Genes are the basic blue print of life in an organism containing the physiological and behavioral characteristics. A gene regulatory network (GRN) is a set of genes, or parts of genes, that interact with each other to control a specific cell function. GRN inference is the reverse engineering approach to predict the biological network from the gene expression data. Biochemical system theory based S-System is a popular model in GRN inference and the model is defined with its different parameters. The task of S-System based GRN inference is its parameter estimation which is an optimization problem. Several studies employed Particle Swarm Optimization (PSO) and other pioneer optimization techniques to estimate S-System model. In this paper several prominent swarm intelligence (SI) techniques have been studied and adapted for S-System parameter estimation. They are Group Search Optimizer, Grey Wolf Optimizer and PSO. Proficiency of optimization techniques are compared to infer GRN from SOS DNA real gene expression data and DREAM 4 Silico data.
| Published in | Computational Biology and Bioinformatics (Volume 4, Issue 5) |
| DOI | 10.11648/j.cbb.20160405.11 |
| Page(s) | 37-44 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Gene Regulatory Network (GRN), GRN Inference, Swarm Intelligence, S-System Model
| [1] | Berg, J. M., Tymoczko, J. L. and Stryer, L.(2002). Biochemistry, 5th Edition. |
| [2] | Pierce, B. A. (2010). Genetics: A Conceptual Approach, 4th edition. |
| [3] | Akhand, M. A. H., Nandi, R. N., Amran, S. M. and K. Murase (2015). Gene Regulatory Network Inference using Maximal Information Coefficient. International Journal of Bioscience, Biochemistry and Bioinformatics, vol. 5, no. 5, pp. 296-310. |
| [4] | Bower, J. M. and Bolouri, H. (2001). Computational modeling of genetic and biochemical networks. MIT Press. |
| [5] | Jong, H. de (2002). Modeling and simulation of genetic regulatory systems: a literature review. J. Comp. Bio., vol. 9, no. 1, pp. 67–103. |
| [6] | Akutsu, T., Miyano, S. and Kuhara, S. (2000), “Inferring qualitative relations in genetic networks and metabolic pathways”, Bioinformatics, vol. 16, pp. 727–734. |
| [7] | Chen, T., He, H. L. and Church, G. M. (1999), Modeling gene expression with differential equations. Proc. Pac. Symp. Biocomput., 4, 29–40. |
| [8] | Tsai, K. Y. and Wang, F. S. (2005). Evolutionary optimization with data collocation for reverse engineering of biological networks. Bioinformatics, vol. 21, no. 7, pp. 1180-1188. |
| [9] | Kikuchi, S., Tominaga, D., Arita, M., Takahashi, K. and Tomita, M. (2003). Dynamic modeling of genetic networks using genetic algorithm and S-system. Bioinformatics, vol. 19, pp. 643–650. |
| [10] | Ueda, T., Ono, I. and Okamoto, M. (2002). Development of system identification technique based on real-coded genetic algorithm. Genome Inform. vol. 13, pp. 386–387. |
| [11] | Noman, N. and Iba, H. (2008). Accelerating differential evolution using an adaptive local search. IEEE Trans. Evol. Computat., vol. 12, no. 1, pp. 107–125. |
| [12] | Liu, P. K. and Wang, F. S. (2008). Inference of biochemical network models in S-system using multiobjective optimization approach. Bioinformatics, vol. 24, no. 8, pp. 1085-1092. |
| [13] | Butte, A. J. and Kohane, I. S. (2000). Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pacific Symposium on Biocomputing, vol. 5, pp. 418-429. |
| [14] | Meyer, P. E., Kontos, K. and Bontempi, G. (2007). Biological Network Inference Using Redundancy Analysis, Bioinformatics Research and Development. Lecture Notes in Computer Science, vol. 4414, pp. 16-27. |
| [15] | Faith, J., Hayete, B., Thaden, J. T., Mogno, I., Wierzbowski, J., et al. (2007). Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PloS Biology, 5 (1), e8 (13 pages). |
| [16] | Savageau, M. A. (1969). Biochemical systems analysis: I. Some mathematical properties of the rate law for the component enzymatic reactions. J. Theoretic. Biol., vol. 25, no. 3, pp. 365–369. |
| [17] | Tominaga, D., Koga, N. and Okamoto, M. (2000). Efficient numerical optimization algorithm based on genetic algorithm for inverse problem. in Proc. Genet. Evol. Computat. Conf., vol. 251. 2000, pp. 251- 258. |
| [18] | Ng, A. Y. (2004). Feature selection, L1 vs. L2 regularization, and rotational invariance. in Proc. 21st ICML, pp. 78–86. |
| [19] | Noman N. and Iba, H. (2005). Inference of gene regulatory networks using S-system and differential evolution. in Proc. Conf. Genet. Evol. Computat. pp. 439-446. |
| [20] | Gonzalez, O. R., K¨uper, C., Jung, K., Naval, P. C. and Mendoza, E. (2007). Parameter estimation using simulated annealing for S-system models of biochemical networks. Bioinformatics, vol. 23, no. 4, pp. 480–486. |
