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CLC number: O223

On-line Access: 2020-03-04

Received: 2019-08-17

Revision Accepted: 2019-10-19

Crosschecked: 2019-11-15

Cited: 0

Clicked: 2520

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jin-feng Pan

https://orcid.org/0000-0001-8567-3121

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.2 P.294-303

http://doi.org/10.1631/FITEE.1900411


Optimal one-bit perturbation in Boolean networks based on cascading aggregation


Author(s):  Jin-feng Pan, Min Meng

Affiliation(s):  School of Mathematics and Information Sciences, Weifang University, Weifang 261061, China; more

Corresponding email(s):   panjinfeng1989@163.com, mengminmath@gmail.com

Key Words:  Large-scale Boolean network, Attractor, Cascading aggregation, One-bit perturbation


Jin-feng Pan, Min Meng. Optimal one-bit perturbation in Boolean networks based on cascading aggregation[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(2): 294-303.

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author="Jin-feng Pan, Min Meng",
journal="Frontiers of Information Technology & Electronic Engineering",
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year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900411"
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T1 - Optimal one-bit perturbation in Boolean networks based on cascading aggregation
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Abstract: 
We investigate the problem of finding optimal one-bit perturbation that maximizes the size of the basin of attractions (BOAs) of desired attractors and minimizes the size of the BOAs of undesired attractors for large-scale Boolean networks by cascading aggregation. First, via the aggregation, a necessary and sufficient condition is given to ensure the invariance of desired attractors after one-bit perturbation. Second, an algorithm is proposed to identify whether the one-bit perturbation will cause the emergence of new attractors or not. Next, the change of the size of BOAs after one-bit perturbation is provided in an algorithm. Finally, the efficiency of the proposed method is verified by a T-cell receptor network.

基于级联聚合算法下的布尔网络最优单点摄动

潘金凤1,孟敏2
1潍坊学院数学与信息科学学院,中国潍坊市,261061
2南洋理工大学电气与电子工程学院,新加坡,639798

摘要:研究级联聚合算法分割下的大型布尔网络最优单点摄动问题;最大化期望吸引子吸引域,同时最小化非期望吸引子吸引域。首先,通过级联聚合算法给出一个在单点摄动下保持期望吸引子不变的充要条件。其次,提出一个判定是否出现新吸引子的算法。然后,提出另一算法给出单点摄动下吸引子吸引域的大小变化。最后,将本文理论应用于寻找T细胞受体网络的最优单点摄动问题。

关键词:大型布尔网络;吸引子;级联聚合算法;单点摄动

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Campbell C, Albert R, 2014. Stabilization of perturbed Boolean network attractors through compensatory interactions. BMC Syst Biol, 8:53.

[2]Cheng DZ, Qi HS, Li ZQ, 2011. Analysis and Control of Boolean Networks: a Semi-tensor Product Approach. Springer, London, UK.

[3]Ding XY, Li HT, Yang QQ, et al., 2017. Stochastic stability and stabilization of n-person random evolutionary Boolean games. Appl Math Comput, 306:1-12.

[4]Fan HB, Feng JE, Meng M, et al., 2020. General decomposition of fuzzy relations: semi-tensor product approach. Fuzzy Sets Syst, 384:75-90.

[5]Hu MX, Shen LZ, Zan XZ, et al., 2016. An efficient algorithm to identify the optimal one-bit perturbation based on the basin-of-state size of Boolean networks. Sci Rep, 6:26247.

[6]Kauffman SA, 1969. Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol, 22(3):437-467.

[7]Klamt S, Saez-Rodriguez J, Lindquist JA, et al., 2006. A methodology for the structural and functional analysis of signaling and regulatory networks. BMC Bioinform, 7:56.

[8]Li HT, Ding XY, 2019. A control Lyapunov function approach to feedback stabilization of logical control networks. SIAM J Contr Optim, 57(2):810-831.

[9]Li HT, Wang YZ, Liu ZB, 2012. Function perturbation impact on the topological structure of Boolean networks. Proc 10th World Congress on Intelligent Control and Automation, p.1241-1246.

[10]Li HT, Xu XJ, Ding XY, 2019. Finite-time stability analysis of stochastic switched Boolean networks with impulsive effect. Appl Math Comput, 347:557-565.

