction of CHF in concentric-tube open thermosiphon using artificial neural network and genetic algorithm

    Science.gov (United States)

    Chen, R. H.; Su, G. H.; Qiu, S. Z.; Fukuda, Kenji

    2010-03-01

    In this paper, an artificial neural network (ANN) for predicting critical heat flux (CHF) of concentric-tube open thermosiphon has been trained successfully based on the experimental data from the literature. The dimensionless input parameters of the ANN are density ratio, ρ l/ ρ v; the ratio of the heated tube length to the inner diameter of the outer tube, L/ D i; the ratio of frictional area, d i/( D i + d o); and the ratio of equivalent heated diameter to characteristic bubble size, D he/[ σ/ g( ρ l- ρ v)]0.5, the output is Kutateladze number, Ku. The predicted values of ANN are found to be in reasonable agreement with the actual values from the experiments with a mean relative error (MRE) of 8.46%. New correlations for predicting CHF were also proposed by using genetic algorithm (GA) and succeeded to correlate the existing CHF data with better accuracy than the existing empirical correlations.

  1. Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

    Directory of Open Access Journals (Sweden)

    Palika Chopra

    2016-01-01

    Full Text Available An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs and Genetic Programming (GP. The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN model with the training function Levenberg-Marquardt (LM for the prediction of concrete compressive strength is the best prediction tool.

  2. A genetic algorithm for multiple relay selection in two-way relaying cognitive radio networks

    KAUST Repository

    Alsharoa, Ahmad M.

    2013-09-01

    In this paper, we investigate a multiple relay selection scheme for two-way relaying cognitive radio networks where primary users and secondary users operate on the same frequency band. More specifically, cooperative relays using Amplifyand- Forward (AF) protocol are optimally selected to maximize the sum rate of the secondary users without degrading the Quality of Service (QoS) of the primary users by respecting a tolerated interference threshold. A strong optimization tool based on genetic algorithm is employed to solve our formulated optimization problem where discrete relay power levels are considered. Our simulation results show that the practical heuristic approach achieves almost the same performance of the optimal multiple relay selection scheme either with discrete or continuous power distributions. Copyright © 2013 by the Institute of Electrical and Electronic Engineers, Inc.

  3. Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Magnier, Laurent; Haghighat, Fariborz [Department of Building, Civil and Environmental Engineering, Concordia University, 1455 de Maisonneuve Blvd. W., BE-351, Montreal, Quebec H3G 1M8 (Canada)

    2010-03-15

    Building optimization involving multiple objectives is generally an extremely time-consuming process. The GAINN approach presented in this study first uses a simulation-based Artificial Neural Network (ANN) to characterize building behaviour, and then combines this ANN with a multiobjective Genetic Algorithm (NSGA-II) for optimization. The methodology has been used in the current study for the optimization of thermal comfort and energy consumption in a residential house. Results of ANN training and validation are first discussed. Two optimizations were then conducted taking variables from HVAC system settings, thermostat programming, and passive solar design. By integrating ANN into optimization the total simulation time was considerably reduced compared to classical optimization methodology. Results of the optimizations showed significant reduction in terms of energy consumption as well as improvement in thermal comfort. Finally, thanks to the multiobjective approach, dozens of potential designs were revealed, with a wide range of trade-offs between thermal comfort and energy consumption. (author)

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

    Science.gov (United States)

    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.

  5. Identification of Autophagosome-associated Proteins and Regulators by Quantitative Proteomic Analysis and Genetic Screens*

    Science.gov (United States)

    Dengjel, Jörn; Høyer-Hansen, Maria; Nielsen, Maria O.; Eisenberg, Tobias; Harder, Lea M.; Schandorff, Søren; Farkas, Thomas; Kirkegaard, Thomas; Becker, Andrea C.; Schroeder, Sabrina; Vanselow, Katja; Lundberg, Emma; Nielsen, Mogens M.; Kristensen, Anders R.; Akimov, Vyacheslav; Bunkenborg, Jakob; Madeo, Frank; Jäättelä, Marja; Andersen, Jens S.

