WorldWideScience

Sample records for gene networks controlling

  1. Gene networks controlling Arabidopsis thaliana flower development.

    Science.gov (United States)

    Ó'Maoiléidigh, Diarmuid Seosamh; Graciet, Emmanuelle; Wellmer, Frank

    2014-01-01

    The formation of flowers is one of the main models for studying the regulatory mechanisms that underlie plant development and evolution. Over the past three decades, extensive genetic and molecular analyses have led to the identification of a large number of key floral regulators and to detailed insights into how they control flower morphogenesis. In recent years, genome-wide approaches have been applied to obtaining a global view of the gene regulatory networks underlying flower formation. Furthermore, mathematical models have been developed that can simulate certain aspects of this process and drive further experimentation. Here, we review some of the main findings made in the field of Arabidopsis thaliana flower development, with an emphasis on recent advances. In particular, we discuss the activities of the floral organ identity factors, which are pivotal for the specification of the different types of floral organs, and explore the experimental avenues that may elucidate the molecular mechanisms and gene expression programs through which these master regulators of flower development act. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

  2. Transcriptional control in the segmentation gene network of Drosophila.

    Science.gov (United States)

    Schroeder, Mark D; Pearce, Michael; Fak, John; Fan, HongQing; Unnerstall, Ulrich; Emberly, Eldon; Rajewsky, Nikolaus; Siggia, Eric D; Gaul, Ulrike

    2004-09-01

    The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross-) regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules) with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a uniform set of

  3. Transcriptional control in the segmentation gene network of Drosophila.

    Directory of Open Access Journals (Sweden)

    Mark D Schroeder

    2004-09-01

    Full Text Available The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross- regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a

  4. Gene networks controlling the initiation of flower development.

    Science.gov (United States)

    Wellmer, Frank; Riechmann, José L

    2010-12-01

    The onset of flower formation is a key regulatory event during the life cycle of angiosperm plants, which marks the beginning of the reproductive phase of development. It has been shown that floral initiation is under tight genetic control, and deciphering the underlying molecular mechanisms has been a main area of interest in plant biology for the past two decades. Here, we provide an overview of the developmental and genetic processes that occur during floral initiation. We further review recent studies that have led to the genome-wide identification of target genes of key floral regulators and discuss how they have contributed to an in-depth understanding of the gene regulatory networks controlling early flower development. We focus especially on a master regulator of floral initiation in Arabidopsis thaliana APETALA1 (AP1), but also outline what is known about the AP1 network in other plant species and the evolutionary implications. Copyright © 2010 Elsevier Ltd. All rights reserved.

  5. Digital Signal Processing and Control for the Study of Gene Networks

    Science.gov (United States)

    Shin, Yong-Jun

    2016-04-01

    Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

  6. Digital Signal Processing and Control for the Study of Gene Networks.

    Science.gov (United States)

    Shin, Yong-Jun

    2016-04-22

    Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

  7. Sensor-coupled fractal gene regulatory networks for locomotion control of a modular snake robot

    DEFF Research Database (Denmark)

    Zahadat, Payam; Christensen, David Johan; Katebi, Serajeddin

    2013-01-01

    In this paper we study fractal gene regulatory network (FGRN) controllers based on sensory information. The FGRN controllers are evolved to control a snake robot consisting of seven simulated ATRON modules. Each module contains three tilt sensors which represent the direction of gravity in the co......In this paper we study fractal gene regulatory network (FGRN) controllers based on sensory information. The FGRN controllers are evolved to control a snake robot consisting of seven simulated ATRON modules. Each module contains three tilt sensors which represent the direction of gravity...

  8. Intervention in gene regulatory networks via greedy control policies based on long-run behavior

    Directory of Open Access Journals (Sweden)

    Ghaffari Noushin

    2009-06-01

    Full Text Available Abstract Background A salient purpose for studying gene regulatory networks is to derive intervention strategies, the goals being to identify potential drug targets and design gene-based therapeutic intervention. Optimal stochastic control based on the transition probability matrix of the underlying Markov chain has been studied extensively for probabilistic Boolean networks. Optimization is based on minimization of a cost function and a key goal of control is to reduce the steady-state probability mass of undesirable network states. Owing to computational complexity, it is difficult to apply optimal control for large networks. Results In this paper, we propose three new greedy stationary control policies by directly investigating the effects on the network long-run behavior. Similar to the recently proposed mean-first-passage-time (MFPT control policy, these policies do not depend on minimization of a cost function and avoid the computational burden of dynamic programming. They can be used to design stationary control policies that avoid the need for a user-defined cost function because they are based directly on long-run network behavior; they can be used as an alternative to dynamic programming algorithms when the latter are computationally prohibitive; and they can be used to predict the best control gene with reduced computational complexity, even when one is employing dynamic programming to derive the final control policy. We compare the performance of these three greedy control policies and the MFPT policy using randomly generated probabilistic Boolean networks and give a preliminary example for intervening in a mammalian cell cycle network. Conclusion The newly proposed control policies have better performance in general than the MFPT policy and, as indicated by the results on the mammalian cell cycle network, they can potentially serve as future gene therapeutic intervention strategies.

  9. Intervention in gene regulatory networks via a stationary mean-first-passage-time control policy.

    Science.gov (United States)

    Vahedi, Golnaz; Faryabi, Babak; Chamberland, Jean-Francois; Datta, Aniruddha; Dougherty, Edward R

    2008-10-01

    A prime objective of modeling genetic regulatory networks is the identification of potential targets for therapeutic intervention. To date, optimal stochastic intervention has been studied in the context of probabilistic Boolean networks, with the control policy based on the transition probability matrix of the associated Markov chain and dynamic programming used to find optimal control policies. Dynamical programming algorithms are problematic owing to their high computational complexity. Two additional computationally burdensome issues that arise are the potential for controlling the network and identifying the best gene for intervention. This paper proposes an algorithm based on mean first-passage time that assigns a stationary control policy for each gene candidate. It serves as an approximation to an optimal control policy and, owing to its reduced computational complexity, can be used to predict the best control gene. Once the best control gene is identified, one can derive an optimal policy or simply utilize the approximate policy for this gene when the network size precludes a direct application of dynamic programming algorithms. A salient point is that the proposed algorithm can be model-free. It can be directly designed from time-course data without having to infer the transition probability matrix of the network.

  10. Fractal gene regulatory networks for robust locomotion control of modular robots

    DEFF Research Database (Denmark)

    Zahadat, Payam; Christensen, David Johan; Schultz, Ulrik Pagh

    2010-01-01

    Designing controllers for modular robots is difficult due to the distributed and dynamic nature of the robots. In this paper fractal gene regulatory networks are evolved to control modular robots in a distributed way. Experiments with different morphologies of modular robot are performed and the ......Designing controllers for modular robots is difficult due to the distributed and dynamic nature of the robots. In this paper fractal gene regulatory networks are evolved to control modular robots in a distributed way. Experiments with different morphologies of modular robot are performed...

  11. Fractal gene regulatory networks for robust locomotion control of modular robots

    DEFF Research Database (Denmark)

    Zahadat, Payam; Christensen, David Johan; Schultz, Ulrik Pagh

    2010-01-01

    Designing controllers for modular robots is difficult due to the distributed and dynamic nature of the robots. In this paper fractal gene regulatory networks are evolved to control modular robots in a distributed way. Experiments with different morphologies of modular robot are performed and the ...

  12. A CoD-based stationary control policy for intervening in large gene regulatory networks.

    Science.gov (United States)

    Ghaffari, Noushin; Ivanov, Ivan; Qian, Xiaoning; Dougherty, Edward R

    2011-10-18

    One of the most important goals of the mathematical modeling of gene regulatory networks is to alter their behavior toward desirable phenotypes. Therapeutic techniques are derived for intervention in terms of stationary control policies. In large networks, it becomes computationally burdensome to derive an optimal control policy. To overcome this problem, greedy intervention approaches based on the concept of the Mean First Passage Time or the steady-state probability mass of the network states were previously proposed. Another possible approach is to use reduction mappings to compress the network and develop control policies on its reduced version. However, such mappings lead to loss of information and require an induction step when designing the control policy for the original network. In this paper, we propose a novel solution, CoD-CP, for designing intervention policies for large Boolean networks. The new method utilizes the Coefficient of Determination (CoD) and the Steady-State Distribution (SSD) of the model. The main advantage of CoD-CP in comparison with the previously proposed methods is that it does not require any compression of the original model, and thus can be directly designed on large networks. The simulation studies on small synthetic networks shows that CoD-CP performs comparable to previously proposed greedy policies that were induced from the compressed versions of the networks. Furthermore, on a large 17-gene gastrointestinal cancer network, CoD-CP outperforms other two available greedy techniques, which is precisely the kind of case for which CoD-CP has been developed. Finally, our experiments show that CoD-CP is robust with respect to the attractor structure of the model. The newly proposed CoD-CP provides an attractive alternative for intervening large networks where other available greedy methods require size reduction on the network and an extra induction step before designing a control policy.

  13. Model checking optimal finite-horizon control for probabilistic gene regulatory networks.

    Science.gov (United States)

    Wei, Ou; Guo, Zonghao; Niu, Yun; Liao, Wenyuan

    2017-12-14

    Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random perturbations to express the impact of external stimuli, is considered to be more suitable for modeling small biological systems intervened by conditions from the outside. In this paper, we apply probabilistic model checking, a formal verification technique, to optimal control for a CS-PBNp that minimizes the expected cost over a finite control horizon. We first describe a procedure of modeling a CS-PBNp using the language provided by a widely used probabilistic model checker PRISM. We then analyze the reward-based temporal properties and the computation in probabilistic model checking; based on the analysis, we provide a method to formulate the optimal control problem as minimum reachability reward properties. Furthermore, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. We conduct experiments on two examples, an apoptosis network and a WNT5A network. Preliminary experiment results show the feasibility and effectiveness of our approach. The approach based on probabilistic model checking for optimal control avoids explicit computation of large-size state transition relations associated with PBNs. It enables a natural depiction of the dynamics of gene regulatory networks, and provides a canonical form to formulate optimal control problems using temporal properties that can be automated solved by leveraging the analysis power of underlying model checking engines. This work will be helpful for further utilization of the advances in formal verification techniques in system biology.

  14. Coordinations between gene modules control the operation of plant amino acid metabolic networks

    Directory of Open Access Journals (Sweden)

    Galili Gad

    2009-01-01

    Full Text Available Abstract Background Being sessile organisms, plants should adjust their metabolism to dynamic changes in their environment. Such adjustments need particular coordination in branched metabolic networks in which a given metabolite can be converted into multiple other metabolites via different enzymatic chains. In the present report, we developed a novel "Gene Coordination" bioinformatics approach and use it to elucidate adjustable transcriptional interactions of two branched amino acid metabolic networks in plants in response to environmental stresses, using publicly available microarray results. Results Using our "Gene Coordination" approach, we have identified in Arabidopsis plants two oppositely regulated groups of "highly coordinated" genes within the branched Asp-family network of Arabidopsis plants, which metabolizes the amino acids Lys, Met, Thr, Ile and Gly, as well as a single group of "highly coordinated" genes within the branched aromatic amino acid metabolic network, which metabolizes the amino acids Trp, Phe and Tyr. These genes possess highly coordinated adjustable negative and positive expression responses to various stress cues, which apparently regulate adjustable metabolic shifts between competing branches of these networks. We also provide evidence implying that these highly coordinated genes are central to impose intra- and inter-network interactions between the Asp-family and aromatic amino acid metabolic networks as well as differential system interactions with other growth promoting and stress-associated genome-wide genes. Conclusion Our novel Gene Coordination elucidates that branched amino acid metabolic networks in plants are regulated by specific groups of highly coordinated genes that possess adjustable intra-network, inter-network and genome-wide transcriptional interactions. We also hypothesize that such transcriptional interactions enable regulatory metabolic adjustments needed for adaptation to the stresses.

  15. HAND2 Target Gene Regulatory Networks Control Atrioventricular Canal and Cardiac Valve Development.

    Science.gov (United States)

    Laurent, Frédéric; Girdziusaite, Ausra; Gamart, Julie; Barozzi, Iros; Osterwalder, Marco; Akiyama, Jennifer A; Lincoln, Joy; Lopez-Rios, Javier; Visel, Axel; Zuniga, Aimée; Zeller, Rolf

    2017-05-23

    The HAND2 transcriptional regulator controls cardiac development, and we uncover additional essential functions in the endothelial to mesenchymal transition (EMT) underlying cardiac cushion development in the atrioventricular canal (AVC). In Hand2-deficient mouse embryos, the EMT underlying AVC cardiac cushion formation is disrupted, and we combined ChIP-seq of embryonic hearts with transcriptome analysis of wild-type and mutants AVCs to identify the functionally relevant HAND2 target genes. The HAND2 target gene regulatory network (GRN) includes most genes with known functions in EMT processes and AVC cardiac cushion formation. One of these is Snai1, an EMT master regulator whose expression is lost from Hand2-deficient AVCs. Re-expression of Snai1 in mutant AVC explants partially restores this EMT and mesenchymal cell migration. Furthermore, the HAND2-interacting enhancers in the Snai1 genomic landscape are active in embryonic hearts and other Snai1-expressing tissues. These results show that HAND2 directly regulates the molecular cascades initiating AVC cardiac valve development. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  16. A highly redundant gene network controls assembly of the outer spore wall in S. cerevisiae.

    Directory of Open Access Journals (Sweden)

    Coney Pei-Chen Lin

    Full Text Available The spore wall of Saccharomyces cerevisiae is a multilaminar extracellular structure that is formed de novo in the course of sporulation. The outer layers of the spore wall provide spores with resistance to a wide variety of environmental stresses. The major components of the outer spore wall are the polysaccharide chitosan and a polymer formed from the di-amino acid dityrosine. Though the synthesis and export pathways for dityrosine have been described, genes directly involved in dityrosine polymerization and incorporation into the spore wall have not been identified. A synthetic gene array approach to identify new genes involved in outer spore wall synthesis revealed an interconnected network influencing dityrosine assembly. This network is highly redundant both for genes of different activities that compensate for the loss of each other and for related genes of overlapping activity. Several of the genes in this network have paralogs in the yeast genome and deletion of entire paralog sets is sufficient to severely reduce dityrosine fluorescence. Solid-state NMR analysis of partially purified outer spore walls identifies a novel component in spore walls from wild type that is absent in some of the paralog set mutants. Localization of gene products identified in the screen reveals an unexpected role for lipid droplets in outer spore wall formation.

  17. Q&A: How do gene regulatory networks control environmental responses in plants?

    Science.gov (United States)

    Sun, Ying; Dinneny, José R

    2018-04-11

    A gene regulatory network (GRN) describes the hierarchical relationship between transcription factors, associated proteins, and their target genes. Studying GRNs allows us to understand how a plant's genotype and environment are integrated to regulate downstream physiological responses. Current efforts in plants have focused on defining the GRNs that regulate functions such as development and stress response and have been performed primarily in genetically tractable model plant species such as Arabidopsis thaliana. Future studies will likely focus on how GRNs function in non-model plants and change over evolutionary time to allow for adaptation to extreme environments. This broader understanding will inform efforts to engineer GRNs to create tailored crop traits.

  18. Sub-circuits of a gene regulatory network control a developmental epithelial-mesenchymal transition.

    Science.gov (United States)

    Saunders, Lindsay R; McClay, David R

    2014-04-01

    Epithelial-mesenchymal transition (EMT) is a fundamental cell state change that transforms epithelial to mesenchymal cells during embryonic development, adult tissue repair and cancer metastasis. EMT includes a complex series of intermediate cell state changes including remodeling of the basement membrane, apical constriction, epithelial de-adhesion, directed motility, loss of apical-basal polarity, and acquisition of mesenchymal adhesion and polarity. Transcriptional regulatory state changes must ultimately coordinate the timing and execution of these cell biological processes. A well-characterized gene regulatory network (GRN) in the sea urchin embryo was used to identify the transcription factors that control five distinct cell changes during EMT. Single transcription factors were perturbed and the consequences followed with in vivo time-lapse imaging or immunostaining assays. The data show that five different sub-circuits of the GRN control five distinct cell biological activities, each part of the complex EMT process. Thirteen transcription factors (TFs) expressed specifically in pre-EMT cells were required for EMT. Three TFs highest in the GRN specified and activated EMT (alx1, ets1, tbr) and the 10 TFs downstream of those (tel, erg, hex, tgif, snail, twist, foxn2/3, dri, foxb, foxo) were also required for EMT. No single TF functioned in all five sub-circuits, indicating that there is no EMT master regulator. Instead, the resulting sub-circuit topologies suggest EMT requires multiple simultaneous regulatory mechanisms: forward cascades, parallel inputs and positive-feedback lock downs. The interconnected and overlapping nature of the sub-circuits provides one explanation for the seamless orchestration by the embryo of cell state changes leading to successful EMT.

  19. A Predictive Coexpression Network Identifies Novel Genes Controlling the Seed-to-Seedling Phase Transition in Arabidopsis thaliana.

    Science.gov (United States)

    Silva, Anderson Tadeu; Ribone, Pamela A; Chan, Raquel L; Ligterink, Wilco; Hilhorst, Henk W M

    2016-04-01

    The transition from a quiescent dry seed to an actively growing photoautotrophic seedling is a complex and crucial trait for plant propagation. This study provides a detailed description of global gene expression in seven successive developmental stages of seedling establishment in Arabidopsis (Arabidopsis thaliana). Using the transcriptome signature from these developmental stages, we obtained a coexpression gene network that highlights interactions between known regulators of the seed-to-seedling transition and predicts the functions of uncharacterized genes in seedling establishment. The coexpressed gene data sets together with the transcriptional module indicate biological functions related to seedling establishment. Characterization of the homeodomain leucine zipper I transcription factor AtHB13, which is expressed during the seed-to-seedling transition, demonstrated that this gene regulates some of the network nodes and affects late seedling establishment. Knockout mutants for athb13 showed increased primary root length as compared with wild-type (Columbia-0) seedlings, suggesting that this transcription factor is a negative regulator of early root growth, possibly repressing cell division and/or cell elongation or the length of time that cells elongate. The signal transduction pathways present during the early phases of the seed-to-seedling transition anticipate the control of important events for a vigorous seedling, such as root growth. This study demonstrates that a gene coexpression network together with transcriptional modules can provide insights that are not derived from comparative transcript profiling alone. © 2016 American Society of Plant Biologists. All Rights Reserved.

  20. Current approaches to gene regulatory network modelling

    Directory of Open Access Journals (Sweden)

    Brazma Alvis

    2007-09-01

    Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

  1. Synthetic gene network restoring endogenous pituitary–thyroid feedback control in experimental Graves’ disease

    Science.gov (United States)

    Saxena, Pratik; Charpin-El Hamri, Ghislaine; Folcher, Marc; Zulewski, Henryk; Fussenegger, Martin

    2016-01-01

    Graves’ disease is an autoimmune disorder that causes hyperthyroidism because of autoantibodies that bind to the thyroid-stimulating hormone receptor (TSHR) on the thyroid gland, triggering thyroid hormone release. The physiological control of thyroid hormone homeostasis by the feedback loops involving the hypothalamus–pituitary–thyroid axis is disrupted by these stimulating autoantibodies. To reset the endogenous thyrotrophic feedback control, we designed a synthetic mammalian gene circuit that maintains thyroid hormone homeostasis by monitoring thyroid hormone levels and coordinating the expression of a thyroid-stimulating hormone receptor antagonist (TSHAntag), which competitively inhibits the binding of thyroid-stimulating hormone or the human autoantibody to TSHR. This synthetic control device consists of a synthetic thyroid-sensing receptor (TSR), a yeast Gal4 protein/human thyroid receptor-α fusion, which reversibly triggers expression of the TSHAntag gene from TSR-dependent promoters. In hyperthyroid mice, this synthetic circuit sensed pathological thyroid hormone levels and restored the thyrotrophic feedback control of the hypothalamus–pituitary–thyroid axis to euthyroid hormone levels. Therapeutic plug and play gene circuits that restore physiological feedback control in metabolic disorders foster advanced gene- and cell-based therapies. PMID:26787873

  2. (Im) Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis

    NARCIS (Netherlands)

    He, F.; Fromion, V.; Westerhoff, H.V.

    2013-01-01

    Background: Metabolic control analysis (MCA) and supply-demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply-demand theory has not yet considered gene-expression regulation explicitly whilst a

  3. An overview of the gene regulatory network controlling trichome development in the model plant, Arabidopsis

    Directory of Open Access Journals (Sweden)

    Sitakanta ePattanaik

    2014-06-01

    Full Text Available Trichomes are specialized epidermal cells located on aerial parts of plants and are associated with a wide array of biological processes. Trichomes protect plants from adverse conditions including UV light and herbivore attack and are also an important source of a number of phytochemicals. The simple unicellular trichomes of Arabidopsis serve as an excellent model to study molecular mechanism of cell differentiation and pattern formation in plants. The emerging picture suggests that the developmental process is controlled by a transcriptional network involving three major groups of transcription factors: the R2R3 MYB, basic helix-loop-helix (bHLH and WD40 repeat (WDR protein. These regulatory proteins form a trimeric activator complex that positively regulates trichome development. The single repeat R3 MYBs act as negative regulators of trichome development. They compete with the R2R3 MYBs to bind the bHLH factor and form a repressor complex. In addition to activator-repressor mechanism, a depletion mechanism may operate in parallel during trichome development. In this mechanism, the bHLH factor traps the WDR protein which results in depletion of WDR protein in neighboring cells. Consequently, the cells with high levels of bHLH and WDR proteins are developed into trichomes. A group of C2H2 zinc finger TFs has also been implicated in trichome development. Phytohormones, including gibberellins and jasmonic acid, play significant roles in this developmental process. Recently, microRNAs have been shown to be involved in trichome development. Furthermore, it has been demonstrated that the activities of the key regulatory proteins involved in trichome development are controlled by the 26S/ubiquitin proteasome system (UPS, highlighting the complexity of the regulatory network controlling this developmental process. To complement several excellent recent relevant reviews, this review focuses on the transcriptional network and hormonal interplay

  4. Introduction: Cancer Gene Networks.

    Science.gov (United States)

    Clarke, Robert

    2017-01-01

    Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary

  5. (Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis.

    Science.gov (United States)

    He, Fei; Fromion, Vincent; Westerhoff, Hans V

    2013-11-21

    Metabolic control analysis (MCA) and supply-demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply-demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect. This study integrates control engineering and classical MCA augmented with supply-demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the 'integral control' (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of 'integral control' should rarely be expected to lead to the 'perfect adaptation': although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily. A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems

  6. A gene regulatory network for apical organ neurogenesis and its spatial control in sea star embryos.

    Science.gov (United States)

    Cheatle Jarvela, Alys M; Yankura, Kristen A; Hinman, Veronica F

    2016-11-15

    How neural stem cells generate the correct number and type of differentiated neurons in appropriate places remains an important question. Although nervous systems are diverse across phyla, in many taxa the larva forms an anterior concentration of serotonergic neurons, or apical organ. The sea star embryo initially has a pan-neurogenic ectoderm, but the genetic mechanism that directs a subset of these cells to generate serotonergic neurons in a particular location is unresolved. We show that neurogenesis in sea star larvae begins with soxc-expressing multipotent progenitors. These give rise to restricted progenitors that express lhx2/9 soxc- and lhx2/9-expressing cells can undergo both asymmetric divisions, allowing for progression towards a particular neural fate, and symmetric proliferative divisions. We show that nested concentric domains of gene expression along the anterior-posterior (AP) axis, which are observed in a great diversity of metazoans, control neurogenesis in the sea star larva by promoting particular division modes and progression towards becoming a neuron. This work explains how spatial patterning in the ectoderm controls progression of neurogenesis in addition to providing spatial cues for neuron location. Modification to the sizes of these AP territories provides a simple mechanism to explain the diversity of neuron number among apical organs. © 2016. Published by The Company of Biologists Ltd.

  7. Reverse engineering transcriptional gene networks.

    Science.gov (United States)

    Belcastro, Vincenzo; di Bernardo, Diego

    2014-01-01

    The aim of this chapter is a step-by-step guide on how to infer gene networks from gene expression profiles. The definition of a gene network is given in Subheading 1, where the different types of networks are discussed. The chapter then guides the readers through a data-gathering process in order to build a compendium of gene expression profiles from a public repository. Gene expression profiles are then discretized and a statistical relationship between genes, called mutual information (MI), is computed. Gene pairs with insignificant MI scores are then discarded by applying one of the described pruning steps. The retained relationships are then used to build up a Boolean adjacency matrix used as input for a clustering algorithm to divide the network into modules (or communities). The gene network can then be used as a hypothesis generator for discovering gene function and analyzing gene signatures. Some case studies are presented, and an online web-tool called Netview is described.

  8. Intelligent networked teleoperation control

    CERN Document Server

    Li, Zhijun; Su, Chun-Yi

    2015-01-01

    This book describes a unified framework for networked teleoperation systems involving multiple research fields: networked control systems for linear and nonlinear forms, bilateral teleoperation, trilateral teleoperation, multilateral teleoperation and cooperative teleoperation. It closely examines networked control as a field at the intersection of systems & control and robotics and presents a number of experimental case studies on testbeds for robotic systems, including networked haptic devices, robotic network systems and sensor network systems. The concepts and results outlined are easy to understand, even for readers fairly new to the subject. As such, the book offers a valuable reference work for researchers and engineers in the fields of systems & control and robotics.

  9. Deconstructing the pluripotency gene regulatory network

    KAUST Repository

    Li, Mo

    2018-04-04

    Pluripotent stem cells can be isolated from embryos or derived by reprogramming. Pluripotency is stabilized by an interconnected network of pluripotency genes that cooperatively regulate gene expression. Here we describe the molecular principles of pluripotency gene function and highlight post-transcriptional controls, particularly those induced by RNA-binding proteins and alternative splicing, as an important regulatory layer of pluripotency. We also discuss heterogeneity in pluripotency regulation, alternative pluripotency states and future directions of pluripotent stem cell research.

  10. Stochastic simulation for the inference of transcriptional control network of yeast cyclins genes

    Czech Academy of Sciences Publication Activity Database

    Vohradský, Jiří

    2012-01-01

    Roč. 40, č. 15 (2012), s. 7096-7103 ISSN 0305-1048 R&D Projects: GA ČR GAP302/11/0229 Institutional support: RVO:61388971 Keywords : Cell cycle * gene expression * protein complexes Subject RIV: CE - Biochemistry Impact factor: 8.278, year: 2012

  11. Small RNA-Controlled Gene Regulatory Networks in Pseudomonas putida

    DEFF Research Database (Denmark)

    Bojanovic, Klara

    Bacteria commonly encounter stressful conditions during growth in their natural environments and in industrial biotechnology applications such as the biobased production of chemicals. As the coordinated regulation of gene expression is necessary to adapt to changing environments, bacteria have...... such as sRNAs and riboswitches. Further, the sRNAome during the growth of bacteria was investigatedand compared to the strain without Hfq protein. Hfq has a big impact on sRNAs and gene expression in P. putida, hence many Hfq-associated sRNAs and mRNAs were found. Together, the results reported here...... response of Pseudomonas putida KT2440 in different conditions via identification ofdifferentially expressed mRNAs and sRNAs. P. putida is a soil bacterium with a versatile metabolism and innate stress endurance traits, which makes it suitable as future cell factory for the production of valuable compounds...

  12. Gene and metabolite regulatory network analysis of early developing fruit tissues highlights new candidate genes for the control of tomato fruit composition and development.

    Science.gov (United States)

    Mounet, Fabien; Moing, Annick; Garcia, Virginie; Petit, Johann; Maucourt, Michael; Deborde, Catherine; Bernillon, Stéphane; Le Gall, Gwénaëlle; Colquhoun, Ian; Defernez, Marianne; Giraudel, Jean-Luc; Rolin, Dominique; Rothan, Christophe; Lemaire-Chamley, Martine

    2009-03-01

    Variations in early fruit development and composition may have major impacts on the taste and the overall quality of ripe tomato (Solanum lycopersicum) fruit. To get insights into the networks involved in these coordinated processes and to identify key regulatory genes, we explored the transcriptional and metabolic changes in expanding tomato fruit tissues using multivariate analysis and gene-metabolite correlation networks. To this end, we demonstrated and took advantage of the existence of clear structural and compositional differences between expanding mesocarp and locular tissue during fruit development (12-35 d postanthesis). Transcriptome and metabolome analyses were carried out with tomato microarrays and analytical methods including proton nuclear magnetic resonance and liquid chromatography-mass spectrometry, respectively. Pairwise comparisons of metabolite contents and gene expression profiles detected up to 37 direct gene-metabolite correlations involving regulatory genes (e.g. the correlations between glutamine, bZIP, and MYB transcription factors). Correlation network analyses revealed the existence of major hub genes correlated with 10 or more regulatory transcripts and embedded in a large regulatory network. This approach proved to be a valuable strategy for identifying specific subsets of genes implicated in key processes of fruit development and metabolism, which are therefore potential targets for genetic improvement of tomato fruit quality.

  13. Early evolutionary acquisition of stomatal control and development gene signalling networks.

    Science.gov (United States)

    Chater, Caspar; Gray, Julie E; Beerling, David J

    2013-10-01

    Fossil stomata of early vascular land plants date back over 418 million years and exhibit properties suggesting that they were operational, including differentially thickened guard cells and sub-stomatal chambers. Molecular studies on basal land plant groups (bryophytes and lycophytes) provide insight into the core genes involved in sensing and translating changes in the drought hormone abscisic acid (ABA), light and concentration of CO2 into changes in stomatal aperture. These studies indicate that early land plants probably possessed the genetic tool kits for stomata to actively respond to environmental/endogenous cues. With these ancestral molecular genetic tool kits in place, stomatal regulation of plant carbon and water relations may have became progressively more effective as hydraulic systems evolved in seed plant lineages. Gene expression and cross-species gene complementation studies suggest that the pathway regulating stomatal fate may also have been conserved across land plant evolution. This emerging area offers a fascinating glimpse into the potential genetic tool kits used by the earliest vascular land plants to build and operate the stomata preserved in the fossil record. Copyright © 2013 Elsevier Ltd. All rights reserved.

  14. A Gene Regulatory Network Cooperatively Controlled by Pdx1 and Sox9 Governs Lineage Allocation of Foregut Progenitor Cells

    DEFF Research Database (Denmark)

    Shih, Hung Ping; Seymour, Philip A; Patel, Nisha A

    2015-01-01

    9 as cooperative inducers of a gene regulatory network that distinguishes the pancreatic from the intestinal lineage. Genetic studies demonstrate dual and cooperative functions for Pdx1 and Sox9 in pancreatic lineage induction and repression of the intestinal lineage choice. Pdx1 and Sox9 bind...... to regulatory sequences near pancreatic and intestinal differentiation genes and jointly regulate their expression, revealing direct cooperative roles for Pdx1 and Sox9 in gene activation and repression. Our study identifies Pdx1 and Sox9 as important regulators of a transcription factor network that initiates...... pancreatic fate and sheds light on the gene regulatory circuitry that governs the development of distinct organs from multi-lineage-competent foregut progenitors....

  15. Attractor Transformation by Impulsive Control in Boolean Control Network

    Directory of Open Access Journals (Sweden)

    Bo Gao

    2013-01-01

    Full Text Available Boolean control networks have recently been attracting considerable interests as computational models for genetic regulatory networks. In this paper, we present an approach of impulsive control for attractor transitions in Boolean control networks based on the recent developed matrix semitensor product theory. The reachability of attractors is estimated, and the controller is also obtained. The general derivation proposed here is exemplified with a kind of gene model, which is the protein-nucleic acid interactions network, on numerical simulations.

  16. Virtualized Network Control (VNC)

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, Thomas [Univ. of Southern California, Los Angeles, CA (United States); Guok, Chin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Ghani, Nasir [Univ. of New Mexico, Albuquerque, NM (United States)

    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.

  17. Inferring gene regression networks with model trees

    Directory of Open Access Journals (Sweden)

    Aguilar-Ruiz Jesus S

    2010-10-01

    Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear

  18. Gene expression profiling in the stress control brain region hypothalamic paraventricular nucleus reveals a novel gene network including Amyloid beta Precursor Protein

    Directory of Open Access Journals (Sweden)

    Deussing Jan M

    2010-10-01

    Full Text Available Abstract Background The pivotal role of stress in the precipitation of psychiatric diseases such as depression is generally accepted. This study aims at the identification of genes that are directly or indirectly responding to stress. Inbred mouse strains that had been evidenced to differ in their stress response as well as in their response to antidepressant treatment were chosen for RNA profiling after stress exposure. Gene expression and regulation was determined by microarray analyses and further evaluated by bioinformatics tools including pathway and cluster analyses. Results Forced swimming as acute stressor was applied to C57BL/6J and DBA/2J mice and resulted in sets of regulated genes in the paraventricular nucleus of the hypothalamus (PVN, 4 h or 8 h after stress. Although the expression changes between the mouse strains were quite different, they unfolded in phases over time in both strains. Our search for connections between the regulated genes resulted in potential novel signalling pathways in stress. In particular, Guanine nucleotide binding protein, alpha inhibiting 2 (GNAi2 and Amyloid β (A4 precursor protein (APP were detected as stress-regulated genes, and together with other genes, seem to be integrated into stress-responsive pathways and gene networks in the PVN. Conclusions This search for stress-regulated genes in the PVN revealed its impact on interesting genes (GNAi2 and APP and a novel gene network. In particular the expression of APP in the PVN that is governing stress hormone balance, is of great interest. The reported neuroprotective role of this molecule in the CNS supports the idea that a short acute stress can elicit positive adaptational effects in the brain.

  19. Identification of key player genes in gene regulatory networks.

    Science.gov (United States)

    Nazarieh, Maryam; Wiese, Andreas; Will, Thorsten; Hamed, Mohamed; Helms, Volkhard

    2016-09-06

    Identifying the gene regulatory networks governing the workings and identity of cells is one of the main challenges in understanding processes such as cellular differentiation, reprogramming or cancerogenesis. One particular challenge is to identify the main drivers and master regulatory genes that control such cell fate transitions. In this work, we reformulate this problem as the optimization problems of computing a Minimum Dominating Set and a Minimum Connected Dominating Set for directed graphs. Both MDS and MCDS are applied to the well-studied gene regulatory networks of the model organisms E. coli and S. cerevisiae and to a pluripotency network for mouse embryonic stem cells. The results show that MCDS can capture most of the known key player genes identified so far in the model organisms. Moreover, this method suggests an additional small set of transcription factors as novel key players for governing the cell-specific gene regulatory network which can also be investigated with regard to diseases. To this aim, we investigated the ability of MCDS to define key drivers in breast cancer. The method identified many known drug targets as members of the MDS and MCDS. This paper proposes a new method to identify key player genes in gene regulatory networks. The Java implementation of the heuristic algorithm explained in this paper is available as a Cytoscape plugin at http://apps.cytoscape.org/apps/mcds . The SageMath programs for solving integer linear programming formulations used in the paper are available at https://github.com/maryamNazarieh/KeyRegulatoryGenes and as supplementary material.

  20. The evolution of controlled multitasked gene networks: the role of introns and other noncoding RNAs in the development of complex organisms.

    Science.gov (United States)

    Mattick, J S; Gagen, M J

    2001-09-01

    Eukaryotic phenotypic diversity arises from multitasking of a core proteome of limited size. Multitasking is routine in computers, as well as in other sophisticated information systems, and requires multiple inputs and outputs to control and integrate network activity. Higher eukaryotes have a mosaic gene structure with a dual output, mRNA (protein-coding) sequences and introns, which are released from the pre-mRNA by posttranscriptional processing. Introns have been enormously successful as a class of sequences and comprise up to 95% of the primary transcripts of protein-coding genes in mammals. In addition, many other transcripts (perhaps more than half) do not encode proteins at all, but appear both to be developmentally regulated and to have genetic function. We suggest that these RNAs (eRNAs) have evolved to function as endogenous network control molecules which enable direct gene-gene communication and multitasking of eukaryotic genomes. Analysis of a range of complex genetic phenomena in which RNA is involved or implicated, including co-suppression, transgene silencing, RNA interference, imprinting, methylation, and transvection, suggests that a higher-order regulatory system based on RNA signals operates in the higher eukaryotes and involves chromatin remodeling as well as other RNA-DNA, RNA-RNA, and RNA-protein interactions. The evolution of densely connected gene networks would be expected to result in a relatively stable core proteome due to the multiple reuse of components, implying that cellular differentiation and phenotypic variation in the higher eukaryotes results primarily from variation in the control architecture. Thus, network integration and multitasking using trans-acting RNA molecules produced in parallel with protein-coding sequences may underpin both the evolution of developmentally sophisticated multicellular organisms and the rapid expansion of phenotypic complexity into uncontested environments such as those initiated in the Cambrian

  1. The ETS transcription factors ELK1 and GABPA regulate different gene networks to control MCF10A breast epithelial cell migration.

    Science.gov (United States)

    Odrowaz, Zaneta; Sharrocks, Andrew D

    2012-01-01

    Members of the ETS transcription factor family often target the same binding regions and hence have the potential to regulate the same genes and downstream biological processes. However, individual family members also preferentially bind to other genomic regions, thus providing the potential for controlling distinct transcriptional programmes and generating specific biological effects. The ETS transcription factor ELK1 controls cell migration in breast epithelial cells through targeting a cohort of genes, independently from another family member GABPA, and therefore achieves biological specificity. Here, we demonstrate that GABPA also controls cell migration in breast epithelial cells. However, GABPA controls the expression of a different network of target genes to ELK1. Both direct and indirect target genes for GABPA are identified and amongst the direct targets we confirm the importance of RAC1 and KIF20A for cell migration. Therefore, although ELK1 and GABPA ultimately control the same biological process, they do so by regulating different cohorts of target genes associated with cytoskeletal functions and cell migration control.

  2. Transcriptomic analysis identifies gene networks regulated by estrogen receptor α (ERα) and ERβ that control distinct effects of different botanical estrogens

    Science.gov (United States)

    Gong, Ping; Madak-Erdogan, Zeynep; Li, Jilong; Cheng, Jianlin; Greenlief, C. Michael; Helferich, William G.; Katzenellenbogen, John A.

    2014-01-01

    The estrogen receptors (ERs) ERα and ERβ mediate the actions of endogenous estrogens as well as those of botanical estrogens (BEs) present in plants. BEs are ingested in the diet and also widely consumed by postmenopausal women as dietary supplements, often as a substitute for the loss of endogenous estrogens at menopause. However, their activities and efficacies, and similarities and differences in gene expression programs with respect to endogenous estrogens such as estradiol (E2) are not fully understood. Because gene expression patterns underlie and control the broad physiological effects of estrogens, we have investigated and compared the gene networks that are regulated by different BEs and by E2. Our aim was to determine if the soy and licorice BEs control similar or different gene expression programs and to compare their gene regulations with that of E2. Gene expression was examined by RNA-Seq in human breast cancer (MCF7) cells treated with control vehicle, BE or E2. These cells contained three different complements of ERs, ERα only, ERα+ERβ, or ERβ only, reflecting the different ratios of these two receptors in different human breast cancers and in different estrogen target cells. Using principal component, hierarchical clustering, and gene ontology and interactome analyses, we found that BEs regulated many of the same genes as did E2. The genes regulated by each BE, however, were somewhat different from one another, with some genes being regulated uniquely by each compound. The overlap with E2 in regulated genes was greatest for the soy isoflavones genistein and S-equol, while the greatest difference from E2 in gene expression pattern was observed for the licorice root BE liquiritigenin. The gene expression pattern of each ligand depended greatly on the cell background of ERs present. Despite similarities in gene expression pattern with E2, the BEs were generally less stimulatory of genes promoting proliferation and were more pro-apoptotic in their

  3. A gene co-expression network predicts functional genes controlling the re-establishment of desiccation tolerance in germinated Arabidopsis thaliana seeds.

    Science.gov (United States)

    Costa, Maria Cecília D; Righetti, Karima; Nijveen, Harm; Yazdanpanah, Farzaneh; Ligterink, Wilco; Buitink, Julia; Hilhorst, Henk W M

    2015-08-01

    During re-establishment of desiccation tolerance (DT), early events promote initial protection and growth arrest, while late events promote stress adaptation and contribute to survival in the dry state. Mature seeds of Arabidopsis thaliana are desiccation tolerant, but they lose desiccation tolerance (DT) while progressing to germination. Yet, there is a small developmental window during which DT can be rescued by treatment with abscisic acid (ABA). To gain temporal resolution and identify relevant genes in this process, data from a time series of microarrays were used to build a gene co-expression network. The network has two regions, namely early response (ER) and late response (LR). Genes in the ER region are related to biological processes, such as dormancy, acquisition of DT and drought, amplification of signals, growth arrest and induction of protection mechanisms (such as LEA proteins). Genes in the LR region lead to inhibition of photosynthesis and primary metabolism, promote adaptation to stress conditions and contribute to seed longevity. Phenotyping of 12 hubs in relation to re-establishment of DT with T-DNA insertion lines indicated a significant increase in the ability to re-establish DT compared with the wild-type in the lines cbsx4, at3g53040 and at4g25580, suggesting the operation of redundant and compensatory mechanisms. Moreover, we show that re-establishment of DT by polyethylene glycol and ABA occurs through partially overlapping mechanisms. Our data confirm that co-expression network analysis is a valid approach to examine data from time series of transcriptome analysis, as it provides promising insights into biologically relevant relations that help to generate new information about the roles of certain genes for DT.

  4. A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks.

    Science.gov (United States)

    Petrovskaya, Olga V; Petrovskiy, Evgeny D; Lavrik, Inna N; Ivanisenko, Vladimir A

    2017-04-01

    Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

  5. Launch Control Network Engineer

    Science.gov (United States)

    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.

  6. PBMC transcriptome profiles identifies potential candidate genes and functional networks controlling the innate and the adaptive immune response to PRRSV vaccine in Pietrain pig

    Science.gov (United States)

    Islam, Md. Aminul; Große-Brinkhaus, Christine; Pröll, Maren Julia; Uddin, Muhammad Jasim; Aqter Rony, Sharmin; Tesfaye, Dawit; Tholen, Ernst; Hoelker, Michael; Schellander, Karl; Neuhoff, Christiane

    2017-01-01

    The porcine reproductive and respiratory syndrome (PRRS) is a devastating viral disease affecting swine production, health and welfare throughout the world. A synergistic action of the innate and the adaptive immune system of the host is essential for mounting a durable protective immunity through vaccination. Therefore, the current study aimed to investigate the transcriptome profiles of peripheral blood mononuclear cells (PBMCs) to characterize the innate and the adaptive immune response to PRRS Virus (PRRSV) vaccination in Pietrain pigs. The Affymetrix gene chip porcine gene 1.0 ST array was used for the transcriptome profiling of PBMCs collected at immediately before (D0), at one (D1) and 28 days (D28) post PRRSV vaccination with three biological replications. With FDR activation, cytokine activity and inflammatory response were enriched during the innate immunity; cytolysis, T cell mediated cytotoxicity, immunoglobulin production were enriched during adaptive immunity to PRRSV vaccination. Significant enrichment of cytokine-cytokine receptor interaction, signaling by interleukins, signaling by the B cell receptor (BCR), viral mRNA translation, IFN-gamma pathway and AP-1 transcription factor network pathways were indicating the involvement of altered genes in the antiviral defense. Network analysis revealed that four network modules were functionally involved with the transcriptional network of innate immunity, and five modules were linked to adaptive immunity in PBMCs. The innate immune transcriptional network was found to be regulated by LCK, STAT3, ATP5B, UBB and RSP17. While TGFß1, IL7R, RAD21, SP1 and GZMB are likely to be predictive for the adaptive immune transcriptional response to PRRSV vaccine in PBMCs. Results of the current immunogenomics study advances our understanding of PRRS in term of host-vaccine interaction, and thereby contribute to design a rationale for disease control strategy. PMID:28278192

  7. Controlling gene networks and cell fate with precision-targeted DNA-binding proteins and small-molecule-based genome readers.

