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Letteratura scientifica selezionata sul tema "Genetic regulatory networks"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 28 gennaio 2023

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Articoli di riviste sul tema "Genetic regulatory networks"

1

Dougherty, Edward R., Tatsuya Akutsu, Paul Dan Cristea, and Ahmed H. Tewfik. "Genetic Regulatory Networks." EURASIP Journal on Bioinformatics and Systems Biology 2007 (2007): 1–2. http://dx.doi.org/10.1155/2007/17321.

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2

Kauffman, Stuart. "Understanding genetic regulatory networks." International Journal of Astrobiology 2, no. 2 (April 2003): 131–39. http://dx.doi.org/10.1017/s147355040300154x.

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Random Boolean networks (RBM) were introduced about 35 years ago as first crude models of genetic regulatory networks. RBNs are comprised of N on–off genes, connected by a randomly assigned regulatory wiring diagram where each gene has K inputs, and each gene is controlled by a randomly assigned Boolean function. This procedure samples at random from the ensemble of all possible NK Boolean networks. The central ideas are to study the typical, or generic properties of this ensemble, and see 1) whether characteristic differences appear as K and biases in Boolean functions are introducted, and 2)

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3

de Jong, H., J. Geiselmann, C. Hernandez, and M. Page. "Genetic Network Analyzer: qualitative simulation of genetic regulatory networks." Bioinformatics 19, no. 3 (February 12, 2003): 336–44. http://dx.doi.org/10.1093/bioinformatics/btf851.

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4

Hunziker, A., C. Tuboly, P. Horvath, S. Krishna, and S. Semsey. "Genetic flexibility of regulatory networks." Proceedings of the National Academy of Sciences 107, no. 29 (July 6, 2010): 12998–3003. http://dx.doi.org/10.1073/pnas.0915003107.

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5

Pan, Wei, Zexu Zhang, and Hongyang Liu. "Multistability of genetic regulatory networks." International Journal of Systems Science 41, no. 1 (January 2010): 107–18. http://dx.doi.org/10.1080/00207720903072381.

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6

Ying, Li, Liu Zeng-Rong, and Zhang Jian-Bao. "Dynamics of network motifs in genetic regulatory networks." Chinese Physics 16, no. 9 (September 2007): 2587–94. http://dx.doi.org/10.1088/1009-1963/16/9/015.

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7

Sadyrbaev, Felix, Inna Samuilik, and Valentin Sengileyev. "On Modelling of Genetic Regulatory Net Works." WSEAS TRANSACTIONS ON ELECTRONICS 12 (August 2, 2021): 73–80. http://dx.doi.org/10.37394/232017.2021.12.10.

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Abstract (sommario):

We consider mathematical model of genetic regulatory networks (GRN). This model consists of a nonlinear system of ordinary differential equations. The vector of solutions X(t) is interpreted as a current state of a network for a given value of time t: Evolution of a network and future states depend heavily on attractors of system of ODE. We discuss this issue for low dimensional networks and show how the results can be applied for the study of large size networks. Examples and visualizations are provided

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8

Weighill, Deborah, Marouen Ben Guebila, Kimberly Glass, John Quackenbush, and John Platig. "Predicting genotype-specific gene regulatory networks." Genome Research 32, no. 3 (February 22, 2022): 524–33. http://dx.doi.org/10.1101/gr.275107.120.

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Understanding how each person's unique genotype influences their individual patterns of gene regulation has the potential to improve our understanding of human health and development, and to refine genotype-specific disease risk assessments and treatments. However, the effects of genetic variants are not typically considered when constructing gene regulatory networks, despite the fact that many disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding. We developed EGRET (Estimating the Genetic Regulatory Effect on

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9

WU, FANG-XIANG. "DELAY-INDEPENDENT STABILITY OF GENETIC REGULATORY NETWORKS WITH TIME DELAYS." Advances in Complex Systems 12, no. 01 (February 2009): 3–19. http://dx.doi.org/10.1142/s0219525909002040.

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In an organism, genes encode proteins, some of which in turn regulate other genes. Such interactions work in highly structured but incredibly complex ways, and make up a genetic regulatory network. Recently, nonlinear delay differential equations have been proposed for describing genetic regulatory networks in the state-space form. In this paper, we study stability properties of genetic regulatory networks with time delays, by the notion of delay-independent stability. We first present necessary and sufficient conditions for delay-independent local stability of genetic regulatory networks with

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You, Xiong, Xueping Liu, and Ibrahim Hussein Musa. "Splitting Strategy for Simulating Genetic Regulatory Networks." Computational and Mathematical Methods in Medicine 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/683235.