| [21] | Kennedy, J. and Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942-1948. |
| [22] | He, S., Wu, Q. H., and Saunders, J. R. (2009). Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior. IEEE Transactions On Evolutionary Computation, vol. 13, no. 5, pp. 973-990. |
| [23] | Mirjalili, S., Mirjalili, S. M., Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, vol. 69, pp. 46–61. |
| [24] | Ronen, M., Rosenberg, R., Shraiman, B. I. and Alon, U. (2002). Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics. Proc. Nat. Acad. Sci., vol. 99, no. 16, p. 10555-60. |
| [25] | DREAM4 In Silico Network Challenge, Available: http://dreamchallenges.org/projectclosed/dream4 [Accessed: 27- Apr- 2016]. |
| [26] | Palafox, L., Noman, N., and Iba, H. (2013). Reverse Engineering of Gene Regulatory Networks Using Dissipative Particle Swarm Optimization. IEEE Trans. on Evolutionary Computation, vol. 17, no. 4, pp. 577-586. |
APA Style
Md Julfikar Islam, M. S. R. Tanveer, M. A. H. Akhand. (2017). Gene Regulatory Network Inference Using Prominent Swarm Intelligence Methods. Computational Biology and Bioinformatics, 4(5), 37-44. https://doi.org/10.11648/j.cbb.20160405.11
ACS Style
Md Julfikar Islam; M. S. R. Tanveer; M. A. H. Akhand. Gene Regulatory Network Inference Using Prominent Swarm Intelligence Methods. Comput. Biol. Bioinform. 2017, 4(5), 37-44. doi: 10.11648/j.cbb.20160405.11
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.cbb.20160405.11,
author = {Md Julfikar Islam and M. S. R. Tanveer and M. A. H. Akhand},
title = {Gene Regulatory Network Inference Using Prominent Swarm Intelligence Methods},
journal = {Computational Biology and Bioinformatics},
volume = {4},
number = {5},
pages = {37-44},
doi = {10.11648/j.cbb.20160405.11},
url = {https://doi.org/10.11648/j.cbb.20160405.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20160405.11},
abstract = {Genes are the basic blue print of life in an organism containing the physiological and behavioral characteristics. A gene regulatory network (GRN) is a set of genes, or parts of genes, that interact with each other to control a specific cell function. GRN inference is the reverse engineering approach to predict the biological network from the gene expression data. Biochemical system theory based S-System is a popular model in GRN inference and the model is defined with its different parameters. The task of S-System based GRN inference is its parameter estimation which is an optimization problem. Several studies employed Particle Swarm Optimization (PSO) and other pioneer optimization techniques to estimate S-System model. In this paper several prominent swarm intelligence (SI) techniques have been studied and adapted for S-System parameter estimation. They are Group Search Optimizer, Grey Wolf Optimizer and PSO. Proficiency of optimization techniques are compared to infer GRN from SOS DNA real gene expression data and DREAM 4 Silico data.},
year = {2017}
}
TY - JOUR T1 - Gene Regulatory Network Inference Using Prominent Swarm Intelligence Methods AU - Md Julfikar Islam AU - M. S. R. Tanveer AU - M. A. H. Akhand Y1 - 2017/01/16 PY - 2017 N1 - https://doi.org/10.11648/j.cbb.20160405.11 DO - 10.11648/j.cbb.20160405.11 T2 - Computational Biology and Bioinformatics JF - Computational Biology and Bioinformatics JO - Computational Biology and Bioinformatics SP - 37 EP - 44 PB - Science Publishing Group SN - 2330-8281 UR - https://doi.org/10.11648/j.cbb.20160405.11 AB - Genes are the basic blue print of life in an organism containing the physiological and behavioral characteristics. A gene regulatory network (GRN) is a set of genes, or parts of genes, that interact with each other to control a specific cell function. GRN inference is the reverse engineering approach to predict the biological network from the gene expression data. Biochemical system theory based S-System is a popular model in GRN inference and the model is defined with its different parameters. The task of S-System based GRN inference is its parameter estimation which is an optimization problem. Several studies employed Particle Swarm Optimization (PSO) and other pioneer optimization techniques to estimate S-System model. In this paper several prominent swarm intelligence (SI) techniques have been studied and adapted for S-System parameter estimation. They are Group Search Optimizer, Grey Wolf Optimizer and PSO. Proficiency of optimization techniques are compared to infer GRN from SOS DNA real gene expression data and DREAM 4 Silico data. VL - 4 IS - 5 ER -
Information
Email: