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Journal articles on the topic 'Genetic regulatory networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 January 2023

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1

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

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2

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

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Random Boolean networks (RBM) were introduced about 35 years ago as first crude models of genetic regulatory networks. RBNs are comprised of N on–off genes, connected by a randomly assigned regulatory wiring diagram where each gene has K inputs, and each gene is controlled by a randomly assigned Boolean function. This procedure samples at random from the ensemble of all possible NK Boolean networks. The central ideas are to study the typical, or generic properties of this ensemble, and see 1) whether characteristic differences appear as K and biases in Boolean functions are introducted, and 2) 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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3

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

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4

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

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5

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

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6

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

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7

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

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

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8

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

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Understanding how each person's unique genotype influences their individual patterns of gene regulation has the potential to improve our understanding of human health and development, and to refine genotype-specific disease risk assessments and treatments. However, the effects of genetic variants are not typically considered when constructing gene regulatory networks, despite the fact that many disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding. We developed EGRET (Estimating the Genetic Regulatory Effect on 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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9

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

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In an organism, genes encode proteins, some of which in turn regulate other genes. Such interactions work in highly structured but incredibly complex ways, and make up a genetic regulatory network. Recently, nonlinear delay differential equations have been proposed for describing genetic regulatory networks in the state-space form. In this paper, we study stability properties of genetic regulatory networks with time delays, by the notion of delay-independent stability. We first present necessary and sufficient conditions for delay-independent local stability of genetic regulatory networks with 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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10

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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11

Bennett, Matthew R., Dmitri Volfson, Lev Tsimring, and Jeff Hasty. "Transient Dynamics of Genetic Regulatory Networks." Biophysical Journal 92, no. 10 (May 2007): 3501–12. http://dx.doi.org/10.1529/biophysj.106.095638.

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12

MATEUS, DANIEL, JEAN-PIERRE GALLOIS, JEAN-PAUL COMET, and PASCALE LE GALL. "SYMBOLIC MODELING OF GENETIC REGULATORY NETWORKS." Journal of Bioinformatics and Computational Biology 05, no. 02b (April 2007): 627–40. http://dx.doi.org/10.1142/s0219720007002850.

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Understanding the functioning of genetic regulatory networks supposes a modeling of biological processes in order to simulate behaviors and to reason on the model. Unfortunately, the modeling task is confronted to incomplete knowledge about the system. To deal with this problem we propose a methodology that uses the qualitative approach developed by Thomas. A symbolic transition system can represent the set of all possible models in a concise and symbolic way. We introduce a new method based on model-checking techniques and symbolic execution to extract constraints on parameters leading to dynamics coherent with known behaviors. Our method allows us to efficiently respond to two kinds of questions: is there any model coherent with a certain hypothetic behavior? Are there behaviors common to all selected models? The first question is illustrated with the example of the mucus production in Pseudomonas aeruginosa while the second one is illustrated with the example of immunity control in bacteriophage lambda.

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13

Luo, Qi, Lili Shi, and Yutian Zhang. "Stochastic stabilization of genetic regulatory networks." Neurocomputing 266 (November 2017): 123–27. http://dx.doi.org/10.1016/j.neucom.2017.05.027.

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14

Tian, Li-Ping, Zhi-Jun Wang, Amin Mohammadbagheri, and Fang-Xiang Wu. "State Observer Design for Delayed Genetic Regulatory Networks." Computational and Mathematical Methods in Medicine 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/761562.

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Genetic regulatory networks are dynamic systems which describe the interactions among gene products (mRNAs and proteins). The internal states of a genetic regulatory network consist of the concentrations of mRNA and proteins involved in it, which are very helpful in understanding its dynamic behaviors. However, because of some limitations such as experiment techniques, not all internal states of genetic regulatory network can be effectively measured. Therefore it becomes an important issue to estimate the unmeasured states via the available measurements. In this study, we design a state observer to estimate the states of genetic regulatory networks with time delays from available measurements. Furthermore, based on linear matrix inequality (LMI) approach, a criterion is established to guarantee that the dynamic of estimation error is globally asymptotically stable. A gene repressillatory network is employed to illustrate the effectiveness of our design approach.

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15

HICKMAN, GRAHAM J., and T. CHARLIE HODGMAN. "INFERENCE OF GENE REGULATORY NETWORKS USING BOOLEAN-NETWORK INFERENCE METHODS." Journal of Bioinformatics and Computational Biology 07, no. 06 (December 2009): 1013–29. http://dx.doi.org/10.1142/s0219720009004448.