    2012-01-01

    Autophagy is one of the major intracellular catabolic pathways, but little is known about the composition of autophagosomes. To study the associated proteins, we isolated autophagosomes from human breast cancer cells using two different biochemical methods and three stimulus types: amino acid deprivation or rapamycin or concanamycin A treatment. The autophagosome-associated proteins were dependent on stimulus, but a core set of proteins was stimulus-independent. Remarkably, proteasomal proteins were abundant among the stimulus-independent common autophagosome-associated proteins, and the activation of autophagy significantly decreased the cellular proteasome level and activity supporting interplay between the two degradation pathways. A screen of yeast strains defective in the orthologs of the human genes encoding for a common set of autophagosome-associated proteins revealed several regulators of autophagy, including subunits of the retromer complex. The combined spatiotemporal proteomic and genetic data sets presented here provide a basis for further characterization of autophagosome biogenesis and cargo selection. PMID:22311637

  6. Multi-Objective Genetic Programming with Redundancy-Regulations for Automatic Construction of Image Feature Extractors

    Science.gov (United States)

    Watchareeruetai, Ukrit; Matsumoto, Tetsuya; Takeuchi, Yoshinori; Kudo, Hiroaki; Ohnishi, Noboru

    We propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multi-objective evolutionary algorithm (MOEA), i.e., NSGA-II. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity as well as convergence rate. Experimental results indicate that the proposed MOGP-based FEP construction system outperforms the two conventional MOEAs (i.e., NSGA-II and SPEA2) for a test problem. Moreover, we compared the programs constructed by the proposed MOGP with four human-designed object recognition programs. The results show that the constructed programs are better than two human-designed methods and are comparable with the other two human-designed methods for the test problem.

  7. Transgenerational latent early-life associated regulation unites environment and genetics across generations.

    Science.gov (United States)

    Lahiri, Debomoy K; Maloney, Bryan; Bayon, Baindu L; Chopra, Nipun; White, Fletcher A; Greig, Nigel H; Nurnberger, John I

    2016-03-01

    The origin of idiopathic diseases is still poorly understood. The latent early-life associated regulation (LEARn) model unites environmental exposures and gene expression while providing a mechanistic underpinning for later-occurring disorders. We propose that this process can occur across generations via transgenerational LEARn (tLEARn). In tLEARn, each person is a 'unit' accumulating preclinical or subclinical 'hits' as in the original LEARn model. These changes can then be epigenomically passed along to offspring. Transgenerational accumulation of 'hits' determines a sporadic disease state. Few significant transgenerational hits would accompany conception or gestation of most people, but these may suffice to 'prime' someone to respond to later-life hits. Hits need not produce symptoms or microphenotypes to have a transgenerational effect. Testing tLEARn requires longitudinal approaches. A recently proposed longitudinal epigenome/envirome-wide association study would unite genetic sequence, epigenomic markers, environmental exposures, patient personal history taken at multiple time points and family history.

  8. Ethylene Control of Fruit Ripening: Revisiting the Complex Network of Transcriptional Regulation1

    Science.gov (United States)

    Chervin, Christian; Bouzayen, Mondher

    2015-01-01

    The plant hormone ethylene plays a key role in climacteric fruit ripening. Studies on components of ethylene signaling have revealed a linear transduction pathway leading to the activation of ethylene response factors. However, the means by which ethylene selects the ripening-related genes and interacts with other signaling pathways to regulate the ripening process are still to be elucidated. Using tomato (Solanum lycopersicum) as a reference species, the present review aims to revisit the mechanisms by which ethylene regulates fruit ripening by taking advantage of new tools available to perform in silico studies at the genome-wide scale, leading to a global view on the expression pattern of ethylene biosynthesis and response genes throughout ripening. Overall, it provides new insights on the transcriptional network by which this hormone coordinates the ripening process and emphasizes the interplay between ethylene and ripening-associated developmental factors and the link between epigenetic regulation and ethylene during fruit ripening. PMID:26511917