Login Register Cart 0
Generic placeholder image

Current Protein & Peptide Science

Editor-in-Chief

ISSN (Print): 1389-2037
ISSN (Online): 1875-5550

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Overview of Gene Regulatory Network Inference Based on Differential Equation Models

Author(s): Bin Yang* and Yuehui Chen

Volume 21, Issue 11, 2020

Page: [1054 - 1059] Pages: 6

DOI: 10.2174/1389203721666200213103350

Price: $65

Abstract

Reconstruction of gene regulatory networks (GRN) plays an important role in understanding the complexity, functionality and pathways of biological systems, which could support the design of new drugs for diseases. Because differential equation models are flexible androbust, these models have been utilized to identify biochemical reactions and gene regulatory networks. This paper investigates the differential equation models for reverse engineering gene regulatory networks. We introduce three kinds of differential equation models, including ordinary differential equation (ODE), time-delayed differential equation (TDDE) and stochastic differential equation (SDE). ODE models include linear ODE, nonlinear ODE and S-system model. We also discuss the evolutionary algorithms, which are utilized to search the optimal structures and parameters of differential equation models. This investigation could provide a comprehensive understanding of differential equation models, and lead to the discovery of novel differential equation models.

Keywords: Gene regulatory network, Differential equation, Gene expression data, Time-delayed, S-system, reverse engineering.