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The modeling of genetic networks especially from microarray and related data has become an important aspect of the biosciences. This review takes a fresh look at a specific family of models used for constructing genetic networks, the so-called Boolean networks. The review outlines the various different types of Boolean network developed to date, from the original Random Boolean Network to the current Probabilistic Boolean Network. In addition, some of the different inference methods available to infer these genetic networks are also examined. Where possible, particular attention is paid to input requirements as well as the efficiency, advantages and drawbacks of each method. Though the Boolean network model is one of many models available for network inference today, it is well established and remains a topic of considerable interest in the field of genetic network inference. Hybrids of Boolean networks with other approaches may well be the way forward in inferring the most informative networks.

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16

Fear, Justin M., Luis G. León-Novelo, Alison M. Morse, Alison R. Gerken, Kjong Van Lehmann, John Tower, Sergey V. Nuzhdin, and Lauren M. McIntyre. "Buffering of Genetic Regulatory Networks inDrosophila melanogaster." Genetics 203, no. 3 (May 18, 2016): 1177–90. http://dx.doi.org/10.1534/genetics.116.188797.

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17

Banks, Richard, and L. Jason Steggles. "A High-Level Petri Net Framework for Genetic Regulatory Networks." Journal of Integrative Bioinformatics 4, no. 3 (December 1, 2007): 1–14. http://dx.doi.org/10.1515/jib-2007-60.

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Summary To understand the function of genetic regulatory networks in the development of cellular systems, we must not only realise the individual network entities, but also the manner by which they interact. Multi-valued networks are a promising qualitative approach for modelling such genetic regulatory networks, however, at present they have limited formal analysis techniques and tools. We present a flexible formal framework for modelling and analysing multi-valued genetic regulatory networks using high-level Petri nets and logic minimization techniques. We demonstrate our approach with a detailed case study in which part of the genetic regulatory network responsible for the carbon starvation stress response in Escherichia coli is modelled and analysed. We then compare and contrast this multivalued model to a corresponding Boolean model and consider their formal relationship.

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18

Thai, M. T., Z. Cai, and D. Z. Du. "Genetic networks: processing data, regulatory network modelling and their analysis." Optimization Methods and Software 22, no. 1 (February 2007): 169–85. http://dx.doi.org/10.1080/10556780600881860.

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19

Ye Wei-Ming, L Bin-Bin, Zhao Chen, and Di Zeng-Ru. "Control of few node genetic regulatory networks." Acta Physica Sinica 62, no. 1 (2013): 010507. http://dx.doi.org/10.7498/aps.62.010507.

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20

IVANOV, IVAN, and EDWARD R. DOUGHERTY. "MODELING GENETIC REGULATORY NETWORKS: CONTINUOUS OR DISCRETE?" Journal of Biological Systems 14, no. 02 (June 2006): 219–29. http://dx.doi.org/10.1142/s0218339006001763.

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Selecting an appropriate mathematical model to describe the dynamical behavior of a genetic regulatory network plays an important part in discovering gene regulatory mechanisms. Whereas fine-scale models can in principle provide a very accurate description of the real genetic regulatory system, one must be aware of the availability and quality of the data used to infer such models. Consequently, pragmatic considerations motivate the selection of a model possessing minimal complexity among those capable of capturing the level of real gene regulation being studied, particularly in relation to the prediction capability of the model. This paper compares fine-scale stochastic-differential-equation models with coarse-scale discrete models in the context of currently available data and with respect to their description of switch-like behavior among specific groups of genes.

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21

Andrecut, M., and S. A. Kauffman. "Mean-field model of genetic regulatory networks." New Journal of Physics 8, no. 8 (August 25, 2006): 148. http://dx.doi.org/10.1088/1367-2630/8/8/148.

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22

Hartemink, A. J., D. K. Gifford, T. S. Jaakkola, and R. A. Young. "Bayesian methods for elucidating genetic regulatory networks." IEEE Intelligent Systems 17, no. 2 (2002): 37–43. http://dx.doi.org/10.1109/5254.999218.

