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Sample records for genetic network controlling

  1. Genetic networks controlling retinal injury

    PubMed Central

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

    2010-01-01

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

  2. Optimizing the controllability of arbitrary networks with genetic algorithm

    NASA Astrophysics Data System (ADS)

    Li, Xin-Feng; Lu, Zhe-Ming

    2016-04-01

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

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

    PubMed Central

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

    2009-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

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

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

    NASA Astrophysics Data System (ADS)

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

    2001-03-01

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

  6. Control of Stochastic Master Equation Models of Genetic Regulatory Networks by Approximating Their Average Behavior

    NASA Astrophysics Data System (ADS)

    Umut Caglar, Mehmet; Pal, Ranadip

    2010-10-01

    The central dogma of molecular biology states that ``information cannot be transferred back from protein to either protein or nucleic acid.'' However, this assumption is not exactly correct in most of the cases. There are a lot of feedback loops and interactions between different levels of systems. These types of interactions are hard to analyze due to the lack of data in the cellular level and probabilistic nature of interactions. Probabilistic models like Stochastic Master Equation (SME) or deterministic models like differential equations (DE) can be used to analyze these types of interactions. SME models based on chemical master equation (CME) can provide detailed representation of genetic regulatory system, but their use is restricted by the large data requirements and computational costs of calculations. The differential equations models on the other hand, have low calculation costs and much more adequate to generate control procedures on the system; but they are not adequate to investigate the probabilistic nature of interactions. In this work the success of the mapping between SME and DE is analyzed, and the success of a control policy generated by DE model with respect to SME model is examined. Index Terms--- Stochastic Master Equation models, Differential Equation Models, Control Policy Design, Systems biology

  7. Comparison of Control Approaches in Genetic Regulatory Networks by Using Stochastic Master Equation Models, Probabilistic Boolean Network Models and Differential Equation Models and Estimated Error Analyzes

    NASA Astrophysics Data System (ADS)

    Caglar, Mehmet Umut; Pal, Ranadip

    2011-03-01

    Central dogma of molecular biology states that ``information cannot be transferred back from protein to either protein or nucleic acid''. However, this assumption is not exactly correct in most of the cases. There are a lot of feedback loops and interactions between different levels of systems. These types of interactions are hard to analyze due to the lack of cell level data and probabilistic - nonlinear nature of interactions. Several models widely used to analyze and simulate these types of nonlinear interactions. Stochastic Master Equation (SME) models give probabilistic nature of the interactions in a detailed manner, with a high calculation cost. On the other hand Probabilistic Boolean Network (PBN) models give a coarse scale picture of the stochastic processes, with a less calculation cost. Differential Equation (DE) models give the time evolution of mean values of processes in a highly cost effective way. The understanding of the relations between the predictions of these models is important to understand the reliability of the simulations of genetic regulatory networks. In this work the success of the mapping between SME, PBN and DE models is analyzed and the accuracy and affectivity of the control policies generated by using PBN and DE models is compared.

  8. Control of Multilayer Networks

    PubMed Central

    Menichetti, Giulia; Dall’Asta, Luca; Bianconi, Ginestra

    2016-01-01

    The controllability of a network is a theoretical problem of relevance in a variety of contexts ranging from financial markets to the brain. Until now, network controllability has been characterized only on isolated networks, while the vast majority of complex systems are formed by multilayer networks. Here we build a theoretical framework for the linear controllability of multilayer networks by mapping the problem into a combinatorial matching problem. We found that correlating the external signals in the different layers can significantly reduce the multiplex network robustness to node removal, as it can be seen in conjunction with a hybrid phase transition occurring in interacting Poisson networks. Moreover we observe that multilayer networks can stabilize the fully controllable multiplex network configuration that can be stable also when the full controllability of the single network is not stable. PMID:26869210

  9. Designing Genetic Feedback Controllers.

    PubMed

    Harris, Andreas W K; Dolan, James A; Kelly, Ciarán L; Anderson, James; Papachristodoulou, Antonis

    2015-08-01

    By incorporating feedback around systems we wish to manipulate, it is possible to improve their performance and robustness properties to meet pre-specified design objectives. For decades control engineers have been successfully implementing feedback controllers for complex mechanical and electrical systems such as aircraft and sports cars. Natural biological systems use feedback extensively for regulation and adaptation but apart from the most basic designs, there is no systematic framework for designing feedback controllers in Synthetic Biology. In this paper we describe how classical approaches from linear control theory can be used to close the loop. This includes the design of genetic circuits using feedback control and the presentation of a biological phase lag controller. PMID:26390502

  10. Adaptation by Plasticity of Genetic Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Brenner, Naama

    2007-03-01

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

  11. Controllability of complex networks.

    PubMed

    Liu, Yang-Yu; Slotine, Jean-Jacques; Barabási, Albert-László

    2011-05-12

    The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system's entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network's degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.

  12. Boolean Modelingof Genetic Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Albert, Réka

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

  13. Genetic Network Programming with Reconstructed Individuals

    NASA Astrophysics Data System (ADS)

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

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

  14. Virtualized Network Control (VNC)

    SciTech Connect

    Lehman, Thomas; Guok, Chin; Ghani, Nasir

    2013-01-31

    The focus of this project was on the development of a "Network Service Plane" as an abstraction model for the control and provisioning of multi-layer networks. The primary motivation for this work were the requirements of next generation networked applications which will need to access advanced networking as a first class resource at the same level as compute and storage resources. A new class of "Intelligent Network Services" were defined in order to facilitate the integration of advanced network services into application specific workflows. This new class of network services are intended to enable real-time interaction between the application co-scheduling algorithms and the network for the purposes of workflow planning, real-time resource availability identification, scheduling, and provisioning actions.

  15. Optimized intelligent control of a 2-degree of freedom robot for rehabilitation of lower limbs using neural network and genetic algorithm

    PubMed Central

    2013-01-01

    Background There is an increasing trend in using robots for medical purposes. One specific area is rehabilitation. Rehabilitation is one of the non-drug treatments in community health which means the restoration of the abilities to maximize independence. It is a prolonged work and costly labor. On the other hand, by using the flexible and efficient robots in rehabilitation area, this process will be more useful for handicapped patients. Methods In this study, a rule-based intelligent control methodology is proposed to mimic the behavior of a healthy limb in a satisfactory way by a 2-DOF planar robot. Inverse kinematic of the planar robot will be solved by neural networks and control parameters will be optimized by genetic algorithm, as rehabilitation progress. Results The results of simulations are presented by defining a physiotherapy simple mode on desired trajectory. MATLAB/Simulink is used for simulations. The system is capable of learning the action of the physiotherapist for each patient and imitating this behaviour in the absence of a physiotherapist that can be called robotherapy. Conclusions In this study, a therapeutic exercise planar 2-DOF robot is designed and controlled for lower-limb rehabilitation. The robot manipulator is controlled by combination of hybrid and adaptive controls. Some safety factors and stability constraints are defined and obtained. The robot is stopped when the safety factors are not satisfied. Kinematics of robot is estimated by an MLP neural network and proper control parameters are achieved using GA optimization. PMID:23945420

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

    NASA Astrophysics Data System (ADS)

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

    2013-06-01

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

  17. Genetic Network Inference Using Hierarchical Structure

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2010-01-01

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

  19. Inferring genetic networks from microarray data.

    SciTech Connect

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

    2004-06-01

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

  20. Systematic mapping of genetic interaction networks.

    PubMed

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

    2009-01-01

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

  1. Genetic Dissection of Rhythmic Motor Networks in Mice

    PubMed Central

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

    2011-01-01

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

  2. Networks of spatial genetic variation across species

    PubMed Central

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

    2009-01-01

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

  3. Evolutionary design of oscillatory genetic networks

    NASA Astrophysics Data System (ADS)

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

    2010-07-01

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

  4. Neural networks for aircraft control

    NASA Technical Reports Server (NTRS)

    Linse, Dennis

    1990-01-01

    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

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

    PubMed

    Radwan, Elsayed; Tazaki, Eiichiro

    2004-08-01

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

  6. chaoptin, prominin, eyes shut and crumbs form a genetic network controlling the apical compartment of Drosophila photoreceptor cells

    PubMed Central

    Gurudev, Nagananda; Yuan, Michaela; Knust, Elisabeth

    2014-01-01

    ABSTRACT The apical surface of epithelial cells is often highly specialised to fulfil cell type-specific functions. Many epithelial cells expand their apical surface by forming microvilli, actin-based, finger-like membrane protrusions. The apical surface of Drosophila photoreceptor cells (PRCs) forms tightly packed microvilli, which are organised into the photosensitive rhabdomeres. As previously shown, the GPI-anchored adhesion protein Chaoptin is required for the stability of the microvilli, whereas the transmembrane protein Crumbs is essential for proper rhabdomere morphogenesis. Here we show that chaoptin synergises with crumbs to ensure optimal rhabdomere width. In addition, reduction of crumbs ameliorates morphogenetic defects observed in PRCs mutant for prominin and eyes shut, known antagonists of chaoptin. These results suggest that these four genes provide a balance of adhesion and anti-adhesion to maintain microvilli development and maintenance. Similar to crumbs mutant PRCs, PRCs devoid of prominin or eyes shut undergo light-dependent retinal degeneration. Given the observation that human orthologues of crumbs, prominin and eyes shut result in progressive retinal degeneration and blindness, the Drosophila eye is ideally suited to unravel the genetic and cellular mechanisms that ensure morphogenesis of PRCs and their maintenance under light-mediated stress. PMID:24705015

  7. Genetic control of inflorescence architecture in legumes

    PubMed Central

    Benlloch, Reyes; Berbel, Ana; Ali, Latifeh; Gohari, Gholamreza; Millán, Teresa; Madueño, Francisco

    2015-01-01

    The architecture of the inflorescence, the shoot system that bears the flowers, is a main component of the huge diversity of forms found in flowering plants. Inflorescence architecture has also a strong impact on the production of fruits and seeds, and on crop management, two highly relevant agronomical traits. Elucidating the genetic networks that control inflorescence development, and how they vary between different species, is essential to understanding the evolution of plant form and to being able to breed key architectural traits in crop species. Inflorescence architecture depends on the identity and activity of the meristems in the inflorescence apex, which determines when flowers are formed, how many are produced and their relative position in the inflorescence axis. Arabidopsis thaliana, where the genetic control of inflorescence development is best known, has a simple inflorescence, where the primary inflorescence meristem directly produces the flowers, which are thus borne in the main inflorescence axis. In contrast, legumes represent a more complex inflorescence type, the compound inflorescence, where flowers are not directly borne in the main inflorescence axis but, instead, they are formed by secondary or higher order inflorescence meristems. Studies in model legumes such as pea (Pisum sativum) or Medicago truncatula have led to a rather good knowledge of the genetic control of the development of the legume compound inflorescence. In addition, the increasing availability of genetic and genomic tools for legumes is allowing to rapidly extending this knowledge to other grain legume crops. This review aims to describe the current knowledge of the genetic network controlling inflorescence development in legumes. It also discusses how the combination of this knowledge with the use of emerging genomic tools and resources may allow rapid advances in the breeding of grain legume crops. PMID:26257753

  8. Genetic control of inflorescence architecture in legumes.

    PubMed

    Benlloch, Reyes; Berbel, Ana; Ali, Latifeh; Gohari, Gholamreza; Millán, Teresa; Madueño, Francisco

    2015-01-01

    The architecture of the inflorescence, the shoot system that bears the flowers, is a main component of the huge diversity of forms found in flowering plants. Inflorescence architecture has also a strong impact on the production of fruits and seeds, and on crop management, two highly relevant agronomical traits. Elucidating the genetic networks that control inflorescence development, and how they vary between different species, is essential to understanding the evolution of plant form and to being able to breed key architectural traits in crop species. Inflorescence architecture depends on the identity and activity of the meristems in the inflorescence apex, which determines when flowers are formed, how many are produced and their relative position in the inflorescence axis. Arabidopsis thaliana, where the genetic control of inflorescence development is best known, has a simple inflorescence, where the primary inflorescence meristem directly produces the flowers, which are thus borne in the main inflorescence axis. In contrast, legumes represent a more complex inflorescence type, the compound inflorescence, where flowers are not directly borne in the main inflorescence axis but, instead, they are formed by secondary or higher order inflorescence meristems. Studies in model legumes such as pea (Pisum sativum) or Medicago truncatula have led to a rather good knowledge of the genetic control of the development of the legume compound inflorescence. In addition, the increasing availability of genetic and genomic tools for legumes is allowing to rapidly extending this knowledge to other grain legume crops. This review aims to describe the current knowledge of the genetic network controlling inflorescence development in legumes. It also discusses how the combination of this knowledge with the use of emerging genomic tools and resources may allow rapid advances in the breeding of grain legume crops.

  9. Probabilistic belief networks for genetic counseling.

    PubMed

    Harris, N L

    1990-05-01

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

  10. Genetic Networks Governing Heart Development

    PubMed Central

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

    2014-01-01

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

  11. Genetic networks influencing fruit ripening

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Tomato is a model for ripening control and the basis of many characterized genes underlying this process. Cloning of the CNR, RIN and NOR genes defined the first ripening-specific transcription factors and provided insight into a ripening control system upstream of ethylene. RIN is a central player ...

  12. Genetic Control Of Malaria Mosquitoes.

    PubMed

    McLean, Kyle Jarrod; Jacobs-Lorena, Marcelo

    2016-03-01

    Experiments demonstrating the feasibility of genetically modifying mosquito vectors to impair their ability to transmit the malaria parasite have been known for well over a decade. However, means to spread resistance or population control genes into wild mosquito populations remains an unsolved challenge. Two recent reports give hope that CRISPR technology may allow such challenge to be overcome. PMID:26809567

  13. Genetic flexibility of regulatory networks.

    PubMed

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

    2010-07-20

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

  14. Genetic control of Aedes mosquitoes

    PubMed Central

    Alphey, Luke; McKemey, Andrew; Nimmo, Derric; Neira Oviedo, Marco; Lacroix, Renaud; Matzen, Kelly; Beech, Camilla

    2013-01-01

    Aedes mosquitoes include important vector species such as Aedes aegypti, the major vector of dengue. Genetic control methods are being developed for several of these species, stimulated by an urgent need owing to the poor effectiveness of current methods combined with an increase in chemical pesticide resistance. In this review we discuss the various genetic strategies that have been proposed, their present status, and future prospects. We focus particularly on those methods that are already being tested in the field, including RIDL and Wolbachia-based approaches. PMID:23816508

  15. Controlling allosteric networks in proteins

    NASA Astrophysics Data System (ADS)

    Dokholyan, Nikolay

    2013-03-01

    We present a novel methodology based on graph theory and discrete molecular dynamics simulations for delineating allosteric pathways in proteins. We use this methodology to uncover the structural mechanisms responsible for coupling of distal sites on proteins and utilize it for allosteric modulation of proteins. We will present examples where inference of allosteric networks and its rewiring allows us to ``rescue'' cystic fibrosis transmembrane conductance regulator (CFTR), a protein associated with fatal genetic disease cystic fibrosis. We also use our methodology to control protein function allosterically. We design a novel protein domain that can be inserted into identified allosteric site of target protein. Using a drug that binds to our domain, we alter the function of the target protein. We successfully tested this methodology in vitro, in living cells and in zebrafish. We further demonstrate transferability of our allosteric modulation methodology to other systems and extend it to become ligh-activatable.

  16. Robust Multiobjective Controllability of Complex Neuronal Networks.

    PubMed

    Tang, Yang; Gao, Huijun; Du, Wei; Lu, Jianquan; Vasilakos, Athanasios V; Kurths, Jurgen

    2016-01-01

    This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain, in which uncertainties in determination of driver nodes and control gains are considered. A framework for robust multiobjective controllability is proposed by introducing interval uncertainties and optimization algorithms. By appropriate definitions of robust multiobjective controllability, a robust nondominated sorting adaptive differential evolution (NSJaDE) is presented by means of the nondominated sorting mechanism and the adaptive differential evolution (JaDE). The simulation experimental results illustrate the satisfactory performance of NSJaDE for robust multiobjective controllability, in comparison with six statistical methods and two multiobjective evolutionary algorithms (MOEAs): nondominated sorting genetic algorithms II (NSGA-II) and nondominated sorting composite differential evolution. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affects the controllability of neuronal networks. We also unveil that driver nodes play a more drastic role than control gains in robust controllability. The developed NSJaDE and obtained results will shed light on the understanding of robustness in controlling realistic complex networks such as transportation networks, power grid networks, biological networks, etc.

  17. Control efficacy of complex networks

    NASA Astrophysics Data System (ADS)

    Gao, Xin-Dong; Wang, Wen-Xu; Lai, Ying-Cheng

    2016-06-01

    Controlling complex networks has become a forefront research area in network science and engineering. Recent efforts have led to theoretical frameworks of controllability to fully control a network through steering a minimum set of driver nodes. However, in realistic situations not every node is accessible or can be externally driven, raising the fundamental issue of control efficacy: if driving signals are applied to an arbitrary subset of nodes, how many other nodes can be controlled? We develop a framework to determine the control efficacy for undirected networks of arbitrary topology. Mathematically, based on non-singular transformation, we prove a theorem to determine rigorously the control efficacy of the network and to identify the nodes that can be controlled for any given driver nodes. Physically, we develop the picture of diffusion that views the control process as a signal diffused from input signals to the set of controllable nodes. The combination of mathematical theory and physical reasoning allows us not only to determine the control efficacy for model complex networks and a large number of empirical networks, but also to uncover phenomena in network control, e.g., hub nodes in general possess lower control centrality than an average node in undirected networks.

  18. Control efficacy of complex networks

    PubMed Central

    Gao, Xin-Dong; Wang, Wen-Xu; Lai, Ying-Cheng

    2016-01-01

    Controlling complex networks has become a forefront research area in network science and engineering. Recent efforts have led to theoretical frameworks of controllability to fully control a network through steering a minimum set of driver nodes. However, in realistic situations not every node is accessible or can be externally driven, raising the fundamental issue of control efficacy: if driving signals are applied to an arbitrary subset of nodes, how many other nodes can be controlled? We develop a framework to determine the control efficacy for undirected networks of arbitrary topology. Mathematically, based on non-singular transformation, we prove a theorem to determine rigorously the control efficacy of the network and to identify the nodes that can be controlled for any given driver nodes. Physically, we develop the picture of diffusion that views the control process as a signal diffused from input signals to the set of controllable nodes. The combination of mathematical theory and physical reasoning allows us not only to determine the control efficacy for model complex networks and a large number of empirical networks, but also to uncover phenomena in network control, e.g., hub nodes in general possess lower control centrality than an average node in undirected networks. PMID:27324438

  19. Fluctuations and Slow Variables in Genetic Networks

    PubMed Central

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

    2003-01-01

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

  20. From gene expressions to genetic networks

    NASA Astrophysics Data System (ADS)

    Cieplak, Marek

    2009-03-01

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

  1. Controllability of structural brain networks

    PubMed Central

    Gu, Shi; Pasqualetti, Fabio; Cieslak, Matthew; Telesford, Qawi K.; Yu, Alfred B.; Kahn, Ari E.; Medaglia, John D.; Vettel, Jean M.; Miller, Michael B.; Grafton, Scott T.; Bassett, Danielle S.

    2015-01-01

    Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function. PMID:26423222

  2. Effects of Macromolecular Crowding on Genetic Networks

    PubMed Central

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

    2011-01-01

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

  3. Attack Vulnerability of Network Controllability

    PubMed Central

    2016-01-01

    Controllability of complex networks has attracted much attention, and understanding the robustness of network controllability against potential attacks and failures is of practical significance. In this paper, we systematically investigate the attack vulnerability of network controllability for the canonical model networks as well as the real-world networks subject to attacks on nodes and edges. The attack strategies are selected based on degree and betweenness centralities calculated for either the initial network or the current network during the removal, among which random failure is as a comparison. It is found that the node-based strategies are often more harmful to the network controllability than the edge-based ones, and so are the recalculated strategies than their counterparts. The Barabási-Albert scale-free model, which has a highly biased structure, proves to be the most vulnerable of the tested model networks. In contrast, the Erdős-Rényi random model, which lacks structural bias, exhibits much better robustness to both node-based and edge-based attacks. We also survey the control robustness of 25 real-world networks, and the numerical results show that most real networks are control robust to random node failures, which has not been observed in the model networks. And the recalculated betweenness-based strategy is the most efficient way to harm the controllability of real-world networks. Besides, we find that the edge degree is not a good quantity to measure the importance of an edge in terms of network controllability. PMID:27588941

  4. Attack Vulnerability of Network Controllability.

    PubMed

    Lu, Zhe-Ming; Li, Xin-Feng

    2016-01-01

    Controllability of complex networks has attracted much attention, and understanding the robustness of network controllability against potential attacks and failures is of practical significance. In this paper, we systematically investigate the attack vulnerability of network controllability for the canonical model networks as well as the real-world networks subject to attacks on nodes and edges. The attack strategies are selected based on degree and betweenness centralities calculated for either the initial network or the current network during the removal, among which random failure is as a comparison. It is found that the node-based strategies are often more harmful to the network controllability than the edge-based ones, and so are the recalculated strategies than their counterparts. The Barabási-Albert scale-free model, which has a highly biased structure, proves to be the most vulnerable of the tested model networks. In contrast, the Erdős-Rényi random model, which lacks structural bias, exhibits much better robustness to both node-based and edge-based attacks. We also survey the control robustness of 25 real-world networks, and the numerical results show that most real networks are control robust to random node failures, which has not been observed in the model networks. And the recalculated betweenness-based strategy is the most efficient way to harm the controllability of real-world networks. Besides, we find that the edge degree is not a good quantity to measure the importance of an edge in terms of network controllability. PMID:27588941

  5. Enhanced energy transport in genetically engineered excitonic networks

    NASA Astrophysics Data System (ADS)

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

    2016-02-01

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

  6. Population Dynamics of Genetic Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Braun, Erez

    2005-03-01

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

  7. Reliability of genetic networks is evolvable

    NASA Astrophysics Data System (ADS)

    Braunewell, Stefan; Bornholdt, Stefan

    2008-06-01

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

  8. Genetic and Molecular Network Analysis of Behavior

    PubMed Central

    Williams, Robert W.; Mulligan, Megan K.

    2014-01-01

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

  9. Character Recognition Using Genetically Trained Neural Networks

    SciTech Connect

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

    1998-10-01

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

  10. The APS control system network

    SciTech Connect

    Sidorowicz, K.V.; McDowell, W.P.

    1995-12-31

    The APS accelerator control system is a distributed system consisting of operator interfaces, a network, and computer-controlled interfaces to hardware. This implementation of a control system has come to be called the {open_quotes}Standard Model.{close_quotes} The operator interface is a UNDC-based workstation with an X-windows graphical user interface. The workstation may be located at any point on the facility network and maintain full functionality. The function of the network is to provide a generalized communication path between the host computers, operator workstations, input/output crates, and other hardware that comprise the control system. The crate or input/output controller (IOC) provides direct control and input/output interfaces for each accelerator subsystem. The network is an integral part of all modem control systems and network performance will determine many characteristics of a control system. This paper will describe the overall APS network and examine the APS control system network in detail. Metrics are provided on the performance of the system under various conditions.

  11. Functional Localization of Genetic Network Programming

    NASA Astrophysics Data System (ADS)

    Eto, Shinji; Hirasawa, Kotaro; Hu, Jinglu

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

  12. Genetic Regulatory Networks in Embryogenesis and Evolution

    NASA Technical Reports Server (NTRS)

    1998-01-01

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

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

    PubMed

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

    2014-01-01

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

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

    PubMed Central

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

    2014-01-01

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

  15. Identification of Interventions to Control Network Crises

    NASA Astrophysics Data System (ADS)

    Sun, Jie; Sahasrabudhe, Sagar; Motter, Adilson

    2012-02-01

    Large-scale crises in financial, social, infrastructure, genetic and ecological networks often result from the spread of disturbances that in isolation would only cause limited damage. Here we present a method to identify and schedule interventions that can mitigate cascading failures in general complex networks. When applied to competition networks, our method shows that the system can often be rescued from global failures through actions that satisfy restrictive constraints typical of real-world conditions. However, under such constraints, interventions that can rescue the system from a propagating cascade exist over specific periods of time that do not always include the early postperturbation period, suggesting that scheduling is critical in the control of network cascades.

  16. Optimal Design of Geodetic Network Using Genetic Algorithms

    NASA Astrophysics Data System (ADS)

    Vajedian, Sanaz; Bagheri, Hosein

    2010-05-01

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

  17. Control of collective network chaos

    NASA Astrophysics Data System (ADS)

    Wagemakers, Alexandre; Barreto, Ernest; Sanjuán, Miguel A. F.; So, Paul

    2014-06-01

    Under certain conditions, the collective behavior of a large globally-coupled heterogeneous network of coupled oscillators, as quantified by the macroscopic mean field or order parameter, can exhibit low-dimensional chaotic behavior. Recent advances describe how a small set of "reduced" ordinary differential equations can be derived that captures this mean field behavior. Here, we show that chaos control algorithms designed using the reduced equations can be successfully applied to imperfect realizations of the full network. To systematically study the effectiveness of this technique, we measure the quality of control as we relax conditions that are required for the strict accuracy of the reduced equations, and hence, the controller. Although the effects are network-dependent, we show that the method is effective for surprisingly small networks, for modest departures from global coupling, and even with mild inaccuracy in the estimate of network heterogeneity.

  18. Control of collective network chaos

    SciTech Connect

    Wagemakers, Alexandre Sanjuán, Miguel A. F.

    2014-06-01

    Under certain conditions, the collective behavior of a large globally-coupled heterogeneous network of coupled oscillators, as quantified by the macroscopic mean field or order parameter, can exhibit low-dimensional chaotic behavior. Recent advances describe how a small set of “reduced” ordinary differential equations can be derived that captures this mean field behavior. Here, we show that chaos control algorithms designed using the reduced equations can be successfully applied to imperfect realizations of the full network. To systematically study the effectiveness of this technique, we measure the quality of control as we relax conditions that are required for the strict accuracy of the reduced equations, and hence, the controller. Although the effects are network-dependent, we show that the method is effective for surprisingly small networks, for modest departures from global coupling, and even with mild inaccuracy in the estimate of network heterogeneity.

  19. Secure network for beamline control

    NASA Astrophysics Data System (ADS)

    Ohata, T.; Fukui, T.; Ishii, M.; Furukawa, Y.; Nakatani, T.; Matsushita, T.; Takeuchi, M.; Tanaka, R.; Ishikawa, T.

    2001-07-01

    In SPring-8, beamline control system is constructed with a highly available distributed network system. The socket based communication protocol is used for the beamline control mainly. Beamline users can control the equipment by sending simple control commands to a server process, which is running on a beamline-managing computer (Ohata et al., SPring-8 beamline control system, ICALEPCS'99, Trieste, Italy, 1999). At the beginning the network was based on the shared topology at all beamlines. Consequently, it has a risk for misapplication of the user's program to access different machines on the network system cross over beamlines. It is serious problem for the SPring-8 beamline control system, because all beamlines controlled with unified software interfaces. We introduced the switching technology and the firewalls to support network access control. Also the virtual networking (VLAN: IEEE 802.1Q) and the gigabit Ethernet technology (IEEE 802.3ab) are introduced. Thus the network security and the reliability are guaranteed at the higher level in SPring-8 beamline.

  20. Controllability of asynchronous Boolean multiplex control networks

    NASA Astrophysics Data System (ADS)

    Luo, Chao; Wang, Xingyuan; Liu, Hong

    2014-09-01

    In this article, the controllability of asynchronous Boolean multiplex control networks (ABMCNs) is studied. First, the model of Boolean multiplex control networks under Harvey' asynchronous update is presented. By means of semi-tensor product approach, the logical dynamics is converted into linear representation, and a generalized formula of control-depending network transition matrices is achieved. Second, a necessary and sufficient condition is proposed to verify that only control-depending fixed points of ABMCNs can be controlled with probability one. Third, using two types of controls, the controllability of system is studied and formulae are given to show: (a) when an initial state is given, the reachable set at time s under a group of specified controls; (b) the reachable set at time s under arbitrary controls; (c) the specific probability values from a given initial state to destination states. Based on the above formulae, an algorithm to calculate overall reachable states from a specified initial state is presented. Moreover, we also discuss an approach to find the particular control sequence which steers the system between two states with maximum probability. Examples are shown to illustrate the feasibility of the proposed scheme.

  1. Network-Control Algorithm

    NASA Technical Reports Server (NTRS)

    Chan, Hak-Wai; Yan, Tsun-Yee

    1989-01-01

    Algorithm developed for optimal routing of packets of data along links of multilink, multinode digital communication network. Algorithm iterative and converges to cost-optimal assignment independent of initial assignment. Each node connected to other nodes through links, each containing number of two-way channels. Algorithm assigns channels according to message traffic leaving and arriving at each node. Modified to take account of different priorities among packets belonging to different users by using different delay constraints or imposing additional penalties via cost function.

  2. Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks.

    PubMed

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

    2015-01-01

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

  3. Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks

    PubMed Central

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

    2015-01-01

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

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

    PubMed

    Tkačik, Gašper; Walczak, Aleksandra M

    2011-04-20

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

  5. Nonlinear control with neural networks

    SciTech Connect

    Malik, S.A.

    1996-12-31

    Research results are presented to show the successful industrial application of neural networks in closed loop. Two distillation columns are used to demonstrate the effectiveness of nonlinear controllers. The two columns chosen for this purpose are very dissimilar in operating characteristics, and dynamic behavior. One of the columns is a crude column, and the second, a depropaniser, is a smaller column in a vapor recovery unit. In earlier work, neural networks had been presented as general function estimators, for prediction of stream compositions and the suitability of the various network architectures for this task had been investigated. This report reviews the successful application of neural networks, as feedback controllers, to large industrial distillation columns. 21 refs.

  6. A control network of Triton

    NASA Technical Reports Server (NTRS)

    Davies, Merton E.; Rogers, Patricia G.; Colvin, Tim R.

    1991-01-01

    A control network for Triton has been computed using a bundle-type analytical triangulation program. The network contains 105 points that were measured on 57 Voyager-2 pictures. The adjustment contained 1010 observation equations and 382 normal equations and resulted in a standard measurement error of 13.36 microns. The coordinates of the control points, the camera orientation angles at the times when the pictures were taken, and Triton's mean radius were determined. A separate statistical analysis confirmed Triton's radius to be 1352.6 + or - 2.4 km. Attempts to tie the control network around the satellite were unsuccessful because discontinuities exist in high-resolution coverage between 66 deg and 289 deg longitude, north of 38 deg latitude, and south of 78 deg latitude.

  7. Neural Networks for Flight Control

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    1996-01-01

    Neural networks are being developed at NASA Ames Research Center to permit real-time adaptive control of time varying nonlinear systems, enhance the fault-tolerance of mission hardware, and permit online system reconfiguration. In general, the problem of controlling time varying nonlinear systems with unknown structures has not been solved. Adaptive neural control techniques show considerable promise and are being applied to technical challenges including automated docking of spacecraft, dynamic balancing of the space station centrifuge, online reconfiguration of damaged aircraft, and reducing cost of new air and spacecraft designs. Our experiences have shown that neural network algorithms solved certain problems that conventional control methods have been unable to effectively address. These include damage mitigation in nonlinear reconfiguration flight control, early performance estimation of new aircraft designs, compensation for damaged planetary mission hardware by using redundant manipulator capability, and space sensor platform stabilization. This presentation explored these developments in the context of neural network control theory. The discussion began with an overview of why neural control has proven attractive for NASA application domains. The more important issues in control system development were then discussed with references to significant technical advances in the literature. Examples of how these methods have been applied were given, followed by projections of emerging application needs and directions.

  8. Delay-independent stability of genetic regulatory networks.

    PubMed

    Wu, Fang-Xiang

    2011-11-01

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

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

    PubMed Central

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

    2011-01-01

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

  10. Genetic Algorithm Based Neural Networks for Nonlinear Optimization

    1994-09-28

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

  11. Genetic and environmental etiology of effortful control.

    PubMed

    Yamagata, Shinji; Takahashi, Yusuke; Kijima, Nobuhiko; Maekawa, Hiroko; Ono, Yutaka; Ando, Juko

    2005-08-01

    We examined whether effortful control (EC), a temperament proposed by Rothbart and Bates (1998), has genetically coherent structure. A self-report measure of EC was administered to 450 Japanese twins (151 males and 299 females, ages 17 to 32 years) including 152 monozygotic and 73 dizygotic pairs. Univariate genetic analysis revealed that AE model fit best for the total EC as well as its subscales. The heritability estimate for total EC was 49%, and the estimates for subscales ranged between 32% and 45%. Multivariate genetic analysis revealed that the subscales of EC were genetically correlated to a high degree and environmentally correlated to a moderate degree. These results suggest that EC has substantial genetic basis and genetically coherent structure, supporting the validity of the construct. The implications to molecular genetic study and study of psychopathology were discussed.

  12. Propagation of genetic variation in gene regulatory networks.

    PubMed

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

    2013-08-01

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

  13. Virtualized Network Control. Final Report

    SciTech Connect

    Ghani, Nasir

    2013-02-01

    This document is the final report for the Virtualized Network Control (VNC) project, which was funded by the United States Department of Energy (DOE) Office of Science. This project was also informally referred to as Advanced Resource Computation for Hybrid Service and TOpology NEtworks (ARCHSTONE). This report provides a summary of the project's activities, tasks, deliverable, and accomplishments. It also provides a summary of the documents, software, and presentations generated as part of this projects activities. Namely, the Appendix contains an archive of the deliverables, documents, and presentations generated a part of this project.

  14. The AMSC network control system

    NASA Technical Reports Server (NTRS)

    Garner, William B.

    1990-01-01

    The American Mobile Satellite Corporation (AMSC) is going to construct, launch, and operate a satellite system in order to provide mobile satellite services to the United States. AMSC is going to build, own, and operate a Network Control System (NCS) for managing the communications usage of the satellites, and to control circuit switched access between mobile earth terminals and feeder-link earth stations. An overview of the major NCS functional and performance requirements, the control system physical architecture, and the logical architecture is provided.

  15. The AMSC network control system

    NASA Astrophysics Data System (ADS)

    Garner, William B.

    The American Mobile Satellite Corporation (AMSC) is going to construct, launch, and operate a satellite system in order to provide mobile satellite services to the United States. AMSC is going to build, own, and operate a Network Control System (NCS) for managing the communications usage of the satellites, and to control circuit switched access between mobile earth terminals and feeder-link earth stations. An overview of the major NCS functional and performance requirements, the control system physical architecture, and the logical architecture is provided.

  16. Evolving artificial neural networks to control chaotic systems

    NASA Astrophysics Data System (ADS)

    Weeks, Eric R.; Burgess, John M.

    1997-08-01

    We develop a genetic algorithm that produces neural network feedback controllers for chaotic systems. The algorithm was tested on the logistic and Hénon maps, for which it stabilizes an unstable fixed point using small perturbations, even in the presence of significant noise. The network training method [D. E. Moriarty and R. Miikkulainen, Mach. Learn. 22, 11 (1996)] requires no previous knowledge about the system to be controlled, including the dimensionality of the system and the location of unstable fixed points. This is the first dimension-independent algorithm that produces neural network controllers using time-series data. A software implementation of this algorithm is available via the World Wide Web.

  17. Controllability of the better chosen partial networks

    NASA Astrophysics Data System (ADS)

    Liu, Xueming; Pan, Linqiang

    2016-08-01

    How to control large complex networks is a great challenge. Recent studies have proved that the whole network can be sufficiently steered by injecting control signals into a minimum set of driver nodes, and the minimum numbers of driver nodes for many real networks are high, indicating that it is difficult to control them. For some large natural and technological networks, it is impossible and not feasible to control the full network. For example, in biological networks like large-scale gene regulatory networks it is impossible to control all the genes. This prompts us to explore the question how to choose partial networks that are easy for controlling and important in networked systems. In this work, we propose a method to achieve this goal. By computing the minimum driver nodes densities of the partial networks of Erdös-Rényi (ER) networks, scale-free (SF) networks and 23 real networks, we find that our method performs better than random method that chooses nodes randomly. Moreover, we find that the nodes chosen by our method tend to be the essential elements of the whole systems, via studying the nodes chosen by our method of a real human signaling network and a human protein interaction network and discovering that the chosen nodes from these networks tend to be cancer-associated genes. The implementation of our method shows some interesting connections between the structure and the controllability of networks, improving our understanding of the control principles of complex systems.