    Science.gov (United States)

    Eguchi, Asuka; Lee, Garrett O; Wan, Fang; Erwin, Graham S; Ansari, Aseem Z

    2014-09-15

    Transcription factors control the fate of a cell by regulating the expression of genes and regulatory networks. Recent successes in inducing pluripotency in terminally differentiated cells as well as directing differentiation with natural transcription factors has lent credence to the efforts that aim to direct cell fate with rationally designed transcription factors. Because DNA-binding factors are modular in design, they can be engineered to target specific genomic sequences and perform pre-programmed regulatory functions upon binding. Such precision-tailored factors can serve as molecular tools to reprogramme or differentiate cells in a targeted manner. Using different types of engineered DNA binders, both regulatory transcriptional controls of gene networks, as well as permanent alteration of genomic content, can be implemented to study cell fate decisions. In the present review, we describe the current state of the art in artificial transcription factor design and the exciting prospect of employing artificial DNA-binding factors to manipulate the transcriptional networks as well as epigenetic landscapes that govern cell fate.

  8. Harnessing gene expression networks to prioritize candidate epileptic encephalopathy genes.

    Science.gov (United States)

    Oliver, Karen L; Lukic, Vesna; Thorne, Natalie P; Berkovic, Samuel F; Scheffer, Ingrid E; Bahlo, Melanie

    2014-01-01

    We apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. We utilized developing brain and adult brain gene expression data from the Allen Human Brain Atlas (AHBA) and compared this to data from Celsius: a large, heterogeneous gene expression data warehouse. We show replicable prioritization results using these three independent gene expression resources, two of which are brain-specific, with small sample size, and the third derived from a heterogeneous collection of tissues with large sample size. Of the nineteen genes that we predicted with the highest likelihood to be true Epileptic Encephalopathy genes, two (GNAO1 and GRIN2B) have recently been independently reported and confirmed. We compare our results to those produced by an established in silico prioritization approach called Endeavour, and finally present gene expression networks for the known and candidate Epileptic Encephalopathy genes. This highlights sub-networks of gene expression, particularly in the network derived from the adult AHBA gene expression dataset. These networks give clues to the likely biological interactions between Epileptic Encephalopathy genes, potentially highlighting underlying mechanisms and avenues for therapeutic targets.

  9. Neural networks for aircraft control

    Science.gov (United States)

    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.

  10. Evolution of evolvability in gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Anton Crombach

    Full Text Available Gene regulatory networks are perhaps the most important organizational level in the cell where signals from the cell state and the outside environment are integrated in terms of activation and inhibition of genes. For the last decade, the study of such networks has been fueled by large-scale experiments and renewed attention from the theoretical field. Different models have been proposed to, for instance, investigate expression dynamics, explain the network topology we observe in bacteria and yeast, and for the analysis of evolvability and robustness of such networks. Yet how these gene regulatory networks evolve and become evolvable remains an open question. An individual-oriented evolutionary model is used to shed light on this matter. Each individual has a genome from which its gene regulatory network is derived. Mutations, such as gene duplications and deletions, alter the genome, while the resulting network determines the gene expression pattern and hence fitness. With this protocol we let a population of individuals evolve under Darwinian selection in an environment that changes through time. Our work demonstrates that long-term evolution of complex gene regulatory networks in a changing environment can lead to a striking increase in the efficiency of generating beneficial mutations. We show that the population evolves towards genotype-phenotype mappings that allow for an orchestrated network-wide change in the gene expression pattern, requiring only a few specific gene indels. The genes involved are hubs of the networks, or directly influencing the hubs. Moreover, throughout the evolutionary trajectory the networks maintain their mutational robustness. In other words, evolution in an alternating environment leads to a network that is sensitive to a small class of beneficial mutations, while the majority of mutations remain neutral: an example of evolution of evolvability.

  11. The transfer and transformation of collective network information in gene-matched networks.

    Science.gov (United States)

    Kitsukawa, Takashi; Yagi, Takeshi

    2015-10-09

    Networks, such as the human society network, social and professional networks, and biological system networks, contain vast amounts of information. Information signals in networks are distributed over nodes and transmitted through intricately wired links, making the transfer and transformation of such information difficult to follow. Here we introduce a novel method for describing network information and its transfer using a model network, the Gene-matched network (GMN), in which nodes (neurons) possess attributes (genes). In the GMN, nodes are connected according to their expression of common genes. Because neurons have multiple genes, the GMN is cluster-rich. We show that, in the GMN, information transfer and transformation were controlled systematically, according to the activity level of the network. Furthermore, information transfer and transformation could be traced numerically with a vector using genes expressed in the activated neurons, the active-gene array, which was used to assess the relative activity among overlapping neuronal groups. Interestingly, this coding style closely resembles the cell-assembly neural coding theory. The method introduced here could be applied to many real-world networks, since many systems, including human society and various biological systems, can be represented as a network of this type.

  12. Controllability of Surface Water Networks

    Science.gov (United States)

    Riasi, M. Sadegh; Yeghiazarian, Lilit

    2017-12-01

    To sustainably manage water resources, we must understand how to control complex networked systems. In this paper, we study surface water networks from the perspective of structural controllability, a concept that integrates classical control theory with graph-theoretic formalism. We present structural controllability theory and compute four metrics: full and target controllability, control centrality and control profile (FTCP) that collectively determine the structural boundaries of the system's control space. We use these metrics to answer the following questions: How does the structure of a surface water network affect its controllability? How to efficiently control a preselected subset of the network? Which nodes have the highest control power? What types of topological structures dominate controllability? Finally, we demonstrate the structural controllability theory in the analysis of a wide range of surface water networks, such as tributary, deltaic, and braided river systems.

  13. Broadband accelerator control network

    International Nuclear Information System (INIS)

    Skelly, J.; Clifford, T.; Frankel, R.

    1983-01-01

    A broadband data communications network has been implemented at BNL for control of the Alternating Gradient Synchrotron (AG) proton accelerator, using commercial CATV hardware, dual coaxial cables as the communications medium, and spanning 2.0 km. A 4 MHz bandwidth Digital Control channel using CSMA-CA protocol is provided for digital data transmission, with 8 access nodes available over the length of the RELWAY. Each node consists of an rf modem and a microprocessor-based store-and-forward message handler which interfaces the RELWAY to a branch line implemented in GPIB. A gateway to the RELWAY control channel for the (preexisting) AGS Computerized Accelerator Operating system has been constructed using an LSI-11/23 microprocessor as a device in a GPIB branch line. A multilayer communications protocol has been defined for the Digital Control Channel, based on the ISO Open Systems Interconnect layered model, and a RELWAY Device Language defined as the required universal language for device control on this channel

  14. Neural Networks for Optimal Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1995-01-01

    Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....

  15. Inferring gene networks from discrete expression data

    KAUST Repository

    Zhang, L.

    2013-07-18

    The modeling of gene networks from transcriptional expression data is an important tool in biomedical research to reveal signaling pathways and to identify treatment targets. Current gene network modeling is primarily based on the use of Gaussian graphical models applied to continuous data, which give a closedformmarginal likelihood. In this paper,we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which generate counts of mRNAtranscripts in cell samples.We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution.We restrict the gene network structures to decomposable graphs and derive the graphs by selecting the covariance matrix of the Gaussian distribution with the hyper-inverse Wishart priors. Furthermore, we incorporate prior network models based on gene ontology information, which avails existing biological information on the genes of interest. We conduct simulation studies to examine the performance of our discrete graphical model and apply the method to two real datasets for gene network inference. © The Author 2013. Published by Oxford University Press. All rights reserved.

  16. Asynchronous control for networked systems

    CERN Document Server

    Rubio, Francisco; Bencomo, Sebastián

    2015-01-01

    This book sheds light on networked control systems; it describes different techniques for asynchronous control, moving away from the periodic actions of classical control, replacing them with state-based decisions and reducing the frequency with which communication between subsystems is required. The text focuses specially on event-based control. Split into two parts, Asynchronous Control for Networked Systems begins by addressing the problems of single-loop networked control systems, laying out various solutions which include two alternative model-based control schemes (anticipatory and predictive) and the use of H2/H∞ robust control to deal with network delays and packet losses. Results on self-triggering and send-on-delta sampling are presented to reduce the need for feedback in the loop. In Part II, the authors present solutions for distributed estimation and control. They deal first with reliable networks and then extend their results to scenarios in which delays and packet losses may occur. The novel ...

  17. Controllability of structural brain networks

    Science.gov (United States)

    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.

  18. Utilizing evolutionary information and gene expression data for estimating gene networks with bayesian network models.

    Science.gov (United States)

    Tamada, Yoshinori; Bannai, Hideo; Imoto, Seiya; Katayama, Toshiaki; Kanehisa, Minoru; Miyano, Satoru

    2005-12-01

    Since microarray gene expression data do not contain sufficient information for estimating accurate gene networks, other biological information has been considered to improve the estimated networks. Recent studies have revealed that highly conserved proteins that exhibit similar expression patterns in different organisms, have almost the same function in each organism. Such conserved proteins are also known to play similar roles in terms of the regulation of genes. Therefore, this evolutionary information can be used to refine regulatory relationships among genes, which are estimated from gene expression data. We propose a statistical method for estimating gene networks from gene expression data by utilizing evolutionarily conserved relationships between genes. Our method simultaneously estimates two gene networks of two distinct organisms, with a Bayesian network model utilizing the evolutionary information so that gene expression data of one organism helps to estimate the gene network of the other. We show the effectiveness of the method through the analysis on Saccharomyces cerevisiae and Homo sapiens cell cycle gene expression data. Our method was successful in estimating gene networks that capture many known relationships as well as several unknown relationships which are likely to be novel. Supplementary information is available at http://bonsai.ims.u-tokyo.ac.jp/~tamada/bayesnet/.

  19. Optimization-Based Approaches to Control of Probabilistic Boolean Networks

    Directory of Open Access Journals (Sweden)

    Koichi Kobayashi

    2017-02-01

    Full Text Available Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs, which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.

  20. Genes2FANs: connecting genes through functional association networks

    Science.gov (United States)

    2012-01-01

    Background Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent. Results Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user’s PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories. Conclusions Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in

  1. Resistance Genes in Global Crop Breeding Networks.

    Science.gov (United States)

    Garrett, K A; Andersen, K F; Asche, F; Bowden, R L; Forbes, G A; Kulakow, P A; Zhou, B

    2017-10-01

    Resistance genes are a major tool for managing crop diseases. The networks of crop breeders who exchange resistance genes and deploy them in varieties help to determine the global landscape of resistance and epidemics, an important system for maintaining food security. These networks function as a complex adaptive system, with associated strengths and vulnerabilities, and implications for policies to support resistance gene deployment strategies. Extensions of epidemic network analysis can be used to evaluate the multilayer agricultural networks that support and influence crop breeding networks. Here, we evaluate the general structure of crop breeding networks for cassava, potato, rice, and wheat. All four are clustered due to phytosanitary and intellectual property regulations, and linked through CGIAR hubs. Cassava networks primarily include public breeding groups, whereas others are more mixed. These systems must adapt to global change in climate and land use, the emergence of new diseases, and disruptive breeding technologies. Research priorities to support policy include how best to maintain both diversity and redundancy in the roles played by individual crop breeding groups (public versus private and global versus local), and how best to manage connectivity to optimize resistance gene deployment while avoiding risks to the useful life of resistance genes. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

  2. Neural Networks For Robot Control

    National Research Council Canada - National Science Library

    Nasr, Chaiban

    2001-01-01

    ...; and optimization of the architecture; (b) Application of artificial neural networks in controlling closed-loop 2D planar robot arm and comparison with the use of proportional-integral-differential (PID) controllers...

  3. Positive train control shared network.

    Science.gov (United States)

    2015-05-01

    The Interoperable Train Control (ITC) Positive : Train Control (PTC) Shared Network (IPSN) : project investigated anticipated industry benefits : and the level of support for the development of : a hosted technological platform for PTC : messaging ac...

  4. Evolving chromosomes and gene regulatory networks

    Indian Academy of Sciences (India)

    Aswin

    Gene expression level unilateral. Other genes epistatic. Collateral damage. Page 25. ok.. is there a phenotype? $ % #. Page 26. Can the regulatory network of. E. coli lacking the xenogene silencing system evolve towards greater fitness? Page 27. Many mutations emerge in a dynamic genome. Inactivation of the global ...

  5. Crowdsourcing the nodulation gene network discovery environment.

    Science.gov (United States)

    Li, Yupeng; Jackson, Scott A

    2016-05-26

    The Legumes (Fabaceae) are an economically and ecologically important group of plant species with the conspicuous capacity for symbiotic nitrogen fixation in root nodules, specialized plant organs containing symbiotic microbes. With the aim of understanding the underlying molecular mechanisms leading to nodulation, many efforts are underway to identify nodulation-related genes and determine how these genes interact with each other. In order to accurately and efficiently reconstruct nodulation gene network, a crowdsourcing platform, CrowdNodNet, was created. The platform implements the jQuery and vis.js JavaScript libraries, so that users are able to interactively visualize and edit the gene network, and easily access the information about the network, e.g. gene lists, gene interactions and gene functional annotations. In addition, all the gene information is written on MediaWiki pages, enabling users to edit and contribute to the network curation. Utilizing the continuously updated, collaboratively written, and community-reviewed Wikipedia model, the platform could, in a short time, become a comprehensive knowledge base of nodulation-related pathways. The platform could also be used for other biological processes, and thus has great potential for integrating and advancing our understanding of the functional genomics and systems biology of any process for any species. The platform is available at http://crowd.bioops.info/ , and the source code can be openly accessed at https://github.com/bioops/crowdnodnet under MIT License.

  6. The nuclear receptor gene nhr-25 plays multiple roles in the Caenorhabditis elegans heterochronic gene network to control the larva-to-adult transition

    Czech Academy of Sciences Publication Activity Database

    Hada, K.; Asahina, Masako; Hasegawa, H.; Kanaho, Y.; Slack, F. J.; Niwa, R.

    2010-01-01

    Roč. 344, č. 2 (2010), s. 1100-1109 ISSN 0012-1606 R&D Projects: GA ČR(CZ) GA204/07/0948; GA ČR(CZ) GD204/09/H058 Institutional research plan: CEZ:AV0Z60220518 Keywords : apl-1 * Caenorhabditis elegans * heterochronic gene * heterochronic gene * let-7 * nuclear receptor * nhr-25 Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 4.094, year: 2010

  7. Network Completion for Static Gene Expression Data

    Directory of Open Access Journals (Sweden)

    Natsu Nakajima

    2014-01-01

    Full Text Available We tackle the problem of completing and inferring genetic networks under stationary conditions from static data, where network completion is to make the minimum amount of modifications to an initial network so that the completed network is most consistent with the expression data in which addition of edges and deletion of edges are basic modification operations. For this problem, we present a new method for network completion using dynamic programming and least-squares fitting. This method can find an optimal solution in polynomial time if the maximum indegree of the network is bounded by a constant. We evaluate the effectiveness of our method through computational experiments using synthetic data. Furthermore, we demonstrate that our proposed method can distinguish the differences between two types of genetic networks under stationary conditions from lung cancer and normal gene expression data.

  8. HSUPA Transport Network Congestion Control

    Directory of Open Access Journals (Sweden)

    Szilveszter Nádas

    2009-01-01

    Full Text Available The introduction of High Speed Uplink Packet Access (HSUPA greatly improves achievable uplink bitrate but it presents new challenges to be solved in the WCDMA radio access network. In the transport network, bandwidth reservation for HSUPA is not efficient and TCP cannot efficiently resolve congestion because of lower layer retransmissions. This paper proposes an HSUPA transport network flow control algorithm that handles congestion situations efficiently and supports Quality of Service differentiation. In the Radio Network Controller (RNC, transport network congestion is detected. Relying on the standardized control frame, the RNC notifies the Node B about transport network congestion. In case of transport network congestion, the Node B part of the HSUPA flow control instructs the air interface scheduler to reduce the bitrate of the flow to eliminate congestion. The performance analysis concentrates on transport network limited scenarios. It is shown that TCP cannot provide efficient congestion control. The proposed algorithm can achieve high end-user perceived throughput, while maintaining low delay, loss, and good fairness in the transport network.

  9. Admission Control in IMS Networks

    Directory of Open Access Journals (Sweden)

    Erik Chromy

    2013-01-01

    Full Text Available In our paper there is an emphasis on simulations of admission control methods in MATLAB environment. The main task of admission control method is to make a decision if the connection requiring network access should be accepted to the network or the access should be rejected. If the connection is accepted to the network, the admission control has to ensure that Quality of Service of this connection will be satisfied, as well as Quality of Service of all other existing connections. We have observed several Measurement based admission control algorithms and the result is the identification of the suitable algorithm which can estimate the required bandwidth.

  10. Mutated Genes in Schizophrenia Map to Brain Networks

    Science.gov (United States)

    ... Matters NIH Research Matters August 12, 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks ... have a high number of spontaneous mutations in genes that form a network in the front region ...

  11. Delays and networked control systems

    CERN Document Server

    Hetel, Laurentiu; Daafouz, Jamal; Johansson, Karl

    2016-01-01

    This edited monograph includes state-of-the-art contributions on continuous time dynamical networks with delays. The book is divided into four parts. The first part presents tools and methods for the analysis of time-delay systems with a particular attention on control problems of large scale or infinite-dimensional systems with delays. The second part of the book is dedicated to the use of time-delay models for the analysis and design of Networked Control Systems. The third part of the book focuses on the analysis and design of systems with asynchronous sampling intervals which occur in Networked Control Systems. The last part of the book exposes several contributions dealing with the design of cooperative control and observation laws for networked control systems. The target audience primarily comprises researchers and experts in the field of control theory, but the book may also be beneficial for graduate students. .

  12. Understanding control of network spreading from network controllability

    Science.gov (United States)

    Sun, Peng Gang; Ma, Xiaoke

    2017-09-01

    How to control the spread of an epidemic or information is a great challenge for us. A dynamic network-based system’s structural controllability provides a new way to control spreading with the minimum input of external signals, and the dynamic system is controllable if the signals can drive it from any initial state to any desired final state in finite time. Therefore, we are motivated to develop a new framework by introducing spreading networks (SNs) to describe the spreading pathways from a global view, and we try to understand the control of the spreading by the structural controllability of the SNs. The SNs are transformed from original networks, in which each node is considered as a single spreading origin. The weights of directed links pointing at its direct contacts in the SNs denote the spreading abilities, which can be determined by a new probability function. Furthermore, we also investigate the impact of the dynamics of network structures on the framework. The results show that sparse homogeneous networks with a higher transmission probability tend to trigger a larger scale of diffusion, which is easier to control. We can also see that an epidemic or information is inclined to diffuse easily on the networks with strong community strengths and heterogeneous community sizes. From the structural controllability of the SNs, we observe that driver nodes for the control of the spread tend not to be the nodes located within the core of original networks or those with high-degree. In addition, the scale of diffusion, the number of driver nodes and positions of nodes are highly associated with the degree distribution of the original networks.

  13. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

  14. Distinct and overlapping gene regulatory networks in BMP- and HDAC-controlled cell fate determination in the embryonic forebrain

    Directory of Open Access Journals (Sweden)

    Scholl Catharina

    2012-07-01

    Full Text Available Abstract Background Both bone morphogenetic proteins (BMPs and histone deacetylases (HDACs have previously been established to play a role in the development of the three major cell types of the central nervous system: neurons, astrocytes, and oligodendrocytes. We have previously established a connection between these two protein families, showing that HDACs suppress BMP-promoted astrogliogenesis in the embryonic striatum. Since HDACs act in the nucleus to effect changes in transcription, an unbiased analysis of their transcriptional targets could shed light on their downstream effects on BMP-signaling. Results Using neurospheres from the embryonic striatum as an in vitro system to analyze this phenomenon, we have performed microarray expression profiling on BMP2- and TSA-treated cultures, followed by validation of the findings with quantitative RT-PCR and protein analysis. In BMP-treated cultures we first observed an upregulation of genes involved in cell-cell communication and developmental processes such as members of BMP and canonical Wnt signaling pathways. In contrast, in TSA-treated cultures we first observed an upregulation of genes involved in chromatin modification and transcription. Interestingly, we could not record direct changes in the protein levels of canonical members of BMP2 signaling, but we did observe an upregulation of both the transcription factor STAT3 and its active isoform phospho-STAT3 at the protein level. Conclusions STAT3 and SMAD1/5/8 interact synergistically to promote astrogliogenesis, and thus we show for the first time that HDACs act to suppress BMP-promoted astrogliogenesis by suppression of the crucial partner STAT3.

  15. Fusion genes in leukemia: an emerging network.

    Science.gov (United States)

    Bohlander, S K

    2000-01-01

    The molecular analysis of recurring chromosome rearrangements, especially of translocations and inversions, has provided us with valuable insight into the pathogenesis of hematological malignancies. Many translocations result in the fusion of genes located at the translocation breakpoints. In recent years we have witnessed a rapid rise in the number of chromosome translocations in leukemias being characterized at the molecular level. However, the number of genes being newly identified as translocation fusion genes has not risen at the same pace. This is due to the fact that several genes are involved in more than one translocation forming fusion genes with a number of other partner genes. Not only does one find star-shaped topologies, with one gene forming fusions with several others (e.g. ETV6/PDGFRB, ETV6/JAK2, ETV6/ABL etc.), but also networks connecting several genes with more than one fusion partner (e.g. ETV6/RUNX1 (AML1), RUNX1/CBFA2T1 (ETO), ETV6/EVI1, RUNX1/EVI1, ETV6/ABL, BCR/ABL). The emergence of such networks with the "recycling" of genes in new fusion combinations suggests that there is a rather limited number of genes which can be altered to cause leukemia. Copyright 2001 S. Karger AG, Basel

  16. Secure network for beamline control

    Science.gov (United States)

    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.

  17. Secure network for beamline control

    International Nuclear Information System (INIS)

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

    2001-01-01

    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

  18. Mutational robustness of gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Aalt D J van Dijk

    Full Text Available Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor-target gene interactions but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive. In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.

  19. Inferring Phylogenetic Networks from Gene Order Data

    Directory of Open Access Journals (Sweden)

    Alexey Anatolievich Morozov

    2013-01-01

    Full Text Available Existing algorithms allow us to infer phylogenetic networks from sequences (DNA, protein or binary, sets of trees, and distance matrices, but there are no methods to build them using the gene order data as an input. Here we describe several methods to build split networks from the gene order data, perform simulation studies, and use our methods for analyzing and interpreting different real gene order datasets. All proposed methods are based on intermediate data, which can be generated from genome structures under study and used as an input for network construction algorithms. Three intermediates are used: set of jackknife trees, distance matrix, and binary encoding. According to simulations and case studies, the best intermediates are jackknife trees and distance matrix (when used with Neighbor-Net algorithm. Binary encoding can also be useful, but only when the methods mentioned above cannot be used.

  20. Gene networks and the neuroendocrine regulation of puberty.

    Science.gov (United States)

    Ojeda, Sergio R; Dubay, Christopher; Lomniczi, Alejandro; Kaidar, Gabi; Matagne, Valerie; Sandau, Ursula S; Dissen, Gregory A

    2010-08-05

    A sustained increase in pulsatile release of gonadotrophin releasing hormone (GnRH) from the hypothalamus is an essential, final event that defines the initiation of mammalian puberty. This increase depends on coordinated changes in transsynaptic and glial-neuronal communication, consisting of activating neuronal and glial excitatory inputs to the GnRH neuronal network and the loss of transsynaptic inhibitory tone. It is now clear that the prevalent excitatory systems stimulating GnRH secretion involve a neuronal component consisting of excitatory amino acids (glutamate) and at least one peptide (kisspeptin), and a glial component that uses growth factors and small molecules for cell-cell signaling. GABAergic and opiatergic neurons provide transsynaptic inhibitory control to the system, but GABA neurons also exert direct excitatory effects on GnRH neurons. The molecular mechanisms that provide encompassing coordination to this cellular network are not known, but they appear to involve a host of functionally related genes hierarchically arranged. We envision that, as observed in other gene networks, the highest level of control in this network is provided by transcriptional regulators that, by directing expression of key subordinate genes, impose an integrative level of coordination to the neuronal and glial subsets involved in initiating the pubertal process. The use of high-throughput and gene manipulation approaches coupled to systems biology strategies should provide not only the experimental bases supporting this concept, but also unveil the existence of crucial components of network control not yet identified. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.

  1. Robustness Analysis of Gene Regulatory Networks

    Science.gov (United States)

    Kadelka, Claus T.

    Cells generally manage to maintain stable phenotypes in the face of widely varying environmental conditions. This fact is particularly surprising since the key step of gene expression is fundamentally a stochastic process. Many hypotheses have been suggested to explain this robustness. First, the special topology of gene regulatory networks (GRNs) seems to be an important factor as they possess feedforward loops and certain other topological features much more frequently than expected. Second, genes often regulate each other in a canalizing fashion: there exists a dominance order amidst the regulators of a gene, which in silico leads to very robust phenotypes. Lastly, an entirely novel gene regulatory mechanism, discovered and studied during the last two decades, which is believed to play an important role in cancer, is shedding some light on how canalization may in fact take place as part of a cell's gene regulatory program. Short segments of single-stranded RNA, so-called microRNAs, which are embedded in several different types of feedforward loops, help smooth out noise and generate canalizing effects in gene regulation by overriding the effect of certain genes on others. Boolean networks and their multi-state extensions have been successfully used to model GRNs for many years. In this dissertation, GRNs are represented in the time- and state-discrete framework of Stochastic Discrete Dynamical Systems (SDDS), which captures the cell-inherent stochasticity. Each gene has finitely many different concentration levels and its concentration at the next time step is determined by a gene-specific update rule that depends on the current concentration of the gene's regulators. The update rules in published gene regulatory networks are often nested canalizing functions. In Chapter 2, this class of functions is introduced, generalized and analyzed with respect to its potential to confer robustness. Chapter 3 describes a simulation study, which supports the hypothesis that

  2. Network Analysis of Human Genes Influencing Susceptibility to Mycobacterial Infections

    Science.gov (United States)

    Lipner, Ettie M.; Garcia, Benjamin J.; Strong, Michael

    2016-01-01

    Tuberculosis and nontuberculous mycobacterial infections constitute a high burden of pulmonary disease in humans, resulting in over 1.5 million deaths per year. Building on the premise that genetic factors influence the instance, progression, and defense of infectious disease, we undertook a systems biology approach to investigate relationships among genetic factors that may play a role in increased susceptibility or control of mycobacterial infections. We combined literature and database mining with network analysis and pathway enrichment analysis to examine genes, pathways, and networks, involved in the human response to Mycobacterium tuberculosis and nontuberculous mycobacterial infections. This approach allowed us to examine functional relationships among reported genes, and to identify novel genes and enriched pathways that may play a role in mycobacterial susceptibility or control. Our findings suggest that the primary pathways and genes influencing mycobacterial infection control involve an interplay between innate and adaptive immune proteins and pathways. Signaling pathways involved in autoimmune disease were significantly enriched as revealed in our networks. Mycobacterial disease susceptibility networks were also examined within the context of gene-chemical relationships, in order to identify putative drugs and nutrients with potential beneficial immunomodulatory or anti-mycobacterial effects. PMID:26751573

  3. Transcriptional delay stabilizes bistable gene networks.

    Science.gov (United States)

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

    2013-08-02

    Transcriptional delay can significantly impact the dynamics of gene networks. Here we examine how such delay affects bistable systems. We investigate several stochastic models of bistable gene networks and find that increasing delay dramatically increases the mean residence times near stable states. To explain this, we introduce a non-Markovian, analytically tractable reduced model. The model shows that stabilization is the consequence of an increased number of failed transitions between stable states. Each of the bistable systems that we simulate behaves in this manner.

  4. An Integrative Approach to Computational Modelling of the Gene Regulatory Network Controlling Clostridium botulinum Type A1 Toxin Production.

    Science.gov (United States)

    Ihekwaba, Adaoha E C; Mura, Ivan; Walshaw, John; Peck, Michael W; Barker, Gary C

    2016-11-01

    Clostridium botulinum produces botulinum neurotoxins (BoNTs), highly potent substances responsible for botulism. Currently, mathematical models of C. botulinum growth and toxigenesis are largely aimed at risk assessment and do not include explicit genetic information beyond group level but integrate many component processes, such as signalling, membrane permeability and metabolic activity. In this paper we present a scheme for modelling neurotoxin production in C. botulinum Group I type A1, based on the integration of diverse information coming from experimental results available in the literature. Experiments show that production of BoNTs depends on the growth-phase and is under the control of positive and negative regulatory elements at the intracellular level. Toxins are released as large protein complexes and are associated with non-toxic components. Here, we systematically review and integrate those regulatory elements previously described in the literature for C. botulinum Group I type A1 into a population dynamics model, to build the very first computational model of toxin production at the molecular level. We conduct a validation of our model against several items of published experimental data for different wild type and mutant strains of C. botulinum Group I type A1. The result of this process underscores the potential of mathematical modelling at the cellular level, as a means of creating opportunities in developing new strategies that could be used to prevent botulism; and potentially contribute to improved methods for the production of toxin that is used for therapeutics.

  5. Inferring the gene network underlying the branching of tomato inflorescence.

    Directory of Open Access Journals (Sweden)

    Laura Astola

    Full Text Available The architecture of tomato inflorescence strongly affects flower production and subsequent crop yield. To understand the genetic activities involved, insight into the underlying network of genes that initiate and control the sympodial growth in the tomato is essential. In this paper, we show how the structure of this network can be derived from available data of the expressions of the involved genes. Our approach starts from employing biological expert knowledge to select the most probable gene candidates behind branching behavior. To find how these genes interact, we develop a stepwise procedure for computational inference of the network structure. Our data consists of expression levels from primary shoot meristems, measured at different developmental stages on three different genotypes of tomato. With the network inferred by our algorithm, we can explain the dynamics corresponding to all three genotypes simultaneously, despite their apparent dissimilarities. We also correctly predict the chronological order of expression peaks for the main hubs in the network. Based on the inferred network, using optimal experimental design criteria, we are able to suggest an informative set of experiments for further investigation of the mechanisms underlying branching behavior.

  6. Network enrichment analysis: extension of gene-set enrichment analysis to gene networks

    Directory of Open Access Journals (Sweden)

    Alexeyenko Andrey

    2012-09-01

    Full Text Available Abstract Background Gene-set enrichment analyses (GEA or GSEA are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis. Results We developed a method of network enrichment analysis (NEA that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study. Conclusions The results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.

  7. Robustness and Accuracy in Sea Urchin Developmental Gene Regulatory Networks.

    Science.gov (United States)

    Ben-Tabou de-Leon, Smadar

    2016-01-01

    Developmental gene regulatory networks robustly control the timely activation of regulatory and differentiation genes. The structure of these networks underlies their capacity to buffer intrinsic and extrinsic noise and maintain embryonic morphology. Here I illustrate how the use of specific architectures by the sea urchin developmental regulatory networks enables the robust control of cell fate decisions. The Wnt-βcatenin signaling pathway patterns the primary embryonic axis while the BMP signaling pathway patterns the secondary embryonic axis in the sea urchin embryo and across bilateria. Interestingly, in the sea urchin in both cases, the signaling pathway that defines the axis controls directly the expression of a set of downstream regulatory genes. I propose that this direct activation of a set of regulatory genes enables a uniform regulatory response and a clear cut cell fate decision in the endoderm and in the dorsal ectoderm. The specification of the mesodermal pigment cell lineage is activated by Delta signaling that initiates a triple positive feedback loop that locks down the pigment specification state. I propose that the use of compound positive feedback circuitry provides the endodermal cells enough time to turn off mesodermal genes and ensures correct mesoderm vs. endoderm fate decision. Thus, I argue that understanding the control properties of repeatedly used regulatory architectures illuminates their role in embryogenesis and provides possible explanations to their resistance to evolutionary change.

  8. Robustness and accuracy in sea urchin developmental gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Smadar eBen-Tabou De-Leon

    2016-02-01

    Full Text Available Developmental gene regulatory networks robustly control the timely activation of regulatory and differentiation genes. The structure of these networks underlies their capacity to buffer intrinsic and extrinsic noise and maintain embryonic morphology. Here I illustrate how the use of specific architectures by the sea urchin developmental regulatory networks enables the robust control of cell fate decisions. The Wnt-βcatenin signaling pathway patterns the primary embryonic axis while the BMP signaling pathway patterns the secondary embryonic axis in the sea urchin embryo and across bilateria. Interestingly, in the sea urchin in both cases, the signaling pathway that defines the axis controls directly the expression of a set of downstream regulatory genes. I propose that this direct activation of a set of regulatory genes enables a uniform regulatory response and a clear cut cell fate decision in the endoderm and in the dorsal ectoderm. The specification of the mesodermal pigment cell lineage is activated by Delta signaling that initiates a triple positive feedback loop that locks down the pigment specification state. I propose that the use of compound positive feedback circuitry provides the endodermal cells enough time to turn off mesodermal genes and ensures correct mesoderm vs. endoderm fate decision. Thus, I argue that understanding the control properties of repeatedly used regulatory architectures illuminates their role in embryogenesis and provides possible explanations to their resistance to evolutionary change.

  9. Ground rules of the pluripotency gene regulatory network.

    KAUST Repository

    Li, Mo

    2017-01-03

    Pluripotency is a state that exists transiently in the early embryo and, remarkably, can be recapitulated in vitro by deriving embryonic stem cells or by reprogramming somatic cells to become induced pluripotent stem cells. The state of pluripotency, which is stabilized by an interconnected network of pluripotency-associated genes, integrates external signals and exerts control over the decision between self-renewal and differentiation at the transcriptional, post-transcriptional and epigenetic levels. Recent evidence of alternative pluripotency states indicates the regulatory flexibility of this network. Insights into the underlying principles of the pluripotency network may provide unprecedented opportunities for studying development and for regenerative medicine.

  10. Multiscale Embedded Gene Co-expression Network Analysis.

    Directory of Open Access Journals (Sweden)

    Won-Min Song

    2015-11-01

    Full Text Available Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3, the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA by: i introducing quality control of co-expression similarities, ii parallelizing embedded network construction, and iii developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs. We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA. MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

  11. Multiscale Embedded Gene Co-expression Network Analysis.

    Science.gov (United States)

    Song, Won-Min; Zhang, Bin

    2015-11-01

    Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

  12. Interactive visualization of gene regulatory networks with associated gene expression time series data

    NARCIS (Netherlands)

    Westenberg, Michel A.; Hijum, Sacha A.F.T. van; Lulko, Andrzej T.; Kuipers, Oscar P.; Roerdink, Jos B.T.M.; Linsen, L; Hagen, H; Hamann, B

    2008-01-01

    We present GENeVis, an application to visualize gene expression time series data in a gene regulatory network context. This is a network of regulator proteins that regulate the expression of their respective target genes. The networks are represented as graphs, in which the nodes represent genes,

  13. A Network Traffic Control Enhancement Approach over Bluetooth Networks

    DEFF Research Database (Denmark)

    Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun

    2003-01-01

    This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated...... as capacity limitations and flow requirements in the network. Simulation shows that the performance of Bluetooth networks could be improved by applying the adaptive distributed network traffic control scheme...... solution of the stated optimization problem that satisfies quality of service requirements and topologically induced constraints in Bluetooth networks, such as link capacity and node resource limitations. The proposed scheme is decentralized and complies with frequent changes of topology as well...

  14. Gene-network analysis identifies susceptibility genes related to glycobiology in autism.

    Directory of Open Access Journals (Sweden)

    Bert van der Zwaag

    Full Text Available The recent identification of copy-number variation in the human genome has opened up new avenues for the discovery of positional candidate genes underlying complex genetic disorders, especially in the field of psychiatric disease. One major challenge that remains is pinpointing the susceptibility genes in the multitude of disease-associated loci. This challenge may be tackled by reconstruction of functional gene-networks from the genes residing in these loci. We applied this approach to autism spectrum disorder (ASD, and identified the copy-number changes in the DNA of 105 ASD patients and 267 healthy individuals with Illumina Humanhap300 Beadchips. Subsequently, we used a human reconstructed gene-network, Prioritizer, to rank candidate genes in the segmental gains and losses in our autism cohort. This analysis highlighted several candidate genes already known to be mutated in cognitive and neuropsychiatric disorders, including RAI1, BRD1, and LARGE. In addition, the LARGE gene was part of a sub-network of seven genes functioning in glycobiology, present in seven copy-number changes specifically identified in autism patients with limited co-morbidity. Three of these seven copy-number changes were de novo in the patients. In autism patients with a complex phenotype and healthy controls no such sub-network was identified. An independent systematic analysis of 13 published autism susceptibility loci supports the involvement of genes related to glycobiology as we also identified the same or similar genes from those loci. Our findings suggest that the occurrence of genomic gains and losses of genes associated with glycobiology are important contributors to the development of ASD.

  15. Hybrid stochastic simplifications for multiscale gene networks

    Directory of Open Access Journals (Sweden)

    Debussche Arnaud

    2009-09-01

    Full Text Available Abstract Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion 123 which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach.

  16. Information and control in networks

    CERN Document Server

    Bernhardsson, Bo; Rantzer, Anders

    2014-01-01

    Information and Control in Networks demonstrates the way in which system dynamics and information flows intertwine as they evolve, and the central role played by information in the control of complex networked systems. It is a milestone on the road to that convergence from traditionally independent development of control theory and information theory which has emerged strongly in the last fifteen years, and is now a very active research field. In addition to efforts in control and information theory, the text is witness to strong research in such diverse fields as computer science, mathematics, and statistics. Aspects that are given specialist treatment include: ·                 data-rate theorems; ·                 computation and control over communication networks; ·                 decentralized stochastic control; ·                 Gaussian networks and Gaussian–Markov random fields; and ·                 routability ...

  17. Maximum entropy networks are more controllable than preferential attachment networks

    International Nuclear Information System (INIS)

    Hou, Lvlin; Small, Michael; Lao, Songyang

    2014-01-01

    A maximum entropy (ME) method to generate typical scale-free networks has been recently introduced. We investigate the controllability of ME networks and Barabási–Albert preferential attachment networks. Our experimental results show that ME networks are significantly more easily controlled than BA networks of the same size and the same degree distribution. Moreover, the control profiles are used to provide insight into control properties of both classes of network. We identify and classify the driver nodes and analyze the connectivity of their neighbors. We find that driver nodes in ME networks have fewer mutual neighbors and that their neighbors have lower average degree. We conclude that the properties of the neighbors of driver node sensitively affect the network controllability. Hence, subtle and important structural differences exist between BA networks and typical scale-free networks of the same degree distribution. - Highlights: • The controllability of maximum entropy (ME) and Barabási–Albert (BA) networks is investigated. • ME networks are significantly more easily controlled than BA networks of the same degree distribution. • The properties of the neighbors of driver node sensitively affect the network controllability. • Subtle and important structural differences exist between BA networks and typical scale-free networks

  18. Learning gene regulatory networks from gene expression data using weighted consensus

    KAUST Repository

    Fujii, Chisato

    2016-08-25

    An accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.

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

    Science.gov (United States)

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

    2016-04-14

    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.

  20. Neural Networks for Flight Control

    Science.gov (United States)

    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.

  1. FUMET: A fuzzy network module extraction technique for gene ...

    Indian Academy of Sciences (India)

    Supplementary figure 1. (A): Visualization of one of the network modules by GeneMania for dataset 4 (B): Visualization of one of the network modules by GeneMania for dataset 1 (C): Visualization of one of the network modules by GeneMania for dataset 3.

  2. Remote control of gene expression.

    Science.gov (United States)

    Long, Xiaochun; Miano, Joseph M

    2007-06-01

    The elucidation of a growing number of species' genomes heralds an unprecedented opportunity to ascertain functional attributes of non-coding sequences. In particular, cis regulatory modules (CRMs) controlling gene expression constitute a rich treasure trove of data to be defined and experimentally validated. Such information will provide insight into cell lineage determination and differentiation and the genetic basis of heritable diseases as well as the development of novel tools for restricting the inactivation of genes to specific cell types or conditions. Historically, the study of CRMs and their individual transcription factor binding sites has been limited to proximal regions around gene loci. Two important by-products of the genomics revolution, artificial chromosome vectors and comparative genomics, have fueled efforts to define an increasing number of CRMs acting remotely to control gene expression. Such regulation from a distance has challenged our perspectives of gene expression control and perhaps the very definition of a gene. This review summarizes current approaches to characterize remote control of gene expression in transgenic mice and inherent limitations for accurately interpreting the essential nature of CRM activity.

  3. Controllability analysis of transcriptional regulatory networks reveals circular control patterns among transcription factors

    DEFF Research Database (Denmark)

    Österlund, Tobias; Bordel, Sergio; Nielsen, Jens

    2015-01-01

    we analyze the topology and organization of nine transcriptional regulatory networks for E. coli, yeast, mouse and human, and we evaluate how the structure of these networks influences two of their key properties, namely controllability and stability. We calculate the controllability for each network......Transcriptional regulation is the most committed type of regulation in living cells where transcription factors (TFs) control the expression of their target genes and TF expression is controlled by other TFs forming complex transcriptional regulatory networks that can be highly interconnected. Here...... as a measure of the organization and interconnectivity of the network. We find that the number of driver nodes n(D) needed to control the whole network is 64% of the TFs in the E. coli transcriptional regulatory network in contrast to only 17% for the yeast network, 4% for the mouse network and 8...

  4. Gene-Expression Profiles in Generalized Aggressive Periodontitis: A Gene Network-Based Microarray Analysis.

    Science.gov (United States)

    Guzeldemir-Akcakanat, Esra; Sunnetci-Akkoyunlu, Deniz; Orucguney, Begum; Cine, Naci; Kan, Bahadır; Yılmaz, Elif Büsra; Gümüşlü, Esen; Savli, Hakan

    2016-01-01

    In this study, molecular biomarkers that play a role in the development of generalized aggressive periodontitis (GAgP) are investigated using gingival tissue samples through omics-based whole-genome transcriptomics while using healthy individuals as background controls. Gingival tissue biopsies from 23 patients with GAgP and 25 healthy individuals were analyzed using gene-expression microarrays with network and pathway analyses to identify gene-expression patterns. To substantiate the results of the microarray studies, real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to assess the messenger RNA (mRNA) expression of MZB1 and DSC1. The microarrays and qRT-PCR resulted in similar gene-expression changes, confirming the reliability of the microarray results at the mRNA level. As a result of the gene-expression microarray studies, four significant gene networks were identified. The most upregulated genes were found as MZB1, TNFRSF17, PNOC, FCRL5, LAX1, BMS1P20, IGLL5, MMP7, SPAG4, and MEI1; the most downregulated genes were found as LOR, LAMB4, AADACL2, MAPT, ARG1, NPR3, AADAC, DSC1, LRRC4, and CHP2. Functions of the identified genes that were involved in gene networks were cellular development, cell growth and proliferation, cellular movement, cell-cell signaling and interaction, humoral immune response, protein synthesis, cell death and survival, cell population and organization, organismal injury and abnormalities, molecular transport, and small-molecule biochemistry. The data suggest new networks that have important functions as humoral immune response and organismal injury/abnormalities. Future analyses may facilitate proteomic profiling analyses to identify gene-expression patterns related to clinical outcome.

  5. Virtualized Network Control. Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Ghani, Nasir [Univ. of New Mexico, Albuquerque, NM (United States)

    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.

  6. Portrait of Candida Species Biofilm Regulatory Network Genes.

    Science.gov (United States)

    Araújo, Daniela; Henriques, Mariana; Silva, Sónia

    2017-01-01

    Most cases of candidiasis have been attributed to Candida albicans, but Candida glabrata, Candida parapsilosis and Candida tropicalis, designated as non-C. albicans Candida (NCAC), have been identified as frequent human pathogens. Moreover, Candida biofilms are an escalating clinical problem associated with significant rates of mortality. Biofilms have distinct developmental phases, including adhesion/colonisation, maturation and dispersal, controlled by complex regulatory networks. This review discusses recent advances regarding Candida species biofilm regulatory network genes, which are key components for candidiasis. Copyright © 2016 Elsevier Ltd. All rights reserved.

  7. Specificity and robustness in transcription control networks.

    Science.gov (United States)

    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.

  8. Genes responsive to elevated CO2 concentrations in triploid white poplar and integrated gene network analysis.

    Directory of Open Access Journals (Sweden)

    Juanjuan Liu

    Full Text Available BACKGROUND: The atmospheric CO2 concentration increases every year. While the effects of elevated CO2 on plant growth, physiology and metabolism have been studied, there is now a pressing need to understand the molecular mechanisms of how plants will respond to future increases in CO2 concentration using genomic techniques. PRINCIPAL FINDINGS: Gene expression in triploid white poplar ((Populus tomentosa ×P. bolleana ×P. tomentosa leaves was investigated using the Affymetrix poplar genome gene chip, after three months of growth in controlled environment chambers under three CO2 concentrations. Our physiological findings showed the growth, assessed as stem diameter, was significantly increased, and the net photosynthetic rate was decreased in elevated CO2 concentrations. The concentrations of four major endogenous hormones appeared to actively promote plant development. Leaf tissues under elevated CO2 concentrations had 5,127 genes with different expression patterns in comparison to leaves under the ambient CO2 concentration. Among these, 8 genes were finally selected for further investigation by using randomized variance model corrective ANOVA analysis, dynamic gene expression profiling, gene network construction, and quantitative real-time PCR validation. Among the 8 genes in the network, aldehyde dehydrogenase and pyruvate kinase were situated in the core and had interconnections with other genes. CONCLUSIONS: Under elevated CO2 concentrations, 8 significantly changed key genes involved in metabolism and responding to stimulus of external environment were identified. These genes play crucial roles in the signal transduction network and show strong correlations with elevated CO2 exposure. This study provides several target genes, further investigation of which could provide an initial step for better understanding the molecular mechanisms of plant acclimation and evolution in future rising CO2 concentrations.

  9. Paper-based Synthetic Gene Networks

    Science.gov (United States)

    Pardee, Keith; Green, Alexander A.; Ferrante, Tom; Cameron, D. Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J.

    2014-01-01

    Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides a new venue for synthetic biologists to operate, and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze-dried onto paper, enabling the inexpensive, sterile and abiotic distribution of synthetic biology-based technologies for the clinic, global health, industry, research and education. For field use, we create circuits with colorimetric outputs for detection by eye, and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors. PMID:25417167

  10. Predicting and Controlling Complex Networks

    Science.gov (United States)

    2015-06-22

    ubiquitous in nature and fundamental to evolution in ecosystems. However, a significant chal- lenge remains in understanding biodiversity since, by the...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

  11. Congestion control in satellite networks

    Science.gov (United States)

    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

  12. Proxy SDN Controller for Wireless Networks

    Directory of Open Access Journals (Sweden)

    Won-Suk Kim

    2016-01-01

    Full Text Available Management of wireless networks as well as wired networks by using software-defined networking (SDN has been highlighted continually. However, control features of a wireless network differ from those of a wired network in several aspects. In this study, we identify the various inefficient points when controlling and managing wireless networks by using SDN and propose SDN-based control architecture called Proxcon to resolve these problems. Proxcon introduces the concept of a proxy SDN controller (PSC for the wireless network control, and the PSC entrusted with the role of a main controller performs control operations and provides the latest network state for a network administrator. To address the control inefficiency, Proxcon supports offloaded SDN operations for controlling wireless networks by utilizing the PSC, such as local control by each PSC, hybrid control utilizing the PSC and the main controller, and locally cooperative control utilizing the PSCs. The proposed architecture and the newly supported control operations can enhance scalability and response time when the logically centralized control plane responds to the various wireless network events. Through actual experiments, we verified that the proposed architecture could address the various control issues such as scalability, response time, and control overhead.

  13. Biomarker Gene Signature Discovery Integrating Network Knowledge

    Directory of Open Access Journals (Sweden)

    Holger Fröhlich

    2012-02-01

    Full Text Available Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step towards a better personalized medicine. During the last decade various methods, mainly coming from the machine learning or statistical domain, have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Here we review the current state of research in this field by giving an overview about so-far proposed approaches.

  14. Opinion control in complex networks

    Science.gov (United States)

    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.

  15. Gene regulation networks generate diverse pigmentation patterns in plants.

    Science.gov (United States)

    Albert, Nick W; Davies, Kevin M; Schwinn, Kathy E

    2014-01-01

    The diversity of pigmentation patterns observed in plants occurs due to the spatial distribution and accumulation of colored compounds, which may also be associated with structural changes to the tissue. Anthocyanins are flavonoids that provide red/purple/blue coloration to plants, often forming complex patterns such as spots, stripes, and vein-associated pigmentation, particularly in flowers. These patterns are determined by the activity of MYB-bHLH-WDR (MBW) transcription factor complexes, which activate the anthocyanin biosynthesis genes, resulting in anthocyanin pigment accumulation. Recently, we established that the MBW complex controlling anthocyanin synthesis acts within a gene regulation network that is conserved within at least the Eudicots. This network involves hierarchy, reinforcement, and feedback mechanisms that allow for stringent and responsive regulation of the anthocyanin biosynthesis genes. The gene network and mobile nature of the WDR and R3-MYB proteins provide exciting new opportunities to explore the basis of pigmentation patterning, and to investigate the evolutionary history of the MBW components in land plants.

  16. Discovering implicit entity relation with the gene-citation-gene network.

    Directory of Open Access Journals (Sweden)

    Min Song

    Full Text Available In this paper, we apply the entitymetrics model to our constructed Gene-Citation-Gene (GCG network. Based on the premise there is a hidden, but plausible, relationship between an entity in one article and an entity in its citing article, we constructed a GCG network of gene pairs implicitly connected through citation. We compare the performance of this GCG network to a gene-gene (GG network constructed over the same corpus but which uses gene pairs explicitly connected through traditional co-occurrence. Using 331,411 MEDLINE abstracts collected from 18,323 seed articles and their references, we identify 25 gene pairs. A comparison of these pairs with interactions found in BioGRID reveal that 96% of the gene pairs in the GCG network have known interactions. We measure network performance using degree, weighted degree, closeness, betweenness centrality and PageRank. Combining all measures, we find the GCG network has more gene pairs, but a lower matching rate than the GG network. However, combining top ranked genes in both networks produces a matching rate of 35.53%. By visualizing both the GG and GCG networks, we find that cancer is the most dominant disease associated with the genes in both networks. Overall, the study indicates that the GCG network can be useful for detecting gene interaction in an implicit manner.

  17. Recurrent neural network based hybrid model for reconstructing gene regulatory network.

    Science.gov (United States)

    Raza, Khalid; Alam, Mansaf

    2016-10-01

    One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks.

    Science.gov (United States)

    Xiang, Zuoshuang; Qin, Tingting; Qin, Zhaohui S; He, Yongqun

    2013-10-16

    The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level. The genome-wide GenoMesh literature mining algorithm was developed by sequentially generating a gene-article matrix, a normalized gene-MeSH term matrix, and a gene-gene matrix. The gene-gene matrix relies on the calculation of pairwise gene dissimilarities based on gene-MeSH relationships. An optimized dissimilarity score was identified from six well-studied functions based on a receiver operating characteristic (ROC) analysis. Based on the studies with well-studied Escherichia coli and less-studied Brucella spp., GenoMesh was found to accurately identify gene functions using weighted MeSH terms, predict gene-gene interactions not reported in the literature, and cluster all the genes studied from an organism using the MeSH-based gene-gene matrix. A web-based GenoMesh literature mining program is also available at: http://genomesh.hegroup.org. GenoMesh also predicts gene interactions and networks among genes associated with specific MeSH terms or user-selected gene lists. The GenoMesh algorithm and web program provide the first genome-wide, MeSH-based literature mining

  19. A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks

    Science.gov (United States)

    2013-01-01

    Background The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level. Results The genome-wide GenoMesh literature mining algorithm was developed by sequentially generating a gene-article matrix, a normalized gene-MeSH term matrix, and a gene-gene matrix. The gene-gene matrix relies on the calculation of pairwise gene dissimilarities based on gene-MeSH relationships. An optimized dissimilarity score was identified from six well-studied functions based on a receiver operating characteristic (ROC) analysis. Based on the studies with well-studied Escherichia coli and less-studied Brucella spp., GenoMesh was found to accurately identify gene functions using weighted MeSH terms, predict gene-gene interactions not reported in the literature, and cluster all the genes studied from an organism using the MeSH-based gene-gene matrix. A web-based GenoMesh literature mining program is also available at: http://genomesh.hegroup.org. GenoMesh also predicts gene interactions and networks among genes associated with specific MeSH terms or user-selected gene lists. Conclusions The GenoMesh algorithm and web program provide the first genome

  20. Constrained target controllability of complex networks

    Science.gov (United States)

    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.

  1. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    concerning canonical, observable state space forms (minimum realizable form) for SISO as wll as MIMO processes. The tests show that all models, after succeeeful training, which is judged by correlation analysis of the prediction errors, are able to perform non-linear system identification, prediction......, simulation and filtering of dynamic, non-linear, multi-variable and noisy processes in a very satisfactory manner. The further examinations mainly concentrate on two models, the Non-linear ARMAX (NARMAX) model representing input/output description, and the Non-linear Innovation state Space (NISS) model (a...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

  2. Identifying key genes in glaucoma based on a benchmarked dataset and the gene regulatory network.

    Science.gov (United States)

    Chen, Xi; Wang, Qiao-Ling; Zhang, Meng-Hui

    2017-10-01

    The current study aimed to identify key genes in glaucoma based on a benchmarked dataset and gene regulatory network (GRN). Local and global noise was added to the gene expression dataset to produce a benchmarked dataset. Differentially-expressed genes (DEGs) between patients with glaucoma and normal controls were identified utilizing the Linear Models for Microarray Data (Limma) package based on benchmarked dataset. A total of 5 GRN inference methods, including Zscore, GeneNet, context likelihood of relatedness (CLR) algorithm, Partial Correlation coefficient with Information Theory (PCIT) and GEne Network Inference with Ensemble of Trees (Genie3) were evaluated using receiver operating characteristic (ROC) and precision and recall (PR) curves. The interference method with the best performance was selected to construct the GRN. Subsequently, topological centrality (degree, closeness and betweenness) was conducted to identify key genes in the GRN of glaucoma. Finally, the key genes were validated by performing reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A total of 176 DEGs were detected from the benchmarked dataset. The ROC and PR curves of the 5 methods were analyzed and it was determined that Genie3 had a clear advantage over the other methods; thus, Genie3 was used to construct the GRN. Following topological centrality analysis, 14 key genes for glaucoma were identified, including IL6 , EPHA2 and GSTT1 and 5 of these 14 key genes were validated by RT-qPCR. Therefore, the current study identified 14 key genes in glaucoma, which may be potential biomarkers to use in the diagnosis of glaucoma and aid in identifying the molecular mechanism of this disease.

  3. Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge

    Directory of Open Access Journals (Sweden)

    Timmer Jens

    2007-02-01

    Full Text Available Abstract Background Cellular processes are controlled by gene-regulatory networks. Several computational methods are currently used to learn the structure of gene-regulatory networks from data. This study focusses on time series gene expression and gene knock-out data in order to identify the underlying network structure. We compare the performance of different network reconstruction methods using synthetic data generated from an ensemble of reference networks. Data requirements as well as optimal experiments for the reconstruction of gene-regulatory networks are investigated. Additionally, the impact of prior knowledge on network reconstruction as well as the effect of unobserved cellular processes is studied. Results We identify linear Gaussian dynamic Bayesian networks and variable selection based on F-statistics as suitable methods for the reconstruction of gene-regulatory networks from time series data. Commonly used discrete dynamic Bayesian networks perform inferior and this result can be attributed to the inevitable information loss by discretization of expression data. It is shown that short time series generated under transcription factor knock-out are optimal experiments in order to reveal the structure of gene regulatory networks. Relative to the level of observational noise, we give estimates for the required amount of gene expression data in order to accurately reconstruct gene-regulatory networks. The benefit of using of prior knowledge within a Bayesian learning framework is found to be limited to conditions of small gene expression data size. Unobserved processes, like protein-protein interactions, induce dependencies between gene expression levels similar to direct transcriptional regulation. We show that these dependencies cannot be distinguished from transcription factor mediated gene regulation on the basis of gene expression data alone. Conclusion Currently available data size and data quality make the reconstruction of

  4. Gene coexpression network analysis as a source of functional annotation for rice genes.

    Directory of Open Access Journals (Sweden)

    Kevin L Childs

    Full Text Available With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional

  5. Cancer classification based on gene expression using neural networks.

    Science.gov (United States)

    Hu, H P; Niu, Z J; Bai, Y P; Tan, X H

    2015-12-21

    Based on gene expression, we have classified 53 colon cancer patients with UICC II into two groups: relapse and no relapse. Samples were taken from each patient, and gene information was extracted. Of the 53 samples examined, 500 genes were considered proper through analyses by S-Kohonen, BP, and SVM neural networks. Classification accuracy obtained by S-Kohonen neural network reaches 91%, which was more accurate than classification by BP and SVM neural networks. The results show that S-Kohonen neural network is more plausible for classification and has a certain feasibility and validity as compared with BP and SVM neural networks.

  6. Inferring a transcriptional regulatory network of the cytokinesis-related genes by network component analysis

    Directory of Open Access Journals (Sweden)

    Kao Cheng-Yan

    2009-11-01

    Full Text Available Abstract Background Network Component Analysis (NCA is a network structure-driven framework for deducing regulatory signal dynamics. In contrast to principal component analysis, which can be employed to select the high-variance genes, NCA makes use of the connectivity structure from transcriptional regulatory networks to infer dynamics of transcription factor activities. Using the budding yeast Saccharomyces cerevisiae as a model system, we aim to deduce regulatory actions of cytokinesis-related genes, using precise spatial proximity (midbody and/or temporal synchronicity (cytokinesis to avoid full-scale computation from genome-wide databases. Results NCA was applied to infer regulatory actions of transcription factor activity from microarray data and partial transcription factor-gene connectivity information for cytokinesis-related genes, which were a subset of genome-wide datasets. No literature has so far discussed the inferred results through NCA are independent of the scale of the gene expression dataset. To avoid full-scale computation from genome-wide databases, four cytokinesis-related gene cases were selected for NCA by running computational analysis over the transcription factor database to confirm the approach being scale-free. The inferred dynamics of transcription factor activity through NCA were independent of the scale of the data matrix selected from the four cytokinesis-related gene sets. Moreover, the inferred regulatory actions were nearly identical to published observations for the selected cytokinesis-related genes in the budding yeast; namely, Mcm1, Ndd1, and Fkh2, which form a transcription factor complex to control expression of the CLB2 cluster (i.e. BUD4, CHS2, IQG1, and CDC5. Conclusion In this study, using S. cerevisiae as a model system, NCA was successfully applied to infer similar regulatory actions of transcription factor activities from two various microarray databases and several partial transcription factor-gene

  7. Inferring a transcriptional regulatory network of the cytokinesis-related genes by network component analysis.

    Science.gov (United States)

    Chen, Shun-Fu; Juang, Yue-Li; Chou, Wei-Kang; Lai, Jin-Mei; Huang, Chi-Ying F; Kao, Cheng-Yan; Wang, Feng-Sheng

    2009-11-27

    Network Component Analysis (NCA) is a network structure-driven framework for deducing regulatory signal dynamics. In contrast to principal component analysis, which can be employed to select the high-variance genes, NCA makes use of the connectivity structure from transcriptional regulatory networks to infer dynamics of transcription factor activities. Using the budding yeast Saccharomyces cerevisiae as a model system, we aim to deduce regulatory actions of cytokinesis-related genes, using precise spatial proximity (midbody) and/or temporal synchronicity (cytokinesis) to avoid full-scale computation from genome-wide databases. NCA was applied to infer regulatory actions of transcription factor activity from microarray data and partial transcription factor-gene connectivity information for cytokinesis-related genes, which were a subset of genome-wide datasets. No literature has so far discussed the inferred results through NCA are independent of the scale of the gene expression dataset. To avoid full-scale computation from genome-wide databases, four cytokinesis-related gene cases were selected for NCA by running computational analysis over the transcription factor database to confirm the approach being scale-free. The inferred dynamics of transcription factor activity through NCA were independent of the scale of the data matrix selected from the four cytokinesis-related gene sets. Moreover, the inferred regulatory actions were nearly identical to published observations for the selected cytokinesis-related genes in the budding yeast; namely, Mcm1, Ndd1, and Fkh2, which form a transcription factor complex to control expression of the CLB2 cluster (i.e. BUD4, CHS2, IQG1, and CDC5). In this study, using S. cerevisiae as a model system, NCA was successfully applied to infer similar regulatory actions of transcription factor activities from two various microarray databases and several partial transcription factor-gene connectivity datasets for selected cytokinesis

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

    Science.gov (United States)

    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

  9. Adaptive optimization and control using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    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.

  10. The inferred cardiogenic gene regulatory network in the mammalian heart.

    Science.gov (United States)

    Bazil, Jason N; Stamm, Karl D; Li, Xing; Thiagarajan, Raghuram; Nelson, Timothy J; Tomita-Mitchell, Aoy; Beard, Daniel A

    2014-01-01

    Cardiac development is a complex, multiscale process encompassing cell fate adoption, differentiation and morphogenesis. To elucidate pathways underlying this process, a recently developed algorithm to reverse engineer gene regulatory networks was applied to time-course microarray data obtained from the developing mouse heart. Approximately 200 genes of interest were input into the algorithm to generate putative network topologies that are capable of explaining the experimental data via model simulation. To cull specious network interactions, thousands of putative networks are merged and filtered to generate scale-free, hierarchical networks that are statistically significant and biologically relevant. The networks are validated with known gene interactions and used to predict regulatory pathways important for the developing mammalian heart. Area under the precision-recall curve and receiver operator characteristic curve are 9% and 58%, respectively. Of the top 10 ranked predicted interactions, 4 have already been validated. The algorithm is further tested using a network enriched with known interactions and another depleted of them. The inferred networks contained more interactions for the enriched network versus the depleted network. In all test cases, maximum performance of the algorithm was achieved when the purely data-driven method of network inference was combined with a data-independent, functional-based association method. Lastly, the network generated from the list of approximately 200 genes of interest was expanded using gene-profile uniqueness metrics to include approximately 900 additional known mouse genes and to form the most likely cardiogenic gene regulatory network. The resultant network supports known regulatory interactions and contains several novel cardiogenic regulatory interactions. The method outlined herein provides an informative approach to network inference and leads to clear testable hypotheses related to gene regulation.

  11. A broadband accelerator control network

    International Nuclear Information System (INIS)

    Skelly, J.; Clifford, T.; Frankel, R.

    1983-01-01

    A broadband data communications network has been implemented at BNL for control of the Alternating Gradient Synchrotron (AGS) proton accelerator, using commercial CATV hardware, dual coaxial cables as the communications medium, and spanning 2.0 km. A 4 MHz bandwidth Digital Control Channel using CSMA-CA protocol is provided for digital data transmission, with 8 access nodes available over the length of the RELWAY. Each node consists of an rf modem and a microprocessor-based store-and-forward message handler which interfaces the RELWAY to a branch line implemented in GPIB. A gateway to the RELWAY control channel for the (preexisting) AGS Computerized Accelerator Operating System has been constructed using an LSI-11/23 microprocessor as a device in a GPIB branch line. A multilayer communications protocol has been defined for the Digital Control Channel, based on the ISO Open Systems Interconnect layered model, and a RELWAY Device Language defined as the required universal language for device control on this channel

  12. Simplified LQG Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another neural network is modelled to form a state controller and trained to produce...

  13. Neural Networks in Nonlinear Aircraft Control

    Science.gov (United States)

    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.

  14. Gene regulatory networks governing lung specification.

    Science.gov (United States)

    Rankin, Scott A; Zorn, Aaron M

    2014-08-01

    The epithelial lining of the respiratory system originates from a small group of progenitor cells in the ventral foregut endoderm of the early embryo. Research in the last decade has revealed a number of paracrine signaling pathways that are critical for the development of these respiratory progenitors. In the post-genomic era the challenge now is to figure out at the genome wide level how these different signaling pathways and their downstream transcription factors interact in a complex "gene regulatory network" (GRN) to orchestrate early lung development. In this prospective, we review our growing understanding of the GRN governing lung specification. We discuss key gaps in our knowledge and describe emerging opportunities that will soon provide an unprecedented understanding of lung development and accelerate our ability to apply this knowledge to regenerative medicine. © 2014 Wiley Periodicals, Inc.

  15. Communication and control for networked complex systems

    CERN Document Server

    Peng, Chen; Han, Qing-Long

    2015-01-01

    This book reports on the latest advances in the study of Networked Control Systems (NCSs). It highlights novel research concepts on NCSs; the analysis and synthesis of NCSs with special attention to their networked character; self- and event-triggered communication schemes for conserving limited network resources; and communication and control co-design for improving the efficiency of NCSs. The book will be of interest to university researchers, control and network engineers, and graduate students in the control engineering, communication and network sciences interested in learning the core principles, methods, algorithms and applications of NCSs.

  16. Networks in biological systems: An investigation of the Gene Ontology as an evolving network

    International Nuclear Information System (INIS)

    Coronnello, C; Tumminello, M; Micciche, S; Mantegna, R.N.

    2009-01-01

    Many biological systems can be described as networks where different elements interact, in order to perform biological processes. We introduce a network associated with the Gene Ontology. Specifically, we construct a correlation-based network where the vertices are the terms of the Gene Ontology and the link between each two terms is weighted on the basis of the number of genes that they have in common. We analyze a filtered network obtained from the correlation-based network and we characterize its evolution over different releases of the Gene Ontology.

  17. Cloud-based Networked Visual Servo Control

    DEFF Research Database (Denmark)

    Wu, Haiyan; Lu, Lei; Chen, Chih-Chung

    2013-01-01

    The performance of vision-based control systems, in particular of highly dynamic vision-based motion control systems, is often limited by the low sampling rate of the visual feedback caused by the long image processing time. In order to overcome this problem, the networked visual servo control...... feedback, ii) a stabilizing control law for the networked visual servo control system with time-varying feedback time delay, and iii) a sending rate scheduling strategy aiming at reducing the communication network load. The performance of the networked visual servo control system with sending rate...

  18. Logistic control in automated transportation networks

    NARCIS (Netherlands)

    Ebben, Mark

    2001-01-01

    Increasing congestion problems lead to a search for alternative transportation systems. Automated transportation networks, possibly underground, are an option. Logistic control systems are essential for future implementations of such automated transportation networks. This book contributes to the

  19. Using evolutionary conserved modules in gene networks as a strategy to leverage high throughput gene expression queries.

    Directory of Open Access Journals (Sweden)

    Jeanne M Serb

    Full Text Available BACKGROUND: Large-scale gene expression studies have not yielded the expected insight into genetic networks that control complex processes. These anticipated discoveries have been limited not by technology, but by a lack of effective strategies to investigate the data in a manageable and meaningful way. Previous work suggests that using a pre-determined seed-network of gene relationships to query large-scale expression datasets is an effective way to generate candidate genes for further study and network expansion or enrichment. Based on the evolutionary conservation of gene relationships, we test the hypothesis that a seed network derived from studies of retinal cell determination in the fly, Drosophila melanogaster, will be an effective way to identify novel candidate genes for their role in mouse retinal development. METHODOLOGY/PRINCIPAL FINDINGS: Our results demonstrate that a number of gene relationships regulating retinal cell differentiation in the fly are identifiable as pairwise correlations between genes from developing mouse retina. In addition, we demonstrate that our extracted seed-network of correlated mouse genes is an effective tool for querying datasets and provides a context to generate hypotheses. Our query identified 46 genes correlated with our extracted seed-network members. Approximately 54% of these candidates had been previously linked to the developing brain and 33% had been previously linked to the developing retina. Five of six candidate genes investigated further were validated by experiments examining spatial and temporal protein expression in the developing retina. CONCLUSIONS/SIGNIFICANCE: We present an effective strategy for pursuing a systems biology approach that utilizes an evolutionary comparative framework between two model organisms, fly and mouse. Future implementation of this strategy will be useful to determine the extent of network conservation, not just gene conservation, between species and will

  20. Gene network analysis of Arabidopsis thaliana flower development through dynamic gene perturbations.

    Science.gov (United States)

    Ó'Maoiléidigh, Diarmuid S; Thomson, Bennett; Raganelli, Andrea; Wuest, Samuel E; Ryan, Patrick T; Kwaśniewska, Kamila; Carles, Cristel C; Graciet, Emmanuelle; Wellmer, Frank

    2015-07-01

    Understanding how flowers develop from undifferentiated stem cells has occupied developmental biologists for decades. Key to unraveling this process is a detailed knowledge of the global regulatory hierarchies that control developmental transitions, cell differentiation and organ growth. These hierarchies may be deduced from gene perturbation experiments, which determine the effects on gene expression after specific disruption of a regulatory gene. Here, we tested experimental strategies for gene perturbation experiments during Arabidopsis thaliana flower development. We used artificial miRNAs (amiRNAs) to disrupt the functions of key floral regulators, and expressed them under the control of various inducible promoter systems that are widely used in the plant research community. To be able to perform genome-wide experiments with stage-specific resolution using the various inducible promoter systems for gene perturbation experiments, we also generated a series of floral induction systems that allow collection of hundreds of synchronized floral buds from a single plant. Based on our results, we propose strategies for performing dynamic gene perturbation experiments in flowers, and outline how they may be combined with versions of the floral induction system to dissect the gene regulatory network underlying flower development. © 2015 The Authors The Plant Journal © 2015 John Wiley & Sons Ltd.

  1. Structure-based control of complex networks with nonlinear dynamics

    Science.gov (United States)

    Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka

    2017-01-01

    What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework’s applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances. PMID:28655847

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

    Directory of Open Access Journals (Sweden)

    Aaron R Wolen

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

  3. Analysis of regulatory networks constructed based on gene ...

    Indian Academy of Sciences (India)

    For pituitary adenoma-specific coexpressed genes, we integrated transcription factor (TF) and microRNA (miRNA) regulation to construct a complex regulatory network from the transcriptional and posttranscriptional perspectives. Network module analysis identified the synergistic regulation of genes by miRNAs and TFs in ...

  4. 2016 Network Games, Control, and Optimization Conference

    CERN Document Server

    Jimenez, Tania; Solan, Eilon

    2017-01-01

    This contributed volume offers a collection of papers presented at the 2016 Network Games, Control, and Optimization conference (NETGCOOP), held at the University of Avignon in France, November 23-25, 2016. These papers highlight the increasing importance of network control and optimization in many networking application domains, such as mobile and fixed access networks, computer networks, social networks, transportation networks, and, more recently, electricity grids and biological networks. Covering a wide variety of both theoretical and applied topics in the areas listed above, the authors explore several conceptual and algorithmic tools that are needed for efficient and robust control operation, performance optimization, and better understanding the relationships between entities that may be acting cooperatively or selfishly in uncertain and possibly adversarial environments. As such, this volume will be of interest to applied mathematicians, computer scientists, engineers, and researchers in other relate...

  5. Identification of gene networks and pathways associated with Guillain-Barre syndrome.

    Directory of Open Access Journals (Sweden)

    Kuo-Hsuan Chang

    Full Text Available BACKGROUND: The underlying change of gene network expression of Guillain-Barré syndrome (GBS remains elusive. We sought to identify GBS-associated gene networks and signaling pathways by analyzing the transcriptional profile of leukocytes in the patients with GBS. METHODS AND FINDINGS: Quantitative global gene expression microarray analysis of peripheral blood leukocytes was performed on 7 patients with GBS and 7 healthy controls. Gene expression profiles were compared between patients and controls after standardization. The set of genes that significantly correlated with GBS was further analyzed by Ingenuity Pathways Analyses. 256 genes and 18 gene networks were significantly associated with GBS (fold change ≥2, P<0.05. FOS, PTGS2, HMGB2 and MMP9 are the top four of 246 significantly up-regulated genes. The most significant disease and altered biological function genes associated with GBS were those involved in inflammatory response, infectious disease, and respiratory disease. Cell death, cellular development and cellular movement were the top significant molecular and cellular functions involved in GBS. Hematological system development and function, immune cell trafficking and organismal survival were the most significant GBS-associated function in physiological development and system category. Several hub genes, such as MMP9, PTGS2 and CREB1 were identified in the associated gene networks. Canonical pathway analysis showed that GnRH, corticotrophin-releasing hormone and ERK/MAPK signaling were the most significant pathways in the up-regulated gene set in GBS. CONCLUSIONS: This study reveals the gene networks and canonical pathways associated with GBS. These data provide not only networks between the genes for understanding the pathogenic properties of GBS but also map significant pathways for the future development of novel therapeutic strategies.

  6. Filtering and control of wireless networked systems

    CERN Document Server

    Zhang, Dan; Yu, Li

    2017-01-01

    This self-contained book, written by leading experts, offers a cutting-edge, in-depth overview of the filtering and control of wireless networked systems. It addresses the energy constraint and filter/controller gain variation problems, and presents both the centralized and the distributed solutions. The first two chapters provide an introduction to networked control systems and basic information on system analysis. Chapters (3–6) then discuss the centralized filtering of wireless networked systems, presenting different approaches to deal with energy efficiency and filter/controller gain variation problems. The next part (chapters 7–10) explores the distributed filtering of wireless networked systems, addressing the main problems of energy constraint and filter gain variation. The final part (chapters 11–14) focuses on the distributed control of wireless networked systems. networked systems for communication and control applications, the bo...

  7. Structure-based control of complex networks with nonlinear dynamics

    Science.gov (United States)

    Zanudo, Jorge G. T.; Yang, Gang; Albert, Reka

    What can we learn about controlling a system solely from its underlying network structure? Here we use a framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors, regardless of the dynamic details and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of classical structural control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case, but not in specific model instances. This work was supported by NSF Grants PHY 1205840 and IIS 1160995. JGTZ is a recipient of a Stand Up To Cancer - The V Foundation Convergence Scholar Award.

  8. Control theory of digitally networked dynamic systems

    CERN Document Server

    Lunze, Jan

    2013-01-01

    The book gives an introduction to networked control systems and describes new modeling paradigms, analysis methods for event-driven, digitally networked systems, and design methods for distributed estimation and control. Networked model predictive control is developed as a means to tolerate time delays and packet loss brought about by the communication network. In event-based control the traditional periodic sampling is replaced by state-dependent triggering schemes. Novel methods for multi-agent systems ensure complete or clustered synchrony of agents with identical or with individual dynamic

  9. Predicting disease-related genes using integrated biomedical networks

    OpenAIRE

    Peng, Jiajie; Bai, Kun; Shang, Xuequn; Wang, Guohua; Xue, Hansheng; Jin, Shuilin; Cheng, Liang; Wang, Yadong; Chen, Jin

    2017-01-01

    Background Identifying the genes associated to human diseases is crucial for disease diagnosis and drug design. Computational approaches, esp. the network-based approaches, have been recently developed to identify disease-related genes effectively from the existing biomedical networks. Meanwhile, the advance in biotechnology enables researchers to produce multi-omics data, enriching our understanding on human diseases, and revealing the complex relationships between genes and diseases. Howeve...

  10. Using Morpholinos to Probe Gene Networks in Sea Urchin.

    Science.gov (United States)

    Materna, Stefan C

    2017-01-01

    The control processes that underlie the progression of development can be summarized in maps of gene regulatory networks (GRNs). A critical step in their assembly is the systematic perturbation of network candidates. In sea urchins the most important method for interfering with expression in a gene-specific way is application of morpholino antisense oligonucleotides (MOs). MOs act by binding to their sequence complement in transcripts resulting in a block in translation or a change in splicing and thus result in a loss of function. Despite the tremendous success of this technology, recent comparisons to mutants generated by genome editing have led to renewed criticism and challenged its reliability. As with all methods based on sequence recognition, MOs are prone to off-target binding that may result in phenotypes that are erroneously ascribed to the loss of the intended target. However, the slow progression of development in sea urchins has enabled extremely detailed studies of gene activity in the embryo. This wealth of knowledge paired with the simplicity of the sea urchin embryo enables careful analysis of MO phenotypes through a variety of methods that do not rely on terminal phenotypes. This article summarizes the use of MOs in probing GRNs and the steps that should be taken to assure their specificity.

  11. Control Augmentation Using Adaptive Fuzzy Neural Networks

    Science.gov (United States)

    Kato, Akio; Wada, Yoshihisa

    Control to improve control characteristics of aircraft, CA (Control Augmentation), is used to realize the desirable motion of aircraft corresponding to pilot's control action. When the control laws using fuzzy inference were designed, trial and error was repeated for optimization of the parameter. Here, in designing control laws using fuzzy neural networks, the systematic optimization of the parameter was possible using the learning algorithm usually used in neural networks, by expressing the fuzzy inference in the form of neural networks. Here, the control laws, which learned the characteristics of the aircraft for one flight condition only, were used in all flight conditions without changing any parameter. Evaluation of the designed control laws showed good performance in all flight conditions. This proves that fuzzy neural networks are an effective and flexible method when applied to control laws for control augmentation of aircraft.

  12. Congestion control of high-speed networks

    Science.gov (United States)

    1993-06-01

    We report on four areas of activity in the past six months. These areas include the following: (1) work on the control of integrated video and image traffic, both at the access to a network and within a high-speed network; (2) more general/game theoretic models for flow control in networks; (3) work on fault management for high-speed heterogeneous networks to improve survivability; and (4) work on all-optical (lightwave) networks of the future, designed to take advantage of the enormous bandwidth capability available at optical frequencies.

  13. Distributed medium access control in wireless networks

    CERN Document Server

    Wang, Ping

    2013-01-01

    This brief investigates distributed medium access control (MAC) with QoS provisioning for both single- and multi-hop wireless networks including wireless local area networks (WLANs), wireless ad hoc networks, and wireless mesh networks. For WLANs, an efficient MAC scheme and a call admission control algorithm are presented to provide guaranteed QoS for voice traffic and, at the same time, increase the voice capacity significantly compared with the current WLAN standard. In addition, a novel token-based scheduling scheme is proposed to provide great flexibility and facility to the network servi

  14. A Regulatory Network Analysis of Orphan Genes in Arabidopsis Thaliana

    Science.gov (United States)

    Singh, Pramesh; Chen, Tianlong; Arendsee, Zebulun; Wurtele, Eve S.; Bassler, Kevin E.

    Orphan genes, which are genes unique to each particular species, have recently drawn significant attention for their potential usefulness for organismal robustness. Their origin and regulatory interaction patterns remain largely undiscovered. Recently, methods that use the context likelihood of relatedness to infer a network followed by modularity maximizing community detection algorithms on the inferred network to find the functional structure of regulatory networks were shown to be effective. We apply improved versions of these methods to gene expression data from Arabidopsis thaliana, identify groups (clusters) of interacting genes with related patterns of expression and analyze the structure within those groups. Focusing on clusters that contain orphan genes, we compare the identified clusters to gene ontology (GO) terms, regulons, and pathway designations and analyze their hierarchical structure. We predict new regulatory interactions and unravel the structure of the regulatory interaction patterns of orphan genes. Work supported by the NSF through Grants DMR-1507371 and IOS-1546858.

  15. Neural Networks Applied to Optimal Flight Control

    OpenAIRE

    McKelvey, Tomas

    1992-01-01

    This paper presents a method for developing control laws for nonlinear systems based on an optimal control formulation. Due to the nonlinearities of the system, no analytical solution exists. The method proposed here uses the 'black box' structure of a neural network to model a feedback control law. The network is trained with the back-propagation learning method by using examples of optimal control produced with a differential dynamic programming technique. Two different optimal control prob...

  16. Semi-supervised prediction of gene regulatory networks using ...

    Indian Academy of Sciences (India)

    2015-09-28

    Sep 28, 2015 ... [Patel N and Wang JTL 2015 Semi-supervised prediction of gene regulatory networks using machine learning algorithms. J. Biosci. 40 731–740]. DOI 10.1007/s12038-015-9558-9. 1. Introduction. 1.1 Background. Using gene expression data to infer gene regulatory net- works (GRNs) is a key approach to ...

  17. Experimentally based sea urchin gene regulatory network and the causal explanation of developmental phenomenology.

    Science.gov (United States)

    Ben-Tabou de-Leon, Smadar; Davidson, Eric H

    2009-01-01

    Gene regulatory networks for development underlie cell fate specification and differentiation. Network topology, logic and dynamics can be obtained by thorough experimental analysis. Our understanding of the gene regulatory network controlling endomesoderm specification in the sea urchin embryo has attained an advanced level such that it explains developmental phenomenology. Here we review how the network explains the mechanisms utilized in development to control the formation of dynamic expression patterns of transcription factors and signaling molecules. The network represents the genomic program controlling timely activation of specification and differentiation genes in the correct embryonic lineages. It can also be used to study evolution of body plans. We demonstrate how comparing the sea urchin gene regulatory network to that of the sea star and to that of later developmental stages in the sea urchin, reveals mechanisms underlying the origin of evolutionary novelty. The experimentally based gene regulatory network for endomesoderm specification in the sea urchin embryo provides unique insights into the system level properties of cell fate specification and its evolution.

  18. PoplarGene: poplar gene network and resource for mining functional information for genes from woody plants

    OpenAIRE

    Qi Liu; Changjun Ding; Yanguang Chu; Jiafei Chen; Weixi Zhang; Bingyu Zhang; Qinjun Huang; Xiaohua Su

    2016-01-01

    Poplar is not only an important resource for the production of paper, timber and other wood-based products, but it has also emerged as an ideal model system for studying woody plants. To better understand the biological processes underlying various traits in poplar, e.g., wood development, a comprehensive functional gene interaction network is highly needed. Here, we constructed a genome-wide functional gene network for poplar (covering ~70% of the 41,335 poplar genes) and created the network...

  19. Trends in Integrated Ship Control Networking

    DEFF Research Database (Denmark)

    Jørgensen, N.; Nielsen, Jens Frederik Dalsgaard

    1997-01-01

    Integrated Ship Control systems can be designed as robust, distributed, autonomous control systems. The EU funded ATOMOS and ATOMOS II projects involves both technical and non technical aspects of this process. A reference modelling concept giving an outline of a generic ISC system covering...... the network and the equipment connected to it, a framework for verification of network functionality and performance by simulation and a general distribution platform for ISC systems, The ATOMOS Network, are results of this work....

  20. Gene network analysis in plant development by genomic technologies.

    Science.gov (United States)

    Wellmer, Frank; Riechmann, José Luis

    2005-01-01

    The analysis of the gene regulatory networks underlying development is of central importance for a better understanding of the mechanisms that control the formation of the different cell-types, tissues or organs of an organism. The recent invention of genomic technologies has opened the possibility of studying these networks at a global level. In this paper, we summarize some of the recent advances that have been made in the understanding of plant development by the application of genomic technologies. We focus on a few specific processes, namely flower and root development and the control of the cell cycle, but we also highlight landmark studies in other areas that opened new avenues of experimentation or analysis. We describe the methods and the strategies that are currently used for the analysis of plant development by genomic technologies, as well as some of the problems and limitations that hamper their application. Since many genomic technologies and concepts were first developed and tested in organisms other than plants, we make reference to work in non-plant species and compare the current state of network analysis in plants to that in other multicellular organisms.

  1. Centralized surveillance and control of satellite networks

    Science.gov (United States)

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

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

  2. Neural Networks for Non-linear Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1994-01-01

    This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....

  3. Control patterns in an healthcare network

    NARCIS (Netherlands)

    Kartseva, V.; Hulstijn, J.; Gordijn, J.; Tan, Y.H.

    2010-01-01

    To keep a network of enterprises sustainable, inter-organizational control measures are needed to detect or prevent opportunistic behaviour of network participants. We present a requirements engineering method for understanding control problems and designing solutions, based on an economic value

  4. A flood-based information flow analysis and network minimization method for gene regulatory networks.

    Science.gov (United States)

    Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias

    2013-04-24

    Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.

  5. Additive Feed Forward Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1999-01-01

    . A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

  6. Reverse Engineering of Gene Regulatory Networks: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Hendrik Hache

    2009-01-01

    Full Text Available Reverse engineering of gene regulatory networks has been an intensively studied topic in bioinformatics since it constitutes an intermediate step from explorative to causative gene expression analysis. Many methods have been proposed through recent years leading to a wide range of mathematical approaches. In practice, different mathematical approaches will generate different resulting network structures, thus, it is very important for users to assess the performance of these algorithms. We have conducted a comparative study with six different reverse engineering methods, including relevance networks, neural networks, and Bayesian networks. Our approach consists of the generation of defined benchmark data, the analysis of these data with the different methods, and the assessment of algorithmic performances by statistical analyses. Performance was judged by network size and noise levels. The results of the comparative study highlight the neural network approach as best performing method among those under study.

  7. Distributed intelligent control and status networking

    Science.gov (United States)

    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.

  8. Distributed controller clustering in software defined networks.

    Directory of Open Access Journals (Sweden)

    Ahmed Abdelaziz

    Full Text Available 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.

  9. Distributed controller clustering in software defined networks

    Science.gov (United States)

    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

  10. A network of genes, genetic disorders, and brain areas.

    Directory of Open Access Journals (Sweden)

    Satoru Hayasaka

    Full Text Available The network-based approach has been used to describe the relationship among genes and various phenotypes, producing a network describing complex biological relationships. Such networks can be constructed by aggregating previously reported associations in the literature from various databases. In this work, we applied the network-based approach to investigate how different brain areas are associated to genetic disorders and genes. In particular, a tripartite network with genes, genetic diseases, and brain areas was constructed based on the associations among them reported in the literature through text mining. In the resulting network, a disproportionately large number of gene-disease and disease-brain associations were attributed to a small subset of genes, diseases, and brain areas. Furthermore, a small number of brain areas were found to be associated with a large number of the same genes and diseases. These core brain regions encompassed the areas identified by the previous genome-wide association studies, and suggest potential areas of focus in the future imaging genetics research. The approach outlined in this work demonstrates the utility of the network-based approach in studying genetic effects on the brain.

  11. A network of genes, genetic disorders, and brain areas.

    Science.gov (United States)

    Hayasaka, Satoru; Hugenschmidt, Christina E; Laurienti, Paul J

    2011-01-01

    The network-based approach has been used to describe the relationship among genes and various phenotypes, producing a network describing complex biological relationships. Such networks can be constructed by aggregating previously reported associations in the literature from various databases. In this work, we applied the network-based approach to investigate how different brain areas are associated to genetic disorders and genes. In particular, a tripartite network with genes, genetic diseases, and brain areas was constructed based on the associations among them reported in the literature through text mining. In the resulting network, a disproportionately large number of gene-disease and disease-brain associations were attributed to a small subset of genes, diseases, and brain areas. Furthermore, a small number of brain areas were found to be associated with a large number of the same genes and diseases. These core brain regions encompassed the areas identified by the previous genome-wide association studies, and suggest potential areas of focus in the future imaging genetics research. The approach outlined in this work demonstrates the utility of the network-based approach in studying genetic effects on the brain.