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23

Faryabi, Babak, Jean-FranÇois Chamberland, Golnaz Vahedi, Aniruddha Datta, and Edward R. Dougherty. "Optimal Intervention in Asynchronous Genetic Regulatory Networks." IEEE Journal of Selected Topics in Signal Processing 2, no. 3 (June 2008): 412–23. http://dx.doi.org/10.1109/jstsp.2008.923853.

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24

Lehmann, Malte, and Kim Sneppen. "Genetic Regulatory Networks that count to 3." Journal of Theoretical Biology 329 (July 2013): 15–19. http://dx.doi.org/10.1016/j.jtbi.2013.03.023.

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25

Longabaugh, William J. R., Eric H. Davidson, and Hamid Bolouri. "Computational representation of developmental genetic regulatory networks." Developmental Biology 283, no. 1 (July 2005): 1–16. http://dx.doi.org/10.1016/j.ydbio.2005.04.023.

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26

Xiong, Hao, and Yoonsuck Choe. "Structural systems identification of genetic regulatory networks." Bioinformatics 24, no. 4 (January 5, 2008): 553–60. http://dx.doi.org/10.1093/bioinformatics/btm623.

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27

Hartemink, A. J., D. K. Gifford, T. S. Jaakkola, and R. A. Young. "Bayesian methods for elucidating genetic regulatory networks." IEEE Intelligent Systems 17, no. 2 (March 2002): 37–43. http://dx.doi.org/10.1109/mis.2002.999218.

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28

Quayle, A. P., and S. Bullock. "Modelling the evolution of genetic regulatory networks." Journal of Theoretical Biology 238, no. 4 (February 2006): 737–53. http://dx.doi.org/10.1016/j.jtbi.2005.06.020.

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29

Aldana, Maximino, Enrique Balleza, Stuart Kauffman, and Osbaldo Resendiz. "Robustness and evolvability in genetic regulatory networks." Journal of Theoretical Biology 245, no. 3 (April 2007): 433–48. http://dx.doi.org/10.1016/j.jtbi.2006.10.027.

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30

Fang-Xiang Wu. "Delay-Independent Stability of Genetic Regulatory Networks." IEEE Transactions on Neural Networks 22, no. 11 (November 2011): 1685–93. http://dx.doi.org/10.1109/tnn.2011.2165556.

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31

Hashimoto, R. F., S. Kim, I. Shmulevich, W. Zhang, M. L. Bittner, and E. R. Dougherty. "Growing genetic regulatory networks from seed genes." Bioinformatics 20, no. 8 (February 10, 2004): 1241–47. http://dx.doi.org/10.1093/bioinformatics/bth074.

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32

AGUILAR-HIDALGO, DANIEL, ANTONIO CÓRDOBA ZURITA, and Ma CARMEN LEMOS FERNÁNDEZ. "COMPLEX NETWORKS EVOLUTIONARY DYNAMICS USING GENETIC ALGORITHMS." International Journal of Bifurcation and Chaos 22, no. 07 (July 2012): 1250156. http://dx.doi.org/10.1142/s0218127412501568.

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Gene regulatory networks set a second order approximation to genetics understanding, where the first order is the knowledge at the single gene activity level. With the increasing number of sequenced genomes, including humans, the time has come to investigate the interactions among myriads of genes that result in complex behaviors. These characteristics are included in the novel discipline of Systems Biology. The composition and unfolding of interactions among genes determine the activity of cells and, when is considered during development, the organogenesis. Hence the interest of building representative networks of gene expression and their time evolution, i.e. the structure as the network dynamics, for certain development processes. The complexity of this kind of problems makes imperative to analyze the problem in the field of network theory and the evolutionary dynamics of complex systems.All this has led us to investigate, in a first step, the evolutionary dynamics in generic networks. Thus, the results can be used in experimental researches in the field of Systems Biology. This research aims to decode the transformation rules governing the evolutionary dynamics in a network. To do this, a genetic algorithm has been implemented in which, starting from initial and ending network states, it is possible to determine the transformation dynamics between these states by using simple acting rules. The network description is the following: (a) The network node values in the initial and ending states can be active or inactive; (b) The network links can act as activators or repressors; (c) A set of rules is established in order to transform the initial state into the ending one; (d) Due to the low connectivity, frequently observed, in gene regulatory networks, each node will hold a maximum of three inputs with no restriction on outputs. The "chromosomes" of the genetic algorithm include two parts, one related to the node links and another related to the transformation rules.The implemented rules are based on certain genetic interactions behavior. The rules and their combinations are compound by logic conditions and set the bases to the network motifs formation, which are the building blocks of the network dynamics.The implemented algorithm is able to find appropriate dynamics in complex networks evolution among different states for several cases.