  18. Propagation of genetic variation in gene regulatory networks

    PubMed Central

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

    2013-01-01

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

  19. Genetic noise control via protein oligomerization

    SciTech Connect

    Ghim, C; Almaas, E

    2008-06-12

    Gene expression in a cell entails random reaction events occurring over disparate time scales. Thus, molecular noise that often results in phenotypic and population-dynamic consequences sets a fundamental limit to biochemical signaling. While there have been numerous studies correlating the architecture of cellular reaction networks with noise tolerance, only a limited effort has been made to understand the dynamical role of protein-protein associations. We have developed a fully stochastic model for the positive feedback control of a single gene, as well as a pair of genes (toggle switch), integrating quantitative results from previous in vivo and in vitro studies. In particular, we explicitly account for the fast protein binding-unbinding kinetics, RNA polymerases, and the promoter/operator sequences of DNA. We find that the overall noise-level is reduced and the frequency content of the noise is dramatically shifted to the physiologically irrelevant high-frequency regime in the presence of protein dimerization. This is independent of the choice of monomer or dimer as transcription factor and persists throughout the multiple model topologies considered. For the toggle switch, we additionally find that the presence of a protein dimer, either homodimer or heterodimer, may significantly reduce its intrinsic switching rate. Hence, the dimer promotes the robust function of bistable switches by preventing the uninduced (induced) state from randomly being induced (uninduced). The specific binding between regulatory proteins provides a buffer that may prevent the propagation of fluctuations in genetic activity. The capacity of the buffer is a non-monotonic function of association-dissociation rates. Since the protein oligomerization per se does not require extra protein components to be expressed, it provides a basis for the rapid control of intrinsic or extrinsic noise. The stabilization of phenotypically important toggle switches, and nested positive feedback loops in

  20. Opinion control in complex networks

    NASA Astrophysics Data System (ADS)

    Masuda, Naoki

    2015-03-01

    In many political elections, the electorate appears to be a composite of partisan and independent voters. Given that partisans are not likely to convert to a different party, an important goal for a political party could be to mobilize independent voters toward the party with the help of strong leadership, mass media, partisans, and the effects of peer-to-peer influence. Based on the exact solution of classical voter model dynamics in the presence of perfectly partisan voters (i.e., zealots), we propose a computational method that uses pinning control strategy to maximize the share of a party in a social network of independent voters. The party, corresponding to the controller or zealots, optimizes the nodes to be controlled given the information about the connectivity of independent voters and the set of nodes that the opposing party controls. We show that controlling hubs is generally a good strategy, but the optimized strategy is even better. The superiority of the optimized strategy is particularly eminent when the independent voters are connected as directed (rather than undirected) networks.

  1. Weight-Control Information Network (WIN)

    MedlinePlus

    ... Feature: Reducing Childhood Obesity The Weight-control Information Network (WIN) Past Issues / Spring - Summer 2010 Table of ... here are tips from the Weight-control Information Network (WIN), an information service of the National Institute ...

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

    PubMed Central

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

    2009-01-01

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

  3. Neural Network Controlled Visual Saccades

    NASA Astrophysics Data System (ADS)

    Johnson, Jeffrey D.; Grogan, Timothy A.

    1989-03-01

    The paper to be presented will discuss research on a computer vision system controlled by a neural network capable of learning through classical (Pavlovian) conditioning. Through the use of unconditional stimuli (reward and punishment) the system will develop scan patterns of eye saccades necessary to differentiate and recognize members of an input set. By foveating only those portions of the input image that the system has found to be necessary for recognition the drawback of computational explosion as the size of the input image grows is avoided. The model incorporates many features found in animal vision systems, and is governed by understandable and modifiable behavior patterns similar to those reported by Pavlov in his classic study. These behavioral patterns are a result of a neuronal model, used in the network, explicitly designed to reproduce this behavior.

  4. Controlling synchronous patterns in complex networks.

    PubMed

    Lin, Weijie; Fan, Huawei; Wang, Ying; Ying, Heping; Wang, Xingang

    2016-04-01

    Although the set of permutation symmetries of a complex network could be very large, few of them give rise to stable synchronous patterns. Here we present a general framework and develop techniques for controlling synchronization patterns in complex network of coupled chaotic oscillators. Specifically, according to the network permutation symmetry, we design a small-size and weighted network, namely the control network, and use it to control the large-size complex network by means of pinning coupling. We argue mathematically that for any of the network symmetries, there always exists a critical pinning strength beyond which the unstable synchronous pattern associated to this symmetry can be stabilized. The feasibility of the control method is verified by numerical simulations of both artificial and real-world networks and demonstrated experimentally in systems of coupled chaotic circuits. Our studies show the controllability of synchronous patterns in complex networks of coupled chaotic oscillators.

  5. Topological constraints on network control profiles

    PubMed Central

    Campbell, Colin; Ruths, Justin; Ruths, Derek; Shea, Katriona; Albert, Réka

    2015-01-01

    Network models are designed to capture properties of empirical networks and thereby provide insight into the processes that underlie the formation of complex systems. As new information concerning network structure becomes available, it becomes possible to design models that more fully capture the properties of empirical networks. A recent advance in our understanding of network structure is the control profile, which summarizes the structural controllability of a network in terms of source nodes, external dilations, and internal dilations. Here, we consider the topological properties–and their formation mechanisms—that constrain the control profile. We consider five representative empirical categories of internal-dilation dominated networks, and show that the number of source and sink nodes, the form of the in- and out-degree distributions, and local complexity (e.g., cycles) shape the control profile. We evaluate network models that are sufficient to produce realistic control profiles, and conclude that holistic network models should similarly consider these properties. PMID:26691951

  6. Combination of uniform design with artificial neural network coupling genetic algorithm: an effective way to obtain high yield of biomass and algicidal compound of a novel HABs control actinomycete

    PubMed Central

    2014-01-01

    Controlling harmful algae blooms (HABs) using microbial algicides is cheap, efficient and environmental-friendly. However, obtaining high yield of algicidal microbes to meet the need of field test is still a big challenge since qualitative and quantitative analysis of algicidal compounds is difficult. In this study, we developed a protocol to increase the yield of both biomass and algicidal compound present in a novel algicidal actinomycete Streptomyces alboflavus RPS, which kills Phaeocystis globosa. To overcome the problem in algicidal compound quantification, we chose algicidal ratio as the index and used artificial neural network to fit the data, which was appropriate for this nonlinear situation. In this protocol, we firstly determined five main influencing factors through single factor experiments and generated the multifactorial experimental groups with a U15(155) uniform-design-table. Then, we used the traditional quadratic polynomial stepwise regression model and an accurate, fully optimized BP-neural network to simulate the fermentation. Optimized with genetic algorithm and verified using experiments, we successfully increased the algicidal ratio of the fermentation broth by 16.90% and the dry mycelial weight by 69.27%. These results suggested that this newly developed approach is a viable and easy way to optimize the fermentation conditions for algicidal microorganisms. PMID:24886410

  7. Network Adaptive Deadband: NCS Data Flow Control for Shared Networks

    PubMed Central

    Díaz-Cacho, Miguel; Delgado, Emma; Prieto, José A. G.; López, Joaquín

    2012-01-01

    This paper proposes a new middleware solution called Network Adaptive Deadband (NAD) for long time operation of Networked Control Systems (NCS) through the Internet or any shared network based on IP technology. The proposed middleware takes into account the network status and the NCS status, to improve the global system performance and to share more effectively the network by several NCS and sensor/actuator data flows. Relationship between network status and NCS status is solved with a TCP-friendly transport flow control protocol and the deadband concept, relating deadband value and transmission throughput. This creates a deadband-based flow control solution. Simulation and experiments in shared networks show that the implemented network adaptive deadband has better performance than an optimal constant deadband solution in the same circumstances. PMID:23208556

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

    PubMed

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

    2016-07-19

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

  9. Genetic algorithms in adaptive fuzzy control

    NASA Technical Reports Server (NTRS)

    Karr, C. Lucas; Harper, Tony R.

    1992-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.

  10. Network Access Control List Situation Awareness

    ERIC Educational Resources Information Center

    Reifers, Andrew

    2010-01-01

    Network security is a large and complex problem being addressed by multiple communities. Nevertheless, current theories in networking security appear to overestimate network administrators' ability to understand network access control lists (NACLs), providing few context specific user analyses. Consequently, the current research generally seems to…

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

    PubMed

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

    2016-07-01

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

  12. Adaptive optimization and control using neural networks

    SciTech Connect

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  13. PHOBOS and Deimos control networks

    NASA Astrophysics Data System (ADS)

    Duxbury, T. C.; Callahan, J. D.

    1989-02-01

    Viking Orbiter images of Phobos and Deimos have been measured to establish global control networks for 98 surface features of the former and 53 of the latter; photogrammetric triangulation has yielded body-fixed coordinates of these control-points, as well as mean triaxial radii of 13.3 x 11.1 x 9.3 km for Phobos and 7.5 x 6.2 x 5.4 for Deimos. Expressions are also obtained for the inertial orientations of these bodies' spin axes and prime meridians. While these expressions should be accurate to a few tenths of a deg for the 1971-1980 period, their accuracy will degrade with time as the orbit accuracy degrades.

  14. Phobos and Deimos control networks

    NASA Technical Reports Server (NTRS)

    Duxbury, Thomas C.; Callahan, John D.

    1989-01-01

    Viking Orbiter images of Phobos and Deimos have been measured to establish global control networks for 98 surface features of the former and 53 of the latter; photogrammetric triangulation has yielded body-fixed coordinates of these control-points, as well as mean triaxial radii of 13.3 x 11.1 x 9.3 km for Phobos and 7.5 x 6.2 x 5.4 for Deimos. Expressions are also obtained for the inertial orientations of these bodies' spin axes and prime meridians. While these expressions should be accurate to a few tenths of a deg for the 1971-1980 period, their accuracy will degrade with time as the orbit accuracy degrades.

  15. The control network of Iapetus

    NASA Astrophysics Data System (ADS)

    Davies, M. E.; Katayama, F. Y.

    1984-08-01

    A control network of the Saturnian satellite Iapetus has been established photogrammetrically from pictures taken by the two Voyager spacecraft. Coordinates of 62 control points have been computed and listed; pixel measurements of these points were made on 14 Voyager 1 and 66 Voyager 2 pictures. Some of these points are identified on the preliminary U.S. Geological Survey map of Iapetus and many are identified by name. The Voyager 1 and Voyager 2 pictures covered limited regions of the satellite's surface and contained no overlapping areas. The longitude system on Iapetus is defined by the crater Almeric; the 276 deg meridian passes through the center of this crater. The obliquity of Iapetus has been measured as 0.4 deg + or - 1.6 deg. The mean radius of Iapetus has been determined at 718 + or - 8 km.

  16. The control network of Iapetus

    NASA Technical Reports Server (NTRS)

    Davies, M. E.; Katayama, F. Y.

    1984-01-01

    A control network of the Saturnian satellite Iapetus has been established photogrammetrically from pictures taken by the two Voyager spacecraft. Coordinates of 62 control points have been computed and listed; pixel measurements of these points were made on 14 Voyager 1 and 66 Voyager 2 pictures. Some of these points are identified on the preliminary U.S. Geological Survey map of Iapetus and many are identified by name. The Voyager 1 and Voyager 2 pictures covered limited regions of the satellite's surface and contained no overlapping areas. The longitude system on Iapetus is defined by the crater Almeric; the 276 deg meridian passes through the center of this crater. The obliquity of Iapetus has been measured as 0.4 deg + or - 1.6 deg. The mean radius of Iapetus has been determined at 718 + or - 8 km.

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

    PubMed

    Xiao, Min; Cao, Jinde

    2008-09-01

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

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

    PubMed

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

    2010-01-01

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

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

    PubMed Central

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

    2013-01-01

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

  20. Pinning impulsive control algorithms for complex network.

    PubMed

    Sun, Wen; Lü, Jinhu; Chen, Shihua; Yu, Xinghuo

    2014-03-01

    In this paper, we further investigate the synchronization of complex dynamical network via pinning control in which a selection of nodes are controlled at discrete times. Different from most existing work, the pinning control algorithms utilize only the impulsive signals at discrete time instants, which may greatly improve the communication channel efficiency and reduce control cost. Two classes of algorithms are designed, one for strongly connected complex network and another for non-strongly connected complex network. It is suggested that in the strongly connected network with suitable coupling strength, a single controller at any one of the network's nodes can always pin the network to its homogeneous solution. In the non-strongly connected case, the location and minimum number of nodes needed to pin the network are determined by the Frobenius normal form of the coupling matrix. In addition, the coupling matrix is not necessarily symmetric or irreducible. Illustrative examples are then given to validate the proposed pinning impulsive control algorithms.

  1. Pinning impulsive control algorithms for complex network

    SciTech Connect

    Sun, Wen; Lü, Jinhu; Chen, Shihua; Yu, Xinghuo

    2014-03-15

    In this paper, we further investigate the synchronization of complex dynamical network via pinning control in which a selection of nodes are controlled at discrete times. Different from most existing work, the pinning control algorithms utilize only the impulsive signals at discrete time instants, which may greatly improve the communication channel efficiency and reduce control cost. Two classes of algorithms are designed, one for strongly connected complex network and another for non-strongly connected complex network. It is suggested that in the strongly connected network with suitable coupling strength, a single controller at any one of the network's nodes can always pin the network to its homogeneous solution. In the non-strongly connected case, the location and minimum number of nodes needed to pin the network are determined by the Frobenius normal form of the coupling matrix. In addition, the coupling matrix is not necessarily symmetric or irreducible. Illustrative examples are then given to validate the proposed pinning impulsive control algorithms.

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

    PubMed

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

    2013-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-11-01

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

  4. Controlling complex networks with conformity behavior

    NASA Astrophysics Data System (ADS)

    Wang, Xu-Wen; Nie, Sen; Wang, Wen-Xu; Wang, Bing-Hong

    2015-09-01

    Controlling complex networks accompanied by common conformity behavior is a fundamental problem in social and physical science. Conformity behavior that individuals tend to follow the majority in their neighborhood is common in human society and animal communities. Despite recent progress in understanding controllability of complex networks, the existent controllability theories cannot be directly applied to networks associated with conformity. Here we propose a simple model to incorporate conformity-based decision making into the evolution of a network system, which allows us to employ the exact controllability theory to explore the controllability of such systems. We offer rigorous theoretical results of controllability for representative regular networks. We also explore real networks in different fields and some typical model networks, finding some interesting results that are different from the predictions of structural and exact controllability theory in the absence of conformity. We finally present an example of steering a real social network to some target states to further validate our controllability theory and tools. Our work offers a more realistic understanding of network controllability with conformity behavior and can have potential applications in networked evolutionary games, opinion dynamics and many other complex networked systems.

  5. Genetic Networks in Mouse Retinal Ganglion Cells

    PubMed Central

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

    2016-01-01

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

  6. Genetic Adaptive Control for PZT Actuators

    NASA Technical Reports Server (NTRS)

    Kim, Jeongwook; Stover, Shelley K.; Madisetti, Vijay K.

    1995-01-01

    A piezoelectric transducer (PZT) is capable of providing linear motion if controlled correctly and could provide a replacement for traditional heavy and large servo systems using motors. This paper focuses on a genetic model reference adaptive control technique (GMRAC) for a PZT which is moving a mirror where the goal is to keep the mirror velocity constant. Genetic Algorithms (GAs) are an integral part of the GMRAC technique acting as the search engine for an optimal PID controller. Two methods are suggested to control the actuator in this research. The first one is to change the PID parameters and the other is to add an additional reference input in the system. The simulation results of these two methods are compared. Simulated Annealing (SA) is also used to solve the problem. Simulation results of GAs and SA are compared after simulation. GAs show the best result according to the simulation results. The entire model is designed using the Mathworks' Simulink tool.

  7. Genetic Network Programming with Intron-Like Nodes

    NASA Astrophysics Data System (ADS)

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

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

  8. Genetic demographic networks: Mathematical model and applications.

    PubMed

    Kimmel, Marek; Wojdyła, Tomasz

    2016-10-01

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

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

    PubMed

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

    2015-10-01

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

  10. Network Monitor and Control of Disruption-Tolerant Networks

    NASA Technical Reports Server (NTRS)

    Torgerson, J. Leigh

    2014-01-01

    For nearly a decade, NASA and many researchers in the international community have been developing Internet-like protocols that allow for automated network operations in networks where the individual links between nodes are only sporadically connected. A family of Disruption-Tolerant Networking (DTN) protocols has been developed, and many are reaching CCSDS Blue Book status. A NASA version of DTN known as the Interplanetary Overlay Network (ION) has been flight-tested on the EPOXI spacecraft and ION is currently being tested on the International Space Station. Experience has shown that in order for a DTN service-provider to set up a large scale multi-node network, a number of network monitor and control technologies need to be fielded as well as the basic DTN protocols. The NASA DTN program is developing a standardized means of querying a DTN node to ascertain its operational status, known as the DTN Management Protocol (DTNMP), and the program has developed some prototypes of DTNMP software. While DTNMP is a necessary component, it is not sufficient to accomplish Network Monitor and Control of a DTN network. JPL is developing a suite of tools that provide for network visualization, performance monitoring and ION node control software. This suite of network monitor and control tools complements the GSFC and APL-developed DTN MP software, and the combined package can form the basis for flight operations using DTN.

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

    PubMed

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

    2013-10-01

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

  12. Genetic control of inflorescence in common bean.

    PubMed

    Guilherme, S R; Ramalho, M A P; de F B Abreu, A; Pereira, L A

    2014-12-04

    The number of pods per common bean plant is a primary component of grain yield, which depends on the number of flowers produced and on the flower set. Thus, a larger number of flowers per plant would increase yield. Lines with inflorescences that had a large number of flowers compared to common bean plants now under cultivation were identified. We analyzed the genetic control of this trait and its association with grain yield. The cultivar BRSMG Talismã was crossed with 2 lines, L.59583 and L.59692, which have a large number of flowers. The F1, F2, and F3 generations were obtained. These generations were assessed together with the parents in a randomized block experimental design with 2 replications. The traits assessed included length of inflorescence, number of pods per inflorescence, number of pods per plant, number of grains per plant, 100-grain weight, and grain yield per plant. Mean genetic components and variance were estimated. The traits length of inflorescence and number of pods per inflorescence exhibited genetic control with predominance that showed an additive effect. In the 2 crosses, genetic control of grain yield and of its primary components showed that the allelic interaction of dominance was high. The wide variability in the traits assessed may be used to increase yield of the common bean plant by increasing the number of flowers on the plant.

  13. Genetics and early disturbances of breathing control.

    PubMed

    Gaultier, Claude; Amiel, Jeanne; Dauger, Stéphane; Trang, Ha; Lyonnet, Stanislas; Gallego, Jorge; Simonneau, Michel

    2004-05-01

    Early disturbances in breathing control, including apneas of prematurity and apparently life-threatening events, account for some cases of sudden infant death syndrome and for a rare disorder called congenital central hypoventilation syndrome (CCHS). Data suggesting a genetic basis for CCHS have been obtained. Recently, we found heterozygous de novo mutations of the PHOX2B gene in 18 of 29 individuals with CCHS. Most mutations consisted of five to nine alanine expansions within a 20-residue polyalanine tract, probably resulting from nonhomologous recombination. Other mutations, generally inherited from one of the parents, in the coding regions of genes involved in the endothelin and RET signaling pathways and in the brain-derived-neurotrophic factor (BDNF) gene have been found in a few CCHS patients. Interestingly, all these genes are involved in the development of neural crest cells. Targeted disruption of these genes in mice has provided information on the pathophysiological mechanisms underlying CCHS. Despite the identification of these genes involved in breathing control, none of the genetically engineered mice developed to date replicate the full human CCHS respiratory phenotype. Recent insights into the genetic basis for CCHS may shed light on the genetics of other early disturbances in breathing control, such as apnea of prematurity and sudden infant death syndrome. PMID:14739359

  14. Minimum-cost control of complex networks

    NASA Astrophysics Data System (ADS)

    Li, Guoqi; Hu, Wuhua; Xiao, Gaoxi; Deng, Lei; Tang, Pei; Pei, Jing; Shi, Luping

    2016-01-01

    Finding the solution for driving a complex network at the minimum energy cost with a given number of controllers, known as the minimum-cost control problem, is critically important but remains largely open. We propose a projected gradient method to tackle this problem, which works efficiently in both synthetic and real-life networks. The study is then extended to the case where each controller can only be connected to a single network node to have the lowest connection complexity. We obtain the interesting insight that such connections basically avoid high-degree nodes of the network, which is in resonance with recent observations on controllability of complex networks. Our results provide the first technical path to enabling minimum-cost control of complex networks, and contribute new insights to locating the key nodes from a minimum-cost control perspective.

  15. Genetic demographic networks: Mathematical model and applications.

    PubMed

    Kimmel, Marek; Wojdyła, Tomasz

    2016-10-01

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

  16. Control of Large-Scale Boolean Networks via Network Aggregation.

    PubMed

    Zhao, Yin; Ghosh, Bijoy K; Cheng, Daizhan

    2016-07-01

    A major challenge to solve problems in control of Boolean networks is that the computational cost increases exponentially when the number of nodes in the network increases. We consider the problem of controllability and stabilizability of Boolean control networks, address the increasing cost problem by partitioning the network graph into several subnetworks, and analyze the subnetworks separately. Easily verifiable necessary conditions for controllability and stabilizability are proposed for a general aggregation structure. For acyclic aggregation, we develop a sufficient condition for stabilizability. It dramatically reduces the computational complexity if the number of nodes in each block of the acyclic aggregation is small enough compared with the number of nodes in the entire Boolean network.

  17. Complex and unexpected dynamics in simple genetic regulatory networks

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  18. Realizing actual feedback control of complex network

    NASA Astrophysics Data System (ADS)

    Tu, Chengyi; Cheng, Yuhua

    2014-06-01

    In this paper, we present the concept of feedbackability and how to identify the Minimum Feedbackability Set of an arbitrary complex directed network. Furthermore, we design an estimator and a feedback controller accessing one MFS to realize actual feedback control, i.e. control the system to our desired state according to the estimated system internal state from the output of estimator. Last but not least, we perform numerical simulations of a small linear time-invariant dynamics network and a real simple food network to verify the theoretical results. The framework presented here could make an arbitrary complex directed network realize actual feedback control and deepen our understanding of complex systems.

  19. Genetic Algorithm Application in Optimization of Wireless Sensor Networks

    PubMed Central

    Norouzi, Ali; Zaim, A. Halim

    2014-01-01

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

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

    PubMed

    Koçer, Sabri; Canal, M Rahmi

    2011-08-01

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

  1. Populus trichocarpa cell wall chemistry and ultrastructure trait variation, genetic control and genetic correlations.

    PubMed

    Porth, Ilga; Klápště, Jaroslav; Skyba, Oleksandr; Lai, Ben S K; Geraldes, Armando; Muchero, Wellington; Tuskan, Gerald A; Douglas, Carl J; El-Kassaby, Yousry A; Mansfield, Shawn D

    2013-02-01

    The increasing ecological and economical importance of Populus species and hybrids has stimulated research into the investigation of the natural variation of the species and the estimation of the extent of genetic control over its wood quality traits for traditional forestry activities as well as the emerging bioenergy sector. A realized kinship matrix based on informative, high-density, biallelic single nucleotide polymorphism (SNP) genetic markers was constructed to estimate trait variance components, heritabilities, and genetic and phenotypic correlations. Seventeen traits related to wood chemistry and ultrastructure were examined in 334 9-yr-old Populus trichocarpa grown in a common-garden plot representing populations spanning the latitudinal range 44° to 58.6°. In these individuals, 9342 SNPs that conformed to Hardy-Weinberg expectations were employed to assess the genomic pair-wise kinship to estimate narrow-sense heritabilities and genetic correlations among traits. The range-wide phenotypic variation in all traits was substantial and several trait heritabilities were > 0.6. In total, 61 significant genetic and phenotypic correlations and a network of highly interrelated traits were identified. The high trait variation, the evidence for moderate to high heritabilities and the identification of advantageous trait combinations of industrially important characteristics should aid in providing the foundation for the enhancement of poplar tree breeding strategies for modern industrial use. PMID:23278123

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

    PubMed

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

    2016-08-01

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

  3. Information theory and the ethylene genetic network

    PubMed Central

    González-García, José S

    2011-01-01

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

  4. Information theory and the ethylene genetic network.

    PubMed

    González-García, José S; Díaz, José

    2011-10-01

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

  5. Information theory and the ethylene genetic network.

    PubMed

    González-García, José S; Díaz, José

    2011-10-01

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

  6. Control of State Transitions in Complex and Biophysical Networks

    NASA Astrophysics Data System (ADS)

    Motter, Adilson; Wells, Daniel; Kath, William

    Noise is a fundamental part of intracellular processes. While the response of biological systems to noise has been studied extensively, there has been limited understanding of how to exploit it to induce a desired cell state. Here I will present a scalable, quantitative method based on the Freidlin-Wentzell action to predict and control noise-induced switching between different states in genetic networks that, conveniently, can also control transitions between stable states in the absence of noise. I will discuss applications of this methodology to predict control interventions that can induce lineage changes and to identify new candidate strategies for cancer therapy. This framework offers a systems approach to identifying the key factors for rationally manipulating network dynamics, and should also find use in controlling other classes of complex networks exhibiting multi-stability. Reference: D. K. Wells, W. L. Kath, and A. E. Motter, Phys. Rev. X 5, 031036 (2015). Work funded by CBC, NCI, NIGMS, and NSF.

  7. Predicting genetic interactions with random walks on biological networks

    PubMed Central

    Chipman, Kyle C; Singh, Ambuj K

    2009-01-01

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

  8. Congestion control and avoidance for ATM networks

    NASA Astrophysics Data System (ADS)

    Wu, Chih-Ming

    1997-10-01

    The flow of papers proposing new schemes to cope with congestion in networks continues unabated. In particular as the deployment of ATM networks advances effective congestion control is required to ensure that these networks can effectively provide the wide range of services that they promise. This paper attempts to evaluate whether recently proposed algorithms are likely to be useful in practice using performance simulation and modeling methods. However the performance is very sensitive to the flow control parameters and identifying an appropriate set of parameters is difficult since it depends heavily on the traffic conditions. The aim of this paper described is to broaden the context within which ATM performance is considered, and outline ongoing work in performance evaluation of ATM networks. This paper presents the complete picture for evaluating the properties of congestion control mechanisms including fairness, overhead, data loss and network utilization are described. It is particularly aimed at estimating the effects of recent congestion control schemes for ATM networks.

  9. Centralized surveillance and control of satellite networks

    NASA Astrophysics Data System (ADS)

    Rzewnicki, S. E.; McBeath, J. W.; Brostrup-Jensen, P.

    Satellite based services and networks are increasing in number. This paper describes how such networks can be operated efficiently using software based systems to do satellite transmission surveillance and remote earth station status, alarm and control monitoring at a centralized operations control center. Arrangements are available to accomplish real time, customer controlled configuration of space segments and earth station equipment. Application of the system elements satellite transmission surveillance, alarm and control central, earth station remote, and customer control terminals - to a number of typical networks is described.

  10. Attitude control of spacecraft using neural networks

    NASA Technical Reports Server (NTRS)

    Vadali, Srinivas R.; Krishnan, S.; Singh, T.

    1993-01-01

    This paper investigates the use of radial basis function neural networks for adaptive attitude control and momentum management of spacecraft. In the first part of the paper, neural networks are trained to learn from a family of open-loop optimal controls parameterized by the initial states and times-to-go. The trained is then used for closed-loop control. In the second part of the paper, neural networks are used for direct adaptive control in the presence of unmodeled effects and parameter uncertainty. The control and learning laws are derived using the method of Lyapunov.

  11. Interdependent networks: the fragility of control

    PubMed Central

    Morris, Richard G.; Barthelemy, Marc

    2013-01-01

    Recent work in the area of interdependent networks has focused on interactions between two systems of the same type. However, an important and ubiquitous class of systems are those involving monitoring and control, an example of interdependence between processes that are very different. In this Article, we introduce a framework for modelling ‘distributed supervisory control' in the guise of an electrical network supervised by a distributed system of control devices. The system is characterised by degrees of freedom salient to real-world systems— namely, the number of control devices, their inherent reliability, and the topology of the control network. Surprisingly, the behavior of the system depends crucially on the reliability of control devices. When devices are completely reliable, cascade sizes are percolation controlled; the number of devices being the relevant parameter. For unreliable devices, the topology of the control network is important and can dramatically reduce the resilience of the system. PMID:24067404

  12. Genetic interaction networks: better understand to better predict

    PubMed Central

    Boucher, Benjamin; Jenna, Sarah

    2013-01-01

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

  13. Evolution through genetically controlled allometry space.

    PubMed

    Langlade, Nicolas B; Feng, Xianzhong; Dransfield, Tracy; Copsey, Lucy; Hanna, Andrew I; Thébaud, Christophe; Bangham, Andrew; Hudson, Andrew; Coen, Enrico

    2005-07-19

    Understanding evolutionary change requires phenotypic differences between organisms to be placed in a genetic context. However, there are few cases where it has been possible to define an appropriate genotypic space for a range of species. Here we address this problem by defining a genetically controlled space that captures variation in shape and size between closely related species of Antirrhinum. The axes of the space are based on an allometric model of leaves from an F2 of an interspecific cross between Antirrhinum majus and Antirrhinum charidemi. Three principal components were found to capture most of the genetic variation in shape and size, allowing a three-dimensional allometric space to be defined. The contribution of individual genetic loci was determined from QTL analysis, allowing each locus to be represented as a vector in the allometric space. Leaf shapes and sizes of 18 different Antirrhinum taxa, encompassing a broad range of leaf morphologies, could be accurately represented as clouds within the space. Most taxa overlapped with, or were near to, at least one other species in the space, so that together they defined a largely interconnected domain of viable forms. It is likely that the pattern of evolution within this domain reflects a combination of directional selection and evolutionary tradeoffs within a high dimensional space.

  14. A Review of Modeling Techniques for Genetic Regulatory Networks

    PubMed Central

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

    2012-01-01

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

  15. Immune allied genetic algorithm for Bayesian network structure learning

    NASA Astrophysics Data System (ADS)

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

    2012-06-01

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

  16. Stress controls the mechanics of collagen networks

    PubMed Central

    Licup, Albert James; Münster, Stefan; Sharma, Abhinav; Sheinman, Michael; Jawerth, Louise M.; Fabry, Ben; Weitz, David A.; MacKintosh, Fred C.

    2015-01-01

    Collagen is the main structural and load-bearing element of various connective tissues, where it forms the extracellular matrix that supports cells. It has long been known that collagenous tissues exhibit a highly nonlinear stress–strain relationship, although the origins of this nonlinearity remain unknown. Here, we show that the nonlinear stiffening of reconstituted type I collagen networks is controlled by the applied stress and that the network stiffness becomes surprisingly insensitive to network concentration. We demonstrate how a simple model for networks of elastic fibers can quantitatively account for the mechanics of reconstituted collagen networks. Our model points to the important role of normal stresses in determining the nonlinear shear elastic response, which can explain the approximate exponential relationship between stress and strain reported for collagenous tissues. This further suggests principles for the design of synthetic fiber networks with collagen-like properties, as well as a mechanism for the control of the mechanics of such networks. PMID:26195769

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed Central

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

    2008-01-01

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

  20. Multivariate analysis of noise in genetic regulatory networks.

    PubMed

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

    2004-08-21

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

  1. GENETIC CONTROL OF CANDIDA ALBICANS BIOFILM DEVELOPMENT

    PubMed Central

    Finkel, Jonathan S.; Mitchell, Aaron P.

    2014-01-01

    Preface Candida species cause frequent infections due to their ability to form biofilms – surface-associated microbial communities – primarily on implanted medical devices. Increasingly, mechanistic studies have identified the gene products that participate directly in Candida albicans biofilm formation, as well as the regulatory circuitry and networks that control their expression and activity. These studies have revealed new mechanisms and signals that govern C. albicans biofilm formation and associated drug resistance, thus providing biological insight and therapeutic foresight. PMID:21189476

  2. Genetic Algorithm based Decentralized PI Type Controller: Load Frequency Control

    NASA Astrophysics Data System (ADS)

    Dwivedi, Atul; Ray, Goshaidas; Sharma, Arun Kumar

    2016-12-01

    This work presents a design of decentralized PI type Linear Quadratic (LQ) controller based on genetic algorithm (GA). The proposed design technique allows considerable flexibility in defining the control objectives and it does not consider any knowledge of the system matrices and moreover it avoids the solution of algebraic Riccati equation. To illustrate the results of this work, a load-frequency control problem is considered. Simulation results reveal that the proposed scheme based on GA is an alternative and attractive approach to solve load-frequency control problem from both performance and design point of views.

  3. Distributed intelligent control and status networking

    NASA Technical Reports Server (NTRS)

    Fortin, Andre; Patel, Manoj

    1993-01-01

    Over the past two years, the Network Control Systems Branch (Code 532) has been investigating control and status networking technologies. These emerging technologies use distributed processing over a network to accomplish a particular custom task. These networks consist of small intelligent 'nodes' that perform simple tasks. Containing simple, inexpensive hardware and software, these nodes can be easily developed and maintained. Once networked, the nodes can perform a complex operation without a central host. This type of system provides an alternative to more complex control and status systems which require a central computer. This paper will provide some background and discuss some applications of this technology. It will also demonstrate the suitability of one particular technology for the Space Network (SN) and discuss the prototyping activities of Code 532 utilizing this technology.

  4. Controlling statistical moments of stochastic dynamical networks

    NASA Astrophysics Data System (ADS)

    Bielievtsov, Dmytro; Ladenbauer, Josef; Obermayer, Klaus

    2016-07-01

    We consider a general class of stochastic networks and ask which network nodes need to be controlled, and how, to stabilize and switch between desired metastable (target) states in terms of the first and second statistical moments of the system. We first show that it is sufficient to directly interfere with a subset of nodes which can be identified using information about the graph of the network only. Then we develop a suitable method for feedback control which acts on that subset of nodes and preserves the covariance structure of the desired target state. Finally, we demonstrate our theoretical results using a stochastic Hopfield network and a global brain model. Our results are applicable to a variety of (model) networks and further our understanding of the relationship between network structure and collective dynamics for the benefit of effective control.

  5. Controlling statistical moments of stochastic dynamical networks.

    PubMed

    Bielievtsov, Dmytro; Ladenbauer, Josef; Obermayer, Klaus

    2016-07-01

    We consider a general class of stochastic networks and ask which network nodes need to be controlled, and how, to stabilize and switch between desired metastable (target) states in terms of the first and second statistical moments of the system. We first show that it is sufficient to directly interfere with a subset of nodes which can be identified using information about the graph of the network only. Then we develop a suitable method for feedback control which acts on that subset of nodes and preserves the covariance structure of the desired target state. Finally, we demonstrate our theoretical results using a stochastic Hopfield network and a global brain model. Our results are applicable to a variety of (model) networks and further our understanding of the relationship between network structure and collective dynamics for the benefit of effective control. PMID:27575147

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

    NASA Technical Reports Server (NTRS)

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

    1997-01-01

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

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

    PubMed

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

    2003-09-16

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

  8. Genetic control of invasive plants species using selfish genetic elements

    PubMed Central

    Hodgins, Kathryn A; Rieseberg, Loren; Otto, Sarah P

    2009-01-01

    Invasive plants cause substantial environmental damage and economic loss. Here, we explore the possibility that a selfish genetic element found in plants called cytoplasmic male sterility (CMS) could be exploited for weed control. CMS is caused by mutations in the mitochondrial genome that sterilize male reproductive organs. We developed an analytical model and a spatial simulation to assess the use of CMS alleles to manage weed populations. Specifically, we examined how fertility, selfing, pollen limitation and dispersal influenced extinction rate and time until extinction in populations where CMS arises. We found that the introduction of a CMS allele can cause rapid population extinction, but only under a restricted set of conditions. Both models suggest that the CMS strategy will be appropriate for species where pollen limitation is negligible, inbreeding depression is high and the fertility advantage of females over hermaphrodites is substantial. In general, spatial structure did not have a strong influence on the simulation outcome, although low pollen dispersal and intermediate levels of seed dispersal tended to reduce population extinction rates. Given these results, the introduction of CMS alleles into a population of invasive plants probably represents an effective control method for only a select number of species. PMID:25567898

  9. Controlling false discoveries in genetic studies.

    PubMed

    van den Oord, Edwin J C G

    2008-07-01

    A false discovery occurs when a researcher concludes that a marker is involved in the etiology of the disease whereas in reality it is not. In genetic studies the risk of false discoveries is very high because only few among the many markers that can be tested will have an effect on the disease. In this article, we argue that it may be best to use methods for controlling false discoveries that would introduce the same ratio of false discoveries divided by all rejected tests into the literature regardless of systematic differences between studies. After a brief discussion of traditional "multiple testing" methods, we show that methods that control the false discovery rate (FDR) may be more suitable to achieve this goal. These FDR methods are therefore discussed in more detail. Instead of merely testing for main effects, it may be important to search for gene-environment/covariate interactions, gene-gene interactions or genetic variants affecting disease subtypes. In the second section, we point out the challenges involved in controlling false discoveries in such searches. The final section discusses the role of replication studies for eliminating false discoveries and the complexities associated with the definition of what constitutes a replication and the design of these studies.