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

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Tesi sul tema "Genetic regulatory networks"

1

Bokes, Pavol. "Genetic regulatory networks." Thesis, University of Nottingham, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.523016.

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Abul, Osman. "Controlling Discrete Genetic Regulatory Networks." Phd thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605739/index.pdf.

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Genetic regulatory networks can model dynamics of cells. They also allow for studying the effect of internal or external interventions. Selectively applying interventions towards a certain objective is known as controlling network dynamics. In this thesis work, the issue of how the external interventions af fect the network is studied. The effects are determined using differential gene expression analysis. The differential gene expression problem is further studied to improve the power of the given method. Control problem for dynamic discrete regulatory networks is formulated. This also addres

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Xiao, Yufei. "Boolean models for genetic regulatory networks." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1498.

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Pal, Ranadip. "Discovering relationships in genetic regulatory networks." Thesis, Texas A&M University, 2004. http://hdl.handle.net/1969.1/1230.

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The development of cDNA microarray technology has made it possible to simultaneously monitor the expression status of thousands of genes. A natural use for this vast amount of information would be to try and ﬁgure out inter-gene relationships by studying the gene expression patterns across different experimental conditions and to build Gene Regulatory Networks from these data. In this thesis, we study some of the issues involved in Genetic Regulatory Networks. One of them is to discover and elucidate multivariate logical predictive relations among gene expressions and to demonstrate how these

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Pal, Ranadip. "Modeling and control of genetic regulatory networks." Thesis, [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1494.

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Parmar, Kiresh. "Time-delayed models of genetic regulatory networks." Thesis, University of Sussex, 2017. http://sro.sussex.ac.uk/id/eprint/70716/.

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In this thesis I have analysed several mathematical models, which represent the dynamics of genetic regulatory networks. Methods of bifurcation analysis and direct numerical simulations were employed to study the biological phenomena that can occur due to the presence of time delays, such as stable periodic oscillations induced by Hopf bifurcations. To highlight the biological implications of time-delayed systems, different models of genetic regulatory networks as relevant to the onset and development of cancer were studied in detail, as well as genetic regulatory networks which describe the e

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Zhao, Dacheng. "Representation and visualization of genetic regulatory networks." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/42131.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (leaves 61-65).<br>We present a new framework, Sonnet, for the interactive visualization of large, complex biological models that are represented as graphs. Sonnet provides a flexible representation framework and graphical user interface for filtering and layout, allowing users to rapidly visualize different aspects of a data set. Many previous approaches have required users to write customized software in order to achieve the same function

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Zhang, Shuqin. "Mathematical models and algorithms for genetic regulatory networks." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38842828.

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Zhang, Shuqin, and 張淑芹. "Mathematical models and algorithms for genetic regulatory networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38842828.

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Santos, Bruno Acácio de Castro Moreira dos. "Small RNAs in gene regulatory networks." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708543.

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Libri sul tema "Genetic regulatory networks"

1

Zhang, Xian, Yantao Wang, and Ligang Wu. Analysis and Design of Delayed Genetic Regulatory Networks. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17098-1.

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Knabe, Johannes F. Computational Genetic Regulatory Networks: Evolvable, Self-organizing Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30296-1.

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The regulatory genome: Gene regulatory networks in development and evolution. Oxford: Academic, 2005.

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Shmulevich, Ilya. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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Shmulevich, Ilya. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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6

R, Dougherty Edward, and Society for Industrial and Applied Mathematics., eds. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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Shmulevich, Ilya. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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8

Babu, M. Madan. Bacterial gene regulation and transcriptional networks. Norfolk, UK: Caister Academic Press, 2013.

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Gene regulatory networks: Methods and protocols. New York: Humana Press, 2012.

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Lamoreux, M. Lynn. The colors of mice: A model genetic network. Chichester, West Sussex: Wiley-Blackwell, 2010.

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Capitoli di libri sul tema "Genetic regulatory networks"

1

Brabazon, Anthony, Michael O’Neill, and Seán McGarraghy. "Genetic Regulatory Networks." In Natural Computing Algorithms, 383–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43631-8_21.

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2

Knabe, Johannes F. "Genetic Regulatory Networks." In Computational Genetic Regulatory Networks: Evolvable, Self-organizing Systems, 19–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30296-1_3.

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3

Banks, Richard, Victor Khomenko, and L. Jason Steggles. "Modeling Genetic Regulatory Networks." In Computational Biology, 73–100. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-474-6_5.

