Relevant bibliographies by topics / Repressilator

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Repressilator'

Author: Grafiati
Published: 23 February 2024

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Journal articles on the topic "Repressilator"

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BULDÚ, JAVIER M., JORDI GARCÍA-OJALVO, ALEXANDRE WAGEMAKERS, and MIGUEL A. F. SANJUÁN. "ELECTRONIC DESIGN OF SYNTHETIC GENETIC NETWORKS." International Journal of Bifurcation and Chaos 17, no. 10 (October 2007): 3507–11. http://dx.doi.org/10.1142/s0218127407019275.

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We propose the use of nonlinear electronic circuits to study synthetic gene regulation networks. Specifically, we have designed two electronic versions of a synthetic genetic clock, known as the "repressilator," making use of appropriate electronic elements linked in the same way as the original biochemical system. We study the effects of coupling in a population of electronic repressilators, with the aim of observing coherent oscillations of the whole population. With these results, we show that this kind of nonlinear circuits can be helpful in the design and understanding of synthetic genetic networks.
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Oliveira, Samuel M. D., Jerome G. Chandraseelan, Antti Häkkinen, Nadia S. M. Goncalves, Olli Yli-Harja, Sofia Startceva, and Andre S. Ribeiro. "Single-cell kinetics of a repressilator when implemented in a single-copy plasmid." Molecular BioSystems 11, no. 7 (2015): 1939–45. http://dx.doi.org/10.1039/c5mb00012b.

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BUŞE, OLGUŢA, ALEXEY KUZNETSOV, and RODRIGO A. PÉREZ. "EXISTENCE OF LIMIT CYCLES IN THE REPRESSILATOR EQUATIONS." International Journal of Bifurcation and Chaos 19, no. 12 (December 2009): 4097–106. http://dx.doi.org/10.1142/s0218127409025237.

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The Repressilator is a genetic regulatory network used to model oscillatory behavior of more complex regulatory networks like the circadian clock. We prove that the Repressilator equations undergo a supercritical Hopf bifurcation as the maximal rate of protein synthesis increases, and find a large range of parameters for which there is a cycle.
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Ushikubo, T., W. Inoue, M. Yoda, and M. Sasai. "3P287 Stochastic Dynamics of Repressilator." Seibutsu Butsuri 44, supplement (2004): S261. http://dx.doi.org/10.2142/biophys.44.s261_3.

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Dukarić, Maša, Hassan Errami, Roman Jerala, Tina Lebar, Valery G. Romanovski, János Tóth, and Andreas Weber. "On three genetic repressilator topologies." Reaction Kinetics, Mechanisms and Catalysis 126, no. 1 (December 18, 2018): 3–30. http://dx.doi.org/10.1007/s11144-018-1519-5.

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Borg, Yanika, Ekkehard Ullner, Afnan Alagha, Ahmed Alsaedi, Darren Nesbeth, and Alexey Zaikin. "Complex and unexpected dynamics in simple genetic regulatory networks." International Journal of Modern Physics B 28, no. 14 (April 25, 2014): 1430006. http://dx.doi.org/10.1142/s0217979214300060.

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One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.
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Müller, Stefan, Josef Hofbauer, Lukas Endler, Christoph Flamm, Stefanie Widder, and Peter Schuster. "A generalized model of the repressilator." Journal of Mathematical Biology 53, no. 6 (September 2, 2006): 905–37. http://dx.doi.org/10.1007/s00285-006-0035-9.

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Kuznetsov, A., and V. Afraimovich. "Heteroclinic cycles in the repressilator model." Chaos, Solitons & Fractals 45, no. 5 (May 2012): 660–65. http://dx.doi.org/10.1016/j.chaos.2012.02.009.

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Knotz, Gabriel, Ulrich Parlitz, and Stefan Klumpp. "Synchronization of a genetic oscillator with the cell division cycle." New Journal of Physics 24, no. 3 (March 1, 2022): 033050. http://dx.doi.org/10.1088/1367-2630/ac5c16.