  10. Controllability of Microbial Contamination in Hydrologic Networks

    NASA Astrophysics Data System (ADS)

    Riasi, M. S.; Yeghiazarian, L.

    2015-12-01

    Microbial contamination in surface water networks is highly dynamic and stochastic, and is characterized by high level of spatial and temporal variability. Controlling water contamination is therefore challenging.Ideally, to control contamination in a flow network, one needs to design a management approach whereby the level of contamination can be controlled everywhere at all times, by controlling it at certain locations in the network. This can be viewed as a control problem in which we aim to efficiently drive the system to a desired state by manipulating few input variables. Such problems reduce to i) finding the best control locations in the network that would impact the whole system; and ii) choosing the time-variant inputs at the control locations to achieve the desired state of the system. In this study, we aim to answer questions like "How controllable is microbial contamination in a watershed flow network?" and "Given the network topology, geometry and environmental drivers, what are the best control locations?".

  11. Advanced mobile networking, sensing, and controls.

    SciTech Connect

    Feddema, John Todd; Kilman, Dominique Marie; Byrne, Raymond Harry; Young, Joseph G.; Lewis, Christopher L.; Van Leeuwen, Brian P.; Robinett, Rush D. III; Harrington, John J.

    2005-03-01

    This report describes an integrated approach for designing communication, sensing, and control systems for mobile distributed systems. Graph theoretic methods are used to analyze the input/output reachability and structural controllability and observability of a decentralized system. Embedded in each network node, this analysis will automatically reconfigure an ad hoc communication network for the sensing and control task at hand. The graph analysis can also be used to create the optimal communication flow control based upon the spatial distribution of the network nodes. Edge coloring algorithms tell us that the minimum number of time slots in a planar network is equal to either the maximum number of adjacent nodes (or degree) of the undirected graph plus some small number. Therefore, the more spread out that the nodes are, the fewer number of time slots are needed for communication, and the smaller the latency between nodes. In a coupled system, this results in a more responsive sensor network and control system. Network protocols are developed to propagate this information, and distributed algorithms are developed to automatically adjust the number of time slots available for communication. These protocols and algorithms must be extremely efficient and only updated as network nodes move. In addition, queuing theory is used to analyze the delay characteristics of Carrier Sense Multiple Access (CSMA) networks. This report documents the analysis, simulation, and implementation of these algorithms performed under this Laboratory Directed Research and Development (LDRD) effort.

  12. Towards a predictive theory for genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Tkacik, Gasper

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

  13. Neural networks and orbit control in accelerators

    SciTech Connect

    Bozoki, E.; Friedman, A.

    1994-07-01

    An overview of the architecture, workings and training of Neural Networks is given. We stress the aspects which are important for the use of Neural Networks for orbit control in accelerators and storage rings, especially its ability to cope with the nonlinear behavior of the orbit response to `kicks` and the slow drift in the orbit response during long-term operation. Results obtained for the two NSLS storage rings with several network architectures and various training methods for each architecture are given.

  14. Secure quantum network coding for controlled repeater networks

    NASA Astrophysics Data System (ADS)

    Shang, Tao; Li, Jiao; Liu, Jian-wei

    2016-07-01

    To realize efficient quantum communication based on quantum repeater, we propose a secure quantum network coding scheme for controlled repeater networks, which adds a controller as a trusted party and is able to control the process of EPR-pair distribution. As the key operations of quantum repeater, local operations and quantum communication are designed to adopt quantum one-time pad to enhance the function of identity authentication instead of local operations and classical communication. Scheme analysis shows that the proposed scheme can defend against active attacks for quantum communication and realize long-distance quantum communication with minimal resource consumption.

  15. Modern control centers and computer networking

    SciTech Connect

    Dy-Liacco, T.E.

    1994-10-01

    The automation of power system operation is generally achieved with the implementation of two control centers, one for the operation of the generation-transmission system and the other for the operation of the distribution system. These control centers are referred to, respectively, as the energy management system (EMS) and the distribution management system (DMS). The EMS may consist of several control centers in a hierarchy. The DMS may be made up of several independent distribution control centers. This article features the fundamental design aspects of modern EMS and DMS control centers (computer networks, distributed processing, and distributed databases), the linking of computer networks, and the communications that support such internetworking. The extension of such networking beyond the confines of system operation to other corporate networks is now made practical by the maturing concepts of client-server architectures and by the availability of modern communication technologies.

  16. Congestion control and routing over satellite networks

    NASA Astrophysics Data System (ADS)

    Cao, Jinhua

    Satellite networks and transmissions find their application in fields of computer communications, telephone communications, television broadcasting, transportation, space situational awareness systems and so on. This thesis mainly focuses on two networking issues affecting satellite networking: network congestion control and network routing optimization. Congestion, which leads to long queueing delays, packet losses or both, is a networking problem that has drawn the attention of many researchers. The goal of congestion control mechanisms is to ensure high bandwidth utilization while avoiding network congestion by regulating the rate at which traffic sources inject packets into a network. In this thesis, we propose a stable congestion controller using data-driven, safe switching control theory to improve the dynamic performance of satellite Transmission Control Protocol/Active Queue Management (TCP/AQM) networks. First, the stable region of the Proportional-Integral (PI) parameters for a nominal model is explored. Then, a PI controller, whose parameters are adaptively tuned by switching among members of a given candidate set, using observed plant data, is presented and compared with some classical AQM policy examples, such as Random Early Detection (RED) and fixed PI control. A new cost detectable switching law with an interval cost function switching algorithm, which improves the performance and also saves the computational cost, is developed and compared with a law commonly used in the switching control literature. Finite-gain stability of the system is proved. A fuzzy logic PI controller is incorporated as a special candidate to achieve good performance at all nominal points with the available set of candidate controllers. Simulations are presented to validate the theory. An effocient routing algorithm plays a key role in optimizing network resources. In this thesis, we briefly analyze Low Earth Orbit (LEO) satellite networks, review the Cross Entropy (CE

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

    PubMed Central

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

    2010-01-01

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

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

    PubMed

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

    2007-01-01

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

  19. Molecular, metabolic, and genetic control: An introduction

    NASA Astrophysics Data System (ADS)

    Tyson, John J.; Mackey, Michael C.

    2001-03-01

    The living cell is a miniature, self-reproducing, biochemical machine. Like all machines, it has a power supply, a set of working components that carry out its necessary tasks, and control systems that ensure the proper coordination of these tasks. In this Special Issue, we focus on the molecular regulatory systems that control cell metabolism, gene expression, environmental responses, development, and reproduction. As for the control systems in human-engineered machines, these regulatory networks can be described by nonlinear dynamical equations, for example, ordinary differential equations, reaction-diffusion equations, stochastic differential equations, or cellular automata. The articles collected here illustrate (i) a range of theoretical problems presented by modern concepts of cellular regulation, (ii) some strategies for converting molecular mechanisms into dynamical systems, (iii) some useful mathematical tools for analyzing and simulating these systems, and (iv) the sort of results that derive from serious interplay between theory and experiment.

  20. Control Networks and Neuromodulators of Early Development

    ERIC Educational Resources Information Center

    Posner, Michael I.; Rothbart, Mary K.; Sheese, Brad E.; Voelker, Pascale

    2012-01-01

    In adults, most cognitive and emotional self-regulation is carried out by a network of brain regions, including the anterior cingulate, insula, and areas of the basal ganglia, related to executive attention. We propose that during infancy, control systems depend primarily upon a brain network involved in orienting to sensory events that includes…

  1. Self-Control in Sparsely Coded Networks

    NASA Astrophysics Data System (ADS)

    Dominguez, D. R. C.; Bollé, D.

    1998-03-01

    A complete self-control mechanism is proposed in the dynamics of neural networks through the introduction of a time-dependent threshold, determined in function of both the noise and the pattern activity in the network. Especially for sparsely coded models this mechanism is shown to considerably improve the storage capacity, the basins of attraction, and the mutual information content.

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

    PubMed Central

    Fogelmark, Karl; Peterson, Carsten; Troein, Carl

    2016-01-01

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

  3. Genetic control of size in Drosophila.

    PubMed Central

    Oldham, S; Böhni, R; Stocker, H; Brogiolo, W; Hafen, E

    2000-01-01

    During the past ten years, significant progress has been made in understanding the basic mechanisms of the development of multicellular organisms. Genetic analysis of the development of Caenorhabditis elegans and Drosophila has unearthed a fruitful number of genes involved in establishing the basic body plan, patterning of limbs, specification of cell fate and regulation of programmed cell death. The genes involved in these developmental processes have been conserved throughout evolution and homologous genes are involved in the patterning of insect and human limbs. Despite these important discoveries, we have learned astonishingly little about one of the most obvious distinctions between animals: their difference in body size. The mass of the smallest mammal, the bumble-bee bat, is 2 g while that of the largest mammal, the blue whale, is 150 t or 150 million grams. Remarkably, even though they are in the same class, body size can vary up to 75-million-fold. Furthermore, this body growth can be finite in the case of most vertebrates or it can occur continuously throughout life, as for trees, molluscs and large crustaceans. Currently, we know comparatively little about the genetic control of body size. In this article we will review recent evidence from vertebrates and particularly from Drosophila that implicates insulin/insulin-like growth factor-I and other growth pathways in the control of cell, organ and body size. PMID:11128988

  4. Modular genetic control of sexually dimorphic behaviors

    PubMed Central

    Xu, Xiaohong; Coats, Jennifer K.; Yang, Cindy F.; Wang, Amy; Ahmed, Osama M.; Alvarado, Maricruz; Izumi, Tetsuro; Shah, Nirao M.

    2012-01-01

    SUMMARY Sex hormones such as estrogen and testosterone are essential for sexually dimorphic behaviors in vertebrates. However, the hormone-activated molecular mechanisms that control the development and function of the underlying neural circuits remain poorly defined. We have identified numerous sexually dimorphic gene expression patterns in the adult mouse hypothalamus and amygdala. We find that adult sex hormones regulate these expression patterns in a sex-specific, regionally-restricted manner, suggesting that these genes regulate sex typical behaviors. Indeed, we find that mice with targeted disruptions of each of four of these genes (Brs3, Cckar, Irs4, Sytl4) exhibit extremely specific deficits in sex specific behaviors, with single genes controlling the pattern or extent of male sexual behavior, male aggression, maternal behavior, or female sexual behavior. Taken together, our findings demonstrate that various components of sexually dimorphic behaviors are governed by separable genetic programs. PMID:22304924

  5. Structurally robust control of complex networks

    NASA Astrophysics Data System (ADS)

    Nacher, Jose C.; Akutsu, Tatsuya

    2015-01-01

    Robust control theory has been successfully applied to numerous real-world problems using a small set of devices called controllers. However, the real systems represented by networks contain unreliable components and modern robust control engineering has not addressed the problem of structural changes on complex networks including scale-free topologies. Here, we introduce the concept of structurally robust control of complex networks and provide a concrete example using an algorithmic framework that is widely applied in engineering. The developed analytical tools, computer simulations, and real network analyses lead herein to the discovery that robust control can be achieved in scale-free networks with exactly the same order of controllers required in a standard nonrobust configuration by adjusting only the minimum degree. The presented methodology also addresses the probabilistic failure of links in real systems, such as neural synaptic unreliability in Caenorhabditis elegans, and suggests a new direction to pursue in studies of complex networks in which control theory has a role.

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

    PubMed Central

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

    2015-01-01

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

  7. Wireless Sensor Networks: Monitoring and Control

    SciTech Connect

    Hastbacka, Mildred; Ponoum, Ratcharit; Bouza, Antonio

    2013-05-31

    The article discusses wireless sensor technologies for building energy monitoring and control. This article, also, addresses wireless sensor networks as well as benefits and challenges of using wireless sensors. The energy savings and market potential of wireless sensors are reviewed.

  8. Global and robust stability analysis of genetic regulatory networks with time-varying delays and parameter uncertainties.

    PubMed

    Fang-Xiang Wu

    2011-08-01

    The study of stability is essential for designing or controlling genetic regulatory networks. This paper addresses global and robust stability of genetic regulatory networks with time delays and parameter uncertainties. Most existing results on this issue are based on the linear matrix inequalities (LMIs) approach, which results in checking the existence of a feasible solution to high dimensional LMIs. Based on M-matrix theory, we will present several novel global stability conditions for genetic regulatory networks with time-varying and time-invariant delays. All of these stability conditions are given in terms of M-matrices, for which there are many and very easy ways to be verified. Then, we extend these results to genetic regulatory networks with time delays and parameter uncertainties. To illustrate the effectiveness of our theoretical results, several genetic regulatory networks are analyzed. Compared with existing results in the literature, we also show that our results are less conservative than existing ones with these illustrative genetic regulatory networks.

  9. Portable control device for networked mobile robots

    DOEpatents

    Feddema, John T.; Byrne, Raymond H.; Bryan, Jon R.; Harrington, John J.; Gladwell, T. Scott

    2002-01-01

    A handheld control device provides a way for controlling one or multiple mobile robotic vehicles by incorporating a handheld computer with a radio board. The device and software use a personal data organizer as the handheld computer with an additional microprocessor and communication device on a radio board for use in controlling one robot or multiple networked robots.

  10. Fast predictive control of networked energy systems

    NASA Astrophysics Data System (ADS)

    Chuang, Frank Fu-Han

    In this thesis we study the optimal control of networked energy systems. Networked energy systems consist of a collection of energy storage nodes and a network of links and inputs which allow energy to be exchanged, injected, or removed from the nodes. The nodes may exchange energy between each other autonomously or via controlled flows between the nodes. Examples of networked systems include building heating, ventilation, and air conditioning (HVAC) systems and networked battery systems. In the building system example, the nodes of the system are rooms which store thermal energy in the air and other elements which have thermal capacity. The rooms transfer energy autonomously through thermal conduction, convection, and radiation. Thermal energy can be injected into or removed from the rooms via conditioned air or slabs. In the case of a networked battery system, the batteries store electrical energy in their chemical cells. The batteries may be electrically linked so that a controller can move electrical charge from one battery to another. Networked energy systems are typically large-scale (contain many states and inputs), affected by uncertain forecasts and disturbances, and require fast computation on cheap embedded platforms. In this thesis, the optimal control technique we study is model predictive control for networked energy systems. Model predictive or receding horizon control is a time-domain optimization-based control technique which uses predictive models of a system to forecast its behavior and minimize a performance cost subject to system constraints. In this thesis we address two primary issues concerning model predictive control for networked energy systems: robustness to uncertainty in forecasts and reducing the complexity of the large-scale optimization problem for use in embedded platforms. The first half of the thesis deals primarily with the efficient computation of robust controllers for dealing with random and adversarial uncertainties in the

  11. Neural-Network Controller For Vibration Suppression

    NASA Technical Reports Server (NTRS)

    Boussalis, Dhemetrios; Wang, Shyh Jong

    1995-01-01

    Neural-network-based adaptive-control system proposed for vibration suppression of flexible space structures. Controller features three-layer neural network and utilizes output feedback. Measurements generated by various sensors on structure. Feed forward path also included to speed up response in case plant exhibits predominantly linear dynamic behavior. System applicable to single-input single-output systems. Work extended to multiple-input multiple-output systems as well.

  12. Optimal Feedback Control of Thermal Networks

    NASA Technical Reports Server (NTRS)

    Papalexandris, Miltiadis

    2003-01-01

    An improved approach to the mathematical modeling of feedback control of thermal networks has been devised. Heretofore software for feedback control of thermal networks has been developed by time-consuming trial-and-error methods that depend on engineers expertise. In contrast, the present approach is a systematic means of developing algorithms for feedback control that is optimal in the sense that it combines performance with low cost of implementation. An additional advantage of the present approach is that a thermal engineer need not be expert in control theory. Thermal networks are lumped-parameter approximations used to represent complex thermal systems. Thermal networks are closely related to electrical networks commonly represented by lumped-parameter circuit diagrams. Like such electrical circuits, thermal networks are mathematically modeled by systems of differential-algebraic equations (DAEs) that is, ordinary differential equations subject to a set of algebraic constraints. In the present approach, emphasis is placed on applications in which thermal networks are subject to constant disturbances and, therefore, integral control action is necessary to obtain steady-state responses. The mathematical development of the present approach begins with the derivation of optimal integral-control laws via minimization of an appropriate cost functional that involves augmented state vectors. Subsequently, classical variational arguments provide optimality conditions in the form of the Hamiltonian equations for the standard linear-quadratic-regulator (LQR) problem. These equations are reduced to an algebraic Riccati equation (ARE) with respect to the augmented state vector. The solution of the ARE leads to the direct computation of the optimal proportional- and integral-feedback control gains. In cases of very complex networks, large numbers of state variables make it difficult to implement optimal controllers in the manner described in the preceding paragraph.

  13. Structural controllability and controlling centrality of temporal networks.

    PubMed

    Pan, Yujian; Li, Xiang

    2014-01-01

    Temporal networks are such networks where nodes and interactions may appear and disappear at various time scales. With the evidence of ubiquity of temporal networks in our economy, nature and society, it's urgent and significant to focus on its structural controllability as well as the corresponding characteristics, which nowadays is still an untouched topic. We develop graphic tools to study the structural controllability as well as its characteristics, identifying the intrinsic mechanism of the ability of individuals in controlling a dynamic and large-scale temporal network. Classifying temporal trees of a temporal network into different types, we give (both upper and lower) analytical bounds of the controlling centrality, which are verified by numerical simulations of both artificial and empirical temporal networks. We find that the positive relationship between aggregated degree and controlling centrality as well as the scale-free distribution of node's controlling centrality are virtually independent of the time scale and types of datasets, meaning the inherent robustness and heterogeneity of the controlling centrality of nodes within temporal networks. PMID:24747676

  14. A candidate multimodal functional genetic network for thermal adaptation

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2014-01-01

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

  16. The control networks of Mimas and Enceladus

    NASA Astrophysics Data System (ADS)

    Davies, M. E.; Katayama, F. Y.

    1983-02-01

    A bundle-type analytical triangulation program is employed to compute control networks for Mimas, whose network encircles the satellite with 110 points measured on 32 Voyager 1 pictures, and Enceladus, whose network does not completely encircle the satellite and contains 71 points measured on 22 Voyager 2 pictures. Many of the control points are identified on illustrations and by name, and their coordinates are presented in tabular form. The analytical triangulation program was used to solve for the mean radii and three principal axes of best-fit ellipsoids. The mean radius of Mimas is 197 + or - 3 km, while that of Enceladus is 251 + or - 5 km.

  17. Controlling extreme events on complex networks

    NASA Astrophysics Data System (ADS)

    Chen, Yu-Zhong; Huang, Zi-Gang; Lai, Ying-Cheng

    2014-08-01

    Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network ``mobile'' can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed.

  18. The control networks of Mimas and Enceladus

    NASA Technical Reports Server (NTRS)

    Davies, M. E.; Katayama, F. Y.

    1983-01-01

    A bundle-type analytical triangulation program is employed to compute control networks for Mimas, whose network encircles the satellite with 110 points measured on 32 Voyager 1 pictures, and Enceladus, whose network does not completely encircle the satellite and contains 71 points measured on 22 Voyager 2 pictures. Many of the control points are identified on illustrations and by name, and their coordinates are presented in tabular form. The analytical triangulation program was used to solve for the mean radii and three principal axes of best-fit ellipsoids. The mean radius of Mimas is 197 + or - 3 km, while that of Enceladus is 251 + or - 5 km.

  19. Non-coding RNAs and complex distributed genetic networks

    NASA Astrophysics Data System (ADS)

    Zhdanov, Vladimir

    2011-08-01

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

  20. Networks of genetic loci and the scientific literature

    NASA Astrophysics Data System (ADS)

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

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

  1. The network of global corporate control.

    PubMed

    Vitali, Stefania; Glattfelder, James B; Battiston, Stefano

    2011-01-01

    The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic "super-entity" that raises new important issues both for researchers and policy makers.

  2. Inferring Network Connectivity by Delayed Feedback Control

    PubMed Central

    Yu, Dongchuan; Parlitz, Ulrich

    2011-01-01

    We suggest a control based approach to topology estimation of networks with elements. This method first drives the network to steady states by a delayed feedback control; then performs structural perturbations for shifting the steady states times; and finally infers the connection topology from the steady states' shifts by matrix inverse algorithm () or -norm convex optimization strategy applicable to estimate the topology of sparse networks from perturbations. We discuss as well some aspects important for applications, such as the topology reconstruction quality and error sources, advantages and disadvantages of the suggested method, and the influence of (control) perturbations, inhomegenity, sparsity, coupling functions, and measurement noise. Some examples of networks with Chua's oscillators are presented to illustrate the reliability of the suggested technique. PMID:21969856

  3. Control of Stochastic and Induced Switching in Biophysical Networks

    NASA Astrophysics Data System (ADS)

    Wells, Daniel K.; Kath, William L.; Motter, Adilson E.

    2015-07-01

    Noise caused by fluctuations at the molecular level is a fundamental part of intracellular processes. While the response of biological systems to noise has been studied extensively, there has been limited understanding of how to exploit it to induce a desired cell state. Here we present a scalable, quantitative method based on the Freidlin-Wentzell action to predict and control noise-induced switching between different states in genetic networks that, conveniently, can also control transitions between stable states in the absence of noise. We apply this methodology to models of cell differentiation and show how predicted manipulations of tunable factors can induce lineage changes, and further utilize it to identify new candidate strategies for cancer therapy in a cell death pathway model. This framework offers a systems approach to identifying the key factors for rationally manipulating biophysical dynamics, and should also find use in controlling other classes of noisy complex networks.

  4. An optimal control approach to probabilistic Boolean networks

    NASA Astrophysics Data System (ADS)

    Liu, Qiuli

    2012-12-01

    External control of some genes in a genetic regulatory network is useful for avoiding undesirable states associated with some diseases. For this purpose, a number of stochastic optimal control approaches have been proposed. Probabilistic Boolean networks (PBNs) as powerful tools for modeling gene regulatory systems have attracted considerable attention in systems biology. In this paper, we deal with a problem of optimal intervention in a PBN with the help of the theory of discrete time Markov decision process. Specifically, we first formulate a control model for a PBN as a first passage model for discrete time Markov decision processes and then find, using a value iteration algorithm, optimal effective treatments with the minimal expected first passage time over the space of all possible treatments. In order to demonstrate the feasibility of our approach, an example is also displayed.

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

    PubMed

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

    2007-02-13

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

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

    PubMed

    Reaves, Allison Cook; Bianchi, Diana W

    2013-05-01

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

  7. Genetic control of Drosophila nerve cord development

    NASA Technical Reports Server (NTRS)

    Skeath, James B.; Thor, Stefan

    2003-01-01

    The Drosophila ventral nerve cord has been a central model system for studying the molecular genetic mechanisms that control CNS development. Studies show that the generation of neural diversity is a multistep process initiated by the patterning and segmentation of the neuroectoderm. These events act together with the process of lateral inhibition to generate precursor cells (neuroblasts) with specific identities, distinguished by the expression of unique combinations of regulatory genes. The expression of these genes in a given neuroblast restricts the fate of its progeny, by activating specific combinations of downstream genes. These genes in turn specify the identity of any given postmitotic cell, which is evident by its cellular morphology and choice of neurotransmitter.

  8. Genetic control of mosquitoes: population suppression strategies.

    PubMed

    Wilke, André Barretto Bruno; Marrelli, Mauro Toledo

    2012-01-01

    Over the last two decades, morbidity and mortality from malaria and dengue fever among other pathogens are an increasing Public Health problem. The increase in the geographic distribution of vectors is accompanied by the emergence of viruses and diseases in new areas. There are insufficient specific therapeutic drugs available and there are no reliable vaccines for malaria or dengue, although some progress has been achieved, there is still a long way between its development and actual field use. Most mosquito control measures have failed to achieve their goals, mostly because of the mosquito's great reproductive capacity and genomic flexibility. Chemical control is increasingly restricted due to potential human toxicity, mortality in no target organisms, insecticide resistance, and other environmental impacts. Other strategies for mosquito control are desperately needed. The Sterile Insect Technique (SIT) is a species-specific and environmentally benign method for insect population suppression, it is based on mass rearing, radiation mediated sterilization, and release of a large number of male insects. Releasing of Insects carrying a dominant lethal gene (RIDL) offers a solution to many of the drawbacks of traditional SIT that have limited its application in mosquitoes while maintaining its environmentally friendly and species-specific utility. The self-limiting nature of sterile mosquitoes tends to make the issues related to field use of these somewhat less challenging than for self-spreading systems characteristic of population replacement strategies. They also are closer to field use, so might be appropriate to consider first. The prospect of genetic control methods against mosquito vectored human diseases is rapidly becoming a reality, many decisions will need to be made on a national, regional and international level regarding the biosafety, social, cultural and ethical aspects of the use and deployment of these vector control methods.

  9. A genetic regulatory network for Xenopus mesendoderm formation.

    PubMed

    Loose, Matthew; Patient, Roger

    2004-07-15

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

  10. Controlling neural network responsiveness: tradeoffs and constraints.

    PubMed

    Keren, Hanna; Marom, Shimon

    2014-01-01

    In recent years much effort is invested in means to control neural population responses at the whole brain level, within the context of developing advanced medical applications. The tradeoffs and constraints involved, however, remain elusive due to obvious complications entailed by studying whole brain dynamics. Here, we present effective control of response features (probability and latency) of cortical networks in vitro over many hours, and offer this approach as an experimental toy for studying controllability of neural networks in the wider context. Exercising this approach we show that enforcement of stable high activity rates by means of closed loop control may enhance alteration of underlying global input-output relations and activity dependent dispersion of neuronal pair-wise correlations across the network. PMID:24808860

  11. Control of fluxes in metabolic networks.

    PubMed

    Basler, Georg; Nikoloski, Zoran; Larhlimi, Abdelhalim; Barabási, Albert-László; Liu, Yang-Yu

    2016-07-01

    Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism. PMID:27197218

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

    NASA Astrophysics Data System (ADS)

    van Oudenaarden, Alexander

    2003-03-01

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

  13. Epidemic Extinction and Control in Heterogeneous Networks

    NASA Astrophysics Data System (ADS)

    Hindes, Jason; Schwartz, Ira B.

    2016-07-01

    We consider epidemic extinction in finite networks with a broad variation in local connectivity. Generalizing the theory of large fluctuations to random networks with a given degree distribution, we are able to predict the most probable, or optimal, paths to extinction in various configurations, including truncated power laws. We find that paths for heterogeneous networks follow a limiting form in which infection first decreases in low-degree nodes, which triggers a rapid extinction in high-degree nodes, and finishes with a residual low-degree extinction. The usefulness of our approach is further demonstrated through optimal control strategies that leverage the dependence of finite-size fluctuations on network topology. Interestingly, we find that the optimal control is a mix of treating both high- and low-degree nodes based on theoretical predictions, in contrast to methods that ignore dynamical fluctuations.

  14. Network control processor for a TDMA system

    NASA Astrophysics Data System (ADS)

    Suryadevara, Omkarmurthy; Debettencourt, Thomas J.; Shulman, R. B.

    Two unique aspects of designing a network control processor (NCP) to monitor and control a demand-assigned, time-division multiple-access (TDMA) network are described. The first involves the implementation of redundancy by synchronizing the databases of two geographically remote NCPs. The two sets of databases are kept in synchronization by collecting data on both systems, transferring databases, sending incremental updates, and the parallel updating of databases. A periodic audit compares the checksums of the databases to ensure synchronization. The second aspect involves the use of a tracking algorithm to dynamically reallocate TDMA frame space. This algorithm detects and tracks current and long-term load changes in the network. When some portions of the network are overloaded while others have excess capacity, the algorithm automatically calculates and implements a new burst time plan.

  15. Epidemic Extinction and Control in Heterogeneous Networks.

    PubMed

    Hindes, Jason; Schwartz, Ira B

    2016-07-01

    We consider epidemic extinction in finite networks with a broad variation in local connectivity. Generalizing the theory of large fluctuations to random networks with a given degree distribution, we are able to predict the most probable, or optimal, paths to extinction in various configurations, including truncated power laws. We find that paths for heterogeneous networks follow a limiting form in which infection first decreases in low-degree nodes, which triggers a rapid extinction in high-degree nodes, and finishes with a residual low-degree extinction. The usefulness of our approach is further demonstrated through optimal control strategies that leverage the dependence of finite-size fluctuations on network topology. Interestingly, we find that the optimal control is a mix of treating both high- and low-degree nodes based on theoretical predictions, in contrast to methods that ignore dynamical fluctuations. PMID:27447531

  16. Population genetics of Chrysoperla externa (Neuroptera: Chrysopidae) and implications for biological control.

    PubMed

    Lavagnini, T C; Morales, A C; Freitas, S

    2015-11-01

    Green lacewings are insects with great potential to be use in the biological control of agricultural pests, but relatively few studies have attempted to understand the genetic structure of these agents, especially those of predatory insects. The purpose of this study was to characterize genetically populations of C. externa using sequences of subunit I of the cytochrome oxidase, a mitochondrial gene, and examine the population structure of this species in sampled areas in São Paulo state. The results indicate high genetic diversity but no genetic structure, detected by AMOVA analysis, and high levels of haplotype sharing in the network. These genetic patterns could be a consequence of environmental homogeneity provided by agroecosystem (citrus orchard), allowing gene flow among populations. Probably there is a unique population in the area sampled that could be used as a population (genetic) source for mass-reared and posterior release in these farms.

  17. Flexible brain network reconfiguration supporting inhibitory control

    PubMed Central

    Spielberg, Jeffrey M.; Miller, Gregory A.; Heller, Wendy; Banich, Marie T.

    2015-01-01

    The ability to inhibit distracting stimuli from interfering with goal-directed behavior is crucial for success in most spheres of life. Despite an abundance of studies examining regional brain activation, knowledge of the brain networks involved in inhibitory control remains quite limited. To address this critical gap, we applied graph theory tools to functional magnetic resonance imaging data collected while a large sample of adults (n = 101) performed a color-word Stroop task. Higher demand for inhibitory control was associated with restructuring of the global network into a configuration that was more optimized for specialized processing (functional segregation), more efficient at communicating the output of such processing across the network (functional integration), and more resilient to potential interruption (resilience). In addition, there were regional changes with right inferior frontal sulcus and right anterior insula occupying more central positions as network hubs, and dorsal anterior cingulate cortex becoming more tightly coupled with its regional subnetwork. Given the crucial role of inhibitory control in goal-directed behavior, present findings identifying functional network organization supporting inhibitory control have the potential to provide additional insights into how inhibitory control may break down in a wide variety of individuals with neurological or psychiatric difficulties. PMID:26216985

  18. Color control of printers by neural networks

    NASA Astrophysics Data System (ADS)

    Tominaga, Shoji

    1998-07-01

    A method is proposed for solving the mapping problem from the 3D color space to the 4D CMYK space of printer ink signals by means of a neural network. The CIE-L*a*b* color system is used as the device-independent color space. The color reproduction problem is considered as the problem of controlling an unknown static system with four inputs and three outputs. A controller determines the CMYK signals necessary to produce the desired L*a*b* values with a given printer. Our solution method for this control problem is based on a two-phase procedure which eliminates the need for UCR and GCR. The first phase determines a neural network as a model of the given printer, and the second phase determines the combined neural network system by combining the printer model and the controller in such a way that it represents an identity mapping in the L*a*b* color space. Then the network of the controller part realizes the mapping from the L*a*b* space to the CMYK space. Practical algorithms are presented in the form of multilayer feedforward networks. The feasibility of the proposed method is shown in experiments using a dye sublimation printer and an ink jet printer.

  19. The Unified Lunar Control Network 2005

    USGS Publications Warehouse

    Archinal, Brent A.; Rosiek, Mark R.; Kirk, Randolph L.; Redding, Bonnie L.

    2006-01-01

    This report documents a new general unified lunar control network and lunar topographic model based on a combination of Clementine images and a previous network derived from Earth-based & Apollo photographs, and Mariner 10, & Galileo images. This photogrammetric network solution is the largest planetary control network ever completed. It includes the determination of the 3-D positions of 272,931 points on the lunar surface and the correction of the camera angles for 43,866 Clementine images, using 546,126 tie point measurements. The solution RMS is 20 ?m (= 0.9 pixels) in the image plane, with the largest residual of 6.4 pixels. The explanation given here, along with the accompanying files, comprises the release of the network information and of global lunar digital elevation models (DEMs) derived from the network. A paper that will describe the solution and network in further detail will be submitted to a refereed journal, and will include additional background information, solution details, discussion of accuracy and precision, and explanatory figures.

  20. Controlling Complex Networks with Compensatory Perturbations

    NASA Astrophysics Data System (ADS)

    Cornelius, Sean; Kath, William; Motter, Adilson

    2012-02-01

    The response of complex networks to perturbations is of critical importance in areas as diverse as ecosystem management, power system design, and cell reprogramming. These systems have the property that localized perturbations can propagate through the network, causing the system as a whole to change behavior and possibly collapse. We will show how this same mechanism can actually be exploited to prevent such failures and, more generally, control a network's behavior. This strategy is based on counteracting a deleterious perturbation through the judicious application of additional, compensatory perturbations---a prospect recently demonstrated heuristically in metabolic and food-web networks. Here, we introduce a method to identify such compensatory perturbations in general complex networks, under arbitrary constraints that restrict the interventions one can actually implement in real systems. Our method accounts for the full nonlinear time evolution of real complex networks, and in fact capitalizes on this behavior to bring the system to a desired target state even when this state is not directly accessible. Altogether, these results provide a new framework for the rescue, control, and reprogramming of complex networks in various domains.

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

    PubMed

    Lindee, Susan

    2014-09-01

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

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

    PubMed

    Lindee, Susan

    2014-09-01

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

  3. The APS control system network upgrade.

    SciTech Connect

    Sidorowicz, K. v.; Leibfritz, D.; McDowell, W. P.

    1999-10-22

    When it was installed,the Advanced Photon Source (APS) control system network was at the state-of-the-art. Different aspects of the system have been reported at previous meetings [1,2]. As loads on the controls network have increased due to newer and faster workstations and front-end computers, we have found performance of the system declining and have implemented an upgraded network. There have been dramatic advances in networking hardware in the last several years. The upgraded APS controls network replaces the original FDDI backbone and shared Ethernet hubs with redundant gigabit uplinks and fully switched 10/100 Ethernet switches with backplane fabrics in excess of 20 Gbits/s (Gbps). The central collapsed backbone FDDI concentrator has been replaced with a Gigabit Ethernet switch with greater than 30 Gbps backplane fabric. Full redundancy of the system has been maintained. This paper will discuss this upgrade and include performance data and performance comparisons with the original network.

  4. Toward controlling perturbations in robotic sensor networks

    NASA Astrophysics Data System (ADS)

    Banerjee, Ashis G.; Majumder, Saikat R.