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33

Kim, Dong-Chul, Jiao Wang, Chunyu Liu, and Jean Gao. "Inference of SNP-Gene Regulatory Networks by Integrating Gene Expressions and Genetic Perturbations." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/629697.

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In order to elucidate the overall relationships between gene expressions and genetic perturbations, we propose a network inference method to infer gene regulatory network where single nucleotide polymorphism (SNP) is involved as a regulator of genes. In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings. In this paper, we propose a SGRN inference method without predefined eQTL information assuming a gene is regulated by a single SNP at most. To evaluate the performance, the proposed method was applied to random data generated from synthetic networks and parameters. There are three main contributions. First, the proposed method provides both the gene regulatory inference and the eQTL identification. Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances. Lastly, the proposed method was also applied to psychiatric disorder data in order to explore how the method works with real data.

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34

Li, Li, Yongqing Yang, and Chuanzhi Bai. "Effect of Leakage Delay on Stability of Neutral-Type Genetic Regulatory Networks." Abstract and Applied Analysis 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/826020.

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The stability of neutral-type genetic regulatory networks with leakage delays is considered. Firstly, we describe the model of genetic regulatory network with neutral delays and leakage delays. Then some sufficient conditions are derived to ensure the asymptotic stability of the genetic regulatory network by the Lyapunov functional method. Further, the effect of leakage delay on stability is discussed. Finally, a numerical example is given to show the effectiveness of the results.

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35

Rachdi, Mustapha, Jules Waku, Hana Hazgui, and Jacques Demongeot. "Entropy as a Robustness Marker in Genetic Regulatory Networks." Entropy 22, no. 3 (February 25, 2020): 260. http://dx.doi.org/10.3390/e22030260.

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Genetic regulatory networks have evolved by complexifying their control systems with numerous effectors (inhibitors and activators). That is, for example, the case for the double inhibition by microRNAs and circular RNAs, which introduce a ubiquitous double brake control reducing in general the number of attractors of the complex genetic networks (e.g., by destroying positive regulation circuits), in which complexity indices are the number of nodes, their connectivity, the number of strong connected components and the size of their interaction graph. The stability and robustness of the networks correspond to their ability to respectively recover from dynamical and structural disturbances the same asymptotic trajectories, and hence the same number and nature of their attractors. The complexity of the dynamics is quantified here using the notion of attractor entropy: it describes the way the invariant measure of the dynamics is spread over the state space. The stability (robustness) is characterized by the rate at which the system returns to its equilibrium trajectories (invariant measure) after a dynamical (structural) perturbation. The mathematical relationships between the indices of complexity, stability and robustness are presented in case of Markov chains related to threshold Boolean random regulatory networks updated with a Hopfield-like rule. The entropy of the invariant measure of a network as well as the Kolmogorov-Sinaï entropy of the Markov transition matrix ruling its random dynamics can be considered complexity, stability and robustness indices; and it is possible to exploit the links between these notions to characterize the resilience of a biological system with respect to endogenous or exogenous perturbations. The example of the genetic network controlling the kinin-kallikrein system involved in a pathology called angioedema shows the practical interest of the present approach of the complexity and robustness in two cases, its physiological normal and pathological, abnormal, dynamical behaviors.

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36

Omholt, Stig W., Erik Plahte, Leiv Øyehaug, and Kefang Xiang. "Gene Regulatory Networks Generating the Phenomena of Additivity, Dominance and Epistasis." Genetics 155, no. 2 (June 1, 2000): 969–80. http://dx.doi.org/10.1093/genetics/155.2.969.