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4

Albert, Réka. "Boolean Modelingof Genetic Regulatory Networks." In Complex Networks, 459–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44485-5_21.

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Schilstra, M., and H. Bolouri. "Models of Genetic Regulatory Networks." In Natural Computing Series, 149–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-06369-9_8.

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Placantonakis, Dimitris G., Mark J. Tomishima, Fabien G. Lafaille, and Lorenz Studer. "Genetic Manipulation of Human Embryonic Stem Cells." In Regulatory Networks in Stem Cells, 75–86. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-227-8_7.

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Banzhaf, W. "Artificial Regulatory Networks and Genetic Programming." In Genetic Programming Theory and Practice, 43–61. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-8983-3_4.

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Salafranca, Julia, Zhichao Ai, Lihui Wang, Irina A. Udalova, and Erinke van Grinsven. "Analysis of Neutrophil Morphology and Function Under Genetic Perturbation of Transcription Factors In Vitro." In Transcription Factor Regulatory Networks, 69–86. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2815-7_6.

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Nicolau, Miguel, Michael O’Neill, and Anthony Brabazon. "Applying Genetic Regulatory Networks to Index Trading." In Lecture Notes in Computer Science, 428–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32964-7_43.

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Kuwahara, Hiroyuki, Chris J. Myers, Michael S. Samoilov, Nathan A. Barker, and Adam P. Arkin. "Automated Abstraction Methodology for Genetic Regulatory Networks." In Transactions on Computational Systems Biology VI, 150–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11880646_7.

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Atti di convegni sul tema "Genetic regulatory networks"

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Le Yu and Zhong Su. "Modelling genetic regulatory networks." In 2008 Asia Simulation Conference - 7th International Conference on System Simulation and Scientific Computing (ICSC). IEEE, 2008. http://dx.doi.org/10.1109/asc-icsc.2008.4675424.

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Lopes, Rui L., and Ernesto Costa. "Genetic programming with genetic regulatory networks." In Proceeding of the fifteenth annual conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2463372.2463488.

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3

Cussat-Blanc, Sylvain, and Wolfgang Banzhaf. "Introduction to Gene Regulatory Networks." In GECCO '15: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2739482.2756586.

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Cussat-Blanc, Sylvain, and Wolfgang Banzhaf. "Introduction to gene regulatory networks." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3067695.3067728.

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Beal, Jacob, and Aaron Adler. "Functional synthesis of genetic regulatory networks." In the 1st annual workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2505351.2505356.

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Mohamadian, Mohammad, Amir Hossein Abolmasoumi, and Hamid Reza Momeni. "Stochastic asymptotic boundedness of genetic regulatory networks." In 2011 23rd Chinese Control and Decision Conference (CCDC). IEEE, 2011. http://dx.doi.org/10.1109/ccdc.2011.5968593.

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Kablar, Natasa A., Vlada Kvrgic, and Dragomir Ilic. "Singularly impulsive model of genetic regulatory networks." In 2012 24th Chinese Control and Decision Conference (CCDC). IEEE, 2012. http://dx.doi.org/10.1109/ccdc.2012.6242925.

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Tian, Li-Ping, and Fang-Xiang Wu. "Delay-Dependent Stability for Genetic Regulatory Networks." In 2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2011. http://dx.doi.org/10.1109/bibm.2011.20.

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Kablar, Natasa A. "Singularly impulsive model of genetic regulatory networks." In Robotics (MMAR). IEEE, 2010. http://dx.doi.org/10.1109/mmar.2010.5587236.

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Ling, Hai, Zhangqing Zhu, and Chunlin Chen. "Dynamics of genetic regulatory networks with delays." In 2012 9th IEEE International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2012. http://dx.doi.org/10.1109/icnsc.2012.6204936.

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Rapporti di organizzazioni sul tema "Genetic regulatory networks"

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McAdams, Harley. Genetic Regulatory Networks: Analysis and Simulation. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada410805.

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Kishore, Nand, Radhakrishnan Balu, and Shashi P. Karna. Modeling Genetic Regulatory Networks Using First-Order Probabilistic Logic. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada582376.

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3

Eshed-Williams, Leor, and Daniel Zilberman. Genetic and cellular networks regulating cell fate at the shoot apical meristem. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7699862.bard.