    2014-06-01

    Robotic sensor networks (RSNs), which consist of networks of sensors placed on mobile robots, are being increasingly used for environment monitoring applications. In particular, a lot of work has been done on simultaneous localization and mapping of the robots, and optimal sensor placement for environment state estimation1. The deployment of RSNs, however, remains challenging in harsh environments where the RSNs have to deal with significant perturbations in the forms of wind gusts, turbulent water flows, sand storms, or blizzards that disrupt inter-robot communication and individual robot stability. Hence, there is a need to be able to control such perturbations and bring the networks to desirable states with stable nodes (robots) and minimal operational performance (environment sensing). Recent work has demonstrated the feasibility of controlling the non-linear dynamics in other communication networks like emergency management systems and power grids by introducing compensatory perturbations to restore network stability and operation2. In this paper, we develop a computational framework to investigate the usefulness of this approach for RSNs in marine environments. Preliminary analysis shows promising performance and identifies bounds on the original perturbations within which it is possible to control the networks.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-02-23

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

  7. Cortical Control of Affective Networks

    PubMed Central

    Kumar, Sunil; Black, Sherilynn J.; Hultman, Rainbo; Szabo, Steven T.; DeMaio, Kristine D.; Du, Jeanette; Katz, Brittany M.; Feng, Guoping; Covington, Herbert E.; Dzirasa, Kafui

    2013-01-01

    Transcranial magnetic stimulation and deep brain stimulation have emerged as therapeutic modalities for treatment refractory depression; however, little remains known regarding the circuitry that mediates the therapeutic effect of these approaches. Here we show that direct optogenetic stimulation of prefrontal cortex (PFC) descending projection neurons in mice engineered to express Chr2 in layer V pyramidal neurons (Thy1–Chr2 mice) models an antidepressant-like effect in mice subjected to a forced-swim test. Furthermore, we show that this PFC stimulation induces a long-lasting suppression of anxiety-like behavior (but not conditioned social avoidance) in socially stressed Thy1–Chr2 mice: an effect that is observed >10 d after the last stimulation. Finally, we use optogenetic stimulation and multicircuit recording techniques concurrently in Thy1–Chr2 mice to demonstrate that activation of cortical projection neurons entrains neural oscillatory activity and drives synchrony across limbic brain areas that regulate affect. Importantly, these neural oscillatory changes directly correlate with the temporally precise activation and suppression of limbic unit activity. Together, our findings show that the direct activation of cortical projection systems is sufficient to modulate activity across networks underlying affective regulation. They also suggest that optogenetic stimulation of cortical projection systems may serve as a viable therapeutic strategy for treating affective disorders. PMID:23325249

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

    NASA Astrophysics Data System (ADS)

    Patra, Tarak; Meenakshisundaram, Venkatesh; Simmons, David

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

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

    SciTech Connect

    Bornholdt, S.; Graudenz, D.

    1993-07-01

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

  10. Engineering modular and tunable genetic amplifiers for scaling transcriptional signals in cascaded gene networks.

    PubMed

    Wang, Baojun; Barahona, Mauricio; Buck, Martin

    2014-08-01

    Synthetic biology aims to control and reprogram signal processing pathways within living cells so as to realize repurposed, beneficial applications. Here we report the design and construction of a set of modular and gain-tunable genetic amplifiers in Escherichia coli capable of amplifying a transcriptional signal with wide tunable-gain control in cascaded gene networks. The devices are engineered using orthogonal genetic components (hrpRS, hrpV and PhrpL) from the hrp (hypersensitive response and pathogenicity) gene regulatory network in Pseudomonas syringae. The amplifiers can linearly scale up to 21-fold the transcriptional input with a large output dynamic range, yet not introducing significant time delay or significant noise during signal amplification. The set of genetic amplifiers achieves different gains and input dynamic ranges by varying the expression levels of the underlying ligand-free activator proteins in the device. As their electronic counterparts, these engineered transcriptional amplifiers can act as fundamental building blocks in the design of biological systems by predictably and dynamically modulating transcriptional signal flows to implement advanced intra- and extra-cellular control functions.

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

    PubMed

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

    2012-07-01

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

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

    PubMed

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

    2010-11-13

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

  13. A GA-based PID active queue management control design for TCP/IP networks

    NASA Astrophysics Data System (ADS)

    Kuo, H.-H.; Chen, C.-K.; Yan, J.-J.; Liao, T.-L.

    2008-02-01

    In this paper, a genetic algorithm-based (GA-based) proportional-integral-derivative (PID) controller as an active queue manager for Internet routers is proposed to reduce packet loss and improve network utilization in TCP/IP networks. Based on the window-based nonlinear dynamics, the TCP network was modeled as a time-delayed system with a saturated input due to the limitations of packet-dropping probability and the effects of propagation delays in TCP networks. An improved genetic algorithm is employed to derive optimal or near optimal PID control gains such that a performance index of integrated-absolute error (IAE) in terms of the error between the router queue length and the desired queue length is minimized. The performance of the proposed control scheme was evaluated in various network scenarios via a series of numerical simulations. The simulation results confirm that the proposed scheme outperforms other AQM schemes.

  14. [The network of official medicines control laboratories].

    PubMed

    Buchheit, K-H; Wanko, R

    2014-10-01

    Licensing, control and surveillance by competent authorities is the basis for ensuring efficacy, safety and quality of medicines in Europe. The control of the quality of medicines by national control laboratories, known as Official Medicines Control Laboratories (OMCLs) is an essential step in this process; it encompasses controls before and after granting a marketing authorisation. For certain groups of biomedical medicines (vaccines for human and veterinary use, medicines derived from human plasma) even each batch is controlled before it can be placed on the market. As single OMCLs would not be able to cope with their task, given the large number and diversity of medicines, in 1994 the OMCL network was founded upon initiative of the European Directorate for the Quality of Medicines & HealthCare, in close collaboration with the Commission of the European Union. Currently 68 OMCLs from 39 countries are part of the network. Prerequisite for the smooth operation of the OMCL network is the harmonisation of the quality management system of the individual OMCLs, based on the ISO 17025 standard, internal guidelines and the European Pharmacopoeia. Compliance with these standards is checked through regular audits, thus creating the basis for mutual recognition of test results. The collaboration in the OMCL network for the surveillance of the medicines market, the official control authority batch release and the fight against counterfeiting and illegal medicines enables OMCLs to keep pace with the developments in the field of medicines and to control the broad spectrum of medicines. In the 20 years since its start, the OMCL network has become a European success story. PMID:25192832

  15. Cuba's Salgen: a provincial informatics network for genetic services to pregnant women and newborns.

    PubMed

    Rodríguez-Vázquez, Miguel; Pérez, Rubén; Valero, Damicel; Santiago, Darío G

    2014-01-01

    The Sancti Spíritus Provincial Medical Genetics Network has been using the Salgen IT platform since 2009 for health care, administrative and research activities concerning pregnant mothers and newborns. The network uses the national Infomed backbone to provide real-time connection between community-based polyclinics in primary health care and the Provincial Medical Genetics Reference Center. The platform has records for 23,025 pregnant women and sequential clinical data on genetic risk assessment in early pregnancy, first trimester ultrasound, sickle cell anemia screening, alpha-fetoprotein levels, cytogenetic antenatal diagnosis, second trimester ultrasound, delivery and newborn characteristics, neonatal metabolic screening, and infant clinical assessment. The system makes health care results immediately available and provides health alerts to enable timely preventive care for pregnant women. It also provides guidelines for processes and practices, and streamlines administrative and monitoring activities through statistical reports. The database generates indicators for assessing fetal growth and applies international standards for antenatal ultrasound quality control. Salgen provides a new source of information for medical research and knowledge management, and its use in this case fulfills Cuba's criteria for an integrated health services network. PMID:25208122

  16. Molecular and genetic control of plant thermomorphogenesis.

    PubMed

    Quint, Marcel; Delker, Carolin; Franklin, Keara A; Wigge, Philip A; Halliday, Karen J; van Zanten, Martijn

    2016-01-01

    Temperature is a major factor governing the distribution and seasonal behaviour of plants. Being sessile, plants are highly responsive to small differences in temperature and adjust their growth and development accordingly. The suite of morphological and architectural changes induced by high ambient temperatures, below the heat-stress range, is collectively called thermomorphogenesis. Understanding the molecular genetic circuitries underlying thermomorphogenesis is particularly relevant in the context of climate change, as this knowledge will be key to rational breeding for thermo-tolerant crop varieties. Until recently, the fundamental mechanisms of temperature perception and signalling remained unknown. Our understanding of temperature signalling is now progressing, mainly by exploiting the model plant Arabidopsis thaliana. The transcription factor PHYTOCHROME INTERACTING FACTOR 4 (PIF4) has emerged as a critical player in regulating phytohormone levels and their activity. To control thermomorphogenesis, multiple regulatory circuits are in place to modulate PIF4 levels, activity and downstream mechanisms. Thermomorphogenesis is integrally governed by various light signalling pathways, the circadian clock, epigenetic mechanisms and chromatin-level regulation. In this Review, we summarize recent progress in the field and discuss how the emerging knowledge in Arabidopsis may be transferred to relevant crop systems. PMID:27250752

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

    NASA Astrophysics Data System (ADS)

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

    2001-01-01

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

  18. Feedback Controller Design for the Synchronization of Boolean Control Networks.

    PubMed

    Liu, Yang; Sun, Liangjie; Lu, Jianquan; Liang, Jinling

    2016-09-01

    This brief investigates the partial and complete synchronization of two Boolean control networks (BCNs). Necessary and sufficient conditions for partial and complete synchronization are established by the algebraic representations of logical dynamics. An algorithm is obtained to construct the feedback controller that guarantees the synchronization of master and slave BCNs. Two biological examples are provided to illustrate the effectiveness of the obtained results.

  19. Controlling extreme events on complex networks.

    PubMed

    Chen, Yu-Zhong; Huang, Zi-Gang; Lai, Ying-Cheng

    2014-01-01

    Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network "mobile" can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed. PMID:25131344

  20. Controlling extreme events on complex networks

    PubMed Central

    Chen, Yu-Zhong; Huang, Zi-Gang; Lai, Ying-Cheng

    2014-01-01

    Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network “mobile” can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed. PMID:25131344

  1. The Network of Global Corporate Control

    PubMed Central

    Vitali, Stefania; Glattfelder, James B.; Battiston, Stefano

    2011-01-01

    The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic “super-entity” that raises new important issues both for researchers and policy makers. PMID:22046252

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

    NASA Technical Reports Server (NTRS)

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

    2002-01-01

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

  3. Optimizing Dynamical Network Structure for Pinning Control.

    PubMed

    Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo

    2016-04-12

    Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights.

  4. REAL TIME CONTROL OF URBAN DRAINAGE NETWORKS

    EPA Science Inventory

    Real-time control (RTC) is a custom-designed, computer-assisted management technology for a specific sewerage network to meet the operational objectives of its collection/conveyance system. RTC can operate in several modes, including a mode that is activated during a wet weather ...

  5. Optimizing Dynamical Network Structure for Pinning Control

    NASA Astrophysics Data System (ADS)

    Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo

    2016-04-01

    Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights.

  6. Distributed control network for optogenetic experiments

    NASA Astrophysics Data System (ADS)

    Kasprowicz, G.; Juszczyk, B.; Mankiewicz, L.

    2014-11-01

    Nowadays optogenetic experiments are constructed to examine social behavioural relations in groups of animals. A novel concept of implantable device with distributed control network and advanced positioning capabilities is proposed. It is based on wireless energy transfer technology, micro-power radio interface and advanced signal processing.

  7. Deep networks for motor control functions

    PubMed Central

    Berniker, Max; Kording, Konrad P.

    2015-01-01

    The motor system generates time-varying commands to move our limbs and body. Conventional descriptions of motor control and learning rely on dynamical representations of our body's state (forward and inverse models), and control policies that must be integrated forward to generate feedforward time-varying commands; thus these are representations across space, but not time. Here we examine a new approach that directly represents both time-varying commands and the resulting state trajectories with a function; a representation across space and time. Since the output of this function includes time, it necessarily requires more parameters than a typical dynamical model. To avoid the problems of local minima these extra parameters introduce, we exploit recent advances in machine learning to build our function using a stacked autoencoder, or deep network. With initial and target states as inputs, this deep network can be trained to output an accurate temporal profile of the optimal command and state trajectory for a point-to-point reach of a non-linear limb model, even when influenced by varying force fields. In a manner that mirrors motor babble, the network can also teach itself to learn through trial and error. Lastly, we demonstrate how this network can learn to optimize a cost objective. This functional approach to motor control is a sharp departure from the standard dynamical approach, and may offer new insights into the neural implementation of motor control. PMID:25852530

  8. Inner structure of capital control networks

    NASA Astrophysics Data System (ADS)

    Battiston, Stefano

    2004-07-01

    We study the topological structure of the network of shareholding relationships in the Italian stock market (MIB) and in two US stock markets (NYSE and NASDAQ). The portfolio diversification and the wealth invested on the market by economical agents have been shown in our previous work to have all a power law behavior. However, a further investigation shows that the inner structure of the capital control network are not at all the same across markets. The shareholding network is a weighted graph, therefore we introduce two quantities analogous to in-degree and out-degree for weighted graphs which measure, respectively: the number of effective shareholders of a stock and the number of companies effectively controlled by a single holder. Combining the information carried by the distributions of these two quantities we are able to extract the backbone of each market and we find that while the MIB splits into several separated groups of interest, the US markets is characterized by very large holders sharing control on overlapping subsets of stocks. This method seems promising for the analysis of the topology of capital control networks in general and not only in the stock market.

  9. Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous

    NASA Technical Reports Server (NTRS)

    Karr, C. L.; Freeman, L. M.; Meredith, D. L.

    1990-01-01

    The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

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

    NASA Astrophysics Data System (ADS)

    Xu, Liming

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

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

    PubMed

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

    2014-01-01

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

  12. Network dysfunction of emotional and cognitive processes in those at genetic risk of bipolar disorder.

    PubMed

    Breakspear, Michael; Roberts, Gloria; Green, Melissa J; Nguyen, Vinh T; Frankland, Andrew; Levy, Florence; Lenroot, Rhoshel; Mitchell, Philip B

    2015-11-01

    The emotional and cognitive vulnerabilities that precede the development of bipolar disorder are poorly understood. The inferior frontal gyrus-a key cortical hub for the integration of cognitive and emotional processes-exhibits both structural and functional changes in bipolar disorder, and is also functionally impaired in unaffected first-degree relatives, showing diminished engagement during inhibition of threat-related emotional stimuli. We hypothesized that this functional impairment of the inferior frontal gyrus in those at genetic risk of bipolar disorder reflects the dysfunction of broader network dynamics underlying the coordination of emotion perception and cognitive control. To test this, we studied effective connectivity in functional magnetic resonance imaging data acquired from 41 first-degree relatives of patients with bipolar disorder, 45 matched healthy controls and 55 participants with established bipolar disorder. Dynamic causal modelling was used to model the neuronal interaction between key regions associated with fear perception (the anterior cingulate), inhibition (the left dorsolateral prefrontal cortex) and the region upon which these influences converge, namely the inferior frontal gyrus. Network models that embodied non-linear, hierarchical relationships were the most strongly supported by data from our healthy control and bipolar participants. We observed a marked difference in the hierarchical influence of the anterior cingulate on the effective connectivity from the dorsolateral prefrontal cortex to the inferior frontal gyrus that is unique to the at-risk cohort. Non-specific, non-hierarchical mechanisms appear to compensate for this network disturbance. We thus establish a specific network disturbance suggesting dysfunction in the processes that support hierarchical relationships between emotion and cognitive control in those at high genetic risk for bipolar disorder. PMID:26373604

  13. Network dysfunction of emotional and cognitive processes in those at genetic risk of bipolar disorder.

    PubMed

    Breakspear, Michael; Roberts, Gloria; Green, Melissa J; Nguyen, Vinh T; Frankland, Andrew; Levy, Florence; Lenroot, Rhoshel; Mitchell, Philip B

    2015-11-01

    The emotional and cognitive vulnerabilities that precede the development of bipolar disorder are poorly understood. The inferior frontal gyrus-a key cortical hub for the integration of cognitive and emotional processes-exhibits both structural and functional changes in bipolar disorder, and is also functionally impaired in unaffected first-degree relatives, showing diminished engagement during inhibition of threat-related emotional stimuli. We hypothesized that this functional impairment of the inferior frontal gyrus in those at genetic risk of bipolar disorder reflects the dysfunction of broader network dynamics underlying the coordination of emotion perception and cognitive control. To test this, we studied effective connectivity in functional magnetic resonance imaging data acquired from 41 first-degree relatives of patients with bipolar disorder, 45 matched healthy controls and 55 participants with established bipolar disorder. Dynamic causal modelling was used to model the neuronal interaction between key regions associated with fear perception (the anterior cingulate), inhibition (the left dorsolateral prefrontal cortex) and the region upon which these influences converge, namely the inferior frontal gyrus. Network models that embodied non-linear, hierarchical relationships were the most strongly supported by data from our healthy control and bipolar participants. We observed a marked difference in the hierarchical influence of the anterior cingulate on the effective connectivity from the dorsolateral prefrontal cortex to the inferior frontal gyrus that is unique to the at-risk cohort. Non-specific, non-hierarchical mechanisms appear to compensate for this network disturbance. We thus establish a specific network disturbance suggesting dysfunction in the processes that support hierarchical relationships between emotion and cognitive control in those at high genetic risk for bipolar disorder.

  14. Controlled drug release from hydrogel nanoparticle networks.

    PubMed

    Huang, Gang; Gao, Jun; Hu, Zhibing; St John, John V; Ponder, Bill C; Moro, Dan

    2004-02-10

    Monodisperse nanoparticles of poly-N-isopropylacrylamide-co-allylamine (PNIPAM-co-allylamine) and PNIPAM-co-acrylic acid (PNIPAM-co-AA) were synthesized. The close-packed PNIPAM-co-allylamine and PNIPAM-co-AA nanoparticles were converted to three-dimensional gel networks by covalently crosslinking neighboring particles at room temperature and neutral pH using glutaric dialdehyde and adipic acid dihydrazide, respectively. Controlled release studies were conducted using dextran markers of various molecular weights as model macromolecular drugs. Release was quantified under various physical conditions, including a range of temperatures and dextran molecular weights. Dextran, entrapped in cavities in the nanoparticle network, was released with a rate regulated by their molecular weights and cavity size. No release from a conventional bulk PNIPAM gel, with high crosslinking density, was observed. The rate of release from the PNIPAM-co-allylamine network was temperature-dependent, being much faster at room temperature than that at human body temperature. In contrast, release of low molecular weight dextrans from the PNIPAM-co-AA network showed a temperature-independent release profile. These nanoparticle networks have several advantages over conventional bulk gels for controlling the release of high molecular weight biomolecules. PMID:14744482

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

    PubMed

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

    2014-03-01

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

  16. Flexible body control using neural networks

    NASA Technical Reports Server (NTRS)

    Mccullough, Claire L.

    1992-01-01

    Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.

  17. [Genetic Control of Circadian Rhythms and Aging].

    PubMed

    Solovyeva, I A; Dobrovolskayaa, E V; Moskalev, A A

    2016-04-01

    The review establishes a link between a group of genes which are conserved in evolution and form a molecular oscillator responsible for generation of circadian rhythms and genetic determinants of aging including associated pathways of intracellular signaling. An analysis of mechanisms of development of age-dependent pathologies is conducted from the viewpoint of circadian genetics. Systematic data of circadian gene expression studies in animals demonstrating different rates of aging from accelerated to negligible are presented. PMID:27529973

  18. Noise Control in Gene Regulatory Networks with Negative Feedback.

    PubMed

    Hinczewski, Michael; Thirumalai, D

    2016-07-01

    Genes and proteins regulate cellular functions through complex circuits of biochemical reactions. Fluctuations in the components of these regulatory networks result in noise that invariably corrupts the signal, possibly compromising function. Here, we create a practical formalism based on ideas introduced by Wiener and Kolmogorov (WK) for filtering noise in engineered communications systems to quantitatively assess the extent to which noise can be controlled in biological processes involving negative feedback. Application of the theory, which reproduces the previously proven scaling of the lower bound for noise suppression in terms of the number of signaling events, shows that a tetracycline repressor-based negative-regulatory gene circuit behaves as a WK filter. For the class of Hill-like nonlinear regulatory functions, this type of filter provides the optimal reduction in noise. Our theoretical approach can be readily combined with experimental measurements of response functions in a wide variety of genetic circuits, to elucidate the general principles by which biological networks minimize noise.

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

    PubMed Central

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

    2011-01-01

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

  20. Network control architecture for solid state lighting

    NASA Astrophysics Data System (ADS)

    Ducharme, Alfred D.; Morgan, Fritz

    2001-12-01

    At the current time most of the attention in the solid-state lighting field has been placed on the blue and white light emitting diodes (LEDs). It has and will continue to be extremely important to concentrate on increasing the efficiencies of these devices. However, one of the most overlooked benefits of LEDs is that they are intrinsically simple to control. In this paper, the authors will discuss a technology that is currently being developed to enable fixtures incorporating LED light engines to be connected to a digital lighting network. A description of such a network enabling device and the results from a technology demonstration of a prototype system are provided.

  1. Gene networks controlling early cerebral cortex arealization.

    PubMed

    Mallamaci, Antonello; Stoykova, Anastassia

    2006-02-01

    Early thalamus-independent steps in the process of cortical arealization take place on the basis of information intrinsic to the cortical primordium, as proposed by Rakic in his classical protomap hypothesis [Rakic, P. (1988)Science, 241, 170-176]. These steps depend on a dense network of molecular interactions, involving genes encoding for diffusible ligands which are released around the borders of the cortical field, and transcription factor genes which are expressed in graded ways throughout this field. In recent years, several labs worldwide have put considerable effort into identifying members of this network and disentangling its topology. In this respect, a considerable amount of knowledge has accumulated and a first, provisional description of the network can be delineated. The aim of this review is to provide an organic synthesis of our current knowledge of molecular genetics of early cortical arealization, i.e. to summarise the mechanisms by which secreted ligands and graded transcription factor genes elaborate positional information and trigger the activation of distinctive area-specific morphogenetic programs.

  2. Seismic active control by neutral networks

    SciTech Connect

    Tang, Yu

    1995-12-31

    A study on the application of artificial neural networks (ANNs) to active structural control under seismic loads is carried out. The structure considered is a single-degree-of-freedom (SDF) system with an active bracing device. The control force is computed by a trained neural network. The feedforward neural network architecture and an adaptive backpropagation training algorithm is used in the study. The neural net is trained to reproduce the function that represents the response-excitation relationship of the SDF system under seismic loads. The input-output training patterns are generated randomly. In the backpropagation training algorithm, the learning rate is determined by ensuring the decrease of the error function at each epoch. The computer program implemented is validated by solving the classification of the XOR problem. Then, the trained ANN is used to compute the control force according to the control strategy. If the control force exceeds the actuator`s capacity limit, it is set equal to that limit. The concept of the control strategy employed herein is to apply the control force at every time step to cancel the system velocity induced at the preceding time step so that the gradual rhythmic buildup of the response is destroyed. The ground motions considered in the numerical example are the 1940 El Centro earthquake and the 1979 Imperial Valley earthquake in California. The system responses with and without the control are calculated and compared. The feasibility and potential of applying ANNs to seismic active control is asserted by the promising results obtained from the numerical examples studied.

  3. Mathematical inference and control of molecular networks from perturbation experiments

    NASA Astrophysics Data System (ADS)

    Mohammed-Rasheed, Mohammed

    in order to affect the time evolution of molecular activity in a desirable manner. In this proposal, we address both the inference and control problems of GRNs. In the first part of the thesis, we consider the control problem. We assume that we are given a general topology network structure, whose dynamics follow a discrete-time Markov chain model. We subsequently develop a comprehensive framework for optimal perturbation control of the network. The aim of the perturbation is to drive the network away from undesirable steady-states and to force it to converge to a unique desirable steady-state. The proposed framework does not make any assumptions about the topology of the initial network (e.g., ergodicity, weak and strong connectivity), and is thus applicable to general topology networks. We define the optimal perturbation as the minimum-energy perturbation measured in terms of the Frobenius norm between the initial and perturbed networks. We subsequently demonstrate that there exists at most one optimal perturbation that forces the network into the desirable steady-state. In the event where the optimal perturbation does not exist, we construct a family of sub-optimal perturbations that approximate the optimal solution arbitrarily closely. In the second part of the thesis, we address the inference problem of GRNs from time series data. We model the dynamics of the molecules using a system of ordinary differential equations corrupted by additive white noise. For large-scale networks, we formulate the inference problem as a constrained maximum likelihood estimation problem. We derive the molecular interactions that maximize the likelihood function while constraining the network to be sparse. We further propose a procedure to recover weak interactions based on the Bayesian information criterion. For small-size networks, we investigated the inference of a globally stable 7-gene melanoma genetic regulatory network from genetic perturbation experiments. We considered five

  4. Networked control of microgrid system of systems

    NASA Astrophysics Data System (ADS)

    Mahmoud, Magdi S.; Rahman, Mohamed Saif Ur; AL-Sunni, Fouad M.

    2016-08-01

    The microgrid has made its mark in distributed generation and has attracted widespread research. However, microgrid is a complex system which needs to be viewed from an intelligent system of systems perspective. In this paper, a network control system of systems is designed for the islanded microgrid system consisting of three distributed generation units as three subsystems supplying a load. The controller stabilises the microgrid system in the presence of communication infractions such as packet dropouts and delays. Simulation results are included to elucidate the effectiveness of the proposed control strategy.

  5. Evolution of Controllability in Interbank Networks

    NASA Astrophysics Data System (ADS)

    Delpini, Danilo; Battiston, Stefano; Riccaboni, Massimo; Gabbi, Giampaolo; Pammolli, Fabio; Caldarelli, Guido

    2013-04-01

    The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected ``hub'' institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies.

  6. Evolution of Controllability in Interbank Networks

    PubMed Central

    Delpini, Danilo; Battiston, Stefano; Riccaboni, Massimo; Gabbi, Giampaolo; Pammolli, Fabio; Caldarelli, Guido

    2013-01-01

    The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected “hub” institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies. PMID:23568033

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

    NASA Astrophysics Data System (ADS)

    Zu, Yun-Xiao; Zhou, Jie

    2012-01-01

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

  8. Neural Networks for Signal Processing and Control

    NASA Astrophysics Data System (ADS)

    Hesselroth, Ted Daniel

    Neural networks are developed for controlling a robot-arm and camera system and for processing images. The networks are based upon computational schemes that may be found in the brain. In the first network, a neural map algorithm is employed to control a five-joint pneumatic robot arm and gripper through feedback from two video cameras. The pneumatically driven robot arm employed shares essential mechanical characteristics with skeletal muscle systems. To control the position of the arm, 200 neurons formed a network representing the three-dimensional workspace embedded in a four-dimensional system of coordinates from the two cameras, and learned a set of pressures corresponding to the end effector positions, as well as a set of Jacobian matrices for interpolating between these positions. Because of the properties of the rubber-tube actuators of the arm, the position as a function of supplied pressure is nonlinear, nonseparable, and exhibits hysteresis. Nevertheless, through the neural network learning algorithm the position could be controlled to an accuracy of about one pixel (~3 mm) after two hundred learning steps. Applications of repeated corrections in each step via the Jacobian matrices leads to a very robust control algorithm since the Jacobians learned by the network have to satisfy the weak requirement that they yield a reduction of the distance between gripper and target. The second network is proposed as a model for the mammalian vision system in which backward connections from the primary visual cortex (V1) to the lateral geniculate nucleus play a key role. The application of hebbian learning to the forward and backward connections causes the formation of receptive fields which are sensitive to edges, bars, and spatial frequencies of preferred orientations. The receptive fields are learned in such a way as to maximize the rate of transfer of information from the LGN to V1. Orientational preferences are organized into a feature map in the primary visual

  9. Parallel processing data network of master and slave transputers controlled by a serial control network

    DOEpatents

    Crosetto, D.B.

    1996-12-31

    The present device provides for a dynamically configurable communication network having a multi-processor parallel processing system having a serial communication network and a high speed parallel communication network. The serial communication network is used to disseminate commands from a master processor to a plurality of slave processors to effect communication protocol, to control transmission of high density data among nodes and to monitor each slave processor`s status. The high speed parallel processing network is used to effect the transmission of high density data among nodes in the parallel processing system. Each node comprises a transputer, a digital signal processor, a parallel transfer controller, and two three-port memory devices. A communication switch within each node connects it to a fast parallel hardware channel through which all high density data arrives or leaves the node. 6 figs.

  10. Parallel processing data network of master and slave transputers controlled by a serial control network

    DOEpatents

    Crosetto, Dario B.

    1996-01-01

    The present device provides for a dynamically configurable communication network having a multi-processor parallel processing system having a serial communication network and a high speed parallel communication network. The serial communication network is used to disseminate commands from a master processor (100) to a plurality of slave processors (200) to effect communication protocol, to control transmission of high density data among nodes and to monitor each slave processor's status. The high speed parallel processing network is used to effect the transmission of high density data among nodes in the parallel processing system. Each node comprises a transputer (104), a digital signal processor (114), a parallel transfer controller (106), and two three-port memory devices. A communication switch (108) within each node (100) connects it to a fast parallel hardware channel (70) through which all high density data arrives or leaves the node.

  11. XROUTE: A knowledge-based routing system using neural networks and genetic algorithms

    SciTech Connect

    Kadaba, N.

    1990-01-01

    This dissertation is concerned with applying alternative methods of artificial intelligence (AI) in conjunction with mathematical methods to Vehicle Routing Problems. The combination of good mathematical models, knowledge-based systems, artificial neural networks, and adaptive genetic algorithms (GA) - which are shown to be synergistic - produces near-optimal results, which none of the individual methods can produce on its own. A significant problem associated with application of the Back Propagation learning paradigm for pattern classification with neural networks is the lack of high accuracy in generalization when the domain is large. In this work, a multiple neural network system is employed, using two self-organizing neural networks that work as feature extractors, producing information that is used to train a generalization neural network. The technique was successfully applied to the selection of control rules for a Traveling Salesman Problem heuristic, thus making it adaptive to the input problem instance. XROUTE provides an interactive visualization system, using state-of-the-art vehicle routing models and AI tools, yet allows an interactive environment for human expertise to be utilized in powerful ways. XROUTE provides an experimental, exploratory framework that allows many variations, and alternatives to problems with different characteristics. XROUTE is dynamic, expandable, and adaptive, and typically outperforms alternative methods in computer-aided vehicle routing.

  12. Dynamic congestion control mechanisms for MPLS networks

    NASA Astrophysics Data System (ADS)

    Holness, Felicia; Phillips, Chris I.

    2001-02-01

    Considerable interest has arisen in congestion control through traffic engineering from the knowledge that although sensible provisioning of the network infrastructure is needed, together with sufficient underlying capacity, these are not sufficient to deliver the Quality of Service required for new applications. This is due to dynamic variations in load. In operational Internet Protocol (IP) networks, it has been difficult to incorporate effective traffic engineering due to the limited capabilities of the IP technology. In principle, Multiprotocol Label Switching (MPLS), which is a connection-oriented label swapping technology, offers new possibilities in addressing the limitations by allowing the operator to use sophisticated traffic control mechanisms. This paper presents a novel scheme to dynamically manage traffic flows through the network by re-balancing streams during periods of congestion. It proposes management-based algorithms that will allow label switched routers within the network to utilize mechanisms within MPLS to indicate when flows are starting to experience frame/packet loss and then to react accordingly. Based upon knowledge of the customer's Service Level Agreement, together with instantaneous flow information, the label edge routers can then instigate changes to the LSP route and circumvent congestion that would hitherto violate the customer contacts.

  13. Genomic and network patterns of schizophrenia genetic variation in human evolutionary accelerated regions.

    PubMed

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

    2015-05-01

    The population persistence of schizophrenia despite associated reductions in fitness and fecundity suggests that the genetic basis of schizophrenia has a complex evolutionary history. A recent meta-analysis of schizophrenia genome-wide association studies offers novel opportunities for assessment of the evolutionary trajectories of schizophrenia-associated loci. In this study, we hypothesize that components of the genetic architecture of schizophrenia are attributable to human lineage-specific evolution. Our results suggest that schizophrenia-associated loci enrich in genes near previously identified human accelerated regions (HARs). Specifically, we find that genes near HARs conserved in nonhuman primates (pHARs) are enriched for schizophrenia-associated loci, and that pHAR-associated schizophrenia genes are under stronger selective pressure than other schizophrenia genes and other pHAR-associated genes. We further evaluate pHAR-associated schizophrenia genes in regulatory network contexts to investigate associated molecular functions and mechanisms. We find that pHAR-associated schizophrenia genes significantly enrich in a GABA-related coexpression module that was previously found to be differentially regulated in schizophrenia affected individuals versus healthy controls. In another two independent networks constructed from gene expression profiles from prefrontal cortex samples, we find that pHAR-associated schizophrenia genes are located in more central positions and their average path lengths to the other nodes are significantly shorter than those of other schizophrenia genes. Together, our results suggest that HARs are associated with potentially important functional roles in the genetic architecture of schizophrenia.

  14. Temperature drift modeling of MEMS gyroscope based on genetic-Elman neural network

    NASA Astrophysics Data System (ADS)

    Chong, Shen; Rui, Song; Jie, Li; Xiaoming, Zhang; Jun, Tang; Yunbo, Shi; Jun, Liu; Huiliang, Cao

    2016-05-01

    In order to improve the temperature drift modeling precision of a tuning fork micro-electromechanical system (MEMS) gyroscope, a novel multiple inputs/single output model based on genetic algorithm (GA) and Elman neural network (Elman NN) is proposed. First, the temperature experiment of MEMS gyroscope is carried out and the outputs of MEMS gyroscope and temperature sensors are collected; then the temperature drift model based on temperature, temperature variation rate and the coupling term is proposed, and the Elman NN is employed to guarantee the generalization ability of the model; at last the genetic algorithm is used to tune the parameters of Elman NN in order to improve the modeling precision. The Allan analysis results validate that, compared to traditional single input/single output model, the novel multiple inputs/single output model can guarantee high accurate fitting ability because the proposed model can provide more plentiful controllable information. By the way, the generalization ability of the Elman neural network can be improved significantly due to the parameters are optimized by genetic algorithm.

  15. Genetic control of reproduction in dairy cows.

    PubMed

    Butler, Stephen T

    2013-01-01

    The advent of AI has markedly improved the production potential of dairy cows in all systems of production and transformed the dairy industry in many countries. Unfortunately, for many years breeding objectives focused solely on milk production. This resulted in a major decline in genetic merit for fertility traits. In recent years, the underlying physiological mechanisms responsible for this decline have started to be unravelled. It is apparent that poor genetic merit for fertility traits is associated with multiple defects across a range of organs and tissues that are antagonistic to achieving satisfactory fertility performance. The principal defects include excessive mobilisation of body condition score, unfavourable metabolic status, delayed resumption of cyclicity, increased incidence of endometritis, dysfunctional oestrus expression and inadequate luteal phase progesterone concentrations. On a positive note, it is possible to identify sires that combine good milk production traits with good fertility traits. Sire genetic merit for daughter fertility traits is improving rapidly in the dairy breeds, including the Holstein. With advances in animal breeding, especially genomic technologies, to identify superior sires, genetic merit for fertility traits can be improved much more quickly than they initially declined.

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

    SciTech Connect

    Gatti, Daniel; Maki, Akira; Chesler, Elissa J; Kirova, Roumyana; Kosyk, Oksana; Lu, Lu; Manly, Kenneth; Matthews, Douglas B.; Qu, Yanhua; Williams, Robert; Perkins, Andy; Langston, Michael A; Threadgill, David; Rusyn, Ivan

    2007-01-01

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

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

    PubMed

    Gilman, Alex; Arkin, Adam P

    2002-01-01

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

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

    PubMed

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

    2016-02-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed Central

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

    2014-01-01

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

  1. Chain functions and scoring functions in genetic networks.

    PubMed

    Gat-Viks, I; Shamir, R

    2003-01-01

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

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

    PubMed

    Williams-Jones, Bryn; Graham, Janice E

    2003-12-01

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

  3. Genetic control of insects of public health importance

    PubMed Central

    Knipling, E. F.; Laven, H.; Craig, G. B.; Pal, R.; Kitzmiller, J. B.; Smith, C. N.; Brown, A. W. A.