« Previous Next »
[1]
Banzhaf, W.; Pillay, N. Why complex systems engineering needs biological development. Complexity, 2010, 13(2), 12-21.
[http://dx.doi.org/10.1002/cplx.20199]
[2]
Bao, W.; Yuan, C.A.; Zhang, Y.; Han, K.; Nandi, A.K.; Honig, B.; Huang, D.S. Mutli-Features Prediction of Protein Translational Modification Sites. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2018, 15(5), 1453-1460.
[http://dx.doi.org/10.1109/TCBB.2017.2752703] [PMID: 28961121]
[3]
Salazar-Ciudad, I.; Jernvall, J. How different types of pattern formation mechanisms affect the evolution of form and development. Evol. Dev., 2004, 6(1), 6-16.
[http://dx.doi.org/10.1111/j.1525-142X.2004.04002.x] [PMID: 15108813]
[4]
Hoffman, R.M. Topical liposome targeting of dyes, melanins, genes, and proteins selectively to hair follicles. J. Drug Target., 1998, 5(2), 67-74.
[http://dx.doi.org/10.3109/10611869808995860] [PMID: 9588863]
[5]
Bao, W.; Chen, Y.; Wang, D. Prediction of protein structure classes with flexible neural tree. Biomed. Mater. Eng., 2014, 24(6), 3797-3806.
[http://dx.doi.org/10.3233/BME-141209] [PMID: 25227096]
[6]
Kuhn, M.; von Mering, C.; Campillos, M.; Jensen, L.J.; Bork, P. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res., 2008, 36(Database issue), D684-D688.
[PMID: 18084021]
[7]
Attur, M.G.; Dave, M.N.; Tsunoyama, K.; Akamatsu, M.; Kobori, M.; Miki, J.; Abramson, S.B.; Katoh, M.; Amin, A.R. “A system biology” approach to bioinformatics and functional genomics in complex human diseases: arthritis. Curr. Issues Mol. Biol., 2002, 4(4), 129-146.
[PMID: 12432964]
[8]
Kitano, H. Computational systems biology. Nature, 2002, 420(6912), 206-210.
[http://dx.doi.org/10.1038/nature01254] [PMID: 12432404]
[9]
Düvel, K.; Yecies, J.L.; Menon, S.; Raman, P.; Lipovsky, A.I.; Souza, A.L.; Triantafellow, E.; Ma, Q.; Gorski, R.; Cleaver, S.; Vander Heiden, M.G.; MacKeigan, J.P.; Finan, P.M.; Clish, C.B.; Murphy, L.O.; Manning, B.D. Activation of a metabolic gene regulatory network downstream of mTOR complex 1. Mol. Cell, 2010, 39(2), 171-183.
[http://dx.doi.org/10.1016/j.molcel.2010.06.022] [PMID: 20670887]
[10]
Zou, M.; Conzen, S.D. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics, 2005, 21(1), 71-79.
[http://dx.doi.org/10.1093/bioinformatics/bth463] [PMID: 15308537]
[11]
Nishiyama, S.; Onoue, N.; Kono, A.; Sato, A.; Yonemori, K.; Tao, R. Characterization of a gene regulatory network underlying astringency loss in persimmon fruit. Planta, 2018, 247(3), 733-743.
[http://dx.doi.org/10.1007/s00425-017-2819-0] [PMID: 29188374]
[12]
Maharana, S.K.; Schlosser, G. A gene regulatory network underlying the formation of pre-placodal ectoderm in Xenopus laevis. BMC Biol., 2018, 16(1), 79.
[http://dx.doi.org/10.1186/s12915-018-0540-5] [PMID: 30012125]
[13]
Karlebach, G.; Shamir, R. Minimally perturbing a gene regulatory network to avoid a disease phenotype: the glioma network as a test case. BMC Syst. Biol., 2010, 4, 15.
[http://dx.doi.org/10.1186/1752-0509-4-15] [PMID: 20184733]
[14]
Madhamshettiwar, P.B.; Maetschke, S.R.; Davis, M.J.; Reverter, A.; Ragan, M.A. Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets. Genome Med., 2012, 4(5), 41.
[http://dx.doi.org/10.1186/gm340] [PMID: 22548828]
[15]
Yuan, L.; Guo, L.H.; Yuan, C.A.; Zhang, Y.H.; Han, K.; Nandi, A.; Honig, B.; Huang, D.S. Integration of Multi-omics Data for Gene Regulatory Network Inference and Application to Breast Cancer. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018, 16(3), 782-791.
[http://dx.doi.org/10.1109/TCBB.2018.2866836]
[16]
Lopes-Ramos, C.M.; Kuijjer, M.L.; Ogino, S.; Fuchs, C.S.; DeMeo, D.L.; Glass, K.; Quackenbush, J. Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism. Cancer Res., 2018, 78(19), 5538-5547.
[http://dx.doi.org/10.1158/0008-5472.CAN-18-0454] [PMID: 30275053]
[17]
Morrissey, E.R.; Juárez, M.A.; Denby, K.J.; Burroughs, N.J. Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression. Biostatistics, 2011, 12(4), 682-694.
