An Optimal Boolean Approach for Computational Modeling of Gene Regulatory Networks from Temporal Gene Expression Profile

Document Type : Original Article


1 Department of Electrical Engineering, Semnan University, Semnan, Iran

2 Department of Electrical Engineering, University of Neyshabur, Neyshabur, Iran


Deciphering the crucial interactions among genes is one of the key issues in understanding the fundamental molecular and intracellular mechanisms of cell. Computational modeling of gene regulatory networks can be used as a powerful tool in various fields of molecular biomedicine such as identification of metabolic, regulator, and signal transduction pathways, analysis of complex genetic diseases, and drug discovery. In this paper, an optimal Boolean approach was proposed for computational modeling of gene regulatory networks from temporal gene expression profile. In this method, the optimal values of the Boolean thresholds of gene expression signals and the parameters of the interaction patterns between target and regulator genes are all designed as a mixed-integer nonlinear programming solved by Genetic Algorithm. To evaluate the performance of the proposed scheme, it has been applied to a well-known time course microarray data and gene regulatory network of Saccharomyces cerevisiae from the literature. The reference network has 11 genes, 9 targets, and 61 regulatory interactions, and the original transcriptional dataset includes 18 time points for each gene expression signal. In this case study, the proposed computational model contains 142 unknown parameters that are optimally determined through optimization. The results demonstrate the efficiency of the proposed approach.