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The shoot apical meristem establishes plant architecture by continuously producing new lateral organs such as leaves, axillary meristems and flowers throughout the plant life cycle. This unique capacity is achieved by a group of self-renewing pluripotent stem cells that give rise to founder cells, which can differentiate into multiple cell and tissue types in response to environmental and developmental cues. Cell fate specification at the shoot apical meristem is programmed primarily by transcription factors acting in a complex gene regulatory network. In this project we proposed to provide si

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Olszewski, Neil, and David Weiss. Role of Serine/Threonine O-GlcNAc Modifications in Signaling Networks. United States Department of Agriculture, September 2010. http://dx.doi.org/10.32747/2010.7696544.bard.

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Significant evidence suggests that serine/threonine-O-linked N-acetyl glucosamine0-(GlcNAc) modifications play a central role in the regulation of plant signaling networks. Forexample, mutations in SPINDLY,) SPY (an O-GlcNAc transferase,) OGT (promote gibberellin GA) (signal transduction and inhibit cytokinin responses. In addition, mutating both Arabidopsis OGTsSEC (and SPY) causes embryo lethality. The long-term goal of this research is to elucidate the mechanism by which Arabidopsis OGTs regulate signaling networks. This project investigated the mechanisms of O-GlcNAc regulation of cytokini

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Li, Li, Joseph Burger, Nurit Katzir, Yaakov Tadmor, Ari Schaffer, and Zhangjun Fei. Characterization of the Or regulatory network in melon for carotenoid biofortification in food crops. United States Department of Agriculture, April 2015. http://dx.doi.org/10.32747/2015.7594408.bard.

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The general goals of the BARD research grant US-4423-11 are to understand how Or regulates carotenoid accumulation and to reveal novel strategies for breeding agricultural crops with enhanced β-carotene level. The original objectives are: 1) to identify the genes and proteins in the Or regulatory network in melon; 2) to genetically and molecularly characterize the candidate genes; and 3) to define genetic and functional allelic variation of these genes in a representative germplasm collection of the C. melo species. Or was found by the US group to causes provitamin A accumulation in chromoplas

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Hovav, Ran, Peggy Ozias-Akins, and Scott A. Jackson. The genetics of pod-filling in peanut under water-limiting conditions. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7597923.bard.

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Pod-filling, an important yield-determining stage is strongly influenced by water stress. This is particularly true for peanut (Arachishypogaea), wherein pods are developed underground and are directly affected by the water condition. Pod-filling in peanut has a significant genetic component as well, since genotypes are considerably varied in their pod-fill (PF) and seed-fill (SF) potential. The goals of this research were to: Examine the effects of genotype, irrigation, and genotype X irrigation on PF and SF. Detect global changes in mRNA and metabolites levels that accompany PF and SF. Explo

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Lers, Amnon, and Gan Susheng. Study of the regulatory mechanism involved in dark-induced Postharvest leaf senescence. United States Department of Agriculture, January 2009. http://dx.doi.org/10.32747/2009.7591734.bard.

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Postharvest leaf senescence contributes to quality losses in flowers and leafy vegetables. The general goal of this research project was to investigate the regulatory mechanisms involved in dark-induced leaf senescence. The regulatory system involved in senescence induction and control is highly complex and possibly involves a network of senescence promoting pathways responsible for activation of the senescence-associated genes. Pathways involving different internal signals and environmental factors may have distinctive importance in different leaf senescence systems. Darkness is known to have

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Friedman, Haya, Julia Vrebalov, and James Giovannoni. Elucidating the ripening signaling pathway in banana for improved fruit quality, shelf-life and food security. United States Department of Agriculture, October 2014. http://dx.doi.org/10.32747/2014.7594401.bard.

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Background : Banana being a monocot and having distinct peel and pulp tissues is unique among the fleshy fruits and hence can provide a more comprehensive understanding of fruit ripening. Our previous research which translated ripening discoveries from tomato, led to the identification of six banana fruit-associated MADS-box genes, and we confirmed the positive role of MaMADS1/2 in banana ripening. The overall goal was to further elucidate the banana ripening signaling pathway as mediated by MADS-boxtranscriptional regulators. Specific objectives were: 1) characterize transcriptional profiles

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Aharoni, Asaph, Zhangjun Fei, Efraim Lewinsohn, Arthur Schaffer, and Yaakov Tadmor. System Approach to Understanding the Metabolic Diversity in Melon. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7593400.bard.

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Fruit quality is determined by numerous genetic factors that affect taste, aroma, ‎color, texture, nutritional value and shelf life. To unravel the genetic components ‎involved in the metabolic pathways behind these traits, the major goal of the project was to identify novel genes that are involved in, or that regulate, these pathways using correlation analysis between genotype, metabolite and gene expression data. The original and specific research objectives were: (1) Collection of replicated fruit from a population of 96 RI lines derived from parents distinguished by great diversity in frui

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