[http://dx.doi.org/10.1093/biostatistics/kxr009] [PMID: 21551122]
[18]
Gama-Castro, S.; Salgado, H.; Santos-Zavaleta, A.; Ledezma-Tejeida, D.; Muñiz-Rascado, L.; García-Sotelo, J.S.; Alquicira-Hernández, K.; Martínez-Flores, I.; Pannier, L.; Castro-Mondragón, J.A.; Medina-Rivera, A.; Solano-Lira, H.; Bonavides-Martínez, C.; Pérez-Rueda, E.; Alquicira-Hernández, S.; Porrón-Sotelo, L.; López-Fuentes, A.; Hernández-Koutoucheva, A.; Del Moral-Chávez, V.; Rinaldi, F.; Collado-Vides, J. RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond. Nucleic Acids Res., 2016, 44(D1), D133-D143.
[http://dx.doi.org/10.1093/nar/gkv1156] [PMID: 26527724]
[19]
Rosenberg, D.K.; Noon, B.R.; Megahan, J.W.; Meslow, E.C. Compensatory behavior of Ensatina eschscholtzii in biological corridors: a field experiment. Can. J. Zool., 1998, 76(1), 117-133.
[http://dx.doi.org/10.1139/z97-178]
[20]
Liu, Z.; He, Q. A Novel Boolean Network for Analyzing the p53 Gene Regulatory Network. Curr. Bioinform., 2016, 11(1), 13-21.
[http://dx.doi.org/10.2174/1574893611666151119215249]
[21]
Kabir, M.; Noman, N.; Iba, H. Reverse engineering gene regulatory network from microarray data using linear time-variant model. BMC Bioinformatics, 2010, 11(Suppl. 1), S56.
[http://dx.doi.org/10.1186/1471-2105-11-S1-S56] [PMID: 20122231]
[22]
Shi, J.; Zhao, J.; Li, T.; Chen, L. Detecting direct associations in a network by information theoretic approaches. Sci. China Math., 2019, 62(5), 823-838.
[http://dx.doi.org/10.1007/s11425-017-9206-0]
[23]
Xing, L.; Guo, M.; Liu, X.; Wang, C.; Zhang, L. Gene Regulatory Networks Reconstruction Using the Flooding-Pruning Hill-Climbing Algorithm. Genes (Basel), 2018, 9(7), 342.
[http://dx.doi.org/10.3390/genes9070342] [PMID: 29986472]
[24]
Mandal, S.; Saha, G.; Pal, R.K. Recurrent neural network based modeling of gene regulatory network using bat algorithm. J. Adv. Math Comput. Sci., 2017, 23(5), 1-16.
[http://dx.doi.org/10.9734/JAMCS/2017/34916]
[25]
Ram, R.; Chetty, M. A Markov-blanket-based model for gene regulatory network inference. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2011, 8(2), 353-367.
[http://dx.doi.org/10.1109/TCBB.2009.70] [PMID: 21233520]
[26]
Wang, J.; Cheung, L.W.; Delabie, J. New probabilistic graphical models for genetic regulatory networks studies. J. Biomed. Inform., 2005, 38(6), 443-455.
[http://dx.doi.org/10.1016/j.jbi.2005.04.003] [PMID: 15996532]
[27]
Wu, S.; Liu, Z.P.; Qiu, X.; Wu, H. High-Dimensional Ordinary Differential Equation Models for Reconstructing Genome-Wide Dynamic Regulatory Networks 2012 symposium of the 21st International Chinese Statistical Association (ICSA), Boston, MA, USA, June 23–26, 2012, 2013, , pp. 173-190..
[28]
Zheng, M.; Liu, G.X.; Wang, H.; Zhou, C.G. Gene Regulatory Network Reconstruction of P38 MAPK Pathway Using Ordinary Differential Equation with Linear Regression Analysis. Advances in Intelligent and Soft Computing, 2009, 116, 299-308.
[http://dx.doi.org/10.1007/978-3-642-03156-4_30]
[29]
Hoon, M.D.; Imoto, S.; Miyano, S. 5th International Conference, DS 2002 Lübeck, GermanyNovember 24–26, 2002Lecture Notes in Computer. Science2002, pp. 267-274.
[30]
de Jong, H.; Page, M. Search for steady states of piecewise-linear differential equation models of genetic regulatory networks. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2008, 5(2), 208-222.
[http://dx.doi.org/10.1109/TCBB.2007.70254] [PMID: 18451430]
[31]
Gebert, J.; Radde, N.; Weber, G.W. Modeling gene regulatory networks with piecewise linear differential equations. Eur. J. Oper. Res., 2007, 181(3), 1148-1165.
[http://dx.doi.org/10.1016/j.ejor.2005.11.044]
[32]
Machina, A.; Ponosov, A. Stability of stationary solutions of piecewise affine differential equations describing gene regulatory networks. J. Math. Anal. Appl., 2011, 380(2), 736-749.
[http://dx.doi.org/10.1016/j.jmaa.2011.02.034]
[33]
