A Robust Control Centrality Applicable to Genetic Regulatory Networks with Structured Uncertainties

Ghassemi Zahan, Z.; Ozgoli, S.; Bolouki, S.

doi:10.22061/jecei.2024.10506.714

نشریات مستقل دانشگاه در سامانه ارزیابی نشریات علمی وزارت علوم

نشریه معماری وشهرسازی پایدار موفق به اخذ رتبه علمی-پژوهشی شد

تعداد نشریات	13
تعداد شماره‌ها	206
تعداد مقالات	2,049
تعداد مشاهده مقاله	2,721,267
تعداد دریافت فایل اصل مقاله	1,972,911

	A Robust Control Centrality Applicable to Genetic Regulatory Networks with Structured Uncertainties
Journal of Electrical and Computer Engineering Innovations (JECEI)
دوره 12، شماره 2، مهر 2024، صفحه 511-524 اصل مقاله (931.54 K)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2024.10506.714
نویسندگان
Z. Ghassemi Zahan¹؛ S. Ozgoli^* ¹؛ S. Bolouki²
¹Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
²Department of Mechanical Engineering, Polytechnique Montreal, Quebec, Canada.
تاریخ دریافت: 13 اسفند 1402، تاریخ بازنگری: 06 تیر 1403، تاریخ پذیرش: 09 مرداد 1403
چکیده
Background and Objectives: In genetic network control, RC-Centrality is introduced as a new control centrality measure to address the control of linear time-invariant networks. The objective of this study is to propose an optimal control centrality metric that quantifies the centrality of individual nodes or groups of nodes within a network. Specifically, RC-Centrality identifies key nodes or node groups that can act as controllers, such as genes regulating the gene expression process. To assess the effectiveness of this method, RC-Centrality is compared with standard centralities in a real genetic network. Additionally, the research delves into the role of uncertainty structure in altering the priority order of RC-Centrality. Methods: The RC-Centrality measure is introduced based on an optimal control problem to address weighted, directed, and signed networks. Robust controllers are designed to ensure Lyapunov stability under uncertainty. A cost function is introduced to measure the performance metric represented by input energy in the presence of uncertainty. Results: The study presents RC-Centrality as an effective measure for identifying key nodes in genetic networks suitable for control. In-silico simulations are conducted to evaluate its performance in comparison to standard centralities. The research highlights the impact of uncertainty structure on the priority of RC-Centrality. Conclusion: RC-Centrality offers a promising approach to identify essential nodes in genetic networks for control purposes. Its performance is demonstrated through simulations, and the study emphasizes the influence of uncertainty structure on the centrality measure's prioritization. This research has implications for understanding and controlling genetic networks, particularly in the presence of uncertainty.
کلیدواژه‌ها
Network analysis and control؛ Network centrality؛ Network controllability, Uncertain systems