    1968-01-01

    During recent years many advances have been made in the development of insect control by genetic manipulation. These methods include the sterile-male technique, now well known, which depends on ionizing radiation or chemosterilization. The recent field experiment carried out by WHO in Rangoon, Burma, on Culex fatigans has demonstrated that naturally occurring cytogenetic mechanisms such as cytoplasmic incompatibility can be used successfully without the use of radiations or chemosterilants. The paper not only describes the experiment on Culex fatigans but also discusses basic concepts and theoretical considerations involved in genetic control of insects of public health importance. The possibility of using genetic mechanisms for the control of other vector species is also discussed. There are a number of problems which require study before genetic control can be used on an operational scale. These problems and suggestions for future research in this field are also outlined. PMID:5302334

  4. Genetic regulatory networks programming hematopoietic stem cells and erythroid lineage specification.

    PubMed

    Swiers, Gemma; Patient, Roger; Loose, Matthew

    2006-06-15

    Erythroid cell production results from passage through cellular hierarchies dependent on differential gene expression under the control of transcription factors responsive to changing niches. We have constructed Genetic Regulatory Networks (GRNs) describing this process, based predominantly on mouse data. Regulatory network motifs identified in E. coli and yeast GRNs are found in combination in these GRNs. Feed-forward motifs with autoregulation generate forward momentum and also control its rate, which is at its lowest in hematopoietic stem cells (HSCs). The simultaneous requirement for multiple regulators in multi-input motifs (MIMs) provides tight control over expression of target genes. Combinations of MIMs, exemplified by the SCL/LMO2 complexes, which have variable content and binding sites, explain how individual regulators can have different targets in HSCs and erythroid cells and possibly also how HSCs maintain stem cell functions while expressing lineage-affiliated genes at low level, so-called multi-lineage priming. MIMs combined with cross-antagonism describe the relationship between PU.1 and GATA-1 and between two of their target genes, Fli-1 and EKLF, with victory for GATA-1 and EKLF leading to erythroid lineage specification. These GRNs are useful repositories for current regulatory information, are accessible in interactive form via the internet, enable the consequences of perturbation to be predicted, and can act as seed networks to organize the rapidly accumulating microarray data.

  5. Problems in the control of genetic disorders.

    PubMed

    Kuliev, A M; Modell, B

    1990-01-01

    Preventive genetics services based on population screening are now an integral part of maternal and child health programmes. New developments in DNA technology, ultrasound scanning, and assay of factors in maternal blood are greatly increasing their potential to improve human health. If these services are to be delivered fairly to populations, there must be much more emphasis on community information, professional education, and service monitoring.

  6. Microturbine control based on fuzzy neural network

    NASA Astrophysics Data System (ADS)

    Yan, Shijie; Bian, Chunyuan; Wang, Zhiqiang

    2006-11-01

    As microturbine generator (MTG) is a clean, efficient, low cost and reliable energy supply system. From outside characteristics of MTG, it is multi-variable, time-varying and coupling system, so it is difficult to be identified on-line and conventional control law adopted before cannot achieve desirable result. A novel fuzzy-neural networks (FNN) control algorithm was proposed in combining with the conventional PID control. In the paper, IF-THEN rules for tuning were applied by a first-order Sugeno fuzzy model with seven fuzzy rules and the membership function was given as the continuous GAUSSIAN function. Some sample data were utilized to train FNN. Through adjusting shape of membership function and weight continually, objective of auto-tuning fuzzy-rules can be achieved. The FNN algorithm had been applied to "100kW Microturbine control and power converter system". The results of simulation and experiment are shown that the algorithm can work very well.

  7. Neural networks as a control methodology

    NASA Technical Reports Server (NTRS)

    Mccullough, Claire L.

    1990-01-01

    While conventional computers must be programmed in a logical fashion by a person who thoroughly understands the task to be performed, the motivation behind neural networks is to develop machines which can train themselves to perform tasks, using available information about desired system behavior and learning from experience. There are three goals of this fellowship program: (1) to evaluate various neural net methods and generate computer software to implement those deemed most promising on a personal computer equipped with Matlab; (2) to evaluate methods currently in the professional literature for system control using neural nets to choose those most applicable to control of flexible structures; and (3) to apply the control strategies chosen in (2) to a computer simulation of a test article, the Control Structures Interaction Suitcase Demonstrator, which is a portable system consisting of a small flexible beam driven by a torque motor and mounted on springs tuned to the first flexible mode of the beam. Results of each are discussed.

  8. Genetic control of flowering time in legumes

    PubMed Central

    Weller, James L.; Ortega, Raúl

    2015-01-01

    The timing of flowering, and in particular the degree to which it is responsive to the environment, is a key factor in the adaptation of a given species to various eco-geographic locations and agricultural practices. Flowering time variation has been documented in many crop legumes, and selection for specific variants has permitted significant expansion and improvement in cultivation, from prehistoric times to the present day. Recent advances in legume genomics have accelerated the process of gene identification and functional analysis, and opened up new prospects for a molecular understanding of flowering time adaptation in this important crop group. Within the legumes, two species have been prominent in flowering time studies; the vernalization-responsive long-day species pea (Pisum sativum) and the warm-season short-day plant soybean (Glycine max). Analysis of flowering in these species is now being complemented by reverse genetics capabilities in the model legumes Medicago truncatula and Lotus japonicus, and the emergence of genome-scale resources in a range of other legumes. This review will outline the insights gained from detailed forward genetic analysis of flowering time in pea and soybean, highlighting the importance of light perception, the circadian clock and the FT family of flowering integrators. It discusses the current state of knowledge on genetic mechanisms for photoperiod and vernalization response, and concludes with a broader discussion of flowering time adaptation across legumes generally. PMID:25914700

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

    NASA Astrophysics Data System (ADS)

    Kim, Byungwhan; Bae, Jungki

    2005-10-01

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

  10. Neural networks for LED color control

    NASA Astrophysics Data System (ADS)

    Ashdown, Ian E.

    2004-01-01

    The design and implementation of an architectural dimming control for multicolor LED-based lighting fixtures is complicated by the need to maintain a consistent color balance under a wide variety of operating conditions. Factors to consider include nonlinear relationships between luminous flux intensity and drive current, junction temperature dependencies, LED manufacturing tolerances and binning parameters, device aging characteristics, variations in color sensor spectral responsitivities, and the approximations introduced by linear color space models. In this paper we formulate this problem as a nonlinear multidimensional function, where maintaining a consistent color balance is equivalent to determining the hyperplane representing constant chromaticity. To be useful for an architectural dimming control design, this determination must be made in real time as the lighting fixture intensity is adjusted. Further, the LED drive current must be continuously adjusted in response to color sensor inputs to maintain constant chromaticity for a given intensity setting. Neural networks are known to be universal approximators capable of representing any continuously differentiable bounded function. We therefore use a radial basis function neural network to represent the multidimensional function and provide the feedback signals needed to maintain constant chromaticity. The network can be trained on the factory floor using individual device measurements such as spectral radiant intensity and color sensor characteristics. This provides a flexible solution that is mostly independent of LED manufacturing tolerances and binning parameters.

  11. Genetic Control of Weight Loss During Pneumonic Burkholderia pseudomallei Infection

    PubMed Central

    Emery, Felicia D.; Parvathareddy, Jyothi; Pandey, Ashutosh K.; Cui, Yan; Williams, Robert W.; Miller, Mark A.

    2014-01-01

    Burkholderia pseudomallei (Bp) is the causal agent of a high morbidity/mortality disease syndrome known as melioidosis. This syndrome can range from acute fulminate disease to chronic, local, and disseminated infections that are often difficult to treat because Bp exhibits resistance to many antibiotics. Bp is a prime candidate for use in biological warfare/terrorism and is classified as a Tier-1 Select Agent by HHS and APHIS. It is known that inbred mouse strains display a range of susceptibility to Bp and that the murine infection model is ideal for studying acute melioidosis. Here we exploit a powerful mouse genetics resource that consists of a large family of BXD type recombinant inbred strains, to perform genome-wide linkage analysis of the weight loss phenotype following pneumonic infection with Bp. We infected parental mice and 32 BXD strains with 50-100 CFU of Bp (strain 1026b) and monitored weight retention each day over an eleven-day time course. Using the computational tools in GeneNetwork, we performed genome-wide linkage analysis to identify an interval on chromosome 12 that appears to control the weight retention trait. We then analysed and ranked positional candidate genes in this interval, several of which have intriguing connections with innate immunity, calcium homeostasis, lipid transport, host cell growth and development, and autophagy. PMID:24687986

  12. The 1982 control network of Mars

    NASA Technical Reports Server (NTRS)

    Davies, M. E.; Katayama, F. Y.

    1983-01-01

    Attention is given to a planet-wide control network of Mars that was computed in September 1982 using a large single-block analytical triangulation with 47,524 measurements of 6853 control points on 1054 Mariner 9 and 757 Viking pictures. In all, 19,139 normal equations were solved, with a resulting standard error of measurement of 18.06 microns. The control points identified by name and letter designation are given, as are the aerographic coordinates of the control points. In addition, the coordinates of the Viking I lander site are given: latitude, 22.480 deg; longitude, 47.962 deg (radius, 3389.32 km). This study expands and updates the previously published network (1978). It is noted that the computation differs in many respects from standard aerial mapping photogrammetric practice. In comparison with aerial mapping photography, the television formats are small and the focal lengths are long; stereo coverage is rare, the scale of the pictures varies greatly, and the residual camera distortions are large.

  13. Optimising Control of Disease Spread on Networks

    NASA Astrophysics Data System (ADS)

    Dybiec, B.; Kleczkowski, A.; Gilligan, C. A.

    2005-05-01

    We consider models for control of epidemics on local, global, small-world and scale-free networks, with only partial information accessible about the status of individuals and their connections. The effectiveness of local (e.g. ring vaccination or culling) versus global (e.g. random vaccination) control measures is evaluated, with the aim of minimising the total cost of an epidemic. The costs include direct costs of treating infected individuals as well as costs of treatment. We first consider a random (global) vaccination strategy designed to stop any potential outbreak. We show that if the costs of the preventive vaccination are ignored, the optimal strategy is to vaccinate the whole population, although most of the resources are wasted on preventing a small number of cases. If the vaccination costs are included, or if a local strategy (within a certain neighbourhood of a symptomatic individual) is chosen, there is an optimum number of treated individuals. Inclusion of non-local contacts (``small-worlds'' or scale-free networks) increases the levels of preventive (random) vaccination and radius of local treatment necessary for stopping the outbreak at a minimal cost. The number of initial foci also influences our choice of optimal strategy. The size of epidemics and the number of treated individuals increase for outbreaks that are initiated from a larger number of initial foci, but the optimal radius of local control actually decreases. The results are important for designing control strategies based on cost effectiveness.

  14. End-to-End Flow Control Using PI Controller for Servo Control over Networks

    NASA Astrophysics Data System (ADS)

    Yashiro, Daisuke; Kubo, Ryogo; Yakoh, Takahiro; Ohnishi, Kouhei

    This paper presents a novel flow control method using a PI controller for servo control over networks. The UDP is known to be effective for motion control systems over networks such as bilateral teleoperation. However, UDP does not have a mechanism for congestion avoidance. The congestion, which causes large communication delay, jitter, and packet loss, deteriorates the performance and stability of control systems over networks. To avoid this congestion, a novel flow control method, which adjusts a packet-sending period in real time, is proposed. The validity of the proposed method is shown by simulation and experimental results.

  15. A comprehensive Network Security Risk Model for process control networks.

    PubMed

    Henry, Matthew H; Haimes, Yacov Y

    2009-02-01

    The risk of cyber attacks on process control networks (PCN) is receiving significant attention due to the potentially catastrophic extent to which PCN failures can damage the infrastructures and commodity flows that they support. Risk management addresses the coupled problems of (1) reducing the likelihood that cyber attacks would succeed in disrupting PCN operation and (2) reducing the severity of consequences in the event of PCN failure or manipulation. The Network Security Risk Model (NSRM) developed in this article provides a means of evaluating the efficacy of candidate risk management policies by modeling the baseline risk and assessing expectations of risk after the implementation of candidate measures. Where existing risk models fall short of providing adequate insight into the efficacy of candidate risk management policies due to shortcomings in their structure or formulation, the NSRM provides model structure and an associated modeling methodology that captures the relevant dynamics of cyber attacks on PCN for risk analysis. This article develops the NSRM in detail in the context of an illustrative example.

  16. A comprehensive Network Security Risk Model for process control networks.

    PubMed

    Henry, Matthew H; Haimes, Yacov Y

    2009-02-01

    The risk of cyber attacks on process control networks (PCN) is receiving significant attention due to the potentially catastrophic extent to which PCN failures can damage the infrastructures and commodity flows that they support. Risk management addresses the coupled problems of (1) reducing the likelihood that cyber attacks would succeed in disrupting PCN operation and (2) reducing the severity of consequences in the event of PCN failure or manipulation. The Network Security Risk Model (NSRM) developed in this article provides a means of evaluating the efficacy of candidate risk management policies by modeling the baseline risk and assessing expectations of risk after the implementation of candidate measures. Where existing risk models fall short of providing adequate insight into the efficacy of candidate risk management policies due to shortcomings in their structure or formulation, the NSRM provides model structure and an associated modeling methodology that captures the relevant dynamics of cyber attacks on PCN for risk analysis. This article develops the NSRM in detail in the context of an illustrative example. PMID:19000078

  17. Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

    NASA Technical Reports Server (NTRS)

    Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

    1998-01-01

    Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

  18. Reveal, a general reverse engineering algorithm for inference of genetic network architectures.

    PubMed

    Liang, S; Fuhrman, S; Somogyi, R

    1998-01-01

    Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

  19. The control network of Mars: April 1991

    NASA Technical Reports Server (NTRS)

    Davies, Merton E.; Rogers, Patricia G.

    1991-01-01

    The modern geodetic control network of Mars was first established based on Mariner 9 images with 1-2 km/pixel resolutions and covered almost the entire Martian surface. The introduction of higher resolution (10-200 meter/pixel) Viking Orbiter images greatly improved the accuracy and density of points in the control network. Analysis of the Viking Lander radio tracking data led to more accurate measurements of Mars' rotation period, spin axis direction, and the lander coordinates relative to the inertial reference frame. The prime meridian on Mars was defined by the Geodesy/Cartography Group of the Mariner 9 Television Team as the crater Airy-0, located about 5 degrees south of the equator. The Viking 1 Lander site was identified on a high resolution Viking frame. The control point measurements form the basis of a least squares solution determined by analytical triangulation after the pixel measurements are corrected for geometric distortions and converted to millimeter coordinates in the camera focal plane. Photogrammetric strips encircling Mars at the equator and at 60 degree north south were used to strengthen the overall net and improve the accuracy of the coordinates of points. In addition, photogrammetric strips along 0, 90, 180, and 270 degrees longitude to the Viking 1 Lander site have all significantly strengthened the control network. Most recently, photogrammetric strips were added to the net along 30 degrees north latitude between 0 and 180 degrees, and along 30 degrees between 180 and 360 degrees. The Viking 1 Lander site and Airy-0 are linked through photogrammetric strips occurring along the 0 degree meridian from Airy-0 to 65 degrees north, from that point through the Viking 1 Lander site to the equator, and along the equator to 180 degrees longitude. The Viking 1 lander site is thus a well calibrated area with coordinates of points accurate to approximately 200 meters relative to the J2000 inertial coordinate system. This will be a useful

  20. Genetic and epigenetic control of RKIP transcription.

    PubMed

    Datar, Ila; Tegegne, Hanna; Qin, Kevin; Al-Mulla, Fahd; Bitar, Milad S; Trumbly, Robert J; Yeung, Kam C

    2014-01-01

    Raf kinase inhibitory protein (RKIP) is known to modulate key signaling cascades and regulate normal physiological processes such as cellular proliferation, differentiation, and apoptosis. The expression of RKIP is found to be downregulated in several cancer metastases and the repressed RKIP expression can be reactivated on treatment with chemotherapeutic agents. RKIP is a proven tumor metastasis suppressor gene and investigating the mechanisms of transcriptional regulation of RKIP is therefore of immense clinical importance. In this review, we discuss the basal expression of RKIP in various tissues and the genetic aspects of the RKIP chromosomal locus including the structure of the RKIP promoter as well as gene regulatory elements such as enhancers. We also review the genetic and epigenetic modulation of RKIP transcription through EZH2, a component of the polycomb repressive complex 2 (PRC2) and sequence specific transcription factors (TFs) BACH1 and Snail. Emerging experimental evidence supports a unifying model in which both these TFs repress RKIP transcription in cancers by recruiting the EZH2 containing repressive complex to the proximal RKIP promoter. Finally, we review the known mechanisms employed by different types of chemotherapeutic agents to activate RKIP expression in cancer cells.

  1. Genetic Control of Active Neural Circuits

    PubMed Central

    Reijmers, Leon; Mayford, Mark

    2009-01-01

    The use of molecular tools to study the neurobiology of complex behaviors has been hampered by an inability to target the desired changes to relevant groups of neurons. Specific memories and specific sensory representations are sparsely encoded by a small fraction of neurons embedded in a sea of morphologically and functionally similar cells. In this review we discuss genetics techniques that are being developed to address this difficulty. In several studies the use of promoter elements that are responsive to neural activity have been used to drive long-lasting genetic alterations into neural ensembles that are activated by natural environmental stimuli. This approach has been used to examine neural activity patterns during learning and retrieval of a memory, to examine the regulation of receptor trafficking following learning and to functionally manipulate a specific memory trace. We suggest that these techniques will provide a general approach to experimentally investigate the link between patterns of environmentally activated neural firing and cognitive processes such as perception and memory. PMID:20057936

  2. Genetic Networks of Liver Metabolism Revealed by Integration of Metabolic and Transcriptional Profiling

    PubMed Central

    Ferrara, Christine T.; Wang, Ping; Neto, Elias Chaibub; Stevens, Robert D.; Bain, James R.; Wenner, Brett R.; Ilkayeva, Olga R.; Keller, Mark P.; Blasiole, Daniel A.; Kendziorski, Christina; Yandell, Brian S.; Newgard, Christopher B.; Attie, Alan D.

    2008-01-01

    Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes. PMID:18369453

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

    PubMed

    Luo, Pei-Gao

    2010-04-01

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

  4. Neural network-based finite horizon stochastic optimal control design for nonlinear networked control systems.

    PubMed

    Xu, Hao; Jagannathan, Sarangapani

    2015-03-01

    The stochastic optimal control of nonlinear networked control systems (NNCSs) using neuro-dynamic programming (NDP) over a finite time horizon is a challenging problem due to terminal constraints, system uncertainties, and unknown network imperfections, such as network-induced delays and packet losses. Since the traditional iteration or time-based infinite horizon NDP schemes are unsuitable for NNCS with terminal constraints, a novel time-based NDP scheme is developed to solve finite horizon optimal control of NNCS by mitigating the above-mentioned challenges. First, an online neural network (NN) identifier is introduced to approximate the control coefficient matrix that is subsequently utilized in conjunction with the critic and actor NNs to determine a time-based stochastic optimal control input over finite horizon in a forward-in-time and online manner. Eventually, Lyapunov theory is used to show that all closed-loop signals and NN weights are uniformly ultimately bounded with ultimate bounds being a function of initial conditions and final time. Moreover, the approximated control input converges close to optimal value within finite time. The simulation results are included to show the effectiveness of the proposed scheme. PMID:25720004

  5. Practical synchronization on complex dynamical networks via optimal pinning control.

    PubMed

    Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu

    2015-07-01

    We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications. PMID:26274112

  6. Analog compound orthogonal neural network control of robotic manipulators

    NASA Astrophysics Data System (ADS)

    Jun, Ye

    2005-12-01

    An analog compound orthogonal neural network is presented which is based on digital compound orthogonal neural networks. The compound neural network's control performance was investigated as applied to a robot control problem. The analog neural network is a Chebyshev neural network with a high speed-learning rate in an on-line manner. Its control algorithm does not relate to controlled plant models. The analog neural network is used as the feedforward controller, and PD is used as the feedback controller in the control system of robots. The excellent performance in system response, tracking accuracy, and robustness was verified through a simulation experiment applied to a robotic manipulator with friction and nonlinear disturbances. The trajectory tracking control showed results in satisfactory effectiveness. This analog neural controller provides a novel approach for the control of uncertain or unknown systems.

  7. Deep Space Network Antenna Logic Controller

    NASA Technical Reports Server (NTRS)

    Ahlstrom, Harlow; Morgan, Scott; Hames, Peter; Strain, Martha; Owen, Christopher; Shimizu, Kenneth; Wilson, Karen; Shaller, David; Doktomomtaz, Said; Leung, Patrick

    2007-01-01

    The Antenna Logic Controller (ALC) software controls and monitors the motion control equipment of the 4,000-metric-ton structure of the Deep Space Network 70-meter antenna. This program coordinates the control of 42 hydraulic pumps, while monitoring several interlocks for personnel and equipment safety. Remote operation of the ALC runs via the Antenna Monitor & Control (AMC) computer, which orchestrates the tracking functions of the entire antenna. This software provides a graphical user interface for local control, monitoring, and identification of faults as well as, at a high level, providing for the digital control of the axis brakes so that the servo of the AMC may control the motion of the antenna. Specific functions of the ALC also include routines for startup in cold weather, controlled shutdown for both normal and fault situations, and pump switching on failure. The increased monitoring, the ability to trend key performance characteristics, the improved fault detection and recovery, the centralization of all control at a single panel, and the simplification of the user interface have all reduced the required workforce to run 70-meter antennas. The ALC also increases the antenna availability by reducing the time required to start up the antenna, to diagnose faults, and by providing additional insight into the performance of key parameters that aid in preventive maintenance to avoid key element failure. The ALC User Display (AUD) is a graphical user interface with hierarchical display structure, which provides high-level status information to the operation of the ALC, as well as detailed information for virtually all aspects of the ALC via drill-down displays. The operational status of an item, be it a function or assembly, is shown in the higher-level display. By pressing the item on the display screen, a new screen opens to show more detail of the function/assembly. Navigation tools and the map button allow immediate access to all screens.

  8. Designing robust control laws using genetic algorithms

    NASA Technical Reports Server (NTRS)

    Marrison, Chris

    1994-01-01

    The purpose of this research is to create a method of finding practical, robust control laws. The robustness of a controller is judged by Stochastic Robustness metrics and the level of robustness is optimized by searching for design parameters that minimize a robustness cost function.

  9. A systems approach to mapping transcriptional networks controlling surfactant homeostasis

    PubMed Central

    2010-01-01

    Background Pulmonary surfactant is required for lung function at birth and throughout life. Lung lipid and surfactant homeostasis requires regulation among multi-tiered processes, coordinating the synthesis of surfactant proteins and lipids, their assembly, trafficking, and storage in type II cells of the lung. The mechanisms regulating these interrelated processes are largely unknown. Results We integrated mRNA microarray data with array independent knowledge using Gene Ontology (GO) similarity analysis, promoter motif searching, protein interaction and literature mining to elucidate genetic networks regulating lipid related biological processes in lung. A Transcription factor (TF) - target gene (TG) similarity matrix was generated by integrating data from different analytic methods. A scoring function was built to rank the likely TF-TG pairs. Using this strategy, we identified and verified critical components of a transcriptional network directing lipogenesis, lipid trafficking and surfactant homeostasis in the mouse lung. Conclusions Within the transcriptional network, SREBP, CEBPA, FOXA2, ETSF, GATA6 and IRF1 were identified as regulatory hubs displaying high connectivity. SREBP, FOXA2 and CEBPA together form a common core regulatory module that controls surfactant lipid homeostasis. The core module cooperates with other factors to regulate lipid metabolism and transport, cell growth and development, cell death and cell mediated immune response. Coordinated interactions of the TFs influence surfactant homeostasis and regulate lung function at birth. PMID:20659319

  10. Geochemical, Genetic, and Community Controls on Mercury

    SciTech Connect

    Wall, Judy D.

    2014-11-10

    The sulfate-reducing bacteria (SRB) are soil bacteria that share two common characteristics, strict anaerobiosis and the ability to respire sulfate. The metabolic activities of these bacteria play significant roles in the global sulfur cycle, anaerobic degradation of biomass, biological metal corrosion in the environment and, recently, degradation of toxic compounds. The accumulation of evidence suggests these bacteria are also key to the production of the neurotoxin methylmercury in environmental settings. We propose to use our experience with the development of genetics in sulfate-reducing bacteria of the genus Desulfovibrio to create mutations that will eliminate the methylation of mercury, thereby identifying the genes essential for this process. This information may allow the environmental monitoring of the mercury methylation potential to learn the location and quantity of the production this toxin. From these data, more accurate predictive models of mercury cycling can be generated.

  11. Genetic Control of Meat Quality Traits

    NASA Astrophysics Data System (ADS)

    Williams, John L.

    Meat was originally produced from non-specialized animals that were used for a variety of purposes, in addition to being a source of food. However, selective breeding has resulted in “improved” breeds of cattle that are now used to produce either milk or beef, and specialized chicken lines that produce eggs or meat. These improved breeds are very productive under appropriate management systems. The selection methods used to create these specialized breeds were based on easily measured phenotypic variations, such as growth rate or physical size. Improvement in the desired trait was achieved by breeding directly from animals displaying the desired phenotype. However, more recently sophisticated genetic models have been developed using statistical approaches that consider phenotypic information collected, not only from individual animals but also from their parents, sibs, and progeny.

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

    PubMed

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

    2015-11-01

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

  13. Criteria for stochastic pinning control of networks of chaotic maps

    SciTech Connect

    Mwaffo, Violet; Porfiri, Maurizio; DeLellis, Pietro

    2014-03-15

    This paper investigates the controllability of discrete-time networks of coupled chaotic maps through stochastic pinning. In this control scheme, the network dynamics are steered towards a desired trajectory through a feedback control input that is applied stochastically to the network nodes. The network controllability is studied by analyzing the local mean square stability of the error dynamics with respect to the desired trajectory. Through the analysis of the spectral properties of salient matrices, a toolbox of conditions for controllability are obtained, in terms of the dynamics of the individual maps, algebraic properties of the network, and the probability distribution of the pinning control. We demonstrate the use of these conditions in the design of a stochastic pinning control strategy for networks of Chirikov standard maps. To elucidate the applicability of the approach, we consider different network topologies and compare five different stochastic pinning strategies through extensive numerical simulations.

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

    PubMed

    Bapteste, E; Bicep, C; Lopez, P

    2012-07-01

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

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

    PubMed

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

    2013-08-30

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

  16. Genetic control of contagious asexuality in the pea aphid.

    PubMed

    Jaquiéry, Julie; Stoeckel, Solenn; Larose, Chloé; Nouhaud, Pierre; Rispe, Claude; Mieuzet, Lucie; Bonhomme, Joël; Mahéo, Frédérique; Legeai, Fabrice; Gauthier, Jean-Pierre; Prunier-Leterme, Nathalie; Tagu, Denis; Simon, Jean-Christophe

    2014-12-01

    Although evolutionary transitions from sexual to asexual reproduction are frequent in eukaryotes, the genetic bases of such shifts toward asexuality remain largely unknown. We addressed this issue in an aphid species where both sexual and obligate asexual lineages coexist in natural populations. These sexual and asexual lineages may occasionally interbreed because some asexual lineages maintain a residual production of males potentially able to mate with the females produced by sexual lineages. Hence, this species is an ideal model to study the genetic basis of the loss of sexual reproduction with quantitative genetic and population genomic approaches. Our analysis of the co-segregation of ∼ 300 molecular markers and reproductive phenotype in experimental crosses pinpointed an X-linked region controlling obligate asexuality, this state of character being recessive. A population genetic analysis (>400-marker genome scan) on wild sexual and asexual genotypes from geographically distant populations under divergent selection for reproductive strategies detected a strong signature of divergent selection in the genomic region identified by the experimental crosses. These population genetic data confirm the implication of the candidate region in the control of reproductive mode in wild populations originating from 700 km apart. Patterns of genetic differentiation along chromosomes suggest bidirectional gene flow between populations with distinct reproductive modes, supporting contagious asexuality as a prevailing route to permanent parthenogenesis in pea aphids. This genetic system provides new insights into the mechanisms of coexistence of sexual and asexual aphid lineages.

  17. Does Childhood Anxiety Evoke Maternal Control? A Genetically Informed Study

    ERIC Educational Resources Information Center

    Eley, Thalia C.; Napolitano, Maria; Lau, Jennifer Y. F.; Gregory, Alice M.

    2010-01-01

    Background: Despite theoretical and empirical support for an association between maternal control and child anxiety, few studies have examined the origins of this association. Furthermore, none use observer-ratings of maternal control within a genetically informative design. This study addressed three questions: 1) do children who experience…

  18. Specificity and robustness in transcription control networks.

    PubMed

    Sengupta, Anirvan M; Djordjevic, Marko; Shraiman, Boris I

    2002-02-19

    Recognition by transcription factors of the regulatory DNA elements upstream of genes is the fundamental step in controlling gene expression. How does the necessity to provide stability with respect to mutation constrain the organization of transcription control networks? We examine the mutation load of a transcription factor interacting with a set of n regulatory response elements as a function of the factor/DNA binding specificity and conclude on theoretical grounds that the optimal specificity decreases with n. The predicted correlation between variability of binding sites (for a given transcription factor) and their number is supported by the genomic data for Escherichia coli. The analysis of E. coli genomic data was carried out using an algorithm suggested by the biophysical model of transcription factor/DNA binding. Complete results of the search for candidate transcription factor binding sites are available at http://www.physics.rockefeller.edu/~boris/public/search_ecoli. PMID:11854503

  19. Network-based production quality control

    NASA Astrophysics Data System (ADS)

    Kwon, Yongjin; Tseng, Bill; Chiou, Richard

    2007-09-01

    This study investigates the feasibility of remote quality control using a host of advanced automation equipment with Internet accessibility. Recent emphasis on product quality and reduction of waste stems from the dynamic, globalized and customer-driven market, which brings opportunities and threats to companies, depending on the response speed and production strategies. The current trends in industry also include a wide spread of distributed manufacturing systems, where design, production, and management facilities are geographically dispersed. This situation mandates not only the accessibility to remotely located production equipment for monitoring and control, but efficient means of responding to changing environment to counter process variations and diverse customer demands. To compete under such an environment, companies are striving to achieve 100%, sensor-based, automated inspection for zero-defect manufacturing. In this study, the Internet-based quality control scheme is referred to as "E-Quality for Manufacturing" or "EQM" for short. By its definition, EQM refers to a holistic approach to design and to embed efficient quality control functions in the context of network integrated manufacturing systems. Such system let designers located far away from the production facility to monitor, control and adjust the quality inspection processes as production design evolves.

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

    PubMed

    Ahnert, Sebastian E

    2013-11-01

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

  1. Genetic Control of Heterochrony in Eucalyptus globulus

    PubMed Central

    Hudson, Corey J.; Freeman, Jules S.; Jones, Rebecca C.; Potts, Brad M.; Wong, Melissa M. L.; Weller, James L.; Hecht, Valérie F. G.; Poethig, R. Scott; Vaillancourt, René E.

    2014-01-01

    A change in the timing or rate of developmental events throughout ontogeny is referred to as heterochrony, and it is a major evolutionary process in plants and animals. We investigated the genetic basis for natural variation in the timing of vegetative phase change in the tree Eucalyptus globulus, which undergoes a dramatic change in vegetative morphology during the juvenile-to-adult transition. Quantitative trait loci analysis in an outcross F2 family derived from crosses between individuals from a coastal population of E. globulus with precocious vegetative phase change and individuals from populations in which vegetative phase change occurs several years later implicated the microRNA EglMIR156.5 as a potential contributor to this heterochronic difference. Additional evidence for the involvement of EglMIR156.5 was provided by its differential expression in trees with early and late phase change. Our findings suggest that changes in the expression of miR156 underlie natural variation in vegetative phase change in E. globulus, and may also explain interspecific differences in the timing of this developmental transition. PMID:24950963

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

    PubMed

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

    2014-03-01

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

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

    PubMed

    Schughart, Klaus

    2010-08-01

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

  4. Genetic architecture of sex determination in fish: applications to sex ratio control in aquaculture.

    PubMed

    Martínez, Paulino; Viñas, Ana M; Sánchez, Laura; Díaz, Noelia; Ribas, Laia; Piferrer, Francesc

    2014-01-01

    Controlling the sex ratio is essential in finfish farming. A balanced sex ratio is usually good for broodstock management, since it enables to develop appropriate breeding schemes. However, in some species the production of monosex populations is desirable because the existence of sexual dimorphism, primarily in growth or first time of sexual maturation, but also in color or shape, can render one sex more valuable. The knowledge of the genetic architecture of sex determination (SD) is convenient for controlling sex ratio and for the implementation of breeding programs. Unlike mammals and birds, which show highly conserved master genes that control a conserved genetic network responsible for gonad differentiation (GD), a huge diversity of SD mechanisms has been reported in fish. Despite theory predictions, more than one gene is in many cases involved in fish SD and genetic differences have been observed in the GD network. Environmental factors also play a relevant role and epigenetic mechanisms are becoming increasingly recognized for the establishment and maintenance of the GD pathways. Although major genetic factors are frequently involved in fish SD, these observations strongly suggest that SD in this group resembles a complex trait. Accordingly, the application of quantitative genetics combined with genomic tools is desirable to address its study and in fact, when applied, it has frequently demonstrated a multigene trait interacting with environmental factors in model and cultured fish species. This scenario has notable implications for aquaculture and, depending upon the species, from chromosome manipulation or environmental control techniques up to classical selection or marker assisted selection programs, are being applied. In this review, we selected four relevant species or fish groups to illustrate this diversity and hence the technologies that can be used by the industry for the control of sex ratio: turbot and European sea bass, two reference species of

  5. Genetic architecture of sex determination in fish: applications to sex ratio control in aquaculture

    PubMed Central

    Martínez, Paulino; Viñas, Ana M.; Sánchez, Laura; Díaz, Noelia; Ribas, Laia; Piferrer, Francesc

    2014-01-01

    Controlling the sex ratio is essential in finfish farming. A balanced sex ratio is usually good for broodstock management, since it enables to develop appropriate breeding schemes. However, in some species the production of monosex populations is desirable because the existence of sexual dimorphism, primarily in growth or first time of sexual maturation, but also in color or shape, can render one sex more valuable. The knowledge of the genetic architecture of sex determination (SD) is convenient for controlling sex ratio and for the implementation of breeding programs. Unlike mammals and birds, which show highly conserved master genes that control a conserved genetic network responsible for gonad differentiation (GD), a huge diversity of SD mechanisms has been reported in fish. Despite theory predictions, more than one gene is in many cases involved in fish SD and genetic differences have been observed in the GD network. Environmental factors also play a relevant role and epigenetic mechanisms are becoming increasingly recognized for the establishment and maintenance of the GD pathways. Although major genetic factors are frequently involved in fish SD, these observations strongly suggest that SD in this group resembles a complex trait. Accordingly, the application of quantitative genetics combined with genomic tools is desirable to address its study and in fact, when applied, it has frequently demonstrated a multigene trait interacting with environmental factors in model and cultured fish species. This scenario has notable implications for aquaculture and, depending upon the species, from chromosome manipulation or environmental control techniques up to classical selection or marker assisted selection programs, are being applied. In this review, we selected four relevant species or fish groups to illustrate this diversity and hence the technologies that can be used by the industry for the control of sex ratio: turbot and European sea bass, two reference species of

  6. Genetically controlled food preference: biochemical mechanisms.

    PubMed Central

    Guarna, M M; Borowsky, R L

    1993-01-01

    Food choice is known to be correlated with genotype in the crustacean Gammarus palustris. Given a choice of Enteromorpha intestinalis (E) over Ulva lactuca (U), individuals homozygous for the Amy II.52 allele have a greater preference for E than do Amy II.55 homozygotes. To account for this correlation, we hypothesized that the proportions of saccharides released by the enzymatic action of Amy II.52 on E or Amy II.55 on U starches differ from and better stimulate feeding than those released by Amy II.52 on U and Amy II.55 on E starches. To test this, the two forms of amylase were purified by glycogen/ethanol precipitation and preparative PAGE. Their product distributions with each of the starches were determined by HPLC. Each amylase/starch combination gave different distributions of the main products: maltose, maltotriose, and maltotetraose. Feeding preference tests using artificial foods containing these sugars showed that the product distributions from Amy II.52/E starch or Amy II.55/U starch were preferred over those from Amy II.52/U or Amy II.55/E. Patterns of preferences for the artificial foods closely matched those observed in earlier experiments in which different genotypes fed on intact algae. Thus, genetic differences in feeding preferences can be understood in terms of variation in biochemical properties of a digestive enzyme. These results highlight a previously unappreciated role for digestive enzymes: in their capacity to modify the chemical nature of environmental stimuli prior to gustation, digestive enzymes can be viewed as having important chemosensory roles. PMID:7685121

  7. Adaptive Process Control with Fuzzy Logic and Genetic Algorithms

    NASA Technical Reports Server (NTRS)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  8. Adaptive process control using fuzzy logic and genetic algorithms

    NASA Technical Reports Server (NTRS)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

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

    NASA Astrophysics Data System (ADS)

    Huang, Zhi; Yuan, Hong

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

  10. Genetic dissection of cardiac growth control pathways

    NASA Technical Reports Server (NTRS)

    MacLellan, W. R.; Schneider, M. D.