Main Subjects

  1. Khezri, R., Hosseini, R., and Mazinani, M. "A Fuzzy Rule-based Expert System for the Prognosis of the Risk of Development of the Breast Cancer." International Journal of Engineering, Transactions A: Basics, 27, No. 10 (2014), 1557-1564. DOI: 10.5829/idosi.ije.2014.27.10a.09
  2. Rowhanimanesh, A., and Akbarzadeh-T, M. R., "Stigmergic cooperation of nanoparticles for swarm fuzzy control of low-density lipoprotein concentration in the arterial wall." Applied Soft Computing 34 (2015), 799-812. DOI: 10.1016/j.asoc.2015.05.013
  3. Shaeiri, Z., and Ghaderi, R., "Modification of the fast global k-means using a fuzzy relation with application in microarray data analysis." International Journal of Engineering, Transactions C: Aspects, 25, No. 4 (2012), 283-292. DOI: 10.5829/idosi.ije.2012.25.04c.03
  4. Nachtigall, P., Bovolenta, L., James, P., Bastian, F., Ney, L., Danillo, P., "A comparative analysis of heart microRNAs in vertebrates brings novel insights into the evolution of genetic regulatory networks." BMC Genomics1 (2021), 1-20.‏ DOI: 10.1186/s12864-021-07441-4
  5. Xiang, C., Min, Li., Ruiqing, Z., Siyu, Z., Fang-X, Wu., Yaohang, Li., and Jianxin, W., "A novel method of gene regulatory network structure inference from gene knock-out expression data." Tsinghua Science and Technology4 (2019), 446-455. DOI: 1 0. 2 6 5 9 9/T ST. 2 0 1 8. 9 0 1 0 0 9 7
  6. Delgado, F. M., and Francisco, G., "Computational methods for Gene Regulatory Networks reconstruction and analysis: A review." Artificial Intelligence in Medicine 95 (2019), 133-145. DOI: 10.1016/j.artmed.2018.10.006
  7. Sanguinetti, G. "Gene regulatory network inference: an introductory survey." Gene Regulatory Networks. Humana Press, New York, NY, (2019), 1-23. Doi: 10.1007/978-1-4939-8882-2_1
  8. Sun, X., Ji, Z., and Q. Nie., "Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples." PLoS Computational Biology3 (2021), e1008379. DOI: 10.1371/journal.pcbi.1008379
  9. Zou, C., and Xingyuan W., "Robust stability of delayed Markovian switching genetic regulatory networks with reaction–diffusion terms." Computers & Mathematics with Applications4 (2020), 1150-1164. DOI: 10.1016/j.camwa.2019.08.024
  10. Ren, F., and Jinde C., "Asymptotic and robust stability of genetic regulatory networks with time-varying delays." Neurocomputing4-6 (2008), 834-842, DOI: 10.1016/j.neucom.2007.03.011
  11. Xiao, S., Xian, Z., Xin, W., and Yantao, W., "A reduced-order approach to analyze stability of genetic regulatory networks with discrete time delays." Neurocomputing 323, (2019), 311-318. org/10.1016/j.neucom.2018.10.005
  12. Zañudo, J., GT., Steven, N., Steinway, and Réka, A., "Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer." Current Opinion in Systems Biology 9 (2018), 1-10. DOI: 10.1016/j.coisb.2018.02.002
  13. Chen, P.CY., and Jeremy W.C., "A Markovian approach to the control of genetic regulatory networks." Biosystems2, (2007), 535-545. DOI: 10.1016/j.biosystems.2006.12.005
  14. Barbuti, R., Gori, R., Milazzo, P., and Nasti, L., "A survey of gene regulatory networks modelling methods: from differential equations, to Boolean and qualitative bioinspired models." Journal of Membrane Computing (2020), 1-20. DOI: 10.1007/s41965-020-00046
  15. Hugues, M., Cui, S., Stefan, H., Jun, P. and loic, P., "Sequential reprogramming of Boolean networks made practical." International Conference on Computational Methods in Systems Biology, (2019). DOI: 10.1007/978-3-030-31304-3_1
  16. Dai, C., and Juan, L., "Inducing pairwise gene interactions from time-series data by EDA based bayesian network." IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, (2006). DOI: 1109/IEMBS.2005.1616308
  17. Hajiramezanali, E., Imani, M., Barga-N, U., Qian, X., and Dougherty, E.R., "Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty." BMC Genomics6 (2019), 1-11. DOI: 10.1186/s12864-019-5720-3
  18. Maróti, Z., Tombácz, D., Prazsák, I., Moldován, N., Csabai, Z., Torma, G., Balázs, Z., Kalmár, T., Dénes, B., Snyder, M. and BoldogkÅ‘i, Z., "Time-course transcriptome analysis of host cell response to poxvirus infection using a dual long-read sequencing approach." BMC Research Notes1 (2021), 1-7. DOI: 10.1186/s13104-021-05657-x
  19. Rowhanimanesh, A., “A Novel Approach for the Analysis of Time-course Gene Expression Data Based on Computing with Words.” Journal of Biomedical Informatics 120 (2021), 103868. DOI: 10.1016/j.jbi.2021.103868