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Abstract Genetic circuits that control specific cellular functions are never fully insulated against influences of other parts of the cell. For example, they are subject to periodic modulation by the cell cycle through volume growth and gene doubling. To investigate possible effects of the cell cycle on oscillatory gene circuits dynamics, we modelled a simple synthetic genetic oscillator, the repressilator, and studied hallmarks of the resulting nonlinear dynamics. We found that the repressilator coupled to the cell cycle shows typical quasiperiodic motion with discrete Fourier spectra and windows in parameter space with synchronization of the two oscillators, with a devil’s stair case indicating the Arnold tongues of synchronization. In the case of identical parameters for the three genes of the repressilator and simultaneous gene duplication, we identify two classes of synchronization windows, symmetric and asymmetric, depending on whether the trajectories satisfy a discrete three-fold rotation symmetry, corresponding to cyclic permutation of the three genes. Unexpectedly changing the gene doubling time revealed that the width of the Arnold tongues is connected to that three-fold symmetry of the synchronization trajectories: non-simultaneous gene duplication increases the width of asymmetric synchronization regions, for some of them by an order of magnitude. By contrast, there is only a small or even a negative effect on the window size for symmetric synchronization. This observation points to a control mechanism of synchronization via the location of the genes on the chromosome.
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Glyzin, S., A. Kolesov, and N. Rozov. "On a mathematical model of a repressilator." St. Petersburg Mathematical Journal 33, no. 5 (August 24, 2022): 797–828. http://dx.doi.org/10.1090/spmj/1727.

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A mathematical model of the simplest three-link oscillatory gene network, the so-called repressilator, is considered. This model is a nonlinear singularly perturbed system of three ordinary differential equations. The existence and stability of a relaxation periodic solution invariant with respect to cyclic permutations of coordinates are investigated for this system.
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Dissertations / Theses on the topic "Repressilator"

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Fryett, Matthew. "The stochastic multi-cellular repressilator." Thesis, University of Aberdeen, 2014. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=211418.

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The discovery of genetic regulatory networks was an important advancement in science. Not only do they help understand how organisms behave but the development of synthetic genetic networks has aided in other fields of science and industry. Many genetic networks have been modelled deterministically by using differential equations to provide an insight into the network's behaviour. However, within a biological environment, a certain degree of intrinsic noise should be expected and the robustness of these networks should be tested. Creating and analysing a genetic network in a biological environment can be a time consuming task so applying stochastic methods, such as the Gillespie Algorithm, to a computer model will provide an important, initial insight into the behaviour of the system. One interesting genetic network is the coupled repressilator due to its relatively simplistic design and the broad, multistable dynamics it offers. The inhomogeneous solutions that it can yield are particularly interesting as they may help explain certain biological phenomena, and may be used as a tool to assist with further research into genetic networks. In this thesis, the Gillespie Algorithm will be applied to the coupled repressilator so that its robustness can be tested. Biologically feasible modifications will be made to the system to produce much more stable and predictable dynamics so that the broad range of solutions can exist within a noisy environment. The methods developed will take into account previously made assumptions and potential errors in biological data so that they can be applied to other genetic system. One further objective in this thesis is to explore computational limitations that may occur when modelling large, stochastic networks. Issues such as rounding errors and dealing with very small and very large numbers were encountered and methods to circumvent these without sacrificing computational run-time will be developed and applied.
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Sun, Honglu. "Identifying and Analyzing Long-term Dynamical Behaviors of Gene Regulatory Networks with Hybrid Modeling." Electronic Thesis or Diss., Ecole centrale de Nantes, 2023. http://www.theses.fr/2023ECDN0043.