    2000-01-01

    Cardiac muscle cells exhibit two related but distinct modes of growth that are highly regulated during development and disease. Cardiac myocytes rapidly proliferate during fetal life but exit the cell cycle irreversibly soon after birth, following which the predominant form of growth shifts from hyperplastic to hypertrophic. Much research has focused on identifying the candidate mitogens, hypertrophic agonists, and signaling pathways that mediate these processes in isolated cells. What drives the proliferative growth of embryonic myocardium in vivo and the mechanisms by which adult cardiac myocytes hypertrophy in vivo are less clear. Efforts to answer these questions have benefited from rapid progress made in techniques to manipulate the murine genome. Complementary technologies for gain- and loss-of-function now permit a mutational analysis of these growth control pathways in vivo in the intact heart. These studies have confirmed the importance of suspected pathways, have implicated unexpected pathways as well, and have led to new paradigms for the control of cardiac growth.

  11. Complex Genetics Control Natural Variation in Arabidopsis thaliana Resistance to Botrytis cinerea

    PubMed Central

    Rowe, Heather C.; Kliebenstein, Daniel J.

    2008-01-01

    The genetic architecture of plant defense against microbial pathogens may be influenced by pathogen lifestyle. While plant interactions with biotrophic pathogens are frequently controlled by the action of large-effect resistance genes that follow classic Mendelian inheritance, our study suggests that plant defense against the necrotrophic pathogen Botrytis cinerea is primarily quantitative and genetically complex. Few studies of quantitative resistance to necrotrophic pathogens have used large plant mapping populations to dissect the genetic structure of resistance. Using a large structured mapping population of Arabidopsis thaliana, we identified quantitative trait loci influencing plant response to B. cinerea, measured as expansion of necrotic lesions on leaves and accumulation of the antimicrobial compound camalexin. Testing multiple B. cinerea isolates, we identified 23 separate QTL in this population, ranging in isolate-specificity from being identified with a single isolate to controlling resistance against all isolates tested. We identified a set of QTL controlling accumulation of camalexin in response to pathogen infection that largely colocalized with lesion QTL. The identified resistance QTL appear to function in epistatic networks involving three or more loci. Detection of multilocus connections suggests that natural variation in specific signaling or response networks may control A. thaliana–B. cinerea interaction in this population. PMID:18845849

  12. Genetic control of cuticular wax compounds in Eucalyptus globulus.

    PubMed

    Gosney, Benjamin J; Potts, Brad M; O'Reilly-Wapstra, Julianne M; Vaillancourt, René E; Fitzgerald, Hugh; Davies, Noel W; Freeman, Jules S

    2016-01-01

    Plant cuticular wax compounds perform functions that are essential for the survival of terrestrial plants. Despite their importance, the genetic control of these compounds is poorly understood outside of model taxa. Here we investigate the genetic basis of variation in cuticular compounds in Eucalyptus globulus using quantitative genetic and quantitative trait loci (QTL) analyses. Quantitative genetic analysis was conducted using 246 open-pollinated progeny from 13 native sub-races throughout the geographic range. QTL analysis was conducted using 112 clonally replicated progeny from an outcross F2 population. Nine compounds exhibited significant genetic variation among sub-races with three exhibiting signals of diversifying selection. Fifty-two QTL were found with co-location of QTL for related compounds commonly observed. Notable among these was the QTL for five wax esters, which co-located with a gene from the KCS family, previously implicated in the biosynthesis of cuticular waxes in Arabidopsis. In combination, the QTL and quantitative genetic analyses suggest the variation and differentiation in cuticular wax compounds within E. globulus has a complex genetic origin. Sub-races exhibited independent latitudinal and longitudinal differentiation in cuticular wax compounds, likely reflecting processes such as historic gene flow and diversifying selection acting upon genes that have diverse functions in distinct biochemical pathways.

  13. Structural permeability of complex networks to control signals

    NASA Astrophysics Data System (ADS)

    Lo Iudice, Francesco; Garofalo, Franco; Sorrentino, Francesco

    2015-09-01

    Many biological, social and technological systems can be described as complex networks. The goal of affecting their behaviour has motivated recent work focusing on the relationship between the network structure and its propensity to be controlled. While this work has provided insight into several relevant problems, a comprehensive approach to address partial and complete controllability of networks is still lacking. Here, we bridge this gap by developing a framework to maximize the diffusion of the control signals through a network, while taking into account physical and economic constraints that inevitably arise in applications. This approach allows us to introduce the network permeability, a unified metric of the propensity of a network to be controllable. The analysis of the permeability of several synthetic and real networks enables us to extract some structural features that deepen our quantitative understanding of the ease with which specific controllability requirements can be met.

  14. Control of tree water networks: A geometric programming approach

    NASA Astrophysics Data System (ADS)

    Sela Perelman, L.; Amin, S.

    2015-10-01

    This paper presents a modeling and operation approach for tree water supply systems. The network control problem is approximated as a geometric programming (GP) problem. The original nonlinear nonconvex network control problem is transformed into a convex optimization problem. The optimization model can be efficiently solved to optimality using state-of-the-art solvers. Two control schemes are presented: (1) operation of network actuators (pumps and valves) and (2) controlled demand shedding allocation between network consumers with limited resources. The dual of the network control problem is formulated and is used to perform sensitivity analysis with respect to hydraulic constraints. The approach is demonstrated on a small branched-topology network and later extended to a medium-size irrigation network. The results demonstrate an intrinsic trade-off between energy costs and demand shedding policy, providing an efficient decision support tool for active management of water systems.

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

    PubMed Central

    Villafuerte, Sandra; Burmeister, Margit

    2003-01-01

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

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

    PubMed Central

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

    2008-01-01

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

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

    PubMed

    Hether, Tyler D; Hohenlohe, Paul A

    2014-04-01

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

  18. Edge orientation for optimizing controllability of complex networks

    NASA Astrophysics Data System (ADS)

    Xiao, Yan-Dong; Lao, Song-Yang; Hou, Lv-Lin; Bai, Liang

    2014-10-01

    Recently, as the controllability of complex networks attracts much attention, how to design and optimize the controllability of networks has become a common and urgent problem in the field of controlling complex networks. Previous work focused on the structural perturbation and neglected the role of edge direction to optimize the network controllability. In a recent work [Phys. Rev. Lett. 103, 228702 (2009), 10.1103/PhysRevLett.103.228702], the authors proposed a simple method to enhance the synchronizability of networks by assignment of link direction while keeping network topology unchanged. However, the controllability is fundamentally different from synchronization. In this work, we systematically propose the definition of assigning direction to optimize controllability, which is called the edge orientation for optimal controllability problem (EOOC). To solve the EOOC problem, we construct a switching network and transfer the EOOC problem to find the maximum independent set of the switching network. We prove that the principle of our optimization method meets the sense of unambiguity and optimum simultaneously. Furthermore, the relationship between the degree-degree correlations and EOOC are investigated by experiments. The results show that the disassortativity pattern could weaken the orientation for optimal controllability, while the assortativity pattern has no correlation with EOOC. All the experimental results of this work verify that the network structure determines the network controllability and the optimization effects.

  19. Edge orientation for optimizing controllability of complex networks.

    PubMed

    Xiao, Yan-Dong; Lao, Song-Yang; Hou, Lv-Lin; Bai, Liang

    2014-10-01

    Recently, as the controllability of complex networks attracts much attention, how to design and optimize the controllability of networks has become a common and urgent problem in the field of controlling complex networks. Previous work focused on the structural perturbation and neglected the role of edge direction to optimize the network controllability. In a recent work [Phys. Rev. Lett. 103, 228702 (2009)], the authors proposed a simple method to enhance the synchronizability of networks by assignment of link direction while keeping network topology unchanged. However, the controllability is fundamentally different from synchronization. In this work, we systematically propose the definition of assigning direction to optimize controllability, which is called the edge orientation for optimal controllability problem (EOOC). To solve the EOOC problem, we construct a switching network and transfer the EOOC problem to find the maximum independent set of the switching network. We prove that the principle of our optimization method meets the sense of unambiguity and optimum simultaneously. Furthermore, the relationship between the degree-degree correlations and EOOC are investigated by experiments. The results show that the disassortativity pattern could weaken the orientation for optimal controllability, while the assortativity pattern has no correlation with EOOC. All the experimental results of this work verify that the network structure determines the network controllability and the optimization effects. PMID:25375546

  20. Observability and Controllability of Nonlinear Networks: The Role of Symmetry

    NASA Astrophysics Data System (ADS)

    Schiff, Steven; Whalen, Andrew; Brennan, Sean; Sauer, Timothy

    2015-03-01

    Observability and controllability are essential concepts to the design of predictive observer models and feedback controllers of networked systems. For example, noncontrollable mathematical models of real systems may have subspaces that influence model behavior, but cannot be controlled by an input. Such subspaces are difficult to determine in complex nonlinear networks. Since most of the present theory was developed for linear networks without symmetries, here we present a numerical and group representational framework, to quantify the observability and controllability of nonlinear networks with explicit symmetries that shows the connection between symmetries and measures of observability and controllability. We numerically observe and theoretically predict that not all symmetries have the same effect on network observation and control. We find that the presence of symmetry in a network may decrease observability and controllability, although networks containing only rotational symmetries remain controllable and observable. These results alter our view of the nature of observability and controllability in complex networks, change our understanding of structural controllability, and affect the design of mathematical models to observe and control such networks. National Academies - Keck Futures Initiative, NSF grant DMS 1216568, and Collaborative Research in Computational Neuroscience NIH Grant 1R01EB014641.

  1. Observability and Controllability of Nonlinear Networks: The Role of Symmetry

    NASA Astrophysics Data System (ADS)

    Whalen, Andrew J.; Brennan, Sean N.; Sauer, Timothy D.; Schiff, Steven J.

    2015-01-01

    Observability and controllability are essential concepts to the design of predictive observer models and feedback controllers of networked systems. For example, noncontrollable mathematical models of real systems have subspaces that influence model behavior, but cannot be controlled by an input. Such subspaces can be difficult to determine in complex nonlinear networks. Since almost all of the present theory was developed for linear networks without symmetries, here we present a numerical and group representational framework, to quantify the observability and controllability of nonlinear networks with explicit symmetries that shows the connection between symmetries and nonlinear measures of observability and controllability. We numerically observe and theoretically predict that not all symmetries have the same effect on network observation and control. Our analysis shows that the presence of symmetry in a network may decrease observability and controllability, although networks containing only rotational symmetries remain controllable and observable. These results alter our view of the nature of observability and controllability in complex networks, change our understanding of structural controllability, and affect the design of mathematical models to observe and control such networks.

  2. The control networks of Tethys and Dione

    NASA Technical Reports Server (NTRS)

    Davies, M. E.; Katayama, F. Y.

    1983-01-01

    Control networks of the Saturnian satellites Tethys and Dione have been established photogrammetrically from pictures taken by the two Voyager spacecraft during their flybys. Coordinates of 110 points on Tethys and 126 points on Dione are listed; selected points are identified on U.S. Geological Survey maps of the satellites, and many are identified by name. Measurements of these points were made on six pictures from Voyager 1 and 21 from Voyager 2 for Tethys, and on 27 pictures from Voyager 1 and one from Voyager 2 for Dione. The longitude systems on the satellites have been defined by craters on their surfaces. The mean radii have been determined as 524 + or - 5 km for Tethys and 559 + or - 5 km for Dione.

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

    PubMed Central

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

    2014-01-01

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

  4. Environmental Control Of A Genetic Process

    NASA Technical Reports Server (NTRS)

    Khosla, Chaitan; Bailey, James E.

    1991-01-01

    E. coli bacteria altered to contain DNA sequence encoding production of hemoglobin made to produce hemoglobin at rates decreasing with increases in concentration of oxygen in culture media. Represents amplification of part of method described in "Cloned Hemoglobin Genes Enhance Growth Of Cells" (NPO-17517). Manipulation of promoter/regulator DNA sequences opens promising new subfield of recombinant-DNA technology for environmental control of expression of selected DNA sequences. New recombinant-DNA fusion gene products, expression vectors, and nucleotide-base sequences will emerge. Likely applications include such aerobic processes as manufacture of cloned proteins and synthesis of metabolites, production of chemicals by fermentation, enzymatic degradation, treatment of wastes, brewing, and variety of oxidative chemical reactions.

  5. Human genetic variation: new challenges and opportunities for doping control.

    PubMed

    Schneider, Angela J; Fedoruk, Matthew N; Rupert, Jim L

    2012-01-01

    Sport celebrates differences in competitors that lead to the often razor-thin margins between victory and defeat. The source of this variation is the interaction between the environment in which the athletes develop and compete and their genetic make-up. However, a darker side of sports may also be genetically influenced: some anti-doping tests are affected by the athlete's genotype. Genetic variation is an issue that anti-doping authorities must address as more is learned about the interaction between genotype and the responses to prohibited practices. To differentiate between naturally occurring deviations in indirect blood and urine markers from those potentially caused by doping, the "biological-passport" program uses intra-individual variability rather than population values to establish an athlete's expected physiological range. The next step in "personalized" doping control may be the inclusion of genetic data, both for the purposes of documenting an athlete's responses to doping agents and doping-control assays as well facilitating athlete and sample identification. Such applications could benefit "clean" athletes but will come at the expense of risks to privacy. This article reviews the instances where genetics has intersected with doping control, and briefly discusses the potential role, and ethical implications, of genotyping in the struggle to eliminate illicit ergogenic practices. PMID:22681541

  6. Human genetic variation: new challenges and opportunities for doping control.

    PubMed

    Schneider, Angela J; Fedoruk, Matthew N; Rupert, Jim L

    2012-01-01

    Sport celebrates differences in competitors that lead to the often razor-thin margins between victory and defeat. The source of this variation is the interaction between the environment in which the athletes develop and compete and their genetic make-up. However, a darker side of sports may also be genetically influenced: some anti-doping tests are affected by the athlete's genotype. Genetic variation is an issue that anti-doping authorities must address as more is learned about the interaction between genotype and the responses to prohibited practices. To differentiate between naturally occurring deviations in indirect blood and urine markers from those potentially caused by doping, the "biological-passport" program uses intra-individual variability rather than population values to establish an athlete's expected physiological range. The next step in "personalized" doping control may be the inclusion of genetic data, both for the purposes of documenting an athlete's responses to doping agents and doping-control assays as well facilitating athlete and sample identification. Such applications could benefit "clean" athletes but will come at the expense of risks to privacy. This article reviews the instances where genetics has intersected with doping control, and briefly discusses the potential role, and ethical implications, of genotyping in the struggle to eliminate illicit ergogenic practices.

  7. Landscape genetic connectivity in a riparian foundation tree is jointly driven by climatic gradients and river networks.

    PubMed

    Cushman, Samuel A; Max, Tamara; Meneses, Nashelly; Evans, Luke M; Ferrier, Sharon; Honchak, Barbara; Whitham, Thomas G; Allan, Gerard J

    2014-07-01

    Fremont cottonwood (Populus fremonti) is a foundation riparian tree species that drives community structure and ecosystem processes in southwestern U.S. ecosystems. Despite its ecological importance, little is known about the ecological and environmental processes that shape its genetic diversity, structure, and landscape connectivity. Here, we combined molecular analyses of 82 populations including 1312 individual trees dispersed over the species' geographical distribution. We reduced the data set to 40 populations and 743 individuals to eliminate admixture with a sibling species, and used multivariate restricted optimization and reciprocal causal modeling to evaluate the effects of river network connectivity and climatic gradients on gene flow. Our results confirmed the following: First, gene flow of Fremont cottonwood is jointly controlled by the connectivity of the river network and gradients of seasonal precipitation. Second, gene flow is facilitated by mid-sized to large rivers, and is resisted by small streams and terrestrial uplands, with resistance to gene flow decreasing with river size. Third, genetic differentiation increases with cumulative differences in winter and spring precipitation. Our results suggest that ongoing fragmentation of riparian habitats will lead to a loss of landscape-level genetic connectivity, leading to increased inbreeding and the concomitant loss of genetic diversity in a foundation species. These genetic effects will cascade to a much larger community of organisms, some of which are threatened and endangered. PMID:25154093

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

    NASA Astrophysics Data System (ADS)

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

    2008-10-01

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

  9. Self-teaching neural network learns difficult reactor control problem

    SciTech Connect

    Jouse, W.C.

    1989-01-01

    A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits.

  10. Systems genetics identifies Sestrin 3 as a regulator of a proconvulsant gene network in human epileptic hippocampus.

    PubMed

    Johnson, Michael R; Behmoaras, Jacques; Bottolo, Leonardo; Krishnan, Michelle L; Pernhorst, Katharina; Santoscoy, Paola L Meza; Rossetti, Tiziana; Speed, Doug; Srivastava, Prashant K; Chadeau-Hyam, Marc; Hajji, Nabil; Dabrowska, Aleksandra; Rotival, Maxime; Razzaghi, Banafsheh; Kovac, Stjepana; Wanisch, Klaus; Grillo, Federico W; Slaviero, Anna; Langley, Sarah R; Shkura, Kirill; Roncon, Paolo; De, Tisham; Mattheisen, Manuel; Niehusmann, Pitt; O'Brien, Terence J; Petrovski, Slave; von Lehe, Marec; Hoffmann, Per; Eriksson, Johan; Coffey, Alison J; Cichon, Sven; Walker, Matthew; Simonato, Michele; Danis, Bénédicte; Mazzuferi, Manuela; Foerch, Patrik; Schoch, Susanne; De Paola, Vincenzo; Kaminski, Rafal M; Cunliffe, Vincent T; Becker, Albert J; Petretto, Enrico

    2015-01-01

    Gene-regulatory network analysis is a powerful approach to elucidate the molecular processes and pathways underlying complex disease. Here we employ systems genetics approaches to characterize the genetic regulation of pathophysiological pathways in human temporal lobe epilepsy (TLE). Using surgically acquired hippocampi from 129 TLE patients, we identify a gene-regulatory network genetically associated with epilepsy that contains a specialized, highly expressed transcriptional module encoding proconvulsive cytokines and Toll-like receptor signalling genes. RNA sequencing analysis in a mouse model of TLE using 100 epileptic and 100 control hippocampi shows the proconvulsive module is preserved across-species, specific to the epileptic hippocampus and upregulated in chronic epilepsy. In the TLE patients, we map the trans-acting genetic control of this proconvulsive module to Sestrin 3 (SESN3), and demonstrate that SESN3 positively regulates the module in macrophages, microglia and neurons. Morpholino-mediated Sesn3 knockdown in zebrafish confirms the regulation of the transcriptional module, and attenuates chemically induced behavioural seizures in vivo.

  11. Systems genetics identifies Sestrin 3 as a regulator of a proconvulsant gene network in human epileptic hippocampus

    PubMed Central

    Johnson, Michael R.; Rossetti, Tiziana; Speed, Doug; Srivastava, Prashant K.; Chadeau-Hyam, Marc; Hajji, Nabil; Dabrowska, Aleksandra; Rotival, Maxime; Razzaghi, Banafsheh; Kovac, Stjepana; Wanisch, Klaus; Grillo, Federico W.; Slaviero, Anna; Langley, Sarah R.; Shkura, Kirill; Roncon, Paolo; De, Tisham; Mattheisen, Manuel; Niehusmann, Pitt; O’Brien, Terence J.; Petrovski, Slave; von Lehe, Marec; Hoffmann, Per; Eriksson, Johan; Coffey, Alison J.; Cichon, Sven; Walker, Matthew; Simonato, Michele; Danis, Bénédicte; Mazzuferi, Manuela; Foerch, Patrik; Schoch, Susanne; De Paola, Vincenzo; Kaminski, Rafal M.; Cunliffe, Vincent T.; Becker, Albert J.; Petretto, Enrico

    2015-01-01

    Gene-regulatory network analysis is a powerful approach to elucidate the molecular processes and pathways underlying complex disease. Here we employ systems genetics approaches to characterize the genetic regulation of pathophysiological pathways in human temporal lobe epilepsy (TLE). Using surgically acquired hippocampi from 129 TLE patients, we identify a gene-regulatory network genetically associated with epilepsy that contains a specialized, highly expressed transcriptional module encoding proconvulsive cytokines and Toll-like receptor signalling genes. RNA sequencing analysis in a mouse model of TLE using 100 epileptic and 100 control hippocampi shows the proconvulsive module is preserved across-species, specific to the epileptic hippocampus and upregulated in chronic epilepsy. In the TLE patients, we map the trans-acting genetic control of this proconvulsive module to Sestrin 3 (SESN3), and demonstrate that SESN3 positively regulates the module in macrophages, microglia and neurons. Morpholino-mediated Sesn3 knockdown in zebrafish confirms the regulation of the transcriptional module, and attenuates chemically induced behavioural seizures in vivo. PMID:25615886

  12. A geometrical approach to control and controllability of nonlinear dynamical networks

    PubMed Central

    Wang, Le-Zhi; Su, Ri-Qi; Huang, Zi-Gang; Wang, Xiao; Wang, Wen-Xu; Grebogi, Celso; Lai, Ying-Cheng

    2016-01-01

    In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control. PMID:27076273

  13. Physiology and genetics of metabolic flux control in Zymomonas mobilis

    SciTech Connect

    Conway, T.

    1992-01-01

    This work seeks to understand the role of gene expression in regulating glycolytic enzyme synthesis in a balance that allows proper glycoltic flux control. The seven genes targeted for study in this laboratory have been cloned and sequenced, and molecular details of regulation have been investigated. Clear that glycolytic enzyme synthesis is coordinated to prevent the build up of toxic metabolic intermediates. The genetic mechanisms responsible for regulating balanced expression of the EntnerDoudoroff and glycolytic genes in Z. mobilis are beginning to be understood. Several layers of genetic control, perhaps in a hierarchal arrangement act in concert to determine the relative abundance of the glycolytic enzymes. These genetic controls involve differential translational efficiency, highly conserved promoter sequences, transcription factors, differential mRNA stabilities, and nucleolytic mRNA processing. The serendipitous cloning of the glucose facilitator, glf, as a result of linkage to several other genes of interest will have a significant impact on the study of Z. mobilis metabolism. The glucose facilitator is being characterized in a genetically reconstituted system in E. coli. Molecular genetic studies indicate that the ratio of glf expression to that of glk, zmf, and edd is carefully regulated, and suggests a critical role in metabolic control. Regulation of glycolytic gene expression is now sufficiently well understood to allow use of the glycolytic genes as tools to manipulate specified enzyme levels for the purpose of analyzing metabolic flux control. The critical genes have been subcloned for stable expression in Z. mobilis and placed under control of a regulated promoter system involving the tac promoter, the lacI repressor, and gene induction in by IPTG. HPLC methods have been developed that allow quantitation of virtually all of the metabolic intermediates in the cell pool.

  14. An extended signal control strategy for urban network traffic flow

    NASA Astrophysics Data System (ADS)

    Yan, Fei; Tian, Fuli; Shi, Zhongke

    2016-03-01

    Traffic flow patterns are in general repeated on a daily or weekly basis. To improve the traffic conditions by using the inherent repeatability of traffic flow, a novel signal control strategy for urban networks was developed via iterative learning control (ILC) approach. Rigorous analysis shows that the proposed learning control method can guarantee the asymptotic convergence. The impacts of the ILC-based signal control strategy on the macroscopic fundamental diagram (MFD) were analyzed by simulations on a test road network. The results show that the proposed ILC strategy can evenly distribute the accumulation in the network and improve the network mobility.

  15. Resolving Conflicts: Modeling Genetic Control of Plant Morphogenesis.

    PubMed

    Coen, Enrico; Rebocho, Alexandra B

    2016-09-26

    Computational modeling of tissue morphogenesis reveals how spatiotemporal patterns of gene activity control tissue shape by introducing several types of tissue conflict. These conflicts reflect genetic modulation of processes that influence the cellular mechanical properties and may underlie the enormous diversity of forms that have evolved in plants and animals. PMID:27676429

  16. PID controller design for nonlinear systems represented by discrete-time local model networks

    NASA Astrophysics Data System (ADS)

    Hametner, Christoph; Mayr, Christian H.; Kozek, Martin; Jakubek, Stefan

    2013-09-01

    This paper deals with proportional-integral-derivative (PID) controller design for nonlinear systems represented by local model networks. The proposed method is based on the concept of parallel distributed compensators where the scheduling of the local model network is adopted for the PID parameters. The proposed design method for nonlinear PID controllers considers closed-loop stability by means of a Lyapunov stability criterion as well as closed-loop performance. All PID parameters are determined by a multi-objective genetic algorithm (multiGA), which handles the trade-off between stability and performance. A simulation example demonstrates the effectiveness of the proposed method.

  17. Prospects of chemosterilant and genetic control of rodents

    PubMed Central

    Marsh, Rex E.; Howard, Walter E.

    1973-01-01

    This paper discusses some requirements of an ideal rodent chemosterilant, analyses the advantages of chemosterilants over other control methods, and compares the potential values of chemosterilants that affect females, males, and both sexes. Examples are given of specific situations where chemosterilants will be valuable in rodent control, together with suggested methods of applying them. The theory and practicability of using genetics in rodent control are also discussed. Neither the chemosterilant nor the genetic method is expected to become a panacea, but their eventual application will be a significant advance in rodent-control technology. Since both approaches are based on sound biological principles and are relatively safe, they should be helpful in regulating rodent populations in the future. PMID:4583051

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

    PubMed

    Sun, Yonghui; Feng, Gang; Cao, Jinde

    2010-12-01

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

  19. Genetic improvement of tomato by targeted control of fruit softening.

    PubMed

    Uluisik, Selman; Chapman, Natalie H; Smith, Rebecca; Poole, Mervin; Adams, Gary; Gillis, Richard B; Besong, Tabot M D; Sheldon, Judith; Stiegelmeyer, Suzy; Perez, Laura; Samsulrizal, Nurul; Wang, Duoduo; Fisk, Ian D; Yang, Ni; Baxter, Charles; Rickett, Daniel; Fray, Rupert; Blanco-Ulate, Barbara; Powell, Ann L T; Harding, Stephen E; Craigon, Jim; Rose, Jocelyn K C; Fich, Eric A; Sun, Li; Domozych, David S; Fraser, Paul D; Tucker, Gregory A; Grierson, Don; Seymour, Graham B

    2016-09-01

    Controlling the rate of softening to extend shelf life was a key target for researchers engineering genetically modified (GM) tomatoes in the 1990s, but only modest improvements were achieved. Hybrids grown nowadays contain 'non-ripening mutations' that slow ripening and improve shelf life, but adversely affect flavor and color. We report substantial, targeted control of tomato softening, without affecting other aspects of ripening, by silencing a gene encoding a pectate lyase. PMID:27454737

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

    PubMed

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

    2015-01-01

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

  1. Nonlinear adaptive PID control for greenhouse environment based on RBF network.

    PubMed

    Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui

    2012-01-01

    This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production.

  2. Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network

    PubMed Central

    Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui

    2012-01-01

    This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production. PMID:22778587

  3. Unified Lunar Control Network 2005 and Topographic Model

    NASA Technical Reports Server (NTRS)

    Archinal, B. A.; Rosiek, M. R.; Redding, B. L.

    2005-01-01

    There are currently two generally accepted lunar control networks. These are the Unified Lunar Control Network (ULCN) and the Clementine Lunar Control Network (CLCN), both derived by M. Davies and T. Colvin at RAND. We address here our efforts to merge and improve these networks into a new ULCN. The ULCN was described in the last major publication about a lunar control network. The statistics on this and the other networks discussed here. Images for this network are from the Apollo, Mariner 10, and Galileo missions, and Earth-based photographs. The importance of this network is that its accuracy is relatively well quantified and published information on the network is available. The CLCN includes measurements on 43,871 Clementine 750-nm images - the largest planetary control network ever computed. This purpose of this network was to determine the geometry for the Clementine Basemap Mosiac (CBM). The geometry of that mosaic was used to produce the Clementine UVVIS digital image model and the Near-Infrared Global Multispectral Map of the Moon from Clementine. Through the extensive use of these products, they and the underlying CLCN in effect define the generally accepted current coordinate system for reporting and describing the location of lunar coordinates. However, no publication describes the CLCN itself.

  4. Implementing controlled-unitary operations over the butterfly network

    SciTech Connect

    Soeda, Akihito; Kinjo, Yoshiyuki; Turner, Peter S.; Murao, Mio

    2014-12-04

    We introduce a multiparty quantum computation task over a network in a situation where the capacities of both the quantum and classical communication channels of the network are limited and a bottleneck occurs. Using a resource setting introduced by Hayashi [1], we present an efficient protocol for performing controlled-unitary operations between two input nodes and two output nodes over the butterfly network, one of the most fundamental networks exhibiting the bottleneck problem. This result opens the possibility of developing a theory of quantum network coding for multiparty quantum computation, whereas the conventional network coding only treats multiparty quantum communication.

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

    PubMed

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

    2016-01-01

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

  6. Minimizing communication cost among distributed controllers in software defined networks

    NASA Astrophysics Data System (ADS)

    Arlimatti, Shivaleela; Elbreiki, Walid; Hassan, Suhaidi; Habbal, Adib; Elshaikh, Mohamed

    2016-08-01

    Software Defined Networking (SDN) is a new paradigm to increase the flexibility of today's network by promising for a programmable network. The fundamental idea behind this new architecture is to simplify network complexity by decoupling control plane and data plane of the network devices, and by making the control plane centralized. Recently controllers have distributed to solve the problem of single point of failure, and to increase scalability and flexibility during workload distribution. Even though, controllers are flexible and scalable to accommodate more number of network switches, yet the problem of intercommunication cost between distributed controllers is still challenging issue in the Software Defined Network environment. This paper, aims to fill the gap by proposing a new mechanism, which minimizes intercommunication cost with graph partitioning algorithm, an NP hard problem. The methodology proposed in this paper is, swapping of network elements between controller domains to minimize communication cost by calculating communication gain. The swapping of elements minimizes inter and intra communication cost among network domains. We validate our work with the OMNeT++ simulation environment tool. Simulation results show that the proposed mechanism minimizes the inter domain communication cost among controllers compared to traditional distributed controllers.

  7. Network device interface for digitally interfacing data channels to a controller via a network

    NASA Technical Reports Server (NTRS)

    Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor)

    2004-01-01

    The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface uses a specialized protocol for communicating across the network bus that uses a low-level instruction set and has low overhead for data communication.

  8. Network device interface for digitally interfacing data channels to a controller via a network

    NASA Technical Reports Server (NTRS)

    Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor)

    2005-01-01

    The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface uses a specialized protocol for communicating across the network bus that uses a low-level instruction set and has low overhead for data communication.

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

    NASA Astrophysics Data System (ADS)

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

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

  10. Neural networks for self-learning control systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Derrick H.; Widrow, Bernard

    1990-01-01

    It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

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

    PubMed

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

    2015-04-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-11-01

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

  14. Scalable Approaches to Control Network Dynamics: Prospects for City Networks

    NASA Astrophysics Data System (ADS)

    Motter, Adilson E.; Gray, Kimberly A.

    2014-07-01

    A city is a complex, emergent system and as such can be conveniently represented as a network of interacting components. A fundamental aspect of networks is that the systemic properties can depend as much on the interactions as they depend on the properties of the individual components themselves. Another fundamental aspect is that changes to one component can affect other components, in a process that may cause the entire or a substantial part of the system to change behavior. Over the past 2 decades, much research has been done on the modeling of large and complex networks involved in communication and transportation, disease propagation, and supply chains, as well as emergent phenomena, robustness and optimization in such systems...

  15. Genetic Control of Organ Shape and Tissue Polarity

    PubMed Central

    Green, Amelia A.; Kennaway, J. Richard; Hanna, Andrew I.; Bangham, J. Andrew; Coen, Enrico

    2010-01-01

    The mechanisms by which genes control organ shape are poorly understood. In principle, genes may control shape by modifying local rates and/or orientations of deformation. Distinguishing between these possibilities has been difficult because of interactions between patterns, orientations, and mechanical constraints during growth. Here we show how a combination of growth analysis, molecular genetics, and modelling can be used to dissect the factors contributing to shape. Using the Snapdragon (Antirrhinum) flower as an example, we show how shape development reflects local rates and orientations of tissue growth that vary spatially and temporally to form a dynamic growth field. This growth field is under the control of several dorsoventral genes that influence flower shape. The action of these genes can be modelled by assuming they modulate specified growth rates parallel or perpendicular to local orientations, established by a few key organisers of tissue polarity. Models in which dorsoventral genes only influence specified growth rates do not fully account for the observed growth fields and shapes. However, the data can be readily explained by a model in which dorsoventral genes also modify organisers of tissue polarity. In particular, genetic control of tissue polarity organisers at ventral petal junctions and distal boundaries allows both the shape and growth field of the flower to be accounted for in wild type and mutants. The results suggest that genetic control of tissue polarity organisers has played a key role in the development and evolution of shape. PMID:21085690

  16. Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition.

    PubMed

    Spreng, R Nathan; Stevens, W Dale; Chamberlain, Jon P; Gilmore, Adrian W; Schacter, Daniel L

    2010-10-15

    Tasks that demand externalized attention reliably suppress default network activity while activating the dorsal attention network. These networks have an intrinsic competitive relationship; activation of one suppresses activity of the other. Consequently, many assume that default network activity is suppressed during goal-directed cognition. We challenge this assumption in an fMRI study of planning. Recent studies link default network activity with internally focused cognition, such as imagining personal future events, suggesting a role in autobiographical planning. However, it is unclear how goal-directed cognition with an internal focus is mediated by these opposing networks. A third anatomically interposed 'frontoparietal control network' might mediate planning across domains, flexibly coupling with either the default or dorsal attention network in support of internally versus externally focused goal-directed cognition, respectively. We tested this hypothesis by analyzing brain activity during autobiographical versus visuospatial planning. Autobiographical planning engaged the default network, whereas visuospatial planning engaged the dorsal attention network, consistent with the anti-correlated domains of internalized and externalized cognition. Critically, both planning tasks engaged the frontoparietal control network. Task-related activation of these three networks was anatomically consistent with independently defined resting-state functional connectivity MRI maps. Task-related functional connectivity analyses demonstrate that the default network can be involved in goal-directed cognition when its activity is coupled with the frontoparietal control network. Additionally, the frontoparietal control network may flexibly couple with the default and dorsal attention networks according to task domain, serving as a cortical mediator linking the two networks in support of goal-directed cognitive processes.

  17. Temporal network structures controlling disease spreading.

    PubMed

    Holme, Petter

    2016-08-01

    We investigate disease spreading on eight empirical data sets of human contacts (mostly proximity networks recording who is close to whom, at what time). We compare three levels of representations of these data sets: temporal networks, static networks, and a fully connected topology. We notice that the difference between the static and fully connected networks-with respect to time to extinction and average outbreak size-is smaller than between the temporal and static topologies. This suggests that, for these data sets, temporal structures influence disease spreading more than static-network structures. To explain the details in the differences between the representations, we use 32 network measures. This study concurs that long-time temporal structures, like the turnover of nodes and links, are the most important for the spreading dynamics. PMID:27627315

  18. Temporal network structures controlling disease spreading.

    PubMed

    Holme, Petter

    2016-08-01

    We investigate disease spreading on eight empirical data sets of human contacts (mostly proximity networks recording who is close to whom, at what time). We compare three levels of representations of these data sets: temporal networks, static networks, and a fully connected topology. We notice that the difference between the static and fully connected networks-with respect to time to extinction and average outbreak size-is smaller than between the temporal and static topologies. This suggests that, for these data sets, temporal structures influence disease spreading more than static-network structures. To explain the details in the differences between the representations, we use 32 network measures. This study concurs that long-time temporal structures, like the turnover of nodes and links, are the most important for the spreading dynamics.

