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Relevant bibliographies by topics / Genetic regulatory networks

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Academic literature on the topic 'Genetic regulatory networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 January 2023

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Journal articles on the topic "Genetic regulatory networks"

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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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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) whether a subclass of this ensemble has properties matching real cells.Such networks behave in an ordered or a chaotic regime, with a phase transition, ‘the edge of chaos’ between the two regimes. Networks with continuous variables exhibit the same two regimes. Substantial evidence suggests that real cells are in the ordered regime. A key concept is that of an attractor. This is a reentrant trajectory of states of the network, called a state cycle. The central biological interpretation is that cell types are attractors. A number of properties differentiate the ordered and chaotic regimes. These include the size and number of attractors, the existence in the ordered regime of a percolating ‘sea’ of genes frozen in the on or off state, with a remainder of isolated twinkling islands of genes, a power law distribution of avalanches of gene activity changes following perturbation to a single gene in the ordered regime versus a similar power law distribution plus a spike of enormous avalanches of gene changes in the chaotic regime, and the existence of branching pathway of ‘differentiation’ between attractors induced by perturbations in the ordered regime.Noise is serious issue, since noise disrupts attractors. But numerical evidence suggests that attractors can be made very stable to noise, and meanwhile, metaplasias may be a biological manifestation of noise. As we learn more about the wiring diagram and constraints on rules controlling real genes, we can build refined ensembles reflecting these properties, study the generic properties of the refined ensembles, and hope to gain insight into the dynamics of real cells.

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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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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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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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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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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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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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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 TFs), which infers a genotype-specific gene regulatory network for each individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, expression quantitative trait locus (eQTL) data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses a technique known as message passing to integrate this prior network with gene expression and TF protein–protein interaction data to produce a refined, genotype-specific regulatory network. We used EGRET to infer gene regulatory networks for two blood-derived cell lines and identified genotype-associated, cell line–specific regulatory differences that we subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential ChIP-seq TF binding. We also inferred EGRET networks for three cell types from each of 119 individuals and identified cell type–specific regulatory differences associated with diseases related to those cell types. EGRET is, to our knowledge, the first method that infers networks reflective of individual genetic variation in a way that provides insight into the genetic regulatory associations driving complex phenotypes.

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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 a single time delay, and then extend the main result to genetic regulatory networks with multiple time delays. To illustrate the main theory, we analyze delay-independent stability of three genetic regulatory networks in E. coli or zebra fish. For E. coli, an autoregulatory network and a repressilatory network are analyzed. The results show that these two genetic regulatory networks with parameters in the physiological range are delay-independently robustly stable. For zebra fish, an autoregulatory network for the gene her1 is analyzed. The result shows that delay-independent stability of this network depends on the initial number of protein molecules, which is in agreement with the existing biological knowledge. The theories presented in this paper provide a very useful complement to the previous work and a framework for further studying the stability of more complex genetic regulatory networks.

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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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Dissertations / Theses on the topic "Genetic regulatory networks"

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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 addresses the needs for various control strategies, e.g., finite horizon, infinite horizon, and various accounting of state and intervention costs. Control schemes for small to large networks are proposed and experimented. A case study is provided to show how the proposals are exploited
also given is the need for and effectiveness of various control schemes.

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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 logical relations based on coarse quantization closely reﬂect corresponding relations in the continuous data. The other issue involves construction of synthetic Probabilistic Boolean Networks with particular attractor structures. These synthetic networks help in testing of various algorithms like Bayesian Connectivity based approach for design of Probabilistic Boolean Networks.

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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 effects of transcription factors in the immune system. A network of an oscillator coupled with a switch was explored, as systems such as these are prevalent in genetic regulatory networks. The effects of time delays on its oscillatory and bistable behaviour were then investigated, the results of which were compared with available results from the literature.