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Utiliser des modèles dynamiques pour révéler les propriétés dynamiques des réseaux de régulation des gènes peut nous aider à mieux comprendre la nature de ces systèmes biologiques et à développer nouveaux traitements médicaux. Dans cette thèse, nous nous concentrons sur une classe de systèmes dynamiques hybrides appelés réseaux de régulation des gènes hybrides (HGRN) et visons à analyser les propriétés dynamiques à long terme. Nous proposons des méthodes pour trouver des cycles limites et analyser leur stabilité, et pour analyser l’accessibilité dans HGRNs. Ceci est suivi d’une étude plus approfondie de certains réseaux d’intérêt pour la biologie des systèmes : Les répressilateurs, et nous trouvons des conditions pour l’existence d’oscillations soutenues dans le répressilateur canonique en dimension 3, et des conditions, décrites par les caractéristiques topologiques des réseaux, pour l’existence d’un attracteur périodique dans les répressilateurs discrets en dimension 4. En résumé, cette thèse propose de nouvelles méthodes pour analyser certaines propriétés des HGRNs qui n’ont pas été étudiées auparavant, par exemple la stabilité des cycles limites à N dimensions, l’accessibilité, etc. Les résultats pourront être développés à l’avenir pour étudier d’autres grands réseaux complexes
Using dynamical models to reveal dynamical properties of gene regulatory networks can help us better understand the nature of these biological systems and develop new medical treatments. In this thesis, we focus on a class of hybrid dynamical systems called Hybrid Gene Regulatory Network (HGRN) and aim to analyze long-term dynamical properties. We propose methods to find limit cycles and analyze their stability, and to analyze the reachability in HGRNs. This is followed by a deeper study of some networks of interest for Systems Biology: The repressilators, and we find conditions for the existenceof sustained oscillations in the 3-dimensional canonical repressilator, and conditions, which are described by topological features of the networks, for the existence of a periodic attractor in discrete 4-dimensional repressilators. In summary, this thesis proposes new methods to analyze some properties of HGRNs that were not investigated before, for instance, the stability of N-dimensional limit cycles, the reachability, etc. The results can be further developed in the future to study other large complex networks
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Book chapters on the topic "Repressilator"

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Lei, Jinzhi. "Repressilator and Oscillating Network." In Encyclopedia of Systems Biology, 1848–51. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_530.

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Sun, Honglu, Maxime Folschette, and Morgan Magnin. "Condition for Periodic Attractor in 4-Dimensional Repressilators." In Computational Methods in Systems Biology, 184–201. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42697-1_13.

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"Repressilator." In Encyclopedia of Genetics, Genomics, Proteomics and Informatics, 1677. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6754-9_14446.

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Murray, Richard M. "Biological Circuit Components." In Biomolecular Feedback Systems. Princeton University Press, 2014. http://dx.doi.org/10.23943/princeton/9780691161532.003.0005.

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This chapter describes some simple circuit components that have been constructed in E. coli cells using the technology of synthetic biology and then considers a more complicated circuit that already appears in natural systems to implement adaptation. It first analyzes the negatively autoregulated gene fabricated in E. coli bacteria, before turning to the toggle switch, which is composed of two genes that mutually repress each other. The chapter next illustrates a dynamical model of a “repressilator”—an oscillatory genetic circuit consisting of three repressors arranged in a ring fashion. The activator–repressor clock is then considered, alongside an incoherent feedforward loop (IFFL). Finally, the chapter examines bacterial chemotaxis, which E. coli use to move in the direction of increasing nutrients.
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Conference papers on the topic "Repressilator"

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Kablar, Natasa A., Vlada Kvrgic, and Dragomir Ilic. "Nonlinear mathematical model of repressilator. Approximation to singular and singularly impulsive dynamical system." In 2012 24th Chinese Control and Decision Conference (CCDC). IEEE, 2012. http://dx.doi.org/10.1109/ccdc.2012.6244016.

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Sun, Honglu, Jean-Paul Comet, Maxime Folschette, and Morgan Magnin. "Condition for Sustained Oscillations in Repressilator Based on a Hybrid Modeling of Gene Regulatory Networks." In 14th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011614300003414.

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Strelkowa, Natalja, and Mauricio Barahona. "Stochastic oscillatory dynamics of generalized repressilators." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756221.

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Wu, Yu, and Junhui Gao. "Numerical Imitation of the Synchronization of Gene Repressilators." In 2015 International Conference on Modeling, Simulation and Applied Mathematics. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/msam-15.2015.41.

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Liu, Zexing, Xian Zhang, and Xin Wang. "Global Exponential Stability of Delayed Coupled Repressilators in Artificial Oscillatory Networks." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029465.

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"SYNCHRONISATION OF BIOLOGICAL CLOCK SIGNALS - Capturing Coupled Repressilators from a Control Systems Perspective." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003121301010106.

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