  19. Genetic networks inducing invasive growth in Saccharomyces cerevisiae identified through systematic genome-wide overexpression.

    PubMed

    Shively, Christian A; Eckwahl, Matthew J; Dobry, Craig J; Mellacheruvu, Dattatreya; Nesvizhskii, Alexey; Kumar, Anuj

    2013-04-01

    The budding yeast Saccharomyces cerevisiae can respond to nutritional and environmental stress by implementing a morphogenetic program wherein cells elongate and interconnect, forming pseudohyphal filaments. This growth transition has been studied extensively as a model signaling system with similarity to processes of hyphal development that are linked with virulence in related fungal pathogens. Classic studies have identified core pseudohyphal growth signaling modules in yeast; however, the scope of regulatory networks that control yeast filamentation is broad and incompletely defined. Here, we address the genetic basis of yeast pseudohyphal growth by implementing a systematic analysis of 4909 genes for overexpression phenotypes in a filamentous strain of S. cerevisiae. Our results identify 551 genes conferring exaggerated invasive growth upon overexpression under normal vegetative growth conditions. This cohort includes 79 genes lacking previous phenotypic characterization. Pathway enrichment analysis of the gene set identifies networks mediating mitogen-activated protein kinase (MAPK) signaling and cell cycle progression. In particular, overexpression screening suggests that nuclear export of the osmoresponsive MAPK Hog1p may enhance pseudohyphal growth. The function of nuclear Hog1p is unclear from previous studies, but our analysis using a nuclear-depleted form of Hog1p is consistent with a role for nuclear Hog1p in repressing pseudohyphal growth. Through epistasis and deletion studies, we also identified genetic relationships with the G2 cyclin Clb2p and phenotypes in filamentation induced by S-phase arrest. In sum, this work presents a unique and informative resource toward understanding the breadth of genes and pathways that collectively constitute the molecular basis of filamentation.

  20. Network device interface for digitally interfacing data channels to a controller a via network

    NASA Technical Reports Server (NTRS)

    Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor)

    2006-01-01

    The present invention provides a network device interface and method for digitally connecting a plurality of data channels to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. In one embodiment, the bus controller transmits messages to the network device interface containing a plurality of bits having a value defined by a transition between first and second states in the bits. The network device interface determines timing of the data sequence of the message and uses the determined timing to communicate with the bus controller.

  1. Network device interface for digitally interfacing data channels to a controller via a network

    NASA Technical Reports Server (NTRS)

    Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor)

    2006-01-01

    The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface is a state machine, such as an ASIC, that operates independent of a processor in communicating with the bus controller and data channels.

  2. [Application of artificial neural network based on the genetic algorithm in predicting the root distribution of winter wheat].

    PubMed

    Luo, Changshou; Zuo, Qiang; Li, Baoguo

    2004-02-01

    In this study, a controlled experiment of winter wheat under water stress at the seedling stage was conducted in soil columns in greenhouse. Based on the data gotten from the experiment, a model to estimate root length density distribution was developed through optimizing the weights of neural network by genetic algorithm. The neural network model was constructed by using forward neural network framework, by applying the strategy of the roulette wheel selection and reserving the most optimizing series of weights, which were composed by real codes. This model was applied to predict the root length density distribution of winter wheat, and the predicted root length density had good agreement with experiment data. The way could save a lot of manpower and material resources for determining the root length density distribution of winter wheat.

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

    PubMed

    Koch, Valerie Gutmann

    2012-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Kobayashi, Takahisa; Simon, Donald L.

    2001-01-01

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

  5. The fatigue life prediction of aluminium alloy using genetic algorithm and neural network

    NASA Astrophysics Data System (ADS)

    Susmikanti, Mike

    2013-09-01

    The behavior of the fatigue life of the industrial materials is very important. In many cases, the material with experiencing fatigue life cannot be avoided, however, there are many ways to control their behavior. Many investigations of the fatigue life phenomena of alloys have been done, but it is high cost and times consuming computation. This paper report the modeling and simulation approaches to predict the fatigue life behavior of Aluminum Alloys and resolves some problems of computation. First, the simulation using genetic algorithm was utilized to optimize the load to obtain the stress values. These results can be used to provide N-cycle fatigue life of the material. Furthermore, the experimental data was applied as input data in the neural network learning, while the samples data were applied for testing of the training data. Finally, the multilayer perceptron algorithm is applied to predict whether the given data sets in accordance with the fatigue life of the alloy. To achieve rapid convergence, the Levenberg-Marquardt algorithm was also employed. The simulations results shows that the fatigue behaviors of aluminum under pressure can be predicted. In addition, implementation of neural networks successfully identified a model for material fatigue life.

  6. Interplay of population genetics and dynamics in the genetic control of mosquitoes

    PubMed Central

    Alphey, Nina; Bonsall, Michael B.

    2014-01-01

    Some proposed genetics-based vector control methods aim to suppress or eliminate a mosquito population in a similar manner to the sterile insect technique. One approach under development in Anopheles mosquitoes uses homing endonuclease genes (HEGs)—selfish genetic elements (inherited at greater than Mendelian rate) that can spread rapidly through a population even if they reduce fitness. HEGs have potential to drive introduced traits through a population without large-scale sustained releases. The population genetics of HEG-based systems has been established using discrete-time mathematical models. However, several ecologically important aspects remain unexplored. We formulate a new continuous-time (overlapping generations) combined population dynamic and genetic model and apply it to a HEG that targets and knocks out a gene that is important for survival. We explore the effects of density dependence ranging from undercompensating to overcompensating larval competition, occurring before or after HEG fitness effects, and consider differences in competitive effect between genotypes (wild-type, heterozygotes and HEG homozygotes). We show that population outcomes—elimination, suppression or loss of the HEG—depend crucially on the interaction between these ecological aspects and genetics, and explain how the HEG fitness properties, the homing rate (drive) and the insect's life-history parameters influence those outcomes. PMID:24522781

  7. Alternate wiring of a KNOXI genetic network underlies differences in leaf development of A. thaliana and C. hirsuta

    PubMed Central

    Rast-Somssich, Madlen I.; Broholm, Suvi; Jenkins, Huw; Canales, Claudia; Vlad, Daniela; Kwantes, Michiel; Bilsborough, Gemma; Dello Ioio, Raffaele; Ewing, Rob M.; Laufs, Patrick; Huijser, Peter; Ohno, Carolyn; Heisler, Marcus G.; Hay, Angela; Tsiantis, Miltos

    2015-01-01

    Two interrelated problems in biology are understanding the regulatory logic and predictability of morphological evolution. Here, we studied these problems by comparing Arabidopsis thaliana, which has simple leaves, and its relative, Cardamine hirsuta, which has dissected leaves comprising leaflets. By transferring genes between the two species, we provide evidence for an inverse relationship between the pleiotropy of SHOOTMERISTEMLESS (STM) and BREVIPEDICELLUS (BP) homeobox genes and their ability to modify leaf form. We further show that cis-regulatory divergence of BP results in two alternative configurations of the genetic networks controlling leaf development. In C. hirsuta, ChBP is repressed by the microRNA164A (MIR164A)/ChCUP-SHAPED COTYLEDON (ChCUC) module and ChASYMMETRIC LEAVES1 (ChAS1), thus creating cross-talk between MIR164A/CUC and AS1 that does not occur in A. thaliana. These different genetic architectures lead to divergent interactions of network components and growth regulation in each species. We suggest that certain regulatory genes with low pleiotropy are predisposed to readily integrate into or disengage from conserved genetic networks influencing organ geometry, thus rapidly altering their properties and contributing to morphological divergence. PMID:26588991

  8. Connected Dominating Set Based Topology Control in Wireless Sensor Networks

    ERIC Educational Resources Information Center

    He, Jing

    2012-01-01

    Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network.…

  9. Full design of fuzzy controllers using genetic algorithms

    NASA Technical Reports Server (NTRS)

    Homaifar, Abdollah; Mccormick, ED

    1992-01-01

    This paper examines the applicability of genetic algorithms (GA) in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.

  10. Full design of fuzzy controllers using genetic algorithms

    NASA Technical Reports Server (NTRS)

    Homaifar, Abdollah; Mccormick, ED

    1992-01-01

    This paper examines the applicability of genetic algorithms in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.

  11. Diversified Control Paths: A Significant Way Disease Genes Perturb the Human Regulatory Network

    PubMed Central

    Wang, Bingbo; Gao, Lin; Zhang, Qingfang; Li, Aimin; Deng, Yue; Guo, Xingli

    2015-01-01

    Background The complexity of biological systems motivates us to use the underlying networks to provide deep understanding of disease etiology and the human diseases are viewed as perturbations of dynamic properties of networks. Control theory that deals with dynamic systems has been successfully used to capture systems-level knowledge in large amount of quantitative biological interactions. But from the perspective of system control, the ways by which multiple genetic factors jointly perturb a disease phenotype still remain. Results In this work, we combine tools from control theory and network science to address the diversified control paths in complex networks. Then the ways by which the disease genes perturb biological systems are identified and quantified by the control paths in a human regulatory network. Furthermore, as an application, prioritization of candidate genes is presented by use of control path analysis and gene ontology annotation for definition of similarities. We use leave-one-out cross-validation to evaluate the ability of finding the gene-disease relationship. Results have shown compatible performance with previous sophisticated works, especially in directed systems. Conclusions Our results inspire a deeper understanding of molecular mechanisms that drive pathological processes. Diversified control paths offer a basis for integrated intervention techniques which will ultimately lead to the development of novel therapeutic strategies. PMID:26284649

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

    PubMed

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

    2014-01-01

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

  13. A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies.

    PubMed

    Bodea, Corneliu A; Neale, Benjamin M; Ripke, Stephan; Daly, Mark J; Devlin, Bernie; Roeder, Kathryn

    2016-05-01

    One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data. PMID:27087321

  14. A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies.

    PubMed

    Bodea, Corneliu A; Neale, Benjamin M; Ripke, Stephan; Daly, Mark J; Devlin, Bernie; Roeder, Kathryn

    2016-05-01

    One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data.

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

    PubMed Central

    2013-01-01

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

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

    PubMed Central

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

    2011-01-01

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

  17. Genetic Algorithm Optimizes Q-LAW Control Parameters

    NASA Technical Reports Server (NTRS)

    Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

    2008-01-01

    A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

  18. Navigating Transcriptional Coregulator Ensembles to Establish Genetic Networks: A GATA Factor Perspective.

    PubMed

    DeVilbiss, A W; Tanimura, N; McIver, S C; Katsumura, K R; Johnson, K D; Bresnick, E H

    2016-01-01

    Complex developmental programs require orchestration of intrinsic and extrinsic signals to control cell proliferation, differentiation, and survival. Master regulatory transcription factors are vital components of the machinery that transduce these stimuli into cellular responses. This is exemplified by the GATA family of transcription factors that establish cell type-specific genetic networks and control the development and homeostasis of systems including blood, vascular, adipose, and cardiac. Dysregulated GATA factor activity/expression underlies anemia, immunodeficiency, myelodysplastic syndrome, and leukemia. Parameters governing the capacity of a GATA factor expressed in multiple cell types to generate cell type-specific transcriptomes include selective coregulator usage and target gene-specific chromatin states. As knowledge of GATA-1 mechanisms in erythroid cells constitutes a solid foundation, we will focus predominantly on GATA-1, while highlighting principles that can be extrapolated to other master regulators. GATA-1 interacts with ubiquitous and lineage-restricted transcription factors, chromatin modifying/remodeling enzymes, and other coregulators to activate or repress transcription and to maintain preexisting transcriptional states. Major unresolved issues include: how does a GATA factor selectively utilize diverse coregulators; do distinct epigenetic landscapes and nuclear microenvironments of target genes dictate coregulator requirements; and do gene cohorts controlled by a common coregulator ensemble function in common pathways. This review will consider these issues in the context of GATA factor-regulated hematopoiesis and from a broader perspective.

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

    NASA Technical Reports Server (NTRS)

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

    2002-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Kobayashi, Takahisa; Simon, Donald L.

    2002-01-01

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

  1. Projection learning algorithm for threshold - controlled neural networks

    SciTech Connect

    Reznik, A.M.

    1995-03-01

    The projection learning algorithm proposed in [1, 2] and further developed in [3] substantially improves the efficiency of memorizing information and accelerates the learning process in neural networks. This algorithm is compatible with the completely connected neural network architecture (the Hopfield network [4]), but its application to other networks involves a number of difficulties. The main difficulties include constraints on interconnection structure and the need to eliminate the state uncertainty of latent neurons if such are present in the network. Despite the encouraging preliminary results of [3], further extension of the applications of the projection algorithm therefore remains problematic. In this paper, which is a continuation of the work begun in [3], we consider threshold-controlled neural networks. Networks of this type are quite common. They represent the receptor neuron layers in some neurocomputer designs. A similar structure is observed in the lower divisions of biological sensory systems [5]. In multilayer projection neural networks with lateral interconnections, the neuron layers or parts of these layers may also have the structure of a threshold-controlled completely connected network. Here the thresholds are the potentials delivered through the projection connections from other parts of the network. The extension of the projection algorithm to the class of threshold-controlled networks may accordingly prove to be useful both for extending its technical applications and for better understanding of the operation of the nervous system in living organisms.

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

    PubMed Central

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

    2013-01-01

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

  3. Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

    PubMed Central

    Svečko, Rajko

    2014-01-01

    This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749

  4. Optimal robust motion controller design using multiobjective genetic algorithm.

    PubMed

    Sarjaš, Andrej; Svečko, Rajko; Chowdhury, Amor

    2014-01-01

    This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm-differential evolution. PMID:24987749

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

    PubMed Central

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

    2011-01-01

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

  6. Temporal network structures controlling disease spreading

    NASA Astrophysics Data System (ADS)

    Holme, Petter

    2016-08-01

    We investigate disease spreading on eight empirical data sets of human contacts (mostly proximity networks recording who is close to whom, at what time). We compare three levels of representations of these data sets: temporal networks, static networks, and a fully connected topology. We notice that the difference between the static and fully connected networks—with respect to time to extinction and average outbreak size—is smaller than between the temporal and static topologies. This suggests that, for these data sets, temporal structures influence disease spreading more than static-network structures. To explain the details in the differences between the representations, we use 32 network measures. This study concurs that long-time temporal structures, like the turnover of nodes and links, are the most important for the spreading dynamics.

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

    PubMed Central

    2015-01-01

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

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

    PubMed

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

    2015-08-01

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

  9. Network device interface for digitally interfacing data channels to a controller via a network

    NASA Technical Reports Server (NTRS)

    Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor)

    2006-01-01

    The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted into digital signals and transmitted back to the controller. In one embodiment, the bus controller sends commands and data a defined bit rate, and the network device interface senses this bit rate and sends data back to the bus controller using the defined bit rate.

  10. Control chart pattern recognition using a back propagation neural network

    NASA Astrophysics Data System (ADS)

    Spoerre, Julie K.; Perry, Marcus B.

    2000-10-01

    In this paper, control chart pattern recognition using artificial neural networks is presented. An important motivation of this research is the growing interest in intelligent manufacturing systems, specifically in the area of Statistical Process Control (SPC). On-line automated process analysis is an important area of research since it allows the interfacing of process control with Computer Integrated Manufacturing (CIM) techniques. A back propagation artificial neural network is used to model X-bar quality control charts and identify process instability situations as specified by the Western Electric Statistical Quality Control handbook. Results indicate that the performance of the back propagation neural network is very accurate in identifying these control chart patterns. This work is significant in that the neural network output can serve as a link to process parameters in a closed-loop control system. In this way, adjustments to the process can be made on-line and quality problems averted.

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

    PubMed Central

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

    2012-01-01

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

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

    PubMed

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

    2012-01-01

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

  13. Cell fate reprogramming by control of intracellular network dynamics

    NASA Astrophysics Data System (ADS)

    Zanudo, Jorge G. T.; Albert, Reka

    Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Although the topic of controlling the dynamics of a system has a long history in control theory, most of this work is not directly applicable to intracellular networks. Here we present a network control method that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our control method takes advantage of certain function-dependent network components and their relation to steady states in order to identify control targets, which are guaranteed to drive any initial state to the target state with 100% effectiveness and need to be applied only transiently for the system to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments. This work was supported by NSF Grant PHY 1205840.

  14. Epigenetics and Why Biological Networks are More Controllable than Expected

    NASA Astrophysics Data System (ADS)

    Motter, Adilson

    2013-03-01

    A fundamental property of networks is that perturbations to one node can affect other nodes, potentially causing the entire system to change behavior or fail. In this talk, I will show that it is possible to exploit this same principle to control network behavior. This approach takes advantage of the nonlinear dynamics inherent to real networks, and allows bringing the system to a desired target state even when this state is not directly accessible or the linear counterpart is not controllable. Applications show that this framework permits both reprogramming a network to a desired task as well as rescuing networks from the brink of failure, which I will illustrate through various biological problems. I will also briefly review the progress our group has made over the past 5 years on related control of complex networks in non-biological domains.

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

    NASA Astrophysics Data System (ADS)

    Siu, Theodore; Vivar, Miguel; Shinbrot, Troy

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

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

    PubMed

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

    1999-01-01

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

  17. Optimum Actuator Selection with a Genetic Algorithm for Aircraft Control

    NASA Technical Reports Server (NTRS)

    Rogers, James L.

    2004-01-01

    The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. For example, the desired actuators produce a pure roll moment without at the same time causing much pitch or yaw. For a typical wing, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements and mission constraints. A genetic algorithm has been developed for finding the best placement for four actuators to produce an uncoupled pitch moment. The genetic algorithm has been extended to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control. A simplified, untapered, unswept wing is the model for each application.

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

    PubMed

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

    2015-09-15

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

  19. Breeding and Genetics Symposium: networks and pathways to guide genomic selection.

    PubMed

    Snelling, W M; Cushman, R A; Keele, J W; Maltecca, C; Thomas, M G; Fortes, M R S; Reverter, A

    2013-02-01

    Many traits affecting profitability and sustainability of meat, milk, and fiber production are polygenic, with no single gene having an overwhelming influence on observed variation. No knowledge of the specific genes controlling these traits has been needed to make substantial improvement through selection. Significant gains have been made through phenotypic selection enhanced by pedigree relationships and continually improving statistical methodology. Genomic selection, recently enabled by assays for dense SNP located throughout the genome, promises to increase selection accuracy and accelerate genetic improvement by emphasizing the SNP most strongly correlated to phenotype although the genes and sequence variants affecting phenotype remain largely unknown. These genomic predictions theoretically rely on linkage disequilibrium (LD) between genotyped SNP and unknown functional variants, but familial linkage may increase effectiveness when predicting individuals related to those in the training data. Genomic selection with functional SNP genotypes should be less reliant on LD patterns shared by training and target populations, possibly allowing robust prediction across unrelated populations. Although the specific variants causing polygenic variation may never be known with certainty, a number of tools and resources can be used to identify those most likely to affect phenotype. Associations of dense SNP genotypes with phenotype provide a 1-dimensional approach for identifying genes affecting specific traits; in contrast, associations with multiple traits allow defining networks of genes interacting to affect correlated traits. Such networks are especially compelling when corroborated by existing functional annotation and established molecular pathways. The SNP occurring within network genes, obtained from public databases or derived from genome and transcriptome sequences, may be classified according to expected effects on gene products. As illustrated by

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

    PubMed

    Jung, Jae-Yoon; Wall, Dennis Paul

    2013-01-01

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

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

    USGS Publications Warehouse

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

    2006-01-01

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

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

    PubMed

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

    2016-09-27

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

  3. Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints

    SciTech Connect

    Kmet', Tibor; Kmet'ova, Maria

    2009-09-09

    A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

  4. Application of genetic algorithms to tuning fuzzy control systems

    NASA Technical Reports Server (NTRS)

    Espy, Todd; Vombrack, Endre; Aldridge, Jack

    1993-01-01

    Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.

  5. A neural-network approach to robotic control

    NASA Technical Reports Server (NTRS)

    Graham, D. P. W.; Deleuterio, G. M. T.

    1993-01-01

    An artificial neural-network paradigm for the control of robotic systems is presented. The approach is based on the Cerebellar Model Articulation Controller created by James Albus and incorporates several extensions. First, recognizing the essential structure of multibody equations of motion, two parallel modules are used that directly reflect the dynamical characteristics of multibody systems. Second, the architecture of the proposed network is imbued with a self-organizational capability which improves efficiency and accuracy. Also, the networks can be arranged in hierarchical fashion with each subsequent network providing finer and finer resolution.

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

    PubMed

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

    2016-04-01

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

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

    PubMed

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

    2016-04-01

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

  8. Control of coupled oscillator networks with application to microgrid technologies.

    PubMed

    Skardal, Per Sebastian; Arenas, Alex

    2015-08-01

    The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions-a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable synchronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself.

  9. Control of coupled oscillator networks with application to microgrid technologies.

    PubMed

    Skardal, Per Sebastian; Arenas, Alex

    2015-08-01

    The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions-a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable synchronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself. PMID:26601231

  10. Control of coupled oscillator networks with application to microgrid technologies

    PubMed Central

    Skardal, Per Sebastian; Arenas, Alex

    2015-01-01

    The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions—a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable synchronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself. PMID:26601231

  11. Autonomous Congestion Control in Delay-Tolerant Networks

    NASA Technical Reports Server (NTRS)

    Burleigh, Scott; Jennings, Esther; Schoolcraft, Joshua

    2006-01-01

    Congestion control is an important feature that directly affects network performance. Network congestion may cause loss of data or long delays. Although this problem has been studied extensively in the Internet, the solutions for Internet congestion control do not apply readily to challenged network environments such as Delay Tolerant Networks (DTN) where end-to-end connectivity may not exist continuously and latency can be high. In DTN, end-to-end rate control is not feasible. This calls for congestion control mechanisms where the decisions can be made autonomously with local information only. We use an economic pricing model and propose a rule-based congestion control mechanism where each router can autonomously decide on whether to accept a bundle (data) based on local information such as available storage and the value and risk of accepting the bundle (derived from historical statistics). Preliminary experimental results show that this congestion control mechanism can protect routers from resource depletion without loss of data.

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2013-09-01

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

  14. Network device interface for digitally interfacing data channels to a controller via a network

    NASA Technical Reports Server (NTRS)

    Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor)

    2007-01-01

    The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is converted into digital signals and transmitted to the controller. In some embodiments, network device interfaces associated with different data channels coordinate communications with the other interfaces based on either a transition in a command message sent by the bus controller or a synchronous clock signal.

  15. Inhibition Controls Asynchronous States of Neuronal Networks

    PubMed Central

    Treviño, Mario

    2016-01-01

    Computations in cortical circuits require action potentials from excitatory and inhibitory neurons. In this mini-review, I first provide a quick overview of findings that indicate that GABAergic neurons play a fundamental role in coordinating spikes and generating synchronized network activity. Next, I argue that these observations helped popularize the notion that network oscillations require a high degree of spike correlations among interneurons which, in turn, produce synchronous inhibition of the local microcircuit. The aim of this text is to discuss some recent experimental and computational findings that support a complementary view: one in which interneurons participate actively in producing asynchronous states in cortical networks. This requires a proper mixture of shared excitation and inhibition leading to asynchronous activity between neighboring cells. Such contribution from interneurons would be extremely important because it would tend to reduce the spike correlation between neighboring pyramidal cells, a drop in redundancy that could enhance the information-processing capacity of neural networks. PMID:27274721

  16. Inhibition Controls Asynchronous States of Neuronal Networks.

    PubMed

    Treviño, Mario

    2016-01-01

    Computations in cortical circuits require action potentials from excitatory and inhibitory neurons. In this mini-review, I first provide a quick overview of findings that indicate that GABAergic neurons play a fundamental role in coordinating spikes and generating synchronized network activity. Next, I argue that these observations helped popularize the notion that network oscillations require a high degree of spike correlations among interneurons which, in turn, produce synchronous inhibition of the local microcircuit. The aim of this text is to discuss some recent experimental and computational findings that support a complementary view: one in which interneurons participate actively in producing asynchronous states in cortical networks. This requires a proper mixture of shared excitation and inhibition leading to asynchronous activity between neighboring cells. Such contribution from interneurons would be extremely important because it would tend to reduce the spike correlation between neighboring pyramidal cells, a drop in redundancy that could enhance the information-processing capacity of neural networks.

  17. Dynamics and control of diseases in networks with community structure.

    PubMed

    Salathé, Marcel; Jones, James H

    2010-04-08

    The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.

  18. Flux Control in Networks of Diffusion Paths

    SciTech Connect

    A. I. Zhmoginov and N. J. Fisch

    2009-07-08

    A class of optimization problems in networks of intersecting diffusion domains of a special form of thin paths has been considered. The system of equations describing stationary solutions is equivalent to an electrical circuit built of intersecting conductors. The solution of an optimization problem has been obtained and extended to the analogous electrical circuit. The interest in this network arises from, among other applications, an application to wave-particle diffusion through resonant interactions in plasma.

  19. NASA Integrated Network Monitor and Control Software Architecture

    NASA Technical Reports Server (NTRS)

    Shames, Peter; Anderson, Michael; Kowal, Steve; Levesque, Michael; Sindiy, Oleg; Donahue, Kenneth; Barnes, Patrick

    2012-01-01

    The National Aeronautics and Space Administration (NASA) Space Communications and Navigation office (SCaN) has commissioned a series of trade studies to define a new architecture intended to integrate the three existing networks that it operates, the Deep Space Network (DSN), Space Network (SN), and Near Earth Network (NEN), into one integrated network that offers users a set of common, standardized, services and interfaces. The integrated monitor and control architecture utilizes common software and common operator interfaces that can be deployed at all three network elements. This software uses state-of-the-art concepts such as a pool of re-programmable equipment that acts like a configurable software radio, distributed hierarchical control, and centralized management of the whole SCaN integrated network. For this trade space study a model-based approach using SysML was adopted to describe and analyze several possible options for the integrated network monitor and control architecture. This model was used to refine the design and to drive the costing of the four different software options. This trade study modeled the three existing self standing network elements at point of departure, and then described how to integrate them using variations of new and existing monitor and control system components for the different proposed deployments under consideration. This paper will describe the trade space explored, the selected system architecture, the modeling and trade study methods, and some observations on useful approaches to implementing such model based trade space representation and analysis.

  20. Selection against genetic defects in conservation schemes while controlling inbreeding

    PubMed Central

    Sonesson, Anna K; Janss, Luc LG; Meuwissen, Theo HE

    2003-01-01

    We studied different genetic models and evaluation systems to select against a genetic disease with additive, recessive or polygenic inheritance in genetic conservation schemes. When using optimum contribution selection with a restriction on the rate of inbreeding (ΔF) to select against a disease allele, selection directly on DNA-genotypes is, as expected, the most efficient strategy. Selection for BLUP or segregation analysis breeding value estimates both need 1–2 generations more to halve the frequency of the disease allele, while these methods do not require knowledge of the disease mutation at the DNA level. BLUP and segregation analysis methods were equally efficient when selecting against a disease with single gene or complex polygene inheritance, i.e. knowledge about the mode of inheritance of the disease was not needed for efficient selection against the disease. Smaller schemes or schemes with a more stringent restriction on ΔF needed more generations to halve the frequency of the disease alleles or the fraction of diseased animals. Optimum contribution selection maintained ΔF at its predefined level, even when selection of females was at random. It is argued that in the investigated small conservation schemes with selection against a genetic defect, control of ΔF is very important. PMID:12927071

  1. Genetic control of astrocyte function in neural circuits

    PubMed Central

    Jahn, Hannah M.; Scheller, Anja; Kirchhoff, Frank

    2015-01-01

    During the last two decades numerous genetic approaches affecting cell function in vivo have been developed. Current state-of-the-art technology permits the selective switching of gene function in distinct cell populations within the complex organization of a given tissue parenchyma. The tamoxifen-inducible Cre/loxP gene recombination and the doxycycline-dependent modulation of gene expression are probably the most popular genetic paradigms. Here, we will review applications of these two strategies while focusing on the interactions of astrocytes and neurons in the central nervous system (CNS) and their impact for the whole organism. Abolishing glial sensing of neuronal activity by selective deletion of glial transmitter receptors demonstrated the impact of astrocytes for higher cognitive functions such as learning and memory, or the more basic body control of muscle coordination. Interestingly, also interfering with glial output, i.e., the release of gliotransmitters can drastically change animal’s physiology like sleeping behavior. Furthermore, such genetic approaches have also been used to restore astrocyte function. In these studies two alternatives were employed to achieve proper genetic targeting of astrocytes: transgenes using the promoter of the human glial fibrillary acidic protein (GFAP) or homologous recombination into the glutamate-aspartate transporter (GLAST) locus. We will highlight their specific properties that could be relevant for their use. PMID:26347607

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

    PubMed Central

    Nakajima, Natsu

    2014-01-01

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

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

    USGS Publications Warehouse

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

    2007-01-01

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

  4. Control and scheduling codesign based on EDF-IAE in networked control systems

    NASA Astrophysics Data System (ADS)

    Yan, Shen; Bing, Guo

    2005-12-01

    Networked control systems are one type of distributed real-time control systems where sensors, actuator and controllers are interconnected by communication networks. In networked control systems, the insertion of the communication network makes the analysis and design of a networked control system more complex. In the past, control system design and CPU scheduling or network scheduling design have normally been separated. But from the point of both the communication protocols and the interacting controlled system, they should not be treated as separate. Thus, a co-design approach is proposed. Firstly, transmission model for the networked control systems is defined when a set of networked control systems is connected to the network, the Rate Monotonic (RM) scheduling algorithm and the Earliest Deadline First (EDF) scheduling algorithm are introduced. Then, applying EDF scheduling algorithm with the Integral of the Absolute Error (IAE) to schedule a set of real-time networked control systems. Finally, a simulation example is given to implement EDF-IAE performance protocol. The simulation results show that EDF-IAE performance protocol is feasible.

  5. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    USGS Publications Warehouse

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  6. Backstepping Control Augmented by Neural Networks For Robot Manipulators

    NASA Astrophysics Data System (ADS)

    Belkheiri, Mohammed; Boudjema, Farès

    2008-06-01

    A new control approach is proposed to address the tracking problem of robot manipulators. In this approach, one relies first on a partially known model to the system to be controlled using a backstepping control strategy. The obtained controller is then augmented by an online neural network that serves as an approximator for the neglected dynamics and modeling errors. The proposed approach is systematic, and exploits the known nonlinear dynamics to derive the stepwise virtual stabilizing control laws. At the final step, an augmented Lyapunov function is introduced to derive the adaptation laws of the network weights. The effectiveness of the proposed controller is demonstrated through computer simulation on PUMA 560 robot.

  7. Seed abscission and fruit dehiscence required for seed dispersal rely on similar genetic networks.

    PubMed

    Balanzà, Vicente; Roig-Villanova, Irma; Di Marzo, Maurizio; Masiero, Simona; Colombo, Lucia

    2016-09-15

    Seed dispersal is an essential trait that enables colonization of new favorable habitats, ensuring species survival. In plants with dehiscent fruits, such as Arabidopsis, seed dispersal depends on two processes: the separation of the fruit valves that protect the seeds (fruit dehiscence) and the detachment of the seeds from the funiculus connecting them to the mother plant (seed abscission). The key factors required to establish a proper lignin pattern for fruit dehiscence are SHATTERPROOF 1 and 2 (SHP1 and SHP2). Here, we demonstrate that the SHP-related gene SEEDSTICK (STK) is a key factor required to establish the proper lignin pattern in the seed abscission zone but in an opposite way. We show that STK acts as a repressor of lignin deposition in the seed abscission zone through the direct repression of HECATE3, whereas the SHP proteins promote lignin deposition in the valve margins by activating INDEHISCENT. The interaction of STK with the SEUSS co-repressor determines the difference in the way STK and SHP proteins control the lignification patterns. Despite this difference in the molecular control of lignification during seed abscission and fruit dehiscence, we show that the genetic networks regulating these two developmental pathways are highly conserved.

  8. Muscle networks: Connectivity analysis of EMG activity during postural control

    NASA Astrophysics Data System (ADS)

    Boonstra, Tjeerd W.; Danna-Dos-Santos, Alessander; Xie, Hong-Bo; Roerdink, Melvyn; Stins, John F.; Breakspear, Michael

    2015-12-01

    Understanding the mechanisms that reduce the many degrees of freedom in the musculoskeletal system remains an outstanding challenge. Muscle synergies reduce the dimensionality and hence simplify the control problem. How this is achieved is not yet known. Here we use network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. We performed connectivity analysis of surface EMG from ten leg muscles to extract the muscle networks while human participants were standing upright in four different conditions. We observed widespread connectivity between muscles at multiple distinct frequency bands. The network topology differed significantly between frequencies and between conditions. These findings demonstrate how muscle networks can be used to investigate the neural circuitry of motor coordination. The presence of disparate muscle networks across frequencies suggests that the neuromuscular system is organized into a multiplex network allowing for parallel and hierarchical control structures.

  9. Muscle networks: Connectivity analysis of EMG activity during postural control

    PubMed Central

    Boonstra, Tjeerd W.; Danna-Dos-Santos, Alessander; Xie, Hong-Bo; Roerdink, Melvyn; Stins, John F.; Breakspear, Michael

    2015-01-01

    Understanding the mechanisms that reduce the many degrees of freedom in the musculoskeletal system remains an outstanding challenge. Muscle synergies reduce the dimensionality and hence simplify the control problem. How this is achieved is not yet known. Here we use network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. We performed connectivity analysis of surface EMG from ten leg muscles to extract the muscle networks while human participants were standing upright in four different conditions. We observed widespread connectivity between muscles at multiple distinct frequency bands. The network topology differed significantly between frequencies and between conditions. These findings demonstrate how muscle networks can be used to investigate the neural circuitry of motor coordination. The presence of disparate muscle networks across frequencies suggests that the neuromuscular system is organized into a multiplex network allowing for parallel and hierarchical control structures. PMID:26634293

  10. Adaptive neural network motion control of manipulators with experimental evaluations.

    PubMed

    Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910

  11. Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

    PubMed Central

    Puga-Guzmán, S.; Moreno-Valenzuela, J.; Santibáñez, V.

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910

  12. Adaptive neural network motion control of manipulators with experimental evaluations.

    PubMed

    Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.

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

    ERIC Educational Resources Information Center

    Chen, Hsinchun

    1995-01-01

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

  14. Optimal control strategy of malaria vector using genetically modified mosquitoes.

    PubMed

    Rafikov, M; Bevilacqua, L; Wyse, A P P

    2009-06-01

    The development of transgenic mosquitoes that are resistant to diseases may provide a new and effective weapon of diseases control. Such an approach relies on transgenic mosquitoes being able to survive and compete with wild-type populations. These transgenic mosquitoes carry a specific code that inhibits the plasmodium evolution in its organism. It is said that this characteristic is hereditary and consequently the disease fades away after some time. Once transgenic mosquitoes are released, interactions between the two populations and inter-specific mating between the two types of mosquitoes take place. We present a mathematical model that considers the generation overlapping and variable environment factors. Based on this continuous model, the malaria vector control is formulated and solved as an optimal control problem, indicating how genetically modified mosquitoes should be introduced in the environment. Numerical simulations show the effectiveness of the proposed control.

  15. Genetic and Environmental Control of Neurodevelopmental Robustness in Drosophila

    PubMed Central

    Mellert, David J.; Williamson, W. Ryan; Shirangi, Troy R.; Card, Gwyneth M.; Truman, James W.