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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.
Includes bibliographical references (leaves 61-65).
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 functionality. With Sonnet, once features of interest are identified, they can be captured as figures for offline presentation. We demonstrate the application of Sonnet to the visualization and manipulation of transcriptional regulatory networks in yeast. Sonnet is particularly well adapted to this application as native presentation of these networks yields dense and difficult to decipher results.
by Dacheng Zhao.
M.Eng.

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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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Books on the topic "Genetic regulatory networks"

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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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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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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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Book chapters on the topic "Genetic regulatory networks"

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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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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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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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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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Conference papers on the topic "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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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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Reports on the topic "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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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 significant understanding of meristem maintenance and cell fate specification by studying four transcription factors acting at the meristem. Our original aim was to identify the direct target genes of WUS, STM, KNAT6 and CNA transcription factor in a genome wide scale and the manner by which they regulate their targets. Our goal was to integrate this data into a regulatory model of cell fate specification in the SAM and to identify key genes within the model for further study. We have generated transgenic plants carrying the four TF with two different tags and preformed chromatin Immunoprecipitation (ChIP) assay to identify the TF direct target genes. Due to unforeseen obstacles we have been delayed in achieving this aim but hope to accomplish it soon. Using the GR inducible system, genetic approach and transcriptome analysis [mRNA-seq] we provided a new look at meristem activity and its regulation of morphogenesis and phyllotaxy and propose a coherent framework for the role of many factors acting in meristem development and maintenance. We provided evidence for 3 different mechanisms for the regulation of WUS expression, DNA methylation, a second receptor pathway - the ERECTA receptor and the CNA TF that negatively regulates WUS expression in its own domain, the Organizing Center. We found that once the WUS expression level surpasses a certain threshold it alters cell identity at the periphery of the inflorescence meristem from floral meristem to carpel fate [FM]. When WUS expression highly elevated in the FM, the meristem turn into indeterminate. We showed that WUS activate cytokinine, inhibit auxin response and represses the genes required for root identity fate and that gradual increase in WUCHEL activity leads to gradual meristem enlargement that affect phyllotaxis. We also propose a model in which the direction of WUS domain expansion laterally or upward affects meristem structure differently. We preformed mRNA-seq on meristems with different size and structure followed by k-means clustering and identified groups of genes that are expressed in specific domains at the meristem. We will integrate this data with the ChIP-seq of the 4 TF to add another layer to the genetic network regulating meristem activity.

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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 cytokinin and gibberellin signaling, identified additional processes regulated by this modification and investigated the regulation of SEC activity. Although SPY is a nucleocytoplasmic protein, its site of action and targets were unknown. Severalstudies suggested that SPY acted in the nucleus where it modified nuclear components such as the DELLA proteins. Using chimeric GFP-SPY fused to a nuclear-export signal or to a nuclear-import signal, we showed that cytosolic, but not nuclear SPY, regulated cytokinin and GA signaling. We also obtained evidence suggesting that GA and SPY affect cytokinin signaling via a DELLA-independent pathway. Although SEC and SPY were believed to have overlapping functions, the role of SEC in cytokinin and GA signaling was unclear. The role of SEC in cytokinin and GA responses was investigated by partially suppressing SPY expression in secplants using a synthetic Spymicro RNA miR(SPY). The possible contribution of SEC to the regulation of GA and cytokinin signaling wastest by determining the resistance of the miR spy secplants to the GA biosynthesis inhibitor paclobutrazol and to cytokinin. We found that the transgenic plants were resistant to paclobutrazol and to cytokinin, butonlyata level similar to spy. Moreover, expressing SEC under the 35S promoter in spy mutant did not complement the spy mutation. Therefore, we believe that SEC does not act with SPY to regulate GA or cytokinin responses. The cellular targets of Spy are largely unknown. We identified the transcription factor TCP15 in a two-hybrid screen for SPY-interacting proteins and showed that both TCP15 and its closely homolog TCP14 were O-GlcNAc modified by bacterially-produced SEC. The significance of the interaction between SPY and these TCPs was examined by over-expressing the minwild-type and spy-4plants. Overexpression of TCP14 or TCP15 in wild-type background produced phenotypes typical of plants with increased cytokinin and reduced GA signaling. TCP14 overexpression phenotypes were strongly suppressed in the spy background, suggesting that TCP14 and TCP15 affect cytokinin and GA signaling and that SPY activates them. In agreement with this hypothesis, we created a tcp14tcp15 double mutant and found that it has defects similar to spyplants. In animals, O-GlcNAc modification is proposed to regulate the activity of the nuclear pore. Therefore, after discovering that SEC modified a nucleoporinNUP) (that also interacts with SPY, we performed genetic experiments exploring the relationship between NUPs and SPY nupspy double mutants exhibited phenotypes consistent with SPY and NUPs functioning in common processes and nupseeds were resistant to GA biosynthesis inhibitors. All eukaryotic OGTs have a TPR domain. Deletion studies with bacterially-expressed SEC demonstrated SEC'sTPR domain inhibits SEC enzymatic activity. Since the TPR domain interacts with other proteins, we propose that regulatory proteins regulate OGT activity by binding and modulating the inhibitory activity of the TPR domain.