Sakamoto, E.; Iba, H. Inferring a system of differential equations for a gene regulatory network by using genetic programming Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, South Korea, South, 27-30 May 2001; IEEE 2001, pp. 720-72.
[34]
Yang, B.; Chen, Y.; Meng, Q. 2009 International Conference on Intelligent Computing, Lecture Notes in Computer Science, 2009, pp. 974-983.
[35]
Dehghannasiri, R.; Esfahani, M.S.; Dougherty, E.R. Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Seattle, WA, USA October 02,2016, pp. 542-543.
[36]
Shen, X.; Vikalo, H. Inferring parameters of gene regulatory networks via particle filtering. EURASIP J. Adv. Signal Process., 2010.204612
[http://dx.doi.org/10.1155/2010/204612]
[37]
Kozlov, K.; Samsonov, A. DEEP-differential evolution entirely parallel method for gene regulatory networks. J. Supercomput., 2011, 57(2), 172-178.
[http://dx.doi.org/10.1007/s11227-010-0390-6] [PMID: 22223930]
[38]
Ren, H.P.; Huang, X.N.; Hao, J.X. Finding Robust Adaptation Gene Regulatory Networks Using Multi-Objective Genetic Algorithm. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2016, 13(3), 571-577.
[http://dx.doi.org/10.1109/TCBB.2015.2430321] [PMID: 27295641]
[39]
Youseph, A.S.K.; Chetty, M.; Karmakar, G. Decoupled Modeling of Gene Regulatory Networks Using Michaelis-Menten Kinetics, ICONIP 2015 Lecture Notes in Computer Science; Springer: Cham, 2015, pp. 497-505.
[40]
Savageau, M.A. Finding multiple roots of nonlinear algebraic equations using s-system methodology. Appl. Math. Comput., 1993, 55, 187-199.
[http://dx.doi.org/10.1016/0096-3003(93)90020-F]
[41]
Noman, N.; Iba, H. Proceedings of the 7th annual conference on Genetic and evolutionary computation, Washington DC, USA June 252005, pp. 439-446.
[42]
Kimura, S.; Ide, K.; Kashihara, A.; Kano, M.; Hatakeyama, M.; Masui, R.; Nakagawa, N.; Yokoyama, S.; Kuramitsu, S.; Konagaya, A. Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics, 2005, 21(7), 1154-1163.
[http://dx.doi.org/10.1093/bioinformatics/bti071] [PMID: 15514004]
[43]
Hsiao, Y.T.; Lee, W.P. Inferring robust gene networks from expression data by a sensitivity-based incremental evolution method. BMC Bioinformatics, 2012, 13(Suppl. 7), S8.
[http://dx.doi.org/10.1186/1471-2105-13-S7-S8] [PMID: 22595005]
[44]
Mandal, S.; Khan, A.; Saha, G.; Pal, R.K. Reverse engineering of gene regulatory networks based on S-systems and Bat algorithm. J. Bioinform. Comput. Biol., 2016, 14(3)1650010
[http://dx.doi.org/10.1142/S0219720016500104] [PMID: 26932274]
[45]
Nakayama, T.; Seno, S.; Takenaka, Y.; Matsuda, H. Inference of S-system models of gene regulatory networks using immune algorithm. J. Bioinform. Comput. Biol., 2011, 9(Suppl. 1), 75-86.
[http://dx.doi.org/10.1142/S0219720011005768] [PMID: 22144255]
[46]
Mandal, S.; Saha, G.; Pal, R.K. S-system based gene regulatory network reconstruction using Firefly algorithm. Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT), 2015, , pp. 1-5..
[47]
Palafox, L.; Noman, N.; Iba, H. Reverse Engineering of Gene Regulatory Networks Using Dissipative Particle Swarm Optimization. IEEE Trans. Evol. Comput., 2013, 17(4), 577-587.
[http://dx.doi.org/10.1109/TEVC.2012.2218610]
[48]
Wu, S.J.; Wu, C.T. Computational optimization for S-type biological systems: cockroach genetic algorithm. Math. Biosci., 2013, 245(2), 299-313.
[http://dx.doi.org/10.1016/j.mbs.2013.07.019] [PMID: 23927855]
[49]
Hsiao, Y.T.; Lee, W.P. Reverse engineering gene regulatory networks: coupling an optimization algorithm with a parameter identification technique. BMC Bioinformatics, 2014, 15(15)(Suppl. 15), S8.
[http://dx.doi.org/10.1186/1471-2105-15-S15-S8] [PMID: 25474560]
[50]
Chowdhury, A.R.; Chetty, M. Network decomposition based large-scale reverse engineering of gene regulatory network. Neurocomputing, 2015, 160(3), 213-227.
[http://dx.doi.org/10.1016/j.neucom.2015.02.020]
[51]
Cho, D.Y.; Cho, K.H.; Zhang, B.T. Identification of biochemical networks by S-tree based genetic programming. Bioinformatics, 2006, 22(13), 1631-1640.
