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Journal articles on the topic 'Repressilator'

Author: Grafiati

Published: 23 February 2024

Last updated: 31 July 2025

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1

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 (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 geneti

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2

Oliveira, Samuel M. D., Jerome G. Chandraseelan, Antti Häkkinen, et al. "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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3

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 (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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4

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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5

Dukarić, Maša, Hassan Errami, Roman Jerala, et al. "On three genetic repressilator topologies." Reaction Kinetics, Mechanisms and Catalysis 126, no. 1 (2018): 3–30. http://dx.doi.org/10.1007/s11144-018-1519-5.

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6

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 (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,

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7

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 (2006): 905–37. http://dx.doi.org/10.1007/s00285-006-0035-9.

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8

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

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9

Golubyatnikov, Vladimir Petrovich. "Questions of the existence of a stable cycle in one model of a molecular repressor." Mathematical structures and modeling, no. 2 (2017): 59–67. https://doi.org/10.24147/2222-8772.2017.2.59-67.

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A nonlinear six-dimensional dynamic system simulating the functioning of the simplest molecular repressilator is considered. Sufficient conditions for the existence of a stable cycle in its phase portrait are established.

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10

Knotz, Gabriel, Ulrich Parlitz, and Stefan Klumpp. "Synchronization of a genetic oscillator with the cell division cycle." New Journal of Physics 24, no. 3 (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 win

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11

Glyzin, S., A. Kolesov, and N. Rozov. "On a mathematical model of a repressilator." St. Petersburg Mathematical Journal 33, no. 5 (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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12

Verdugo, Anael. "Hopf Bifurcation Analysis of the Repressilator Model." American Journal of Computational Mathematics 08, no. 02 (2018): 137–52. http://dx.doi.org/10.4236/ajcm.2018.82011.

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13

Buzzi, Claudio A., and Jaume Llibre. "Hopf bifurcation in the full repressilator equations." Mathematical Methods in the Applied Sciences 38, no. 7 (2014): 1428–36. http://dx.doi.org/10.1002/mma.3158.

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14

Golubyatnikov, Vladimir P., Natalia B. Ayupova, Natalia E. Bondarenko, and Alina V. Glubokikh. "Hidden attractors and nonlocal oscillations in gene networks models." Russian Journal of Numerical Analysis and Mathematical Modelling 39, no. 2 (2024): 75–81. http://dx.doi.org/10.1515/rnam-2024-0007.

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Abstract We study periodic trajectories of nonlinear dynamical systems considered as models of the simplest molecular repressilator. In the phase portraits of these systems, we find hidden attractors and nonlocal oscillations. The cases of nonuniqueness of cycles in these portraits are described as well.

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15

Potapov, Ilya, Boris Zhurov, and Evgeny Volkov. "Multi-stable dynamics of the non-adiabatic repressilator." Journal of The Royal Society Interface 12, no. 104 (2015): 20141315. http://dx.doi.org/10.1098/rsif.2014.1315.

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The assumption of the fast binding of transcription factors (TFs) to promoters is a typical point in studies of synthetic genetic circuits functioning in bacteria. Although the assumption is effective for simplifying the models, it becomes questionable in the light of in vivo measurements of the times TF spends searching for its cognate DNA sites. We investigated the dynamics of the full idealized model of the paradigmatic genetic oscillator, the repressilator, using deterministic mathematical modelling and stochastic simulations. We found (using experimentally approved parameter values) that

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16

Volkov, E. I., and B. A. Zhurov. "Dynamic Behavior of an Isolated Repressilator with Feedback." Radiophysics and Quantum Electronics 56, no. 10 (2014): 697–707. http://dx.doi.org/10.1007/s11141-014-9474-0.

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17

Pett, J. Patrick, Matthew Kondoff, Grigory Bordyugov, Achim Kramer, and Hanspeter Herzel. "Co-existing feedback loops generate tissue-specific circadian rhythms." Life Science Alliance 1, no. 3 (2018): e201800078. http://dx.doi.org/10.26508/lsa.201800078.

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Gene regulatory feedback loops generate autonomous circadian rhythms in mammalian tissues. The well-studied core clock network contains many negative and positive regulations. Multiple feedback loops have been discussed as primary rhythm generators but the design principles of the core clock and differences between tissues are still under debate. Here we use global optimization techniques to fit mathematical models to circadian gene expression profiles for different mammalian tissues. It turns out that for every investigated tissue multiple model parameter sets reproduce the experimental data.

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18

Bratsun, D. А., E. S. Lorgov, and A. O. Poluyanov. "On the repressilator stability with time-delayed gene expression." ВЕСТНИК ПЕРМСКОГО УНИВЕРСИТЕТА. ФИЗИКА, no. 2 (2018): 75–87. http://dx.doi.org/10.17072/1994-3598-2018-2-75-87.