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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 chromoplasts in cauliflower. Preliminary genetic study from the Israeli group revealed that the melon Or gene (CmOr) completely co-segregated with fruit flesh color in a segregating mapping population and in a wide melon germplasm collection, which set the stage for the funded research. Major conclusions and achievements include: 1). CmOris proved to be the gene that controls melon fruit flesh color and represents the previously described gflocus in melon. 2). Genetic and molecular analyses of CmOridentify and confirm a single SNP that is responsible for the orange and non-orange phenotypes in melon fruit. 3). Alteration of the evolutionarily conserved arginine in an OR protein to both histidine or alanine greatly enhances its ability to promote carotenoid accumulation. 4). OR promotes massive carotenoid accumulation due to its dual functions in regulating both chromoplast biogenesis and carotenoid biosynthesis. 5). A bulk segregant transcriptome (BSRseq) analysis identifies a list of genes associated with the CmOrregulatory network. 6). BSRseq is proved to be an effective approach for gene discovery. 7). Screening of an EMS mutation library identifies a low β mutant, which contains low level of carotenoids due to a mutation in CmOrto produce a truncated form of OR protein. 8). low β exhibits lower germination rate and slow growth under salt stress condition. 9). Postharvest storage of fruit enhances carotenoid accumulation, which is associated with chromoplast development. Our research uncovers the molecular mechanisms underlying the Or-regulated high level of carotenoid accumulation via regulating carotenoidbiosynthetic capacity and storage sink strength. The findings provide mechanistic insights into how carotenoid accumulation is controlled in plants. Our research also provides general and reliable molecular markers for melon-breeding programs to select orange varieties, and offers effective genetic tools for pro-vitamin A enrichment in other important crops via the rapidly developed genome editing technology. The newly discovered low β mutant could lead to a better understanding of the Or gene function and its association with stress response, which may explain the high conservation of the Or gene among various plant species.

