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Sample records for genetic network controlling

  1. Genetically determined phenotype covariation networks control bone strength.

    PubMed

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

    2010-07-01

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

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

    PubMed Central

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

    2008-01-01

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

  3. Genetic network driven control of PHBV copolymer composition.

    PubMed

    Iadevaia, Sergio; Mantzaris, Nikos V

    2006-03-09

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

  4. Genetic networks controlling the development of midbrain dopaminergic neurons

    PubMed Central

    Prakash, Nilima; Wurst, Wolfgang

    2006-01-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

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

  8. 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

  9. A wavelet neural network based on genetic algorithm and its application to gain scheduling flight control

    NASA Astrophysics Data System (ADS)

    Sun, Xun; Zhang, Weiguo; Yin, Wei; Li, Aijun

    2006-11-01

    As enlarging of the flight envelop, the aerodynamic derivative of the airplane varies enormous. The gain scheduling method is usually used to deal with it. But the workload is enormously and the stability is difficulty to be assured. To solve the above problem, a large envelope wavelet neural network gain scheduling flight control law design method based on genetic algorithm is presented in this paper. Wavelet has good time accuracy in high frequency-domain and the good frequency accuracy in low frequency-domain. Neural network has the self-learning character. In this method, wavelet function instead of Sigmoid function as the excitation function. So the two merits are merged and the high nonlinear function approximation capability could be achieved. In order to obtain higher accuracy and faster speed, genetic algorithm is used to optimize the parameters of the wavelet neural network. This method is used in design the large envelope gain scheduling flight control law. This simulation results show that good control capability could be achieved in large envelope and the system is still stable when modeling error is 20%. In the situation of 20% modeling error, the maximum overshoot is only 12m and it is 35% of the maximum overshoot using normal method.

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

    PubMed

    Moradi, Hojjatullah; Majd, Vahid Johari

    2016-05-01

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

  11. Induction of erythropoiesis using human vascular networks genetically engineered for controlled erythropoietin release.

    PubMed

    Lin, Ruei-Zeng; Dreyzin, Alexandra; Aamodt, Kristie; Li, Dan; Jaminet, Shou-Ching S; Dudley, Andrew C; Melero-Martin, Juan M

    2011-11-17

    For decades, autologous ex vivo gene therapy has been postulated as a potential alternative to parenteral administration of recombinant proteins. However, achieving effective cellular engraftment of previously retrieved patient cells is challenging. Recently, our ability to engineer vasculature in vivo has allowed for the introduction of instructions into tissues by genetically modifying the vascular cells that build these blood vessels. In the present study, we genetically engineered human blood-derived endothelial colony-forming cells (ECFCs) to express erythropoietin (EPO) under the control of a tetracycline-regulated system, and generated subcutaneous vascular networks capable of systemic EPO release in immunodeficient mice. These ECFC-lined vascular networks formed functional anastomoses with the mouse vasculature, allowing direct delivery of recombinant human EPO into the bloodstream. After activation of EPO expression, erythropoiesis was induced in both normal and anemic mice, a process that was completely reversible. This approach could relieve patients from frequent EPO injections, reducing the medical costs associated with the management of anemia. We propose this ECFC-based gene-delivery strategy as a viable alternative technology when routine administration of recombinant proteins is needed.

  12. Induction of erythropoiesis using human vascular networks genetically engineered for controlled erythropoietin release

    PubMed Central

    Lin, Ruei-Zeng; Dreyzin, Alexandra; Aamodt, Kristie; Li, Dan; Jaminet, Shou-Ching S.; Dudley, Andrew C.

    2011-01-01

    For decades, autologous ex vivo gene therapy has been postulated as a potential alternative to parenteral administration of recombinant proteins. However, achieving effective cellular engraftment of previously retrieved patient cells is challenging. Recently, our ability to engineer vasculature in vivo has allowed for the introduction of instructions into tissues by genetically modifying the vascular cells that build these blood vessels. In the present study, we genetically engineered human blood–derived endothelial colony-forming cells (ECFCs) to express erythropoietin (EPO) under the control of a tetracycline-regulated system, and generated subcutaneous vascular networks capable of systemic EPO release in immunodeficient mice. These ECFC-lined vascular networks formed functional anastomoses with the mouse vasculature, allowing direct delivery of recombinant human EPO into the bloodstream. After activation of EPO expression, erythropoiesis was induced in both normal and anemic mice, a process that was completely reversible. This approach could relieve patients from frequent EPO injections, reducing the medical costs associated with the management of anemia. We propose this ECFC-based gene-delivery strategy as a viable alternative technology when routine administration of recombinant proteins is needed. PMID:21937702

  13. Understanding genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Kauffman, Stuart

    2003-04-01

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

  14. Genetic Networks in Osseointegration

    PubMed Central

    Nishimura, I.

    2013-01-01

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

  15. Dynamic Multiple-Threshold Call Admission Control Based on Optimized Genetic Algorithm in Wireless/Mobile Networks

    NASA Astrophysics Data System (ADS)

    Wang, Shengling; Cui, Yong; Koodli, Rajeev; Hou, Yibin; Huang, Zhangqin

    Due to the dynamics of topology and resources, Call Admission Control (CAC) plays a significant role for increasing resource utilization ratio and guaranteeing users' QoS requirements in wireless/mobile networks. In this paper, a dynamic multi-threshold CAC scheme is proposed to serve multi-class service in a wireless/mobile network. The thresholds are renewed at the beginning of each time interval to react to the changing mobility rate and network load. To find suitable thresholds, a reward-penalty model is designed, which provides different priorities between different service classes and call types through different reward/penalty policies according to network load and average call arrival rate. To speed up the running time of CAC, an Optimized Genetic Algorithm (OGA) is presented, whose components such as encoding, population initialization, fitness function and mutation etc., are all optimized in terms of the traits of the CAC problem. The simulation demonstrates that the proposed CAC scheme outperforms the similar schemes, which means the optimization is realized. Finally, the simulation shows the efficiency of OGA.

  16. Boolean genetic network model for the control of C. elegans early embryonic cell cycles

    PubMed Central

    2013-01-01

    Background In Caenorhabditis elegans early embryo, cell cycles only have two phases: DNA synthesis and mitosis, which are different from the typical 4-phase cell cycle. Modeling this cell-cycle process into network can fill up the gap in C. elegans cell-cycle study and provide a thorough understanding on the cell-cycle regulations and progressions at the network level. Methods In this paper, C. elegans early embryonic cell-cycle network has been constructed based on the knowledge of key regulators and their interactions from literature studies. A discrete dynamical Boolean model has been applied in computer simulations to study dynamical properties of this network. The cell-cycle network is compared with random networks and tested under several perturbations to analyze its robustness. To investigate whether our proposed network could explain biological experiment results, we have also compared the network simulation results with gene knock down experiment data. Results With the Boolean model, this study showed that the cell-cycle network was stable with a set of attractors (fixed points). A biological pathway was observed in the simulation, which corresponded to a whole cell-cycle progression. The C. elegans network was significantly robust when compared with random networks of the same size because there were less attractors and larger basins than random networks. Moreover, the network was also robust under perturbations with no significant change of the basin size. In addition, the smaller number of attractors and the shorter biological pathway from gene knock down network simulation interpreted the shorter cell-cycle lengths in mutant from the RNAi gene knock down experiment data. Hence, we demonstrated that the results in network simulation could be verified by the RNAi gene knock down experiment data. Conclusions A C. elegans early embryonic cell cycles network was constructed and its properties were analyzed and compared with those of random networks

  17. Peer Network Drinking Predicts Increased Alcohol Use From Adolescence to Early Adulthood After Controlling for Genetic and Shared Environmental Selection

    PubMed Central

    Cruz, Jennifer E.; Emery, Robert E.; Turkheimer, Eric

    2013-01-01

    Research consistently links adolescents' and young adults' drinking with their peers' alcohol intake. In interpreting this correlation, 2 essential questions are often overlooked. First, which peers are more important, best friends or broader social networks? Second, do peers cause increased drinking, or do young people select friends whose drinking habits match their own? The present study combines social network analyses with family (twin and sibling) designs to answer these questions via data from the National Longitudinal Study of Adolescent Health. Analysis of peer nomination data from 134 schools (n = 82,629) and 1,846 twin and sibling pairs shows that peer network substance use predicts changes in drinking from adolescence into young adult life even after controlling for genetic and shared environmental selection, as well as best friend substance use. This effect was particularly strong for high-intensity friendships. Although the peer-adolescent drinking correlation is partially explained by selection, the present finding offers powerful evidence that peers also cause increased drinking. PMID:22390657

  18. Synchronization of electronic genetic networks.

    PubMed

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

    2006-03-01

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

  19. A Double-Deck Elevator Group Supervisory Control System with Destination Floor Guidance System Using Genetic Network Programming

    NASA Astrophysics Data System (ADS)

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

    The Elevator Group Supervisory Control Systems (EGSCS) are the control systems that systematically manage three or more elevators in order to efficiently transport the passengers in buildings. Double-deck elevators, where two elevators are connected with each other, serve passengers at two consecutive floors simultaneously. Double-deck Elevator systems (DDES) become more complex in their behavior than conventional single-deck elevator systems (SDES). Recently, Artificial Intelligence (AI) technology has been used in such complex systems. Genetic Network Programming (GNP), a graph-based evolutionary method, has been applied to EGSCS and its advantages are shown in some papers. GNP can obtain the strategy of a new hall call assignment to the optimal elevator when it performs crossover and mutation operations to judgment nodes and processing nodes. Meanwhile, Destination Floor Guidance System (DFGS) is installed in DDES, so that passengers can also input their destinations at elevator halls. In this paper, we have applied GNP to DDES and compared DFGS with normal systems. The waiting time and traveling time of DFGS are all improved because of getting more information from DFGS. The simulations showed the effectiveness of the double-deck elevators with DFGS in different building traffics.

  20. Genetic network models: a comparative study

    NASA Astrophysics Data System (ADS)

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

    2001-06-01

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

  1. Network growth models and genetic regulatory networks

    NASA Astrophysics Data System (ADS)

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

    2006-03-01

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

  2. Network growth models and genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Socolar, Joshua; Foster, David; Kauffman, Stuart

    2006-03-01

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

  3. 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.

  4. 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.

  5. 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.

  6. Temporal Genetic Modifications after Controlled Cortical Impact—Understanding Traumatic Brain Injury through a Systematic Network Approach

    PubMed Central

    Wong, Yung-Hao; Wu, Chia-Chou; Wu, John Chung-Che; Lai, Hsien-Yong; Chen, Kai-Yun; Jheng, Bo-Ren; Chen, Mien-Cheng; Chang, Tzu-Hao; Chen, Bor-Sen

    2016-01-01

    Traumatic brain injury (TBI) is a primary injury caused by external physical force and also a secondary injury caused by biological processes such as metabolic, cellular, and other molecular events that eventually lead to brain cell death, tissue and nerve damage, and atrophy. It is a common disease process (as opposed to an event) that causes disabilities and high death rates. In order to treat all the repercussions of this injury, treatment becomes increasingly complex and difficult throughout the evolution of a TBI. Using high-throughput microarray data, we developed a systems biology approach to explore potential molecular mechanisms at four time points post-TBI (4, 8, 24, and 72 h), using a controlled cortical impact (CCI) model. We identified 27, 50, 48, and 59 significant proteins as network biomarkers at these four time points, respectively. We present their network structures to illustrate the protein–protein interactions (PPIs). We also identified UBC (Ubiquitin C), SUMO1, CDKN1A (cyclindependent kinase inhibitor 1A), and MYC as the core network biomarkers at the four time points, respectively. Using the functional analytical tool MetaCore™, we explored regulatory mechanisms and biological processes and conducted a statistical analysis of the four networks. The analytical results support some recent findings regarding TBI and provide additional guidance and directions for future research. PMID:26861311

  7. Temporal Genetic Modifications after Controlled Cortical Impact--Understanding Traumatic Brain Injury through a Systematic Network Approach.

    PubMed

    Wong, Yung-Hao; Wu, Chia-Chou; Wu, John Chung-Che; Lai, Hsien-Yong; Chen, Kai-Yun; Jheng, Bo-Ren; Chen, Mien-Cheng; Chang, Tzu-Hao; Chen, Bor-Sen

    2016-02-06

    Traumatic brain injury (TBI) is a primary injury caused by external physical force and also a secondary injury caused by biological processes such as metabolic, cellular, and other molecular events that eventually lead to brain cell death, tissue and nerve damage, and atrophy. It is a common disease process (as opposed to an event) that causes disabilities and high death rates. In order to treat all the repercussions of this injury, treatment becomes increasingly complex and difficult throughout the evolution of a TBI. Using high-throughput microarray data, we developed a systems biology approach to explore potential molecular mechanisms at four time points post-TBI (4, 8, 24, and 72 h), using a controlled cortical impact (CCI) model. We identified 27, 50, 48, and 59 significant proteins as network biomarkers at these four time points, respectively. We present their network structures to illustrate the protein-protein interactions (PPIs). We also identified UBC (Ubiquitin C), SUMO1, CDKN1A (cyclindependent kinase inhibitor 1A), and MYC as the core network biomarkers at the four time points, respectively. Using the functional analytical tool MetaCore™, we explored regulatory mechanisms and biological processes and conducted a statistical analysis of the four networks. The analytical results support some recent findings regarding TBI and provide additional guidance and directions for future research.

  8. 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.

  9. 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.

  10. Evolving Neural Networks for Nonlinear Control.

    DTIC Science & Technology

    1996-09-30

    An approach to creating Amorphous Recurrent Neural Networks (ARNN) using Genetic Algorithms (GA) called 2pGA has been developed and shown to be...effective in evolving neural networks for the control and stabilization of both linear and nonlinear plants, the optimal control for a nonlinear regulator

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

    PubMed

    Aminiazar, Wahab; Najafi, Farid; Nekoui, Mohammad Ali

    2013-08-14

    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. 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. 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. 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.

  12. 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

  13. 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

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

    PubMed

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

    2013-06-01

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

  15. Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology.

    PubMed

    Li, Yongqiang; Abbaspour, Mohammadreza R; Grootendorst, Paul V; Rauth, Andrew M; Wu, Xiao Yu

    2015-08-01

    This study was performed to optimize the formulation of polymer-lipid hybrid nanoparticles (PLN) for the delivery of an ionic water-soluble drug, verapamil hydrochloride (VRP) and to investigate the roles of formulation factors. Modeling and optimization were conducted based on a spherical central composite design. Three formulation factors, i.e., weight ratio of drug to lipid (X1), and concentrations of Tween 80 (X2) and Pluronic F68 (X3), were chosen as independent variables. Drug loading efficiency (Y1) and mean particle size (Y2) of PLN were selected as dependent variables. The predictive performance of artificial neural networks (ANN) and the response surface methodology (RSM) were compared. As ANN was found to exhibit better recognition and generalization capability over RSM, multi-objective optimization of PLN was then conducted based upon the validated ANN models and continuous genetic algorithms (GA). The optimal PLN possess a high drug loading efficiency (92.4%, w/w) and a small mean particle size (∼100nm). The predicted response variables matched well with the observed results. The three formulation factors exhibited different effects on the properties of PLN. ANN in coordination with continuous GA represent an effective and efficient approach to optimize the PLN formulation of VRP with desired properties.

  16. 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

  17. Study on the idity fuzzy neural network controller based on improved genetic algorithm of intelligent temperature control system in vegetable greenhouse

    NASA Astrophysics Data System (ADS)

    Zhang, Su; Yuan, Hongbo; Zhou, Yuhong; Wang, Nan

    2009-07-01

    In order to create the environment that the suitable crop grows, direct against the characteristic of the system of the greenhouse. The aim of the research was to study the intelligent temperature control system in vegetable greenhouse. Based on computer automatic control ,a kind of intelligent temperature control system in vegetable greenhouse was designed. The design thought of systematic hardwares such as temperature collection system, temperature display, control system, heater control circuit in the heater were expounded in detail The control algorithm of the system was improved and system simulation was made by using MATLAB finally. The control algorithm of the system was improved by a new fuzzy neural network controller. The stimulation curve showed that the system had better controlling and tracking performances ,higher accuracy of controlling the temperature. And this system and host epigyny computer could constitute the secondary computer control system which was favorable for realizing the centralized management of the production.

  18. Network analyses structure genetic diversity in independent genetic worlds

    PubMed Central

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

    2009-01-01

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

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

    PubMed

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

    2010-01-05

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

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

    NASA Astrophysics Data System (ADS)

    Takagi, Hideyuki

    1993-12-01

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

  1. Bimodality in Network Control

    NASA Astrophysics Data System (ADS)

    Jia, Tao; Liu, Yang-Yu; Posfai, Marton; Slotine, Jean-Jacques; Barabasi, Albert-Laszlo

    2013-03-01

    Controlling complex systems is a fundamental challenge of network science. Recent tools enable us to identify the minimum driver nodes, from which we can control a system. They also indicate a multiplicity of minimum driver node sets (MDS's): multiple combinations of the same number of nodes can achieve control over the system. This multiplicity allows us to classify individual nodes as critical if they are involved in all control configurations, intermittent if they occasionally act as driver nodes and redundant if they do not play any role in control. We develop computational and analytical framework analyzing nodes in each category in both model and real networks. We find that networks with identical degree distribution can be in two distinct control modes, ``centralized'' or ``distributed'', with drastic change on the role of each node in maintaining the controllability and orders of magnitude difference in the number of MDS's. In analyzing both model and real networks, we find that the two modes can be inferred directly from the network's degree distribution. Finally we show that the two control modes can be switched by small structural perturbations, leading to potential applications of control theory in real systems.

  2. Evolving complex dynamics in electronic models of genetic networks

    NASA Astrophysics Data System (ADS)

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

    2004-09-01

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

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

    PubMed

    He, Min; Li, Wei

    2007-03-01

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

  4. Genetic networks controlled by the bacterial replication initiator and transcription factor DnaA in Bacillus subtilis.

    PubMed

    Washington, Tracy A; Smith, Janet L; Grossman, Alan D

    2017-10-01

    DnaA is the widely conserved bacterial AAA+ ATPase that functions as both the replication initiator and a transcription factor. In many organisms, DnaA controls expression of its own gene and likely several others during growth and in response to replication stress. To evaluate the effects of DnaA on gene expression, separate from its role in replication initiation, we analyzed changes in mRNA levels in Bacillus subtilis cells with and without dnaA, using engineered strains in which dnaA is not essential. We found that dnaA was required for many of the changes in gene expression in response to replication stress. We also found that dnaA indirectly affected expression of several regulons during growth, including those controlled by the transcription factors Spo0A, AbrB, PhoP, SinR, RemA, Rok and YvrH. These effects were largely mediated by the effects of DnaA on expression of sda. DnaA activates transcription of sda, and Sda inhibits histidine protein kinases required for activation of the transcription factor Spo0A. We also found that loss of dnaA caused a decrease in the development of genetic competence. Together, our results indicate that DnaA plays an important role in modulating cell physiology, separate from its role in replication initiation. © 2017 John Wiley & Sons Ltd.

  5. Robust Reachability of Boolean Control Networks.

    PubMed

    Li, Fangfei; Tang, Yang

    2016-04-20

    Boolean networks serve a powerful tool in analysis of genetic regulatory networks since it emphasizes the fundamental principles and establishes a nature framework for capturing the dynamics of regulation of cellular states. In this paper, the robust reachability of Boolean control networks is investigated by means of semi-tensor product. Necessary and sufficient conditions for the robust reachability of Boolean control networks are provided, in which control inputs relying on disturbances or not are considered, respectively. Besides, the corresponding control algorithms are developed for these two cases. A reduced model of the lac operon in the Escherichia coli is presented to show the effectiveness of the presented results.

  6. Controllability analysis of networks

    NASA Astrophysics Data System (ADS)

    Lombardi, Anna; Hörnquist, Michael

    2007-05-01

    The concept of controllability of linear systems from control theory is applied to networks inspired by biology. A node is in this context controllable if an external signal can be applied which can adjust the level (e.g., protein concentration) of the node in a finite time to an arbitrary value, regardless of the levels of the other nodes. The property of being downstream of the node to which the input is applied turns out to be a necessary but not a sufficient condition for being controllable. An interpretation of the controllability matrix, when applied to networks, is also given. Finally, two case studies are provided in order to better explain the concepts, as well as some results for a gene regulatory network of fission yeast.

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

    PubMed

    Thompson, Ethan G; Galitski, Timothy

    2012-01-01

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

  8. 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.

  9. Launch Control Network Engineer

    NASA Technical Reports Server (NTRS)

    Medeiros, Samantha

    2017-01-01

    The Spaceport Command and Control System (SCCS) is being built at the Kennedy Space Center in order to successfully launch NASA’s revolutionary vehicle that allows humans to explore further into space than ever before. During my internship, I worked with the Network, Firewall, and Hardware teams that are all contributing to the huge SCCS network project effort. I learned the SCCS network design and the several concepts that are running in the background. I also updated and designed documentation for physical networks that are part of SCCS. This includes being able to assist and build physical installations as well as configurations. I worked with the network design for vehicle telemetry interfaces to the Launch Control System (LCS); this allows the interface to interact with other systems at other NASA locations. This network design includes the Space Launch System (SLS), Interim Cryogenic Propulsion Stage (ICPS), and the Orion Multipurpose Crew Vehicle (MPCV). I worked on the network design and implementation in the Customer Avionics Interface Development and Analysis (CAIDA) lab.

  10. Genetic algorithm for neural networks optimization

    NASA Astrophysics Data System (ADS)

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

    2004-11-01

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

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

    NASA Astrophysics Data System (ADS)

    Zheng, Bao-Hua

    2017-07-01

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

  12. Splitting Strategy for Simulating Genetic Regulatory Networks

    PubMed Central

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

    2014-01-01

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

  13. Exact controllability of complex networks

    PubMed Central

    Yuan, Zhengzhong; Zhao, Chen; Di, Zengru; Wang, Wen-Xu; Lai, Ying-Cheng

    2013-01-01

    Controlling complex networks is of paramount importance in science and engineering. Despite the recent development of structural controllability theory, we continue to lack a framework to control undirected complex networks, especially given link weights. Here we introduce an exact controllability paradigm based on the maximum multiplicity to identify the minimum set of driver nodes required to achieve full control of networks with arbitrary structures and link-weight distributions. The framework reproduces the structural controllability of directed networks characterized by structural matrices. We explore the controllability of a large number of real and model networks, finding that dense networks with identical weights are difficult to be controlled. An efficient and accurate tool is offered to assess the controllability of large sparse and dense networks. The exact controllability framework enables a comprehensive understanding of the impact of network properties on controllability, a fundamental problem towards our ultimate control of complex systems. PMID:24025746

  14. A Gene-Phenotype Network Based on Genetic Variability for Drought Responses Reveals Key Physiological Processes in Controlled and Natural Environments

    PubMed Central

    Rengel, David; Arribat, Sandrine; Maury, Pierre; Martin-Magniette, Marie-Laure; Hourlier, Thibaut; Laporte, Marion; Varès, Didier; Carrère, Sébastien; Grieu, Philippe; Balzergue, Sandrine; Gouzy, Jérôme

    2012-01-01

    Identifying the connections between molecular and physiological processes underlying the diversity of drought stress responses in plants is key for basic and applied science. Drought stress response involves a large number of molecular pathways and subsequent physiological processes. Therefore, it constitutes an archetypical systems biology model. We first inferred a gene-phenotype network exploiting differences in drought responses of eight sunflower (Helianthus annuus) genotypes to two drought stress scenarios. Large transcriptomic data were obtained with the sunflower Affymetrix microarray, comprising 32423 probesets, and were associated to nine morpho-physiological traits (integrated transpired water, leaf transpiration rate, osmotic potential, relative water content, leaf mass per area, carbon isotope discrimination, plant height, number of leaves and collar diameter) using sPLS regression. Overall, we could associate the expression patterns of 1263 probesets to six phenotypic traits and identify if correlations were due to treatment, genotype and/or their interaction. We also identified genes whose expression is affected at moderate and/or intense drought stress together with genes whose expression variation could explain phenotypic and drought tolerance variability among our genetic material. We then used the network model to study phenotypic changes in less tractable agronomical conditions, i.e. sunflower hybrids subjected to different watering regimes in field trials. Mapping this new dataset in the gene-phenotype network allowed us to identify genes whose expression was robustly affected by water deprivation in both controlled and field conditions. The enrichment in genes correlated to relative water content and osmotic potential provides evidence of the importance of these traits in agronomical conditions. PMID:23056196

  15. 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.

  16. Controlling General Polynomial Networks

    NASA Astrophysics Data System (ADS)

    Cuneo, N.; Eckmann, J.-P.

    2014-06-01

    We consider networks of massive particles connected by non-linear springs. Some particles interact with heat baths at different temperatures, which are modeled as stochastic driving forces. The structure of the network is arbitrary, but the motion of each particle is 1D. For polynomial interactions, we give sufficient conditions for Hörmander's "bracket condition" to hold, which implies the uniqueness of the steady state (if it exists), as well as the controllability of the associated system in control theory. These conditions are constructive; they are formulated in terms of inequivalence of the forces (modulo translations) and/or conditions on the topology of the connections. We illustrate our results with examples, including "conducting chains" of variable cross-section. This then extends the results for a simple chain obtained in Eckmann et al. in (Commun Math Phys 201:657-697, 1999).

  17. The causes of epistasis in genetic networks.

    PubMed

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

    2012-02-01

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

  18. 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

  19. 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.

  20. Noise in genetic and neural networks

    NASA Astrophysics Data System (ADS)

    Swain, Peter S.; Longtin, André

    2006-06-01

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

  1. Genetic networks influencing fruit ripening

    USDA-ARS?s Scientific Manuscript database

    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 ...

  2. Bridging genetic networks and queueing theory

    NASA Astrophysics Data System (ADS)

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

    2004-02-01

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

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. From gene expressions to genetic networks

    NASA Astrophysics Data System (ADS)

    Cieplak, Marek

    2009-03-01

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

  9. 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.

  10. Effects of macromolecular crowding on genetic networks.

    PubMed

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

    2011-12-21

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

  11. Controllability of structural brain networks

    NASA Astrophysics Data System (ADS)

    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.

    2015-10-01

    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.

  12. Neural Networks For Robot Control

    DTIC Science & Technology

    2001-04-17

    following: (a) Application of artificial neural networks (multi-layer perceptrons, MLPs) for 2D planar robot arm by using the dynamic backpropagation...methods for the adjustment of parameters; and optimization of the architecture; (b) Application of artificial neural networks in controlling closed...studies in controlling dynamic robot arms by using neural networks in real-time process; (2) Research of optimal architectures used in closed-loop systems in order to compare with adaptive and robust control.

  13. 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

  14. 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

  15. 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.

  16. Convergence Analysis of Genetic Algorithms for Topology Control in MANETs

    DTIC Science & Technology

    2009-01-01

    tering algorithm in mobile ad hoc networks using genetic algorith - mic approach,” in Prof. of the Global Telecommunications Conference (GLOBECOM...Convergence Analysis of Genetic Algorithms for Topology Control in MANETs Cem Şafak Şahin, Stephen Gundry, Elkin Urrea, M. Ümit Uyar, Michael...Christian.Pizzo@us.army.mil Abstract—We describe and verify convergence properties of our forced-based genetic algorithm (FGA) as a decentralized topology

  17. 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

  18. Realistic control of network dynamics.

    PubMed

    Cornelius, Sean P; Kath, William L; Motter, Adilson E

    2013-01-01

    The control of complex networks is of paramount importance in areas as diverse as ecosystem management, emergency response and cell reprogramming. A fundamental property of networks is that perturbations to one node can affect other nodes, potentially causing the entire system to change behaviour or fail. Here we show that it is possible to exploit the same principle to control network behaviour. Our approach accounts for the nonlinear dynamics inherent to real systems, and allows bringing the system to a desired target state even when this state is not directly accessible due to constraints that limit the allowed interventions. Applications show that this framework permits reprogramming a network to a desired task, as well as rescuing networks from the brink of failure-which we illustrate through the mitigation of cascading failures in a power-grid network and the identification of potential drug targets in a signalling network of human cancer.

  19. Realistic Control of Network Dynamics

    PubMed Central

    Cornelius, Sean P.; Kath, William L.; Motter, Adilson E.

    2014-01-01

    The control of complex networks is of paramount importance in areas as diverse as ecosystem management, emergency response, and cell reprogramming. 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. Here, we show that it is possible to exploit the same principle to control network behavior. Our approach accounts for the nonlinear dynamics inherent to real systems, and allows bringing the system to a desired target state even when this state is not directly accessible due to constraints that limit the allowed interventions. Applications show that this framework permits reprogramming a network to a desired task as well as rescuing networks from the brink of failure—which we illustrate through the mitigation of cascading failures in a power-grid network and the identification of potential drug targets in a signaling network of human cancer. PMID:23803966

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. Physical controllability of complex networks

    PubMed Central

    Wang, Le-Zhi; Chen, Yu-Zhong; Wang, Wen-Xu; Lai, Ying-Cheng

    2017-01-01

    A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control. PMID:28074900

  5. Physical controllability of complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Le-Zhi; Chen, Yu-Zhong; Wang, Wen-Xu; Lai, Ying-Cheng

    2017-01-01

    A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control.

  6. Training product unit neural networks with genetic algorithms

    NASA Technical Reports Server (NTRS)

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

    1991-01-01

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

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

    PubMed

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

    2017-09-01

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

  8. Control of collective network chaos.

    PubMed

    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.

  9. 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.

  10. 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.

  11. How social and genetic factors predict friendship networks

    PubMed Central

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

    2012-01-01

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

  12. 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.

  13. 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.

  14. 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.

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

    PubMed

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

    2015-06-18

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

  16. Information transmission in genetic regulatory networks: a review

    NASA Astrophysics Data System (ADS)

    Tkačik, Gašper; Walczak, Aleksandra M.

    2011-04-01

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

  17. Cybersecurity of Critical Control Networks

    DTIC Science & Technology

    2015-07-14

    Wertzberger, Casey Glatter, William Mahoney, Robin Gandhi, Kenneth Dick . An Integrated Framework for Control System Simulation and Regulatory...authors Robin Gandhi, William Mahoney, Ken Dick , Zachary Wilson. ADACS – A Language for Monitoring Regulatory Compliance in Control Systems; Second...Dependable Systems and Networks, Estoril, Portugal, authors Robin Gandhi, William Mahoney, Ken Dick . Cryptanalysis and Improvements of the

  18. Controlling centrality in complex networks

    PubMed Central

    Nicosia, V.; Criado, R.; Romance, M.; Russo, G.; Latora, V.

    2012-01-01

    Spectral centrality measures allow to identify influential individuals in social groups, to rank Web pages by popularity, and even to determine the impact of scientific researches. The centrality score of a node within a network crucially depends on the entire pattern of connections, so that the usual approach is to compute node centralities once the network structure is assigned. We face here with the inverse problem, that is, we study how to modify the centrality scores of the nodes by acting on the structure of a given network. We show that there exist particular subsets of nodes, called controlling sets, which can assign any prescribed set of centrality values to all the nodes of a graph, by cooperatively tuning the weights of their out-going links. We found that many large networks from the real world have surprisingly small controlling sets, containing even less than 5 – 10% of the nodes. PMID:22355732

  19. Control Capacity in Complex Networks

    NASA Astrophysics Data System (ADS)

    Jia, Tao; Liu, Yang-Yu; Slotine, Jean-Jacques; Barabasi, Albert-Laszlo

    2012-02-01

    By combining tools from control theory and network science, an efficient methodology was proposed to identify the minimum sets of driver nodes, whose time-dependent control can guide the whole network to any desired final state. Yet, this minimum driver set (MDS) is usually not unique, but one can often achieve multiple potential control configurations with the same number of driver nodes. Given that some nodes may appear in some MDSs but not in other, a crucial question remain unanswered: what is the role of individual node in controlling a complex system? We first classify a node as critical, redundant, or ordinary if it appears in all, no, or some MDSs. Then we introduce the concept of control capacity as a measure of the frequency that a node is in the MDSs, which quantifies the importance of a given node in maintaining Controllability. To avoid impractical enumeration of all MDSs, we propose an algorithm that uniformly samples the MDS. We use it to explore the control capacity of nodes in complex networks and study how it is related to other characteristics of the network topology.

  20. Communicating Networked Control Systems

    DTIC Science & Technology

    2007-03-31

    Bahamas, pages 1010-1015. 64. Carmen Del Vecchio and I.C. Paschalidis, “Supply Contracts with Service Level Requirements”, Proceedings of the IFAC...control using Monte Carlo sensing,” Proc. IEEE International Conference on Robotics and Automation, pp. 3058-3063, 2005. 10. S.B. Andersson, A.A. Handzel, V...Analysis, Madrid Spain. 20. S. Andersson and D. Hristu-Varsakelis, “Language-based feedback control using Monte -Carlo sensing”, to be subm. To IEEE Int’l

  1. 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.

  2. A unified lunar control network

    NASA Technical Reports Server (NTRS)

    Davies, Merton E.; Colvin, Tim R.; Meyer, Donald L.

    1987-01-01

    Mapping network control on the Moon is composed of a number of independent regional networks. These networks frequently have different origins but never have common ties, even in overlapping areas. The objective of the unified network program is to tie the regional networks into a single consistent planetwide control network. The plan is to start with the best defined regions, create common ties with neighboring data sets, and then expand into poorly defined regions. The most accurately defined points on the Moon are locations of the laser ranging retroreflectors and the VLBI measurements of the locations of the Apollo 15, 16, 17 ALSEP stations. Recent values for the coordinates of the retroreflectors have been received. The accuracy of these locations is about 30 m and their locations are used to define the center-of-mass and, hence, the origin of the unified lunar coordinate system. The coordinates of the retroreflectors are given in both principal axis and mean Earth/Polar axis systems. Mean Earth/Polar axis coordinates have been recommended by the IAU for the Moon. The difference in the coordinates is important, more than 600 m in latitude and longitude.

  3. 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.

  4. Control of Boolean networks: hardness results and algorithms for tree structured networks.

    PubMed

    Akutsu, Tatsuya; Hayashida, Morihiro; Ching, Wai-Ki; Ng, Michael K

    2007-02-21

    Finding control strategies of cells is a challenging and important problem in the post-genomic era. This paper considers theoretical aspects of the control problem using the Boolean network (BN), which is a simplified model of genetic networks. It is shown that finding a control strategy leading to the desired global state is computationally intractable (NP-hard) in general. Furthermore, this hardness result is extended for BNs with considerably restricted network structures. These results justify existing exponential time algorithms for finding control strategies for probabilistic Boolean networks (PBNs). On the other hand, this paper shows that the control problem can be solved in polynomial time if the network has a tree structure. Then, this algorithm is extended for the case where the network has a few loops and the number of time steps is small. Though this paper focuses on theoretical aspects, biological implications of the theoretical results are also discussed.

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

    PubMed

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

    2006-04-01

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

  6. Optimization of multicast optical networks with genetic algorithm

    NASA Astrophysics Data System (ADS)

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

    2007-11-01

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

  7. Gene networks controlling petal organogenesis.

    PubMed

    Huang, Tengbo; Irish, Vivian F

    2016-01-01

    One of the biggest unanswered questions in developmental biology is how growth is controlled. Petals are an excellent organ system for investigating growth control in plants: petals are dispensable, have a simple structure, and are largely refractory to environmental perturbations that can alter their size and shape. In recent studies, a number of genes controlling petal growth have been identified. The overall picture of how such genes function in petal organogenesis is beginning to be elucidated. This review will focus on studies using petals as a model system to explore the underlying gene networks that control organ initiation, growth, and final organ morphology.

  8. Multilevel modeling for inference of genetic regulatory networks

    NASA Astrophysics Data System (ADS)

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

    2005-12-01

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

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

    PubMed

    Silverman, Edwin K; Loscalzo, Joseph

    2012-08-01

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

  10. Network Medicine Approaches to the Genetics of Complex Diseases

    PubMed Central

    Silverman, Edwin K.; Loscalzo, Joseph

    2013-01-01

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

  11. Spectral coarse grained controllability of complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Pei; Xu, Shuang

    2017-07-01

    With the accumulation of interaction data from various systems, a fundamental question in network science is how to reduce the sizes while keeping certain properties of complex networks. Combined the spectral coarse graining theory and the structural controllability of complex networks, we explore the structural controllability of undirected complex networks during coarse graining processes. We evidence that the spectral coarse grained controllability (SCGC) properties for the Erdös-Rényi (ER) random networks, the scale-free (SF) random networks and the small-world (SW) random networks are distinct from each other. The SW networks are very robust, while the SF networks are sensitive during the coarse graining processes. As an emergent properties for the dense ER networks, during the coarse graining processes, there exists a threshold value of the coarse grained sizes, which separates the controllability of the reduced networks into robust and sensitive to coarse graining. Investigations on some real-world complex networks indicate that the SCGC properties are varied among different categories and different kinds of networks, some highly organized social or biological networks are more difficult to be controlled, while many man-made power networks and infrastructure networks can keep the controllability properties during the coarse graining processes. Furthermore, we speculate that the SCGC properties of complex networks may depend on their degree distributions. The associated investigations have potential implications in the control of large-scale complex networks, as well as in the understanding of the organization of complex networks.

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

    PubMed

    Iadevaia, Sergio; Mantzaris, Nikos V

    2007-02-20

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

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

    PubMed

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

    2011-05-24

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

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

    PubMed Central

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

    2011-01-01

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

  16. 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.

  17. 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.

  18. Coverage Control in Sensor Networks

    NASA Astrophysics Data System (ADS)

    Wang, Bang

    Sensors are devices that convert physical stimulus into recordable signals. Sensors have facilitated people to understand, monitor, and control machines and environments for many decades. A sensor node consists of not only sensor unit but also microcontroller unit, communication unit, storage unit, and power supply for producing, collecting, storing, processing, and delivering sensory data. The size and cost of a single sensor node has been reducing with the continuous advances of micro-electro-mechanical systems (MEMS) techniques. The miniaturization of sensor nodes has promoted the emergence of sensor networks, which normally consists of a large number of sensor nodes collaborating to accomplish advanced tasks. Applications of sensor networks are in a wide range, including battlefield surveillance, environmental monitoring, biological detection, smart space, industrial diagnostics, etc. Despite promising applications, there are also great challenges in designing, implementing, and operating sensor networks. Many research issues have been studied, and many solution approaches have been proposed for sensor networks. In this chapter, we provide some backgrounds and introduction about sensors, sensor nodes, and sensor networks.

  19. Robustness of network controllability in cascading failure

    NASA Astrophysics Data System (ADS)

    Chen, Shi-Ming; Xu, Yun-Fei; Nie, Sen

    2017-04-01

    It is demonstrated that controlling complex networks in practice needs more inputs than that predicted by the structural controllability framework. Besides, considering the networks usually faces to the external or internal failure, we define parameters to evaluate the control cost and the variation of controllability after cascades, exploring the effect of number of control inputs on the controllability for random networks and scale-free networks in the process of cascading failure. For different topological networks, the results show that the robustness of controllability will be stronger through allocating different control inputs and edge capacity.

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

    DTIC Science & Technology

    2009-10-01

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

  1. Predicting and Controlling Complex Networks

    DTIC Science & Technology

    2015-06-22

    networks and control . . . . . . . . . . . . . . . . . . . 7 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically...Ni, Y.-C. Lai, and C. Grebogi, “Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games,” Physical Review E 83...of Physics B 76, 179-183 (2010). 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games Biodiversity is

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

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

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

  3. Congestion control in satellite networks

    NASA Astrophysics Data System (ADS)

    Byun, Do Jun

    Due to exponential increases in internet traffic, Active Queue Management (AQM) has been heavily studied by numerous researchers. However, little is known about AQM in satellite networks. A microscopic examination of queueing behavior in satellite networks is conducted to identify problems with applying existing AQM methods. A new AQM method is proposed to overcome the problems and it is validated using a realistic emulation environment and a mathematical model. Three problems that were discovered during the research are discussed in this dissertation. The first problem is oscillatory queueing, which is caused by high buffering due to Performance Enhancing Proxy (PEP) in satellite networks where congestion control after the PEP buffering does not effectively control traffic senders. Existing AQMs that can solve this problem have tail drop queueing that results in consecutive packet drops (global synchronization). A new AQM method called Adaptive Virtual Queue Random Early Detection (AVQRED) is proposed to solve this problem. The second problem is unfair bandwidth sharing caused by inaccurate measurements of per-flow bandwidth usage. AVQRED is enhanced to accurately measure per-flow bandwidth usage to solve this problem without adding much complexity to the algorithm. The third problem is queueing instability caused by buffer flow control where TCP receive windows are adjusted to flow control traffic senders instead of dropping received packets during congestion. Although buffer flow control is quite attractive to satellite networks, queueing becomes unstable because accepting packets instead of dropping them aggravates the congestion level. Furthermore, buffer flow control has abrupt reductions in the TCP receive window size due to high PEP buffering causing more instability. AVQRED with packet drop is proposed to solve this problem. Networks with scarce bandwidth and high propagation delays can not afford to have an unstable AQM. In this research, three problems

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

    PubMed

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

    2016-11-01

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

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

    PubMed Central

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

    2016-01-01

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

  6. 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.

  7. Controllability of flow-conservation networks

    NASA Astrophysics Data System (ADS)

    Zhao, Chen; Zeng, An; Jiang, Rui; Yuan, Zhengzhong; Wang, Wen-Xu

    2017-07-01

    The ultimate goal of exploring complex networks is to control them. As such, controllability of complex networks has been intensively investigated. Despite recent advances in studying the impact of a network's topology on its controllability, a comprehensive understanding of the synergistic impact of network topology and dynamics on controllability is still lacking. Here, we explore the controllability of flow-conservation networks, trying to identify the minimal number of driver nodes that can guide the network to any desirable state. We develop a method to analyze the controllability on flow-conservation networks based on exact controllability theory, transforming the original analysis on adjacency matrix to Laplacian matrix. With this framework, we systematically investigate the impact of some key factors of networks, including link density, link directionality, and link polarity, on the controllability of these networks. We also obtain the analytical equations by investigating the network's structural properties approximatively and design the efficient tools. Finally, we consider some real networks with flow dynamics, finding that their controllability is significantly different from that predicted by only considering the topology. These findings deepen our understanding of network controllability with flow-conservation dynamics and provide a general framework to incorporate real dynamics in the analysis of network controllability.

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

    NASA Astrophysics Data System (ADS)

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

    2017-09-01

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

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

    PubMed

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

    2008-01-01

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

  10. Optimal finite horizon control in gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Liu, Qiuli

    2013-06-01

    As a paradigm for modeling gene regulatory networks, probabilistic Boolean networks (PBNs) form a subclass of Markov genetic regulatory networks. To date, many different stochastic optimal control approaches have been developed to find therapeutic intervention strategies for PBNs. A PBN is essentially a collection of constituent Boolean networks via a probability structure. Most of the existing works assume that the probability structure for Boolean networks selection is known. Such an assumption cannot be satisfied in practice since the presence of noise prevents the probability structure from being accurately determined. In this paper, we treat a case in which we lack the governing probability structure for Boolean network selection. Specifically, in the framework of PBNs, the theory of finite horizon Markov decision process is employed to find optimal constituent Boolean networks with respect to the defined objective functions. In order to illustrate the validity of our proposed approach, an example is also displayed.

  11. 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

  12. 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.

  13. 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

  14. 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

  15. Constrained target controllability of complex networks

    NASA Astrophysics Data System (ADS)

    Guo, Wei-Feng; Zhang, Shao-Wu; Wei, Ze-Gang; Zeng, Tao; Liu, Fei; Zhang, Jingsong; Wu, Fang-Xiang; Chen, Luonan

    2017-06-01

    It is of great theoretical interest and practical significance to study how to control a system by applying perturbations to only a few driver nodes. Recently, a hot topic of modern network researches is how to determine driver nodes that allow the control of an entire network. However, in practice, to control a complex network, especially a biological network, one may know not only the set of nodes which need to be controlled (i.e. target nodes), but also the set of nodes to which only control signals can be applied (i.e. constrained control nodes). Compared to the general concept of controllability, we introduce the concept of constrained target controllability (CTC) of complex networks, which concerns the ability to drive any state of target nodes to their desirable state by applying control signals to the driver nodes from the set of constrained control nodes. To efficiently investigate the CTC of complex networks, we further design a novel graph-theoretic algorithm called CTCA to estimate the ability of a given network to control targets by choosing driver nodes from the set of constrained control nodes. We extensively evaluate the CTC of numerous real complex networks. The results indicate that biological networks with a higher average degree are easier to control than biological networks with a lower average degree, while electronic networks with a lower average degree are easier to control than web networks with a higher average degree. We also show that our CTCA can more efficiently produce driver nodes for target-controlling the networks than existing state-of-the-art methods. Moreover, we use our CTCA to analyze two expert-curated bio-molecular networks and compare to other state-of-the-art methods. The results illustrate that our CTCA can efficiently identify proven drug targets and new potentials, according to the constrained controllability of those biological networks.

  16. Genetic Control of Potassium Channels.

    PubMed

    Amin, Ahmad S; Wilde, Arthur A M

    2016-06-01

    Approximately 80 genes in the human genome code for pore-forming subunits of potassium (K(+)) channels. Rare variants (mutations) in K(+) channel-encoding genes may cause heritable arrhythmia syndromes. Not all rare variants in K(+) channel-encoding genes are necessarily disease-causing mutations. Common variants in K(+) channel-encoding genes are increasingly recognized as modifiers of phenotype in heritable arrhythmia syndromes and in the general population. Although difficult, distinguishing pathogenic variants from benign variants is of utmost importance to avoid false designations of genetic variants as disease-causing mutations.

  17. Networked Dynamic Systems: Identification, Controllability, and Randomness

    NASA Astrophysics Data System (ADS)

    Nabi-Abdolyousefi, Marzieh

    The presented dissertation aims to develop a graph-centric framework for the analysis and synthesis of networked dynamic systems (NDS) consisting of multiple dynamic units that interact via an interconnection topology. We examined three categories of network problems, namely, identification, controllability, and randomness. In network identification, as a subclass of inverse problems, we made an explicit relation between the input-output behavior of an NDS and the underlying interacting network. In network controllability, we provided structural and algebraic insights into features of the network that enable external signal(s) to control the state of the nodes in the network for certain classes of interconnections, namely, path, circulant, and Cartesian networks. We also examined the relation between network controllability and the symmetry structure of the graph. Motivated by the analysis results for the controllability and observability of deterministic networks, a natural question is whether randomness in the network layer or in the layer of inputs and outputs generically leads to favorable system theoretic properties. In this direction, we examined system theoretic properties of random networks including controllability, observability, and performance of optimal feedback controllers and estimators. We explored some of the ramifications of such an analysis framework in opinion dynamics over social networks and sensor networks in estimating the real-time position of a Seaglider from experimental data.

  18. 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

    Cai, Guanjing; Zheng, Wei; Yang, Xujun; Zhang, Bangzhou; Zheng, Tianling

    2014-05-24

    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.

  19. SOCIAL NETWORKS HELP CONTROL HYPERTENSION

    PubMed Central

    Shaya, Fadia T.; Chirikov, Viktor V.; Mullins, C. Daniel; Shematek, Jon; Howard, DeLeonardo; Foster, Clyde; Saunders, Elijah

    2013-01-01

    Cardiovascular health disparities continue to pose a major public health problem. We evaluated the effect of education administered within social networks on the improvement of hypertension in 248 African Americans compared to historical controls. Patients formed clusters with peers and attended monthly hypertension education sessions. We assessed the likelihood of reaching goal below predefined SBP and DBP thresholds as well as the absolute reduction in SBP and DBP, controlling for diabetes, smoking, baseline hypertension, and demographics. The intervention group was more likely to have ever reached treatment goal at 12 months follow-up (OR=1.72, P=0.11). At 18 months of follow-up, the MVP group had a statistically significant larger drop in SBP (−4.82 mmHg, P<0.0001) and DBP (−3.37 mmHg, P=0.01) than the control. The clustering of patients in social networks around hypertension education has a positive impact on the management of hypertension in minority populations and may help address cardiovascular health disparities. PMID:23282122

  20. 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…

  1. 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…

  2. Structural Controllability of Temporal Networks with a Single Switching Controller.

    PubMed

    Yao, Peng; Hou, Bao-Yu; Pan, Yu-Jian; Li, Xiang

    2017-01-01

    Temporal network, whose topology evolves with time, is an important class of complex networks. Temporal trees of a temporal network describe the necessary edges sustaining the network as well as their active time points. By a switching controller which properly selects its location with time, temporal trees are used to improve the controllability of the network. Therefore, more nodes are controlled within the limited time. Several switching strategies to efficiently select the location of the controller are designed, which are verified with synthetic and empirical temporal networks to achieve better control performance.

  3. Structural Controllability of Temporal Networks with a Single Switching Controller

    PubMed Central

    Yao, Peng; Hou, Bao-Yu; Pan, Yu-Jian; Li, Xiang

    2017-01-01

    Temporal network, whose topology evolves with time, is an important class of complex networks. Temporal trees of a temporal network describe the necessary edges sustaining the network as well as their active time points. By a switching controller which properly selects its location with time, temporal trees are used to improve the controllability of the network. Therefore, more nodes are controlled within the limited time. Several switching strategies to efficiently select the location of the controller are designed, which are verified with synthetic and empirical temporal networks to achieve better control performance. PMID:28107538

  4. Flow Control Using Neural Networks

    DTIC Science & Technology

    2007-11-02

    FEB 93 - 31 DEC 96 4. TITLE AND SUBTITLE 5 . FUNDING NUMBERS FLOW CONTROL USING NEURAL NETWORKS F49620-93-1-0135 61102F 6. AUTHOR(S) 2307/BS THORWALD...OFFICE OF SCIENTIFIC RESEARCH (AFOSRO AGENCY REPORT NUMBER 110 DUNCAN AVENUE, ROOM B115 BOLLING AFB DC 20332- 8050 11. SUPPLEMENTARY NOTES 12a...signals. Figure 5 shows a time series for an actuator that performs a ramp motion in the streamwise direction over about 1 % of the TS period and remains

  5. 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.

  6. 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.

  7. H ∞ predictive control of networked control systems

    NASA Astrophysics Data System (ADS)

    Xia, Yuanqing; Li, Li; Liu, Guo-Ping; Shi, Peng

    2011-06-01

    This article is concerned with the problem of H ∞ predictive control of networked control system with random network delay. A new control scheme termed networked predictive control is proposed. This scheme mainly consists of the control prediction generator and network delay compensator. While designing the predictor, the control input to the actuator may be different due to networked induced time-delay and data dropout, and two cases are considered depending on the way that the observer obtains the plant control input u t . The necessary and sufficient conditions are given for the closed-loop networked predictive control system to be stochastically stable for different u t and random network delays in controller to actuator channel (CAC) and sensor to controller channel (SCC). A simulation study shows the effectiveness of the proposed scheme.

  8. Genetic networks: between theory and experimentation

    NASA Astrophysics Data System (ADS)

    Bottani, Samuel; Mazurie, Aurélien

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

  9. 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

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

    PubMed

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

    2013-01-01

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

  11. 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.

  12. 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.

  13. 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.

  14. Neural Networks in Nonlinear Aircraft Control

    NASA Technical Reports Server (NTRS)

    Linse, Dennis J.

    1990-01-01

    Recent research indicates that artificial neural networks offer interesting learning or adaptive capabilities. The current research focuses on the potential for application of neural networks in a nonlinear aircraft control law. The current work has been to determine which networks are suitable for such an application and how they will fit into a nonlinear control law.

  15. A Full Bayesian Approach for Boolean Genetic Network Inference

    PubMed Central

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

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Sevim, Volkan; Rikvold, Per Arne

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

  17. Clone size distributions in networks of genetic similarity

    NASA Astrophysics Data System (ADS)

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

    2006-12-01

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

  18. Wind power prediction based on genetic neural network

    NASA Astrophysics Data System (ADS)

    Zhang, Suhan

    2017-04-01

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

  19. MicroRNA-dependent genetic networks during neural development.

    PubMed

    Abernathy, Daniel G; Yoo, Andrew S

    2015-01-01

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

  20. 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.

  1. 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

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

    PubMed

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

    2016-01-01

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

  3. 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.

  4. 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

  5. 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.

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

    PubMed

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

    2012-01-01

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

  7. 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.

  8. 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.

  9. Genetic origins of social networks in rhesus macaques

    PubMed Central

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

    2013-01-01

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

  10. Tuning of a neuro-fuzzy controller by genetic algorithm.

    PubMed

    Seng, T L; Bin Khalid, M; Yusof, R

    1999-01-01

    Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance.

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

    PubMed

    Sun, Yan V

    2012-10-01

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

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

    PubMed Central

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

    2012-01-01

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

  13. Hierarchical genetic networks and noncoding RNAs

    NASA Astrophysics Data System (ADS)

    Zhdanov, Vladimir P.

    2010-12-01

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

  14. 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.

  15. 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.

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

    PubMed

    Norouzi, Ali; Zaim, A Halim

    2014-01-01

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

  17. 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

  18. 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

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. Threshold control of chaotic neural network.

    PubMed

    He, Guoguang; Shrimali, Manish Dev; Aihara, Kazuyuki

    2008-01-01

    The chaotic neural network constructed with chaotic neurons exhibits rich dynamic behaviour with a nonperiodic associative memory. In the chaotic neural network, however, it is difficult to distinguish the stored patterns in the output patterns because of the chaotic state of the network. In order to apply the nonperiodic associative memory into information search, pattern recognition etc. it is necessary to control chaos in the chaotic neural network. We have studied the chaotic neural network with threshold activated coupling, which provides a controlled network with associative memory dynamics. The network converges to one of its stored patterns or/and reverse patterns which has the smallest Hamming distance from the initial state of the network. The range of the threshold applied to control the neurons in the network depends on the noise level in the initial pattern and decreases with the increase of noise. The chaos control in the chaotic neural network by threshold activated coupling at varying time interval provides controlled output patterns with different temporal periods which depend upon the control parameters.

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

    PubMed

    Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei

    2015-08-01

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

  5. Electronic circuit analog of synthetic genetic networks: Revisited

    NASA Astrophysics Data System (ADS)

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

    2017-06-01

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

  6. Controlling contagion processes in activity driven networks.

    PubMed

    Liu, Suyu; Perra, Nicola; Karsai, Márton; Vespignani, Alessandro

    2014-03-21

    The vast majority of strategies aimed at controlling contagion processes on networks consider the connectivity pattern of the system either quenched or annealed. However, in the real world, many networks are highly dynamical and evolve, in time, concurrently with the contagion process. Here, we derive an analytical framework for the study of control strategies specifically devised for a class of time-varying networks, namely activity-driven networks. We develop a block variable mean-field approach that allows the derivation of the equations describing the coevolution of the contagion process and the network dynamic. We derive the critical immunization threshold and assess the effectiveness of three different control strategies. Finally, we validate the theoretical picture by simulating numerically the spreading process and control strategies in both synthetic networks and a large-scale, real-world, mobile telephone call data set.

  7. 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

  8. 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.

  9. Neural network architecture for crossbar switch control

    NASA Technical Reports Server (NTRS)

    Troudet, Terry P.; Walters, Stephen M.

    1991-01-01

    A Hopfield neural network architecture for the real-time control of a crossbar switch for switching packets at maximum throughput is proposed. The network performance and processing time are derived from a numerical simulation of the transitions of the neural network. A method is proposed to optimize electronic component parameters and synaptic connections, and it is fully illustrated by the computer simulation of a VLSI implementation of 4 x 4 neural net controller. The extension to larger size crossbars is demonstrated through the simulation of an 8 x 8 crossbar switch controller, where the performance of the neural computation is discussed in relation to electronic noise and inhomogeneities of network components.

  10. Neural network architecture for crossbar switch control

    NASA Technical Reports Server (NTRS)

    Troudet, Terry P.; Walters, Stephen M.