[11]Liu JY, Liu Y, Guo YQ, et al., 2019. Sampled-data state-feedback stabilization of probabilistic Boolean control networks: a control Lyapunov function approach. IEEE Trans Cybern, in press.

[12]Liu M, 2015. Analysis and Synthesis of Boolean Networks. Licentiate Thesis, KTH School of Information and Communication Technology, Sweden.

[13]Liu Y, Li BW, Lu JQ, et al., 2017. Pinning control for the disturbance decoupling problem of Boolean networks. IEEE Trans Autom Contr, 62(12):6595-6601.

[14]Liu YS, Zheng YT, Li HT, et al., 2018. Control design for output tracking of delayed Boolean control networks. J Comput Appl Math, 327:188-195.

[15]Lu JQ, Zhong J, Huang C, et al., 2016. On pinning controllability of Boolean control networks. IEEE Trans Autom Contr, 61(6):1658-1663.

[16]Lu JQ, Li HT, Liu Y, et al., 2017. Survey on semi-tensor product method with its applications in logical networks and other finite-valued systems. IET Contr Theory Appl, 11(13):2040-2047.

[17]Lu JQ, Li ML, Liu Y, et al., 2018a. Nonsingularity of Grain-like cascade FSRs via semi-tensor product. Sci China Inform Sci, 61:010204.

[18]Lu JQ, Sun LJ, Liu Y, et al., 2018b. Stabilization of Boolean control networks under aperiodic sampled-data control. SIAM J Contr Optim, 56(6):4385-4404.

[19]Lu JQ, Li ML, Huang TW, et al., 2018c. The transformation between the Galois NLFSRs and the Fibonacci NLFSRs via semi-tensor product of matrices. Automatica, 96:393-397.

[20]Ostrowski M, Paulevé L, Schaub T, et al., 2016. Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming. Biosystems, 149:139-153.

[21]Shmulevich I, Dougherty ER, Zhang W, 2002a. From Boolean to probabilistic Boolean networks as models of genetic regulatory networks. Proc IEEE, 90(11):1778-1792.

[22]Shmulevich I, Dougherty ER, Zhang W, 2002b. Control of stationary behavior in probabilistic Boolean networks by means of structural intervention. J Biol Syst, 10(4): 431-445.

[23]Shmulevich I, Dougherty ER, Zhang W, 2002c. Gene perturbation and intervention in probabilistic Boolean networks. Bioinformatics, 18(10):1319-1331.

[24]Wang B, Feng JE, 2019. On detectability of probabilistic Boolean networks. Inform Sci, 483:383-395.

[25]Wang B, Feng JE, Meng M, 2019. Model matching of switched asynchronous sequential machines via matrix approach. Int J Contr, 92(10):2430-2440.

[26]Xiao YF, Dougherty ER, 2007. The impact of function perturbations in Boolean networks. Bioinformatics, 23(10):1265-1273.

[27]Xu XJ, Li HT, Li YL, et al., 2018. Output tracking control of Boolean control networks with impulsive effects. Math Methods Appl Sci, 41(4):1554-1564.

[28]Yu YY, Feng JE, Pan JF, et al., 2019a. Block decoupling of Boolean control networks. IEEE Trans Autom Contr, 64(8):3129-3140.

[29]Yu YY, Wang B, Feng JE, 2019b. Input observability of Boolean control networks. Neurocomputing, 333:22-28.

[30]Zhang LQ, Feng JE, Feng XH, et al., 2014. Further results on disturbance decoupling of mix-valued logical networks. IEEE Trans Autom Contr, 59(6):1630-1634.

[31]Zhao Y, Kim J, Filippone M, 2013. Aggregation algorithm towards large-scale Boolean network analysis. IEEE Trans Autom Contr, 58(8):1976-1985.

[32]Zhao Y, Ghosh BK, Cheng DZ, 2016. Control of large-scale Boolean networks via network aggregation. IEEE Trans Neur Netw Learn Syst, 27(7):1527-1536.

[33]Zhong J, Li BW, Liu Y, et al., 2020. Output feedback stabilizer design of Boolean networks based on network structure. Front Inform Technol Electron Eng, 21(2):247-259.

[34]Zhu QX, Liu Y, Lu JQ, et al., 2018. On the optimal control of Boolean control networks. SIAM J Contr Optim, 56(2):1321-1341.

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