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Abstract We show how the phenomena of genetic dominance, overdominance, additivity, and epistasis are generic features of simple diploid gene regulatory networks. These regulatory network models are together sufficiently complex to catch most of the suggested molecular mechanisms responsible for generating dominant mutations. These include reduced gene dosage, expression or protein activity (haploinsufficiency), increased gene dosage, ectopic or temporarily altered mRNA expression, increased or constitutive protein activity, and dominant negative effects. As classical genetics regards the phenomenon of dominance to be generated by intralocus interactions, we have studied two one-locus models, one with a negative autoregulatory feedback loop, and one with a positive autoregulatory feedback loop. To include the phenomena of epistasis and downstream regulatory effects, a model of a three-locus signal transduction network is also analyzed. It is found that genetic dominance as well as overdominance may be an intra- as well as interlocus interaction phenomenon. In the latter case the dominance phenomenon is intimately connected to either feedback-mediated epistasis or downstream-mediated epistasis. It appears that in the intra- as well as the interlocus case there is considerable room for additive gene action, which may explain to some degree the predictive power of quantitative genetic theory, with its emphasis on this type of gene action. Furthermore, the results illuminate and reconcile the prevailing explanations of heterosis, and they support the old conjecture that the phenomenon of dominance may have an evolutionary explanation related to life history strategy.

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37

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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38

Zhang, Shu Qin, Wai Ki Ching, Michael K. Ng, and Tatsuya Akutsu. "Simulation study in Probabilistic Boolean Network models for genetic regulatory networks." International Journal of Data Mining and Bioinformatics 1, no. 3 (2007): 217. http://dx.doi.org/10.1504/ijdmb.2007.011610.

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39

Demongeot, Jacques, and Jules Waku. "Robustness in biological regulatory networks II: Application to genetic threshold Boolean random regulatory networks (getBren)." Comptes Rendus Mathematique 350, no. 3-4 (February 2012): 225–28. http://dx.doi.org/10.1016/j.crma.2012.01.019.

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40

WANG, Pei, and Jin-Hu LV. "Control of Genetic Regulatory Networks: Opportunities and Challenges." Acta Automatica Sinica 39, no. 12 (March 28, 2014): 1969–79. http://dx.doi.org/10.3724/sp.j.1004.2013.01969.

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41

Faryabi, B., E. R. Dougherty, and A. Datta. "On approximate stochastic control in genetic regulatory networks." IET Systems Biology 1, no. 6 (November 1, 2007): 361–68. http://dx.doi.org/10.1049/iet-syb:20070015.

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42

Chen, L., and K. Aihara. "Stability of genetic regulatory networks with time delay." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 49, no. 5 (May 2002): 602–8. http://dx.doi.org/10.1109/tcsi.2002.1001949.

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43

Tkačik, Gašper, and Aleksandra M. Walczak. "Information transmission in genetic regulatory networks: a review." Journal of Physics: Condensed Matter 23, no. 15 (April 1, 2011): 153102. http://dx.doi.org/10.1088/0953-8984/23/15/153102.

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Wahde, Mattias, and John Hertz. "Coarse-grained reverse engineering of genetic regulatory networks." Biosystems 55, no. 1-3 (February 2000): 129–36. http://dx.doi.org/10.1016/s0303-2647(99)00090-8.

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Qiu, Zhipeng. "The asymptotical behavior of cyclic genetic regulatory networks." Nonlinear Analysis: Real World Applications 11, no. 2 (April 2010): 1067–86. http://dx.doi.org/10.1016/j.nonrwa.2009.01.051.

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Kauffman, Stuart. "The ensemble approach to understand genetic regulatory networks." Physica A: Statistical Mechanics and its Applications 340, no. 4 (September 2004): 733–40. http://dx.doi.org/10.1016/j.physa.2004.05.018.

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Conlon, Erin M., Gulhan Alpargu, and Jeffrey L. Blanchard. "Comparative Genomics Approaches to Identifying Genetic Regulatory Networks." CHANCE 19, no. 3 (June 2006): 45–48. http://dx.doi.org/10.1080/09332480.2006.10722801.

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Sun, Yonghui, Gang Feng, and Jinde Cao. "Stochastic stability of Markovian switching genetic regulatory networks." Physics Letters A 373, no. 18-19 (April 2009): 1646–52. http://dx.doi.org/10.1016/j.physleta.2009.03.017.

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Plahte, Erik, Arne B. Gjuvsland, and Stig W. Omholt. "Propagation of genetic variation in gene regulatory networks." Physica D: Nonlinear Phenomena 256-257 (August 2013): 7–20. http://dx.doi.org/10.1016/j.physd.2013.04.002.

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Jiang, Nan, Xiaoyang Liu, Wenwu Yu, and Jun Shen. "Finite-time stochastic synchronization of genetic regulatory networks." Neurocomputing 167 (November 2015): 314–21. http://dx.doi.org/10.1016/j.neucom.2015.04.064.

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