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19

Pett, J. Patrick, Anja Korenčič, Felix Wesener, Achim Kramer, and Hanspeter Herzel. "Feedback Loops of the Mammalian Circadian Clock Constitute Repressilator." PLOS Computational Biology 12, no. 12 (2016): e1005266. http://dx.doi.org/10.1371/journal.pcbi.1005266.

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20

Hellen, Edward H., Syamal K. Dana, Boris Zhurov, and Evgeny Volkov. "Electronic Implementation of a Repressilator with Quorum Sensing Feedback." PLoS ONE 8, no. 5 (2013): e62997. http://dx.doi.org/10.1371/journal.pone.0062997.

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21

Bratsun, Dmitry Anatolievich, and Maksim Dmitrievich Buzmakov. "Repressilator with time-delayed gene expression. Part II. Stochastic description." Computer Research and Modeling 13, no. 3 (2021): 587–609. http://dx.doi.org/10.20537/2076-7633-2021-13-3-587-609.

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22

Bratsun, Dmitry Anatolievich, Eugeny Sergeevich Lorgov, and Alexander Olegovich Poluyanov. "Repressilator with time-delayed gene expression. Part I. Deterministic description." Computer Research and Modeling 10, no. 2 (2018): 241–59. http://dx.doi.org/10.20537/2076-7633-2018-10-2-241-259.

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23

Shum, Henry, Victor V. Yashin, and Anna C. Balazs. "Self-assembly of microcapsules regulated via the repressilator signaling network." Soft Matter 11, no. 18 (2015): 3542–49. http://dx.doi.org/10.1039/c5sm00201j.

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24

Deo, Ishan, and Krishnacharya Khare. "A simple electronic circuit demonstrating Hopf bifurcation for an advanced undergraduate laboratory." American Journal of Physics 90, no. 12 (2022): 908–13. http://dx.doi.org/10.1119/5.0062969.

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A nonlinear electronic circuit comprising of three nodes with a feedback loop is analyzed. The system has two stable states, a uniform state and a sinusoidal oscillating state, and it transitions from one to another by means of a Hopf bifurcation. The stability of this system is analyzed with nonlinear equations derived from a repressilator-like transistor circuit. The apparatus is simple and inexpensive, and the experiment demonstrates aspects of nonlinear dynamical systems in an advanced undergraduate laboratory setting.

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25

Hara, Shinji, Tetsuya Iwasaki, and Yutaka Hori. "Instability margin analysis for parametrized LTI systems with application to repressilator." Automatica 136 (February 2022): 110047. http://dx.doi.org/10.1016/j.automatica.2021.110047.

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26

Strelkowa, Natalja, and Mauricio Barahona. "Transient dynamics around unstable periodic orbits in the generalized repressilator model." Chaos: An Interdisciplinary Journal of Nonlinear Science 21, no. 2 (2011): 023104. http://dx.doi.org/10.1063/1.3574387.

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27

Kim, Keun-Young, David Lepzelter, and Jin Wang. "Single molecule dynamics and statistical fluctuations of gene regulatory networks: A repressilator." Journal of Chemical Physics 126, no. 3 (2007): 034702. http://dx.doi.org/10.1063/1.2424933.

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28

Agrawal, Vidit, Shivpal Singh Kang, and Sudeshna Sinha. "Realization of morphing logic gates in a repressilator with quorum sensing feedback." Physics Letters A 378, no. 16-17 (2014): 1099–103. http://dx.doi.org/10.1016/j.physleta.2014.02.015.

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29

Chandraseelan, Jerome G., Samuel M. D. Oliveira, Antti Häkkinen, et al. "Effects of temperature on the dynamics of the LacI-TetR-CI repressilator." Molecular BioSystems 9, no. 12 (2013): 3117. http://dx.doi.org/10.1039/c3mb70203k.

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30

Glyzin, S. D., A. Yu Kolesov, and N. Kh Rozov. "Existence and stability of the relaxation cycle in a mathematical repressilator model." Mathematical Notes 101, no. 1-2 (2017): 71–86. http://dx.doi.org/10.1134/s0001434617010072.

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31

Pokhilko, Alexandra, Aurora Piñas Fernández, Kieron D. Edwards, Megan M. Southern, Karen J. Halliday, and Andrew J. Millar. "The clock gene circuit in Arabidopsis includes a repressilator with additional feedback loops." Molecular Systems Biology 8, no. 1 (2012): 574. http://dx.doi.org/10.1038/msb.2012.6.