  12. A provisional regulatory gene network for specification of endomesoderm in the sea urchin embryo

    Science.gov (United States)

    Davidson, Eric H.; Rast, Jonathan P.; Oliveri, Paola; Ransick, Andrew; Calestani, Cristina; Yuh, Chiou-Hwa; Minokawa, Takuya; Amore, Gabriele; Hinman, Veronica; Arenas-Mena, Cesar; hide

    2002-01-01

    We present the current form of a provisional DNA sequence-based regulatory gene network that explains in outline how endomesodermal specification in the sea urchin embryo is controlled. The model of the network is in a continuous process of revision and growth as new genes are added and new experimental results become available; see http://www.its.caltech.edu/mirsky/endomeso.htm (End-mes Gene Network Update) for the latest version. The network contains over 40 genes at present, many newly uncovered in the course of this work, and most encoding DNA-binding transcriptional regulatory factors. The architecture of the network was approached initially by construction of a logic model that integrated the extensive experimental evidence now available on endomesoderm specification. The internal linkages between genes in the network have been determined functionally, by measurement of the effects of regulatory perturbations on the expression of all relevant genes in the network. Five kinds of perturbation have been applied: (1) use of morpholino antisense oligonucleotides targeted to many of the key regulatory genes in the network; (2) transformation of other regulatory factors into dominant repressors by construction of Engrailed repressor domain fusions; (3) ectopic expression of given regulatory factors, from genetic expression constructs and from injected mRNAs; (4) blockade of the beta-catenin/Tcf pathway by introduction of mRNA encoding the intracellular domain of cadherin; and (5) blockade of the Notch signaling pathway by introduction of mRNA encoding the extracellular domain of the Notch receptor. The network model predicts the cis-regulatory inputs that link each gene into the network. Therefore, its architecture is testable by cis-regulatory analysis. Strongylocentrotus purpuratus and Lytechinus variegatus genomic BAC recombinants that include a large number of the genes in the network have been sequenced and annotated. Tests of the cis-regulatory predictions of

  13. Motif Participation by Genes in E. coli Transcriptional Networks

    Directory of Open Access Journals (Sweden)

    Michael eMayo

    2012-09-01

    Full Text Available Motifs are patterns of recurring connections among the genes of genetic networks that occur more frequently than would be expected from randomized networks with the same degree sequence. Although the abundance of certain three-node motifs, such as the feed-forward loop, is positively correlated with a networks’ ability to tolerate moderate disruptions to gene expression, little is known regarding the connectivity of individual genes participating in multiple motifs. Using the transcriptional network of the bacterium Escherichia coli, we investigate this feature by reconstructing the distribution of genes participating in feed-forward loop motifs from its largest connected network component. We contrast these motif participation distributions with those obtained from model networks built using the preferential attachment mechanism employed by many biological and man-made networks. We report that, although some of these model networks support a motif participation distribution that appears qualitatively similar to that obtained from the bacterium Escherichia coli, the probability for a node to support a feed-forward loop motif may instead be strongly influenced by only a few master transcriptional regulators within the network. From these analyses we conclude that such master regulators may be a crucial ingredient to describe coupling among feed-forward loop motifs in transcriptional regulatory networks.

  14. Network aspects of the Fermilab control system

    International Nuclear Information System (INIS)

    Barton, H.R. Jr.

    1977-01-01

    The control system of the Fermi National Accelerator is a heavily computerized network of distributed processors. One part of the control system includes a multidrop network of eleven Lockheed MAC-16 processors, a Digital Equipment Corporation PDP-11 computer, a Xerox 530, and a Control Data 6600 system. These computers exchange information using serial hardware and dedicated cable buses. The individual functions of the central processing units in this network, the message protocols for computer communications, and design guidelines for future distributed processing control systems are discussed

  15. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...... concerning canonical, observable state space forms (minimum realizable form) for SISO as wll as MIMO processes. The tests show that all models, after succeeeful training, which is judged by correlation analysis of the prediction errors, are able to perform non-linear system identification, prediction...

  16. MPC control of water supply networks

    DEFF Research Database (Denmark)

    Baunsgaard, Kenneth Marx Hoe; Ravn, Ole; Kallesoe, Carsten Skovmose

    2016-01-01

    This paper investigates the modelling and predictive control of a drinking water supply network with the aim of minimising the energy and economic cost. A model predictive controller, MPC, is applied to a nonlinear model of a drinking water network that follows certain constraints to maintain...... consumer pressure desire. A model predictive controller, MPC, is based on a simple model that models the main characteristics of a water distribution network, optimizes a desired cost minimisation, and keeps the system inside specified constraints. In comparison to a logic (on/off) control design......, controlling the drinking water supply network with the MPC showed reduction of the energy and the economic cost of running the system. This has been achieved by minimising actuator control effort and by shifting the actuator use towards the night time, where energy prices are lower. Along with energy cost...

  17. Disease candidate gene identification and prioritization using protein interaction networks

    Directory of Open Access Journals (Sweden)

    Aronow Bruce J

    2009-02-01

    Full Text Available Abstract Background Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN analyses. Results For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds", and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance. Conclusion Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.

  18. Advanced mobile networking, sensing, and controls.

    Energy Technology Data Exchange (ETDEWEB)

    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.

  19. Gene Regulatory Network Reconstruction Using Conditional Mutual Information

    Directory of Open Access Journals (Sweden)

    Xiaodong Wang

    2008-06-01

    Full Text Available The inference of gene regulatory network from expression data is an important area of research that provides insight to the inner workings of a biological system. The relevance-network-based approaches provide a simple and easily-scalable solution to the understanding of interaction between genes. Up until now, most works based on relevance network focus on the discovery of direct regulation using correlation coefficient or mutual information. However, some of the more complicated interactions such as interactive regulation and coregulation are not easily detected. In this work, we propose a relevance network model for gene regulatory network inference which employs both mutual information and conditional mutual information to determine the interactions between genes. For this purpose, we propose a conditional mutual information estimator based on adaptive partitioning which allows us to condition on both discrete and continuous random variables. We provide experimental results that demonstrate that the proposed regulatory network inference algorithm can provide better performance when the target network contains coregulated and interactively regulated genes.

  20. Robust Multiobjective Controllability of Complex Neuronal Networks.

    Science.gov (United States)

    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.

  1. Neural networks and orbit control in accelerators

    International Nuclear Information System (INIS)

    Bozoki, E.; Friedman, A.

    1994-01-01

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

  2. Fused Regression for Multi-source Gene Regulatory Network Inference.

    Directory of Open Access Journals (Sweden)

    Kari Y Lam

    2016-12-01

    Full Text Available Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.

  3. A fuzzy network module extraction technique for gene expression data

    Indian Academy of Sciences (India)

    2014-05-01

    May 1, 2014 ... tion. Therefore, in this study, the structural properties of the co-expression network inferred from gene expression microarray data were compared with the topological prop- erties of the known, well-established network data of the same organism. We use a Web application called. topoGSA (Glaab et al.

  4. Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles.

    Directory of Open Access Journals (Sweden)

    Carlos Prieto

    Full Text Available BACKGROUND: Analysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co-transcribed genes. However, most studies done at global "omic" scale are not focused on human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. Moreover, the technical noise present in genome-wide expression microarrays is another well reported problem that many times is not addressed with robust statistical methods, and the estimation of errors in the data is not provided. METHODOLOGY/PRINCIPAL FINDINGS: Human genome-wide expression data from a controlled set of normal-healthy tissues is used to build a confident human gene coexpression network avoiding both pathological and technical noise. To achieve this we describe a new method that combines several statistical and computational strategies: robust normalization and expression signal calculation; correlation coefficients obtained by parametric and non-parametric methods; random cross-validations; and estimation of the statistical accuracy and coverage of the data. All these methods provide a series of coexpression datasets where the level of error is measured and can be tuned. To define the errors, the rates of true positives are calculated by assignment to biological pathways. The results provide a confident human gene coexpression network that includes 3327 gene-nodes and 15841 coexpression-links and a comparative analysis shows good improvement over previously published datasets. Further functional analysis of a subset core network, validated by two independent methods, shows coherent biological modules that share common transcription factors. The network reveals a map of coexpression clusters organized in well defined functional constellations. Two major regions in this network correspond to genes involved in nuclear and mitochondrial

  5. Congestion control and routing over satellite networks

    Science.gov (United States)

    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

  6. Control of autonomous robot using neural networks

    Science.gov (United States)

    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.

  7. Network performance for graphical control systems

    International Nuclear Information System (INIS)

    Clout, P.; Geib, M.; Westervelt, R.

    1992-01-01

    Vsystem is a toolbox for building graphically-based control systems. The real-tiem database component, Vaccess, includes all the networking support necessary to build multi-computer control systems. Vaccess has two modes of database access, synchronous and asynchronous. Vdraw is another component of Vsystem that allows developers and users to develop control screens and windows by drawing rather than programming. Based on X-windows, Vsystem provides the possibility of running Vdraw either on the workstation with the graphics or on the computer with the database. We have made some measurements on the cpu loading, elapsed time and the network loading to give some guidance in system configuration performance. It will be seen that asynchronous network access gives large performance increases and that the network database change notification protocol can be either more or less efficient than the X-window network protocol, depending on the graphical representation of the data. (author)

  8. Flight control with adaptive critic neural network

    Science.gov (United States)

    Han, Dongchen

    2001-10-01

    In this dissertation, the adaptive critic neural network technique is applied to solve complex nonlinear system control problems. Based on dynamic programming, the adaptive critic neural network can embed the optimal solution into a neural network. Though trained off-line, the neural network forms a real-time feedback controller. Because of its general interpolation properties, the neurocontroller has inherit robustness. The problems solved here are an agile missile control for U.S. Air Force and a midcourse guidance law for U.S. Navy. In the first three papers, the neural network was used to control an air-to-air agile missile to implement a minimum-time heading-reverse in a vertical plane corresponding to following conditions: a system without constraint, a system with control inequality constraint, and a system with state inequality constraint. While the agile missile is a one-dimensional problem, the midcourse guidance law is the first test-bed for multiple-dimensional problem. In the fourth paper, the neurocontroller is synthesized to guide a surface-to-air missile to a fixed final condition, and to a flexible final condition from a variable initial condition. In order to evaluate the adaptive critic neural network approach, the numerical solutions for these cases are also obtained by solving two-point boundary value problem with a shooting method. All of the results showed that the adaptive critic neural network could solve complex nonlinear system control problems.

  9. A comprehensive network and pathway analysis of human deafness genes.

    Science.gov (United States)

    Stamatiou, Georgios A; Stankovic, Konstantina M

    2013-07-01

    To perform comprehensive network and pathway analyses of the genes known to cause genetic hearing loss. In silico analysis of deafness genes using ingenuity pathway analysis (IPA). Genes relevant for hearing and deafness were identified through PubMed literature searches and the Hereditary Hearing Loss Homepage. The genes were assembled into 3 groups: 63 genes that cause nonsyndromic deafness, 107 genes that cause nonsyndromic or syndromic sensorineural deafness, and 112 genes associated with otic capsule development and malformations. Each group of genes was analyzed using IPA to discover the most interconnected, that is, "nodal" molecules, within the most statistically significant networks (p deafness (GPCR), or with predisposition to otosclerosis (TGFB1), but also novel genes that have not been described in the cochlea (HNF4A) and signaling kinases (ERK 1/2). A number of molecules that are likely to be key mediators of genetic hearing loss were identified through three different network and pathway analyses. The molecules included new candidate genes for deafness. Therapies targeting these molecules may be useful to treat deafness.

  10. Plug & Play Control of Hydraulic Networks

    DEFF Research Database (Denmark)

    Jensen, Tom Nørgaard

    2012-01-01

    Process Control research program, which the work presented here is a part of. An industrial case study involving a large-scale hydraulic network with non-linear dynamics is studied. The hydraulic network underlies a district heating system, which provides heating water to a number of end-users in a city...

  11. Network Diffusion-Based Prioritization of Autism Risk Genes Identifies Significantly Connected Gene Modules

    Directory of Open Access Journals (Sweden)

    Ettore Mosca

    2017-09-01

    Full Text Available Autism spectrum disorder (ASD is marked by a strong genetic heterogeneity, which is underlined by the low overlap between ASD risk gene lists proposed in different studies. In this context, molecular networks can be used to analyze the results of several genome-wide studies in order to underline those network regions harboring genetic variations associated with ASD, the so-called “disease modules.” In this work, we used a recent network diffusion-based approach to jointly analyze multiple ASD risk gene lists. We defined genome-scale prioritizations of human genes in relation to ASD genes from multiple studies, found significantly connected gene modules associated with ASD and predicted genes functionally related to ASD risk genes. Most of them play a role in synapsis and neuronal development and function; many are related to syndromes that can be in comorbidity with ASD and the remaining are involved in epigenetics, cell cycle, cell adhesion and cancer.

  12. Semi-supervised prediction of gene regulatory networks using ...

    Indian Academy of Sciences (India)

    Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we ...

  13. Semi-supervised prediction of gene regulatory networks using ...

    Indian Academy of Sciences (India)

    2015-09-28

    Sep 28, 2015 ... Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data.

  14. Indeterminacy of reverse engineering of Gene Regulatory Networks: the curse of gene elasticity.

    Directory of Open Access Journals (Sweden)

    Arun Krishnan

    2007-06-01

    Full Text Available Gene Regulatory Networks (GRNs have become a major focus of interest in recent years. A number of reverse engineering approaches have been developed to help uncover the regulatory networks giving rise to the observed gene expression profiles. However, this is an overspecified problem due to the fact that more than one genotype (network wiring can give rise to the same phenotype. We refer to this phenomenon as "gene elasticity." In this work, we study the effect of this particular problem on the pure, data-driven inference of gene regulatory networks.We simulated a four-gene network in order to produce "data" (protein levels that we use in lieu of real experimental data. We then optimized the network connections between the four genes with a view to obtain the original network that gave rise to the data. We did this for two different cases: one in which only the network connections were optimized and the other in which both the network connections as well as the kinetic parameters (given as reaction probabilities in our case were estimated. We observed that multiple genotypes gave rise to very similar protein levels. Statistical experimentation indicates that it is impossible to differentiate between the different networks on the basis of both equilibrium as well as dynamic data.We show explicitly that reverse engineering of GRNs from pure expression data is an indeterminate problem. Our results suggest the unsuitability of an inferential, purely data-driven approach for the reverse engineering transcriptional networks in the case of gene regulatory networks displaying a certain level of complexity.

  15. Structurally robust control of complex networks

    Science.gov (United States)

    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.

  16. Synchronizability on complex networks via pinning control

    Indian Academy of Sciences (India)

    article/fulltext/pram/080/04/0593-0606 ... Numerical simulations show that different pinning strategies have different pinning synchronizability on the same complex network, and the synchronizability with pinning control is consistent with one ...

  17. Analysis of deterministic cyclic gene regulatory network models with delays

    CERN Document Server

    Ahsen, Mehmet Eren; Niculescu, Silviu-Iulian

    2015-01-01

    This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory chapter provides some insights into molecular biology and GRNs. The mathematical tools necessary for studying the GRN model are then reviewed, in particular Hill functions and Schwarzian derivatives. One chapter is devoted to the analysis of GRNs under negative feedback with time delays and a special case of a homogenous GRN is considered. Asymptotic stability analysis of GRNs under positive feedback is then considered in a separate chapter, in which conditions leading to bi-stability are derived. Graduate and advanced undergraduate students and researchers in control engineering, applied mathematics, systems biology and synthetic biology will find this brief to be a clear and concise introduction to the modeling and analysis of GRNs.

  18. A complex network analysis of hypertension-related genes

    Science.gov (United States)

    Wang, Huan; Xu, Chuan-Yun; Hu, Jing-Bo; Cao, Ke-Fei

    2014-01-01

    In this paper, a network of hypertension-related genes is constructed by analyzing the correlations of gene expression data among the Dahl salt-sensitive rat and two consomic rat strains. The numerical calculations show that this sparse and assortative network has small-world and scale-free properties. Further, 16 key hub genes (Col4a1, Lcn2, Cdk4, etc.) are determined by introducing an integrated centrality and have been confirmed by biological/medical research to play important roles in hypertension.

  19. SELANSI: a toolbox for simulation of stochastic gene regulatory networks.

    Science.gov (United States)

    Pájaro, Manuel; Otero-Muras, Irene; Vázquez, Carlos; Alonso, Antonio A

    2018-03-01

    Gene regulation is inherently stochastic. In many applications concerning Systems and Synthetic Biology such as the reverse engineering and the de novo design of genetic circuits, stochastic effects (yet potentially crucial) are often neglected due to the high computational cost of stochastic simulations. With advances in these fields there is an increasing need of tools providing accurate approximations of the stochastic dynamics of gene regulatory networks (GRNs) with reduced computational effort. This work presents SELANSI (SEmi-LAgrangian SImulation of GRNs), a software toolbox for the simulation of stochastic multidimensional gene regulatory networks. SELANSI exploits intrinsic structural properties of gene regulatory networks to accurately approximate the corresponding Chemical Master Equation with a partial integral differential equation that is solved by a semi-lagrangian method with high efficiency. Networks under consideration might involve multiple genes with self and cross regulations, in which genes can be regulated by different transcription factors. Moreover, the validity of the method is not restricted to a particular type of kinetics. The tool offers total flexibility regarding network topology, kinetics and parameterization, as well as simulation options. SELANSI runs under the MATLAB environment, and is available under GPLv3 license at https://sites.google.com/view/selansi. antonio@iim.csic.es. © The Author(s) 2017. Published by Oxford University Press.

  20. Topology control with IPD network creation games

    International Nuclear Information System (INIS)

    Scholz, Jan C; Greiner, Martin O W

    2007-01-01

    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

  1. Topology control with IPD network creation games

    Energy Technology Data Exchange (ETDEWEB)

    Scholz, Jan C [Frankfurt Institute for Advanced Studies and Frankfurt International Graduate School for Science, Johann Wolfgang Goethe Universitaet, Max-von-Laue-Strasse 1, D-60438 Frankfurt (Germany); Greiner, Martin O W [Corporate Technology, Information and Communications, Siemens AG, D-81730 Munich (Germany)

    2007-06-15

    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.

  2. Decentralized Networked Control of Building Structures

    Czech Academy of Sciences Publication Activity Database

    Bakule, Lubomír; Rehák, Branislav; Papík, Martin

    2016-01-01

    Roč. 31, č. 11 (2016), s. 871-886 ISSN 1093-9687 R&D Projects: GA ČR GA13-02149S Institutional support: RVO:67985556 Keywords : decentralized control * networked control * building structures Subject RIV: BC - Control Systems Theory Impact factor: 5.786, year: 2016

  3. Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae

    Directory of Open Access Journals (Sweden)

    Kluger Yuval

    2006-03-01

    Full Text Available Abstract Background Gene expression and transcription factor (TF binding data have been used to reveal gene transcriptional regulatory networks. Existing knowledge of gene regulation can be presented using gene connectivity networks. However, these composite connectivity networks do not specify the range of biological conditions of the activity of each link in the network. Results We present a novel method that utilizes the expression and binding patterns of the neighboring nodes of each link in existing experimentally-based, literature-derived gene transcriptional regulatory networks and extend them in silico using TF-gene binding motifs and a compendium of large expression data from Saccharomyces cerevisiae. Using this method, we predict several hundreds of new transcriptional regulatory TF-gene links, along with experimental conditions in which known and predicted links become active. This approach unravels new links in the yeast gene transcriptional regulatory network by utilizing the known transcriptional regulatory interactions, and is particularly useful for breaking down the composite transcriptional regulatory network to condition specific networks. Conclusion Our methods can facilitate future binding experiments, as they can considerably help focus on the TFs that must be surveyed to understand gene regulation. (Supplemental material and the latest version of the MATLAB implementation of the United Signature Algorithm is available online at 1 or [see Additional files 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] Additional File 1 overview of supplemental data Click here for file Additional File 2 experimental conditions for each link in figure 5. These are the experimental conditions in which the links are likely to be active. Click here for file Additional File 3 experimental conditions for each link in figure 7. These are the experimental conditions in which the links are likely to be active. Click here for file Additional File 4 Alon

  4. Data acquisition and control network

    International Nuclear Information System (INIS)

    Hajjar, Victor.

    1983-02-01

    We have participated in the construction of the CELLO detector on the PETRA e + e - Collider in Hamburg in order to test some of the current high energy physics theories. Some 60.000 channels collecting the detector informations are connected to the main computer through the CAMAC acquisition system and specialized ROMULUS subsystems. Each of these subsystems is monitored by its dedicated microprocessor using a CAMAC dataway spy module. All these microprocessors are connected to the main computer through a ''STAR'' type network. Data are read out by the main computer (PDP11-45) and concentrated in a circular type buffer. They are then filtered and transfered to a PDP11-55, also in the network, for storing [fr

  5. Network Communication for Low Level RF Control

    International Nuclear Information System (INIS)

    Liu Weiqing; Yin Chengke; Zhang Tongxuan; Fu Zechuan; Liu Jianfei

    2009-01-01

    Low Level RF (LLRF) control system for storage ring of Shanghai Synchrotron Radiation Facility (SSRF) has been built by digital technology. The settings of parameters and the feedback loop status are carried out through the network communication interface, and the local oscillation and clock, which is the important component of the digital LLRF control system, are also configured through network communication. NIOS II processor was employed as a core to build the embedded system with a real-time operating system MicroC/OS-II, finally Lightweight TCP/IP (LwIP) was used to achieve the communication interface. The communication network is stable after a long-term operation. (authors)

  6. Control systems with network delay

    OpenAIRE

    Şabanoviç, Asif; Sabanovic, Asif; Ohnishi, Kouhei; Yashiro, Daisuke; Acer, Merve; Ş.-Behliloviç, Nadira; S.-Behlilovic, Nadira

    2009-01-01

    In this paper motion control systems with delay in measurement and control channels are discussed and a new structure of the observer-predictor is proposed. The feature of the proposed system is enforcement of the convergence in both the estimation and the prediction of the plant output in the presence of the variable, unknown delay in both measurement and in the control channels. The estimation is based on the available data – undelayed control input, the delayed measurement of position o...

  7. The network of global corporate control.

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

  9. Slave nodes and the controllability of metabolic networks

    International Nuclear Information System (INIS)

    Kim, Dong-Hee; Motter, Adilson E

    2009-01-01

    Recent work on synthetic rescues has shown that the targeted deletion of specific metabolic genes can often be used to rescue otherwise non-viable mutants. This raises a fundamental biophysical question: to what extent can the whole-cell behavior of a large metabolic network be controlled by constraining the flux of one or more reactions in the network? This touches upon the issue of the number of degrees of freedom contained by one such network. Using the metabolic network of Escherichia coli as a model system, here we address this question theoretically by exploring not only reaction deletions, but also a continuum of all possible reaction expression levels. We show that the behavior of the metabolic network can be largely manipulated by the pinned expression of a single reaction. In particular, a relevant fraction of the metabolic reactions exhibits canalizing interactions, in that the specification of one reaction flux determines cellular growth as well as the fluxes of most other reactions in optimal steady states. The activity of individual reactions can thus be used as surrogates to monitor and possibly control cellular growth and other whole-cell behaviors. In addition to its implications for the study of control processes, our methodology provides a new approach to study how the integrated dynamics of the entire metabolic network emerges from the coordinated behavior of its component parts.

  10. TimeXNet: identifying active gene sub-networks using time-course gene expression profiles.

    Science.gov (United States)

    Patil, Ashwini; Nakai, Kenta

    2014-01-01

    Time-course gene expression profiles are frequently used to provide insight into the changes in cellular state over time and to infer the molecular pathways involved. When combined with large-scale molecular interaction networks, such data can provide information about the dynamics of cellular response to stimulus. However, few tools are currently available to predict a single active gene sub-network from time-course gene expression profiles. We introduce a tool, TimeXNet, which identifies active gene sub-networks with temporal paths using time-course gene expression profiles in the context of a weighted gene regulatory and protein-protein interaction network. TimeXNet uses a specialized form of the network flow optimization approach to identify the most probable paths connecting the genes with significant changes in expression at consecutive time intervals. TimeXNet has been extensively evaluated for its ability to predict novel regulators and their associated pathways within active gene sub-networks in the mouse innate immune response and the yeast osmotic stress response. Compared to other similar methods, TimeXNet identified up to 50% more novel regulators from independent experimental datasets. It predicted paths within a greater number of known pathways with longer overlaps (up to 7 consecutive edges) within these pathways. TimeXNet was also shown to be robust in the presence of varying amounts of noise in the molecular interaction network. TimeXNet is a reliable tool that can be used to study cellular response to stimuli through the identification of time-dependent active gene sub-networks in diverse biological systems. It is significantly better than other similar tools. TimeXNet is implemented in Java as a stand-alone application and supported on Linux, MS Windows and Macintosh. The output of TimeXNet can be directly viewed in Cytoscape. TimeXNet is freely available for non-commercial users.

  11. Combining many interaction networks to predict gene function and analyze gene lists.

    Science.gov (United States)

    Mostafavi, Sara; Morris, Quaid

    2012-05-01

    In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Singular Perturbation Analysis and Gene Regulatory Networks with Delay

    Science.gov (United States)

    Shlykova, Irina; Ponosov, Arcady

    2009-09-01

    There are different ways of how to model gene regulatory networks. Differential equations allow for a detailed description of the network's dynamics and provide an explicit model of the gene concentration changes over time. Production and relative degradation rate functions used in such models depend on the vector of steeply sloped threshold functions which characterize the activity of genes. The most popular example of the threshold functions comes from the Boolean network approach, where the threshold functions are given by step functions. The system of differential equations becomes then piecewise linear. The dynamics of this system can be described very easily between the thresholds, but not in the switching domains. For instance this approach fails to analyze stationary points of the system and to define continuous solutions in the switching domains. These problems were studied in [2], [3], but the proposed model did not take into account a time delay in cellular systems. However, analysis of real gene expression data shows a considerable number of time-delayed interactions suggesting that time delay is essential in gene regulation. Therefore, delays may have a great effect on the dynamics of the system presenting one of the critical factors that should be considered in reconstruction of gene regulatory networks. The goal of this work is to apply the singular perturbation analysis to certain systems with delay and to obtain an analog of Tikhonov's theorem, which provides sufficient conditions for constracting the limit system in the delay case.

  13. Evaluation of gene association methods for coexpression network construction and biological knowledge discovery.

    Directory of Open Access Journals (Sweden)

    Sapna Kumari

    Full Text Available BACKGROUND: Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. METHODS AND RESULTS: In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. CONCLUSIONS: We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

  14. NOVANET: communications network for a control system

    International Nuclear Information System (INIS)

    Hill, J.R.; Severyn, J.R.; VanArsdall, P.J.

    1983-01-01

    NOVANET is a control system oriented fiber optic local area network that was designed to meet the unique and often conflicting requirements of the Nova laser control system which will begin operation in 1984. The computers and data acquisition devices that form the distributed control system for a large laser fusion research facility need reliable, high speed communications. Both control/status messages and experimental data must be handled. A subset of NOVANET is currently operating on the two beam Novette laser system

  15. Learning gene regulatory networks from only positive and unlabeled data

    Directory of Open Access Journals (Sweden)

    Elkan Charles

    2010-05-01

    Full Text Available Abstract Background Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact. Results A recent advance in research on data mining is a method capable of learning a classifier from only positive and unlabeled examples, that does not need labeled negative examples. Applied to the reconstruction of gene regulatory networks, we show that this method significantly outperforms the current state of the art of machine learning methods. We assess the new method using both simulated and experimental data, and obtain major performance improvement. Conclusions Compared to unsupervised methods for gene network inference, supervised methods are potentially more accurate, but for training they need a complete set of known regulatory connections. A supervised method that can be trained using only positive and unlabeled data, as presented in this paper, is especially beneficial for the task of inferring gene regulatory networks, because only an incomplete set of known regulatory connections is available in public databases such as RegulonDB, TRRD, KEGG, Transfac, and IPA.

  16. Using GeneReg to construct time delay gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Qian Ziliang

    2010-05-01

    Full Text Available Abstract Background Understanding gene expression and regulation is essential for understanding biological mechanisms. Because gene expression profiling has been widely used in basic biological research, especially in transcription regulation studies, we have developed GeneReg, an easy-to-use R package, to construct gene regulatory networks from time course gene expression profiling data; More importantly, this package can provide information about time delays between expression change in a regulator and that of its target genes. Findings The R package GeneReg is based on time delay linear regression, which can generate a model of the expression levels of regulators at a given time point against the expression levels of their target genes at a later time point. There are two parameters in the model, time delay and regulation coefficient. Time delay is the time lag during which expression change of the regulator is transmitted to change in target gene expression. Regulation coefficient expresses the regulation effect: a positive regulation coefficient indicates activation and negative indicates repression. GeneReg was implemented on a real Saccharomyces cerevisiae cell cycle dataset; more than thirty percent of the modeled regulations, based entirely on gene expression files, were found to be consistent with previous discoveries from known databases. Conclusions GeneReg is an easy-to-use, simple, fast R package for gene regulatory network construction from short time course gene expression data. It may be applied to study time-related biological processes such as cell cycle, cell differentiation, or causal inference.

  17. Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks.

    Science.gov (United States)

    Saik, Olga V; Demenkov, Pavel S; Ivanisenko, Timofey V; Bragina, Elena Yu; Freidin, Maxim B; Goncharova, Irina A; Dosenko, Victor E; Zolotareva, Olga I; Hofestaedt, Ralf; Lavrik, Inna N; Rogaev, Evgeny I; Ivanisenko, Vladimir A

    2018-02-13

    Hypertension and bronchial asthma are a major issue for people's health. As of 2014, approximately one billion adults, or ~ 22% of the world population, have had hypertension. As of 2011, 235-330 million people globally have been affected by asthma and approximately 250,000-345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets. Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension - 713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in

  18. Admission Control Methods in IMS Networks

    Directory of Open Access Journals (Sweden)

    Erik Chromy

    2016-09-01

    Full Text Available In this paper we present admission control methods for IMS network. The task of RACS block is to accept or reject new connection into the network. The main goal of the admission control method is to ensure the Quality of Service not only for new connection but also for already accepted connections. We discuss and compare three admission control algorithms in the paper from the qualitative parameters point of view.

  19. Optimal control learning with artificial neural networks

    International Nuclear Information System (INIS)

    Martinez, J.M.; Parey, C.; Houkari, M.

    1993-01-01

    This paper shows neural networks capabilities in optimal control applications of non linear dynamic systems. Our method is issued of a classical method concerning the direct research of the optimal control using gradient techniques. We show that neural approach and backpropagation paradigm are able to solve efficiently equations relative to necessary conditions for an optimizing solution. We have taken into account the known capabilities of multi layered networks in approximation functions. And for dynamic systems, we have generalized the indirect learning of inverse model adaptive architecture that is capable to define an optimal control in relation to a temporal criterion. (orig.)

  20. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    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 cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....... and controlled have thus become essential factors for efficient 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...

  1. Exploiting traffic periodicity in industrial control networks

    NARCIS (Netherlands)

    Barbosa, R.R.R.; Sadre, R.; Pras, Aiko

    Industrial control systems play a major role in the operation of critical infrastructure assets. Due to the polling mechanisms typically used to retrieve data from field devices, industrial control network traffic exhibits strong periodic patterns. This paper presents a novel approach that uses

  2. On the Design of Energy Efficient Optical Networks with Software Defined Networking Control Across Core and Access Networks

    DEFF Research Database (Denmark)

    Wang, Jiayuan; Yan, Ying; Dittmann, Lars

    2013-01-01

    This paper presents a Software Defined Networking (SDN) control plane based on an overlay GMPLS control model. The SDN control platform manages optical core networks (WDM/DWDM networks) and the associated access networks (GPON networks), which makes it possible to gather global information...

  3. Relaxation rates of gene expression kinetics reveal the feedback signs of autoregulatory gene networks

    Science.gov (United States)

    Jia, Chen; Qian, Hong; Chen, Min; Zhang, Michael Q.

    2018-03-01

    The transient response to a stimulus and subsequent recovery to a steady state are the fundamental characteristics of a living organism. Here we study the relaxation kinetics of autoregulatory gene networks based on the chemical master equation model of single-cell stochastic gene expression with nonlinear feedback regulation. We report a novel relation between the rate of relaxation, characterized by the spectral gap of the Markov model, and the feedback sign of the underlying gene circuit. When a network has no feedback, the relaxation rate is exactly the decaying rate of the protein. We further show that positive feedback always slows down the relaxation kinetics while negative feedback always speeds it up. Numerical simulations demonstrate that this relation provides a possible method to infer the feedback topology of autoregulatory gene networks by using time-series data of gene expression.

  4. Admission Control Methods in IMS Networks

    Directory of Open Access Journals (Sweden)

    Michal Cuba

    2016-01-01

    Full Text Available The article deals with solving the problem of ensuring Quality of Service (QoS in IP Multimedia Subsystem (IMS networks. Admission Control methods (AC are used to prevent network congestion and the decrease of QoS. The main function of AC is to maximize utilization of network resources and to ensure the level of QoS. Four methods were chosen for comparison. These methods are described in the main part of the article. The last part deals with simulations of these methods in the software MATLAB.

  5. Decentralized control of ecological and biological networks through Evolutionary Network Control

    Directory of Open Access Journals (Sweden)

    Alessandro Ferrarini

    2016-09-01

    Full Text Available Evolutionary Network Control (ENC has been recently introduced to allow the control of any kind of ecological and biological networks, with an arbitrary number of nodes and links, acting from inside and/or outside. To date, ENC has been applied using a centralized approach where an arbitrary number of network nodes and links could be tamed. This approach has shown to be effective in the control of ecological and biological networks. However a decentralized control, where only one node and the correspondent input/output links are controlled, could be more economic from a computational viewpoint, in particular when the network is very large (i.e. big data. In this view, ENC is upgraded here to realize the decentralized control of ecological and biological nets.

  6. Functional analysis of prognostic gene expression network genes in metastatic breast cancer models.

    Directory of Open Access Journals (Sweden)

    Thomas R Geiger

    Full Text Available Identification of conserved co-expression networks is a useful tool for clustering groups of genes enriched for common molecular or cellular functions [1]. The relative importance of genes within networks can frequently be inferred by the degree of connectivity, with those displaying high connectivity being significantly more likely to be associated with specific molecular functions [2]. Previously we utilized cross-species network analysis to identify two network modules that were significantly associated with distant metastasis free survival in breast cancer. Here, we validate one of the highly connected genes as a metastasis associated gene. Tpx2, the most highly connected gene within a proliferation network specifically prognostic for estrogen receptor positive (ER+ breast cancers, enhances metastatic disease, but in a tumor autonomous, proliferation-independent manner. Histologic analysis suggests instead that variation of TPX2 levels within disseminated tumor cells may influence the transition between dormant to actively proliferating cells in the secondary site. These results support the co-expression network approach for identification of new metastasis-associated genes to provide new information regarding the etiology of breast cancer progression and metastatic disease.

  7. Learning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus

    KAUST Repository

    Fujii, Chisato

    2015-04-16

    Gene regulatory networks analyze the relationships between genes allowing us to un- derstand the gene regulatory interactions in systems biology. Gene expression data from the microarray experiments is used to obtain the gene regulatory networks. How- ever, the microarray data is discrete, noisy and non-linear which makes learning the networks a challenging problem and existing gene network inference methods do not give consistent results. Current state-of-the-art study uses the average-ranking-based consensus method to combine and average the ranked predictions from individual methods. However each individual method has an equal contribution to the consen- sus prediction. We have developed a linear programming-based consensus approach which uses learned weights from linear programming among individual methods such that the methods have di↵erent weights depending on their performance. Our result reveals that assigning di↵erent weights to individual methods rather than giving them equal weights improves the performance of the consensus. The linear programming- based consensus method is evaluated and it had the best performance on in silico and Saccharomyces cerevisiae networks, and the second best on the Escherichia coli network outperformed by Inferelator Pipeline method which gives inconsistent results across a wide range of microarray data sets.

  8. Toward controlling perturbations in robotic sensor networks

    Science.gov (United States)

    Banerjee, Ashis G.; Majumder, Saikat R.

    2014-06-01

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

  9. Informed walks: whispering hints to gene hunters inside networks' jungle.

    Science.gov (United States)

    Bourdakou, Marilena M; Spyrou, George M

    2017-10-11

    Systemic approaches offer a different point of view on the analysis of several types of molecular associations as well as on the identification of specific gene communities in several cancer types. However, due to lack of sufficient data needed to construct networks based on experimental evidence, statistical gene co-expression networks are widely used instead. Many efforts have been made to exploit the information hidden in these networks. However, these approaches still need to capitalize comprehensively the prior knowledge encrypted into molecular pathway associations and improve their efficiency regarding the discovery of both exclusive subnetworks as candidate biomarkers and conserved subnetworks that may uncover common origins of several cancer types. In this study we present the development of the Informed Walks model based on random walks that incorporate information from molecular pathways to mine candidate genes and gene-gene links. The proposed model has been applied to TCGA (The Cancer Genome Atlas) datasets from seven different cancer types, exploring the reconstructed co-expression networks of the whole set of genes and driving to highlighted sub-networks for each cancer type. In the sequel, we elucidated the impact of each subnetwork on the indication of underlying exclusive and common molecular mechanisms as well as on the short-listing of drugs that have the potential to suppress the corresponding cancer type through a drug-repurposing pipeline. We have developed a method of gene subnetwork highlighting based on prior knowledge, capable to give fruitful insights regarding the underlying molecular mechanisms and valuable input to drug-repurposing pipelines for a variety of cancer types.

  10. Listening to the noise: random fluctuations reveal gene network parameters

    Energy Technology Data Exchange (ETDEWEB)

    Munsky, Brian [Los Alamos National Laboratory; Khammash, Mustafa [UCSB

    2009-01-01

    The cellular environment is abuzz with noise. The origin of this noise is attributed to the inherent random motion of reacting molecules that take part in gene expression and post expression interactions. In this noisy environment, clonal populations of cells exhibit cell-to-cell variability that frequently manifests as significant phenotypic differences within the cellular population. The stochastic fluctuations in cellular constituents induced by noise can be measured and their statistics quantified. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. This establishes a potentially powerful approach for the identification of gene networks and offers a new window into the workings of these networks.

  11. Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network.

    Science.gov (United States)

    Kordmahalleh, Mina Moradi; Sefidmazgi, Mohammad Gorji; Harrison, Scott H; Homaifar, Abdollah

    2017-01-01

    The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications. We have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN. Our HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network

  12. Network-based integration of GWAS and gene expression identifies a HOX-centric network associated with serous ovarian cancer risk

    Science.gov (United States)

    Kar, Siddhartha P.; Tyrer, Jonathan P.; Li, Qiyuan; Lawrenson, Kate; Aben, Katja K.H.; Anton-Culver, Hoda; Antonenkova, Natalia; Chenevix-Trench, Georgia; Baker, Helen; Bandera, Elisa V.; Bean, Yukie T.; Beckmann, Matthias W.; Berchuck, Andrew; Bisogna, Maria; Bjørge, Line; Bogdanova, Natalia; Brinton, Louise; Brooks-Wilson, Angela; Butzow, Ralf; Campbell, Ian; Carty, Karen; Chang-Claude, Jenny; Chen, Yian Ann; Chen, Zhihua; Cook, Linda S.; Cramer, Daniel; Cunningham, Julie M.; Cybulski, Cezary; Dansonka-Mieszkowska, Agnieszka; Dennis, Joe; Dicks, Ed; Doherty, Jennifer A.; Dörk, Thilo; du Bois, Andreas; Dürst, Matthias; Eccles, Diana; Easton, Douglas F.; Edwards, Robert P.; Ekici, Arif B.; Fasching, Peter A.; Fridley, Brooke L.; Gao, Yu-Tang; Gentry-Maharaj, Aleksandra; Giles, Graham G.; Glasspool, Rosalind; Goode, Ellen L.; Goodman, Marc T.; Grownwald, Jacek; Harrington, Patricia; Harter, Philipp; Hein, Alexander; Heitz, Florian; Hildebrandt, Michelle A.T.; Hillemanns, Peter; Hogdall, Estrid; Hogdall, Claus K.; Hosono, Satoyo; Iversen, Edwin S.; Jakubowska, Anna; Paul, James; Jensen, Allan; Ji, Bu-Tian; Karlan, Beth Y; Kjaer, Susanne K.; Kelemen, Linda E.; Kellar, Melissa; Kelley, Joseph; Kiemeney, Lambertus A.; Krakstad, Camilla; Kupryjanczyk, Jolanta; Lambrechts, Diether; Lambrechts, Sandrina; Le, Nhu D.; Lee, Alice W.; Lele, Shashi; Leminen, Arto; Lester, Jenny; Levine, Douglas A.; Liang, Dong; Lissowska, Jolanta; Lu, Karen; Lubinski, Jan; Lundvall, Lene; Massuger, Leon; Matsuo, Keitaro; McGuire, Valerie; McLaughlin, John R.; McNeish, Iain A.; Menon, Usha; Modugno, Francesmary; Moysich, Kirsten B.; Narod, Steven A.; Nedergaard, Lotte; Ness, Roberta B.; Nevanlinna, Heli; Odunsi, Kunle; Olson, Sara H.; Orlow, Irene; Orsulic, Sandra; Weber, Rachel Palmieri; Pearce, Celeste Leigh; Pejovic, Tanja; Pelttari, Liisa M.; Permuth-Wey, Jennifer; Phelan, Catherine M.; Pike, Malcolm C.; Poole, Elizabeth M.; Ramus, Susan J.; Risch, Harvey A.; Rosen, Barry; Rossing, Mary Anne; Rothstein, Joseph H.; Rudolph, Anja; Runnebaum, Ingo B.; Rzepecka, Iwona K.; Salvesen, Helga B.; Schildkraut, Joellen M.; Schwaab, Ira; Shu, Xiao-Ou; Shvetsov, Yurii B; Siddiqui, Nadeem; Sieh, Weiva; Song, Honglin; Southey, Melissa C.; Sucheston-Campbell, Lara E.; Tangen, Ingvild L.; Teo, Soo-Hwang; Terry, Kathryn L.; Thompson, Pamela J; Timorek, Agnieszka; Tsai, Ya-Yu; Tworoger, Shelley S.; van Altena, Anne M.; Van Nieuwenhuysen, Els; Vergote, Ignace; Vierkant, Robert A.; Wang-Gohrke, Shan; Walsh, Christine; Wentzensen, Nicolas; Whittemore, Alice S.; Wicklund, Kristine G.; Wilkens, Lynne R.; Woo, Yin-Ling; Wu, Xifeng; Wu, Anna; Yang, Hannah; Zheng, Wei; Ziogas, Argyrios; Sellers, Thomas A.; Monteiro, Alvaro N. A.; Freedman, Matthew L.; Gayther, Simon A.; Pharoah, Paul D. P.

    2015-01-01

    Background Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by co-expression may also be enriched for additional EOC risk associations. Methods We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly co-expressed with each selected TF gene in the unified microarray data set of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this data set were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls). Results Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P<0.05 and FDR<0.05). These results were replicated (P<0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network. Conclusion We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development. Impact Network analysis integrating large, context-specific data sets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization. PMID:26209509

  13. iRegulon: from a gene list to a gene regulatory network using large motif and track collections.

    Directory of Open Access Journals (Sweden)

    Rekin's Janky

    2014-07-01

    Full Text Available Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying a co-expressed gene set using cis-regulatory sequence analysis. iRegulon implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets. We increase the accuracy of network inference by using very large motif collections of up to ten thousand position weight matrices collected from various species, and linking these to candidate human TFs via a motif2TF procedure. We validate iRegulon on gene sets derived from ENCODE ChIP-seq data with increasing levels of noise, and we compare iRegulon with existing motif discovery methods. Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data. In particular, we over-activate p53 in breast cancer cells, followed by RNA-seq and ChIP-seq, and could identify an extensive up-regulated network controlled directly by p53. Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY. Finally, we generalize our computational framework to include regulatory tracks such as ChIP-seq data and show how motif and track discovery can be combined to map functional regulatory interactions among co-expressed genes. iRegulon is available as a Cytoscape plugin from http://iregulon.aertslab.org.

  14. Finding gene regulatory network candidates using the gene expression knowledge base.

    Science.gov (United States)

    Venkatesan, Aravind; Tripathi, Sushil; Sanz de Galdeano, Alejandro; Blondé, Ward; Lægreid, Astrid; Mironov, Vladimir; Kuiper, Martin

    2014-12-10

    Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

  15. Fan-out in gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Sauro Herbert M

    2010-12-01

    Full Text Available Abstract Background In synthetic biology, gene regulatory circuits are often constructed by combining smaller circuit components. Connections between components are achieved by transcription factors acting on promoters. If the individual components behave as true modules and certain module interface conditions are satisfied, the function of the composite circuits can in principle be predicted. Results In this paper, we investigate one of the interface conditions: fan-out. We quantify the fan-out, a concept widely used in electrical engineering, to indicate the maximum number of the downstream inputs that an upstream output transcription factor can regulate. The fan-out is shown to be closely related to retroactivity studied by Del Vecchio, et al. An efficient operational method for measuring the fan-out is proposed and shown to be applied to various types of module interfaces. The fan-out is also shown to be enhanced by self-inhibitory regulation on the output. The potential role of an inhibitory regulation is discussed. Conclusions The proposed estimation method for fan-out not only provides an experimentally efficient way for quantifying the level of modularity in gene regulatory circuits but also helps characterize and design module interfaces, enabling the modular construction of gene circuits.

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

    Directory of Open Access Journals (Sweden)

    Fei Xiao

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

  17. Multivariate Entropy Characterizes the Gene Expression and Protein-Protein Networks in Four Types of Cancer

    Directory of Open Access Journals (Sweden)

    Angel Juarez-Flores

    2018-02-01

    Full Text Available There is an important urgency to detect cancer at early stages to treat it, to improve the patients’ lifespans, and even to cure it. In this work, we determined the entropic contributions of genes in cancer networks. We detected sudden changes in entropy values in melanoma, hepatocellular carcinoma, pancreatic cancer, and squamous lung cell carcinoma associated to transitions from healthy controls to cancer. We also identified the most relevant genes involved in carcinogenic process of the four types of cancer with the help of entropic changes in local networks. Their corresponding proteins could be used as potential targets for treatments and as biomarkers of cancer.

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

    Science.gov (United States)

    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.

  19. Modeling, Optimization & Control of Hydraulic Networks

    DEFF Research Database (Denmark)

    Tahavori, Maryamsadat

    2014-01-01

    Water supply systems consist of a number of pumping stations, which deliver water to the customers via pipeline networks and elevated reservoirs. A huge amount of drinking water is lost before it reaches to end-users due to the leakage in pipe networks. A cost effective solution to reduce leakage...... systems. To have better understanding of water leakage, to control pressure and leakage effectively and for optimal design of water supply system, suitable modeling is an important prerequisite. Therefore a model with the main objective of pressure control and consequently leakage reduction is presented...

  20. An algebra-based method for inferring gene regulatory networks.

    Science.gov (United States)

    Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard

    2014-03-26

    The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the

  1. Next Generation Network Routing and Control Plane

    DEFF Research Database (Denmark)

    Fu, Rong

    proved, the dominating Border Gateway Protocol (BGP) cannot address all the issues that in inter-domain QoS routing. Thus a new protocol or network architecture has to be developed to be able to carry the inter-domain traffic with the QoS and TE consideration. Moreover, the current network control also...... (RACF) provides the platform that enables cooperation and ubiquitous integration between networks. In this paper, we investigate in the network architecture, protocols and algorithms for inter-domain QoS routing and traffic engineering. The PCE based inter-domain routing architecture is enhanced...... with Domain Path Vector based protocol that compute the domain level path dynamically for the further inter-domain path routing mechanism Backward Recursive Path Computation (BRPC). Furthermore, several algorithms are proposed to compute the domain-level path under more than one constrains (multi...

  2. The capacity for multistability in small gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Grotewold Erich

    2009-09-01

    Full Text Available Abstract Background Recent years have seen a dramatic increase in the use of mathematical modeling to gain insight into gene regulatory network behavior across many different organisms. In particular, there has been considerable interest in using mathematical tools to understand how multistable regulatory networks may contribute to developmental processes such as cell fate determination. Indeed, such a network may subserve the formation of unicellular leaf hairs (trichomes in the model plant Arabidopsis thaliana. Results In order to investigate the capacity of small gene regulatory networks to generate multiple equilibria, we present a chemical reaction network (CRN-based modeling formalism and describe a number of methods for CRN analysis in a parameter-free context. These methods are compared and applied to a full set of one-component subnetworks, as well as a large random sample from 40,680 similarly constructed two-component subnetworks. We find that positive feedback and cooperativity mediated by transcription factor (TF dimerization is a requirement for one-component subnetwork bistability. For subnetworks with two components, the presence of these processes increases the probability that a randomly sampled subnetwork will exhibit multiple equilibria, although we find several examples of bistable two-component subnetworks that do not involve cooperative TF-promoter binding. In the specific case of epidermal differentiation in Arabidopsis, dimerization of the GL3-GL1 complex and cooperative sequential binding of GL3-GL1 to the CPC promoter are each independently sufficient for bistability. Conclusion Computational methods utilizing CRN-specific theorems to rule out bistability in small gene regulatory networks are far superior to techniques generally applicable to deterministic ODE systems. Using these methods to conduct an unbiased survey of parameter-free deterministic models of small networks, and the Arabidopsis epidermal cell

  3. Modification of gene duplicability during the evolution of protein interaction network.

    Directory of Open Access Journals (Sweden)

    Matteo D'Antonio

    2011-04-01

    Full Text Available Duplications of genes encoding highly connected and essential proteins are selected against in several species but not in human, where duplicated genes encode highly connected proteins. To understand when and how gene duplicability changed in evolution, we compare gene and network properties in four species (Escherichia coli, yeast, fly, and human that are representative of the increase in evolutionary complexity, defined as progressive growth in the number of genes, cells, and cell types. We find that the origin and conservation of a gene significantly correlates with the properties of the encoded protein in the protein-protein interaction network. All four species preserve a core of singleton and central hubs that originated early in evolution, are highly conserved, and accomplish basic biological functions. Another group of hubs appeared in metazoans and duplicated in vertebrates, mostly through vertebrate-specific whole genome duplication. Such recent and duplicated hubs are frequently targets of microRNAs and show tissue-selective expression, suggesting that these are alternative mechanisms to control their dosage. Our study shows how networks modified during evolution and contributes to explaining the occurrence of somatic genetic diseases, such as cancer, in terms of network perturbations.

  4. Rodent Zic Genes in Neural Network Wiring.

    Science.gov (United States)

    Herrera, Eloísa

    2018-01-01

    The formation of the nervous system is a multistep process that yields a mature brain. Failure in any of the steps of this process may cause brain malfunction. In the early stages of embryonic development, neural progenitors quickly proliferate and then, at a specific moment, differentiate into neurons or glia. Once they become postmitotic neurons, they migrate to their final destinations and begin to extend their axons to connect with other neurons, sometimes located in quite distant regions, to establish different neural circuits. During the last decade, it has become evident that Zic genes, in addition to playing important roles in early development (e.g., gastrulation and neural tube closure), are involved in different processes of late brain development, such as neuronal migration, axon guidance, and refinement of axon terminals. ZIC proteins are therefore essential for the proper wiring and connectivity of the brain. In this chapter, we review our current knowledge of the role of Zic genes in the late stages of neural circuit formation.

  5. Schizophrenia gene networks and pathways and their applications for novel candidate gene selection.

    Directory of Open Access Journals (Sweden)

    Jingchun Sun

    2010-06-01

    Full Text Available Schizophrenia (SZ is a heritable, complex mental disorder. We have seen limited success in finding causal genes for schizophrenia from numerous conventional studies. Protein interaction network and pathway-based analysis may provide us an alternative and effective approach to investigating the molecular mechanisms of schizophrenia.We selected a list of schizophrenia candidate genes (SZGenes using a multi-dimensional evidence-based approach. The global network properties of proteins encoded by these SZGenes were explored in the context of the human protein interactome while local network properties were investigated by comparing SZ-specific and cancer-specific networks that were extracted from the human interactome. Relative to cancer genes, we observed that SZGenes tend to have an intermediate degree and an intermediate efficiency on a perturbation spreading throughout the human interactome. This suggested that schizophrenia might have different pathological mechanisms from cancer even though both are complex diseases. We conducted pathway analysis using Ingenuity System and constructed the first schizophrenia molecular network (SMN based on protein interaction networks, pathways and literature survey. We identified 24 pathways overrepresented in SZGenes and examined their interactions and crosstalk. We observed that these pathways were related to neurodevelopment, immune system, and retinoic X receptor (RXR. Our examination of SMN revealed that schizophrenia is a dynamic process caused by dysregulation of the multiple pathways. Finally, we applied the network/pathway approach to identify novel candidate genes, some of which could be verified by experiments.This study provides the first comprehensive review of the network and pathway characteristics of schizophrenia candidate genes. Our preliminary results suggest that this systems biology approach might prove promising for selection of candidate genes for complex diseases. Our findings have

  6. Gene Network for Identifying the Entropy Changes of Different Modules in Pediatric Sepsis

    Directory of Open Access Journals (Sweden)

    Jing Yang

    2016-12-01

    Full Text Available Background/Aims: Pediatric sepsis is a disease that threatens life of children. The incidence of pediatric sepsis is higher in developing countries due to various reasons, such as insufficient immunization and nutrition, water and air pollution, etc. Exploring the potential genes via different methods is of significance for the prevention and treatment of pediatric sepsis. This study aimed to identify potential genes associated with pediatric sepsis utilizing analysis of gene network and entropy. Methods: The mRNA expression in the blood samples collected from 20 septic children and 30 healthy controls was quantified by using Affymetrix HG-U133A microarray. Two condition-specific protein-protein interaction networks (PINs, one for the healthy control and the other one for the children with sepsis, were deduced by combining the fundamental human PINs with gene expression profiles in the two phenotypes. Subsequently, distinct modules from the two conditional networks were extracted by adopting a maximal clique-merging approach. Delta entropy (ΔS was calculated between sepsis and control modules. Results: Then, key genes displaying changes in gene composition were identified by matching the control and sepsis modules. Two objective modules were obtained, in which ribosomal protein RPL4 and RPL9 as well as TOP2A were probably considered as the key genes differentiating sepsis from healthy controls. Conclusion: According to previous reports and this work, TOP2A is the potential gene therapy target for pediatric sepsis. The relationship between pediatric sepsis and RPL4 and RPL9 needs further investigation.

  7. GeneNetwork: framework for web-based genetics

    NARCIS (Netherlands)

    Sloan, Zachary; Arends, Danny; Broman, Karl W.; Centeno, Arthur; Furlotte, Nicholas; Nijveen, H.; Yan, Lei; Zhou, Xiang; Williams, Robert W.; Prins, Pjotr

    2016-01-01

    GeneNetwork (GN) is a free and open source (FOSS) framework for web-based genetics that can be deployed anywhere. GN allows biologists to upload high-throughput experimental data, such as expression data from microarrays and RNA-seq, and also `classic' phenotypes, such as disease phenotypes. These

  8. FUMET: A fuzzy network module extraction technique for gene ...

    Indian Academy of Sciences (India)

    FUMET: A fuzzy network module extraction technique for gene expression data. PRIYAKSHI MAHANTA, HASIN AFZAL AHMED, DHRUBA KUMAR BHATTACHARYYA and ASHISH GHOSH http://www.ias.ac.in/jbiosci. J. Biosci. 39(3), June 2014, 351–364, © Indian Academy of Sciences. Supplementary material ...

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

    Science.gov (United States)

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

    2017-06-16

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

  10. Construction of coffee transcriptome networks based on gene annotation semantics

    Directory of Open Access Journals (Sweden)

    Castillo Luis F.

    2012-12-01

    Full Text Available Gene annotation is a process that encompasses multiple approaches on the analysis of nucleic acids or protein sequences in order to assign structural and functional characteristics to gene models. When thousands of gene models are being described in an organism genome, construction and visualization of gene networks impose novel challenges in the understanding of complex expression patterns and the generation of new knowledge in genomics research. In order to take advantage of accumulated text data after conventional gene sequence analysis, this work applied semantics in combination with visualization tools to build transcriptome networks from a set of coffee gene annotations. A set of selected coffee transcriptome sequences, chosen by the quality of the sequence comparison reported by Basic Local Alignment Search Tool (BLAST and Interproscan, were filtered out by coverage, identity, length of the query, and e-values. Meanwhile, term descriptors for molecular biology and biochemistry were obtained along the Wordnet dictionary in order to construct a Resource Description Framework (RDF using Ruby scripts and Methontology to find associations between concepts. Relationships between sequence annotations and semantic concepts were graphically represented through a total of 6845 oriented vectors, which were reduced to 745 non-redundant associations. A large gene network connecting transcripts by way of relational concepts was created where detailed connections remain to be validated for biological significance based on current biochemical and genetics frameworks. Besides reusing text information in the generation of gene connections and for data mining purposes, this tool development opens the possibility to visualize complex and abundant transcriptome data, and triggers the formulation of new hypotheses in metabolic pathways analysis.

  11. Hexacopter trajectory control using a neural network

    Science.gov (United States)

    Artale, V.; Collotta, M.; Pau, G.; Ricciardello, A.

    2013-10-01

    The modern flight control systems are complex due to their non-linear nature. In fact, modern aerospace vehicles are expected to have non-conventional flight envelopes and, then, they must guarantee a high level of robustness and adaptability in order to operate in uncertain environments. Neural Networks (NN), with real-time learning capability, for flight control can be used in applications with manned or unmanned aerial vehicles. Indeed, using proven lower level control algorithms with adaptive elements that exhibit long term learning could help in achieving better adaptation performance while performing aggressive maneuvers. In this paper we show a mathematical modeling and a Neural Network for a hexacopter dynamics in order to develop proper methods for stabilization and trajectory control.

  12. Controls from remote through Social networks

    Directory of Open Access Journals (Sweden)

    Alessandra Ingrao

    2016-03-01

    Full Text Available The Author focuses on the recently reformed provisions regulating the employer’s power to control from remote the employees’ activities (art. 4 of the Workers Statute, with particular regard to controls performed by means of Social networks.Such controls are in fact extremely powerful due to the versatile and multi-purpose character of Social networks, which may also be used as a working device. A widespread case law shows indeed that employer’s controls may cost a worker his job.Therefore, after the reform, all employees will have to read carefully the employer’s Privacy policies, before accessing socials during the worktime to express opinions and/or frustrations.

  13. Genome-Scale Networks Link Neurodegenerative Disease Genes to α-Synuclein through Specific Molecular Pathways.

    Science.gov (United States)

    Khurana, Vikram; Peng, Jian; Chung, Chee Yeun; Auluck, Pavan K; Fanning, Saranna; Tardiff, Daniel F; Bartels, Theresa; Koeva, Martina; Eichhorn, Stephen W; Benyamini, Hadar; Lou, Yali; Nutter-Upham, Andy; Baru, Valeriya; Freyzon, Yelena; Tuncbag, Nurcan; Costanzo, Michael; San Luis, Bryan-Joseph; Schöndorf, David C; Barrasa, M Inmaculada; Ehsani, Sepehr; Sanjana, Neville; Zhong, Quan; Gasser, Thomas; Bartel, David P; Vidal, Marc; Deleidi, Michela; Boone, Charles; Fraenkel, Ernest; Berger, Bonnie; Lindquist, Susan

    2017-02-22

    Numerous genes and molecular pathways are implicated in neurodegenerative proteinopathies, but their inter-relationships are poorly understood. We systematically mapped molecular pathways underlying the toxicity of alpha-synuclein (α-syn), a protein central to Parkinson's disease. Genome-wide screens in yeast identified 332 genes that impact α-syn toxicity. To "humanize" this molecular network, we developed a computational method, TransposeNet. This integrates a Steiner prize-collecting approach with homology assignment through sequence, structure, and interaction topology. TransposeNet linked α-syn to multiple parkinsonism genes and druggable targets through perturbed protein trafficking and ER quality control as well as mRNA metabolism and translation. A calcium signaling hub linked these processes to perturbed mitochondrial quality control and function, metal ion transport, transcriptional regulation, and signal transduction. Parkinsonism gene interaction profiles spatially opposed in the network (ATP13A2/PARK9 and VPS35/PARK17) were highly distinct, and network relationships for specific genes (LRRK2/PARK8, ATXN2, and EIF4G1/PARK18) were confirmed in patient induced pluripotent stem cell (iPSC)-derived neurons. This cross-species platform connected diverse neurodegenerative genes to proteinopathy through specific mechanisms and may facilitate patient stratification for targeted therapy. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  14. Network Operations Control Center Block 3 modifications

    Science.gov (United States)

    Garcia, E. A.

    1978-01-01

    The Network Operations Control Center (NOCC) Block 3 configuration is in the process of being upgraded to provide the capabilities required to support Voyager and Pioneer Venus 1978 Project commitments as well as to support Deep Space Station changes arising from the Mark 3 DSN Data Subsystem implementation. Information is given on the hardware and software changes necessary to implement such capabilities.

  15. Deep networks for motor control functions

    Directory of Open Access Journals (Sweden)

    Max eBerniker

    2015-03-01

    Full Text Available 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 nonlinear 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.

  16. Synchronizability on complex networks via pinning control

    Indian Academy of Sciences (India)

    Synchronizability on complex networks via pinning control. YI LIANG1,2 and XINGYUAN WANG1,∗. 1Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology,. Dalian 116024, China. 2Department of Electronics and Information Engineering, Yili Normal College,. Yining 835000, China. ∗.

  17. Inferring dynamic gene networks under varying conditions for transcriptomic network comparison.

    Science.gov (United States)

    Shimamura, Teppei; Imoto, Seiya; Yamaguchi, Rui; Nagasaki, Masao; Miyano, Satoru

    2010-04-15

    Elucidating the differences between cellular responses to various biological conditions or external stimuli is an important challenge in systems biology. Many approaches have been developed to reverse engineer a cellular system, called gene network, from time series microarray data in order to understand a transcriptomic response under a condition of interest. Comparative topological analysis has also been applied based on the gene networks inferred independently from each of the multiple time series datasets under varying conditions to find critical differences between these networks. However, these comparisons often lead to misleading results, because each network contains considerable noise due to the limited length of the time series. We propose an integrated approach for inferring multiple gene networks from time series expression data under varying conditions. To the best of our knowledge, our approach is the first reverse-engineering method that is intended for transcriptomic network comparison between varying conditions. Furthermore, we propose a state-of-the-art parameter estimation method, relevance-weighted recursive elastic net, for providing higher precision and recall than existing reverse-engineering methods. We analyze experimental data of MCF-7 human breast cancer cells stimulated by epidermal growth factor or heregulin with several doses and provide novel biological hypotheses through network comparison. The software NETCOMP is available at http://bonsai.ims.u-tokyo.ac.jp/ approximately shima/NETCOMP/.

  18. FastGCN: a GPU accelerated tool for fast gene co-expression networks.

    Science.gov (United States)

    Liang, Meimei; Zhang, Futao; Jin, Gulei; Zhu, Jun

    2015-01-01

    Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.

  19. FastGCN: a GPU accelerated tool for fast gene co-expression networks.

    Directory of Open Access Journals (Sweden)

    Meimei Liang

    Full Text Available Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.

  20. Characterization of Genes for Beef Marbling Based on Applying Gene Coexpression Network

    Directory of Open Access Journals (Sweden)

    Dajeong Lim

    2014-01-01

    Full Text Available Marbling is an important trait in characterization beef quality and a major factor for determining the price of beef in the Korean beef market. In particular, marbling is a complex trait and needs a system-level approach for identifying candidate genes related to the trait. To find the candidate gene associated with marbling, we used a weighted gene coexpression network analysis from the expression value of bovine genes. Hub genes were identified; they were topologically centered with large degree and BC values in the global network. We performed gene expression analysis to detect candidate genes in M. longissimus with divergent marbling phenotype (marbling scores 2 to 7 using qRT-PCR. The results demonstrate that transmembrane protein 60 (TMEM60 and dihydropyrimidine dehydrogenase (DPYD are associated with increasing marbling fat. We suggest that the network-based approach in livestock may be an important method for analyzing the complex effects of candidate genes associated with complex traits like marbling or tenderness.

  1. Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

    Directory of Open Access Journals (Sweden)

    Vipin Narang

    Full Text Available Human gene regulatory networks (GRN can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs. Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data accompanying this manuscript.

  2. Developing integrated crop knowledge networks to advance candidate gene discovery.

    Science.gov (United States)

    Hassani-Pak, Keywan; Castellote, Martin; Esch, Maria; Hindle, Matthew; Lysenko, Artem; Taubert, Jan; Rawlings, Christopher

    2016-12-01

    The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.

  3. A network approach to analyzing highly recombinant malaria parasite genes.

    Directory of Open Access Journals (Sweden)

    Daniel B Larremore

    Full Text Available The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs, and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences.

  4. A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes

    Science.gov (United States)

    Larremore, Daniel B.; Clauset, Aaron; Buckee, Caroline O.

    2013-01-01

    The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences. PMID:24130474

  5. A gene regulatory network armature for T-lymphocyte specification

    Energy Technology Data Exchange (ETDEWEB)

    Fung, Elizabeth-sharon [Los Alamos National Laboratory

    2008-01-01

    Choice of a T-lymphoid fate by hematopoietic progenitor cells depends on sustained Notch-Delta signaling combined with tightly-regulated activities of multiple transcription factors. To dissect the regulatory network connections that mediate this process, we have used high-resolution analysis of regulatory gene expression trajectories from the beginning to the end of specification; tests of the short-term Notchdependence of these gene expression changes; and perturbation analyses of the effects of overexpression of two essential transcription factors, namely PU.l and GATA-3. Quantitative expression measurements of >50 transcription factor and marker genes have been used to derive the principal components of regulatory change through which T-cell precursors progress from primitive multipotency to T-lineage commitment. Distinct parts of the path reveal separate contributions of Notch signaling, GATA-3 activity, and downregulation of PU.l. Using BioTapestry, the results have been assembled into a draft gene regulatory network for the specification of T-cell precursors and the choice of T as opposed to myeloid dendritic or mast-cell fates. This network also accommodates effects of E proteins and mutual repression circuits of Gfil against Egr-2 and of TCF-l against PU.l as proposed elsewhere, but requires additional functions that remain unidentified. Distinctive features of this network structure include the intense dose-dependence of GATA-3 effects; the gene-specific modulation of PU.l activity based on Notch activity; the lack of direct opposition between PU.l and GATA-3; and the need for a distinct, late-acting repressive function or functions to extinguish stem and progenitor-derived regulatory gene expression.

  6. Towards Controlling Latency in Wireless Networks

    KAUST Repository

    Bouacida, Nader

    2017-04-24

    Wireless networks are undergoing an unprecedented revolution in the last decade. With the explosion of delay-sensitive applications in the Internet (i.e., online gaming and VoIP), latency becomes a major issue for the development of wireless technology. Taking advantage of the significant decline in memory prices, industrialists equip the network devices with larger buffering capacities to improve the network throughput by limiting packets drops. Over-buffering results in increasing the time that packets spend in the queues and, thus, introducing more latency in networks. This phenomenon is known as “bufferbloat”. While throughput is the dominant performance metric, latency also has a huge impact on user experience not only for real-time applications but also for common applications like web browsing, which is sensitive to latencies in order of hundreds of milliseconds. Concerns have arisen about designing sophisticated queue management schemes to mitigate the effects of such phenomenon. My thesis research aims to solve bufferbloat problem in both traditional half-duplex and cutting-edge full-duplex wireless systems by reducing delay while maximizing wireless links utilization and fairness. Our work shed lights on buffer management algorithms behavior in wireless networks and their ability to reduce latency resulting from excessive queuing delays inside oversized static network buffers without a significant loss in other network metrics. First of all, we address the problem of buffer management in wireless full-duplex networks by using Wireless Queue Management (WQM), which is an active queue management technique for wireless networks. Our solution is based on Relay Full-Duplex MAC (RFD-MAC), an asynchronous media access control protocol designed for relay full-duplexing. Compared to the default case, our solution reduces the end-to-end delay by two orders of magnitude while achieving similar throughput in most of the cases. In the second part of this thesis

  7. Prioritization of Susceptibility Genes for Ectopic Pregnancy by Gene Network Analysis

    Science.gov (United States)

    Liu, Ji-Long; Zhao, Miao

    2016-01-01

    Ectopic pregnancy is a very dangerous complication of pregnancy, affecting 1%–2% of all reported pregnancies. Due to ethical constraints on human biopsies and the lack of suitable animal models, there has been little success in identifying functionally important genes in the pathogenesis of ectopic pregnancy. In the present study, we developed a random walk–based computational method named TM-rank to prioritize ectopic pregnancy–related genes based on text mining data and gene network information. Using a defined threshold value, we identified five top-ranked genes: VEGFA (vascular endothelial growth factor A), IL8 (interleukin 8), IL6 (interleukin 6), ESR1 (estrogen receptor 1) and EGFR (epidermal growth factor receptor). These genes are promising candidate genes that can serve as useful diagnostic biomarkers and therapeutic targets. Our approach represents a novel strategy for prioritizing disease susceptibility genes. PMID:26840308

  8. Fusion Control of Flexible Logic Control and Neural Network

    Directory of Open Access Journals (Sweden)

    Lihua Fu

    2014-01-01

    Full Text Available Based on the basic physical meaning of error E and error variety EC, this paper analyzes the logical relationship between them and uses Universal Combinatorial Operation Model in Universal Logic to describe it. Accordingly, a flexible logic control method is put forward to realize effective control on multivariable nonlinear system. In order to implement fusion control with artificial neural network, this paper proposes a new neuron model of Zero-level Universal Combinatorial Operation in Universal Logic. And the artificial neural network of flexible logic control model is implemented based on the proposed neuron model. Finally, stability control, anti-interference control of double inverted-pendulum system, and free walking of cart pendulum system on a level track are realized, showing experimentally the feasibility and validity of this method.

  9. Toxic Diatom Aldehydes Affect Defence Gene Networks in Sea Urchins.

    Science.gov (United States)

    Varrella, Stefano; Romano, Giovanna; Costantini, Susan; Ruocco, Nadia; Ianora, Adrianna; Bentley, Matt G; Costantini, Maria

    2016-01-01

    Marine organisms possess a series of cellular strategies to counteract the negative effects of toxic compounds, including the massive reorganization of gene expression networks. Here we report the modulated dose-dependent response of activated genes by diatom polyunsaturated aldehydes (PUAs) in the sea urchin Paracentrotus lividus. PUAs are secondary metabolites deriving from the oxidation of fatty acids, inducing deleterious effects on the reproduction and development of planktonic and benthic organisms that feed on these unicellular algae and with anti-cancer activity. Our previous results showed that PUAs target several genes, implicated in different functional processes in this sea urchin. Using interactomic Ingenuity Pathway Analysis we now show that the genes targeted by PUAs are correlated with four HUB genes, NF-κB, p53, δ-2-catenin and HIF1A, which have not been previously reported for P. lividus. We propose a working model describing hypothetical pathways potentially involved in toxic aldehyde stress response in sea urchins. This represents the first report on gene networks affected by PUAs, opening new perspectives in understanding the cellular mechanisms underlying the response of benthic organisms to diatom exposure.

  10. Toxic Diatom Aldehydes Affect Defence Gene Networks in Sea Urchins.

    Directory of Open Access Journals (Sweden)

    Stefano Varrella

    Full Text Available Marine organisms possess a series of cellular strategies to counteract the negative effects of toxic compounds, including the massive reorganization of gene expression networks. Here we report the modulated dose-dependent response of activated genes by diatom polyunsaturated aldehydes (PUAs in the sea urchin Paracentrotus lividus. PUAs are secondary metabolites deriving from the oxidation of fatty acids, inducing deleterious effects on the reproduction and development of planktonic and benthic organisms that feed on these unicellular algae and with anti-cancer activity. Our previous results showed that PUAs target several genes, implicated in different functional processes in this sea urchin. Using interactomic Ingenuity Pathway Analysis we now show that the genes targeted by PUAs are correlated with four HUB genes, NF-κB, p53, δ-2-catenin and HIF1A, which have not been previously reported for P. lividus. We propose a working model describing hypothetical pathways potentially involved in toxic aldehyde stress response in sea urchins. This represents the first report on gene networks affected by PUAs, opening new perspectives in understanding the cellular mechanisms underlying the response of benthic organisms to diatom exposure.

  11. A hyperstable neural network for the modelling and control of ...

    Indian Academy of Sciences (India)

    A hyperstable neural network for the modelling and control of nonlinear systems ... Computer control; neural networks; nonlinear systems; adaptive control. ... control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure.

  12. Flexible body control using neural networks

    Science.gov (United States)

    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.

  13. Control Plane Strategies for Elastic Optical Networks

    DEFF Research Database (Denmark)

    Turus, Ioan

    (Generalized Multi-Protocol Label Switching)-based control framework in accordance with existing IETF standards and recommendations. The usual approach of extending capacity in transport networks by incrementally adding more optical resources results in a very inefficient usage and determines a high power...... Networks (EONs) concept is proposed as a solution to enable a more flexible handling of the optical capacity and allows an increase of available capacity over the existing optical infrastructure. One main requirement for enabling EONs is to have a flexible spectrum structure (i.e.Flex-Grid) which allows...... the spectrum to be used as an on-demand resource. Flex-Grid raises new challenges for controlling the dynamic spectrum slots environment. This thesis addresses, as part of the Celtic project “Elastic Optical Networks” (EONet), the control of Flex-Grid architectures by extending the capabilities of a GMPLS...

  14. A network analysis of the human T-cell activation gene network identifies JAGGED1 as a therapeutic target for autoimmune diseases.

    Directory of Open Access Journals (Sweden)

    Ricardo Palacios

    2007-11-01

    Full Text Available Understanding complex diseases will benefit the recognition of the properties of the gene networks that control biological functions. Here, we set out to model the gene network that controls T-cell activation in humans, which is critical for the development of autoimmune diseases such as Multiple Sclerosis (MS. The network was established on the basis of the quantitative expression from 104 individuals of 20 genes of the immune system, as well as on biological information from the Ingenuity database and Bayesian inference. Of the 31 links (gene interactions identified in the network, 18 were identified in the Ingenuity database and 13 were new and we validated 7 of 8 interactions experimentally. In the MS patients network, we found an increase in the weight of gene interactions related to Th1 function and a decrease in those related to Treg and Th2 function. Indeed, we found that IFN-ss therapy induces changes in gene interactions related to T cell proliferation and adhesion, although these gene interactions were not restored to levels similar to controls. Finally, we identify JAG1 as a new therapeutic target whose differential behaviour in the MS network was not modified by immunomodulatory therapy. In vitro treatment with a Jagged1 agonist peptide modulated the T-cell activation network in PBMCs from patients with MS. Moreover, treatment of mice with experimental autoimmune encephalomyelitis with the Jagged1 agonist ameliorated the disease course, and modulated Th2, Th1 and Treg function. This study illustrates how network analysis can predict therapeutic targets for immune intervention and identified the immunomodulatory properties of Jagged1 making it a new therapeutic target for MS and other autoimmune diseases.

  15. Optical network control plane for multi-domain networking

    DEFF Research Database (Denmark)

    Manolova, Anna Vasileva

    process are not enough for efficient TE in mesh multi-domain networks. Enhancing the protocol with multi-path dissemination capability, combined with the employment of an end-to-end TE metric proves to be a highly efficient solution. Simulation results show good performance characteristics of the proposed...... that the applied routing protocol and the topology of the multi-domain nework have very strong influence on the efficiency of the applied restoration techniques. Finally, different challenges of the integration of the GMPLS control framework with the novel Optical Burst Switching technology are analyzed. Existing...

  16. Epigenetic Modulation of Brain Gene Networks for Cocaine and Alcohol Abuse

    Directory of Open Access Journals (Sweden)

    Sean P Farris

    2015-05-01

    Full Text Available Cocaine and alcohol are two substances of abuse that prominently affect the central nervous system (CNS. Repeated exposure to cocaine and alcohol leads to longstanding changes in gene expression, and subsequent functional CNS plasticity, throughout multiple brain regions. Epigenetic modifications of histones are one proposed mechanism guiding these enduring changes to the transcriptome. Characterizing the large number of available biological relationships as network models can reveal unexpected biochemical relationships. Clustering analysis of variation from whole-genome sequencing of gene expression (RNA-Seq and histone H3 lysine 4 trimethylation (H3K4me3 events (ChIP-Seq revealed the underlying structure of the transcriptional and epigenomic landscape within hippocampal postmortem brain tissue of drug abusers and control cases. Distinct sets of interrelated networks for cocaine and alcohol abuse were determined for each abusive substance. The network approach identified subsets of functionally related genes that are regulated in agreement with H3K4me3 changes, suggesting cause and effect relationships between this epigenetic mark and gene expression. Gene expression networks consisted of recognized substrates for addiction, such as the dopamine- and cAMP-regulated neuronal phosphoprotein PPP1R1B / DARPP-32 and the vesicular glutamate transporter SLC17A7 / VGLUT1 as well as potentially novel molecular targets for substance abuse. Through a systems biology based approach our results illustrate the utility of integrating epigenetic and transcript expression to establish relevant biological networks in the human brain for addiction. Future work with laboratory models may clarify the functional relevance of these gene networks for cocaine and alcohol, and provide a framework for the development of medications for the treatment of addiction.

  17. Transcriptome network analysis reveals potential candidate genes for ankylosing spondylitis.

    Science.gov (United States)

    Zhu, Z-Q; Tang, J-S; Cao, X-J

    2013-12-01

    Ankylosing spondylitis (AS) is a chronic, inflammatory arthritis and autoimmune disease. The main symptom of AS is inflammatory spinal pain; with time, some patients develop ankylosis and spinal immobility. We aim to find cure available for ankylosing spondylitis. We used the GSE11886 series to identify potential genes that related to AS to construct a regulation network. In the network, some of TFs and target genes have been proved related with AS in previous study, such as NFKB1, STAT1, STAT4, TNFSF10, IL2RA, and IL2RB. We also found some new TFs (Franscription Factors) and target genes response to AS, such as BXDC5, and EGFR. Further analysis indicated some significant pathways are associated with AS, including antigen processing and presentation and cytokine-cytokine receptor interaction, etc.; although not significant, there was evident that they play an important role in AS progression, such as apoptosis and systemic lupus erythematosus. Therefore, it is demonstrated that transcriptome network analysis is useful in identification of the candidate genes in AS.

  18. Evolution of Controllability in Interbank Networks

    Science.gov (United States)

    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.

  19. Listening to the Noise: Random Fluctuations Reveal Gene Network Parameters

    Science.gov (United States)

    Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

    2010-03-01

    The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant prototypical differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, single molecule fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.

  20. Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Liang Jinghang

    2012-08-01

    Full Text Available Abstract Background Various computational models have been of interest due to their use in the modelling of gene regulatory networks (GRNs. As a logical model, probabilistic Boolean networks (PBNs consider molecular and genetic noise, so the study of PBNs provides significant insights into the understanding of the dynamics of GRNs. This will ultimately lead to advances in developing therapeutic methods that intervene in the process of disease development and progression. The applications of PBNs, however, are hindered by the complexities involved in the computation of the state transition matrix and the steady-state distribution of a PBN. For a PBN with n genes and N Boolean networks, the complexity to compute the state transition matrix is O(nN22n or O(nN2n for a sparse matrix. Results This paper presents a novel implementation of PBNs based on the notions of stochastic logic and stochastic computation. This stochastic implementation of a PBN is referred to as a stochastic Boolean network (SBN. An SBN provides an accurate and efficient simulation of a PBN without and with random gene perturbation. The state transition matrix is computed in an SBN with a complexity of O(nL2n, where L is a factor related to the stochastic sequence length. Since the minimum sequence length required for obtaining an evaluation accuracy approximately increases in a polynomial order with the number of genes, n, and the number of Boolean networks, N, usually increases exponentially with n, L is typically smaller than N, especially in a network with a large number of genes. Hence, the computational efficiency of an SBN is primarily limited by the number of genes, but not directly by the total possible number of Boolean networks. Furthermore, a time-frame expanded SBN enables an efficient analysis of the steady-state distribution of a PBN. These findings are supported by the simulation results of a simplified p53 network, several randomly generated networks and a

  1. DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

    Directory of Open Access Journals (Sweden)

    Pi-Jing Wei

    2017-01-01

    Full Text Available Integration of multi-omics data of cancer can help people to explore cancers comprehensively. However, with a large volume of different omics and functional data being generated, there is a major challenge to distinguish functional driver genes from a sea of inconsequential passenger genes that accrue stochastically but do not contribute to cancer development. In this paper, we present a gene length-based network method, named DriverFinder, to identify driver genes by integrating somatic mutations, copy number variations, gene-gene interaction network, tumor expression, and normal expression data. To illustrate the performance of DriverFinder, it is applied to four cancer types from The Cancer Genome Atlas including breast cancer, head and neck squamous cell carcinoma, thyroid carcinoma, and kidney renal clear cell carcinoma. Compared with some conventional methods, the results demonstrate that the proposed method is effective. Moreover, it can decrease the influence of gene length in identifying driver genes and identify some rare mutated driver genes.

  2. Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer.

    Science.gov (United States)

    Chiu, Yu-Chiao; Wang, Li-Ju; Hsiao, Tzu-Hung; Chuang, Eric Y; Chen, Yidong

    2017-10-03

    With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.

  3. Networked control of microgrid system of systems

    Science.gov (United States)

    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.

  4. Evolutionary signatures amongst disease genes permit novel methods for gene prioritization and construction of informative gene-based networks.

    Directory of Open Access Journals (Sweden)

    Nolan Priedigkeit

    2015-02-01

    Full Text Available Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC, is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting "disease map" network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks.

  5. On the Design of Energy Efficient Optical Networks with Software Defined Networking Control Across Core and Access Networks

    DEFF Research Database (Denmark)

    Wang, Jiayuan; Yan, Ying; Dittmann, Lars

    2013-01-01

    This paper presents a Software Defined Networking (SDN) control plane based on an overlay GMPLS control model. The SDN control platform manages optical core networks (WDM/DWDM networks) and the associated access networks (GPON networks), which makes it possible to gather global information...... and enable wider areas' energy efficiency networking. The energy related information of the networks and the types of the traffic flows are collected and utilized for the end-to-end QoS provision. Dynamic network simulation results show that by applying different routing algorithms according to the type...

  6. Network graph analysis of gene-gene interactions in genome-wide association study data.

    Science.gov (United States)

    Lee, Sungyoung; Kwon, Min-Seok; Park, Taesung

    2012-12-01

    Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

  7. Structures and Boolean Dynamics in Gene Regulatory Networks

    Science.gov (United States)

    Szedlak, Anthony

    This dissertation discusses the topological and dynamical properties of GRNs in cancer, and is divided into four main chapters. First, the basic tools of modern complex network theory are introduced. These traditional tools as well as those developed by myself (set efficiency, interset efficiency, and nested communities) are crucial for understanding the intricate topological properties of GRNs, and later chapters recall these concepts. Second, the biology of gene regulation is discussed, and a method for disease-specific GRN reconstruction developed by our collaboration is presented. This complements the traditional exhaustive experimental approach of building GRNs edge-by-edge by quickly inferring the existence of as of yet undiscovered edges using correlations across sets of gene expression data. This method also provides insight into the distribution of common mutations across GRNs. Third, I demonstrate that the structures present in these reconstructed networks are strongly related to the evolutionary histories of their constituent genes. Investigation of how the forces of evolution shaped the topology of GRNs in multicellular organisms by growing outward from a core of ancient, conserved genes can shed light upon the ''reverse evolution'' of normal cells into unicellular-like cancer states. Next, I simulate the dynamics of the GRNs of cancer cells using the Hopfield model, an infinite range spin-glass model designed with the ability to encode Boolean data as attractor states. This attractor-driven approach facilitates the integration of gene expression data into predictive mathematical models. Perturbations representing therapeutic interventions are applied to sets of genes, and the resulting deviations from their attractor states are recorded, suggesting new potential drug targets for experimentation. Finally, I extend the Hopfield model to modular networks, cyclic attractors, and complex attractors, and apply these concepts to simulations of the cell cycle

  8. Interacting with Networks : How Does Structure Relate to Controllability in Single-Leader, Consensus Networks?

    NARCIS (Netherlands)

    Egerstedt, Magnus; Martini, Simone; Cao, Ming; Camlibel, Kanat; Bicchi, Antonio

    As networked dynamical systems appear around us at an increasing rate, questions concerning how to manage and control such systems are becoming more important. Examples include multiagent robotics, distributed sensor networks, interconnected manufacturing chains, and data networks. In response to

  9. Reconstruction of Gene Networks of Iron Response in Shewanella oneidensis

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Yunfeng [ORNL; Harris, Daniel P [ORNL; Luo, Feng [Clemson University; Joachimiak, Marcin [Clemson University; Wu, Liyou [University of Oklahoma; Dehal, Paramvir [Lawrence Berkeley National Laboratory (LBNL); Jacobsen, Janet [Lawrence Berkeley National Laboratory (LBNL); Yang, Zamin Koo [ORNL; Gao, Haichun [University of Oklahoma; Arkin, Adam [Lawrence Berkeley National Laboratory (LBNL); Palumbo, Anthony Vito [ORNL; Zhou, Jizhong [University of Oklahoma

    2009-01-01

    It is of great interest to study the iron response of the -proteobacterium Shewanella oneidensis since it possesses a high content of iron and is capable of utilizing iron for anaerobic respiration. We report here that the iron response in S. oneidensis is a rapid process. To gain more insights into the bacterial response to iron, temporal gene expression profiles were examined for iron depletion and repletion, resulting in identification of iron-responsive biological pathways in a gene co-expression network. Iron acquisition systems, including genes unique to S. oneidensis, were rapidly and strongly induced by iron depletion, and repressed by iron repletion. Some were required for iron depletion, as exemplified by the mutational analysis of the putative siderophore biosynthesis protein SO3032. Unexpectedly, a number of genes related to anaerobic energy metabolism were repressed by iron depletion and induced by repletion, which might be due to the iron storage potential of their protein products. Other iron-responsive biological pathways include protein degradation, aerobic energy metabolism and protein synthesis. Furthermore, sequence motifs enriched in gene clusters as well as their corresponding DNA-binding proteins (Fur, CRP and RpoH) were identified, resulting in a regulatory network of iron response in S. oneidensis. Together, this work provides an overview of iron response and reveals novel features in S. oneidensis, including Shewanella-specific iron acquisition systems, and suggests the intimate relationship between anaerobic energy metabolism and iron response.

  10. Comparison of evolutionary algorithms in gene regulatory network model inference.

    LENUS (Irish Health Repository)

    2010-01-01

    ABSTRACT: BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.

  11. Coordinated Voltage Control of Active Distribution Network

    Directory of Open Access Journals (Sweden)

    Xie Jiang

    2016-01-01

    Full Text Available This paper presents a centralized coordinated voltage control method for active distribution network to solve off-limit problem of voltage after incorporation of distributed generation (DG. The proposed method consists of two parts, it coordinated primal-dual interior point method-based voltage regulation schemes of DG reactive powers and capacitors with centralized on-load tap changer (OLTC controlling method which utilizes system’s maximum and minimum voltages, to improve the qualified rate of voltage and reduce the operation numbers of OLTC. The proposed coordination has considered the cost of capacitors. The method is tested using a radial edited IEEE-33 nodes distribution network which is modelled using MATLAB.

  12. Call Admission Control in Mobile Cellular Networks

    CERN Document Server

    Ghosh, Sanchita

    2013-01-01

    Call Admission Control (CAC) and Dynamic Channel Assignments (DCA) are important decision-making problems in mobile cellular communication systems. Current research in mobile communication considers them as two independent problems, although the former greatly depends on the resulting free channels obtained as the outcome of the latter. This book provides a solution to the CAC problem, considering DCA as an integral part of decision-making for call admission. Further, current technical resources ignore movement issues of mobile stations and fluctuation in network load (incoming calls) in the control strategy used for call admission. In addition, the present techniques on call admission offers solution globally for the entire network, instead of considering the cells independently.      CAC here has been formulated by two alternative approaches. The first approach aimed at handling the uncertainty in the CAC problem by employing fuzzy comparators.  The second approach is concerned with formulation of CAC ...

  13. Prioritisation and network analysis of Crohn's disease susceptibility genes.

    Directory of Open Access Journals (Sweden)

    Daniele Muraro

    Full Text Available Recent Genome-Wide Association Studies (GWAS have revealed numerous Crohn's disease susceptibility genes and a key challenge now is in understanding how risk polymorphisms in associated genes might contribute to development of this disease. For a gene to contribute to disease phenotype, its risk variant will likely adversely communicate with a variety of other gene products to result in dysregulation of common signaling pathways. A vital challenge is to elucidate pathways of potentially greatest influence on pathological behaviour, in a manner recognizing how multiple relevant genes may yield integrative effect. In this work we apply mathematical analysis of networks involving the list of recently described Crohn's susceptibility genes, to prioritise pathways in relation to their potential development of this disease. Prioritisation was performed by applying a text mining and a diffusion based method (GRAIL, GPEC. Prospective biological significance of the resulting prioritised list of proteins is highlighted by changes in their gene expression levels in Crohn's patients intestinal tissue in comparison with healthy donors.

  14. Transcriptional networks and chromatin remodeling controlling adipogenesis

    DEFF Research Database (Denmark)

    Siersbæk, Rasmus; Nielsen, Ronni; Mandrup, Susanne

    2012-01-01

    Adipocyte differentiation is tightly controlled by a transcriptional cascade, which directs the extensive reprogramming of gene expression required to convert fibroblast-like precursor cells into mature lipid-laden adipocytes. Recent global analyses of transcription factor binding and chromatin...... remodeling have revealed 'snapshots' of this cascade and the chromatin landscape at specific time-points of differentiation. These studies demonstrate that multiple adipogenic transcription factors co-occupy hotspots characterized by an open chromatin structure and specific epigenetic modifications....... Such transcription factor hotspots are likely to represent key signaling nodes which integrate multiple adipogenic signals at specific chromatin sites, thereby facilitating coordinated action on gene expression....

  15. Gene Profiling of Aortic Valve Interstitial Cells under Elevated Pressure Conditions: Modulation of Inflammatory Gene Networks

    Directory of Open Access Journals (Sweden)

    James N. Warnock

    2011-01-01

    Full Text Available The study aimed to identify mechanosensitive pathways and gene networks that are stimulated by elevated cyclic pressure in aortic valve interstitial cells (VICs and lead to detrimental tissue remodeling and/or pathogenesis. Porcine aortic valve leaflets were exposed to cyclic pressures of 80 or 120 mmHg, corresponding to diastolic transvalvular pressure in normal and hypertensive conditions, respectively. Linear, two-cycle amplification of total RNA, followed by microarray was performed for transcriptome analysis (with qRT-PCR validation. A combination of systems biology modeling and pathway analysis identified novel genes and molecular mechanisms underlying the biological response of VICs to elevated pressure. 56 gene transcripts related to inflammatory response mechanisms were differentially expressed. TNF-α, IL-1α, and IL-1β were key cytokines identified from the gene network model. Also of interest was the discovery that pentraxin 3 (PTX3 was significantly upregulated under elevated pressure conditions (41-fold change. In conclusion, a gene network model showing differentially expressed inflammatory genes and their interactions in VICs exposed to elevated pressure has been developed. This system overview has detected key molecules that could be targeted for pharmacotherapy of aortic stenosis in hypertensive patients.

  16. Neural Network Controller for the Pressurized Water Reactor Power Control

    International Nuclear Information System (INIS)

    Haggag, S.S.; Kotb, S.A.

    2017-01-01

    Although there have been some severe nuclear accidents such as Three Mile Island (USA), Chernobyl (Ukraine) and Fukushima (Japan), nuclear fission energy is still a source of clean energy that can substitute fossil fuels in a centralized way and in a great amount with commercial availability and economic competitiveness. Since the pressurized water reactor (PWR) is the most widely used nuclear fission reactor, it is safe, stable and efficient operation is meaningful to the current rebirth of the nuclear fission energy industry. Power-level regulation is an important technique which can deeply affect the operation stability and efficiency of PWRs (Pressurized Water Reactors ). This paper presents the effect of utilizing the Neural Network controller methodology in the power control model of the PWR. The Neural Network Controller was tested on a PWR model using the Matlab Simulink Interface. Two case studies were performed on the model using both the Neural Network method and the traditional rod speed program for controlling the nuclear power plant variables. The proposed controller presents a higher performance than that of the traditional rod speed program controller.

  17. Polynomial-Time Algorithm for Controllability Test of a Class of Boolean Biological Networks

    Directory of Open Access Journals (Sweden)

    Koichi Kobayashi

    2010-01-01

    Full Text Available In recent years, Boolean-network-model-based approaches to dynamical analysis of complex biological networks such as gene regulatory networks have been extensively studied. One of the fundamental problems in control theory of such networks is the problem of determining whether a given substance quantity can be arbitrarily controlled by operating the other substance quantities, which we call the controllability problem. This paper proposes a polynomial-time algorithm for solving this problem. Although the algorithm is based on a sufficient condition for controllability, it is easily computable for a wider class of large-scale biological networks compared with the existing approaches. A key to this success in our approach is to give up computing Boolean operations in a rigorous way and to exploit an adjacency matrix of a directed graph induced by a Boolean network. By applying the proposed approach to a neurotransmitter signaling pathway, it is shown that it is effective.

  18. Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

    Directory of Open Access Journals (Sweden)

    Olszewski Kellen L

    2007-07-01

    Full Text Available Abstract Background The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes. Results We developed Nearest Neighbor Networks (NNN, a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the

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

    Science.gov (United States)

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

    2017-02-01

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

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

    Science.gov (United States)

    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.

  1. Flexible Tube-Based Network Control, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The Innovation Laboratory, Inc. builds a control system which controls the topology of an air traffic flow network and the network flow properties which enables Air...

  2. An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

    Science.gov (United States)

    1996-01-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

  3. Crosstalk between pathways enhances the controllability of signalling networks.

    Science.gov (United States)

    Wang, Dingjie; Jin, Suoqin; Zou, Xiufen

    2016-02-01

    The control of complex networks is one of the most challenging problems in the fields of biology and engineering. In this study, the authors explored the controllability and control energy of several signalling networks, which consisted of many interconnected pathways, including networks with a bow-tie architecture. On the basis of the theory of structure controllability, they revealed that biological mechanisms, such as cross-pathway interactions, compartmentalisation and so on make the networks easier to fully control. Furthermore, using numerical simulations for two realistic examples, they demonstrated that the control energy of normal networks with crosstalk is lower than in networks without crosstalk. These results indicate that the biological networks are optimally designed to achieve their normal functions from the viewpoint of the control theory. The authors' work provides a comprehensive understanding of the impact of network structures and properties on controllability.

  4. Inferring Drosophila gap gene regulatory network: Pattern analysis of simulated gene expression profiles and stability analysis

    NARCIS (Netherlands)

    Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.

    2009-01-01

    Background: Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori

  5. Neural networks as a control methodology

    Science.gov (United States)

    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.

  6. Transcriptional regulatory networks underlying gene expression changes in Huntington's disease.

    Science.gov (United States)

    Ament, Seth A; Pearl, Jocelynn R; Cantle, Jeffrey P; Bragg, Robert M; Skene, Peter J; Coffey, Sydney R; Bergey, Dani E; Wheeler, Vanessa C; MacDonald, Marcy E; Baliga, Nitin S; Rosinski, Jim; Hood, Leroy E; Carroll, Jeffrey B; Price, Nathan D

    2018-03-26

    Transcriptional changes occur presymptomatically and throughout Huntington's disease (HD), motivating the study of transcriptional regulatory networks (TRNs) in HD We reconstructed a genome-scale model for the target genes of 718 transcription factors (TFs) in the mouse striatum by integrating a model of genomic binding sites with transcriptome profiling of striatal tissue from HD mouse models. We identified 48 differentially expressed TF-target gene modules associated with age- and CAG repeat length-dependent gene expression changes in Htt CAG knock-in mouse striatum and replicated many of these associations in independent transcriptomic and proteomic datasets. Thirteen of 48 of these predicted TF-target gene modules were also differentially expressed in striatal tissue from human disease. We experimentally validated a specific model prediction that SMAD3 regulates HD-related gene expression changes using chromatin immunoprecipitation and deep sequencing (ChIP-seq) of mouse striatum. We found CAG repeat length-dependent changes in the genomic occupancy of SMAD3 and confirmed our model's prediction that many SMAD3 target genes are downregulated early in HD. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

  7. Network Security via Biometric Recognition of Patterns of Gene Expression

    Science.gov (United States)

    Shaw, Harry C.

    2016-01-01

    Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT (Information Technology) organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time assays of gene expression products.

  8. Network Security via Biometric Recognition of Patterns of Gene Expression

    Science.gov (United States)

    Shaw, Harry C.

    2016-01-01

    Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time expression and assay of gene expression products.

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

    Directory of Open Access Journals (Sweden)

    Dave Vrushank

    2010-07-01

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

  10. Rbfox2 controls autoregulation in RNA-binding protein networks.

    Science.gov (United States)

    Jangi, Mohini; Boutz, Paul L; Paul, Prakriti; Sharp, Phillip A

    2014-03-15

    The tight regulation of splicing networks is critical for organismal development. To maintain robust splicing patterns, many splicing factors autoregulate their expression through alternative splicing-coupled nonsense-mediated decay (AS-NMD). However, as negative autoregulation results in a self-limiting window of splicing factor expression, it is unknown how variations in steady-state protein levels can arise in different physiological contexts. Here, we demonstrate that Rbfox2 cross-regulates AS-NMD events within RNA-binding proteins to alter their expression. Using individual nucleotide-resolution cross-linking immunoprecipitation coupled to high-throughput sequencing (iCLIP) and mRNA sequencing, we identified >200 AS-NMD splicing events that are bound by Rbfox2 in mouse embryonic stem cells. These "silent" events are characterized by minimal apparent splicing changes but appreciable changes in gene expression upon Rbfox2 knockdown due to degradation of the NMD-inducing isoform. Nearly 70 of these AS-NMD events fall within genes encoding RNA-binding proteins, many of which are autoregulated. As with the coding splicing events that we found to be regulated by Rbfox2, silent splicing events are evolutionarily conserved and frequently contain the Rbfox2 consensus UGCAUG. Our findings uncover an unexpectedly broad and multilayer regulatory network controlled by Rbfox2 and offer an explanation for how autoregulatory splicing networks are tuned.

  11. Towards structural controllability of local-world networks

    International Nuclear Information System (INIS)

    Sun, Shiwen; Ma, Yilin; Wu, Yafang; Wang, Li; Xia, Chengyi

    2016-01-01

    Controlling complex networks is of vital importance in science and engineering. Meanwhile, local-world effect is an important ingredient which should be taken into consideration in the complete description of real-world complex systems. In this letter, structural controllability of a class of local-world networks is investigated. Through extensive numerical simulations, firstly, effects of local world size M and network size N on structural controllability are examined. For local-world networks with sparse topological configuration, compared to network size, local-world size can induce stronger influence on controllability, however, for dense networks, controllability is greatly affected by network size and local-world effect can be neglected. Secondly, relationships between controllability and topological properties are analyzed. Lastly, the robustness of local-world networks under targeted attacks regarding structural controllability is discussed. These results can help to deepen the understanding of structural complexity and connectivity patterns of complex systems. - Highlights: • Structural controllability of a class of local-world networks is investigated. • For sparse local-world networks, compared to network size, local-world size can bring stronger influence on controllability. • For dense networks, controllability is greatly affected by network size and the effect of local-world size can be neglected. • Structural controllability against targeted node attacks is discussed.

  12. Towards structural controllability of local-world networks

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Shiwen, E-mail: sunsw80@126.com [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China); Ma, Yilin; Wu, Yafang; Wang, Li; Xia, Chengyi [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China)

    2016-05-20

    Controlling complex networks is of vital importance in science and engineering. Meanwhile, local-world effect is an important ingredient which should be taken into consideration in the complete description of real-world complex systems. In this letter, structural controllability of a class of local-world networks is investigated. Through extensive numerical simulations, firstly, effects of local world size M and network size N on structural controllability are examined. For local-world networks with sparse topological configuration, compared to network size, local-world size can induce stronger influence on controllability, however, for dense networks, controllability is greatly affected by network size and local-world effect can be neglected. Secondly, relationships between controllability and topological properties are analyzed. Lastly, the robustness of local-world networks under targeted attacks regarding structural controllability is discussed. These results can help to deepen the understanding of structural complexity and connectivity patterns of complex systems. - Highlights: • Structural controllability of a class of local-world networks is investigated. • For sparse local-world networks, compared to network size, local-world size can bring stronger influence on controllability. • For dense networks, controllability is greatly affected by network size and the effect of local-world size can be neglected. • Structural controllability against targeted node attacks is discussed.

  13. Metabolic gene polymorphism frequencies in control populations

    DEFF Research Database (Denmark)

    Garte, Seymour; Gaspari, Laura; Alexandrie, Anna-Karin

    2001-01-01

    Using the International Project on Genetic Susceptibility to Environmental Carcinogens (GSEC) database containing information on over 15,000 control (noncancer) subjects, the allele and genotype frequencies for many of the more commonly studied metabolic genes (CYP1A1, CYP2E1, CYP2D6, GSTM1, GSTT1...

  14. Metabolic gene polymorphism frequencies in control populations.

    NARCIS (Netherlands)

    Garte, S.; Gaspari, L.; Alexandrie, A.K.; Ambrosone, C.; Autrup, H.; Autrup, J.L.; Baranova, H.; Bathum, L.; Benhamou, S.; Boffetta, P.; Bouchardy, C.; Breskvar, K.; Brockmoller, J.; Cascorbi, I.; Clapper, M.L.; Coutelle, C.; Daly, A.; Dell'Omo, M.; Dolzan, V.; Dresler, C.M.; Fryer, A.; Haugen, A.; Hein, D.W.; Hildesheim, A.; Hirvonen, A.; Hsieh, L.L.; Ingelman-Sundberg, M.; Kalina, I.; Kang, D.; Kihara, M.; Kiyohara, C.; Kremers, P.; Lazarus, P.; Marchand, L. le; Lechner, M.C.; Lieshout, E.M.M. van; London, S.; Manni, J.J.; Maugard, C.M.; Morita, S.; Nazar-Stewart, V.; Noda, K.; Oda, Y.; Parl, F.F.; Pastorelli, R.; Persson, I.; Peters, W.H.M.; Rannug, A.; Rebbeck, T.R.; Risch, A.; Roelandt, L.; Romkes, M.; Ryberg, D.; Salagovic, J.; Schoket, B.; Seidegard, J.; Shields, P.G.; Sim, E.; Sinnet, D.; Strange, R.C.; Stucker, I.; Sugimura, H.; To-Figueras, J.; Vineis, P.; Yu, M.C.; Taioli, E.

    2001-01-01

    Using the International Project on Genetic Susceptibility to Environmental Carcinogens (GSEC) database containing information on over 15,000 control (noncancer) subjects, the allele and genotype frequencies for many of the more commonly studied metabolic genes (CYP1A1, CYP2E1, CYP2D6, GSTM1, GSTT1,

  15. Analysis and design of networked control systems

    CERN Document Server

    You, Keyou; Xie, Lihua

    2015-01-01

    This monograph focuses on characterizing the stability and performance consequences of inserting limited-capacity communication networks within a control loop. The text shows how integration of the ideas of control and estimation with those of communication and information theory can be used to provide important insights concerning several fundamental problems such as: ·         minimum data rate for stabilization of linear systems over noisy channels; ·         minimum network requirement for stabilization of linear systems over fading channels; and ·         stability of Kalman filtering with intermittent observations. A fundamental link is revealed between the topological entropy of linear dynamical systems and the capacities of communication channels. The design of a logarithmic quantizer for the stabilization of linear systems under various network environments is also extensively discussed and solutions to many problems of Kalman filtering with intermittent observations are de...

  16. neural network based load frequency control for restructuring power

    African Journals Online (AJOL)

    2012-03-01

    Mar 1, 2012 ... Abstract. In this study, an artificial neural network (ANN) application of load frequency control. (LFC) of a Multi-Area power system by using a neural network controller is presented. The comparison between a conventional Proportional Integral (PI) controller and the proposed artificial neural networks ...

  17. Efficient Access Control in Multimedia Social Networks

    Science.gov (United States)

    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.

  18. Bilingual Contexts Modulate the Inhibitory Control Network

    Directory of Open Access Journals (Sweden)

    Jing Yang

    2018-03-01

    Full Text Available The present functional magnetic resonance imaging (fMRI study investigated influences of language contexts on inhibitory control and the underlying neural processes. Thirty Cantonese–Mandarin–English trilingual speakers, who were highly proficient in Cantonese (L1 and Mandarin (L2, and moderately proficient in English (L3, performed a picture-naming task in three dual-language contexts (L1-L2, L2-L3, and L1-L3. After each of the three naming tasks, participants performed a flanker task, measuring contextual effects on the inhibitory control system. Behavioral results showed a typical flanker effect in the L2-L3 and L1-L3 condition, but not in the L1-L2 condition, which indicates contextual facilitation on inhibitory control performance by the L1-L2 context. Whole brain analysis of the fMRI data acquired during the flanker tasks showed more neural activations in the right prefrontal cortex and subcortical areas in the L2-L3 and L1-L3 condition on one hand as compared to the L1-L2 condition on the other hand, suggesting greater involvement of the cognitive control areas when participants were performing the flanker task in L2-L3 and L1-L3 contexts. Effective connectivity analyses displayed a cortical-subcortical-cerebellar circuitry for inhibitory control in the trilinguals. However, contrary to the right-lateralized network in the L1-L2 condition, functional networks for inhibitory control in the L2-L3 and L1-L3 condition are less integrated and more left-lateralized. These findings provide a novel perspective for investigating the interaction between bilingualism (multilingualism and inhibitory control by demonstrating instant behavioral effects and neural plasticity as a function of changes in global language contexts.

  19. Identification of Plagl1/Zac1 binding sites and target genes establishes its role in the regulation of extracellular matrix genes and the imprinted gene network.

    Science.gov (United States)

    Varrault, Annie; Dantec, Christelle; Le Digarcher, Anne; Chotard, Laëtitia; Bilanges, Benoit; Parrinello, Hugues; Dubois, Emeric; Rialle, Stéphanie; Severac, Dany; Bouschet, Tristan; Journot, Laurent

    2017-10-13

    PLAGL1/ZAC1 undergoes parental genomic imprinting, is paternally expressed, and is a member of the imprinted gene network (IGN). It encodes a zinc finger transcription factor with anti-proliferative activity and is a candidate tumor suppressor gene on 6q24 whose expression is frequently lost in various neoplasms. Conversely, gain of PLAGL1 function is responsible for transient neonatal diabetes mellitus, a rare genetic disease that results from defective pancreas development. In the present work, we showed that Plagl1 up-regulation was not associated with DNA damage-induced cell cycle arrest. It was rather associated with physiological cell cycle exit that occurred with contact inhibition, growth factor withdrawal, or cell differentiation. To gain insights into Plagl1 mechanism of action, we identified Plagl1 target genes by combining chromatin immunoprecipitation and genome-wide transcriptomics in transfected cell lines. Plagl1-elicited gene regulation correlated with multiple binding to the proximal promoter region through a GC-rich motif. Plagl1 target genes included numerous genes involved in signaling, cell adhesion, and extracellular matrix composition, including collagens. Plagl1 targets also included 22% of the 409 genes that make up the IGN. Altogether, this work identified Plagl1 as a transcription factor that coordinated the regulation of a subset of IGN genes and controlled extracellular matrix composition. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  20. Virtual mutagenesis of the yeast cyclins genetic network reveals complex dynamics of transcriptional control networks.

    Directory of Open Access Journals (Sweden)

    Eliska Vohradska

    Full Text Available Study of genetic networks has moved from qualitative description of interactions between regulators and regulated genes to the analysis of the interaction dynamics. This paper focuses on the analysis of dynamics of one particular network--the yeast cyclins network. Using a dedicated mathematical model of gene expression and a procedure for computation of the parameters of the model from experimental data, a complete numerical model of the dynamics of the cyclins genetic network was attained. The model allowed for performing virtual experiments on the network and observing their influence on the expression dynamics of the genes downstream in the regulatory cascade. Results show that when the network structure is more complicated, and the regulatory interactions are indirect, results of gene deletion are highly unpredictable. As a consequence of quantitative behavior of the genes and their connections within the network, causal relationship between a regulator and target gene may not be discovered by gene deletion. Without including the dynamics of the system into the network, its functional properties cannot be studied and interpreted correctly.

  1. Controller area network for monitor and control in ALMA

    Science.gov (United States)

    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.

  2. Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm.

    Science.gov (United States)

    Mandal, Sudip; Saha, Goutam; Pal, Rajat Kumar

    2017-08-01

    Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.

  3. A hybrid network-based method for the detection of disease-related genes

    Science.gov (United States)

    Cui, Ying; Cai, Meng; Dai, Yang; Stanley, H. Eugene

    2018-02-01

    Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein-protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.

  4. Identification and Position Control of Marine Helm using Artificial Neural Network Neural Network

    Directory of Open Access Journals (Sweden)

    Hui ZHU

    2008-02-01

    Full Text Available If nonlinearities such as saturation of the amplifier gain and motor torque, gear backlash, and shaft compliances- just to name a few - are considered in the position control system of marine helm, traditional control methods are no longer sufficient to be used to improve the performance of the system. In this paper an alternative approach to traditional control methods - a neural network reference controller - is proposed to establish an adaptive control of the position of the marine helm to achieve the controlled variable at the command position. This neural network controller comprises of two neural networks. One is the plant model network used to identify the nonlinear system and the other the controller network used to control the output to follow the reference model. The experimental results demonstrate that this adaptive neural network reference controller has much better control performance than is obtained with traditional controllers.

  5. Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

    Science.gov (United States)

    Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

    2016-01-01

    We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

  6. RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

    Directory of Open Access Journals (Sweden)

    Marco Grimaldi

    Full Text Available RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems.

  7. Heart morphogenesis gene regulatory networks revealed by temporal expression analysis.

    Science.gov (United States)

    Hill, Jonathon T; Demarest, Bradley; Gorsi, Bushra; Smith, Megan; Yost, H Joseph

    2017-10-01

    During embryogenesis the heart forms as a linear tube that then undergoes multiple simultaneous morphogenetic events to obtain its mature shape. To understand the gene regulatory networks (GRNs) driving this phase of heart development, during which many congenital heart disease malformations likely arise, we conducted an RNA-seq timecourse in zebrafish from 30 hpf to 72 hpf and identified 5861 genes with altered expression. We clustered the genes by temporal expression pattern, identified transcription factor binding motifs enriched in each cluster, and generated a model GRN for the major gene batteries in heart morphogenesis. This approach predicted hundreds of regulatory interactions and found batteries enriched in specific cell and tissue types, indicating that the approach can be used to narrow the search for novel genetic markers and regulatory interactions. Subsequent analyses confirmed the GRN using two mutants, Tbx5 and nkx2-5 , and identified sets of duplicated zebrafish genes that do not show temporal subfunctionalization. This dataset provides an essential resource for future studies on the genetic/epigenetic pathways implicated in congenital heart defects and the mechanisms of cardiac transcriptional regulation. © 2017. Published by The Company of Biologists Ltd.

  8. Signal Network Analysis of Plant Genes Responding to Ionizing Radiation

    International Nuclear Information System (INIS)

    Kim, Dong Sub; Kim, Jinbaek; Kim, Sang Hoon

    2012-12-01

    In this project, we irradiated Arabidopsis plants with various doses of gamma-rays at the vegetative and reproductive stages to assess their radiation sensitivity. After the gene expression profiles and an analysis of the antioxidant response, we selected several Arabidopsis genes for uses of 'Radio marker genes (RMG)' and conducted over-expression and knock-down experiments to confirm the radio sensitivity. Based on these results, we applied two patents for the detection of two RMG (At3g28210 and At4g37990) and development of transgenic plants. Also, we developed a Genechip for use of high-throughput screening of Arabidopsis genes responding only to ionizing radiation and identified RMG to detect radiation leaks. Based on these results, we applied two patents associated with the use of Genechip for different types of radiation and different growth stages. Also, we conducted co-expression network study of specific expressed probes against gamma-ray stress and identified expressed patterns of duplicated genes formed by whole/500kb segmental genome duplication

  9. Synchronizability on complex networks via pinning control

    Indian Academy of Sciences (India)

    logical neural networks, electric power grids, social networks, etc., can be described by models of complex networks. So far, complex networks have been intensively investi- gated across many fields of science and engineering [1–5]. The synchronization of all dynamical nodes in a network is one of the most interesting and ...

  10. An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.

    Science.gov (United States)

    Valentini, Giorgio; Paccanaro, Alberto; Caniza, Horacio; Romero, Alfonso E; Re, Matteo

    2014-06-01

    In the context of "network medicine", gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different "informativeness" embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation. Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further enhance disease gene ranking results, by adopting both

  11. Netter: re-ranking gene network inference predictions using structural network properties.

    Science.gov (United States)

    Ruyssinck, Joeri; Demeester, Piet; Dhaene, Tom; Saeys, Yvan

    2016-02-09

    Many algorithms have been developed to infer the topology of gene regulatory networks from gene expression data. These methods typically produce a ranking of links between genes with associated confidence scores, after which a certain threshold is chosen to produce the inferred topology. However, the structural properties of the predicted network do not resemble those typical for a gene regulatory network, as most algorithms only take into account connections found in the data and do not include known graph properties in their inference process. This lowers the prediction accuracy of these methods, limiting their usability in practice. We propose a post-processing algorithm which is applicable to any confidence ranking of regulatory interactions obtained from a network inference method which can use, inter alia, graphlets and several graph-invariant properties to re-rank the links into a more accurate prediction. To demonstrate the potential of our approach, we re-rank predictions of six different state-of-the-art algorithms using three simple network properties as optimization criteria and show that Netter can improve the predictions made on both artificially generated data as well as the DREAM4 and DREAM5 benchmarks. Additionally, the DREAM5 E.coli. community prediction inferred from real expression data is further improved. Furthermore, Netter compares favorably to other post-processing algorithms and is not restricted to correlation-like predictions. Lastly, we demonstrate that the performance increase is robust for a wide range of parameter settings. Netter is available at http://bioinformatics.intec.ugent.be. Network inference from high-throughput data is a long-standing challenge. In this work, we present Netter, which can further refine network predictions based on a set of user-defined graph properties. Netter is a flexible system which can be applied in unison with any method producing a ranking from omics data. It can be tailored to specific prior

  12. Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Alina Sîrbu

    2015-05-01

    Full Text Available Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions. Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

  13. Complex Dynamic Behavior in Simple Gene Regulatory Networks

    Science.gov (United States)

    Santillán Zerón, Moisés

    2007-02-01

    Knowing the complete genome of a given species is just a piece of the puzzle. To fully unveil the systems behavior of an organism, an organ, or even a single cell, we need to understand the underlying gene regulatory dynamics. Given the complexity of the whole system, the ultimate goal is unattainable for the moment. But perhaps, by analyzing the most simple genetic systems, we may be able to develop the mathematical techniques and procedures required to tackle more complex genetic networks in the near future. In the present work, the techniques for developing mathematical models of simple bacterial gene networks, like the tryptophan and lactose operons are introduced. Despite all of the underlying assumptions, such models can provide valuable information regarding gene regulation dynamics. Here, we pay special attention to robustness as an emergent property. These notes are organized as follows. In the first section, the long historical relation between mathematics, physics, and biology is briefly reviewed. Recently, the multidisciplinary work in biology has received great attention in the form of systems biology. The main concepts of this novel science are discussed in the second section. A very slim introduction to the essential concepts of molecular biology is given in the third section. In the fourth section, a brief introduction to chemical kinetics is presented. Finally, in the fifth section, a mathematical model for the lactose operon is developed and analyzed..

  14. Reward Network Immediate Early Gene Expression in Mood Disorders

    Directory of Open Access Journals (Sweden)

    Alfred J. Robison

    2017-04-01

    Full Text Available Over the past three decades, it has become clear that aberrant function of the network of interconnected brain regions responsible for reward processing and motivated behavior underlies a variety of mood disorders, including depression and anxiety. It is also clear that stress-induced changes in reward network activity underlying both normal and pathological behavior also cause changes in gene expression. Here, we attempt to define the reward circuitry and explore the known and potential contributions of activity-dependent changes in gene expression within this circuitry to stress-induced changes in behavior related to mood disorders, and contrast some of these effects with those induced by exposure to drugs of abuse. We focus on a series of immediate early genes regulated by stress within this circuitry and their connections, both well-explored and relatively novel, to circuit function and subsequent reward-related behaviors. We conclude that IEGs play a crucial role in stress-dependent remodeling of reward circuitry, and that they may serve as inroads to the molecular, cellular, and circuit-level mechanisms of mood disorder etiology and treatment.

  15. Pluripotency gene network dynamics: System views from parametric analysis.

    Science.gov (United States)

    Akberdin, Ilya R; Omelyanchuk, Nadezda A; Fadeev, Stanislav I; Leskova, Natalya E; Oschepkova, Evgeniya A; Kazantsev, Fedor V; Matushkin, Yury G; Afonnikov, Dmitry A; Kolchanov, Nikolay A

    2018-01-01

    Multiple experimental data demonstrated that the core gene network orchestrating self-renewal and differentiation of mouse embryonic stem cells involves activity of Oct4, Sox2 and Nanog genes by means of a number of positive feedback loops among them. However, recent studies indicated that the architecture of the core gene network should also incorporate negative Nanog autoregulation and might not include positive feedbacks from Nanog to Oct4 and Sox2. Thorough parametric analysis of the mathematical model based on this revisited core regulatory circuit identified that there are substantial changes in model dynamics occurred depending on the strength of Oct4 and Sox2 activation and molecular complexity of Nanog autorepression. The analysis showed the existence of four dynamical domains with different numbers of stable and unstable steady states. We hypothesize that these domains can constitute the checkpoints in a developmental progression from naïve to primed pluripotency and vice versa. During this transition, parametric conditions exist, which generate an oscillatory behavior of the system explaining heterogeneity in expression of pluripotent and differentiation factors in serum ESC cultures. Eventually, simulations showed that addition of positive feedbacks from Nanog to Oct4 and Sox2 leads mainly to increase of the parametric space for the naïve ESC state, in which pluripotency factors are strongly expressed while differentiation ones are repressed.

  16. Deciphering energy-associated gene networks operating in the response of Arabidopsis plants to stress and nutritional cues.

    Science.gov (United States)

    Avin-Wittenberg, Tamar; Tzin, Vered; Angelovici, Ruthie; Less, Hadar; Galili, Gad

    2012-06-01

    Plants need to continuously adjust their transcriptome in response to various stresses that lead to inhibition of photosynthesis and the deprivation of cellular energy. This adjustment is triggered in part by a coordinated re-programming of the energy-associated transcriptome to slow down photosynthesis and activate other energy-promoting gene networks. Therefore, understanding the stress-related transcriptional networks of genes belonging to energy-associated pathways is of major importance for engineering stress tolerance. In a bioinformatics approach developed by our group, termed 'gene coordination', we previously divided genes encoding for enzymes and transcription factors in Arabidopsis thaliana into three clusters, displaying altered coordinated transcriptional behaviors in response to multiple biotic and abiotic stresses (Plant Cell, 23, 2011, 1264). Enrichment analysis indicated further that genes controlling energy-associated metabolism operate as a compound network in response to stress. In the present paper, we describe in detail the network association of genes belonging to six central energy-associated pathways in each of these three clusters described in our previous paper. Our results expose extensive stress-associated intra- and inter-pathway interactions between genes from these pathways, indicating that genes encoding proteins involved in energy-associated metabolism are expressed in a highly coordinated manner. We also provide examples showing that this approach can be further utilized to elucidate candidate genes for stress tolerance and functions of isozymes. © 2012 The Authors. The Plant Journal © 2012 Blackwell Publishing Ltd.

  17. Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data

    Directory of Open Access Journals (Sweden)

    Sungyoung Lee

    2012-12-01

    Full Text Available Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs. For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR is one of the powerful and efficient methods for detecting high-order gene-gene (GxG interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI. Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

  18. Comparative analysis of weighted gene co-expression networks in human and mouse.

    Science.gov (United States)

    Eidsaa, Marius; Stubbs, Lisa; Almaas, Eivind

    2017-01-01

    The application of complex network modeling to analyze large co-expression data sets has gained traction during the last decade. In particular, the use of the weighted gene co-expression network analysis framework has allowed an unbiased and systems-level investigation of genotype-phenotype relationships in a wide range of systems. Since mouse is an important model organism for biomedical research on human disease, it is of great interest to identify similarities and differences in the functional roles of human and mouse orthologous genes. Here, we develop a novel network comparison approach which we demonstrate by comparing two gene-expression data sets from a large number of human and mouse tissues. The method uses weighted topological overlap alongside the recently developed network-decomposition method of s-core analysis, which is suitable for making gene-centrality rankings for weighted networks. The aim is to identify globally central genes separately in the human and mouse networks. By comparing the ranked gene lists, we identify genes that display conserved or diverged centrality-characteristics across the networks. This framework only assumes a single threshold value that is chosen from a statistical analysis, and it may be applied to arbitrary network structures and edge-weight distributions, also outside the context of biology. When conducting the comparative network analysis, both within and across the two species, we find a clear pattern of enrichment of transcription factors, for the homeobox domain in particular, among the globally central genes. We also perform gene-ontology term enrichment analysis and look at disease-related genes for the separate networks as well as the network comparisons. We find that gene ontology terms related to regulation and development are generally enriched across the networks. In particular, the genes FOXE3, RHO, RUNX2, ALX3 and RARA, which are disease genes in either human or mouse, are on the top-10 list of globally

  19. Neural Networks for Modeling and Control of Particle Accelerators

    CERN Document Server

    Edelen, A.L.; Chase, B.E.; Edstrom, D.; Milton, S.V.; Stabile, P.

    2016-01-01

    We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

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

    Science.gov (United States)

    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.

  1. Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network

    Directory of Open Access Journals (Sweden)

    Delyon Bernard

    2010-11-01

    Full Text Available Abstract Background A wide range of techniques is now available for analyzing regulatory networks. Nonetheless, most of these techniques fail to interpret large-scale transcriptional data at the post-translational level. Results We address the question of using large-scale transcriptomic observation of a system perturbation to analyze a regulatory network which contained several types of interactions - transcriptional and post-translational. Our method consisted of post-processing the outputs of an open-source tool named BioQuali - an automatic constraint-based analysis mimicking biologist's local reasoning on a large scale. The post-processing relied on differences in the behavior of the transcriptional and post-translational levels in the network. As a case study, we analyzed a network representation of the genes and proteins controlled by an oncogene in the context of Ewing's sarcoma. The analysis allowed us to pinpoint active interactions specific to this cancer. We also identified the parts of the network which were incomplete and should be submitted for further investigation. Conclusions The proposed approach is effective for the qualitative analysis of cancer networks. It allows the integrative use of experimental data of various types in order to identify the specific information that should be considered a priority in the initial - and possibly very large - experimental dataset. Iteratively, new dataset can be introduced into the analysis to improve the network representation and make it more specific.

  2. Pitx2 modulates a Tbx5-dependent gene regulatory network to maintain atrial rhythm.

    Science.gov (United States)

    Nadadur, Rangarajan D; Broman, Michael T; Boukens, Bastiaan; Mazurek, Stefan R; Yang, Xinan; van den Boogaard, Malou; Bekeny, Jenna; Gadek, Margaret; Ward, Tarsha; Zhang, Min; Qiao, Yun; Martin, James F; Seidman, Christine E; Seidman, Jon; Christoffels, Vincent; Efimov, Igor R; McNally, Elizabeth M; Weber, Christopher R; Moskowitz, Ivan P

    2016-08-31

    Cardiac rhythm is extremely robust, generating 2 billion contraction cycles during the average human life span. Transcriptional control of cardiac rhythm is poorly understood. We found that removal of the transcription factor gene Tbx5 from the adult mouse caused primary spontaneous and sustained atrial fibrillation (AF). Atrial cardiomyocytes from the Tbx5-mutant mice exhibited action potential abnormalities, including spontaneous depolarizations, which were rescued by chelating free calcium. We identified a multitiered transcriptional network that linked seven previously defined AF risk loci: TBX5 directly activated PITX2, and TBX5 and PITX2 antagonistically regulated membrane effector genes Scn5a, Gja1, Ryr2, Dsp, and Atp2a2 In addition, reduced Tbx5 dose by adult-specific haploinsufficiency caused decreased target gene expression, myocardial automaticity, and AF inducibility, which were all rescued by Pitx2 haploinsufficiency in mice. These results defined a transcriptional architecture for atrial rhythm control organized as an incoherent feed-forward loop, driven by TBX5 and modulated by PITX2. TBX5/PITX2 interplay provides tight control of atrial rhythm effector gene expression, and perturbation of the co-regulated network caused AF susceptibility. This work provides a model for the molecular mechanisms underpinning the genetic implication of multiple AF genome-wide association studies loci and will contribute to future efforts to stratify patients for AF risk by genotype. Copyright © 2016, American Association for the Advancement of Science.

  3. Quantitative and logic modelling of gene and molecular networks

    Science.gov (United States)

    Le Novère, Nicolas

    2015-01-01

    Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms. PMID:25645874

  4. Design of experiment for nonlinear dynamic gene regulatory network identification.

    Science.gov (United States)

    Lu, Tao

    2017-04-04

    The gene regulatory network (GRN) is critical for understanding the regulatory interaction between genes. Time-course microarray experiments provide ample information for constructing GRN. The designs for microarray experiments serve different purposes. However, the experiment design specifically for GRN identification is still sparse. In this article, we use a simulation-based approach to deal with design problems in the framework of nonparametric differential equations. We investigate a number of feasible designs. In particular, we evaluate whether earlier samplings can result in more useful information for GRN identification. We also evaluate the effectiveness of two strategies: more frequent samplings per replicate with fewer replicates versus fewer samplings per replicate with more replicates while keeping the total number of samplings constant. The results of our investigation provide quantitative guidance for designing and selecting microarray experiments for the purpose of GRN identification.

  5. GeneNetwork: A Toolbox for Systems Genetics.

    Science.gov (United States)

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

    2017-01-01

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

  6. [Gene networks that regulate secondary metabolism in actinomycetes: pleiotropic regulators].

    Science.gov (United States)

    Rabyk, M V; Ostash, B O; Fedorenko, V O

    2014-01-01

    Current advances in the research and practical applications of pleiotropic regulatory genes for antibiotic production in actinomycetes are reviewed. The basic regulatory mechanisms found in these bacteria are outlined. Examples described in the review show the importance of the manipulation of regulatory systems that affect the synthesis of antibiotics for the metabolic engineering of the actinomycetes. Also, the study of these genes is the basis for the development of genetic engineering approaches towards the induction of "cryptic" part of the actinomycetes secondary metabolome, which capacity for production of biologically active compounds is much bigger than the diversity of antibiotics underpinned by traditional microbiological screening. Besides the practical problems, the study of regulatory genes for antibiotic biosynthesis will provide insights into the process of evolution of complex regulatory systems that coordinate the expression of gene operons, clusters and regulons, involved in the control of secondary metabolism and morphogenesis of actinomycetes.

  7. Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining.

    Science.gov (United States)

    Hur, Junguk; Ozgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

    2012-12-20

    Fever is one of the most common adverse events of vaccines. The detailed mechanisms of fever and vaccine-associated gene interaction networks are not fully understood. In the present study, we employed a genome-wide, Centrality and Ontology-based Network Discovery using Literature data (CONDL) approach to analyse the genes and gene interaction networks associated with fever or vaccine-related fever responses. Over 170,000 fever-related articles from PubMed abstracts and titles were retrieved and analysed at the sentence level using natural language processing techniques to identify genes and vaccines (including 186 Vaccine Ontology terms) as well as their interactions. This resulted in a generic fever network consisting of 403 genes and 577 gene interactions. A vaccine-specific fever sub-network consisting of 29 genes and 28 gene interactions was extracted from articles that are related to both fever and vaccines. In addition, gene-vaccine interactions were identified. Vaccines (including 4 specific vaccine names) were found to directly interact with 26 genes. Gene set enrichment analysis was performed using the genes in the generated interaction networks. Moreover, the genes in these networks were prioritized using network centrality metrics. Making scientific discoveries and generating new hypotheses were possible by using network centrality and gene set enrichment analyses. For example, our study found that the genes in the generic fever network were more enriched in cell death and responses to wounding, and the vaccine sub-network had more gene enrichment in leukocyte activation and phosphorylation regulation. The most central genes in the vaccine-specific fever network are predicted to be highly relevant to vaccine-induced fever, whereas genes that are central only in the generic fever network are likely to be highly relevant to generic fever responses. Interestingly, no Toll-like receptors (TLRs) were found in the gene-vaccine interaction network. Since

  8. Computation and control with neural networks

    Science.gov (United States)

    Corneliusen, A.; Terdal, P.; Knight, T.; Spencer, J.

    1990-08-01

    As energies have increased exponentially with time, so have the size and complexity of accelerators and control systems. Neural networks (NNs) may offer the kinds of improvements in computation and control that are needed to maintain acceptable functionality. For control, their associative characteristics could provide signal conversion or data translation. Because they can do any computation such as least-squares, they can close feedback loops autonomously to provide intelligent control at the point of action rather than at a central location that requires transfers, conversions, hand-shaking and other costly repetitions like input protection. Both computation and control can be integrated on a single chip, a printed circuit or an optical equivalent that is also inherently faster through full parallel operation. For such reasons one expects lower costs and better results. Such systems could be optimized by integrating sensor and signal-processing functions. Distributed nets of such hardware could communicate and provide global monitoring and multiprocessing in various ways, e.g. via token, slotted or parallel rings (or Steiner trees), for compatibility with existing systems. Problems and advantages of this approach, such as an optimal, real-time Turing machine, are discussed. Simple examples are simulated and hardware implemented using discrete elements that demonstrate some basic characteristics of learning and parallelism. Future "microprocessors" are predicted and requested on this basis.

  9. Network-based production quality control

    Science.gov (United States)

    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.

  10. A systematic molecular circuit design method for gene networks under biochemical time delays and molecular noises

    Directory of Open Access Journals (Sweden)

    Chang Yu-Te

    2008-11-01

    Full Text Available Abstract Background Gene networks in nanoscale are of nonlinear stochastic process. Time delays are common and substantial in these biochemical processes due to gene transcription, translation, posttranslation protein modification and diffusion. Molecular noises in gene networks come from intrinsic fluctuations, transmitted noise from upstream genes, and the global noise affecting all genes. Knowledge of molecular noise filtering and biochemical process delay compensation in gene networks is crucial to understand the signal processing in gene networks and the design of noise-tolerant and delay-robust gene circuits for synthetic biology. Results A nonlinear stochastic dynamic model with multiple time delays is proposed for describing a gene network under process delays, intrinsic molecular fluctuations, and extrinsic molecular noises. Then, the stochastic biochemical processing scheme of gene regulatory networks for attenuating these molecular noises and compensating process delays is investigated from the nonlinear signal processing perspective. In order to improve the robust stability for delay toleration and noise filtering, a robust gene circuit for nonlinear stochastic time-delay gene networks is engineered based on the nonlinear robust H∞ stochastic filtering scheme. Further, in order to avoid solving these complicated noise-tolerant and delay-robust design problems, based on Takagi-Sugeno (T-S fuzzy time-delay model and linear matrix inequalities (LMIs technique, a systematic gene circuit design method is proposed to simplify the design procedure. Conclusion The proposed gene circuit design method has much potential for application to systems biology, synthetic biology and drug design when a gene regulatory network has to be designed for improving its robust stability and filtering ability of disease-perturbed gene network or when a synthetic gene network needs to perform robustly under process delays and molecular noises.

  11. Energy efficient topology control algorithm for wireless mesh networks

    CSIR Research Space (South Africa)

    Aron, FO

    2008-08-01

    Full Text Available The control of the topology of a network makes it possible for the network nodes to reduce their power of transmission while ensuring that network connectivity is preserved. This paper explains the need for energy consumption control in Wireless...

  12. Modern computer networks and distributed intelligence in accelerator controls

    International Nuclear Information System (INIS)

    Briegel, C.

    1991-01-01

    Appropriate hardware and software network protocols are surveyed for accelerator control environments. Accelerator controls network topologies are discussed with respect to the following criteria: vertical versus horizontal and distributed versus centralized. Decision-making considerations are provided for accelerator network architecture specification. Current trends and implementations at Fermilab are discussed

  13. CRISPR loci reveal networks of gene exchange in archaea

    Directory of Open Access Journals (Sweden)

    Brodt Avital

    2011-12-01

    Full Text Available Abstract Background CRISPR (Clustered, Regularly, Interspaced, Short, Palindromic Repeats loci provide prokaryotes with an adaptive immunity against viruses and other mobile genetic elements. CRISPR arrays can be transcribed and processed into small crRNA molecules, which are then used by the cell to target the foreign nucleic acid. Since spacers are accumulated by active CRISPR/Cas systems, the sequences of these spacers provide a record of the past "infection history" of the organism. Results Here we analyzed all currently known spacers present in archaeal genomes and identified their source by DNA similarity. While nearly 50% of archaeal spacers matched mobile genetic elements, such as plasmids or viruses, several others matched chromosomal genes of other organisms, primarily other archaea. Thus, networks of gene exchange between archaeal species were revealed by the spacer analysis, including many cases of inter-genus and inter-species gene transfer events. Spacers that recognize viral sequences tend to be located further away from the leader sequence, implying that there exists a selective pressure for their retention. Conclusions CRISPR spacers provide direct evidence for extensive gene exchange in archaea, especially within genera, and support the current dogma where the primary role of the CRISPR/Cas system is anti-viral and anti-plasmid defense. Open peer review This article was reviewed by: Profs. W. Ford Doolittle, John van der Oost, Christa Schleper (nominated by board member Prof. J Peter Gogarten

  14. RNAi-Based Identification of Gene-Specific Nuclear Cofactor Networks Regulating Interleukin-1 Target Genes

    Directory of Open Access Journals (Sweden)

    Johanna Meier-Soelch

    2018-04-01

    Full Text Available The potent proinflammatory cytokine interleukin (IL-1 triggers gene expression through the NF-κB signaling pathway. Here, we investigated the cofactor requirements of strongly regulated IL-1 target genes whose expression is impaired in p65 NF-κB-deficient murine embryonic fibroblasts. By two independent small-hairpin (shRNA screens, we examined 170 genes annotated to encode nuclear cofactors for their role in Cxcl2 mRNA expression and identified 22 factors that modulated basal or IL-1-inducible Cxcl2 levels. The functions of 16 of these factors were validated for Cxcl2 and further analyzed for their role in regulation of 10 additional IL-1 target genes by RT-qPCR. These data reveal that each inducible gene has its own (quantitative requirement of cofactors to maintain basal levels and to respond to IL-1. Twelve factors (Epc1, H2afz, Kdm2b, Kdm6a, Mbd3, Mta2, Phf21a, Ruvbl1, Sin3b, Suv420h1, Taf1, and Ube3a have not been previously implicated in inflammatory cytokine functions. Bioinformatics analysis indicates that they are components of complex nuclear protein networks that regulate chromatin functions and gene transcription. Collectively, these data suggest that downstream from the essential NF-κB signal each cytokine-inducible target gene has further subtle requirements for individual sets of nuclear cofactors that shape its transcriptional activation profile.

  15. Toxoplasma gondii gene expression is under the control of regulatory pathways acting through chromatin structure

    Directory of Open Access Journals (Sweden)

    Bougdour A.

    2008-09-01

    Full Text Available The activity state of a gene is determined by a complex regulatory network of co-acting factors affecting the structure of the chromatin into which the gene is embedded. While significant changes of the transcriptome occur during cell differentiation in apicomplexan parasites, basic mechanisms controlling gene expression are still unknown. Recent studies support and expand the concept of the chromatin environment being key factor for the control of transcriptional activity in these lower eukaryotes organisms. Here, we review recent advances in the field of epigenetic gene regulation in Toxoplasma gondii, the model apicomplexan.

  16. Pinning control of complex networked systems synchronization, consensus and flocking of networked systems via pinning

    CERN Document Server

    Su, Housheng

    2013-01-01

    Synchronization, consensus and flocking are ubiquitous requirements in networked systems. Pinning Control of Complex Networked Systems investigates these requirements by using the pinning control strategy, which aims to control the whole dynamical network with huge numbers of nodes by imposing controllers for only a fraction of the nodes. As the direct control of every node in a dynamical network with huge numbers of nodes might be impossible or unnecessary, it’s then very important to use the pinning control strategy for the synchronization of complex dynamical networks. The research on pinning control strategy in consensus and flocking of multi-agent systems can not only help us to better understand the mechanisms of natural collective phenomena, but also benefit applications in mobile sensor/robot networks. This book offers a valuable resource for researchers and engineers working in the fields of control theory and control engineering.   Housheng Su is an Associate Professor at the Department of Contro...

  17. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

    The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....

  18. Network resource control for grid workflow management systems

    NARCIS (Netherlands)

    Strijkers, R.J.; Cristea, M.; Korkhov, V.; Marchal, D.; Belloum, A.; Laat, C.de; Meijer, R.J.

    2010-01-01

    Grid workflow management systems automate the orchestration of scientific applications with large computational and data processing needs, but lack control over network resources. Consequently, the management system cannot prevent multiple communication intensive applications to compete for network

  19. Control and estimation methods over communication networks

    CERN Document Server

    Mahmoud, Magdi S

    2014-01-01

    This book provides a rigorous framework in which to study problems in the analysis, stability and design of networked control systems. Four dominant sources of difficulty are considered: packet dropouts, communication bandwidth constraints, parametric uncertainty, and time delays. Past methods and results are reviewed from a contemporary perspective, present trends are examined, and future possibilities proposed. Emphasis is placed on robust and reliable design methods. New control strategies for improving the efficiency of sensor data processing and reducing associated time delay are presented. The coverage provided features: ·        an overall assessment of recent and current fault-tolerant control algorithms; ·        treatment of several issues arising at the junction of control and communications; ·        key concepts followed by their proofs and efficient computational methods for their implementation; and ·        simulation examples (including TrueTime simulations) to...

  20. Ethylene-Related Gene Expression Networks in Wood Formation

    Directory of Open Access Journals (Sweden)

    Carolin Seyfferth

    2018-03-01

    Full Text Available Thickening of tree stems is the result of secondary growth, accomplished by the meristematic activity of the vascular cambium. Secondary growth of the stem entails developmental cascades resulting in the formation of secondary phloem outwards and secondary xylem (i.e., wood inwards of the stem. Signaling and transcriptional reprogramming by the phytohormone ethylene modifies cambial growth and cell differentiation, but the molecular link between ethylene and secondary growth remains unknown. We addressed this shortcoming by analyzing expression profiles and co-expression networks of ethylene pathway genes using the AspWood transcriptome database which covers all stages of secondary growth in aspen (Populus tremula stems. ACC synthase expression suggests that the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC is synthesized during xylem expansion and xylem cell maturation. Ethylene-mediated transcriptional reprogramming occurs during all stages of secondary growth, as deduced from AspWood expression profiles of ethylene-responsive genes. A network centrality analysis of the AspWood dataset identified EIN3D and 11 ERFs as hubs. No overlap was found between the co-expressed genes of the EIN3 and ERF hubs, suggesting target diversification and hence independent roles for these transcription factor families during normal wood formation. The EIN3D hub was part of a large co-expression gene module, which contained 16 transcription factors, among them several new candidates that have not been earlier connected to wood formation and a VND-INTERACTING 2 (VNI2 homolog. We experimentally demonstrated Populus EIN3D function in ethylene signaling in Arabidopsis thaliana. The ERF hubs ERF118 and ERF119 were connected on the basis of their expression pattern and gene co-expression module composition to xylem cell expansion and secondary cell wall formation, respectively. We hereby establish data resources for ethylene-responsive genes and

  1. Mechanistically Consistent Reduced Models of Synthetic Gene Networks

    Science.gov (United States)

    Mier-y-Terán-Romero, Luis; Silber, Mary; Hatzimanikatis, Vassily

    2013-01-01

    Designing genetic networks with desired functionalities requires an accurate mathematical framework that accounts for the essential mechanistic details of the system. Here, we formulate a time-delay model of protein translation and mRNA degradation by systematically reducing a detailed mechanistic model that explicitly accounts for the ribosomal dynamics and the cleaving of mRNA by endonucleases. We exploit various technical and conceptual advantages that our time-delay model offers over the mechanistic model to probe the behavior of a self-repressing gene over wide regions of parameter space. We show that a heuristic time-delay model of protein synthesis of a commonly used form yields a notably different prediction for the parameter region where sustained oscillations occur. This suggests that such heuristics can lead to erroneous results. The functional forms that arise from our systematic reduction can be used for every system that involves transcription and translation and they could replace the commonly used heuristic time-delay models for these processes. The results from our analysis have important implications for the design of synthetic gene networks and stress that such design must be guided by a combination of heuristic models and mechanistic models that include all relevant details of the process. PMID:23663853

  2. Equipment to Support Development of Neuronal Network Controlled Robots

    Science.gov (United States)

    2016-06-25

    growth and training of neuronal neural networks to control robot arms. This work was done to learn the properties of the neurons and neuronal network , by...Equipment to Support Development of Neuronal Network Controlled Robots With this award, our team purchased an ALA 2-channel stimulus generator, an...peer-reviewed journals: Number of Papers published in non peer-reviewed journals: Final Report: Equipment to Support Development of Neuronal Network

  3. A core filamentation response network in Candida albicans is restricted to eight genes.

    Directory of Open Access Journals (Sweden)

    Ronny Martin

    Full Text Available Although morphological plasticity is a central virulence trait of Candida albicans, the number of filament-associated genes and the interplay of mechanisms regulating their expression remain unknown. By correlation-based network modeling of the transcriptional response to different defined external stimuli for morphogenesis we identified a set of eight genes with highly correlated expression patterns, forming a core filamentation response. This group of genes included ALS3, ECE1, HGT2, HWP1, IHD1 and RBT1 which are known or supposed to encode for cell- wall associated proteins as well as the Rac1 guanine nucleotide exchange factor encoding gene DCK1 and the unknown function open reading frame orf19.2457. The validity of network modeling was confirmed using a dataset of advanced complexity that describes the transcriptional response of C. albicans during epithelial invasion as well as comparing our results with other previously published transcriptome studies. Although the set of core filamentation response genes was quite small, several transcriptional regulators are involved in the control of their expression, depending on the environmental condition.

  4. Performance of the TRISTAN computer control network

    International Nuclear Information System (INIS)

    Koiso, H.; Abe, K.; Akiyama, A.; Katoh, T.; Kikutani, E.; Kurihara, N.; Kurokawa, S.; Oide, K.; Shinomoto, M.

    1985-01-01

    An N-to-N token ring network of twenty-four minicomputers controls the TRISTAN accelerator complex. The computers are linked by optical fiber cables with 10 Mbps transmission speed. The software system is based on the NODAL, a multi-computer interpreter language developed at CERN SPS. Typical messages exchanged between computers are NODAL programs and NODAL variables transmitted by the EXEC and the REMIT commands. These messages are exchanged as a cluster of packets whose maximum size is 512 bytes. At present, eleven minicomputers are connected to the network and the total length of the ring is 1.5 km. In this condition, the maximum attainable throughput is 980 kbytes/s. The response of a pair of an EXEC and a REMIT transactions which transmit a NODAL array A and one line of program 'REMIT A' and immediately remit the A is measured to be 95+0.039/chi/ ms, where /chi/ is the array size in byte. In ordinary accelerator operations, the maximum channel utilization is 2%, the average packet length is 96 bytes and the transmission rate is 10 kbytes/s

  5. Controllability of giant connected components in a directed network.

    Science.gov (United States)

    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=p_{m}. We find that, for ER networks, the peak value of the driver node density remains the same regardless of its average degree 〈k〉 and that it is determined by p_{m}〈k〉. 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.

  6. Fault Detection for Quantized Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Wei-Wei Che

    2013-01-01

    Full Text Available The fault detection problem in the finite frequency domain for networked control systems with signal quantization is considered. With the logarithmic quantizer consideration, a quantized fault detection observer is designed by employing a performance index which is used to increase the fault sensitivity in finite frequency domain. The quantized measurement signals are dealt with by utilizing the sector bound method, in which the quantization error is treated as sector-bounded uncertainty. By using the Kalman-Yakubovich-Popov (GKYP Lemma, an iterative LMI-based optimization algorithm is developed for designing the quantized fault detection observer. And a numerical example is given to illustrate the effectiveness of the proposed method.

  7. Urgent epidemic control mechanism for aviation networks

    KAUST Repository

    Peng, Chengbin

    2011-01-01

    In the current century, the highly developed transportation system can not only boost the economy, but also greatly accelerate the spreading of epidemics. While some epidemic diseases may infect quite a number of people ahead of our awareness, the health care resources such as vaccines and the medical staff are usually locally or even globally insufficient. In this research, with the network of major aviation routes as an example, we present a method to determine the optimal locations to allocate the medical service in order to minimize the impact of the infectious disease with limited resources. Specifically, we demonstrate that when the medical resources are insufficient, we should concentrate our efforts on the travelers with the objective of effectively controlling the spreading rate of the epidemic diseases. © 2011 Springer-Verlag Berlin Heidelberg.

  8. Accelerator and feedback control simulation using neural networks

    International Nuclear Information System (INIS)

    Nguyen, D.; Lee, M.; Sass, R.; Shoaee, H.

    1991-05-01

    Unlike present constant model feedback system, neural networks can adapt as the dynamics of the process changes with time. Using a process model, the ''Accelerator'' network is first trained to simulate the dynamics of the beam for a given beam line. This ''Accelerator'' network is then used to train a second ''Controller'' network which performs the control function. In simulation, the networks are used to adjust corrector magnetics to control the launch angle and position of the beam to keep it on the desired trajectory when the incoming beam is perturbed. 4 refs., 3 figs

  9. Controllability of Weighted and Directed Networks with Nonidentical Node Dynamics

    Directory of Open Access Journals (Sweden)

    Linying Xiang

    2013-01-01

    Full Text Available The concept of controllability from control theory is applied to weighted and directed networks with heterogenous linear or linearized node dynamics subject to exogenous inputs, where the nodes are grouped into leaders and followers. Under this framework, the controllability of the controlled network can be decomposed into two independent problems: the controllability of the isolated leader subsystem and the controllability of the extended follower subsystem. Some necessary and/or sufficient conditions for the controllability of the leader-follower network are derived based on matrix theory and graph theory. In particular, it is shown that a single-leader network is controllable if it is a directed path or cycle, but it is uncontrollable for a complete digraph or a star digraph in general. Furthermore, some approaches to improving the controllability of a heterogenous network are presented. Some simulation examples are given for illustration and verification.

  10. Discovery of core biotic stress responsive genes in Arabidopsis by weighted gene co-expression network analysis.

    Science.gov (United States)

    Amrine, Katherine C H; Blanco-Ulate, Barbara; Cantu, Dario

    2015-01-01

    Intricate signal networks and transcriptional regulators translate the recognition of pathogens into defense responses. In this study, we carried out a gene co-expression analysis of all currently publicly available microarray data, which were generated in experiments that studied the interaction of the model plant Arabidopsis thaliana with microbial pathogens. This work was conducted to identify (i) modules of functionally related co-expressed genes that are differentially expressed in response to multiple biotic stresses, and (ii) hub genes that may function as core regulators of disease responses. Using Weighted Gene Co-expression Network Analysis (WGCNA) we constructed an undirected network leveraging a rich curated expression dataset comprising 272 microarrays that involved microbial infections of Arabidopsis plants with a wide array of fungal and bacterial pathogens with biotrophic, hemibiotrophic, and necrotrophic lifestyles. WGCNA produced a network with scale-free and small-world properties composed of 205 distinct clusters of co-expressed genes. Modules of functionally related co-expressed genes that are differentially regulated in response to multiple pathogens were identified by integrating differential gene expression testing with functional enrichment analyses of gene ontology terms, known disease associated genes, transcriptional regulators, and cis-regulatory elements. The significance of functional enrichments was validated by comparisons with randomly generated networks. Network topology was then analyzed to identify intra- and inter-modular gene hubs. Based on high connectivity, and centrality in meta-modules that are clearly enriched in defense responses, we propose a list of 66 target genes for reverse genetic experiments to further dissect the Arabidopsis immune system. Our results show that statistical-based data trimming prior to network analysis allows the integration of expression datasets generated by different groups, under different

  11. Dynamic Network Security Control Using Software Defined Networking

    Science.gov (United States)

    2016-03-24

    rapidly respond to host level security events using SDN flow table updates, role-based flow classes , and Advanced Messaging Queuing Protocol to auto...the success of most organizations. One approach is to apply host and network-based security systems, which typically come in the form of antivirus or...intrusion detection/prevention products to man- age these threats. However, since traditional networks require manual configuration, an antivirus alert

  12. Gene, protein and network of male sterility in rice

    Directory of Open Access Journals (Sweden)

    Wang eKun

    2013-04-01

    Full Text Available Rice is one of the most important model crop plants whose heterosis has been well exploited in commercial hybrid seed production via a variety of types of male sterile lines. Hybrid rice cultivation area is steadily expanding around the world, especially in Southern Asia. Characterization of genes and proteins related to male sterility aims to understand how and why the male sterility occurs, and which proteins are the key players for microspores abortion. Recently, a series of genes and proteins related to cytoplasmic male sterility, photoperiod sensitive male sterility, self-incompatibility and other types of microspores deterioration have been characterized through genetics or proteomics. Especially the latter, offers us a powerful and high throughput approach to discern the novel proteins involving in male-sterile pathways which may help us to breed artificial male-sterile system. This represents an alternative tool to meet the critical challenge of further development of hybrid rice. In this paper, we reviewed the recent developments in our understanding of male sterility in rice hybrid production across gene, protein and integrated network levels, and also, present a perspective on the engineering of male sterile lines for hybrid rice production.

  13. GenCLiP: a software program for clustering gene lists by literature profiling and constructing gene co-occurrence networks related to custom keywords

    Directory of Open Access Journals (Sweden)

    Zhou Yi-Bo

    2008-07-01

    Full Text Available Abstract Background Biomedical researchers often want to explore pathogenesis and pathways regulated by abnormally expressed genes, such as those identified by microarray analyses. Literature mining is an important way to assist in this task. Many literature mining tools are now available. However, few of them allows the user to make manual adjustments to zero in on what he/she wants to know in particular. Results We present our software program, GenCLiP (Gene Cluster with Literature Profiles, which is based on the methods presented by Chaussabel and Sher (Genome Biol 2002, 3(10:RESEARCH0055 that search gene lists to identify functional clusters of genes based on up-to-date literature profiling. Four features were added to this previously described method: the ability to 1 manually curate keywords extracted from the literature, 2 search genes and gene co-occurrence networks related to custom keywords, 3 compare analyzed gene results with negative and positive controls generated by GenCLiP, and 4 calculate probabilities that the resulting genes and gene networks are randomly related. In this paper, we show with a set of differentially expressed genes between keloids and normal control, how implementation of functions in GenCLiP successfully identified keywords related to the pathogenesis of keloids and unknown gene pathways involved in the pathogenesis of keloids. Conclusion With regard to the identification of disease-susceptibility genes, GenCLiP allows one to quickly acquire a primary pathogenesis profile and identify pathways involving abnormally expressed genes not previously associated with the disease.

  14. Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease-gene network.

    Science.gov (United States)

    Zhang, Yuji; Tao, Cui; Jiang, Guoqian; Nair, Asha A; Su, Jian; Chute, Christopher G; Liu, Hongfang

    2014-01-01

    A huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and deciphering disease mechanisms. However, little effort has been devoted to investigating associations among drugs, diseases, and genes in an integrative manner. We propose a novel network-based computational framework to identify statistically over-expressed subnetwork patterns, called network motifs, in an integrated disease-drug-gene network extracted from Semantic MEDLINE. The framework consists of two steps. The first step is to construct an association network by extracting pair-wise associations between diseases, drugs and genes in Semantic MEDLINE using a domain pattern driven strategy. A Resource Description Framework (RDF)-linked data approach is used to re-organize the data to increase the flexibility of data integration, the interoperability within domain ontologies, and the efficiency of data storage. Unique associations among drugs, diseases, and genes are extracted for downstream network-based analysis. The second step is to apply a network-based approach to mine the local network structure of this heterogeneous network. Significant network motifs are then identified as the backbone of the network. A simplified network based on those significant motifs is then constructed to facilitate discovery. We implemented our computational framework and identified five network motifs, each of which corresponds to specific biological meanings. Three case studies demonstrate that novel associations are derived from the network topology analysis of reconstructed networks of significant

  15. Characterizing genes with distinct methylation patterns in the context of protein-protein interaction network: application to human brain tissues.

    Science.gov (United States)

    Li, Yongsheng; Xu, Juan; Chen, Hong; Zhao, Zheng; Li, Shengli; Bai, Jing; Wu, Aiwei; Jiang, Chunjie; Wang, Yuan; Su, Bin; Li, Xia

    2013-01-01

    DNA methylation is an essential epigenetic mechanism involved in transcriptional control. However, how genes with different methylation patterns are assembled in the protein-protein interaction network (PPIN) remains a mystery. In the present study, we systematically dissected the characterization of genes with different methylation patterns in the PPIN. A negative association was detected between the methylation levels in the brain tissues and topological centralities. By focusing on two classes of genes with considerably different methylation levels in the brain tissues, namely the low methylated genes (LMGs) and high methylated genes (HMGs), we found that their organizing principles in the PPIN are distinct. The LMGs tend to be the center of the PPIN, and attacking them causes a more deleterious effect on the network integrity. Furthermore, the LMGs express their functions in a modular pattern and substantial differences in functions are observed between the two types of genes. The LMGs are enriched in the basic biological functions, such as binding activity and regulation of transcription. More importantly, cancer genes, especially recessive cancer genes, essential genes, and aging-related genes were all found more often in the LMGs. Additionally, our analysis presented that the intra-classes communications are enhanced, but inter-classes communications are repressed. Finally, a functional complementation was revealed between methylation and miRNA regulation in the human genome. We have elucidated the assembling principles of genes with different methylation levels in the context of the PPIN, providing key insights into the complex epigenetic regulation mechanisms.

  16. The impact of gene expression variation on the robustness and evolvability of a developmental gene regulatory network.

    Directory of Open Access Journals (Sweden)

    David A Garfield

    2013-10-01

    Full Text Available Regulatory interactions buffer development against genetic and environmental perturbations, but adaptation requires phenotypes to change. We investigated the relationship between robustness and evolvability within the gene regulatory network underlying development of the larval skeleton in the sea urchin Strongylocentrotus purpuratus. We find extensive variation in gene expression in this network throughout development in a natural population, some of which has a heritable genetic basis. Switch-like regulatory interactions predominate during early development, buffer expression variation, and may promote the accumulation of cryptic genetic variation affecting early stages. Regulatory interactions during later development are typically more sensitive (linear, allowing variation in expression to affect downstream target genes. Variation in skeletal morphology is associated primarily with expression variation of a few, primarily structural, genes at terminal positions within the network. These results indicate that the position and properties of gene interactions within a network can have important evolutionary consequences independent of their immediate regulatory role.

  17. On the Control of Consensus Networks: Theory and Applications

    Science.gov (United States)

    Hudoba de Badyn, Mathias

    Signed networks allow the study of positive and negative interactions between agents. In this thesis, three papers are presented that address controllability of networked dynamics. First, controllability of signed consensus networks is approached from a symmetry perspective, for both linear and nonlinear consensus protocols. It is shown that the graph-theoretic property of signed networks known as structural balance renders the consensus protocol uncontrollable when coupled with a certain type of symmetry. Stabilizability and output controllability of signed linear consensus is also examined, as well as a data-driven approach to finding bipartite consensus stemming from structural balance for signed nonlinear consensus. Second, an algorithm is constructed that allows one to grow a network while preserving controllability, and some generalizations of this algorithm are presented. Submodular optimization is used to analyze a second algorithm that adds nodes to a network to maximize the network connectivity.

  18. Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory

    Science.gov (United States)

    Sayyed-Ahmad, Abdallah; Tuncay, Kagan; Ortoleva, Peter J

    2007-01-01

    Background Gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding. Results Our approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented. Conclusion Multiplex time series data can be used for the construction of the network of

  19. Inferring Drosophila gap gene regulatory network: A parameter sensitivity and perturbation analysis

    NARCIS (Netherlands)

    Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.

    2009-01-01

    Background: Inverse modelling of gene regulatory networks (GRNs) capable of simulating continuous spatio-temporal biological processes requires accurate data and a good description of the system. If quantitative relations between genes cannot be extracted from direct measurements, an efficient

  20. Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in Brachypodium distachyon

    Directory of Open Access Journals (Sweden)

    Satoru Koda

    2017-11-01

    Full Text Available We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX model with a group smoothly clipped absolute deviation (SCAD method using a time-series transcriptome dataset in a model grass, Brachypodium distachyon. To reveal the diurnal changes in the transcriptome in B. distachyon, we performed RNA-seq analysis of its leaves sampled through a diurnal cycle of over 48 h at 4 h intervals using three biological replications, and identified 3,621 periodic genes through our wavelet analysis. The expression data are feasible to infer network sparsity based on ARX models. We found that genes involved in biological processes such as transcriptional regulation, protein degradation, and post-transcriptional modification and photosynthesis are significantly enriched in the periodic genes, suggesting that these processes might be regulated by circadian rhythm in B. distachyon. On the basis of the time-series expression patterns of the periodic genes, we constructed a chronological gene co-expression network and identified putative transcription factors encoding genes that might be involved in the time-specific regulatory transcriptional network. Moreover, we inferred a transcriptional network composed of the periodic genes in B. distachyon, aiming to identify genes associated with other genes through variable selection by grouping time points for each gene. Based on the ARX model with the group SCAD regularization using our time-series expression datasets of the periodic genes, we constructed gene networks and found that the networks represent typical scale-free structure. Our findings demonstrate that the diurnal changes in the transcriptome in B. distachyon leaves have a sparse network structure, demonstrating the spatiotemporal gene regulatory network over the cyclic phase transitions in B. distachyon diurnal growth.

  1. Diurnal Transcriptome and Gene Network Represented through Sparse Modeling inBrachypodium distachyon.

    Science.gov (United States)

    Koda, Satoru; Onda, Yoshihiko; Matsui, Hidetoshi; Takahagi, Kotaro; Yamaguchi-Uehara, Yukiko; Shimizu, Minami; Inoue, Komaki; Yoshida, Takuhiro; Sakurai, Tetsuya; Honda, Hiroshi; Eguchi, Shinto; Nishii, Ryuei; Mochida, Keiichi

    2017-01-01

    We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX) model with a group smoothly clipped absolute deviation (SCAD) method using a time-series transcriptome dataset in a model grass, Brachypodium distachyon . To reveal the diurnal changes in the transcriptome in B. distachyon , we performed RNA-seq analysis of its leaves sampled through a diurnal cycle of over 48 h at 4 h intervals using three biological replications, and identified 3,621 periodic genes through our wavelet analysis. The expression data are feasible to infer network sparsity based on ARX models. We found that genes involved in biological processes such as transcriptional regulation, protein degradation, and post-transcriptional modification and photosynthesis are significantly enriched in the periodic genes, suggesting that these processes might be regulated by circadian rhythm in B. distachyon . On the basis of the time-series expression patterns of the periodic genes, we constructed a chronological gene co-expression network and identified putative transcription factors encoding genes that might be involved in the time-specific regulatory transcriptional network. Moreover, we inferred a transcriptional network composed of the periodic genes in B. distachyon , aiming to identify genes associated with other genes through variable selection by grouping time points for each gene. Based on the ARX model with the group SCAD regularization using our time-series expression datasets of the periodic genes, we constructed gene networks and found that the networks represent typical scale-free structure. Our findings demonstrate that the diurnal changes in the transcriptome in B. distachyon leaves have a sparse network structure, demonstrating the spatiotemporal gene regulatory network over the cyclic phase transitions in B. distachyon diurnal growth.

  2. Application of reflective memory network in Tokamak fast controller

    International Nuclear Information System (INIS)

    Weng Chuqiao; Zhang Ming; Liu Rui; Zheng Wei; Zhuang Ge

    2014-01-01

    A specific application of reflective memory network in Tokamak fast controller was introduced in this paper. The PMC-5565 reflective memory card and ACC-5565 network hub were used to build a reflective memory real-time network to test its real- time function. The real-time, rapidity and determinacy of the time delay for fast controller controlling power device under the reflective memory network were tested in the LabVIEW RT real-time operation system. Depending on the reflective memory technology, the data in several fast controllers were synchronized, and multiple control tasks using a single control task were finished. The experiment results show that the reflective memory network can meet the real-time requirements for fast controller to perform the feedback control over devices. (authors)

  3. Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders

    Directory of Open Access Journals (Sweden)

    Qingying Meng

    2016-05-01

    Full Text Available Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient–host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control and hippocampus (cognitive processing from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine.

  4. Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

    Science.gov (United States)

    Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

    2016-05-01

    Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  5. Literature-Based Discovery of IFN-γ and Vaccine-Mediated Gene Interaction Networks

    Directory of Open Access Journals (Sweden)

    Arzucan Özgür

    2010-01-01

    Full Text Available Interferon-gamma (IFN-γ regulates various immune responses that are often critical for vaccine-induced protection. In order to annotate the IFN-γ-related gene interaction network from a large amount of IFN-γ research reported in the literature, a literature-based discovery approach was applied with a combination of natural language processing (NLP and network centrality analysis. The interaction network of human IFN-γ (Gene symbol: IFNG and its vaccine-specific subnetwork were automatically extracted using abstracts from all articles in PubMed. Four network centrality metrics were further calculated to rank the genes in the constructed networks. The resulting generic IFNG network contains 1060 genes and 26313 interactions among these genes. The vaccine-specific subnetwork contains 102 genes and 154 interactions. Fifty six genes such as TNF, NFKB1, IL2, IL6, and MAPK8 were ranked among the top 25 by at least one of the centrality methods in one or both networks. Gene enrichment analysis indicated that these genes were classified in various immune mechanisms such as response to extracellular stimulus, lymphocyte activation, and regulation of apoptosis. Literature evidence was manually curated for the IFN-γ relatedness of 56 genes and vaccine development relatedness for 52 genes. This study also generated many new hypotheses worth further experimental studies.

  6. Combined analysis of gene regulatory network and SNV information enhances identification of potential gene markers in mouse knockout studies with small number of samples.

    Science.gov (United States)

    Hur, Benjamin; Chae, Heejoon; Kim, Sun

    2015-01-01

    RNA-sequencing is widely used to measure gene expression level at the whole genome level. Comparing expression data from control and case studies provides good insight on potential gene markers for phenotypes. However, discovering gene markers that represent phenotypic differences in a small number of samples remains a challenging task, since finding gene markers using standard differential expressed gene methods produces too many candidate genes and the number of candidates varies at different threshold values. In addition, in a small number of samples, the statistical power is too low to discriminate whether gene expressions were altered by genetic differences or not. In this study, to address this challenge, we purpose a four-step filtering method that predicts gene markers from RNA-sequencing data of mouse knockout studies by utilizing a gene regulatory network constructed from omics data in the public domain, biological knowledge from curated pathways, and information of single-nucleotide variants. Our prediction method was not only able to reduce the number of candidate genes than the differentialy expressed gene-only filtered method, but also successfully predicted significant genes that were reported in research findings of the data contributors.

  7. Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.

    Directory of Open Access Journals (Sweden)

    Xiaobo Guo

    Full Text Available Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs. It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC curve and the precision-recall (PR curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.

  8. Near-Minimal Node Control of Networked Evolutionary Games

    NARCIS (Netherlands)

    Riehl, James Robert; Cao, Ming

    2014-01-01

    We investigate a problem related to the controllability of networked evolutionary games, first presenting an algorithm that computes a near-minimal set of nodes to drive all nodes in a tree network to a desired strategy, and then briefly discussing an algorithm that works for arbitrary networks

  9. Training- and education system in network control rooms

    Energy Technology Data Exchange (ETDEWEB)

    Eichner, M.; Koentges, U.

    1987-03-01

    The training system for the project network control center of Duesseldorf city is described. Reasons for the implementation of the training system are presented, the main requirements are brought forward, and the envisaged solutions are described. The main training element is a network simulator including also the network protection systems. The software requirements are outlined.

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

    Science.gov (United States)

    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...

  11. Hybrid-Controlled Neurofuzzy Networks Analysis Resulting in Genetic Regulatory Networks Reconstruction

    Science.gov (United States)

    Manshaei, Roozbeh; Sobhe Bidari, Pooya; Aliyari Shoorehdeli, Mahdi; Feizi, Amir; Lohrasebi, Tahmineh; Malboobi, Mohammad Ali; Kyan, Matthew; Alirezaie, Javad

    2012-01-01

    Reverse engineering of gene regulatory networks (GRNs) is the process of estimating genetic interactions of a cellular system from gene expression data. In this paper, we propose a novel hybrid systematic algorithm based on neurofuzzy network for reconstructing GRNs from observational gene expression data when only a medium-small number of measurements are available. The approach uses fuzzy logic to transform gene expression values into qualitative descriptors that can be evaluated by using a set of defined rules. The algorithm uses neurofuzzy network to model genes effects on other genes followed by four stages of decision making to extract gene interactions. One of the main features of the proposed algorithm is that an optimal number of fuzzy rules can be easily and rapidly extracted without overparameterizing. Data analysis and simulation are conducted on microarray expression profiles of S. cerevisiae cell cycle and demonstrate that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO. The accuracy of the proposed approach is evaluated in terms of recall and F-score for the network reconstruction task. PMID:25969749

  12. Discovering disease-associated genes in weighted protein-protein interaction networks

    Science.gov (United States)

    Cui, Ying; Cai, Meng; Stanley, H. Eugene

    2018-04-01

    Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

  13. Complex systems and networks dynamics, controls and applications

    CERN Document Server

    Yu, Xinghuo; Chen, Guanrong; Yu, Wenwu

    2016-01-01

    This elementary book provides some state-of-the-art research results on broad disciplinary sciences on complex networks. It presents an in-depth study with detailed description of dynamics, controls and applications of complex networks. The contents of this book can be summarized as follows. First, the dynamics of complex networks, for example, the cluster dynamic analysis by using kernel spectral methods, community detection algorithms in bipartite networks, epidemiological modeling with demographics and epidemic spreading on multi-layer networks, are studied. Second, the controls of complex networks are investigated including topics like distributed finite-time cooperative control of multi-agent systems by applying homogenous-degree and Lyapunov methods, composite finite-time containment control for disturbed second-order multi-agent systems, fractional-order observer design of multi-agent systems, chaos control and anticontrol of complex systems via Parrondos game and many more. Third, the applications of ...

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

    Directory of Open Access Journals (Sweden)

    Jun-ichi Satoh

    2009-01-01

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

  15. A Genome-Wide Association Study and Complex Network Identify Four Core Hub Genes in Bipolar Disorder.

    Science.gov (United States)

    Xie, Zengyan; Yang, Xianyan; Deng, Xiaoya; Ma, Mingyue; Shu, Kunxian

    2017-12-19

    Bipolar disorder is a common and severe mental illness with unsolved pathophysiology. A genome-wide association study (GWAS) has been used to find a number of risk genes, but it is difficult for a GWAS to find genes indirectly associated with a disease. To find core hub genes, we introduce a network analysis after the GWAS was conducted. Six thousand four hundred fifty eight single nucleotide polymorphisms (SNPs) with p sifted out from Wellcome Trust Case Control Consortium (WTCCC) dataset and mapped to 2045 genes, which are then compared with the protein-protein network. One hundred twelve genes with a degree >17 were chosen as hub genes from which five significant modules and four core hub genes ( FBXL13 , WDFY2 , bFGF , and MTHFD1L ) were found. These core hub genes have not been reported to be directly associated with BD but may function by interacting with genes directly related to BD. Our method engenders new thoughts on finding genes indirectly associated with, but important for, complex diseases.

  16. A Genome-Wide Association Study and Complex Network Identify Four Core Hub Genes in Bipolar Disorder

    Directory of Open Access Journals (Sweden)

    Zengyan Xie

    2017-12-01

    Full Text Available Bipolar disorder is a common and severe mental illness with unsolved pathophysiology. A genome-wide association study (GWAS has been used to find a number of risk genes, but it is difficult for a GWAS to find genes indirectly associated with a disease. To find core hub genes, we introduce a network analysis after the GWAS was conducted. Six thousand four hundred fifty eight single nucleotide polymorphisms (SNPs with p < 0.01 were sifted out from Wellcome Trust Case Control Consortium (WTCCC dataset and mapped to 2045 genes, which are then compared with the protein–protein network. One hundred twelve genes with a degree >17 were chosen as hub genes from which five significant modules and four core hub genes (FBXL13, WDFY2, bFGF, and MTHFD1L were found. These core hub genes have not been reported to be directly associated with BD but may function by interacting with genes directly related to BD. Our method engenders new thoughts on finding genes indirectly associated with, but important for, complex diseases.

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

    Science.gov (United States)

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

    2006-01-01

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

  18. Mapping Gene Regulatory Networks in Drosophila Eye Development by Large-Scale Transcriptome Perturbations and Motif Inference

    Directory of Open Access Journals (Sweden)

    Delphine Potier

    2014-12-01

    Full Text Available Genome control is operated by transcription factors (TFs controlling their target genes by binding to promoters and enhancers. Conceptually, the interactions between TFs, their binding sites, and their functional targets are represented by gene regulatory networks (GRNs. Deciphering in vivo GRNs underlying organ development in an unbiased genome-wide setting involves identifying both functional TF-gene interactions and physical TF-DNA interactions. To reverse engineer the GRNs of eye development in Drosophila, we performed RNA-seq across 72 genetic perturbations and sorted cell types and inferred a coexpression network. Next, we derived direct TF-DNA interactions using computational motif inference, ultimately connecting 241 TFs to 5,632 direct target genes through 24,926 enhancers. Using this network, we found network motifs, cis-regulatory codes, and regulators of eye development. We validate the predicted target regions of Grainyhead by ChIP-seq and identify this factor as a general cofactor in the eye network, being bound to thousands of nucleosome-free regions.

  19. Robust Planning and Control Using Neural Networks

    Science.gov (United States)

    1990-06-30

    hyperspace . We have been investigating CMAC neural networks with tapered, rather than rectangular, receptive fields. Such networks promise better (continuous...CMOS Logic Cell Arrays.’ UNH Intelligent Structures Group Report ECE.IS.90.01, Feb. 6,1990. Miller, W. T., Box, B. A., Whitney, E. C., and Glynn, J...M., ’Design and Implementation of a High Speed CMAC Neural Network Using Logic Programmable CMOS Logic Cell Arrays." To be presented at the Naval

  20. DAF-12 Regulates a Connected Network of Genes to Ensure Robust Developmental Decisions

    Science.gov (United States)

    Stuckenholz, Carsten; Labhart, Paul; Alexiadis, Vassili; Martin, René; Knölker, Hans-Joachim; Fisher, Alfred L.

    2011-01-01

    The nuclear receptor DAF-12 has roles in normal development, the decision to pursue dauer development in unfavorable conditions, and the modulation of adult aging. Despite the biologic importance of DAF-12, target genes for this receptor are largely unknown. To identify DAF-12 targets, we performed chromatin immunoprecipitation followed by hybridization to whole-genome tiling arrays. We identified 1,175 genomic regions to be bound in vivo by DAF-12, and these regions are enriched in known DAF-12 binding motifs and act as DAF-12 response elements in transfected cells and in transgenic worms. The DAF-12 target genes near these binding sites include an extensive network of interconnected heterochronic and microRNA genes. We also identify the genes encoding components of the miRISC, which is required for the control of target genes by microRNA, as a target of DAF-12 regulation. During reproductive development, many of these target genes are misregulated in daf-12(0) mutants, but this only infrequently results in developmental phenotypes. In contrast, we and others have found that null daf-12 mutations enhance the phenotypes of many miRISC and heterochronic target genes. We also find that environmental fluctuations significantly strengthen the weak heterochronic phenotypes of null daf-12 alleles. During diapause, DAF-12 represses the expression of many heterochronic and miRISC target genes, and prior work has demonstrated that dauer formation can suppress the heterochronic phenotypes of many of these target genes in post-dauer development. Together these data are consistent with daf-12 acting to ensure developmental robustness by committing the animal to adult or dauer developmental programs despite variable internal or external conditions. PMID:21814518

  1. Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks.

    Science.gov (United States)

    Wang, Yi Kan; Hurley, Daniel G; Schnell, Santiago; Print, Cristin G; Crampin, Edmund J

    2013-01-01

    We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

  2. Connectivity Gradients Between the Default Mode and Attention Control Networks

    Science.gov (United States)

    Ferguson, Michael A.; Lopez-Larson, Melissa; Yurgelun-Todd, Deborah

    2011-01-01

    Abstract Functional imaging studies have shown reduced activity within the default mode network during attention-demanding tasks. The network circuitry underlying this suppression remains unclear. Proposed hypotheses include an attentional switch in the right anterior insula and reciprocal inhibition between the default mode and attention control networks. We analyzed resting state blood oxygen level dependent (BOLD) data from 1278 subjects from 26 sites and constructed whole-brain maps of functional connectivity between 7266 regions of interest (ROIs) covering the gray matter at ∼5 mm resolution. ROIs belonging to the default mode network and attention control network were identified based on correlation to six published seed locations. Spatial heterogeneity of correlation between the default mode and attention control networks was observed, with smoothly varying gradients in every hub of both networks that ranged smoothly from weakly but significantly anticorrelated to positively correlated. Such gradients were reproduced in 3 separate groups of subjects. Anticorrelated subregions were identified in major hubs of both networks. Between-network connectivity gradients strengthen with age during late adolescence and early adulthood, with associated sharpening of the boundaries of the default mode network, integration of the insula and cingulate with frontoparietal attentional regions, and decreasing correlation between the default mode and attention control networks with age. PMID:22076305

  3. Adaptive Control for Robotic Manipulators Base on RBF Neural Network

    Directory of Open Access Journals (Sweden)

    MA Jing

    2013-09-01

    Full Text Available An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties. The first scheme consists of a PD feedback and a dynamic compensator which is composed by neural network controller and variable structure controller. Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation result s show that the kind of the control scheme is effective and has good robustness.

  4. Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: environmental factors

    Directory of Open Access Journals (Sweden)

    Frank Emmert-Streib

    2013-02-01

    Full Text Available The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I observational gene expression data: normal environmental condition, (II interventional gene expression data: growth in rich media, (III interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.

  5. Neural networks for function approximation in nonlinear control

    Science.gov (United States)

    Linse, Dennis J.; Stengel, Robert F.

    1990-01-01

    Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

  6. a positive control plasmid for reporter gene assay

    African Journals Online (AJOL)

    STORAGESEVER

    2008-07-04

    Jul 4, 2008 ... qualification as a positive control for luciferase reporter gene assays. Key words: Reporter gene plasmid, luciferase assay, cytomegalovirus promoter/enhancer, human melanoma cell line. INTRODUCTION. Reporter genes, often called reporters, have become a precious tool in studies of gene expression ...

  7. Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.

    Directory of Open Access Journals (Sweden)

    Yishai Shimoni

    2010-06-01

    Full Text Available Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are

  8. Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.

    Science.gov (United States)

    Shimoni, Yishai; Fink, Marc Y; Choi, Soon-gang; Sealfon, Stuart C

    2010-06-24

    Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any

  9. Developing a dynamic control system for mine compressed air networks

    OpenAIRE

    Van Heerden, S.W.; Pelzer, R.; Marais, J.H.

    2014-01-01

    Mines in general, make use of compressed air systems for daily operational activities. Compressed air on mines is traditionally distributed via compressed air ring networks where multiple shafts are supplied with compressed air from an integral system. These compressed air networks make use of a number of compressors feeding the ring from various locations in the network. While these mines have sophisticated control systems to control these compressors, they are not dynamic systems. Compresso...

  10. Network-guided analysis of genes with altered somatic copy number and gene expression reveals pathways commonly perturbed in metastatic melanoma.

    Directory of Open Access Journals (Sweden)

    Armand Valsesia

    2011-04-01

    Full Text Available Cancer genomes frequently contain somatic copy number alterations (SCNA that can significantly perturb the expression level of affected genes and thus disrupt pathways controlling normal growth. In melanoma, many studies have focussed on the copy number and gene expression levels of the BRAF, PTEN and MITF genes, but little has been done to identify new genes using these parameters at the genome-wide scale. Using karyotyping, SNP and CGH arrays, and RNA-seq, we have identified SCNA affecting gene expression ('SCNA-genes' in seven human metastatic melanoma cell lines. We showed that the combination of these techniques is useful to identify candidate genes potentially involved in tumorigenesis. Since few of these alterations were recurrent across our samples, we used a protein network-guided approach to determine whether any pathways were enriched in SCNA-genes in one or more samples. From this unbiased genome-wide analysis, we identified 28 significantly enriched pathway modules. Comparison with two large, independent melanoma SCNA datasets showed less than 10% overlap at the individual gene level, but network-guided analysis revealed 66% shared pathways, including all but three of the pathways identified in our data. Frequently altered pathways included WNT, cadherin signalling, angiogenesis and melanogenesis. Additionally, our results emphasize the potential of the EPHA3 and FRS2 gene products, involved in angiogenesis and migration, as possible therapeutic targets in melanoma. Our study demonstrates the utility of network-guided approaches, for both large and small datasets, to identify pathways recurrently perturbed in cancer.

  11. System Identification for Nonlinear Control Using Neural Networks

    Science.gov (United States)

    Stengel, Robert F.; Linse, Dennis J.

    1990-01-01

    An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.

  12. Active Engine Mounting Control Algorithm Using Neural Network

    Directory of Open Access Journals (Sweden)

    Fadly Jashi Darsivan

    2009-01-01

    Full Text Available This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.

  13. Neural network predicts sequence of TP53 gene based on DNA chip

    DEFF Research Database (Denmark)

    Spicker, J.S.; Wikman, F.; Lu, M.L.

    2002-01-01

    We have trained an artificial neural network to predict the sequence of the human TP53 tumor suppressor gene based on a p53 GeneChip. The trained neural network uses as input the fluorescence intensities of DNA hybridized to oligonucleotides on the surface of the chip and makes between zero...

  14. Deterministic learning enhanced neutral network control of unmanned helicopter

    Directory of Open Access Journals (Sweden)

    Yiming Jiang

    2016-11-01

    Full Text Available In this article, a neural network–based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design.

  15. Positive train control interoperability and networking research : final report.

    Science.gov (United States)

    2015-12-01

    This document describes the initial development of an ITC PTC Shared Network (IPSN), a hosted : environment to support the distribution, configuration management, and IT governance of Interoperable : Train Control (ITC) Positive Train Control (PTC) s...

  16. Using a Control System Ethernet Network as a Field Bus

    CERN Document Server

    De Van, William R; Lawson, Gregory S; Wagner, William H; Wantland, David M; Williams, Ernest

    2005-01-01

    A major component of a typical accelerator distributed control system (DCS) is a dedicated, large-scale local area communications network (LAN). The SNS EPICS-based control system uses a LAN based on the popular IEEE-802.3 set of standards (Ethernet). Since the control system network infrastructure is available throughout the facility, and since Ethernet-based controllers are readily available, it is tempting to use the control system LAN for "fieldbus" communications to low-level control devices (e.g. vacuum controllers; remote I/O). These devices may or may not be compatible with the high-level DCS protocols. This paper presents some of the benefits and risks of combining high-level DCS communications with low-level "field bus" communications on the same network, and describes measures taken at SNS to promote compatibility between devices connected to the control system network.

  17. The biocontrol agent Pseudomonas chlororaphis PA23 primes Brassica napus defenses through distinct gene networks.

    Science.gov (United States)

    Duke, Kelly A; Becker, Michael G; Girard, Ian J; Millar, Jenna L; Dilantha Fernando, W G; Belmonte, Mark F; de Kievit, Teresa R

    2017-06-19

    The biological control agent Pseudomonas chlororaphis PA23 is capable of protecting Brassica napus (canola) from the necrotrophic fungus Sclerotinia sclerotiorum via direct antagonism. While we have elucidated bacterial genes and gene products responsible biocontrol, little is known about how the host plant responds to bacterial priming on the leaf surface, including global changes in gene activity in the presence and absence of S. sclerotiorum. Application of PA23 to the aerial surfaces of canola plants reduced the number of S. sclerotiorum lesion-forming petals by 91.1%. RNA sequencing of the host pathogen interface showed that pretreatment with PA23 reduced the number of genes upregulated in response to S. sclerotiorum by 16-fold. By itself, PA23 activated unique defense networks indicative of defense priming. Genes encoding MAMP-triggered immunity receptors detecting flagellin and peptidoglycan were downregulated in PA23 only-treated plants, consistent with post-stimulus desensitization. Downstream, we observed reactive oxygen species (ROS) production involving low levels of H 2 O 2 and overexpression of genes associated with glycerol-3-phosphate (G3P)-mediated systemic acquired resistance (SAR). Leaf chloroplasts exhibited increased thylakoid membrane structures and chlorophyll content, while lipid metabolic processes were upregulated. In addition to directly antagonizing S. sclerotiorum, PA23 primes the plant defense response through induction of unique local and systemic defense networks. This study provides novel insight into the effects of biocontrol agents applied to the plant phyllosphere. Understanding these interactions will aid in the development of biocontrol systems as an alternative to chemical pesticides for protection of important crop systems.

  18. Distributed Estimation and Control for Robotic Networks

    NARCIS (Netherlands)

    Simonetto, A.

    2012-01-01

    Mobile robots that communicate and cooperate to achieve a common task have been the subject of an increasing research interest in recent years. These possibly heterogeneous groups of robots communicate locally via a communication network and therefore are usually referred to as robotic networks.

  19. Local control of cognitive radio networks

    NARCIS (Netherlands)

    Doerr, C.; Grunwald, D.; Sicker, D.C.

    2009-01-01

    In a network deployment, a cognitive radio will have to perform two fundamental tasks. First, each cognitive radio needs to optimize its internal operation, and second, it needs to derive a configuration that will enable and optimize communication with other nodes in the network. This latter

  20. Large maneuverable flight control using neural networks dynamic inversion

    Science.gov (United States)

    Yang, Enquan; Gao, Jinyuan

    2003-09-01

    An adaptive dynamic-inversion-based neural network is applied to aircraft large maneuverable flight control. Neural network is used to cancel the inversion error which may arise from imperfect modeling or approximate inversion. Simulation results for an aircraft model are presented to illustrate the performance of the flight control system.

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

    Science.gov (United States)

    He, Jing

    2012-01-01

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

  2. A control model for district heating networks with storage

    NARCIS (Netherlands)

    Scholten, Tjeert; De Persis, Claudio; Tesi, Pietro

    2014-01-01

    In [1] pressure control of hydraulic networks is investigated. We extend this work to district heating systems with storage capabilities and derive a model taking the topology of the network into account. The goal for the derived model is that it should allow for control of the storage level and

  3. Lessons from a gene regulatory network: echinoderm skeletogenesis provides insights into evolution, plasticity and morphogenesis.

    Science.gov (United States)

    Ettensohn, Charles A

    2009-01-01

    Significant new insights have emerged from the analysis of a gene regulatory network (GRN) that underlies the development of the endoskeleton of the sea urchin embryo. Comparative studies have revealed ways in which this GRN has been modified (and conserved) during echinoderm evolution, and point to mechanisms associated with the evolution of a new cell lineage. The skeletogenic GRN has also recently been used to study the long-standing problem of developmental plasticity. Other recent findings have linked this transcriptional GRN to morphoregulatory proteins that control skeletal anatomy. These new studies highlight powerful new ways in which GRNs can be used to dissect development and the evolution of morphogenesis.

  4. Deciphering the onychophoran 'segmentation gene cascade': Gene expression reveals limited involvement of pair rule gene orthologs in segmentation, but a highly conserved segment polarity gene network.

    Science.gov (United States)

    Janssen, Ralf; Budd, Graham E

    2013-10-01

    The hallmark of the arthropods is their segmented body, although origin of segmentation, however, is unresolved. In order to shed light on the origin of segmentation we investigated orthologs of pair rule genes (PRGs) and segment polarity genes (SPGs) in a member of the closest related sister-group to the arthropods, the onychophorans. Our gene expression data analysis suggests that most of the onychophoran PRGs do not play a role in segmentation. One possible exception is the even-skipped (eve) gene that is expressed in the posterior end of the onychophoran where new segments are likely patterned, and is also expressed in segmentation-gene typical transverse stripes in at least a number of newly formed segments. Other onychophoran PRGs such as runt (run), hairy/Hes (h/Hes) and odd-skipped (odd) do not appear to have a function in segmentation at all. Onychophoran PRGs that act low in the segmentation gene cascade in insects, however, are potentially involved in segment-patterning. Most obvious is that from the expression of the pairberry (pby) gene ortholog that is expressed in a typical SPG-pattern. Since this result suggested possible conservation of the SPG-network we further investigated SPGs (and associated factors) such as Notum in the onychophoran. We find that the expression patterns of SPGs in arthropods and the onychophoran are highly conserved, suggesting a conserved SPG-network in these two clades, and indeed also in an annelid. This may suggest that the common ancestor of lophotrochozoans and ecdysozoans was already segmented utilising the same SPG-network, or that the SPG-network was recruited independently in annelids and onychophorans/arthropods. © 2013 Elsevier Inc. All rights reserved.

  5. Neural network predictive control of a heat exchanger

    OpenAIRE

    2011-01-01

    Abstract The study attempts to show that using the neural network predictive control (NNPC) structure for control of thermal processes can lead to energy savings. The advantage of the NNPC is that it is not a linear-model-based strategy and the control input constraints are directly included into the synthesis. In the designed approach, the neural network is used as a nonlinear process model to predict the future behaviour of the controlled process with distributed parameters. The ...

  6. Topology Control in Aerial Multi-Beam Directional Networks

    Science.gov (United States)

    2017-04-24

    Topology Control in Aerial Multi-Beam Directional Networks Brian Proulx, Nathaniel M. Jones, Jennifer Madiedo, Greg Kuperman {brian.proulx, njones...significant interference. Topology control (i.e., selecting a subset of neighbors to communicate with) is vital to reduce the interference. Good topology ...underlying challenges to topology control in multi-beam direction networks. Two topology control algorithms are developed: a centralized algorithm

  7. Design and Simulation Analysis for Integrated Vehicle Chassis-Network Control System Based on CAN Network

    Directory of Open Access Journals (Sweden)

    Wei Yu

    2016-01-01

    Full Text Available Due to the different functions of the system used in the vehicle chassis control, the hierarchical control strategy also leads to many kinds of the network topology structure. According to the hierarchical control principle, this research puts forward the integrated control strategy of the chassis based on supervision mechanism. The purpose is to consider how the integrated control architecture affects the control performance of the system after the intervention of CAN network. Based on the principle of hierarchical control and fuzzy control, a fuzzy controller is designed, which is used to monitor and coordinate the ESP, AFS, and ARS. And the IVC system is constructed with the upper supervisory controller and three subcontrol systems on the Simulink platform. The network topology structure of IVC is proposed, and the IVC communication matrix based on CAN network communication is designed. With the common sensors and the subcontrollers as the CAN network independent nodes, the network induced delay and packet loss rate on the system control performance are studied by simulation. The results show that the simulation method can be used for designing the communication network of the vehicle.

  8. Optimal traffic control in highway transportation networks using linear programming

    KAUST Repository

    Li, Yanning

    2014-06-01

    This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.

  9. Designing communication and remote controlling of virtual instrument network system

    International Nuclear Information System (INIS)

    Lei Lin; Wang Houjun; Zhou Xue; Zhou Wenjian

    2005-01-01

    In this paper, a virtual instrument network through the LAN and finally remote control of virtual instruments is realized based on virtual instrument and LabWindows/CVI software platform. The virtual instrument network system is made up of three subsystems. There are server subsystem, telnet client subsystem and local instrument control subsystem. This paper introduced virtual instrument network structure in detail based on LabWindows. Application procedure design of virtual instrument network communication, the Client/the programming mode of the server, remote PC and server communication far realizing, the control power of the workstation is transmitted, server program and so on essential technical were introduced. And virtual instruments network may connect to entire Internet on. Above-mentioned technology, through measuring the application in the electronic measurement virtual instrument network that is already built up, has verified the actual using value of the technology. Experiment and application validate that this design is resultful

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

    Science.gov (United States)

    Motter, Adilson

    2013-03-01

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

  11. Cell fate reprogramming by control of intracellular network dynamics

    Science.gov (United States)

    Zanudo, Jorge G. T.; Albert, Reka

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

  12. A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe

    Science.gov (United States)

    Berto, Stefano; Perdomo-Sabogal, Alvaro; Gerighausen, Daniel; Qin, Jing; Nowick, Katja

    2016-01-01

    Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies. PMID:27014338

  13. Global analysis of the human pathophenotypic similarity gene network merges disease module components.

    Science.gov (United States)

    Reyes-Palomares, Armando; Rodríguez-López, Rocío; Ranea, Juan A G; Sánchez-Jiménez, Francisca; Sánchez Jiménez, Francisca; Medina, Miguel Angel

    2013-01-01

    The molecular complexity of genetic diseases requires novel approaches to break it down into coherent biological modules. For this purpose, many disease network models have been created and analyzed. We highlight two of them, "the human diseases networks" (HDN) and "the orphan disease networks" (ODN). However, in these models, each single node represents one disease or an ambiguous group of diseases. In these cases, the notion of diseases as unique entities reduces the usefulness of network-based methods. We hypothesize that using the clinical features (pathophenotypes) to define pathophenotypic connections between disease-causing genes improve our understanding of the molecular events originated by genetic disturbances. For this, we have built a pathophenotypic similarity gene network (PSGN) and compared it with the unipartite projections (based on gene-to-gene edges) similar to those used in previous network models (HDN and ODN). Unlike these disease network models, the PSGN uses semantic similarities. This pathophenotypic similarity has been calculated by comparing pathophenotypic annotations of genes (human abnormalities of HPO terms) in the "Human Phenotype Ontology". The resulting network contains 1075 genes (nodes) and 26197 significant pathophenotypic similarities (edges). A global analysis of this network reveals: unnoticed pairs of genes showing significant pathophenotypic similarity, a biological meaningful re-arrangement of the pathological relationships between genes, correlations of biochemical interactions with higher similarity scores and functional biases in metabolic and essential genes toward the pathophenotypic specificity and the pleiotropy, respectively. Additionally, pathophenotypic similarities and metabolic interactions of genes associated with maple syrup urine disease (MSUD) have been used to merge into a coherent pathological module.Our results indicate that pathophenotypes contribute to identify underlying co-dependencies among disease

  14. Tissue and cell-type co-expression networks of transcription factors and wood component genes in Populus trichocarpa.

    Science.gov (United States)

    Shi, Rui; Wang, Jack P; Lin, Ying-Chung; Li, Quanzi; Sun, Ying-Hsuan; Chen, Hao; Sederoff, Ronald R; Chiang, Vincent L

    2017-05-01

    Co-expression networks based on transcriptomes of Populus trichocarpa major tissues and specific cell types suggest redundant control of cell wall component biosynthetic genes by transcription factors in wood formation. We analyzed the transcriptomes of five tissues (xylem, phloem, shoot, leaf, and root) and two wood forming cell types (fiber and vessel) of Populus trichocarpa to assemble gene co-expression subnetworks associated with wood formation. We identified 165 transcription factors (TFs) that showed xylem-, fiber-, and vessel-specific expression. Of these 165 TFs, 101 co-expressed (correlation coefficient, r > 0.7) with the 45 secondary cell wall cellulose, hemicellulose, and lignin biosynthetic genes. Each cell wall component gene co-expressed on average with 34 TFs, suggesting redundant control of the cell wall component gene expression. Co-expression analysis showed that the 101 TFs and the 45 cell wall component genes each has two distinct groups (groups 1 and 2), based on their co-expression patterns. The group 1 TFs (44 members) are predominantly xylem and fiber specific, and are all highly positively co-expressed with the group 1 cell wall component genes (30 members), suggesting their roles as major wood formation regulators. Group 1 TFs include a lateral organ boundary domain gene (LBD) that has the highest number of positively correlated cell wall component genes (36) and TFs (47). The group 2 TFs have 57 members, including 14 vessel-specific TFs, and are generally less correlated with the cell wall component genes. An exception is a vessel-specific basic helix-loop-helix (bHLH) gene that negatively correlates with 20 cell wall component genes, and may function as a key transcriptional suppressor. The co-expression networks revealed here suggest a well-structured transcriptional homeostasis for cell wall component biosynthesis during wood formation.

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

    Directory of Open Access Journals (Sweden)

    Richard A Notebaart

    2008-01-01

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

  16. NDRC: A Disease-Causing Genes Prioritized Method Based on Network Diffusion and Rank Concordance.

    Science.gov (United States)

    Fang, Minghong; Hu, Xiaohua; Wang, Yan; Zhao, Junmin; Shen, Xianjun; He, Tingting

    2015-07-01

    Disease-causing genes prioritization is very important to understand disease mechanisms and biomedical applications, such as design of drugs. Previous studies have shown that promising candidate genes are mostly ranked according to their relatedness to known disease genes or closely related disease genes. Therefore, a dangling gene (isolated gene) with no edges in the network can not be effectively prioritized. These approaches tend to prioritize those genes that are highly connected in the PPI network while perform poorly when they are applied to loosely connected disease genes. To address these problems, we propose a new disease-causing genes prioritization method that based on network diffusion and rank concordance (NDRC). The method is evaluated by leave-one-out cross validation on 1931 diseases in which at least one gene is known to be involved, and it is able to rank the true causal gene first in 849 of all 2542 cases. The experimental results suggest that NDRC significantly outperforms other existing methods such as RWR, VAVIEN, DADA and PRINCE on identifying loosely connected disease genes and successfully put dangling genes as potential candidate disease genes. Furthermore, we apply NDRC method to study three representative diseases, Meckel syndrome 1, Protein C deficiency and Peroxisome biogenesis disorder 1A (Zellweger). Our study has also found that certain complex disease-causing genes can be divided into several modules that are closely associated with different disease phenotype.

  17. Implementation of neural network based non-linear predictive control

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1999-01-01

    This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...

  18. Network analysis reveals the relationship among wood properties, gene expression levels and genotypes of natural Populus trichocarpa accessions.

    Science.gov (United States)

    Porth, Ilga; Klápště, Jaroslav; Skyba, Oleksandr; Friedmann, Michael C; Hannemann, Jan; Ehlting, Juergen; El-Kassaby, Yousry A; Mansfield, Shawn D; Douglas, Carl J

    2013-11-01

    High-throughput approaches have been widely applied to elucidate the genetic underpinnings of industrially important wood properties. Wood traits are polygenic in nature, but gene hierarchies can be assessed to identify the most important gene variants controlling specific traits within complex networks defining the overall wood phenotype. We tested a large set of genetic, genomic, and phenotypic information in an integrative approach to predict wood properties in Populus trichocarpa. Nine-yr-old natural P. trichocarpa trees including accessions with high contrasts in six traits related to wood chemistry and ultrastructure were profiled for gene expression on 49k Nimblegen (Roche NimbleGen Inc., Madison, WI, USA) array elements and for 28,831 polymorphic single nucleotide polymorphisms (SNPs). Pre-selected transcripts and SNPs with high statistical dependence on phenotypic traits were used in Bayesian network learning procedures with a stepwise K2 algorithm to infer phenotype-centric networks. Transcripts were pre-selected at a much lower logarithm of Bayes factor (logBF) threshold than SNPs and were not accommodated in the networks. Using persistent variables, we constructed cross-validated networks for variability in wood attributes, which contained four to six variables with 94-100% predictive accuracy. Accommodated gene variants revealed the hierarchy in the genetic architecture that underpins substantial phenotypic variability, and represent new tools to support the maximization of response to selection. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

  19. Control of coupled oscillator networks with application to microgrid technologies

    Science.gov (United States)

    Skardal, Per Sebastian; Arenas, Alex

    2015-01-01

    The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions—a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable synchronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself. PMID:26601231

  20. Reliable and Congestion Control Protocols for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Kirti Kharb

    2016-01-01

    Full Text Available The objective of this paper is to analyze review and different congestion control protocols that are employed at the transport layer and some of them working at the medium access control layer in wireless sensor networks. Firstly, a brief introduction is given about wireless sensor networks and how congestion occurs in such networks. Secondly, the concept of congestion is discussed. Thirdly, the reason of occurrence of congestion in wireless sensor networks is analyzed. After that, congestion control and why it is needed in the wireless sensor networks is discussed. Then, a brief review of different congestion control and reliable data transport mechanisms are discussed. Finally, a comparative analysis of different protocols is made depending on their performance on various parameters such as - traffic direction, energy conservation characteristic, efficiency etc. and the paper is concluded.

  1. Control of coupled oscillator networks with application to microgrid technologies

    Science.gov (United States)

    Arenas, Alex

    The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions-a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable syn- chronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself.

  2. Positioning the expanded akirin gene family of Atlantic salmon within the transcriptional networks of myogenesis

    Energy Technology Data Exchange (ETDEWEB)

    Macqueen, Daniel J., E-mail: djm59@st-andrews.ac.uk [Laboratory of Physiological and Evolutionary Genomics, Scottish Oceans Institute, University of St Andrews, St Andrews, Fife KY16 8LB (United Kingdom); Bower, Neil I., E-mail: nib@st-andrews.ac.uk [Laboratory of Physiological and Evolutionary Genomics, Scottish Oceans Institute, University of St Andrews, St Andrews, Fife KY16 8LB (United Kingdom); Johnston, Ian A., E-mail: iaj@st-andrews.ac.uk [Laboratory of Physiological and Evolutionary Genomics, Scottish Oceans Institute, University of St Andrews, St Andrews, Fife KY16 8LB (United Kingdom)

    2010-10-01

    Research highlights: {yields} The expanded akirin gene family of Atlantic salmon was characterised. {yields} akirin paralogues are regulated between mono- and multi-nucleated muscle cells. {yields} akirin paralogues positioned within known genetic networks controlling myogenesis. {yields} Co-expression of akirin paralogues is evident across cell types/during myogenesis. {yields} Selection has likely maintained common regulatory elements among akirin paralogues. -- Abstract: Vertebrate akirin genes usually form a family with one-to-three members that regulate gene expression during the innate immune response, carcinogenesis and myogenesis. We recently established that an expanded family of eight akirin genes is conserved across salmonid fish. Here, we measured mRNA levels of the akirin family of Atlantic salmon (Salmo salar L.) during the differentiation of primary myoblasts cultured from fast-skeletal muscle. Using hierarchical clustering and correlation, the data was positioned into a network of expression profiles including twenty further genes that regulate myogenesis. akirin1(2b) was not significantly regulated during the maturation of the cell culture. akirin2(1a) and 2(1b), along with IGF-II and several igfbps, were most highly expressed in mononuclear cells, then significantly and constitutively downregulated as differentiation proceeded and myotubes formed/matured. Conversely, akirin1(1a), 1(1b), 1(2a), 2(2a) and 2(2b) were expressed at lowest levels when mononuclear cells dominated the culture and highest levels when confluent layers of myotubes were evident. However, akirin1(2a) and 2(2a) were first upregulated earlier than akirin1(1a), 1(1b) and 2(2b), when rates of myoblast proliferation were highest. Interestingly, akirin1(1b), 1(2a), 2(2a) and 2(2b) formed part of a module of co-expressed genes involved in muscle differentiation, including myod1a, myog, mef2a, 14-3-3{beta} and 14-3-3{gamma}. All akirin paralogues were expressed ubiquitously across ten

  3. Positioning the expanded akirin gene family of Atlantic salmon within the transcriptional networks of myogenesis

    International Nuclear Information System (INIS)

    Macqueen, Daniel J.; Bower, Neil I.; Johnston, Ian A.

    2010-01-01

    Research highlights: → The expanded akirin gene family of Atlantic salmon was characterised. → akirin paralogues are regulated between mono- and multi-nucleated muscle cells. → akirin paralogues positioned within known genetic networks controlling myogenesis. → Co-expression of akirin paralogues is evident across cell types/during myogenesis. → Selection has likely maintained common regulatory elements among akirin paralogues. -- Abstract: Vertebrate akirin genes usually form a family with one-to-three members that regulate gene expression during the innate immune response, carcinogenesis and myogenesis. We recently established that an expanded family of eight akirin genes is conserved across salmonid fish. Here, we measured mRNA levels of the akirin family of Atlantic salmon (Salmo salar L.) during the differentiation of primary myoblasts cultured from fast-skeletal muscle. Using hierarchical clustering and correlation, the data was positioned into a network of expression profiles including twenty further genes that regulate myogenesis. akirin1(2b) was not significantly regulated during the maturation of the cell culture. akirin2(1a) and 2(1b), along with IGF-II and several igfbps, were most highly expressed in mononuclear cells, then significantly and constitutively downregulated as differentiation proceeded and myotubes formed/matured. Conversely, akirin1(1a), 1(1b), 1(2a), 2(2a) and 2(2b) were expressed at lowest levels when mononuclear cells dominated the culture and highest levels when confluent layers of myotubes were evident. However, akirin1(2a) and 2(2a) were first upregulated earlier than akirin1(1a), 1(1b) and 2(2b), when rates of myoblast proliferation were highest. Interestingly, akirin1(1b), 1(2a), 2(2a) and 2(2b) formed part of a module of co-expressed genes involved in muscle differentiation, including myod1a, myog, mef2a, 14-3-3β and 14-3-3γ. All akirin paralogues were expressed ubiquitously across ten tissues, although mRNA levels

  4. Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering.

    Directory of Open Access Journals (Sweden)

    Chuan Gao

    2016-07-01

    Full Text Available Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues.

  5. Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

    Directory of Open Access Journals (Sweden)

    Lodowski Kerrie H

    2009-01-01

    Full Text Available Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, unique from all extant network inference algorithms. The method is superior to dynamic Bayesian network modeling in a simulation study. Temporal gene expression data from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol are used for modeling. Genes from major neuronal pathways are identified as putative components of the alcohol response mechanism. Nine of these genes have associations with alcohol reported in literature. Several other potentially relevant genes, compatible with independent results from literature mining, may play a role in the response to alcohol. Additional, previously unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.

  6. Robust receding horizon control for networked and distributed nonlinear systems

    CERN Document Server

    Li, Huiping

    2017-01-01

    This book offers a comprehensive, easy-to-understand overview of receding-horizon control for nonlinear networks. It presents novel general strategies that can simultaneously handle general nonlinear dynamics, system constraints, and disturbances arising in networked and large-scale systems and which can be widely applied. These receding-horizon-control-based strategies can achieve sub-optimal control performance while ensuring closed-loop stability: a feature attractive to engineers. The authors address the problems of networked and distributed control step-by-step, gradually increasing the level of challenge presented. The book first introduces the state-feedback control problems of nonlinear networked systems and then studies output feedback control problems. For large-scale nonlinear systems, disturbance is considered first, then communication delay separately, and lastly the simultaneous combination of delays and disturbances. Each chapter of this easy-to-follow book not only proposes and analyzes novel ...

  7. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System...... Identification, Prediction, Simulation and Control of a dynamic, non-linear and noisy process. Further, the difficulties to control a practical non-linear laboratory process in a satisfactory way by using a traditional controller are overcomed by using a trained neural network to perform non-linear System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...

  8. Reverse-engineering of gene networks for regulating early blood development from single-cell measurements.

    Science.gov (United States)

    Wei, Jiangyong; Hu, Xiaohua; Zou, Xiufen; Tian, Tianhai

    2017-12-28

    Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. However, a significant challenge in computational biology and bioinformatics is how to derive quantitative information from the single-cell observations and how to develop sophisticated mathematical models to describe the dynamic properties of regulatory networks using the derived quantitative information. This work designs an integrated approach to reverse-engineer gene networks for regulating early blood development based on singel-cell experimental observations. The wanderlust algorithm is initially used to develop the pseudo-trajectory for the activities of a number of genes. Since the gene expression data in the developed pseudo-trajectory show large fluctuations, we then use Gaussian process regression methods to smooth the gene express data in order to obtain pseudo-trajectories with much less fluctuations. The proposed integrated framework consists of both bioinformatics algorithms to reconstruct the regulatory network and mathematical models using differential equations to describe the dynamics of gene expression. The developed approach is applied to study the network regulating early blood cell development. A graphic model is constructed for a regulatory network with forty genes and a dynamic model using differential equations is developed for a network of nine genes. Numerical results suggests that the proposed model is able to match experimental data very well. We also examine the networks with more regulatory relations and numerical results show that more regulations may exist. We test the possibility of auto-regulation but numerical simulations do not support the positive auto-regulation. In addition, robustness is used as an importantly additional criterion to select candidate

  9. Distributed synchronization of coupled neural networks via randomly occurring control.

    Science.gov (United States)

    Tang, Yang; Wong, Wai Keung

    2013-03-01

    In this paper, we study the distributed synchronization and pinning distributed synchronization of stochastic coupled neural networks via randomly occurring control. Two Bernoulli stochastic variables are used to describe the occurrences of distributed adaptive control and updating law according to certain probabilities. Both distributed adaptive control and updating law for each vertex in a network depend on state information on each vertex's neighborhood. By constructing appropriate Lyapunov functions and employing stochastic analysis techniques, we prove that the distributed synchronization and the distributed pinning synchronization of stochastic complex networks can be achieved in mean square. Additionally, randomly occurring distributed control is compared with periodically intermittent control. It is revealed that, although randomly occurring control is an intermediate method among the three types of control in terms of control costs and convergence rates, it has fewer restrictions to implement and can be more easily applied in practice than periodically intermittent control.

  10. Neural-Network Control Of Prosthetic And Robotic Hands

    Science.gov (United States)

    Buckley, Theresa M.

    1991-01-01

    Electronic neural networks proposed for use in controlling robotic and prosthetic hands and exoskeletal or glovelike electromechanical devices aiding intact but nonfunctional hands. Specific to patient, who activates grasping motion by voice command, by mechanical switch, or by myoelectric impulse. Patient retains higher-level control, while lower-level control provided by neural network analogous to that of miniature brain. During training, patient teaches miniature brain to perform specialized, anthropomorphic movements unique to himself or herself.

  11. IDENTIFICATION AND CONTROL OF AN ASYNCHRONOUS MACHINE USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    A ZERGAOUI

    2000-06-01

    Full Text Available In this work, we present the application of artificial neural networks to the identification and control of the asynchronous motor, which is a complex nonlinear system with variable internal dynamics.  We show that neural networks can be applied to control the stator currents of the induction motor.  The results of the different simulations are presented to evaluate the performance of the neural controller proposed.

  12. Integrating quantitative knowledge into a qualitative gene regulatory network.

    Directory of Open Access Journals (Sweden)

    Jérémie Bourdon

    2011-09-01

    Full Text Available Despite recent improvements in molecular techniques, biological knowledge remains incomplete. Any theorizing about living systems is therefore necessarily based on the use of heterogeneous and partial information. Much current research has focused successfully on the qualitative behaviors of macromolecular networks. Nonetheless, it is not capable of taking into account available quantitative information such as time-series protein concentration variations. The present work proposes a probabilistic modeling framework that integrates both kinds of information. Average case analysis methods are used in combination with Markov chains to link qualitative information about transcriptional regulations to quantitative information about protein concentrations. The approach is illustrated by modeling the carbon starvation response in Escherichia coli. It accurately predicts the quantitative time-series evolution of several protein concentrations using only knowledge of discrete gene interactions and a small number of quantitative observations on a single protein concentration. From this, the modeling technique also derives a ranking of interactions with respect to their importance during the experiment considered. Such a classification is confirmed by the literature. Therefore, our method is principally novel in that it allows (i a hybrid model that integrates both qualitative discrete model and quantities to be built, even using a small amount of quantitative information, (ii new quantitative predictions to be derived, (iii the robustness and relevance of interactions with respect to phenotypic criteria to be precisely quantified, and (iv the key features of the model to be extracted that can be used as a guidance to design future experiments.

  13. Network Candidate Genes in Breeding for Drought Tolerant Crops

    Directory of Open Access Journals (Sweden)

    Christoph Tim Krannich

    2015-07-01

    Full Text Available Climate change leading to increased periods of low water availability as well as increasing demands for food in the coming years makes breeding for drought tolerant crops a high priority. Plants have developed diverse strategies and mechanisms to survive drought stress. However, most of these represent drought escape or avoidance strategies like early flowering or low stomatal conductance that are not applicable in breeding for crops with high yields under drought conditions. Even though a great deal of research is ongoing, especially in cereals, in this regard, not all mechanisms involved in drought tolerance are yet understood. The identification of candidate genes for drought tolerance that have a high potential to be used for breeding drought tolerant crops represents a challenge. Breeding for drought tolerant crops has to focus on acceptable yields under water-limited conditions and not on survival. However, as more and more knowledge about the complex networks and the cross talk during drought is available, more options are revealed. In addition, it has to be considered that conditioning a crop for drought tolerance might require the production of metabolites and might cost the plants energy and resources that cannot be used in terms of yield. Recent research indicates that yield penalty exists and efficient breeding for drought tolerant crops with acceptable yields under well-watered and drought conditions might require uncoupling yield penalty from drought tolerance.

  14. Neural Network Based Load Frequency Control for Restructuring ...

    African Journals Online (AJOL)

    The comparison between a conventional Proportional Integral (PI) controller and the proposed artificial neural networks controller is showed that the proposed controller can generate an improved ... The same technique is then applied to control a system compose of two single units tied together though a power line.

  15. Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

    Science.gov (United States)

    Hur, Junguk; Özgür, Arzucan; He, Yongqun

    2017-03-14

    Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types. Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of

  16. Adaptive Dynamics, Control, and Extinction in Networked Populations

    Science.gov (United States)

    2015-07-09

    extinction . VI. CONCLUSIONS We have presented a method for predicting extinction in stochastic network systems by analyzing a pair-based proxy model...including games on networks (e.g., [40], [41]). Further, we expect that our method of continuously varying a parameter while tracking the path to extinction ...Adaptive Dynamics, Control, and Extinction in Networked Populations Ira B. Schwartz US Naval Research Laboratory Code 6792 Nonlinear System Dynamics

  17. Combining affinity propagation clustering and mutual information network to investigate key genes in fibroid.

    Science.gov (United States)

    Chen, Qian-Song; Wang, Dan; Liu, Bao-Lian; Gao, Shu-Feng; Gao, Dan-Li; Li, Gui-Rong

    2017-07-01

    The aim of the present study was to investigate key genes in fibroids based on the multiple affinity propogation-Krzanowski and Lai (mAP-KL) method, which included the maxT multiple hypothesis, Krzanowski and Lai (KL) cluster quality index, affinity propagation (AP) clustering algorithm and mutual information network (MIN) constructed by the context likelihood of relatedness (CLR) algorithm. In order to achieve this goal, mAP-KL was initially implemented to investigate exemplars in fibroid, and the maxT function was employed to rank the genes of training and test sets, and the top 200 genes were obtained for further study. In addition, the KL cluster index was applied to determine the quantity of clusters and the AP clustering algorithm was conducted to identify the clusters and their exemplars. Subsequently, the support vector machine (SVM) model was selected to evaluate the classification performance of mAP-KL. Finally, topological properties (degree, closeness, betweenness and transitivity) of exemplars in MIN constructed according to the CLR algorithm were assessed to investigate key genes in fibroid. The SVM model validated that the classification between normal controls and fibroid patients by mAP-KL had a good performance. A total of 9 clusters and exemplars were identified based on mAP-KL, which were comprised of CALCOCO2 , COL4A2 , COPS8 , SNCG , PA2G4 , C17orf70 , MARK3 , BTNL3 and TBC1D13 . By accessing the topological analysis for exemplars in MIN, SNCG and COL4A2 were identified as the two most significant genes of four types of methods, and they were denoted as key genes in the progress of fibroid. In conclusion, two key genes ( SNCG and COL4A2 ) and 9 exemplars were successfully investigated, and these may be potential biomarkers for the detection and treatment of fibroid.

  18. Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

    Science.gov (United States)

    Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

    2014-01-01

    Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

  19. Identification of human disease genes from interactome network using graphlet interaction.

    Directory of Open Access Journals (Sweden)

    Xiao-Dong Wang

    Full Text Available Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes.

  20. Molecular evolution constraints in the floral organ specification gene regulatory network module across 18 angiosperm genomes.

    Science.gov (United States)

    Davila-Velderrain, Jose; Servin-Marquez, Andres; Alvarez-Buylla, Elena R

    2014-03-01

    The gene regulatory network of floral organ cell fate specification of Arabidopsis thaliana is a robust developmental regulatory module. Although such finding was proposed to explain the overall conservation of floral organ types and organization among angiosperms, it has not been confirmed that the network components are conserved at the molecular level among flowering plants. Using the genomic data that have accumulated, we address the conservation of the genes involved in this network and the forces that have shaped its evolution during the divergence of angiosperms. We recovered the network gene homologs for 18 species of flowering plants spanning nine families. We found that all the genes are highly conserved with no evidence of positive selection. We studied the sequence conservation features of the genes in the context of their known biological function and the strength of the purifying selection acting upon them in relation to their placement within the network. Our results suggest an association between protein length and sequence conservation, evolutionary rates, and functional category. On the other hand, we found no significant correlation between the strength of purifying selection and gene placement. Our results confirm that the studied robust developmental regulatory module has been subjected to strong functional constraints. However, unlike previous studies, our results do not support the notion that network topology plays a major role in constraining evolutionary rates. We speculate that the dynamical functional role of genes within the network and not just its connectivity could play an important role in constraining evolution.

  1. Connectivity in gene coexpression networks negatively correlates with rates of molecular evolution in flowering plants.

    Science.gov (United States)

    Masalia, Rishi R; Bewick, Adam J; Burke, John M

    2017-01-01

    Gene coexpression networks are a useful tool for summarizing transcriptomic data and providing insight into patterns of gene regulation in a variety of species. Though there has been considerable interest in studying the evolution of network topology across species, less attention has been paid to the relationship between network position and patterns of molecular evolution. Here, we generated coexpression networks from publicly available expression data for seven flowering plant taxa (Arabidopsis thaliana, Glycine max, Oryza sativa, Populus spp., Solanum lycopersicum, Vitis spp., and Zea mays) to investigate the relationship between network position and rates of molecular evolution. We found a significant negative correlation between network connectivity and rates of molecular evolution, with more highly connected (i.e., "hub") genes having significantly lower nonsynonymous substitution rates and dN/dS ratios compared to less highly connected (i.e., "peripheral") genes across the taxa surveyed. These findings suggest that more centrally located hub genes are, on average, subject to higher levels of evolutionary constraint than are genes located on the periphery of gene coexpression networks. The consistency of this result across disparate taxa suggests that it holds for flowering plants in general, as opposed to being a species-specific phenomenon.

  2. A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

    Science.gov (United States)

    Kentzoglanakis, Kyriakos; Poole, Matthew

    2012-01-01

    In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

  3. Characterization of differentially expressed genes using high-dimensional co-expression networks

    DEFF Research Database (Denmark)

    Coelho Goncalves de Abreu, Gabriel; Labouriau, Rodrigo S.

    2010-01-01

    We present a technique to characterize differentially expressed genes in terms of their position in a high-dimensional co-expression network. The set-up of Gaussian graphical models is used to construct representations of the co-expression network in such a way that redundancy and the propagation...... that allow to make effective inference in problems with high degree of complexity (e.g. several thousands of genes) and small number of observations (e.g. 10-100) as typically occurs in high throughput gene expression studies. Taking advantage of the internal structure of decomposable graphical models, we...... construct a compact representation of the co-expression network that allows to identify the regions with high concentration of differentially expressed genes. It is argued that differentially expressed genes located in highly interconnected regions of the co-expression network are less informative than...

  4. Submodularity in dynamics and control of networked systems

    CERN Document Server

    Clark, Andrew; Bushnell, Linda; Poovendran, Radha

    2016-01-01

    This book presents a framework for the control of networked systems utilizing submodular optimization techniques. The main focus is on selecting input nodes for the control of networked systems, an inherently discrete optimization problem with applications in power system stability, social influence dynamics, and the control of vehicle formations. The first part of the book is devoted to background information on submodular functions, matroids, and submodular optimization, and presents algorithms for distributed submodular optimization that are scalable to large networked systems. In turn, the second part develops a unifying submodular optimization approach to controlling networked systems based on multiple performance and controllability criteria. Techniques are introduced for selecting input nodes to ensure smooth convergence, synchronization, and robustness to environmental and adversarial noise. Submodular optimization is the first unifying approach towards guaranteeing both performance and controllabilit...

  5. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    Science.gov (United States)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  6. A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks.

    Science.gov (United States)

    Gregoretti, Francesco; Belcastro, Vincenzo; di Bernardo, Diego; Oliva, Gennaro

    2010-04-21

    The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes--as is the case in biological networks--due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications.

  7. Filippov systems and quasi-synchronization control for switched networks.

    Science.gov (United States)

    Liu, Xiaoyang; Cao, Jinde; Yu, Wenwu

    2012-09-01

    This paper is concerned with the quasi-synchronization issue of linearly coupled networks with discontinuous nonlinear functions in each isolated node. Under the framework of Filippov systems, the existence and boundedness of solutions for such complex networks can be guaranteed by the matrix measure approach. A design method is presented for the synchronization controllers of coupled networks with non-identical discontinuous systems. Moreover, a sufficient condition is derived to ensure the quasi-synchronization of switched coupled complex networks with discontinuous isolated nodes, which could be controlled by some designed linear controllers. The obtained results extend the previous work on the synchronization issue of coupled complex networks with Lipschitz continuous conditions. Numerical simulations on the coupled chaotic systems are given to demonstrate the effectiveness of the theoretical results.

  8. Integrated control platform for converged optical and wireless networks

    DEFF Research Database (Denmark)

    Yan, Ying

    The next generation of broadband access networks is expected to be heterogeneous. Multiple wired and wireless systems can be integrated, in order to simultaneously provide seamless access with an appropriate Quality of Service (QoS). Wireless networks support ubiquitous connectivity yet low data...... control platform design. To achieve an integrated and unified control platform, enhanced signalling protocol plays an important role in gluing the two different technologies. Consequently, an integrated resource management system is developed. Furthermore, and admission control scheme for connections...... are distributed based on the network states, channel conditions, and QoS requirements. A new aspect in the design of future network is the energy efficiency. An energy management mechanism is proposed and evaluated for the optical network. With regard to power saving, a sleep mode operation is developed...

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

    Science.gov (United States)

    Qiu, Yushan; Tamura, Takeyuki; Ching, Wai-Ki; Akutsu, Tatsuya

    2014-01-01

    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. 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. 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.

  10. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    Science.gov (United States)

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  11. Controllability and observability of Boolean networks arising from biology

    Science.gov (United States)

    Li, Rui; Yang, Meng; Chu, Tianguang

    2015-02-01

    Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.

  12. NetVenn: an integrated network analysis web platform for gene lists.

    Science.gov (United States)

    Wang, Yi; Thilmony, Roger; Gu, Yong Q

    2014-07-01

    Many lists containing biological identifiers, such as gene lists, have been generated in various genomics projects. Identifying the overlap among gene lists can enable us to understand the similarities and differences between the data sets. Here, we present an interactome network-based web application platform named NetVenn for comparing and mining the relationships among gene lists. NetVenn contains interactome network data publically available for several species and supports a user upload of customized interactome network data. It has an efficient and interactive graphic tool that provides a Venn diagram view for comparing two to four lists in the context of an interactome network. NetVenn also provides a comprehensive annotation of genes in the gene lists by using enriched terms from multiple functional databases. In addition, it allows for mapping the gene expression data, providing information of transcription status of genes in the network. The power graph analysis tool is integrated in NetVenn for simplified visualization of gene relationships in the network. NetVenn is freely available at http://probes.pw.usda.gov/NetVenn or http://wheat.pw.usda.gov/NetVenn. Published by Oxford University Press Nucleic Acids Research 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

  13. An approach for reduction of false predictions in reverse engineering of gene regulatory networks.

    Science.gov (United States)

    Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar

    2018-05-14

    A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives

  14. SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zhi Chen

    2014-01-01

    Full Text Available Well-designed network topology provides vital support for routing, data fusion, and target tracking in wireless sensor networks (WSNs. Self-organization feature map (SOFM neural network is a major branch of artificial neural networks, which has self-organizing and self-learning features. In this paper, we propose a cluster-based topology control algorithm for WSNs, named SOFMHTC, which uses SOFM neural network to form a hierarchical network structure, completes cluster head selection by the competitive learning among nodes, and takes the node residual energy and the distance to the neighbor nodes into account in the clustering process. In addition, the approach of dynamically adjusting the transmitting power of the cluster head nodes is adopted to optimize the network topology. Simulation results show that SOFMHTC may get a better energy-efficient performance and make more balanced energy consumption compared with some existing algorithms in WSNs.

  15. Regulatory networks controlling nitrogen sensing and uptake in Candida albicans.

    Directory of Open Access Journals (Sweden)

    Shruthi Ramachandra

    Full Text Available Nitrogen is one of the key nutrients for microbial growth. During infection, pathogenic fungi like C. albicans need to acquire nitrogen from a broad range of different and changing sources inside the host. Detecting the available nitrogen sources and adjusting the expression of genes for their uptake and degradation is therefore cruci