  20. Jose, M., Alvarez, M., Brooks, D., Swift, J., and Coruzzi, G.M., "Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks." Annual Review of Plant Biology 72, (2021), 105-131.‏ DOI: 10.1146/annurev-arplant-081320-090914
  21. Bähler, J., "Cell-cycle control of gene expression in budding and fission yeast." Annu. Rev. Genet. 39 (2005), 69-94. DOI: 1146/annurev.genet.39.110304.095808
  22. Kaderali, L., and Radde, N., "Inferring gene regulatory networks from expression data." Computational Intelligence in Bioinformatics. Springer, Berlin, Heidelberg, (2008), 33-74. DOI: 10.1007/978-3-540-76803-6_2
  23. Radde, N., and Kaderali, L., "Bayesian inference of gene regulatory networks using gene expression time series data." International Conference on Bioinformatics Research and Development. Springer, Berlin, Heidelberg, 2007. DOI: 10.1007/978-3-540-71233-6_1
  24. Fangting, Li., Tao, L., Ying, Lu., Ouyang, , and Tang, C., "The yeast cell-cycle network is robustly designed." Proceedings of the National Academy of Sciences 101.14 (2004), 4781-4786. DOI: 10.1073/pnas.0305937101
  25. Spellman, P.T., Shelock, G., Zhang, M.Q., Q.Z., Lyer, V.R., Anders, K., A., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B., "Comprehensive identification of cell cycle–regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization." Molecular Biology of the Cell12, (1998), 3273-3297. DOI: 10.1091/mbc.9.12.3273
  26. De Boor, C., “A practical guide to splines”. Vol. 27. New York: Springer-verlag, (1978).
  27. Yang, X.S., Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons, 2010.
  28. Rowhanimanesh, A., and Akbarzadeh-T, M.R., "Perception-based heuristic granular search: Exploiting uncertainty for analysis of certain functions." Scientia Iranica 18, No. 3 (2011): 617-626. DOI: 10.1016/j.scient.2011.04.015
  1. Zarepor-A, A., and Mosalman-Y, H., "Location Allocation of Earthquake Relief Centers in Yazd City Based on Whale Optimization Algorithm." International Journal of Engineering, Transactions B: Applications, 34, No. 5 (2021), 1184-1194. DOI: 10.5829/ije.2021.34.05b.12
  2. Rowhanimanesh, A., and Efati, S., "A novel approach to improve the performance of evolutionary methods for nonlinear constrained optimization." Advances in Artificial Intelligence, (2012). DOI: 10.1155/2012/540861
  3. Mohammadi, S., and Babagoli, M., "A Hybrid Modified Grasshopper Optimization Algorithm and Genetic Algorithm to Detect and Prevent DDoS Attacks." International Journal of Engineering, Transactions A: Basics, 34, No. 4 (2021), 811-824. DOI: 10.5829/ije.2021.34.04a.07
  4. Rowhanimanesh, A., and Akbarzadeh-T, M.R., "Perception-based evolutionary optimization: Outline of a novel approach to optimization and problem solving." In Proceedings of IEEE International Conference on Systems, Man and Cybernetics (2010), 4270-4275. DOI: 1109/ICSMC.2010.5642481
  5. Mohebbi,, Barouei, J., AkbarzadehT, M.R., Rowhanimanesh, A., Habibi-N, M.B., Yavarmanesh, M., "Modeling and optimization of viscosity in enzyme-modified cheese by fuzzy logic and genetic algorithm." Computers and Electronics in Agriculture 62.2, (2008), 260-265. DOI: 10.1016/j.compag.2008.01.010
  6. Rowhanimanesh, A., Karimpour, A., Pariz, N., "Optimal path planning for controllability of switched linear systems using multi-level constrained GA." Applications of Soft Computing (2009): 399-408. DOI: 10.1007/978-3-540-89619-7_39
  7. Aalaei, S., Shahraki, H., Rowhanimanesh, A., Eslam, S., "Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets." Iranian Journal of Basic Medical Sciences5, (2016), DOI: 10.22038/ijbms.2016.6931
  8. Parvane, M., Rahimi, E., Jafarinejad, F., "Optimization of quantum cellular automata circuits by genetic algorithm." International Journal of Engineering, Transactions B: Applications, 33, No. 2, (2020), 229-236. DOI: 10.5829/ije.2020.33.02b.07
  9. Yazdi, H.S., Rowhanimanesh, A., Modares, H., "A general insight into the effect of neuron structure on classification." Knowledge & Information Systems1, (2012), 135-154. DOI: 10.1007/s10115-011-0392-6
  10. Rowhanimanesh, A., Khajekaramoin, A., Akbarzadeh-T, M.R. "Evolutionary constrained design of seismically excited buildings: sensor placement." Applications of Soft Computing (2009): 159-169. DOI: 10.1007/978-3-540-89619-7_16
  11. Davani Motlagh, A., Sadeghian, M.S., Javid, A.H., Asgari, M.S., "Optimization of Dam Reservoir Operation Using Grey Wolf Optimization and Genetic Algorithms (A Case Study of Taleghan Dam)." International Journal of Engineering, Transactions A: Basics, 34, No. 7 (2021), 1644-1652. DOI: 10.5829/ije.2021.34.07a.09