    2016-01-01

    Interindividual differences in neuronal wiring may contribute to behavioral individuality and affect susceptibility to neurological disorders. To investigate the causes and potential consequences of wiring variation in Drosophila melanogaster, we focused on a hemilineage of ventral nerve cord interneurons that exhibits morphological variability. We find that late-born subclasses of the 12A hemilineage are highly sensitive to genetic and environmental variation. Neurons in the second thoracic segment are particularly variable with regard to two developmental decisions, whereas its segmental homologs are more robust. This variability “hotspot” depends on Ultrabithorax expression in the 12A neurons, indicating variability is cell-intrinsic and under genetic control. 12A development is more variable and sensitive to temperature in long-established laboratory strains than in strains recently derived from the wild. Strains with a high frequency of one of the 12A variants also showed a high frequency of animals with delayed spontaneous flight initiation, whereas other wing-related behaviors did not show such a correlation and were thus not overtly affected by 12A variation. These results show that neurodevelopmental robustness is variable and under genetic control in Drosophila and suggest that the fly may serve as a model for identifying conserved gene pathways that stabilize wiring in stressful developmental environments. Moreover, some neuronal lineages are variation hotspots and thus may be more amenable to evolutionary change. PMID:27223118

  16. Genetics and evolution of triatomines: from phylogeny to vector control

    PubMed Central

    Gourbière, S; Dorn, P; Tripet, F; Dumonteil, E

    2012-01-01

    Triatomines are hemipteran bugs acting as vectors of the protozoan parasite Trypanosoma cruzi. This parasite causes Chagas disease, one of the major parasitic diseases in the Americas. Studies of triatomine genetics and evolution have been particularly useful in the design of rational vector control strategies, and are reviewed here. The phylogeography of several triatomine species is now slowly emerging, and the struggle to reconcile the phenotypic, phylogenetic, ecological and epidemiological species concepts makes for a very dynamic field. Population genetic studies using different markers indicate a wide range of population structures, depending on the triatomine species, ranging from highly fragmented to mobile, interbreeding populations. Triatomines transmit T. cruzi in the context of complex interactions between the insect vectors, their bacterial symbionts and the parasites; however, an integrated view of the significance of these interactions in triatomine biology, evolution and in disease transmission is still lacking. The development of novel genetic markers, together with the ongoing sequencing of the Rhodnius prolixus genome and more integrative studies, will provide key tools to expanding our understanding of these important insect vectors and allow the design of improved vector control strategies. PMID:21897436

  17. Potential Genetic Risk Factors for Chronic TMD: Genetic Associations from the OPPERA Case Control Study

    PubMed Central

    Smith, Shad B.; Maixner, Dylan; Greenspan, Joel; Dubner, Ron; Fillingim, Roger; Ohrbach, Richard; Knott, Charles; Slade, Gary; Bair, Eric; Gibson, Dustin G.; Zaykin, Dmitri V.; Weir, Bruce; Maixner, William; Diatchenko, Luda

    2011-01-01

    Genetic factors play a role in the etiology of persistent pain conditions, putatively by modulating underlying processes such as nociceptive sensitivity, psychological well-being, inflammation, and autonomic response. However, to date, only a few genes have been associated with temporomandibular disorders (TMD). This study evaluated 358 genes involved in pain processes, comparing allelic frequencies between 166 cases with chronic TMD and 1442 controls enrolled in the OPPERA (Orofacial Pain: Prospective Evaluation and Risk Assessment) study cooperative agreement. To enhance statistical power, 182 TMD cases and 170 controls from a similar study were included in the analysis. Genotyping was performed using the Pain Research Panel, an Affymetrix gene chip representing 3295 single nucleotide polymorphisms, including ancestry-informative markers that were used to adjust for population stratification. Adjusted associations between genetic markers and TMD case status were evaluated using logistic regression. The OPPERA findings provided evidence supporting previously-reported associations between TMD and two genes: HTR2A and COMT. Other genes were revealed as potential new genetic risk factors for TMD, including NR3C1, CAMK4, CHRM2, IFRD1, and GRK5. While these findings need to be replicated in independent cohorts, the genes potentially represent important markers of risk for TMD and they identify potential targets for therapeutic intervention. PMID:22074755

  18. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  19. Autonomous Congestion Control in Delay-Tolerant Networks

    NASA Technical Reports Server (NTRS)

    Burleigh, Scott C.; Jennings, Esther H.

    2005-01-01

    Congestion control is an important feature that directly affects network performance. Network congestion may cause loss of data or long delays. Although this problem has been studied extensively in the Internet, the solutions for Internet congestion control do not apply readily to challenged network environments such as Delay Tolerant Networks (DTN) where end-to-end connectivity may not exist continuously and latency can be high. In DTN, end-to-end rate control is not feasible. This calls for congestion control mechanisms where the decisions can be made autonomously with local information only. We use an economic pricing model and propose a rule-based congestion control mechanism where each router can autonomously decide on whether to accept a bundle (data) based on local information such as available storage and the value and risk of accepting the bundle (derived from historical statistics).

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

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

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

  1. Thermoelastic steam turbine rotor control based on neural network

    NASA Astrophysics Data System (ADS)

    Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

    2015-12-01

    Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

  2. Scale invariance analysis for genetic networks applying homogeneity.

    PubMed

    Bernuau, Emmanuel; Efimov, Denis; Perruquetti, Wilfrid

    2016-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1997-04-01

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

  4. Controllability and observability of Boolean networks arising from biology

    NASA Astrophysics Data System (ADS)

    Li, Rui; Yang, Meng; Chu, Tianguang

    2015-02-01

    Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.

  5. Controllability and observability of Boolean networks arising from biology.

    PubMed

    Li, Rui; Yang, Meng; Chu, Tianguang

    2015-02-01

    Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.

  6. One Community’s Effort to Control Genetic Disease

    PubMed Central

    Puffenberger, Erik G.; Morton, D. Holmes

    2012-01-01

    In 1989, we established a small community health clinic to provide care for uninsured Amish and Mennonite children with genetic disorders. Over 20 years, we have used publicly available molecular data and sophisticated technologies to improve diagnostic efficiency, control laboratory costs, reduce hospitalizations, and prevent major neurological impairments within a rural underserved community. These actions allowed the clinic’s 2010 operating budget of $1.5 million to save local communities an estimated $20 to $25 million in aggregate medical costs. This exposes an unsettling fact: our failure to improve the lot of most people stricken with genetic disease is no longer a matter of scientific ignorance or prohibitive costs but of choices we make about how to implement existing knowledge and resources. PMID:22594747

  7. Genetic and physiological controls of growth under water deficit.

    PubMed

    Tardieu, François; Parent, Boris; Caldeira, Cecilio F; Welcker, Claude

    2014-04-01

    The sensitivity of expansive growth to water deficit has a large genetic variability, which is higher than that of photosynthesis. It is observed in several species, with some genotypes stopping growth in a relatively wet soil, whereas others continue growing until the lower limit of soil-available water. The responses of growth to soil water deficit and evaporative demand share an appreciable part of their genetic control through the colocation of quantitative trait loci as do the responses of the growth of different organs to water deficit. This result may be caused by common mechanisms of action discussed in this paper (particularly, plant hydraulic properties). We propose that expansive growth, putatively linked to hydraulic processes, determines the sink strength under water deficit, whereas photosynthesis determines source strength. These findings have large consequences for plant modeling under water deficit and for the design of breeding programs. PMID:24569846

  8. The deep space network, volume 18. [Deep Space Instrumentation Facility, Ground Communication Facility, and Network Control System

    NASA Technical Reports Server (NTRS)

    1973-01-01

    The objectives, functions, and organization of the Deep Space Network are summarized. The Deep Space Instrumentation Facility, the Ground Communications Facility, and the Network Control System are described.

  9. Neural Networks for Modeling and Control of Particle Accelerators

    DOE PAGES

    Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.

    2016-04-01

    Myriad nonlinear and complex physical phenomena are host to particle accelerators. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems,more » as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Moreover, many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. For the purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We also describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.« less

  10. Evolutionary Computation with Spatial Receding Horizon Control to Minimize Network Coding Resources

    PubMed Central

    Leeson, Mark S.

    2014-01-01

    The minimization of network coding resources, such as coding nodes and links, is a challenging task, not only because it is a NP-hard problem, but also because the problem scale is huge; for example, networks in real world may have thousands or even millions of nodes and links. Genetic algorithms (GAs) have a good potential of resolving NP-hard problems like the network coding problem (NCP), but as a population-based algorithm, serious scalability and applicability problems are often confronted when GAs are applied to large- or huge-scale systems. Inspired by the temporal receding horizon control in control engineering, this paper proposes a novel spatial receding horizon control (SRHC) strategy as a network partitioning technology, and then designs an efficient GA to tackle the NCP. Traditional network partitioning methods can be viewed as a special case of the proposed SRHC, that is, one-step-wide SRHC, whilst the method in this paper is a generalized N-step-wide SRHC, which can make a better use of global information of network topologies. Besides the SRHC strategy, some useful designs are also reported in this paper. The advantages of the proposed SRHC and GA for the NCP are illustrated by extensive experiments, and they have a good potential of being extended to other large-scale complex problems. PMID:24883371

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

    PubMed

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

    2009-09-01

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

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

    PubMed

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

    2014-04-11

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

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

    PubMed Central

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

    2014-01-01

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

  14. Post-translational control of genetic circuits using Potyvirus proteases.

    PubMed

    Fernandez-Rodriguez, Jesus; Voigt, Christopher A

    2016-07-27

    Genetic engineering projects often require control over when a protein is degraded. To this end, we use a fusion between a degron and an inactivating peptide that can be added to the N-terminus of a protein. When the corresponding protease is expressed, it cleaves the peptide and the protein is degraded. Three protease:cleavage site pairs from Potyvirus are shown to be orthogonal and active in exposing degrons, releasing inhibitory domains and cleaving polyproteins. This toolbox is applied to the design of genetic circuits as a means to control regulator activity and degradation. First, we demonstrate that a gate can be constructed by constitutively expressing an inactivated repressor and having an input promoter drive the expression of the protease. It is also shown that the proteolytic release of an inhibitory domain can improve the dynamic range of a transcriptional gate (200-fold repression). Next, we design polyproteins containing multiple repressors and show that their cleavage can be used to control multiple outputs. Finally, we demonstrate that the dynamic range of an output can be improved (8-fold to 190-fold) with the addition of a protease-cleaved degron. Thus, controllable proteolysis offers a powerful tool for modulating and expanding the function of synthetic gene circuits. PMID:27298256

  15. Energy scaling and reduction in controlling complex networks.

    PubMed

    Chen, Yu-Zhong; Wang, Le-Zhi; Wang, Wen-Xu; Lai, Ying-Cheng

    2016-04-01

    Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks. PMID:27152220

  16. Energy scaling and reduction in controlling complex networks

    NASA Astrophysics Data System (ADS)

    Chen, Yu-Zhong; Wang, Le-Zhi; Wang, Wen-Xu; Lai, Ying-Cheng

    2016-04-01

    Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks.

  17. An approach for in vitro genetic networks assembly

    NASA Astrophysics Data System (ADS)

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

    2004-03-01

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

  18. Public authority control strategy for opinion evolution in social networks

    NASA Astrophysics Data System (ADS)

    Chen, Xi; Xiong, Xi; Zhang, Minghong; Li, Wei

    2016-08-01

    This paper addresses the need to deal with and control public opinion and rumors. Existing strategies to control public opinion include degree, random, and adaptive bridge control strategies. In this paper, we use the HK model to present a public opinion control strategy based on public authority (PA). This means utilizing the influence of expert or high authority individuals whose opinions we control to obtain the optimum effect in the shortest time possible and thus reach a consensus of public opinion. Public authority (PA) is only influenced by individuals' attributes (age, economic status, and education level) and not their degree distribution; hence, in this paper, we assume that PA complies with two types of public authority distribution (normal and power-law). According to the proposed control strategy, our experiment is based on random, degree, and public authority control strategies in three different social networks (small-world, scale-free, and random) and we compare and analyze the strategies in terms of convergence time (T), final number of controlled agents (C), and comprehensive efficiency (E). We find that different network topologies and the distribution of the PA in the network can influence the final controlling effect. While the effect of PA strategy differs in different network topology structures, all structures achieve comprehensive efficiency with any kind of public authority distribution in any network. Our findings are consistent with several current sociological phenomena and show that in the process of public opinion/rumor control, considerable attention should be paid to high authority individuals.

  19. Public authority control strategy for opinion evolution in social networks.

    PubMed

    Chen, Xi; Xiong, Xi; Zhang, Minghong; Li, Wei

    2016-08-01

    This paper addresses the need to deal with and control public opinion and rumors. Existing strategies to control public opinion include degree, random, and adaptive bridge control strategies. In this paper, we use the HK model to present a public opinion control strategy based on public authority (PA). This means utilizing the influence of expert or high authority individuals whose opinions we control to obtain the optimum effect in the shortest time possible and thus reach a consensus of public opinion. Public authority (PA) is only influenced by individuals' attributes (age, economic status, and education level) and not their degree distribution; hence, in this paper, we assume that PA complies with two types of public authority distribution (normal and power-law). According to the proposed control strategy, our experiment is based on random, degree, and public authority control strategies in three different social networks (small-world, scale-free, and random) and we compare and analyze the strategies in terms of convergence time (T), final number of controlled agents (C), and comprehensive efficiency (E). We find that different network topologies and the distribution of the PA in the network can influence the final controlling effect. While the effect of PA strategy differs in different network topology structures, all structures achieve comprehensive efficiency with any kind of public authority distribution in any network. Our findings are consistent with several current sociological phenomena and show that in the process of public opinion/rumor control, considerable attention should be paid to high authority individuals. PMID:27586601

  20. Public authority control strategy for opinion evolution in social networks.

    PubMed

    Chen, Xi; Xiong, Xi; Zhang, Minghong; Li, Wei

    2016-08-01

    This paper addresses the need to deal with and control public opinion and rumors. Existing strategies to control public opinion include degree, random, and adaptive bridge control strategies. In this paper, we use the HK model to present a public opinion control strategy based on public authority (PA). This means utilizing the influence of expert or high authority individuals whose opinions we control to obtain the optimum effect in the shortest time possible and thus reach a consensus of public opinion. Public authority (PA) is only influenced by individuals' attributes (age, economic status, and education level) and not their degree distribution; hence, in this paper, we assume that PA complies with two types of public authority distribution (normal and power-law). According to the proposed control strategy, our experiment is based on random, degree, and public authority control strategies in three different social networks (small-world, scale-free, and random) and we compare and analyze the strategies in terms of convergence time (T), final number of controlled agents (C), and comprehensive efficiency (E). We find that different network topologies and the distribution of the PA in the network can influence the final controlling effect. While the effect of PA strategy differs in different network topology structures, all structures achieve comprehensive efficiency with any kind of public authority distribution in any network. Our findings are consistent with several current sociological phenomena and show that in the process of public opinion/rumor control, considerable attention should be paid to high authority individuals.

  1. The Life-Changing Magic of Nonlinearity in Network Control

    NASA Astrophysics Data System (ADS)

    Cornelius, Sean

    The proper functioning and reliability of many man-made and natural systems is fundamentally tied to our ability to control them. Indeed, applications as diverse as ecosystem management, emergency response and cell reprogramming all, at their heart, require us to drive a system to--or keep it in--a desired state. This process is complicated by the nonlinear dynamics inherent to most real systems, which has traditionally been viewed as the principle obstacle to their control. In this talk, I will discuss two ways in which nonlinearity turns this view on its head, in fact representing an asset to the control of complex systems. First, I will show how nonlinearity in the form of multistability allows one to systematically design control interventions that can deliberately induce ``reverse cascading failures'', in which a network spontaneously evolves to a desirable (rather than a failed) state. Second, I will show that nonlinearity in the form of time-varying dynamics unexpectedly makes temporal networks easier to control than their static counterparts, with the former enjoying dramatic and simultaneous reductions in all costs of control. This is true despite the fact that temporality tends to fragment a network's structure, disrupting the paths that allow the directly-controlled or ``driver'' nodes to communicate with the rest of the network. Taken together, these studies shed new light on the crucial role of nonlinearity in network control, and provide support to the idea we can control nonlinearity, rather than letting nonlinearity control us.

  2. The dynamic control of signal transduction networks in cancer cells.

    PubMed

    Kolch, Walter; Halasz, Melinda; Granovskaya, Marina; Kholodenko, Boris N

    2015-09-01

    Cancer is often considered a genetic disease. However, much of the enormous plasticity of cancer cells to evolve different phenotypes, to adapt to challenging microenvironments and to withstand therapeutic assaults is encoded by the structure and spatiotemporal dynamics of signal transduction networks. In this Review, we discuss recent concepts concerning how the rich signalling dynamics afforded by these networks are regulated and how they impinge on cancer cell proliferation, survival, invasiveness and drug resistance. Understanding this dynamic circuitry by mathematical modelling could pave the way to new therapeutic approaches and personalized treatments.

  3. Morphology of fluvial networks on Titan: Evidence for structural control

    NASA Astrophysics Data System (ADS)

    Burr, Devon M.; Drummond, Sarah A.; Cartwright, Richard; Black, Benjamin A.; Perron, J. Taylor

    2013-09-01

    Although Titan’s surface shows clear evidence of erosional modification, such as fluvial incision, evidence for tectonism has been less apparent. On Earth, fluvial networks with strongly preferred orientations are often associated with structural features, such as faults or joints, that influence flow or erodibility. We delineated and classified the morphologies of fluvial drainages on Titan and discovered evidence of structural control. Fluvial networks were delineated both on synthetic aperture radar (SAR) images covering ∼40% of Titan from the Cassini Titan Radar Mapper up through T71 and on visible light images of the Huygens landing site collected by the Descent Imager/Spectral Radiometer (DISR). The delineated networks were assigned to one of three morphologic classes-dendritic, parallel or rectangular-using a quantitative terrestrial drainage pattern classification algorithm modified for use with Titan data. We validated our modified algorithm by applying it to synthetic fluvial networks produced by a landscape evolution model with no structural control of drainage orientations, and confirmed that only a small fraction of the networks are falsely identified as structurally controlled. As a second validation, we confirmed that our modified algorithm correctly classifies terrestrial networks that are classified in multiple previous works as rectangular. Application of this modified algorithm to our Titan networks results in a classification of rectangular for one-half of the SAR and DISR networks. A review of the geological context of the four terrestrial rectangular networks indicates that tensional stresses formed the structures controlling those terrestrial drainages. Based on the similar brittle response of rock and cryogenic ice to stress, we infer that structures formed under tension are the most likely cause of the rectangular Titan networks delineated here. The distribution of these rectangular networks suggests that tensional stresses on Titan may

  4. Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

    PubMed

    Zhang, Yanjun; Tao, Gang; Chen, Mou

    2016-09-01

    This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.

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

    NASA Astrophysics Data System (ADS)

    Potoyan, Davit

    2015-03-01

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

  6. On neural networks in identification and control of dynamic systems

    NASA Technical Reports Server (NTRS)

    Phan, Minh; Juang, Jer-Nan; Hyland, David C.

    1993-01-01

    This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts.

  7. Autonomous Congestion Control in Delay-Tolerant Networks

    NASA Technical Reports Server (NTRS)

    Burleigh, Scott; Jennings, Esther; Schoolcraft, Joshua

    2006-01-01

    This presentation highlights communication congestion control in delay-tolerant networks (DTNs). Large-scale future space exploration will offer complex communication challenges that may be best addressed by establishing a network infrastructure. However, current internet techniques for congestion control are not well suited for operation of a network over interplanetary distances. An alternative, delay-tolerant technique for congestion control in a delay-tolerant network is presented. A simple DTN was constructed and an experimental congestion control mechanism was applied. The mechanism appeared to be effective and each router was able to make its bundle acceptance decisions autonomously. Future research will examine more complex topologies and alternative bundle acceptance rules that might enhance performance.

  8. Enhancing complex network controllability by minimum link direction reversal

    NASA Astrophysics Data System (ADS)

    Hou, Lvlin; Lao, Songyang; Small, Michael; Xiao, Yandong

    2015-07-01

    Controllability of complex networks has recently become one of the most popular research fields, but the importance of link direction for controllability has not been systematically considered. We propose a method to enhance controllability of a directed network by changing the direction of a small fraction of links while keeping the total number of links unchanged. The main idea of the method is to find candidate links based on the matching path. Extensive numerical simulation on many modeled networks demonstrates that this method is effective. Furthermore, we find that the nodes linked to candidate links have a distinct character, which provide us with a strategy to improve the controllability based on the local structure. Since the whole topology of many real networks is not visible and we only get some local structure information, this strategy is potentially more practical compared to those that demand complete topology information.

  9. Controlling natural convection in a closed thermosyphon using neural networks

    NASA Astrophysics Data System (ADS)

    Cammarata, L.; Fichera, A.; Pagano, A.

    . The aim of this paper is to present a neural network-based approach to identification and control of a rectangular natural circulation loop. The first part of the paper defines a NARMAX model for the prediction of the experimental oscillating behavior characterizing the fluid temperature. The model has been generalized and implemented by means of a Multilayer Perceptron Neural Network that has been trained to simulate the system experimental dynamics. In the second part of the paper, the NARMAX model has been used to simulate the plant during the training of another neural network aiming to suppress the undesired oscillating behavior of the system. In order to define the neural controller, a cascade of several couples of neural networks representing both the system and the controller has been used, the number of couples coinciding with the number of steps in which the control action is exerted.

  10. Adaptive artificial neural network for autonomous robot control

    NASA Technical Reports Server (NTRS)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.

  11. Recognition with self-control in neural networks

    NASA Astrophysics Data System (ADS)

    Lewenstein, Maciej; Nowak, Andrzej

    1989-10-01

    We present a theory of fully connected neural networks that incorporates mechanisms of dynamical self-control of recognition process. Using a functional integral technique, we formulate mean-field dynamics for such systems.

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

    PubMed Central

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

    2011-01-01

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

  13. Multi-loop networked process control: a synchronized approach.

    PubMed

    Das, M; Ghosh, R; Goswami, B; Chandra, A K; Balasubramanian, R; Luksch, P; Gupta, A

    2009-01-01

    Modern day process control uses digital controllers which are based on the principle of distributed rather than centralized control. Distributing controllers, sensors and actuators across a plant entails considerable wiring which can be reduced substantially by integrating the components of a control loop over a network. The other advantages include greater flexibility and higher reliability with lower hardware redundancy. The controllers and sensors are on a network and can take over the function of a failed component automatically, without the need of manual reconfiguration, thus eliminating the need of having a redundant component for each and every component. Though elaborate techniques have been developed for Single Input Single Output (SISO) systems, the major challenge lies in extending these ideas to control a practical process plant where de-centralized control is actually achieved through control of individual SISO control loops derived through de-coupling of the original system. Multiple loops increase network load and hence the sampling times associated with the control loops and makes synchronization difficult. This paper presents a methodology by which network based process control can be applied to practical process plants, with a simple direct synchronization mechanism. PMID:19028386

  14. Network device interface for digitally interfacing data channels to a controller via a network

    NASA Technical Reports Server (NTRS)

    Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor)

    2009-01-01

    A communications system and method are provided for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is converted into digital signals and transmitted to the controller. Network device interfaces associated with different data channels can coordinate communications with the other interfaces based on either a transition in a command message sent by the bus controller or a synchronous clock signal.

  15. Modeling and controlling the two-phase dynamics of the p53 network: a Boolean network approach

    NASA Astrophysics Data System (ADS)

    Lin, Guo-Qiang; Ao, Bin; Chen, Jia-Wei; Wang, Wen-Xu; Di, Zeng-Ru

    2014-12-01

    Although much empirical evidence has demonstrated that p53 plays a key role in tumor suppression, the dynamics and function of the regulatory network centered on p53 have not yet been fully understood. Here, we develop a Boolean network model to reproduce the two-phase dynamics of the p53 network in response to DNA damage. In particular, we map the fates of cells into two types of Boolean attractors, and we find that the apoptosis attractor does not exist for minor DNA damage, reflecting that the cell is reparable. As the amount of DNA damage increases, the basin of the repair attractor shrinks, accompanied by the rising of the apoptosis attractor and the expansion of its basin, indicating that the cell becomes more irreparable with more DNA damage. For severe DNA damage, the repair attractor vanishes, and the apoptosis attractor dominates the state space, accounting for the exclusive fate of death. Based on the Boolean network model, we explore the significance of links, in terms of the sensitivity of the two-phase dynamics, to perturbing the weights of links and removing them. We find that the links are either critical or ordinary, rather than redundant. This implies that the p53 network is irreducible, but tolerant of small mutations at some ordinary links, and this can be interpreted with evolutionary theory. We further devised practical control schemes for steering the system into the apoptosis attractor in the presence of DNA damage by pinning the state of a single node or perturbing the weight of a single link. Our approach offers insights into understanding and controlling the p53 network, which is of paramount importance for medical treatment and genetic engineering.

  16. A targeted controlled force injection of genetic material in vivo

    PubMed Central

    Ahlén, Gustaf; Frelin, Lars; Holmström, Fredrik; Smetham, Grant; Augustyn, Steve; Sällberg, Matti

    2016-01-01

    A general limitation in gene delivery is the cellular uptake in lager animals including humans. Several approaches have been tested including liposomes, micro-needles, in vivo electro-transfer, ballistic delivery, and needle-free delivery. All these techniques have individual limitations. One approach reproducibly delivering genetic material in muscle tissue in nonhuman primates is hydrodynamic injection, a forced injection of a volume equaling the volume of the tissue to be transfected thereby causing an increased local pressure resulting in an improved uptake of genetic material. We transferred the principle of hydrodynamic injection to a device, where a small injection volume can be delivered to a targeted tissue volume, termed in vivo intracellular injection (IVIN). The device is based on needle(s) with apertures along the needle shafts, where multiple needles can fix the tissue volume to be transfected. The apertures direct the injection from a central needle outward or inward to the centroid of a geometric arrangement thereby targeting the tissue to be transfected. With a controlled force, this results in a targeted injection with increased transfection efficiency. We here show that the IVIN technology reproducibly improved plasmid uptake and expression and the immunogenicity. The IVIN technology can be generally applied to a targeted delivery of genetic materials. PMID:27069951

  17. Genetic control of asexual development in aspergillus fumigatus.

    PubMed

    Alkhayyat, Fahad; Chang Kim, Sun; Yu, Jae-Hyuk

    2015-01-01

    Aspergillus fumigatus is one of the most common fungi found in the environment. It is an opportunistic human pathogen causing invasive pulmonary aspergillosis with a high mortality rate in immunocompromised patients. Conidia, the asexual spores, serve as the main dispersal and infection agent allowing entrance of the fungus into the host through the respiratory tract. Therefore, understanding the asexual developmental process that gives rise to the conidia is of great interest to the scientific community and is currently the focus of an immense load of research being conducted. We have been studying the genetic basis that controls asexual development and gliotoxin biosynthesis in A. fumigatus. In this review, we discuss the genetic regulatory system that dictates conidiation in this important fungus by covering the roles of crucial genetic factors from the upstream heterotrimeric G-protein signaling components to the more specific downstream central activators of the conidiation pathway. In addition, other key asexual regulators including the velvet regulators, the Flb proteins and their associated regulatory factors are discussed.

  18. Hybrid modeling and receding horizon control of sewer networks

    NASA Astrophysics Data System (ADS)

    Joseph-Duran, Bernat; Ocampo-Martinez, Carlos; Cembrano, Gabriela

    2014-11-01

    In this work, a control-oriented sewer network model is presented based on a hybrid linear modeling framework. The model equations are described independently for each network element, thus allowing the model to be applied to a broad class of networks. A parameter calibration procedure using data obtained from simulation software that solves the physically based model equations is described and validation results are given for a case study. Using the control model equations, an optimal control problem to minimize flooding and pollution is formulated to be solved by means of mixed-integer linear or quadratic programming. A receding horizon control strategy based on this optimal control problem is applied to the case study using the simulation software as a virtual reality. Results of this closed-loop simulation tests show the effectiveness of the proposed approach in fulfilling the control objectives while complying with physical and operational constraints.

  19. Adaptive Quality of Transmission Control in Elastic Optical Network

    NASA Astrophysics Data System (ADS)

    Cai, Xinran

    Optical fiber communication is becoming increasingly important due to the burgeoning demand in the internet capacity. However, traditional wavelength division multiplexing (WDM) technique fails to address such demand because of its inefficient spectral utilization. As a result, elastic optical networking (EON) has been under extensive investigation recently. Such network allows sub-wavelength and super-wavelength channel accommodation, and mitigates the stranded bandwidth problem in the WDM network. In addition, elastic optical network is also able to dynamically allocate the spectral resources of the network based on channel conditions and impairments, and adaptively control the quality of transmission of a channel. This application requires two aspects to be investigated: an efficient optical performance monitoring scheme and networking control and management algorithms to reconfigure the network in a dynamic fashion. This thesis focuses on the two aspects discussed above about adaptive QoT control. We demonstrated a supervisory channel method for optical signal to noise ratio (OSNR) and chromatic dispersion (CD) monitoring. In addition, our proof-of-principle testbed experiments show successful impairment aware reconfiguration of the network with modulation format switching (MFS) only and MFS combined with lightpath rerouting (LR) for hundred-GHz QPSK superchannels undergoing time-varying OSNR impairment.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  2. Ideomotor feedback control in a recurrent neural network.

    PubMed

    Galtier, Mathieu

    2015-06-01

    The architecture of a neural network controlling an unknown environment is presented. It is based on a randomly connected recurrent neural network from which both perception and action are simultaneously read and fed back. There are two concurrent learning rules implementing a sort of ideomotor control: (i) perception is learned along the principle that the network should predict reliably its incoming stimuli; (ii) action is learned along the principle that the prediction of the network should match a target time series. The coherent behavior of the neural network in its environment is a consequence of the interaction between the two principles. Numerical simulations show a promising performance of the approach, which can be turned into a local and better "biologically plausible" algorithm.

  3. Quality of service policy control in virtual private networks

    NASA Astrophysics Data System (ADS)

    Yu, Yiqing; Wang, Hongbin; Zhou, Zhi; Zhou, Dongru

    2004-04-01

    This paper studies the QoS of VPN in an environment where the public network prices connection-oriented services based on source, destination and grade of service, and advertises these prices to its VPN customers (users). As different QoS technologies can produce different QoS, there are according different traffic classification rules and priority rules. The internet service provider (ISP) may need to build complex mechanisms separately for each node. In order to reduce the burden of network configuration, we need to design policy control technologies. We considers mainly directory server, policy server, policy manager and policy enforcers. Policy decision point (PDP) decide its control according to policy rules. In network, policy enforce point (PEP) decide its network controlled unit. For InterServ and DiffServ, we will adopt different policy control methods as following: (1) In InterServ, traffic uses resource reservation protocol (RSVP) to guarantee the network resource. (2) In DiffServ, policy server controls the DiffServ code points and per hop behavior (PHB), its PDP distributes information to each network node. Policy server will function as following: information searching; decision mechanism; decision delivering; auto-configuration. In order to prove the effectiveness of QoS policy control, we make the corrective simulation.

  4. Adaptive mechanism-based congestion control for networked systems

    NASA Astrophysics Data System (ADS)

    Liu, Zhi; Zhang, Yun; Chen, C. L. Philip

    2013-03-01

    In order to assure the communication quality in network systems with heavy traffic and limited bandwidth, a new ATRED (adaptive thresholds random early detection) congestion control algorithm is proposed for the congestion avoidance and resource management of network systems. Different to the traditional AQM (active queue management) algorithms, the control parameters of ATRED are not configured statically, but dynamically adjusted by the adaptive mechanism. By integrating with the adaptive strategy, ATRED alleviates the tuning difficulty of RED (random early detection) and shows a better control on the queue management, and achieve a more robust performance than RED under varying network conditions. Furthermore, a dynamic transmission control protocol-AQM control system using ATRED controller is introduced for the systematic analysis. It is proved that the stability of the network system can be guaranteed when the adaptive mechanism is finely designed. Simulation studies show the proposed ATRED algorithm achieves a good performance in varying network environments, which is superior to the RED and Gentle-RED algorithm, and providing more reliable service under varying network conditions.

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

    PubMed

    Yoon, Yourim; Kim, Yong-Hyuk

    2013-10-01

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

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

    PubMed

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

    2007-06-21

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

  7. Using EDF-IAE to integrate scheduling and control for networked control systems

    NASA Astrophysics Data System (ADS)

    Shen, Yan; Guo, Bing; Li, Xunbo

    2007-12-01

    Networked control systems are one type of distributed real-time control systems where sensors, actuator and controllers are interconnected by communication networks. The insertion of the communication network makes the analysis and design of a networked control system complex. So the performance of the control loops not only depends on the design of the control algorithms, but also on the scheduling of the shared network resource. A co-design method based on the integrated Earliest Deadline First (EDF) scheduling algorithm and the Integral of the Absolute Error (IAE) is proposed in order that the communication medium is more efficiently used and control performance is improved. The presented method can adjust the sampling period, handle three types of message and guarantees real-time transmission of periodic and aperiodic message, and non-real time message. The simulation results show that the proposed co-design method is available and improve the system resources efficiency.

  8. Prediction and control of chaotic processes using nonlinear adaptive networks

    SciTech Connect

    Jones, R.D.; Barnes, C.W.; Flake, G.W.; Lee, K.; Lewis, P.S.; O'Rouke, M.K.; Qian, S.

    1990-01-01

    We present the theory of nonlinear adaptive networks and discuss a few applications. In particular, we review the theory of feedforward backpropagation networks. We then present the theory of the Connectionist Normalized Linear Spline network in both its feedforward and iterated modes. Also, we briefly discuss the theory of stochastic cellular automata. We then discuss applications to chaotic time series, tidal prediction in Venice lagoon, finite differencing, sonar transient detection, control of nonlinear processes, control of a negative ion source, balancing a double inverted pendulum and design advice for free electron lasers and laser fusion targets.

  9. STIMULUS: End-System Network Interface Controller for 100 Gb/s Wide Area Networks

    SciTech Connect

    Zarkesh-Ha, Payman

    2014-09-12

    The main goal of this research grant is to develop a system-level solution leveraging novel technologies that enable network communications at 100 Gb/s or beyond. University of New Mexico in collaboration with Acadia Optronics LLC has been working on this project to develop the 100 Gb/s Network Interface Controller (NIC) under this Department of Energy (DOE) grant.

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

    PubMed

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

    2011-10-01

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

  11. Feedback control and output feedback control for the stabilisation of switched Boolean networks

    NASA Astrophysics Data System (ADS)

    Li, Fangfei; Yu, Zhaoxu

    2016-02-01

    This paper presents the feedback control and output feedback control for the stabilisation of switched Boolean network. A necessary condition for the existence of a state feedback controller for the stabilisation of switched Boolean networks under arbitrary switching signal is derived first, and constructive procedures for feedback control and output feedback control design are provided. An example is introduced to show the effectiveness of this paper.

  12. Neural network based dynamic controllers for industrial robots.

    PubMed

    Oh, S Y; Shin, W C; Kim, H G

    1995-09-01

    The industrial robot's dynamic performance is frequently measured by positioning accuracy at high speeds and a good dynamic controller is essential that can accurately compute robot dynamics at a servo rate high enough to ensure system stability. A real-time dynamic controller for an industrial robot is developed here using neural networks. First, an efficient time-selectable hidden layer architecture has been developed based on system dynamics localized in time, which lends itself to real-time learning and control along with enhanced mapping accuracy. Second, the neural network architecture has also been specially tuned to accommodate servo dynamics. This not only facilitates the system design through reduced sensing requirements for the controller but also enhances the control performance over the control architecture neglecting servo dynamics. Experimental results demonstrate the controller's excellent learning and control performances compared with a conventional controller and thus has good potential for practical use in industrial robots.

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

    Shekarchizadeh, Hajar; Tikani, Reza; Kadivar, Mahdi

    2014-09-01

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

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

    PubMed

    Shekarchizadeh, Hajar; Tikani, Reza; Kadivar, Mahdi

    2014-09-01

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

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

    PubMed

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

    2015-01-01

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

  17. Applying Trusted Network Technology To Process Control Systems

    NASA Astrophysics Data System (ADS)

    Okhravi, Hamed; Nicol, David

    Interconnections between process control networks and enterprise networks expose instrumentation and control systems and the critical infrastructure components they operate to a variety of cyber attacks. Several architectural standards and security best practices have been proposed for industrial control systems. However, they are based on older architectures and do not leverage the latest hardware and software technologies. This paper describes new technologies that can be applied to the design of next generation security architectures for industrial control systems. The technologies are discussed along with their security benefits and design trade-offs.

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

    PubMed Central

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

    2009-01-01

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

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

    PubMed

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

    2009-07-29

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

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

    PubMed

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

    2015-12-01

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