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32

Dilão, Rui. "The regulation of gene expression in eukaryotes: Bistability and oscillations in repressilator models." Journal of Theoretical Biology 340 (January 2014): 199–208. http://dx.doi.org/10.1016/j.jtbi.2013.09.010.

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33

TOKUDA, ISAO T., ALEXANDRE WAGEMAKERS, and MIGUEL A. F. SANJUÁN. "PREDICTING THE SYNCHRONIZATION OF A NETWORK OF ELECTRONIC REPRESSILATORS." International Journal of Bifurcation and Chaos 20, no. 06 (2010): 1751–60. http://dx.doi.org/10.1142/s0218127410026800.

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Synchronization of coupled oscillators is by now a very well studied subject. A large number of analytical and computational tools are available for the treatment of experimental results. This article focuses on a method recently proposed to construct a phase model from experimental data. The advantage of this method is that it extracts a phase model in a noninvasive manner without any prior knowledge of the experimental system. The aim of the present research is to apply this methodology to a network of electronic genetic oscillators. These electronic circuits mimic the dynamics of a syntheti

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34

Ayupova, N. B., V. P. Golubyatnikov, and M. V. Kazantsev. "On the existence of a cycle in an asymmetric model of a molecular repressilator." Numerical Analysis and Applications 10, no. 2 (2017): 101–7. http://dx.doi.org/10.1134/s199542391702001x.

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35

Rim, D. N., P. Cremades, and Pablo Federico Kaluza. "A simple electronic device to experiment with the Hopf bifurcation." Revista Mexicana de Física E 65, no. 1 (2019): 58. http://dx.doi.org/10.31349/revmexfise.65.58.

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We present a simple low-cost electronic circuit that is able to show two different dynamical regimens with oscillations of voltages and with constant values of them. This device is designed as a negative feedback three-node network inspired in the genetic repressilator. The circuit's behavior is modeled by a system of differential equations which is studied in several different ways by applying the dynamical system formalism, making numerical simulations and constructing and measuring it experimentally. We find that the most important characteristics of the Hopf bifurcation can be found and co

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36

Ayupova, N. B., and V. P. Golubyatnikov. "On the uniqueness of a cycle in an asymmetric three-dimensional model of a molecular repressilator." Journal of Applied and Industrial Mathematics 8, no. 2 (2014): 153–57. http://dx.doi.org/10.1134/s199047891402001x.

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37

Perez-Carrasco, Ruben, Chris P. Barnes, Yolanda Schaerli, Mark Isalan, James Briscoe, and Karen M. Page. "Combining a Toggle Switch and a Repressilator within the AC-DC Circuit Generates Distinct Dynamical Behaviors." Cell Systems 6, no. 4 (2018): 521–30. http://dx.doi.org/10.1016/j.cels.2018.02.008.

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38

Glyzin, Sergey D., Andgey Yu Kolesov, and Nikolay Kh Rozov. "New Approach to Gene Network Modeling." Modeling and Analysis of Information Systems 26, no. 3 (2019): 365–404. http://dx.doi.org/10.18255/1818-1015-2019-3-365-404.

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The article is devoted to the mathematical modeling of artificial genetic networks. A phenomenological model of the simplest genetic network called repressilator is considered. This network contains three elements unidirectionally coupled into a ring. More specifically, the first of them inhibits the synthesis of the second, the second inhibits the synthesis of the third, and the third, which closes the cycle, inhibits the synthesis of the first one. The interaction of the protein concentrations and of mRNA (message RNA) concentration is surprisingly similar to the interaction of six ecologica

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39

Skornyakov, Vladimir, Maria Skornyakova, Antonina Shurygina, and Pavel Skornyakov. "Finite-state discrete-time Markov chain models of gene regulatory networks." F1000Research 3 (September 12, 2014): 220. http://dx.doi.org/10.12688/f1000research.4669.1.

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In this study, Markov chain models of gene regulatory networks (GRN) are developed. These models make it possible to apply the well-known theory and tools of Markov chains to GRN analysis. A new kind of finite interaction graph called a combinatorial net is introduced to represent formally a GRN and its transition graphs constructed from interaction graphs. The system dynamics are defined as a random walk on the transition graph, which is a Markov chain. A novel concurrent updating scheme (evolution rule) is developed to determine transitions in a transition graph. The proposed scheme is based

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40

Shum, Henry, and Anna C. Balazs. "Synthetic quorum sensing in model microcapsule colonies." Proceedings of the National Academy of Sciences 114, no. 32 (2017): 8475–80. http://dx.doi.org/10.1073/pnas.1702288114.

