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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

[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 10_th 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.