مراجع
[1] P. H. Guzzi, S. Roy, Biological network analysis: trends, approaches, graphical theory and algorithms. Amsterdam: Elsevier, 2020. [2] X. Chen, M. Xu, Y. An, “Identifying the essential nodes in network pharmacology based on multilayer network combined with random walk algorithm,” J. Biomed. Inform., 114: 103666, 2021. [3] J. Lü, P. Wang, “Identifying important nodes in bio-molecular networks,” in Modeling and Analysis of Bio-molecular Networks, J. Lü and P. Wang, Eds., Singapore: Springer Singapore: 315-396, 2020. [4] M. Ashtiani et al., “A systematic survey of centrality measures for protein-protein interaction networks,” BMC Syst. Biol., 12(1), 2018. [5] J. Wang, R. Peng, Z. Zhang, Y. Zhang, Y. Dai, Y. Sun, “Identification and validation of key genes in hepatocellular carcinoma by bioinformatics analysis,” BioMed Res. Int., 6662114, 2021. [6] H. B. Chakrapani, S. Chourasia, S. Gupta, T. Kumar D, G. P. Doss C, R. Haldar, “Effective utilisation of influence maximization technique for the identification of significant nodes in breast cancer gene networks,” Comput. Biol. Med., 133, 2021. [7] M. Newman, Networks: An Introduction. OUP Oxford, 2010. [8] V. Latora, V. Nicosia, G. Russo, Complex networks: principles, methods and applications. Cambridge, United Kingdom; New York, NY: Cambridge University Press, 2017. [9] S. Gómez, “Centrality in networks: Finding the most important nodes,” in Business and Consumer Analytics: New Ideas, P. Moscato and N. J. de Vries, Eds., Cham: Springer International Publishing, 401-433, 2019. [10] M. G. Everett, S. P. Borgatti, “The centrality of groups and classes,” J. Math. Sociol., 23(3): 181-201, 1999. [11] J. Tang et al., “Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization,” Phys. Stat. Mech. Its Appl., 513: 477-496, 2019. [12] G. Baggio, F. Pasqualetti, S. Zampieri, “Energy-Aware controllability of complex networks,” Annu. Rev. Control Robot. Auton. Syst., 5(1): 465-489, 2022. [13] G. Lindmark, C. Altafini, “Centrality measures and the role of non-normality for network control energy reduction,” IEEE Control Syst. Lett., 5(3): 1013-1018, 2021. [14] G. Lindmark, C. Altafini, “Combining centrality measures for control energy reduction in network controllability problems,” in Proc. 2019 18th European Control Conference (ECC): 1518-1523, 2019. [15] N. Bof, G. Baggio, S. Zampieri, “On the role of network centrality in the controllability of complex networks,” IEEE Trans. Control Netw. Syst., 4(3): 643-653, 2017. [16] G. Lindmark, C. Altafini, “Minimum energy control for complex networks,” Sci. Rep., 8(1), 2018. [17] L. Geris, D. Gomez-Cabrero, Uncertainty in Biology: A Computational Modeling Approach. in Studies in Mechanobiology, Tissue Engineering and Biomaterials. Springer International Publishing, 2015. [18] F. Lin, Robust control design: an optimal control approach, John Wiley & Sons, 2007. [19] Y. Lin, C. Lin, “Optimal control approach for robust control design of neutral systems,” Optim. Control Appl. Methods, 30(1): 87-102, 2009. [20] Y. Y. Liu, J. J. Slotine, A. L. Barabási, “Control centrality and hierarchical structure in complex networks,” PLoS ONE, 7(9): e44459, 2012. [21] U. Usman, A. Mahmood, L. Wang, “Robust control centrality,” in Proc. 2019 Chinese Control Conference (CCC): 5486-5491, 2019. [22] M. Siami, S. Bolouki, B. Bamieh, N. Motee, “Centrality measures in linear consensus networks with structured network uncertainties,” IEEE Trans. Control Netw. Syst., 5(3): 924-934, 2018. [23] Y. Ghaedsharaf, M. Siami, C. Somarakis, N. Motee, “Centrality in time-delay consensus networks with structured uncertainties,” Automatica, 125: 109378, 2021. [24] M. Amodio et al., “Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference,” Patterns, 2(7): 100288, 2021. [25] G. Batt, B. Yordanov, R. Weiss, C. Belta, “Robustness analysis and tuning of synthetic gene networks,” Bioinformatics, 23(18): 2415-2422, 2007. [26] P. V. Chanekar, E. Nozari, J. Cortes, “Energy-Transfer edge centrality and its role in enhancing network controllability,” IEEE Trans. Netw. Sci. Eng., 8(1): 331-346, 2021. [27] L. Zhai, X. Yan, G. Zhang, “Bi-directional h-index: A new measure of node centrality in weighted and directed networks,” J. Informetr., 12(1): 299-314, 2018. [28] H. Ando, M. Bell, F. Kurauchi, K. I. Wong, K. F. Cheung, “Connectivity evaluation of large road network by capacity-weighted eigenvector centrality analysis,” Transp. Transp. Sci., 17(4): 648-674, 2021. [29] E. C. Baek, R. Hyon, K. López, E. S. Finn, M. A. Porter, C. Parkinson, “In-degree centrality in a social network is linked to coordinated neural activity,” Nat. Commun., 13(1), 2022. [30] X. Qi, E. Fuller, Q. Wu, Y. Wu, C. Q. Zhang, “Laplacian centrality: A new centrality measure for weighted networks,” Inf. Sci., 194: 240-253, 2012. [31] J. Laeuchli, Y. Ramírez-Cruz, R. Trujillo-Rasua, “Analysis of centrality measures under differential privacy models,” Appl. Math. Comput., 412: 126546, 2022. [32] Y. Li, Y. Sheng, X. Ye, “Group centrality algorithms based on the h-index for identifying influential nodes in large-scale networks,” Int. J. Innovative Comput. Inf. Control, 16(4): 1183-1201, 2020. [33] M. R. F. Mendonca, A. M. S. Barreto, A. Ziviani, “Approximating network centrality measures using node embedding and machine learning,” IEEE Trans. Netw. Sci. Eng., 8,(1): 220-230, 2021. [34] X. Li, Y. Liu, Y. Jiang, X. Liu, “Identifying social influence in complex networks: A novel conductance eigenvector centrality model,” Neurocomput., 210: 141-154, 2016. [35] Y. Yang, X. Wang, Y. Chen, M. Hu, C. Ruan, “A novel centrality of influential nodes identification in complex networks,” IEEE Access, 8: 58742-58751, 2020. [36] J. J. Molitierno, Applications of combinatorial matrix theory to Laplacian matrices of graphs. CRC Press, 2016. [37] F. L. Lewis, D. L. Vrabie, V. L. Syrmos, Optimal control, 3rd ed. Hoboken: Wiley, 2012. [38] V. V. Prasolov, S. Ivanov, Problems and theorems in linear algebra. in Translations of mathematical monographs, no. v. 134. Providence, R.I: American Mathematical Society, 1994. [39] C. Tu, R. P. Rocha, M. Corbetta, S. Zampieri, M. Zorzi, S. Suweis, “Warnings and caveats in brain controllability,” NeuroImage, 176: 83-91, 2018. [40] M. Imani, U. Braga-Neto, “Optimal control of gene regulatory networks with unknown cost function,” in Proc. 2018 Annual American Control Conference (ACC): 3939-3944, 2018. [41] C. Li, L. Chen, K. Aihara, “Stability of genetic networks with SUM regulatory logic: Lur’e system and LMI approach,” IEEE Trans. Circuits Syst. Regul. Pap., 53(11): 2451-2458, 2006. [42] X. Zhang, Y. Wang, L. Wu, Analysis and Design of Delayed Genetic Regulatory Networks, vol. 207. in Studies in Systems, Decision and Control, vol. 207. Cham: Springer International Publishing, 2019. [43] Y. Mori, Y. Kuroe, B. Ingalls, “A method for design of expression tracking controllers for gene regulatory networks,” IFAC-PapersOnLine, 52(26): 135-142, 2019. [44] Y. Y. Liu, A. L. Barabási, “Control principles of complex systems,” Rev. Mod. Phys., 88, 2016. [45] Y. J. Shin, L. Bleris, “Linear control theory for gene network modeling,” PLOS ONE, 5(9), 2010.
آمار تعداد مشاهده مقاله: 65 تعداد دریافت فایل اصل مقاله: 38

سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب

پیوندهای مفید

پیوندهای مفید

اخبار و اعلانات

آمار

A Robust Control Centrality Applicable to Genetic Regulatory Networks with Structured Uncertainties