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Biological quorum sensing refers to the ability of cells to gauge their population density and collectively initiate a new behavior once a critical density is reached. Designing synthetic materials systems that exhibit quorum sensing-like behavior could enable the fabrication of devices with both self-recognition and self-regulating functionality. Herein, we develop models for a colony of synthetic microcapsules that communicate by producing and releasing signaling molecules. Production of the chemicals is regulated by a biomimetic negative feedback loop, the “repressilator” network. Through t

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41

YAMASHINO, Takafumi. "From a Repressilator-Based Circadian Clock Mechanism to an External Coincidence Model Responsible for Photoperiod and Temperature Control of Plant Architecture inArabodopsis thaliana." Bioscience, Biotechnology, and Biochemistry 77, no. 1 (2013): 10–16. http://dx.doi.org/10.1271/bbb.120765.

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42

Narayanan, G., M. Syed Ali, Rajagopal Karthikeyan, Grienggrai Rajchakit, and Anuwat Jirawattanapanit. "Impulsive control strategies of mRNA and protein dynamics on fractional-order genetic regulatory networks with actuator saturation and its oscillations in repressilator model." Biomedical Signal Processing and Control 82 (April 2023): 104576. http://dx.doi.org/10.1016/j.bspc.2023.104576.

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43

Kosey, Dipali, and Shailza Singh. "Computational design of molecular motors as nanocircuits in Leishmaniasis." F1000Research 6 (January 31, 2017): 94. http://dx.doi.org/10.12688/f1000research.10701.1.

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Cutaneous leishmaniasis is the most common form of lesihmaniasis, caused by Leishmania major and is spread by the bite of a sandfly.This species infects the macrophages and dendritic cells Due to multi-drug resistance, there is a need for a new therapeutic technique. Recently, a novel molecular motor of Leishmania, Myosin XXI, was classified and characterized. In addition, the drug resistance in this organism has been linked with the overexpression of ABC transporters. Systems biology aims to study the simulation and modeling of natural biological systems whereas synthetic biology deals with b

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44

Kosey, Dipali, and Shailza Singh. "Computational design of molecular motors as nanocircuits in Leishmaniasis." F1000Research 6 (August 3, 2017): 94. http://dx.doi.org/10.12688/f1000research.10701.2.

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Cutaneous leishmaniasis is the most common form of leishmaniasis, caused by Leishmania major and is spread by the bite of a sandfly.This species infects the macrophages and dendritic cells Due to multi-drug resistance, there is a need for a new therapeutic technique. Recently, a novel molecular motor of Leishmania, Myosin XXI, was classified and characterized. In addition, the drug resistance in this organism has been linked with the overexpression of ABC transporters. Systems biology aims to study the simulation and modeling of natural biological systems whereas synthetic biology deals with b

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45

Smith, Robert W., Sluijs Bob van, and Christian Fleck. "Designing synthetic networks in silico: a generalised evolutionary algorithm approach." BMC Systems Biology 11, no. 1 (2017): 118. https://doi.org/10.1186/s12918-017-0499-9.

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<strong>Background: </strong>Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised <i>in silico</i> evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy m

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46

Yoda, M., and M. Sasai. "2P308 Stochastics dynamics of coupled repressilators." Seibutsu Butsuri 45, supplement (2005): S196. http://dx.doi.org/10.2142/biophys.45.s196_4.

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47

Potapov, Iliya Sergeevich, and Evgeny Izrailevich Volkov. "Dynamics analysis of coupled synthetic genetic repressilators." Computer Research and Modeling 2, no. 4 (2010): 403–18. http://dx.doi.org/10.20537/2076-7633-2010-2-4-403-418.

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48

Golubyatnikov, V. P. "ON NON-UNIQUENESS OF CYCLES IN 3D MODELS OF CIRCULAR GENE NETWORKS." Челябинский физико-математический журнал 9, no. 1 (2024): 23–34. http://dx.doi.org/10.47475/2500-0101-2024-9-1-23-34.

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We describe 3-dimensional dynamical systems with piecewise linear right hand sides which simulate functioning of simplest molecualr repressilators and contain infinite oneparametric families of cycles in their phase portraits. An analogous dynamical system with step functions in its right hand sides is constructed; its phase portrait contains two piecewise linear cycles. A surface separating these two cycles is described.

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49

Stankevich, N., and E. Volkov. "Evolution of quasiperiodicity in quorum-sensing coupled identical repressilators." Chaos: An Interdisciplinary Journal of Nonlinear Science 30, no. 4 (2020): 043122. http://dx.doi.org/10.1063/1.5140696.

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50

Garcia-Ojalvo, J., M. B. Elowitz, and S. H. Strogatz. "Modeling a synthetic multicellular clock: Repressilators coupled by quorum sensing." Proceedings of the National Academy of Sciences 101, no. 30 (2004): 10955–60. http://dx.doi.org/10.1073/pnas.0307095101.

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