    1991-01-01

    A Hopfield neural network architecture for the real-time control of a crossbar switch for switching packets at maximum throughput is proposed. The network performance and processing time are derived from a numerical simulation of the transitions of the neural network. A method is proposed to optimize electronic component parameters and synaptic connections, and it is fully illustrated by the computer simulation of a VLSI implementation of 4 x 4 neural net controller. The extension to larger size crossbars is demonstrated through the simulation of an 8 x 8 crossbar switch controller, where the performance of the neural computation is discussed in relation to electronic noise and inhomogeneities of network components.

  11. 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.

  12. 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

  13. Adaptive Neural Network Controller for ATM Traffic

    DTIC Science & Technology

    1996-12-01

    IEEE Communications Magazine (October 1995). 2. Baum, Eric B...Adaptive Control in ATM Networks," IEEE Communications Magazine (October 1995). 9. Evanowsky, John B. "Information for the Warrior," IEEE Communications Magazine (October...Network Applications in ATM," IEEE Communications Magazine (October 1995). 78 16. Imrich, et al. "A counter based congestion control for ATM

  14. 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.

  15. 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

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

    PubMed

    Ziebarth, Jesse D; Cui, Yan

    2017-01-01

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

  17. 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

  18. Stress controls the mechanics of collagen networks.

    PubMed

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

    2015-08-04

    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.

  19. 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.

  20. 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.

  1. Neutral networks for control, identification and diagnosis

    NASA Astrophysics Data System (ADS)

    Rauch, H. E.; Schaechter, D. B.

    1994-11-01

    Advances in the theory and technology of artificial neural networks provide the potential for new approaches to the problems of control, identification, and diagnosis for large, complex systems. However, these approaches must be validated for specific applications before they can be exploited effectively. Because of the unique capabilities they offer, neural networks should play an important role in space exploration systems operations. After a brief introduction to neural networks is presented, some applications of neural networks to identification and control of space systems are described and discussed. They span the spectrum of relatively straightforward to rather complex applications. An explanation of how neural networks can be applied to such important tasks as fault diagnosis and accommodation is presented. Neural networks are shown to be part of the hierarchy of intelligent control where a higher order decision element monitors and supervises lower order elements for sensing and actuation.

  2. 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.

  3. Distributed controller clustering in software defined networks.

    PubMed

    Abdelaziz, Ahmed; Fong, Ang Tan; Gani, Abdullah; Garba, Usman; Khan, Suleman; Akhunzada, Adnan; Talebian, Hamid; Choo, Kim-Kwang Raymond

    2017-01-01

    Software Defined Networking (SDN) is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs) brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN) SDN and Open Network Operating System (ONOS) controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability.

  4. Distributed controller clustering in software defined networks

    PubMed Central

    Gani, Abdullah; Akhunzada, Adnan; Talebian, Hamid; Choo, Kim-Kwang Raymond

    2017-01-01

    Software Defined Networking (SDN) is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs) brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN) SDN and Open Network Operating System (ONOS) controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability. PMID:28384312

  5. Detecting controlling nodes of boolean regulatory networks.

    PubMed

    Schober, Steffen; Kracht, David; Heckel, Reinhard; Bossert, Martin

    2011-10-11

    Boolean models of regulatory networks are assumed to be tolerant to perturbations. That qualitatively implies that each function can only depend on a few nodes. Biologically motivated constraints further show that functions found in Boolean regulatory networks belong to certain classes of functions, for example, the unate functions. It turns out that these classes have specific properties in the Fourier domain. That motivates us to study the problem of detecting controlling nodes in classes of Boolean networks using spectral techniques. We consider networks with unbalanced functions and functions of an average sensitivity less than 23k, where k is the number of controlling variables for a function. Further, we consider the class of 1-low networks which include unate networks, linear threshold networks, and networks with nested canalyzing functions. We show that the application of spectral learning algorithms leads to both better time and sample complexity for the detection of controlling nodes compared with algorithms based on exhaustive search. For a particular algorithm, we state analytical upper bounds on the number of samples needed to find the controlling nodes of the Boolean functions. Further, improved algorithms for detecting controlling nodes in large-scale unate networks are given and numerically studied.

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

    PubMed

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

    2010-12-03

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

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

    PubMed

    Vercruysse, Steven; Kuiper, Martin

    2005-01-15

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

  8. Genetic Networks Activated by Blast Injury to the Eye

    DTIC Science & Technology

    2014-08-01

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

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

    DTIC Science & Technology

    2013-03-01

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

  10. Genetic control of leaf curl in maize.

    PubMed

    Entringer, G C; Guedes, F L; Oliveira, A A; Nascimento, J P; Souza, J C

    2014-03-17

    Among the many implications of climatic change on agriculture, drought is expected to continue to have a major impact on agribusinesses. Leaf curling is an anatomical characteristic that might be potentially used to enhance plant tolerance to water deficit. Hence, we aimed to study the genetic control of leaf curl in maize. From 2 contrasting inbred lines for the trait, generations F1, F2, and the backcrosses were obtained. All of these generations were evaluated in a randomized block design with 2 replicates. Leaf curl samples were collected from 3 leaves above the first ear at the tasseling stage, and quantified by dividing the width of the leaf blade with natural curling against its extended width. The mean and variance components were estimated by the weighted least square method. It was found that the trait studied has predominance of the additive effects, with genetic control being attributed to few genes that favor selection and exhibit minimal influence from the environment.

  11. Controlling congestion on complex networks: fairness, efficiency and network structure.

    PubMed

    Buzna, Ľuboš; Carvalho, Rui

    2017-08-22

    We consider two elementary (max-flow and uniform-flow) and two realistic (max-min fairness and proportional fairness) congestion control schemes, and analyse how the algorithms and network structure affect throughput, the fairness of flow allocation, and the location of bottleneck edges. The more realistic proportional fairness and max-min fairness algorithms have similar throughput, but path flow allocations are more unequal in scale-free than in random regular networks. Scale-free networks have lower throughput than their random regular counterparts in the uniform-flow algorithm, which is favoured in the complex networks literature. We show, however, that this relation is reversed on all other congestion control algorithms for a region of the parameter space given by the degree exponent γ and average degree 〈k〉. Moreover, the uniform-flow algorithm severely underestimates the network throughput of congested networks, and a rich phenomenology of path flow allocations is only present in the more realistic α-fair family of algorithms. Finally, we show that the number of paths passing through an edge characterises the location of a wide range of bottleneck edges in these algorithms. Such identification of bottlenecks could provide a bridge between the two fields of complex networks and congestion control.

  12. Genetic Control of Lateral Root Formation in Cereals.

    PubMed

    Yu, Peng; Gutjahr, Caroline; Li, Chunjian; Hochholdinger, Frank

    2016-11-01

    Cereals form complex root systems composed of different root types. Lateral root formation is a major determinant of root architecture and is instrumental for the efficient uptake of water and nutrients. Positioning and patterning of lateral roots and cell types involved in their formation are unique in monocot cereals. Recent discoveries advanced the molecular understanding of the intrinsic genetic control of initiation and elongation of lateral roots in cereals by distinct, in part root-type-specific genetic programs. Moreover, molecular networks modulating the plasticity of lateral root formation in response to water and nutrient availability and arbuscular mycorrhizal fungal colonization have been identified. These novel discoveries provide a better mechanistic understanding of postembryonic lateral root development in cereals.

  13. 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.

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

    PubMed

    Ramadan, Emad; Naef, Ahmed; Ahmed, Moataz

    2016-07-25

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

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

    PubMed Central

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

    2008-01-01

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

  16. 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.

  17. Molecular network control through boolean canalization.

    PubMed

    Murrugarra, David; Dimitrova, Elena S

    2015-12-01

    Boolean networks are an important class of computational models for molecular interaction networks. Boolean canalization, a type of hierarchical clustering of the inputs of a Boolean function, has been extensively studied in the context of network modeling where each layer of canalization adds a degree of stability in the dynamics of the network. Recently, dynamic network control approaches have been used for the design of new therapeutic interventions and for other applications such as stem cell reprogramming. This work studies the role of canalization in the control of Boolean molecular networks. It provides a method for identifying the potential edges to control in the wiring diagram of a network for avoiding undesirable state transitions. The method is based on identifying appropriate input-output combinations on undesirable transitions that can be modified using the edges in the wiring diagram of the network. Moreover, a method for estimating the number of changed transitions in the state space of the system as a result of an edge deletion in the wiring diagram is presented. The control methods of this paper were applied to a mutated cell-cycle model and to a p53-mdm2 model to identify potential control targets.

  18. Genetic control of the innate immune response

    PubMed Central

    Wells, Christine A; Ravasi, Timothy; Faulkner, Geoffrey J; Carninci, Piero; Okazaki, Yasushi; Hayashizaki, Yoshihide; Sweet, Matthew; Wainwright, Brandon J; Hume, David A

    2003-01-01

    Background Susceptibility to infectious diseases is directed, in part, by the interaction between the invading pathogen and host macrophages. This study examines the influence of genetic background on host-pathogen interactions, by assessing the transcriptional responses of macrophages from five inbred mouse strains to lipopolysaccharide (LPS), a major determinant of responses to gram-negative microorganisms. Results The mouse strains examined varied greatly in the number, amplitude and rate of induction of genes expressed in response to LPS. The response was attenuated in the C3H/HeJlpsd strain, which has a mutation in the LPS receptor Toll-like receptor 4 (TLR4). Variation between mouse strains allowed clustering into early (C57Bl/6J and DBA/2J) and delayed (BALB/c and C3H/ARC) transcriptional phenotypes. There was no clear correlation between gene induction patterns and variation at the Bcg locus (Slc11A1) or propensity to bias Th1 versus Th2 T cell activation responses. Conclusion Macrophages from each strain responded to LPS with unique gene expression profiles. The variation apparent between genetic backgrounds provides insights into the breadth of possible inflammatory responses, and paradoxically, this divergence was used to identify a common transcriptional program that responds to TLR4 signalling, irrespective of genetic background. Our data indicates that many additional genetic loci control the nature and the extent of transcriptional responses promoted by a single pathogen-associated molecular pattern (PAMP), such as LPS. PMID:12826024

  19. 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.

  20. The control network of Mercury: April 1991

    NASA Technical Reports Server (NTRS)

    Davies, Merton E.; Rogers, Patricia G.

    1991-01-01

    Features identified on Mariner 10 high resolution images of Mercury, acquired during three flybys between 1974 and 1975, form the basis of Mercury's planetwide control network. Although images from all three flybys are used in the net, the large amount of contiguous coverage from the second flyby, a southern bright-side pass, make these images the strongest contributors to the control net. Mercury is in synchronous rotation with a period of 58.6462 days and its spin axis is approximately normal to the equatorial plane. The 20 degree meridian is defined by the crater Hun Kal, located just south of the equator. The control network computations involve the photogrammetric determination of control point coordinates and an analytical triangulation solution. The current control network computations for Mercury are performed in the J2000 coordinate system according to the International Astronomical Union (IAU) convention. In recent years, updates to the control network have included improved trajectory solutions and modification of the standard radii (2439) at several points based on Earth-based radar altimetry data. The current status of the control network calculations is presented. Improvements were made to existing control points and new control points were added to the net to strengthen the overall network and improve the standard error of measurement.

  1. 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?".

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

    PubMed

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

    2012-12-01

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

  3. Applications of Neural Networks to Adaptive Control

    DTIC Science & Technology

    1989-12-01

    DTIC ;- E py 00 NAVAL POSTGRADUATE SCHOOL Monterey, California I.$ RDTIC IELECTE fl THESIS BEG7V°U APPLICATIONS OF NEURAL NETWORKS TO ADAPTIVE CONTROL...Second keader E . Robert Wood, Chairman, Department of Aeronautics and Astronautics Gordoii E . Schacher, Dean of Faculty and Graduate Education ii ABSTRACT...23: Network Dynamic Stability for q(t) . ............................. 55 ix Figure 24: Network Dynamic Stability for e (t

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

    PubMed

    Fogelmark, Karl; Peterson, Carsten; Troein, Carl

    2016-01-01

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

  5. 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

  6. Effect of correlations on controllability transition in network control

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

    The network control problem has recently attracted an increasing amount of attention, owing to concerns including the avoidance of cascading failures of power-grids and the management of ecological networks. It has been proven that numerical control can be achieved if the number of control inputs exceeds a certain transition point. In the present study, we investigate the effect of degree correlation on the numerical controllability in networks whose topological structures are reconstructed from both real and modeling systems, and we find that the transition point of the number of control inputs depends strongly on the degree correlation in both undirected and directed networks with moderately sparse links. More interestingly, the effect of the degree correlation on the transition point cannot be observed in dense networks for numerical controllability, which contrasts with the corresponding result for structural controllability. In particular, for directed random networks and scale-free networks, the influence of the degree correlation is determined by the types of correlations. Our approach provides an understanding of control problems in complex sparse networks.

  7. 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

  8. Control of autonomous robot using neural networks

    NASA Astrophysics Data System (ADS)

    Barton, Adam; Volna, Eva

    2017-07-01

    The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.

  9. Predictive Control of Large Complex Networks

    NASA Astrophysics Data System (ADS)

    Haber, Aleksandar; Motter, Adilson E.

    Networks of coupled dynamical subsystems are increasingly used to represent complex natural and engineered systems. While recent technological developments give us improved means to actively control the dynamics of individual subsystems in various domains, network control remains a challenging problem due to difficulties imposed by intrinsic nonlinearities, control constraints, and the large-scale nature of the systems. In this talk, we will present a model predictive control approach that is effective while accounting for these realistic properties of complex networks. Our method can systematically identify control interventions that steer the trajectory to a desired state, even in the presence of strong nonlinearities and constraints. Numerical tests show that the method is applicable to a variety of networks, ranging from power grids to chemical reaction systems.

  10. Structural control of reaction-diffusion networks

    NASA Astrophysics Data System (ADS)

    Xuan, Qi; Du, Fang; Dong, Hui; Yu, Li; Chen, Guanrong

    2011-09-01

    Recent studies revealed that reaction-diffusion (RD) dynamics can be significantly influenced by the structure of the underlying network. In this paper, a framework is established to study a closely related problem, i.e., to control the proportion of active particles in an RD process by adjusting the structure of the underlying diffusion network. Both distributed and centralized rewiring and reweighting control schemes are proposed for unweighted and weighted networks, respectively. Simulations show that the proportion of active particles can indeed be controlled to a certain extent even when the distributed control mechanism is totally random, while quite high precision can be achieved by centralized control schemes. More interestingly, it is found that the reactants in heterogeneous networks have wider controllable ranges than those in homogeneous networks with similar numbers of nodes and links, if only the weights of links are changed with a fixed bound. Therefore, it is believed that heterogeneous networks fit the changeable environment better, which provides another explanation for some common observations on many heterogeneous real-world networks.

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

    PubMed

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

    2012-01-01

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

  12. 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…

  13. Advanced telerobotic control using neural networks

    NASA Technical Reports Server (NTRS)

    Pap, Robert M.; Atkins, Mark; Cox, Chadwick; Glover, Charles; Kissel, Ralph; Saeks, Richard

    1993-01-01

    Accurate Automation is designing and developing adaptive decentralized joint controllers using neural networks. We are then implementing these in hardware for the Marshall Space Flight Center PFMA as well as to be usable for the Remote Manipulator System (RMS) robot arm. Our design is being realized in hardware after completion of the software simulation. This is implemented using a Functional-Link neural network.

  14. Advanced telerobotic control using neural networks

    NASA Technical Reports Server (NTRS)

    Pap, Robert M.; Atkins, Mark; Cox, Chadwick; Glover, Charles; Kissel, Ralph; Saeks, Richard

    1993-01-01

    Accurate Automation is designing and developing adaptive decentralized joint controllers using neural networks. We are then implementing these in hardware for the Marshall Space Flight Center PFMA as well as to be usable for the Remote Manipulator System (RMS) robot arm. Our design is being realized in hardware after completion of the software simulation. This is implemented using a Functional-Link neural network.

  15. Neural networks applications to control and computations

    NASA Technical Reports Server (NTRS)

    Luxemburg, Leon A.

    1994-01-01

    Several interrelated problems in the area of neural network computations are described. First an interpolation problem is considered, then a control problem is reduced to a problem of interpolation by a neural network via Lyapunov function approach, and finally a new, faster method of learning as compared with the gradient descent method, was introduced.

  16. 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…

  17. 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.

  18. Human genetic technology: who shall control?

    PubMed

    Blank, R H

    1984-01-01

    The biotechnical "revolution" has fast come upon us. It promises to produce both substantial benefits and difficult dilemmas for individuals and society. Despite the growing attention being paid to biotechnology, a major unanswered question is who shall control the development and use of the powerful array of human genetic and reproductive innovations. Should the decisions be left to individual consumers and private industry or should they be made by the government or other social institutions? After briefly reviewing development in human genetics and reproduction and describing trends toward commercialization of them, this article discusses the dilemmas these trends raise for a democratic society. It argues for the urgent need to delineate societal goals and priorities for the future and for technology assessment as early as possible in the developmental process. The article concludes by presenting some examples of the social policy problems now emerging.

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

    PubMed

    Pietak, Alexis; Levin, Michael

    2017-09-01

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

  20. Genetic and environmental control of salmonella invasion.

    PubMed

    Altier, Craig

    2005-02-01

    An early step in the pathogenesis of non-typhoidal Salmonella species is the ability to penetrate the intestinal epithelial monolayer. This process of cell invasion requires the production and transport of secreted effector proteins by a type III secretion apparatus encoded in Salmonella pathogenicity island I (SPI-1). The control of invasion involves a number of genetic regulators and environmental stimuli in complex relationships. SPI-1 itself encodes several transcriptional regulators (HilA, HilD, HilC, and InvF) with overlapping sets of target genes. These regulators are, in turn, controlled by both positive and regulators outside SPI-1, including the two-component regulators BarA/SirA and PhoP/Q, and the csr post-transcriptional control system. Additionally, several environmental conditions are known to regulate invasion, including pH, osmolarity, oxygen tension, bile, Mg2+ concentration, and short chain fatty acids. This review will discuss the current understanding of invasion control, with emphasis on the interaction of environmental factors with genetic regulators that leads to productive infection.

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

    SciTech Connect

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

    2008-11-06

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

  2. Engineering microbial phenotypes through rewiring of genetic networks.

    PubMed

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

    2017-03-21

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

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. 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.

  8. Topology control with IPD network creation games

    NASA Astrophysics Data System (ADS)

    Scholz, Jan C.; Greiner, Martin O. W.

    2007-06-01

    Network creation games couple a two-players game with the evolution of network structure. A vertex player may increase its own payoff with a change of strategy or with a modification of its edge-defined neighbourhood. By referring to the iterated prisoners dilemma (IPD) game we show that this evolutionary dynamics converges to network-Nash equilibria, where no vertex is able to improve its payoff. The resulting network structure exhibits a strong dependence on the parameter of the payoff matrix. Degree distributions and cluster coefficients are also strongly affected by the specific interactions chosen for the neighbourhood exploration. This allows network creation games to be seen as a promising artificial-social-systems approach for a distributive topology control of complex networked systems.