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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. Explore the response of the duplicate peanut pod transcriptome to drought stress. Study how entire duplicated PF regulatory processes are networked within a polyploid organism. Discover locus-specific SNP markers and map pod quality traits under different environments. The research included genotypes and segregating populations from Israel and US that are varied in PF, SF and their tolerance to water deficit. Initially, an extensive field trial was conducted to investigate the effects of genotype, irrigation, and genotype X irrigation on PF and SF. Significant irrigation and genotypic effect was observed for the two main PF related traits, "seed ratio" and "dead-end ratio", demonstrating that reduction in irrigation directly influences the developing pods as a result of low water potential. Although the Irrigation × Genotype interaction was not statistically significant, one genotype (line 53) was found to be more sensitive to low irrigation treatments. Two RNAseq studies were simultaneously conducted in IL and the USA to characterize expression changes that accompany shell ("source") and seed ("sink") biogenesis in peanut. Both studies showed that SF and PF processes are very dynamic and undergo very rapid change in the accumulation of RNA, nutrients, and oil. Some genotypes differ in transcript accumulation rates, which can explain their difference in SF and PF potential; like cvHanoch that was found to be more enriched than line 53 in processes involving the generation of metabolites and energy at the beginning of seed development. Interestingly, an opposite situation was found in pericarp development, wherein rapid cell wall maturation processes were up-regulated in line 53. Although no significant effect was found for the irrigation level on seed transcriptome in general, and particularly on subgenomic assignment (that was found almost comparable to a 1:1 for A- and B- subgenomes), more specific homoeologous expression changes associated with particular biosynthesis pathways were found. For example, some significant A- and B- biases were observed in particular parts of the oil related gene expression network and several candidate genes with potential influence on oil content and SF were further examined. Substation achievement of the current program was the development and application of new SNP detection and mapping methods for peanut. Two major efforts on this direction were performed. In IL, a GBS approach was developed to map pod quality traits on Hanoch X 53 F2/F3 generations. Although the GBS approach was found to be less effective for our genetic system, it still succeeded to find significant mapping locations for several traits like testa color (linkage A10), number of seeds/pods (A5) and pod wart resistance (B7). In the USA, a SNP array was developed and applied for peanut, which is based on whole genome re-sequencing of 20 genotypes. This chip was used to map pod quality related traits in a Tifrunner x NC3033 RIL population. It was phenotyped for three years, including a new x-ray method to phenotype seed-fill and seed density. The total map size was 1229.7 cM with 1320 markers assigned. Based on this linkage map, 21 QTLs were identified for the traits 16/64 weight, kernel percentage, seed and pod weight, double pod and pod area. Collectively, this research serves as the first fundamental effort in peanut for understanding the PF and SF components, as a whole, and as influenced by the irrigation level. Results of the proposed study will also generate information and materials that will benefit peanut breeding by facilitating selection for reduced linkage drag during introgression of disease resistance traits into elite cultivars. BARD Report - Project4540 Page 2 of 10

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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 a role in enhancement of postharvest leaf senescence and for getting an insight into its regulatory mechanism/s we have applied molecular genetics and functional genomics approaches. The original objectives were: 1. Identification of dark-induced SAGs in Arabidopsis using enhancer/promoter trap lines and microarray approaches; 2. Molecular and functional characterization of the identified genes by analyzing their expression and examining the phenotypes in related knockout mutant plants; 3. Initial studies of promoter sequences for selected early dark-induced SAGs. Since genomic studies of senescence, with emphasis on dark-induced senescence, were early-on published which included information on potential regulatory genes we decided to use this new information. This is instead of using the uncharacterized enhancer/promoter trap lines as originally planned. We have also focused on specific relevant genes identified in the two laboratories. Based on the available genomic analyses of leaf senescence 10 candidate genes hypothesized to have a regulatory role in dark-induced senescence were subjected to both expression as well as functional analyses. For most of these genes senescence-specific regulation was confirmed, however, functional analyses using knock-out mutants indicated no consequence to senescence progression. The transcription factor WARK75 was found to be specifically expressed during natural and dark-induced leaf senescence. Functional analysis demonstrated that in detached leaves senescence under darkness was significantly delayed while no phenotypic consequences could be observed on growth and development, including no effect on natural leaf senescence,. Thus, WARKY75 is suggested to have a role in dark-induced senescence, but not in natural senescence. Another regulatory gene identified to have a role in senescence is MKK9 encoding for a Mitogen-Activated Protein Kinase Kinase 9 which is upregulated during senescence in harvested leaves as well as in naturally senescing leaves. MKK9 can specifically phosphorylate another kinase, MPK6. Both knockouts of MKK9 and MPK6 displayed a significantly senescence delay in harvested leaves and possibly function as a phosphorelay that regulates senescence. To our knowledge, this is the first report that clearly demonstrates the involvement of a MAP kinase pathway in senescence. This research not only revealed a new signal transduction pathway, but more important provided significant insights into the regulatory mechanisms underlying senescence in harvested leaves. In an additional line of research we have employed the promoter of the senescence-induced BFN1 gene as a handle for identifying components of the regulatory mechanism. This gene was shown to be activated during darkinduced senescence of detached leaves, as well as natural senescence. This was shown by following protein accumulation and promoter activity which demonstrated that this promoter is activated during dark-induced senescence. Analysis of the promoter established that, at least some of the regulatory sequences reside in an 80 bps long fragment of the promoter. Overall, progress was made in identification of components with a role in dark-induced senescence in this project. Further studies should be done in order to better understand the function of these components and develop approaches for modulating the progress of senescence in crop plants for the benefit of agriculture.