[http://dx.doi.org/10.1093/bioinformatics/btl122] [PMID: 16585066]
[52]
Yang, B.; Zhang, W. 2015 International Conference on Intelligent Computing, Lecture Notes in Computer Science, 2015, pp. 351-359.
[53]
Yang, B.; Zhang, W. Using Restricted Additive Tree Model for Identifying the Large-Scale Gene Regulatory Networks; Lect. Notes Comput.Sci., 2015, 9226, 351-359..
[http://dx.doi.org/10.1007/978-3-319-22186-1_34]
[54]
Yang, B.; Liu, S.; Zhang, W. Reverse engineering of gene regulatory network using restricted gene expression programming. J. Bioinform. Comput. Biol., 2016, 14(5)1650021
[http://dx.doi.org/10.1142/S0219720016500219] [PMID: 27338130]
[55]
Yang, B.; Zhang, W.; Wang, H. Stock Market Forecasting Using Restricted Gene Expression Programming. Comput. Intell. Neurosci., 2019, 20197198962
[http://dx.doi.org/10.1155/2019/7198962] [PMID: 30867661]
[56]
Yu, B.; Xu, J.M.; Li, S.; Chen, C.; Chen, R.X.; Wang, L.; Zhang, Y.; Wang, M.H. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method. Oncotarget, 2017, 8(46), 80373-80392.
[http://dx.doi.org/10.18632/oncotarget.21268] [PMID: 29113310]
[57]
Yang, B.; Chen, Y.; Zhang, W.; Lv, J.; Bao, W.; Huang, D.S. HSCVFNT: Inference of Time-Delayed Gene Regulatory Network Based on Complex-Valued Flexible Neural Tree Model. Int. J. Mol. Sci., 2018, 19(10)E3178
[http://dx.doi.org/10.3390/ijms19103178] [PMID: 30326663]
[58]
Silvescu, A.; Honavar, V. Temporal boolean network models of genetic networks and their inference from gene expression time series. Complex Syst., 2001, 13, 54-75.
[59]
Kim, S.; Kim, J.; Cho, K.H. Inferring gene regulatory networks from temporal expression profiles under time-delay and noise. Comput. Biol. Chem., 2007, 31(4), 239-245.
[http://dx.doi.org/10.1016/j.compbiolchem.2007.03.013] [PMID: 17631421]
[60]
Huang, T.; Liu, L.; Qian, Z.; Tu, K.; Li, Y.; Xie, L. Using GeneReg to construct time delay gene regulatory networks. BMC Res. Notes, 2010, 3(1), 142.
[http://dx.doi.org/10.1186/1756-0500-3-142] [PMID: 20500822]
[61]
Chowdhury, A.R.; Chetty, M.; Vinh, N.X. 20th International Conference on Neural Information Processing, Lecture Notes in Computer Science, 2013, pp. 616-623.
[62]
Chowdhury, A.R.; Chetty, M.; Vinh, N.X. 20th International Conference on Neural Information Processing, Lecture Notes in Computer Science, 2013, pp. 624-631.
[63]
Chowdhury, A.R.; Chetty, M.; Vinh, N.X. Incorporating time-delays in S-System model for reverse engineering genetic networks. BMC Bioinformatics, 2013, 14(1), 196.
[http://dx.doi.org/10.1186/1471-2105-14-196] [PMID: 23777625]
[64]
Yang, B.; Zhang, W.; Wang, H.; Song, C.; Chen, Y. TDSDMI: Inference of time-delayed gene regulatory network using S-system model with delayed mutual information. Comput. Biol. Med., 2016, 72, 218-225.
[http://dx.doi.org/10.1016/j.compbiomed.2016.03.024] [PMID: 27058285]
[65]
Shmulevich, I.; Aitchison, J.D. Deterministic and stochastic models of genetic regulatory networks. Methods Enzymol., 2009, 467, 335-356.
[http://dx.doi.org/10.1016/S0076-6879(09)67013-0] [PMID: 19897099]
[66]
Wang, T.Y.; Chen, K.C.; Hsu, D.F.; Kao, C.Y. Combining Agent-Based Models with Stochastic Differential Equations for Gene Regulatory Networks Ninth IEEE International Conference on Bioinformatics and Bioengineering, Taichung, Taiwan, IEEE Computer Society Washington, DC, USA, 2009, pp. 405-409..
[67]
Chowdhury, A.R.; Chetty, M.; Evans, R. Stochastic S-system modeling of gene regulatory network. Cogn. Neurodyn., 2015, 9(5), 535-547.
[http://dx.doi.org/10.1007/s11571-015-9346-0] [PMID: 26379803]
[68]
Tian, T. Stochastic models for inferring genetic regulation from microarray gene expression data. Biosystems, 2010, 99(3), 192-200.
[http://dx.doi.org/10.1016/j.biosystems.2009.11.002] [PMID: 19945503]

Rights & Permissions Print Cite
Article Metrics
32
© 2024 Bentham Science Publishers | Privacy Policy