  9. 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.

  10. 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.

  11. Non-coding RNAs and complex distributed genetic networks

    NASA Astrophysics Data System (ADS)

    Zhdanov, Vladimir P.

    2011-08-01

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

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

    PubMed

    Juang, Chia-Feng

    2004-04-01

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

  13. 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.

  14. 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.

  15. 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.

  16. Structural Controllability and Controlling Centrality of Temporal Networks

    PubMed Central

    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

  17. 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.

  18. 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.

  19. 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.

  20. Differential Network Analysis Reveals Genetic Effects on Catalepsy Modules

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2015-08-19

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

  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. Combining neural networks and genetic algorithms for hydrological flow forecasting

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

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

  4. Genetic control of glycolysis in human erythrocytes

    SciTech Connect

    Gilroy, T.E.; Brewer, G.J.; Sing, C.F.

    1980-03-01

    We have studied heritability of the concentration of each glycolytic intermediate and adenine nucleotide in the cytosol of human erythrocytes obtained from a random sample of apparently healthy young individuals. Preliminary to analysis of heritability, each trait was statistically described and the effects attributable to variation in measured concomitants were removed by regression. Heritability was estimated using the family-set method. This method removes covariances between the index case, sibling and first cousin, due to those environmental determinants of the phenotypic values that are shared with a matched, unrelated control member of the family set. It also removes covariances due to environments that are shared by siblings and first cousins. Heritability was estimated by employing the fact that the variance of differences between first cousins minus the variance of differences between full siblings estimates three-fourths of the additive genetic variance. The heritability estimates for G6P, F6P, ATP and some other metabolite concentrations are high and significantly greater than zero. The heritabilities of G6P and F6P are likely attributable to genetic variation in the in vivo activity of HK and/or PFK, because the concentrations of these metabolites are tightly controlled by the two regulatory enzymes. Statistically significant heritability estimates for HK and PFK mass action ratios stronglysuggest genes are responsible for a portion of the quantitative variation in these enzyme activities. Since HK and PFK regulate glycolysis and the production of ATP, genetic variation in their activities might be causally related to the heritability of ATP concentration.

  5. Control of Stochastic and Induced Switching in Biophysical Networks

    PubMed Central

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

    2015-01-01

    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. PMID:26451275

  6. 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.

  7. 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.

  8. Seed maturation: Simplification of control networks in plants.

    PubMed

    Devic, Martine; Roscoe, Thomas

    2016-11-01

    Networks controlling developmental or metabolic processes in plants are often complex as a consequence of the duplication and specialisation of the regulatory genes as well as the numerous levels of transcriptional and post-transcriptional controls added during evolution. Networks serve to accommodate multicellular complexity and increase robustness to environmental changes. Mathematical simplification by regrouping genes or pathways in a limited number of hubs has facilitated the construction of models for complex traits. In a complementary approach, a biological simplification can be achieved by using genetic modification to understand the core and singular ancestral function of the network, which is likely to be more prevalent within the plant kingdom rather than specific to a species. With this viewpoint, we review examples of simplification successfully undertaken in yeast and other organisms. A strategy of progressive complementation of single, double and triple mutants of seed maturation confirmed the fundamental role of the AFL sub-family of B3 transcription factors as master regulators of seed maturation, illustrating that biological simplification of complex networks could be more widely applied in plants. Defining minimal control networks will facilitate evolutionary comparisons of regulatory processes and the identification of an essential gene set for synthetic biology.

  9. Supervisory Control of Networked Control Systems

    DTIC Science & Technology

    2006-01-15

    consisting of 3 Koala robots [Lem06b]. The robots are controlled by MICA2 wireless processor modules. The robots communicate over the MICA2’s...preliminary documentation of a wireless autonomous robotic testbed. The system consists of 3 Koala (K-team Inc.) robots that are controlled by the MICA2...by this project. MICA-KoalaBot Hardware: The Koala robot is an autonomous wheeled vehicle that has 16 infrared (IR) proximity sensors around its

  10. Controlling Directed Protein Interaction Networks in Cancer.

    PubMed

    Kanhaiya, Krishna; Czeizler, Eugen; Gratie, Cristian; Petre, Ion

    2017-09-04

    Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in each case a protein-protein interaction network and focus on the survivability-essential proteins specific to each cancer type. We show that these essential proteins are efficiently controllable from a relatively small computable set of driver nodes. Moreover, we adjust the method to find the driver nodes among FDA-approved drug-target nodes. We find that, while many of the drugs acting on the driver nodes are part of known cancer therapies, some of them are not used for the cancer types analyzed here; some drug-target driver nodes identified by our algorithms are not known to be used in any cancer therapy. Overall we show that a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine.

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

    PubMed

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

    2009-01-01

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

  12. 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. Genetic and Transcriptional Control of Bone Formation

    PubMed Central

    Javed, Amjad; Chen, Haiyan; Ghori, Farah Y.

    2010-01-01

    Synopsis An exquisite interplay of developmental cues, transcription factors, coregulatory and signaling proteins support formation of skeletal elements of the jaw during embryogenesis and the dynamic remodeling of alveolar bone in the post-natal life. These molecules promote initial condensation of the mesenchyme, commitment of the mesenchymal progenitor to osteogenic lineage cells, and differentiation of committed osteoblast to mature osteocyte within mineralized bone. Parallel regulatory network promote formation of the functional ostoclast from mononuclear cells to support continuous bone remodeling within the alveolar bone. With an ever expanding list of new regulatory factors, the complexities of the molecular mechanisms that control gene expression in skeletal cells are being further appreciated. This review examines the multifunctional roles of prominent nuclear proteins, cytokines, hormones and paracrine factors that control osteogenesis. PMID:20713262

  14. Model Predictive Control of Sewer Networks

    NASA Astrophysics Data System (ADS)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.

    2017-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.

  15. Control of fluxes in metabolic networks

    PubMed Central

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

    2016-01-01

    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

  16. Controlling neural network responsiveness: tradeoffs and constraints

    PubMed Central

    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

  17. 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.

  18. 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.

  19. Scheduled Controller Design of Congestion Control Considering Network Resource Constraints

    NASA Astrophysics Data System (ADS)

    Naito, Hiroyuki; Azuma, Takehito; Fujita, Masayuki

    In this paper, we consider a dynamical model of computer networks and derive a synthesis method for congestion control. First, we show a model of TCP/AQM (Transmission Control Protocol/Active Queue Management) as a dynamical model of computer networks. The dynamical model of TCP/AQM networks consists of models of TCP window size, queue length and AQM mechanisms. Second, we propose to describe the dynamical model of TCP/AQM networks as linear systems with self-scheduling parameters, which also depend on information delay. Here we focus on the constraints on the maximum queue length and TCP window-size, which are the network resources in TCP/AQM networks. We derive TCP/AQM networks as the LPV system (linear parameter varying system) with information delay and self-scheduling parameter. We design a memoryless state feedback controller of the LPV system based on a gain-scheduling method. Finally, the effectiveness of the proposed method is evaluated by using MATLAB and the well-known ns-2 (Network Simulator Ver.2) simulator.

  20. The unified lunar control network: 1994 version

    NASA Technical Reports Server (NTRS)

    Davies, Merton E.; Colvin, Tim R.; Meyer, Donald L.; Nelson, Sandra

    1994-01-01

    The objective of the unified lunar control network is to combine a series of control networks into one compatible network with its origin at the center of mass of the Moon and its coordinates referred to the mean Earth/polar axis system. The initial unified system contained 130 nearside points from Apollo data and 1026 from telescopic data. It also contained ten Mariner 10 points. The total number of points was 1166. The current network includes modifications to the past network and extends the coverage. Coordinates of points north of the Apollo region have been recomputed based on Galileo images from the second Earth-Moon flyby. Coordinates of points in the Apollo region were held fixed; however, coordinates of points north of the Apollo region in the telescopic region and many Mariner 10 points were recomputed. All of the Mariner 10 points were remeasured and integrated into the network. Additional points in the Apollo region including the farside have been added. The unified network now contains 1478 points. Apollo, Mariner 10, and Galileo pictures all contained some farside points. The coordinates of the 1478 points are available only in the microfiche supplement to this paper.

  1. 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.

  2. Linear control theory for gene network modeling.

    PubMed

    Shin, Yong-Jun; Bleris, Leonidas

    2010-09-16

    Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

  3. Social Network Privacy via Evolving Access Control

    NASA Astrophysics Data System (ADS)

    di Crescenzo, Giovanni; Lipton, Richard J.

    We study the problem of limiting privacy loss due to data shared in a social network, where the basic underlying assumptions are that users are interested in sharing data and cannot be assumed to constantly follow appropriate privacy policies. Note that if these two assumptions do not hold, social network privacy is theoretically very easy to achieve; for instance, via some form of access control and confidentiality transformation on the data.

  4. Boiler-turbine control system design using a genetic algorithm

    SciTech Connect

    Dimeo, R.; Lee, K.Y.

    1995-12-01

    This paper discusses the application of a genetic algorithm to control system design for a boiler-turbine plant. In particular the authors study the ability of the genetic algorithm to develop a proportional-integral (PI) controller and a state feedback controller for a non-linear multi-input/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller development. A sketch of the genetic algorithm (GA) is presented and its strategy as a method of control system design is discussed. Results are presented for two different control systems that have been designed with the genetic algorithm.

  5. 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.

  6. 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.

  7. Structure Learning of Bayesian Networks Using Dual Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Lee, Jaehun; Chung, Wooyong; Kim, Euntai

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

  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. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

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

    PubMed Central

    Wang, Baojun; Barahona, Mauricio; Buck, Martin

    2014-01-01

    Synthetic biology aims to control and reprogram signal processing pathways within living cells so as to realize repurposed, beneficial applications. Here we report the design and construction of a set of modular and gain-tunable genetic amplifiers in Escherichia coli capable of amplifying a transcriptional signal with wide tunable-gain control in cascaded gene networks. The devices are engineered using orthogonal genetic components (hrpRS, hrpV and PhrpL) from the hrp (hypersensitive response and pathogenicity) gene regulatory network in Pseudomonas syringae. The amplifiers can linearly scale up to 21-fold the transcriptional input with a large output dynamic range, yet not introducing significant time delay or significant noise during signal amplification. The set of genetic amplifiers achieves different gains and input dynamic ranges by varying the expression levels of the underlying ligand-free activator proteins in the device. As their electronic counterparts, these engineered transcriptional amplifiers can act as fundamental building blocks in the design of biological systems by predictably and dynamically modulating transcriptional signal flows to implement advanced intra- and extra-cellular control functions. PMID:25030903

  15. Multiobjective Genetic Algorithm applied to dengue control.

    PubMed

    Florentino, Helenice O; Cantane, Daniela R; Santos, Fernando L P; Bannwart, Bettina F

    2014-12-01

    Dengue fever is an infectious disease caused by a virus of the Flaviridae family and transmitted to the person by a mosquito of the genus Aedes aegypti. This disease has been a global public health problem because a single mosquito can infect up to 300 people and between 50 and 100 million people are infected annually on all continents. Thus, dengue fever is currently a subject of research, whether in the search for vaccines and treatments for the disease or efficient and economical forms of mosquito control. The current study aims to study techniques of multiobjective optimization to assist in solving problems involving the control of the mosquito that transmits dengue fever. The population dynamics of the mosquito is studied in order to understand the epidemic phenomenon and suggest strategies of multiobjective programming for mosquito control. A Multiobjective Genetic Algorithm (MGA_DENGUE) is proposed to solve the optimization model treated here and we discuss the computational results obtained from the application of this technique. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. 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.

  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. Stochastic Congestion Control in Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Kim, Hyung Seok; Lee, Seok; Kim, Namhoon

    In this paper, an effective congestion control algorithm is proposed to increase the end-to-end delivery success ratio of upstream traffic by reduction of buffer drop probabilities and their deviation in wireless sensor networks. According to the queue length of parent and child nodes, each child node chooses one of the parents as the next hop to the sink and controls the delay before transmission begins. It balances traffics among parents and mitigates congestion based on congestion level of a node. Simulation results show that the proposed algorithm reduces buffer drop probabilities and their deviation and increases the end-to-end delivery success ratio in wireless sensor networks.

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

    PubMed

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

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  1. Observability of Boolean multiplex control networks

    NASA Astrophysics Data System (ADS)

    Wu, Yuhu; Xu, Jingxue; Sun, Xi-Ming; Wang, Wei

    2017-04-01

    Boolean multiplex (multilevel) networks (BMNs) are currently receiving considerable attention as theoretical arguments for modeling of biological systems and system level analysis. Studying control-related problems in BMNs may not only provide new views into the intrinsic control in complex biological systems, but also enable us to develop a method for manipulating biological systems using exogenous inputs. In this article, the observability of the Boolean multiplex control networks (BMCNs) are studied. First, the dynamical model and structure of BMCNs with control inputs and outputs are constructed. By using of Semi-Tensor Product (STP) approach, the logical dynamics of BMCNs is converted into an equivalent algebraic representation. Then, the observability of the BMCNs with two different kinds of control inputs is investigated by giving necessary and sufficient conditions. Finally, examples are given to illustrate the efficiency of the obtained theoretical results.

  2. Observability of Boolean multiplex control networks

    PubMed Central

    Wu, Yuhu; Xu, Jingxue; Sun, Xi-Ming; Wang, Wei

    2017-01-01

    Boolean multiplex (multilevel) networks (BMNs) are currently receiving considerable attention as theoretical arguments for modeling of biological systems and system level analysis. Studying control-related problems in BMNs may not only provide new views into the intrinsic control in complex biological systems, but also enable us to develop a method for manipulating biological systems using exogenous inputs. In this article, the observability of the Boolean multiplex control networks (BMCNs) are studied. First, the dynamical model and structure of BMCNs with control inputs and outputs are constructed. By using of Semi-Tensor Product (STP) approach, the logical dynamics of BMCNs is converted into an equivalent algebraic representation. Then, the observability of the BMCNs with two different kinds of control inputs is investigated by giving necessary and sufficient conditions. Finally, examples are given to illustrate the efficiency of the obtained theoretical results. PMID:28452370

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

    PubMed

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

    2015-12-01

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

  4. 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

  5. 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.

  6. 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

  7. 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.

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

    PubMed Central

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

    2014-01-01

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

  9. 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.

  10. Wireless Networking for Control: Technologies and Models

    NASA Astrophysics Data System (ADS)

    Johansson, Mikael; Jäntti, Riku

    This chapter discusses technologies and models for low power wireless industrial communication. The aim of the text is to narrow the gap between the models used in the theoretical control literature with models that arise when tools from communication theory are used to model emerging standards for industrial wireless. The chapter provides a tutorial overview covering basic concepts and models for wireless propagation, medium access control, multi-hop networking, routing and transport protocols. Throughout, an effort is made to describe both key technologies and associated models of control-relevant characteristics such as latency and loss. Some existing and emerging specifications and standards, including Zigbee, WirelessHART and ISA100, are described in some detail, and links are made between the developed models and useful network abstractions for control design.

  11. 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

  12. 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

  13. 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.

  14. Controllable Buoys and Networked Buoy Systems

    NASA Technical Reports Server (NTRS)

    Davoodi, Faranak (Inventor); Davoudi, Farhooman (Inventor)

    2017-01-01

    Buoyant sensor networks are described, comprising floating buoys with sensors and energy harvesting capabilities. The buoys can control their buoyancy and motion, and can organize communication in a distributed fashion. Some buoys may have tethered underwater vehicles with a smart spooling system that allows the vehicles to dive deep underwater while remaining in communication and connection with the buoys.

  15. 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 ...

  16. Optimizing Dynamical Network Structure for Pinning Control

    PubMed Central

    Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo

    2016-01-01

    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. PMID:27067020

  17. 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 ...

  18. A unified lunar control network: April 1991

    NASA Technical Reports Server (NTRS)

    Davies, Merton E.

    1991-01-01

    This program was designed to combine and transform various control networks of the Moon into a common center-of-mass coordinate system. The first phase, dealing with the near side, was completed and published. This report contains coordinates of 1166 points on the near side of the Moon.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. Disturbance Decoupling of Singular Boolean Control Networks.

    PubMed

    Liu, Yang; Li, Bowen; Lou, Jungang

    2016-01-01

    This paper investigates the controller designing for disturbance decoupling problem (DDP) of singular Boolean control networks (SBCNs). Using semi-tensor product (STP) of matrices and the Implicit Function Theorem, a SBCN is converted into the standard BCN. Based on the redundant variable separation technique, both state feedback and output feedback controllers are designed to solve the DDP of the SBCN. Sufficient conditions are also given to analyze the invariance of controllers concerning the DDP of the SBCN with function perturbation. Two illustrative examples are presented to support the effectiveness of these obtained results.

  4. Characteristics of the TRISTAN control computer network

    NASA Astrophysics Data System (ADS)

    Kurokawa, Shin-ichi; Akiyama, Atsuyoshi; Katoh, Tadahiko; Kikutani, Eiji; Koiso, Haruyo; Oide, Katsunobu; Shinomoto, Manabu; Kurihara, Michio; Abe, Ken-ichi

    1986-06-01

    Twenty-four minicomputers forming an N-to- N token-ring network control the TRISTAN accelerator complex. The computers are linked by optical fiber cables with 10 Mbps transmission speed. The software system is based on NODAL, a multicomputer interpretive language developed at the CERN SPS. The high-level services offered to the users of the network are remote execution by the EXEC, EXEC-P and IMEX commands of NODAL and uniform file access throughout the system. The network software was designed to achieve the fast response of the EXEC command. The performance of the network is also reported. Tasks that overload the minicomputers are processed on the KEK central computers. One minicomputer in the network serves as a gateway to KEKNET, which connects the minicomputer network and the central computers. The communication with the central computers is managed within the framework of the KEK NODAL system. NODAL programs communicate with the central computers calling NODAL functions; functions for exchanging data between a data set on the central computers and a NODAL variable, submitting a batch job to the central computers, checking the status of the submitted job, etc. are prepared.

  5. 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. © The Author (2015). Published by Oxford University Press on behalf

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

    Treesearch

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

    2010-01-01

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

  7. Quality control in bio-monitoring networks, Spanish Aerobiology Network.

    PubMed

    Oteros, Jose; Galán, Carmen; Alcázar, Purificación; Domínguez-Vilches, Eugenio

    2013-01-15

    Several of the airborne biological particles, such as pollen grains and fungal spores, are known to generate human health problems including allergies and infections. A number of aerobiologists have focused their research on these airborne particles. The Spanish Aerobiology Network (REA) was set up in 1992, and since then dozens of research groups have worked on a range of related topics, including the standardization of study methods and the quality control of data generated by this network. In 2010, the REA started work on an inter-laboratory survey for proficiency testing purposes. The main goal of the study reported in the present paper was to determine the performance of technicians in the REA network using an analytical method that could be implemented by other bio-monitoring networks worldwide. The results recorded by each technician were compared with the scores obtained for a bounded mean of all results. The performance of each technician was expressed in terms of the relative error made in counting each of several pollen types. The method developed and implemented here proved appropriate for proficiency testing in interlaboratory studies involving bio-monitoring networks, and enabled the source of data quality problems to be pinpointed. The test revealed a variation coefficient of 10%. The relative error was significant for 3.5% of observations. In overall terms, the REA staff performed well, in accordance with the REA Management and Quality Manual. These findings serve to guarantee the quality of the data obtained, which can reliably be used for research purposes and published in the media in order to help prevent pollen-related health problems.