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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 and quality of MaMADS1/2 repressed fruit; 2) reveal the role of additional MaMADSgenes in ripening; 3) develop a model of fruit MaMADS-box mode of action; and 4) isolate new components of the banana ripening signaling pathway. Major conclusion: The functions of the banana MaMADS1-5 have been examined by complimenting the rinor the TAGL1-suppressed lines of tomato. Only MaMADS5 exhibited partial complementation of TAGL1-suppressed and rinlines, suggesting that while similar genes play corresponding roles in ripening, evolutionary divergence makes heterologous complementation studies challenging. Nevertheless, the partial complementation of tomato TAGL1-surpessed and rinlines with MaMADS5 suggests this gene is likely an important ripening regulator in banana, worthy of further study. RNA-seqtranscriptome analysis during ripening was performed on WT and MaMADS2-suppressed lines revealing additional candidate genes contributing to ripening control mechanisms. In summary, we discovered 39 MaMADS-box genes in addition to homologues of CNR, NOR and HB-1 expressed in banana fruits, and which were shown in tomato to play necessary roles in ripening. For most of these genes the expression in peel and pulp was similar. However, a number of key genes were differentially expressed between these tissues indicating that the regulatory components which are active in peel and pulp include both common and tissue-specific regulatory systems, a distinction as compared to the more uniform tomato fruit pericarp. Because plant hormones are well documented to affect fruit ripening, the expressions of genes within the auxin, gibberellin, abscisic acid, jasmonic acid, salicylic and ethylene signal transduction and synthesis pathways were targeted in our transcriptome analysis. Genes’ expression associated with these pathways generally declined during normal ripening in both peel and pulp, excluding cytokinin and ethylene, and this decline was delayed in MaMADS2-suppressed banana lines. Hence, we suggest that normal MaMADS2 activity promotes the observed downward expression within these non-ethylene pathways (especially in the pulp), thus enabling ripening progression. In contrast, the expressions of ACSand ACOof the ethylene biosynthesis pathway increase in peel and pulp during ripening and are delayed/inhibited in the transgenic bananas, explaining the reduced ethylene production of MaMADS2-suppressed lines. Inferred by the different genes’ expression in peel and pulp of the gibberellins, salicylic acid and cytokinins pathways, it is suggested that hormonal regulation in these tissues is diverse. These results provide important insights into possible avenues of ripening control in the diverse fruit tissues of banana which was not previously revealed in other ripening systems. As such, our transcriptome analysis of WT and ripening delayed banana mutants provides a starting point for further characterization of ripening. In this study we also developed novel evidence that the cytoskeleton may have a positive role in ripening as components of this pathway were down-regulated by MaMADS2 suppression. The mode of cytoskeleton involvement in fruit ripening remains unclear but presents a novel new frontier in ripening investigations. In summary, this project yielded functional understanding of the role and mode of action of MaMADS2 during ripening, pointing to both induction of ethylene and suppression of non-ethylene hormonal singling pathways. Furthermore, our data suggest important roles for cytoskeleton components and MaMADS5 in the overall banana ripening control network. Implications: The project revealed new molecular components/genes involved in banana ripening and refines our understanding of ripening responses in the peel and pulp tissues of this important species. This information is novel as compared to that derived from the more uniform carpel tissues of other highly studied ripening systems including tomato and grape. The work provides specific target genes for potential modification through genetic engineering or for exploration of useful genetic diversity in traditional breeding. The results from the project might point toward improved methods or new treatments to improve banana fruit storage and quality.

