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Articoli di riviste sul tema "Genetic regulatory networks"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 28 gennaio 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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Abstract (sommario):

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

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3

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

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4

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

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5

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

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6

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

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7

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

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Abstract (sommario):

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

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8

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

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

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9

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

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

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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 dyn

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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 observ

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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 inp

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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 det

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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 th

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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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Abstract (sommario):

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 repr

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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

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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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Abstract (sommario):

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 network

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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 phen

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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,

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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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44

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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45

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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46

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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47

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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48

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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49

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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50

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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