  8. 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

  9. 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.

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

    PubMed

    Nikolaev, Nikolay Y; Iba, Hitoshi

    2002-10-01

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

  11. Genetic control of biennial bearing in apple

    PubMed Central

    Guitton, Baptiste; Kelner, Jean-Jacques; Velasco, Riccardo; Gardiner, Susan E.; Chagné, David; Costes, Evelyne

    2012-01-01

    Although flowering in mature fruit trees is recurrent, floral induction can be strongly inhibited by concurrent fruiting, leading to a pattern of irregular fruiting across consecutive years referred to as biennial bearing. The genetic determinants of biennial bearing in apple were investigated using the 114 flowering individuals from an F1 population of 122 genotypes, from a ‘Starkrimson’ (strong biennial bearer)בGranny Smith’ (regular bearer) cross. The number of inflorescences, and the number and the mass of harvested fruit were recorded over 6 years and used to calculate 26 variables and indices quantifying yield, precocity of production, and biennial bearing. Inflorescence traits exhibited the highest genotypic effect, and three quantitative trait loci (QTLs) on linkage group (LG) 4, LG8, and LG10 explained 50% of the phenotypic variability for biennial bearing. Apple orthologues of flowering and hormone-related genes were retrieved from the whole-genome assembly of ‘Golden Delicious’ and their position was compared with QTLs. Four main genomic regions that contain floral integrator genes, meristem identity genes, and gibberellin oxidase genes co-located with QTLs. The results indicated that flowering genes are less likely to be responsible for biennial bearing than hormone-related genes. New hypotheses for the control of biennial bearing emerged from QTL and candidate gene co-locations and suggest the involvement of different physiological processes such as the regulation of flowering genes by hormones. The correlation between tree architecture and biennial bearing is also discussed. PMID:21963613

  12. Genetic control of biennial bearing in apple.

    PubMed

    Guitton, Baptiste; Kelner, Jean-Jacques; Velasco, Riccardo; Gardiner, Susan E; Chagné, David; Costes, Evelyne

    2012-01-01

    Although flowering in mature fruit trees is recurrent, floral induction can be strongly inhibited by concurrent fruiting, leading to a pattern of irregular fruiting across consecutive years referred to as biennial bearing. The genetic determinants of biennial bearing in apple were investigated using the 114 flowering individuals from an F(1) population of 122 genotypes, from a 'Starkrimson' (strong biennial bearer)×'Granny Smith' (regular bearer) cross. The number of inflorescences, and the number and the mass of harvested fruit were recorded over 6 years and used to calculate 26 variables and indices quantifying yield, precocity of production, and biennial bearing. Inflorescence traits exhibited the highest genotypic effect, and three quantitative trait loci (QTLs) on linkage group (LG) 4, LG8, and LG10 explained 50% of the phenotypic variability for biennial bearing. Apple orthologues of flowering and hormone-related genes were retrieved from the whole-genome assembly of 'Golden Delicious' and their position was compared with QTLs. Four main genomic regions that contain floral integrator genes, meristem identity genes, and gibberellin oxidase genes co-located with QTLs. The results indicated that flowering genes are less likely to be responsible for biennial bearing than hormone-related genes. New hypotheses for the control of biennial bearing emerged from QTL and candidate gene co-locations and suggest the involvement of different physiological processes such as the regulation of flowering genes by hormones. The correlation between tree architecture and biennial bearing is also discussed.

  13. Genetic control of seed proteins in wheat.

    PubMed

    Dhaliwal, H S

    1977-09-01

    Electrophoretic profiles of crude protein extracts from seed of F1 hybrids and reciprocal crosses among diploid, tetraploid and hexaploid wheats were compared with those of their respective parental species. The electrophoretic patterns within each of three pairs of reciprocal crosses, T.boeoticum X T.urartu, T.monococcun X T. urartu and T.dicoccum X T. araraticum, were different from one another but were identical with those of their respective maternal parents. Protein bands characteristic of the paternal parents were missing in F1 hybrid seed suggesting that the major seed proteins in wheat were presumably regulated by genotype of the maternal parent rather than by the seed genotype. However, in another three pairs of reciprocal crosses, T.boeoticum X T. durum, T.dicoccum X T.aestivum and T. zhukovskyi x T. aestivum, protein bands attributable to the paternal parents were present in the F1 hybrid seeds indicating that the seed proteins were not always exclusively regulated by the maternal genotype. The expression of paternal genomes is presumably determined by dosage and genetic affinity of the maternal and paternal genomes in the hybrid endosperm. The maternal regulation of seed protein content is probably accomplished through the maternal control over seed size. The seed protein quality may, however, depend upon the extent of expression of the paternal genome.

  14. 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.

  15. 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

  16. 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.

  17. 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

  18. Genetic control of midbrain dopaminergic neuron development.

    PubMed

    Blaess, Sandra; Ang, Siew-Lan

    2015-01-01

    Midbrain dopaminergic neurons are involved in regulating motor control, reward behavior, and cognition. Degeneration or dysfunction of midbrain dopaminergic neurons is implicated in several neuropsychiatric disorders such as Parkinson's disease, substance use disorders, depression, and schizophrenia. Understanding the developmental processes that generate midbrain dopaminergic neurons will facilitate the generation of dopaminergic neurons from stem cells for cell replacement therapies to substitute degenerating cells in Parkinson's disease patients and will forward our understanding on how functional diversity of dopaminergic neurons in the adult brain is established. Midbrain dopaminergic neurons develop in a multistep process. Following the induction of the ventral midbrain, a distinct dopaminergic progenitor domain is specified and dopaminergic progenitors undergo proliferation, neurogenesis, and differentiation. Subsequently, midbrain dopaminergic neurons acquire a mature dopaminergic phenotype, migrate to their final position and establish projections and connections to their forebrain targets. This review will discuss insights gained on the signaling network of secreted molecules, cell surface receptors, and transcription factors that regulate specification and differentiation of midbrain dopaminergic progenitors and neurons, from the induction of the ventral midbrain to the migration of dopaminergic neurons. For further resources related to this article, please visit the WIREs website. The authors have declared no conflicts of interest for this article. © 2015 Medical Research Council.

  19. 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

  20. 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.

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed Central

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

    2003-01-01

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

  3. 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.

  4. 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.

  5. Synchrony and Control of Neuronal Networks.

    NASA Astrophysics Data System (ADS)

    Schiff, Steven

    2001-03-01

    Cooperative behavior in the brain stems from the nature and strength of the interactions between neurons within a networked ensemble. Normal network activity takes place in a state of partial synchrony between neurons, and some pathological behaviors, such as epilepsy and tremor, appear to share a common feature of increased interaction strength. We have focused on the parallel paths of both detecting and characterizing the nonlinear synchronization present within neuronal networks, and employing feedback control methodology using electrical fields to modulate that neuronal activity. From a theoretical perspective, we see evidence for nonlinear generalized synchrony in networks of neurons that linear techniques are incapable of detecting (PRE 54: 6708, 1996), and we have described a decoherence transition between asymmetric nonlinear systems that is experimentally observable (PRL 84: 1689, 2000). In addition, we have seen evidence for unstable dimension variability in real neuronal systems that indicates certain physical limits of modelability when observing such systems (PRL 85, 2490, 2000). From an experimental perspective, we have achieved success in modulating epileptic seizures in neuronal networks using electrical fields. Extracellular neuronal activity is continuously recorded during field application through differential extracellular recording techniques, and the applied electric field strength is continuously updated using a computer controlled proportional feedback algorithm. This approach appears capable of sustained amelioration of seizure events when used with negative feedback. In negative feedback mode, such findings may offer a novel technology for seizure control. In positive feedback mode, adaptively applied electric fields may offer a more physiological means for neural modulation for prosthetic purposes than previously possible (J. Neuroscience, 2001).

  6. Adaptive neural networks for mobile robotic control

    NASA Astrophysics Data System (ADS)

    Burnett, Jeff R.; Dagli, Cihan H.

    2001-03-01

    Movement of a differential drive robot has non-linear dependence on the current position and orientation. A controller must be able to deal with the non-linearity of the plant. The controller must either linearize the plant and deal with special cases, or be non-linear itself. Once the controller is designed, implementation on a real robotic platform presents challenges due to the varying parameters of the plant. Robots of the same model may have different motor frictions. The surface the robot maneuvers on may change e.g. carpet to tile. Batteries will drain, providing less power over time. A feed-forward neural network controller could overcome these challenges. The network could learn the non- linearities of the plant and monitor the error for parameter changes and adapt to them. In this manner, a single controller can be designed for an ideal robot, and then used to populate a multi-robot colony without manually fine tuning the controller for each robot. This paper shall demonstrate such a controller, outlining design in simulation and implementation on Khepera robotic platforms.

  7. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

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

    PubMed Central

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

    2015-01-01

    The population persistence of schizophrenia despite associated reductions in fitness and fecundity suggests that the genetic basis of schizophrenia has a complex evolutionary history. A recent meta-analysis of schizophrenia genome-wide association studies offers novel opportunities for assessment of the evolutionary trajectories of schizophrenia-associated loci. In this study, we hypothesize that components of the genetic architecture of schizophrenia are attributable to human lineage-specific evolution. Our results suggest that schizophrenia-associated loci enrich in genes near previously identified human accelerated regions (HARs). Specifically, we find that genes near HARs conserved in nonhuman primates (pHARs) are enriched for schizophrenia-associated loci, and that pHAR-associated schizophrenia genes are under stronger selective pressure than other schizophrenia genes and other pHAR-associated genes. We further evaluate pHAR-associated schizophrenia genes in regulatory network contexts to investigate associated molecular functions and mechanisms. We find that pHAR-associated schizophrenia genes significantly enrich in a GABA-related coexpression module that was previously found to be differentially regulated in schizophrenia affected individuals versus healthy controls. In another two independent networks constructed from gene expression profiles from prefrontal cortex samples, we find that pHAR-associated schizophrenia genes are located in more central positions and their average path lengths to the other nodes are significantly shorter than those of other schizophrenia genes. Together, our results suggest that HARs are associated with potentially important functional roles in the genetic architecture of schizophrenia. PMID:25681384

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

    PubMed

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

    2015-05-01

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

  11. 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.

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

    PubMed

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

    2016-03-28

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

  13. 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

  14. 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

  15. Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay.

    PubMed

    Pan, Indranil; Das, Saptarshi; Gupta, Amitava

    2011-01-01

    An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS). The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz. the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared. The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers.

  16. Integrated network control and performance monitoring

    NASA Astrophysics Data System (ADS)

    Schaefer, D. J.

    A brief description is given of an integrated satellite system monitor utilizing remote control operation from a centralized satellite network operations center. This monitoring facility can measure selected Time Division Multiple Access (TDMA) transmission parameters in real time and report the measurement results to the central satellite operations center having responsibility for overall system operation. The monitor system, while similar to other existing TDMA monitors, is unique in that it features dual rate and frequency band operation in a frequency-hopped environment.

  17. Multifractal nature of network induced time delay in networked control systems

    NASA Astrophysics Data System (ADS)

    Tian, Yu-Chu; Yu, Zu-Guo; Fidge, Colin

    2007-01-01

    When modelling and simulating networked control systems (NCSs) over TCP/IP network protocols, we obtained network traffic data sets with irregular behaviour. Analysing the data sets revealed multifractal network traffic. Typical data sets are given in this Letter together with our preliminary analysis. The network architecture and traffic specifications that generated the multifractal traffic are also described in detail.

  18. 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.

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

    PubMed

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

    2017-05-12

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

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

    PubMed Central

    Pinault, Didier; O'Brien, Terence J.

    2005-01-01

    and network mechanisms responsible for the switch from physiological, wake-related, natural oscillations into pathological spike-and-wave discharges? We speculate on possible answers to this, building particularly on recent findings from genetic models in rats. PMID:21909233

  1. 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.

  2. Stabilization of model-based networked control systems

    SciTech Connect

    Miranda, Francisco; Abreu, Carlos; Mendes, Paulo M.

    2016-06-08

    A class of networked control systems called Model-Based Networked Control Systems (MB-NCSs) is considered. Stabilization of MB-NCSs is studied using feedback controls and simulation of stabilization for different feedbacks is made with the purpose to reduce the network trafic. The feedback control input is applied in a compensated model of the plant that approximates the plant dynamics and stabilizes the plant even under slow network conditions. Conditions for global exponential stabilizability and for the choosing of a feedback control input for a given constant time between the information moments of the network are derived. An optimal control problem to obtain an optimal feedback control is also presented.

  3. Stabilization of model-based networked control systems

    NASA Astrophysics Data System (ADS)

    Miranda, Francisco; Abreu, Carlos; Mendes, Paulo M.

    2016-06-01

    A class of networked control systems called Model-Based Networked Control Systems (MB-NCSs) is considered. Stabilization of MB-NCSs is studied using feedback controls and simulation of stabilization for different feedbacks is made with the purpose to reduce the network trafic. The feedback control input is applied in a compensated model of the plant that approximates the plant dynamics and stabilizes the plant even under slow network conditions. Conditions for global exponential stabilizability and for the choosing of a feedback control input for a given constant time between the information moments of the network are derived. An optimal control problem to obtain an optimal feedback control is also presented.

  4. Payload Invariant Control via Neural Networks: Development and Experimental Evaluation

    DTIC Science & Technology

    1989-12-01

    control is proposed and experimentally evaluated. An Adaptive Model-Based Neural Network Controller (AMBNNC) uses multilayer perceptron artificial neural ... networks to estimate the payload during high speed manipulator motion. The payload estimate adapts the feedforward compensator to unmodeled system

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

    PubMed

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

    2010-03-01

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

  6. Converting genetic network oscillations into somite spatial patterns

    NASA Astrophysics Data System (ADS)

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

    2008-08-01

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

  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. 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.

  9. 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.

  10. 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.

  11. Self-Tuned Congestion Control for Multiprocessor Networks

    DTIC Science & Technology

    2005-01-01

    multiprocessor networks, including virtual cut-through [15] networks and wormhole networks [6, 5]. However, in this paper we evaluate the technique in the...context of wormhole switched, k-ary,n-cube networks. Simulation results for a 16-ary,2-cube (256 node net- work) show that our congestion control...through the net- work. Each packet is composed of flits (flow control units) that are transferred between network nodes.1 Both wormhole routing and cut

  12. The control network of Mars: October 1986

    NASA Technical Reports Server (NTRS)

    Davies, Merton E.

    1987-01-01

    The control network of Mars is composed of Mariner 9 frames which essentially give full coverage of the planet at low resolution. Superimposed on and tied to this network are strips of Viking mapping frames (resolution 100 to 250 m per pixel) which encircle the equator and 60 deg north latitude and multiple longitude ties between these latitude strips. There are multiple ties between these strips and the Viking 1 lander site. In the future another strip will be established at 60 deg south latitude. Because the Viking 1 lander site has been accurately located, the coordinates of points in its vicinity can be determined with an error of less than 100 m relative to an inertial coordinate system.

  13. Controller area network for monitor and control in ALMA

    NASA Astrophysics Data System (ADS)

    Brooks, Michael J.

    2000-06-01

    The Controller Area Network (CAN), initially developed for the automotive industry, is becoming increasingly popular in industrial process control applications. The need for distributed low data rate monitor and control networking in industry is similar to the needs of the various instrumentation and support equipment in a modern radio telescope. In particular, immunity to noise and low radio frequency emission characteristics are common to both domains. The Atacama Large Millimeter Array project has adopted CAN technology for use in local monitor and control applications at each of its 64 antennas. A standard interface slave node providing flexible I/O options is under development and a simple application-level protocol making use of CAN to access these nodes in a master/slave fashion has been implemented. This paper will present the work which has been completed to date including experiences in the use of CAN in an astronomical environment. In addition, analysis and simulation of CAN networks is compared with the performance of our implementation in the lab.

  14. Efficient Access Control in Multimedia Social Networks

    NASA Astrophysics Data System (ADS)

    Sachan, Amit; Emmanuel, Sabu

    Multimedia social networks (MMSNs) have provided a convenient way to share multimedia contents such as images, videos, blogs, etc. Contents shared by a person can be easily accessed by anybody else over the Internet. However, due to various privacy, security, and legal concerns people often want to selectively share the contents only with their friends, family, colleagues, etc. Access control mechanisms play an important role in this situation. With access control mechanisms one can decide the persons who can access a shared content and who cannot. But continuously growing content uploads and accesses, fine grained access control requirements (e.g. different access control parameters for different parts in a picture), and specific access control requirements for multimedia contents can make the time complexity of access control to be very large. So, it is important to study an efficient access control mechanism suitable for MMSNs. In this chapter we present an efficient bit-vector transform based access control mechanism for MMSNs. The proposed approach is also compatible with other requirements of MMSNs, such as access rights modification, content deletion, etc. Mathematical analysis and experimental results show the effectiveness and efficiency of our proposed approach.

  15. Comprehensive Control of Networked Control Systems with Multistep Delay

    PubMed Central

    Jiang, Jie

    2014-01-01

    In networked control systems with multi-step delay, long time-delay causes vacant sampling and controller design difficulty. In order to solve the above problems, comprehensive control methods are proposed in this paper. Time-delay compensation control and linear-quadratic-Guassian (LQG) optimal control are adopted and the systems switch different controllers between two different states. LQG optimal controller is used with probability 1 − α in normal state, which is shown to render the systems mean square exponentially stable. Time-delay compensation controller is used with probability α in abnormal state to compensate vacant sampling and long time-delay. In addition, a buffer window is established at the actuator of the systems to store some history control inputs which are used to estimate the control state of present sampling period under the vacant sampling cases. The comprehensive control methods simplify control design which is easier to be implemented in engineering. The performance of the systems is also improved. Simulation results verify the validity of the proposed theory. PMID:25101322

  16. Population genetics of autocidal control and strain replacement.

    PubMed

    Gould, Fred; Schliekelman, Paul

    2004-01-01

    The concept that an insect species' genome could be altered in a manner that would result in the control of that species (i.e., autocidal control) or in the replacement of a pestiferous strain of the species with a more benign genotype was first proposed in the mid-twentieth century. A major research effort in population genetics and ecology followed and led to the development of a set of classical genetic control approaches that included use of sterile males, conditional lethal genes, translocations, compound chromosomes, and microbe-mediated infertility. Although there have been a number of major successes in application of classical genetic control, research in this area has declined in the past 20 years for technical and societal reasons. Recent advances in molecular biology and transgenesis research have renewed interest in genetically based control methods because these advances may remove some major technical problems that have constrained effective genetic manipulation of pest species. Population genetic analyses suggest that transgenic manipulations may enable development of strains that would be 10 to over 100 times more efficient than strains developed by classical methods. Some of the proposed molecular approaches to genetic control involve modifications of classical approaches such as conditional lethality, whereas others are novel. Experience from the classical era of genetic control research indicates that the population structure and population dynamics of the target population will determine which, if any, genetic control approaches would be appropriate for addressing a specific problem. As such, there continues to be a need for ongoing communication between scientists who are developing strains and those who study the native pest populations.