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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 fruit development and quality phenotypes, (2) Phenotypic and metabolic profiling of mature fruit from all 96 RI lines and their parents, (3) 454 pyrosequencing of cDNA representing mRNA of mature fruit from each line to facilitate gene expression analysis based on relative EST abundance, (4) Development of a database modeled after an existing database developed for tomato introgression lines (ILs) to facilitate online data analysis by members of this project and by researchers around the world. The main functions of the database will be to store and present metabolite and gene expression data so that correlations can be drawn between variation in target traits or metabolites across the RI population members and variation in gene expression to identify candidate genes which may impact phenotypic and chemical traits of interest, (5) Selection of RI lines for segregation and/or hybridization (crosses) analysis to ascertain whether or not genes associated with traits through gene expression/metabolite correlation analysis are indeed contributors to said traits. The overall research strategy was to utilize an available recombinant inbred population of melon (Cucumis melo L.) derived from phenotypically diverse parents and for which over 800 molecular markers have been mapped for the association of metabolic trait and gene expression QTLs. Transcriptomic data were obtained by high throughput sequencing using the Illumina platform instead of the originally planned 454 platform. The change was due to the fast advancement and proven advantages of the Illumina platform, as explained in the first annual scientific report. Metabolic data were collected using both targeted (sugars, organic acids, carotenoids) and non-targeted metabolomics analysis methodologies. Genes whose expression patterns were associated with variation of particular metabolites or fruit quality traits represent candidates for the molecular mechanisms that underlie them. Candidate genes that may encode enzymes catalyzingbiosynthetic steps in the production of volatile compounds of interest, downstream catabolic processes of aromatic amino acids and regulatory genes were selected and are in the process of functional analyses. Several of these are genes represent unanticipated effectors of compound accumulation that could not be identified using traditional approaches. According to the original plan, the Cucurbit Genomics Network (http://www.icugi.org/), developed through an earlier BARD project (IS-3333-02), was expanded to serve as a public portal for the extensive metabolomics and transcriptomic data resulting from the current project. Importantly, this database was also expanded to include genomic and metabolomic resources of all the cucurbit crops, including genomes of cucumber and watermelon, EST collections, genetic maps, metabolite data and additional information. In addition, the database provides tools enabling researchers to identify genes, the expression patterns of which correlate with traits of interest. The project has significantly expanded the existing EST resource for melon and provides new molecular tools for marker-assisted selection. This information will be opened to the public by the end of 2013, upon the first publication describing the transcriptomic and metabolomics resources developed through the project. In addition, well-characterized RI lines are available to enable targeted breeding for genes of interest. Segregation of the RI lines for specific metabolites of interest has been shown, demonstrating the utility in these lines and our new molecular and metabolic data as a basis for selection targeting specific flavor, quality, nutritional and/or defensive compounds. To summarize, all the specific goals of the project have been achieved and in many cases exceeded. Large scale trascriptomic and metabolomic resources have been developed for melon and will soon become available to the community. The usefulness of these has been validated. A number of novel genes involved in fruit ripening have been selected and are currently being functionally analyzed. We thus fully addressed our obligations to the project. In our view, however, the potential value of the project outcomes as ultimately manifested may be far greater than originally anticipated. The resources developed and expanded under this project, and the tools created for using them will enable us, and others, to continue to employ resulting data and discoveries in future studies with benefits both in basic and applied agricultural - scientific research.

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