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

    PubMed

    Nagata, Yuko; Chu, Khim Hoong

    2003-11-01

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

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

    PubMed

    Lam, H K; Leung, Frank H F

    2004-12-01

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

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

    PubMed Central

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

    2011-01-01

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

  20. 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

  1. Genetic control of telomere integrity in Schizosaccharomyces pombe: rad3(+) and tel1(+) are parts of two regulatory networks independent of the downstream protein kinases chk1(+) and cds1(+).

    PubMed Central

    Matsuura, A; Naito, T; Ishikawa, F

    1999-01-01

    The Schizosaccharomyces pombe checkpoint gene named rad3(+) encodes an ATM-homologous protein kinase that shares a highly conserved motif with proteins involved in DNA metabolism. Previous studies have shown that Rad3 fulfills its function via the regulation of the Chk1 and Cds1 protein kinases. Here we describe a novel role for Rad3 in the control of telomere integrity. Mutations in the rad3(+) gene alleviated telomeric silencing and produced shortened lengths in the telomere repeat tracts. Genetic analysis revealed that the other checkpoint rad mutations rad1, rad17, and rad26 belong to the same phenotypic class with rad3 with regard to control of the telomere length. Of these mutations, rad3 and rad26 have a drastic effect on telomere shortening. tel1(+), another ATM homologue in S. pombe, carries out its telomere maintenance function in parallel with the checkpoint rad genes. Furthermore, either a single or double disruption of cds1(+) and chk1(+) caused no obvious changes in the telomeric DNA structure. Our results demonstrate a novel role of the S. pombe ATM homologues that is independent of chk1(+) and cds1(+). PMID:10430579

  2. Learning genetic epistasis using Bayesian network scoring criteria

    PubMed Central

    2011-01-01

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

  3. 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

  4. Minimum energy control and optimal-satisfactory control of Boolean control network

    NASA Astrophysics Data System (ADS)

    Li, Fangfei; Lu, Xiwen

    2013-12-01

    In the literatures, to transfer the Boolean control network from the initial state to the desired state, the expenditure of energy has been rarely considered. Motivated by this, this Letter investigates the minimum energy control and optimal-satisfactory control of Boolean control network. Based on the semi-tensor product of matrices and Floyd's algorithm, minimum energy, constrained minimum energy and optimal-satisfactory control design for Boolean control network are given respectively. A numerical example is presented to illustrate the efficiency of the obtained results.

  5. Gene network dynamics controlling keratinocyte migration

    PubMed Central

    Busch, Hauke; Camacho-Trullio, David; Rogon, Zbigniew; Breuhahn, Kai; Angel, Peter; Eils, Roland; Szabowski, Axel

    2008-01-01

    Translation of large-scale data into a coherent model that allows one to simulate, predict and control cellular behavior is far from being resolved. Assuming that long-term cellular behavior is reflected in the gene expression kinetics, we infer a dynamic gene regulatory network from time-series measurements of DNA microarray data of hepatocyte growth factor-induced migration of primary human keratinocytes. Transferring the obtained interactions to the level of signaling pathways, we predict in silico and verify in vitro the necessary and sufficient time-ordered events that control migration. We show that pulse-like activation of the proto-oncogene receptor Met triggers a responsive state, whereas time sequential activation of EGF-R is required to initiate and maintain migration. Context information for enhancing, delaying or stopping migration is provided by the activity of the protein kinase A signaling pathway. Our study reveals the complex orchestration of multiple pathways controlling cell migration. PMID:18594517

  6. 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.

  7. Consensus and Stability Analysis of Networked Multiagent Predictive Control Systems.

    PubMed

    Liu, Guo-Ping

    2017-04-01

    This paper is concerned with the consensus and stability problem of multiagent control systems via networks with communication delays and data loss. A networked multiagent predictive control scheme is proposed to achieve output consensus and also compensate for the communication delays and data loss actively. The necessary and sufficient conditions of achieving both consensus and stability of the closed-loop networked multiagent control systems are derived. An important result that is obtained is that the consensus and stability of closed-loop networked multiagent predictive control systems are not related to the communication delays and data loss. An example illustrates the performance of the networked multiagent predictive control scheme.

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

    NASA Astrophysics Data System (ADS)

    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.

  9. 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.

  10. Reverse Genetics Approaches to Control Arenavirus.

    PubMed

    Martínez-Sobrido, Luis; Cheng, Benson Yee Hin; de la Torre, Juan Carlos

    2016-01-01

    Several arenavirus cause hemorrhagic fever disease in humans and pose a significant public health problem in their endemic regions. To date, no licensed vaccines are available to combat human arenavirus infections, and anti-arenaviral drug therapy is limited to an off-label use of ribavirin that is only partially effective. The development of arenavirus reverse genetics approaches provides investigators with a novel and powerful approach for the investigation of the arenavirus molecular and cell biology. The use of cell-based minigenome systems has allowed examining the cis- and trans-acting factors involved in arenavirus replication and transcription and the identification of novel anti-arenaviral drug targets without requiring the use of live forms of arenaviruses. Likewise, it is now feasible to rescue infectious arenaviruses entirely from cloned cDNAs containing predetermined mutations in their genomes to investigate virus-host interactions and mechanisms of pathogenesis, as well as to facilitate screens to identify anti-arenaviral drugs and development of novel live-attenuated arenavirus vaccines. Recently, reverse genetics have also allowed the generation of tri-segmented arenaviruses expressing foreign genes, facilitating virus detection and opening the possibility of implementing live-attenuated arenavirus-based vaccine vector approaches. Likewise, the development of single-cycle infectious, reporter-expressing, arenaviruses has provided a new experimental method to study some aspects of the biology of highly pathogenic arenaviruses without the requirement of high-security biocontainment required to study HF-causing arenaviruses. In this chapter we summarize the current knowledge on arenavirus reverse genetics and the implementation of plasmid-based reverse genetics techniques for the development of arenavirus vaccines and vaccine vectors.

  11. Reverse Genetics Approaches to Control Arenavirus

    PubMed Central

    Martínez-Sobrido, Luis; Cheng, Benson Yee Hin; de la Torre, Juan Carlos

    2016-01-01

    Several arenavirus cause hemorrhagic fever disease in humans and pose a significant public health problem in their endemic regions. To date, no licensed vaccines are available to combat human arenavirus infections, and anti-arenaviral drug therapy is limited to an off-label use of ribavirin that is only partially effective. The development of arenavirus reverse genetics approaches provides investigators with a novel and powerful approach for the investigation of the arenavirus molecular and cell biology. The use of cell-based minigenome systems has allowed examining the cis- and trans-acting factors involved in arenavirus replication and transcription and the identification of novel anti-arenaviral drug targets without requiring the use of live forms of arenaviruses. Likewise, it is now feasible to rescue infectious arenaviruses entirely from cloned cDNAs containing predetermined mutations in their genomes to investigate virus-host interactions and mechanisms of pathogenesis, as well as to facilitate screens to identify anti-arenaviral drugs and development of novel live-attenuated arenavirus vaccines. Recently, reverse genetics have also allowed the generation of tri-segmented arenaviruses expressing foreign genes, facilitating virus detection and opening the possibility of implementing live-attenuated arenavirus-based vaccine vector approaches. Likewise, the development of single-cycle infectious, reporter-expressing, arenaviruses has provided a new experimental method to study some aspects of the biology of highly pathogenic arenaviruses without the requirement of high-security biocontainment required to study HF-causing arenaviruses. In this chapter we summarize the current knowledge on arenavirus reverse genetics and the implementation of plasmid-based reverse genetics techniques for the development of arenavirus vaccines and vaccine vectors. PMID:27076139

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

    PubMed

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

    2016-09-23

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

  13. 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.

  14. Enhanced vaccine control of epidemics in adaptive networks.

    PubMed

    Shaw, Leah B; Schwartz, Ira B

    2010-04-01

    We study vaccine control for disease spread on an adaptive network modeling disease avoidance behavior. Control is implemented by adding Poisson-distributed vaccination of susceptibles. We show that vaccine control is much more effective in adaptive networks than in static networks due to feedback interaction between the adaptive network rewiring and the vaccine application. When compared to extinction rates in static social networks, we find that the amount of vaccine resources required to sustain similar rates of extinction are as much as two orders of magnitude lower in adaptive networks.

  15. Predictive Control of Networked Multiagent Systems via Cloud Computing.

    PubMed

    Liu, Guo-Ping

    2017-01-18

    This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.

  16. 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.

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

    NASA Astrophysics Data System (ADS)

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

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

  18. 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.

  19. 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

  20. 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

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

    PubMed Central

    2013-01-01

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

  2. Genetic Control of the German Cockroach.

    DTIC Science & Technology

    1977-04-17

    The authors investigated the possibility of controlling the German cockroach, Blattella germanica , by using chromosome translocations. The first...translocations for control in B. germanica . Such mechanisms are potentially capable of reducing field populations.

  3. 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.

  4. On control of singleton attractors in multiple Boolean networks: integer programming-based method

    PubMed Central

    2014-01-01

    Background Boolean network (BN) is a mathematical model for genetic network and control of genetic networks has become an important issue owing to their potential application in the field of drug discovery and treatment of intractable diseases. Early researches have focused primarily on the analysis of attractor control for a randomly generated BN. However, one may also consider how anti-cancer drugs act in both normal and cancer cells. Thus, the development of controls for multiple BNs is an important and interesting challenge. Results In this article, we formulate three novel problems about attractor control for two BNs (i.e., normal cell and cancer cell). The first is about finding a control that can significantly damage cancer cells but has a limited damage to normal cells. The second is about finding a control for normal cells with a guaranteed damaging effect on cancer cells. Finally, we formulate a definition for finding a control for cancer cells with limited damaging effect on normal cells. We propose integer programming-based methods for solving these problems in a unified manner, and we conduct computational experiments to illustrate the efficiency and the effectiveness of our method for our multiple-BN control problems. Conclusions We present three novel control problems for multiple BNs that are realistic control models for gene regulation networks and adopt an integer programming approach to address these problems. Experimental results indicate that our proposed method is useful and effective for moderate size BNs. PMID:24565276

  5. 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.

  6. Network analysis of skin tumor progression identifies a rewired genetic architecture affecting inflammation and tumor susceptibility

    PubMed Central

    2011-01-01

    Background Germline polymorphisms can influence gene expression networks in normal mammalian tissues and can affect disease susceptibility. We and others have shown that analysis of this genetic architecture can identify single genes and whole pathways that influence complex traits, including inflammation and cancer susceptibility. Whether germline variants affect gene expression in tumors that have undergone somatic alterations, and the extent to which these variants influence tumor progression, is unknown. Results Using an integrated linkage and genomic analysis of a mouse model of skin cancer that produces both benign tumors and malignant carcinomas, we document major changes in germline control of gene expression during skin tumor development resulting from cell selection, somatic genetic events, and changes in the tumor microenvironment. The number of significant expression quantitative trait loci (eQTL) is progressively reduced in benign and malignant skin tumors when compared to normal skin. However, novel tumor-specific eQTL are detected for several genes associated with tumor susceptibility, including IL18 (Il18), Granzyme E (Gzme), Sprouty homolog 2 (Spry2), and Mitogen-activated protein kinase kinase 4 (Map2k4). Conclusions We conclude that the genetic architecture is substantially altered in tumors, and that eQTL analysis of tumors can identify host factors that influence the tumor microenvironment, mitogen-activated protein (MAP) kinase signaling, and cancer susceptibility. PMID:21244661

  7. Genes in new environments: genetics and evolution in biological control.

    PubMed

    Roderick, George K; Navajas, Maria

    2003-11-01

    The availability of new genetic technologies has positioned the field of biological control as a test bed for theories in evolutionary biology and for understanding practical aspects of the release of genetically manipulated material. Purposeful introductions of pathogens, parasites, predators and herbivores, when considered as replicated semi-natural field experiments, show the unpredictable nature of biological colonization. The characteristics of organisms and their environments that determine this variation in the establishment and success of biological control can now be explored using genetic tools. Lessons from studies of classical biological control can help inform researchers and policy makers about the risks that are associated with the release of genetically modified organisms, particularly with respect to long-term evolutionary changes.

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

    PubMed Central

    2010-01-01

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

  9. 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.

  10. 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.

  11. Control of vector populations using genetically modified mosquitoes.

    PubMed

    Wilke, André Barreto Bruno; Gomes, Almério de Castro; Natal, Delsio; Marrelli, Mauro Toledo

    2009-10-01

    The ineffectiveness of current strategies for chemical control of mosquito vectors raises the need for developing novel approaches. Thus, we carried out a literature review of strategies for genetic control of mosquito populations based on the sterile insect technique. One of these strategies consists of releasing radiation-sterilized males into the population; another, of integrating a dominant lethal gene under the control of a specific promoter into immature females. Advantages of these approaches over other biological and chemical control strategies include: highly species-specific, environmentally safety, low production cost, and high efficacy. The use of this genetic modification technique will constitute an important tool for integrated vector management.

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

    PubMed

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

    2016-04-01

    Association studies are widely utilized to analyze complex traits but their ability to disclose genetic architectures is often limited by statistical constraints, and functional insights are usually minimal in nonmodel organisms like forest trees. We developed an approach to integrate association mapping results with co-expression networks. We tested single nucleotide polymorphisms (SNPs) in 2652 candidate genes for statistical associations with wood density, stiffness, microfibril angle and ring width in a population of 1694 white spruce trees (Picea glauca). Associations mapping identified 229-292 genes per wood trait using a statistical significance level of P < 0.05 to maximize discovery. Over-representation of genes associated for nearly all traits was found in a xylem preferential co-expression group developed in independent experiments. A xylem co-expression network was reconstructed with 180 wood associated genes and several known MYB and NAC regulators were identified as network hubs. The network revealed a link between the gene PgNAC8, wood stiffness and microfibril angle, as well as considerable within-season variation for both genetic control of wood traits and gene expression. Trait associations were distributed throughout the network suggesting complex interactions and pleiotropic effects. Our findings indicate that integration of association mapping and co-expression networks enhances our understanding of complex wood traits. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

  13. Tuning of active vibration controllers for ACTEX by genetic algorithm

    NASA Astrophysics Data System (ADS)

    Kwak, Moon K.; Denoyer, Keith K.

    1999-06-01

    This paper is concerned with the optimal tuning of digitally programmable analog controllers on the ACTEX-1 smart structures flight experiment. The programmable controllers for each channel include a third order Strain Rate Feedback (SRF) controller, a fifth order SRF controller, a second order Positive Position Feedback (PPF) controller, and a fourth order PPF controller. Optimal manual tuning of several control parameters can be a difficult task even though the closed-loop control characteristics of each controller are well known. Hence, the automatic tuning of individual control parameters using Genetic Algorithms is proposed in this paper. The optimal control parameters of each control law are obtained by imposing a constraint on the closed-loop frequency response functions using the ACTEX mathematical model. The tuned control parameters are then uploaded to the ACTEX electronic control electronics and experiments on the active vibration control are carried out in space. The experimental results on ACTEX will be presented.

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

    PubMed

    Alter, Orly

    2007-01-01

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

  15. Experimental results of a predictive neural network HVAC controller

    SciTech Connect

    Jeannette, E.; Assawamartbunlue, K.; Kreider, J.F.; Curtiss, P.S.

    1998-12-31

    Proportional, integral, and derivative (PID) control is widely used in many HVAC control processes and requires constant attention for optimal control. Artificial neural networks offer the potential for improved control of processes through predictive techniques. This paper introduces and shows experimental results of a predictive neural network (PNN) controller applied to an unstable hot water system in an air-handling unit. Actual laboratory testing of the PNN and PID controllers show favorable results for the PNN controller.

  16. 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.

  17. A constrained evolutionary computation method for detecting controlling regions of cortical networks.

    PubMed

    Tang, Yang; Wang, Zidong; Gao, Huijun; Swift, Stephen; Kurths, Jürgen

    2012-01-01

    Controlling regions in cortical networks, which serve as key nodes to control the dynamics of networks to a desired state, can be detected by minimizing the eigenratio R and the maximum imaginary part \\sigma of an extended connection matrix. Until now, optimal selection of the set of controlling regions is still an open problem and this paper represents the first attempt to include two measures of controllability into one unified framework. The detection problem of controlling regions in cortical networks is converted into a constrained optimization problem (COP), where the objective function R is minimized and \\sigma is regarded as a constraint. Then, the detection of controlling regions of a weighted and directed complex network (e.g., a cortical network of a cat), is thoroughly investigated. The controlling regions of cortical networks are successfully detected by means of an improved dynamic hybrid framework (IDyHF). Our experiments verify that the proposed IDyHF outperforms two recently developed evolutionary computation methods in constrained optimization field and some traditional methods in control theory as well as graph theory. Based on the IDyHF, the controlling regions are detected in a microscopic and macroscopic way. Our results unveil the dependence of controlling regions on the number of driver nodes l and the constraint r. The controlling regions are largely selected from the regions with a large in-degree and a small out-degree. When r=+ \\infty, there exists a concave shape of the mean degrees of the driver nodes, i.e., the regions with a large degree are of great importance to the control of the networks when l is small and the regions with a small degree are helpful to control the networks when l increases. When r=0, the mean degrees of the driver nodes increase as a function of l. We find that controlling \\sigma is becoming more important in controlling a cortical network with increasing l. The methods and results of detecting controlling

  18. 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.

  19. 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.

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

    PubMed Central

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

    2003-01-01

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

  1. Universal framework for edge controllability of complex networks.

    PubMed

    Pang, Shao-Peng; Wang, Wen-Xu; Hao, Fei; Lai, Ying-Cheng

    2017-06-26

    Dynamical processes occurring on the edges in complex networks are relevant to a variety of real-world situations. Despite recent advances, a framework for edge controllability is still required for complex networks of arbitrary structure and interaction strength. Generalizing a previously introduced class of processes for edge dynamics, the switchboard dynamics, and exploit- ing the exact controllability theory, we develop a universal framework in which the controllability of any node is exclusively determined by its local weighted structure. This framework enables us to identify a unique set of critical nodes for control, to derive analytic formulas and articulate efficient algorithms to determine the exact upper and lower controllability bounds, and to evaluate strongly structural controllability of any given network. Applying our framework to a large number of model and real-world networks, we find that the interaction strength plays a more significant role in edge controllability than the network structure does, due to a vast range between the bounds determined mainly by the interaction strength. Moreover, transcriptional regulatory networks and electronic circuits are much more strongly structurally controllable (SSC) than other types of real-world networks, directed networks are more SSC than undirected networks, and sparse networks are typically more SSC than dense networks.

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

    PubMed Central

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

    2009-01-01

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

  3. 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

  4. Controllability of giant connected components in a directed network

    NASA Astrophysics Data System (ADS)

    Liu, Xueming; Pan, Linqiang; Stanley, H. Eugene; Gao, Jianxi

    2017-04-01

    When controlling a complex networked system it is not feasible to control the full network because many networks, including biological, technological, and social systems, are massive in size and complexity. But neither is it necessary to control the full network. In complex networks, the giant connected components provide the essential information about the entire system. How to control these giant connected components of a network remains an open question. We derive the mathematical expression of the degree distributions for four types of giant connected components and develop an analytic tool for studying the controllability of these giant connected components. We find that for both Erdős-Rényi (ER) networks and scale-free (SF) networks with p fraction of remaining nodes, the minimum driver node density to control the giant component first increases and then decreases as p increases from zero to one, showing a peak at a critical point p =pm . We find that, for ER networks, the peak value of the driver node density remains the same regardless of its average degree and that it is determined by pm . In addition, we find that for SF networks the minimum driver node densities needed to control the giant components of networks decrease as the degree distribution exponents increase. Comparing the controllability of the giant components of ER networks and SF networks, we find that when the fraction of remaining nodes p is low, the giant in-connected, out-connected, and strong-connected components in ER networks have lower controllability than those in SF networks.

  5. Structural permeability of complex networks to control signals

    PubMed Central

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

    2015-01-01

    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. PMID:26391186

  6. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2009-07-01

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

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

    PubMed Central

    Hether, Tyler D.; Hohenlohe, Paul A.

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1999-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-08-01

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

  11. Enterprise network control and management: traffic flow models

    NASA Astrophysics Data System (ADS)

    Maruyama, William; George, Mark S.; Hernandez, Eileen; LoPresto, Keith; Uang, Yea

    1999-11-01

    The exponential growth and dramatic increase in demand for network bandwidth is expanding the market for broadband satellite networks. It is critical to rapidly deliver ubiquitous satellite communication networks that are differentiated by lower cost and increased Quality of Service (QoS). There is a need to develop new network architectures, control and management systems to meet the future commercial and military traffic requirements, services and applications. The next generation communication networks must support legacy and emerging network traffic while providing user negotiated levels of QoS. Network resources control algorithms must be designed to provide the guaranteed performance levels for voice, video and data having different service requirements. To evaluate network architectures and performance, it is essential to understand the network traffic characteristics.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. Signaling networks in the control of pluripotency.

    PubMed

    Zhao, Hanzhi; Jin, Ying

    2017-10-01

    Embryonic stem cells (ESCs) are characterized by their ability of unlimited self-renewal in vitro and pluripotent developmental potential, which endows them with great values in basic research and future clinical application. However, realization of full potential of ESCs is dependent on the elucidation of molecular mechanisms governing ESCs, among which signaling pathways play critical roles. A great deal of efforts has been made in the past decades to understand what and how signaling pathways contribute to the establishment and maintenance of pluripotency. In this review, we discuss signaling networks in both mouse and human ESCs, focusing on signals involved in the control of self-renewal and differentiation. In addition, the modulation of signaling pathways by pluripotency-associated transcription factors is also briefly summarized. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Genetic Control of Contagious Asexuality in the Pea Aphid

    PubMed Central

    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

    2014-01-01

    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. PMID:25473828

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

    NASA Astrophysics Data System (ADS)

    Reca, Juan; MartíNez, Juan

    2006-05-01

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

  20. 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…

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

    PubMed Central

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

    2017-01-01

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

  3. 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.

  4. Optimizing controllability of edge dynamics in complex networks by perturbing network structure

    NASA Astrophysics Data System (ADS)

    Pang, Shaopeng; Hao, Fei

    2017-03-01

    Using the minimum input signals to drive the dynamics in complex networks toward some desired state is a fundamental issue in the field of network controllability. For a complex network with the dynamical process defined on its edges, the controllability of this network is optimal if it can be fully controlled by applying one input signal to an arbitrary non-isolated vertex of it. In this paper, the adding-edge strategy and turning-edge strategy are proposed to optimize the controllability by minimum structural perturbations. Simulations and analyses indicate that the minimum number of adding-edges required for the optimal controllability is equal to the minimum number of turning-edges, and networks with positively correlated in- and out-degrees are easier to achieve optimal controllability. Furthermore, both the strategies have the capacity to reveal the relationship between certain structural properties of a complex network and its controllability of edge dynamics.

  5. 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

  6. 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

  7. A Combined Computational and Genetic Approach Uncovers Network Interactions of the Cyanobacterial Circadian Clock.

    PubMed

    Boyd, Joseph S; Cheng, Ryan R; Paddock, Mark L; Sancar, Cigdem; Morcos, Faruck; Golden, Susan S

    2016-09-15

    Two-component systems (TCS) that employ histidine kinases (HK) and response regulators (RR) are critical mediators of cellular signaling in bacteria. In the model cyanobacterium Synechococcus elongatus PCC 7942, TCSs control global rhythms of transcription that reflect an integration of time information from the circadian clock with a variety of cellular and environmental inputs. The HK CikA and the SasA/RpaA TCS transduce time information from the circadian oscillator to modulate downstream cellular processes. Despite immense progress in understanding of the circadian clock itself, many of the connections between the clock and other cellular signaling systems have remained enigmatic. To narrow the search for additional TCS components that connect to the clock, we utilized direct-coupling analysis (DCA), a statistical analysis of covariant residues among related amino acid sequences, to infer coevolution of new and known clock TCS components. DCA revealed a high degree of interaction specificity between SasA and CikA with RpaA, as expected, but also with the phosphate-responsive response regulator SphR. Coevolutionary analysis also predicted strong specificity between RpaA and a previously undescribed kinase, HK0480 (herein CikB). A knockout of the gene for CikB (cikB) in a sasA cikA null background eliminated the RpaA phosphorylation and RpaA-controlled transcription that is otherwise present in that background and suppressed cell elongation, supporting the notion that CikB is an interactor with RpaA and the clock network. This study demonstrates the power of DCA to identify subnetworks and key interactions in signaling pathways and of combinatorial mutagenesis to explore the phenotypic consequences. Such a combined strategy is broadly applicable to other prokaryotic systems. Signaling networks are complex and extensive, comprising multiple integrated pathways that respond to cellular and environmental cues. A TCS interaction model, based on DCA, independently

  8. A Combined Computational and Genetic Approach Uncovers Network Interactions of the Cyanobacterial Circadian Clock

    PubMed Central

    Boyd, Joseph S.; Cheng, Ryan R.; Paddock, Mark L.; Sancar, Cigdem

    2016-01-01

    ABSTRACT Two-component systems (TCS) that employ histidine kinases (HK) and response regulators (RR) are critical mediators of cellular signaling in bacteria. In the model cyanobacterium Synechococcus elongatus PCC 7942, TCSs control global rhythms of transcription that reflect an integration of time information from the circadian clock with a variety of cellular and environmental inputs. The HK CikA and the SasA/RpaA TCS transduce time information from the circadian oscillator to modulate downstream cellular processes. Despite immense progress in understanding of the circadian clock itself, many of the connections between the clock and other cellular signaling systems have remained enigmatic. To narrow the search for additional TCS components that connect to the clock, we utilized direct-coupling analysis (DCA), a statistical analysis of covariant residues among related amino acid sequences, to infer coevolution of new and known clock TCS components. DCA revealed a high degree of interaction specificity between SasA and CikA with RpaA, as expected, but also with the phosphate-responsive response regulator SphR. Coevolutionary analysis also predicted strong specificity between RpaA and a previously undescribed kinase, HK0480 (herein CikB). A knockout of the gene for CikB (cikB) in a sasA cikA null background eliminated the RpaA phosphorylation and RpaA-controlled transcription that is otherwise present in that background and suppressed cell elongation, supporting the notion that CikB is an interactor with RpaA and the clock network. This study demonstrates the power of DCA to identify subnetworks and key interactions in signaling pathways and of combinatorial mutagenesis to explore the phenotypic consequences. Such a combined strategy is broadly applicable to other prokaryotic systems. IMPORTANCE Signaling networks are complex and extensive, comprising multiple integrated pathways that respond to cellular and environmental cues. A TCS interaction model, based on

  9. Nano-Resonators for RF-Enabled Networked-Control

    DTIC Science & Technology

    2006-01-01

    Networked Control System As a further embodiment of the NCS environment we consider the application to a velocity estimation problem often... Networked Control System Co-simulation for Co-design,” Proc. American Control Conf. Denver, CO, USA, June, 2003. [12] R.H. Brown and S.C. Schneider

  10. Adaptive Control of Visually Guided Grasping in Neural Networks

    DTIC Science & Technology

    1990-03-12

    U01ITU S.WM NONnumsen Adaptive Control of Visually Guided Grasping in Neural Networks AFOSR-89-&CO030 88-NL-209 L AUTHOrSF 2313/A8 00 61102F (V) Dr...FINAL REPORT ADAPTIVE CONTROL OF VISUALLY GUIDED GRASPING IN NEURAL NETWORKS Neurogen Laboratories Inc. Project Summary Research performed for AFOSR...arm’s length in position and 6 degrees in orientation. Keywords: Neural Networks , Adaptive Motor Control, Sensory-Motor sensation Introduction The human

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

    NASA Astrophysics Data System (ADS)

    Ruehle, Fabian

    2017-08-01

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

  12. 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

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

    PubMed

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

    2011-05-01

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

  14. Modeling, monitoring and control based on neural networks

    SciTech Connect

    Fu, Chi Yung

    1995-04-14

    The cost of a fabrication line such as one in a semiconductor house has increased dramatically over the years and it is possibly already past the point that some new start-up company can have sufficient capital to build a new fabrication line. Such capital-intensive manufacturing needs better utilization of resources and management of equipment to maximize its productivity. In order to maximize the return from such a capital-intensive manufacturing line, we need to address the following: (1) increasing the yield, (2) enhancing the flexibility of the fabrication line, (3) improving quality, and finally (4) minimizing the down time of the processing equipment. Because of the significant advances now made in the fields of artificial neural networks, fuzzy logic, machine learning and genetic algorithms, we advocate the use of these new tools to in manufacturing. We term the applications of these and other tools that mimic human intelligence to manufacturing neural manufacturing. This paper will address the effort at Lawrence Livermore National Laboratory (LLNL) to use artificial neural networks to address certain semiconductor process modeling, monitoring and control questions.

  15. Local control network and internetwork ISO-OSI reference model

    SciTech Connect

    Damsker, D.

    1983-05-01

    The paper describes a new local control network architecture. The new control network is totally distributed and redundantly hardware and software structured, based on a bus configuration and on CSMA/CD media access control. The architecture of the control structure and of the data communications structure for both Local Network and Internetwork is discussed in comparison with ISO-OSI and Local Area Network IEEE Standard 802 (Draft) Reference Models. A previous paper dealt with the physical implementation of this concept. The present paper is more software structure oriented.

  16. 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.

  17. 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.

  18. 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.

  19. Design and Analysis of Packet Switched Networks for Control Systems

    SciTech Connect

    Custodi, G.L.

    1999-08-11

    This paper contains a methodology for analyzing and designing a computer network for application to complex control systems. The focus is on the analysis and design of a local area network (LAN) for realizing the high-level control network that interconnects input-output controllers with devices for monitoring and analysis and with high-level controllers such as supervisory PLCs. Part of the development given in this paper can also be applied to the device-level network (fieldbus) that interconnects input-output controllers with sensors, actuators, and other devices in the system being controlled. The high-level network and the device-level network form a two-layer architecture that is typical in control applications. A procedure is given for generating a network design with a hierarchical hub topology having full redundancy. Then in terms of a graph model of the network, procedures are given for studying network availability and analyzing the information flow rates through the links and internal nodes of the network'

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

    PubMed Central

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

    2008-01-01

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

  1. Gate-controlled rectifying behavior in C70@SWNT networks.

    PubMed

    Guo, Ao; Fu, Yunyi; Liu, Jia; Guan, Lunhui; Shi, Zujin; Gu, Zhennan; Huang, Ru; Zhang, Xing

    2006-05-25

    We report the gate-controlled rectification behavior in C(70)@SWNT networks at room temperature in air. The electrical transport characteristics can be fitted well with the conventional Schottky diode model. The origin of the rectifying behavior in fullerene peapod networks device is qualitatively discussed. This paper demonstrates a strategy for diode fabrication based on peapod networks.

  2. Modulations among the alerting, orienting and executive control networks.

    PubMed

    Callejas, Alicia; Lupiàñez, Juan; Funes, María Jesús; Tudela, Pío

    2005-11-01

    This paper reports a series of experiments that were carried out in order to study the attentional system. Three networks make up this system, and each of them specializes in particular processes. The executive control network specializes in control processes, such as conflict resolution or detection of errors; the orienting network directs the processing system to the source of input and enhances its processing; the alerting network prepares the system for a fast response by maintaining an adequate level of activation in the cognitive system. Recently, Fan and collaborators [J Cogn Neurosci 14(3):340-347, 2002] designed a task to measure the efficiency of each network. We modified Fan's task to test the influences among the networks. We found that the executive control network is inhibited by the alerting network, whereas the orienting network raises the efficiency of the executive control network (Experiment 1). We also found that the alerting network influences the orienting network by speeding up its time course function (Experiment 2). Results were replicated in a third experiment, proving the effects to be stable over time, participants and experimental context, and to be potentially important as a tool for neuropsychological assessment.

  3. 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.

  4. 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.

  5. 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

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

    PubMed

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

    2015-03-27

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

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2014-01-01

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

  9. Cell Fate Reprogramming by Control of Intracellular Network Dynamics

    PubMed Central

    Zañudo, Jorge G. T.; Albert, Réka

    2015-01-01

    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. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network 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 helper 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. PMID:25849586

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

    PubMed

    Dumas, Marc-Emmanuel

    2012-10-01

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

  11. 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.

  12. Implementing controlled-unitary operations over the butterfly network

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

    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.

  13. 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.

  14. 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.

  15. An improved method for network congestion control

    NASA Astrophysics Data System (ADS)

    Qiao, Xiaolin

    2013-03-01

    The rapid progress of the wireless network technology has great convenience on the people's life and work. However, because of its openness, the mobility of the terminal and the changing topology, the wireless network is more susceptible to security attacks. Authentication and key agreement is the base of the network security. The authentication and key agreement mechanism can prevent the unauthorized user from accessing the network, resist malicious network to deceive the lawful user, encrypt the session data by using the exchange key and provide the identification of the data origination. Based on characteristics of the wireless network, this paper proposed a key agreement protocol for wireless network. The authentication of protocol is based on Elliptic Curve Cryptosystems and Diffie-Hellman.

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

    PubMed

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

    2015-04-01

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

  17. 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.

  18. 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.

  19. Performance evaluation of network-based steam generator level control

    SciTech Connect

    Li, Q.; Jiang, J.

    2006-07-01

    The performance of a network-based control system (NCS) is systematically and quantitatively analyzed by simulation and experiments. A U-Tube Steam Generator (UTSG) is used as the process in this study. The simulation results show that the offset and rise time are largely unaffected by either network-induced delays or data loss, but the overshoot, range, and settling time can be influenced by delay and/or data loss. In addition, the simulation results also indicate that Model Predictive Control (MPC) is more robust to tolerate network-induced delays and data loss than its PI counterpart. The experimental tests show that the introduction of a network (Foundation Field-bus in this case) to the control loop does not degrade the overall control system performance if the network is used for small number of control loops, but it may degrade the control system performance if more control loops are added. (authors)

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

    PubMed Central

    Wolf, Jochen B.W; Trillmich, Fritz

    2008-01-01

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

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

    PubMed

    Wolf, Jochen B W; Trillmich, Fritz

    2008-09-22

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

  2. 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.

  3. Application of neural networks to unsteady aerodynamic control

    NASA Technical Reports Server (NTRS)

    Faller, William E.; Schreck, Scott J.; Luttges, Marvin W.

    1994-01-01

    The problem under consideration in this viewgraph presentation is to understand, predict, and control the fluid mechanics of dynamic maneuvers, unsteady boundary layers, and vortex dominated flows. One solution is the application of neural networks demonstrating closed-loop control. Neural networks offer unique opportunities: simplify modeling of three dimensional, vortex dominated, unsteady separated flow fields; are effective means for controlling unsteady aerodynamics; and address integration of sensors, controllers, and time lags into adaptive control systems.

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

    PubMed

    Hao, Yibin; Shan, Guoyong; Nan, Kejun

    2017-03-01

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

  5. 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...

  6. 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.

  7. 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.

  8. Genetic control of anastomosis in Podospora anserina.

    PubMed

    Tong, Laetitia Chan Ho; Silar, Philippe; Lalucque, Hervé

    2014-09-01

    We developed a new microscopy procedure to study anastomoses in the model ascomycete Podospora anserina and compared it with the previous method involving the formation of balanced heterokaryons. Both methods showed a good correlation. Heterokaryon formation was less quantifiable, but enabled to observe very rare events. Microscopic analysis evidenced that anastomoses were greatly influence by growth conditions and were severely impaired in the IDC mutants of the PaMpk1, PaMpk2, IDC1 and PaNox1 pathways. Yet some mutants readily formed heterokaryons, albeit with a delay when compared to the wild type. We also identified IDC(821), a new mutant presenting a phenotype similar to the other IDC mutants, including lack of anastomosis. Complete genome sequencing revealed that IDC(821) was affected in the orthologue of the Neurospora crassa So gene known to control anastomosis in several other ascomycetes.

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

    PubMed Central

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

    2016-01-01

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

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. Design of PID-type controllers using multiobjective genetic algorithms.

    PubMed

    Herreros, Alberto; Baeyens, Enrique; Perán, José R

    2002-10-01

    The design of a PID controller is a multiobjective problem. A plant and a set of specifications to be satisfied are given. The designer has to adjust the parameters of the PID controller such that the feedback interconnection of the plant and the controller satisfies the specifications. These specifications are usually competitive and any acceptable solution requires a tradeoff among them. An approach for adjusting the parameters of a PID controller based on multiobjective optimization and genetic algorithms is presented in this paper. The MRCD (multiobjective robust control design) genetic algorithm has been employed. The approach can be easily generalized to design multivariable coupled and decentralized PID loops and has been successfully validated for a large number of experimental cases.

  16. 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.