Volume 17, Issue 2 (9-2020)                   JSDP 2020, 17(2): 112-101 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Bayat R, Sadeghi M, Aref M R. Modeling gene regulatory networks: Classical models, optimal perturbation for identification of network. JSDP. 2020; 17 (2) :112-101
URL: http://jsdp.rcisp.ac.ir/article-1-918-en.html
Yazd University
Abstract:   (815 Views)
Deep understanding of molecular biology has allowed emergence of new technologies like DNA decryption.  On the other hand, advancements of molecular biology have made manipulation of genetic systems simpler than ever; this promises extraordinary progress in biological, medical and biotechnological applications.  This is not an unrealistic goal since genes which are regulated by gene regulatory networks (GRNs) are the core governors of life processes at the molecular level. In fact, manipulation of GRNs would be the ultimate strategy for optimal purposeful control of cell’s life.  GRNs are in charge of regulating the amounts of all the inter-cellular as well as intra-cellular molecular species produced all the time in all living organisms.  Manipulation of a GRN requires comprehensive knowledge about nodes and interconnections.  This paper deals with both aspects in networks having more than fifty nodes.  In the first part of the paper, restrictions of probabilistic models in modeling node behavior are discussed, i.e.: 1) unfeasibility of reliably predicting the next state of GRN based on its current state, 2) impossibility of modelling logical relations among genes, and 3) scarcity of biological data needed for model identification.  These findings which are supported by arguments from probability theory suggest that probabilistic models should not be used for analysis and prediction of node behavior in GRNs.  Next part of the paper focuses on models of GRN structure.  It is shown that the use of multi-tree models for structure for GRN poses severe limitations on network behavior, i.e. 1) increase in signal entropy while passing through the network, 2) decrease in signal bandwidth while passing through the network, and 3) lack of feedback as a key element for oscillatory and/or autonomous behavior (a requirement for any biological network).  To demonstrate that, these restrictions are consequences of model selection, we use information theoretic arguments.  At the last and the most important part of the paper we look into the gene perturbation experiments from a network-theoretic perspective to show that multi-perturbation experiments are not as informative as assumed so far.  A generally accepted belief among researches states that multi-perturbation experiments are more informative than single-perturbation ones, i.e., multiple simultaneously applied perturbations provide more information than a single perturbation.  It is shown that single-perturbation experiments are optimal for identification of network structure, provided the ultimate goal is to discover correct subnet structures. 
Full-Text [PDF 3633 kb]   (253 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2018/10/23 | Accepted: 2019/01/26 | Published: 2020/09/14 | ePublished: 2020/09/14

References
1. [1]G. Battail, "Heredity as an encoded communication process," IEEE Trans. Information Theory, vol. 56, no. 2, pp. 678-687, Feb. 2010. [DOI:10.1109/TIT.2009.2037044]
2. [2] L. M. Adleman, "Molecular computation of solutions to combinatorial problems," Science, vol. 266, no. 11, pp. 1021-1025, Nov. 1994. [DOI:10.1126/science.7973651] [PMID]
3. [3] S. A. Salehi, et al, "Computing mathematical functions using DNA via fractional coding," Nature Genetics, May 2018. [DOI:10.1038/s41598-018-26709-6] [PMID] [PMCID]
4. [4] S. M. H. Tabatabaei Yazdi, et al, "Mutually uncorrelated primers for DNA-based data storage," IEEE Trans. Information Theory, vol. 64, no. 9, pp. 6283-6296, Sept. 2018. [DOI:10.1109/TIT.2018.2792488]
5. [5] M. K. Gupta, "Quest for error correction in biology," IEEE Eng. in Medicine and Biology Mag, vol. 26, no. 1, Jan. 2006. [DOI:10.1109/MEMB.2006.1578663] [PMID]
6. [6] B. Alberts, et al, Molecular biology of the cell, 6th edition, Garland Science, New York, 2014.
7. [7] S. Das, et al, Handbook of research on computational methodologies in gene regulatory networks, Hershey, New York, 2010. [DOI:10.4018/978-1-60566-685-3]
8. [8] J. J. Pasternak, An introduction to human molecular genetics: Mechanisms of inherited diseases, 2nd edition, Wiley, New York, 2005. [DOI:10.1002/0471719188]
9. [9] Beom S. Lee, et al, "A computational algorithm for personalized medicine in schizophrenia," Schizophrenia Research, vol. 192, pp. 131-136, Feb. 2018. [DOI:10.1016/j.schres.2017.05.001] [PMID]
10. [10] N. Kornienko, et al, "Interfacing nature's catalytic machinery with synthetic materials for semi-artificial photosynthesis," Nature Nanotechnology, vol. 13, pp. 890-899, Oct. 2018. [DOI:10.1038/s41565-018-0251-7] [PMID]
11. [11] M.Banf, Seung Y. Rhee, "Computational inference of gene regulatory networks: Approaches, limitations and opportunities," Biochimica et Biophysica Acta, vol. 1860, no. 1, pp. 41-52, Jan 2017. [DOI:10.1016/j.bbagrm.2016.09.003] [PMID]
12. [12] N. Friedman, et al, "Using Bayesian networks to analyze expression data," J. Computational Biology, vol. 7, no. 3, pp. 601-620, March 2000. [DOI:10.1089/106652700750050961] [PMID]
13. [13] F. Fages, et al, "Influence networks compared with reaction networks: Semantics, expressivity and attractors," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 15, no. 4, pp. 1138 - 1151, July 2018. [DOI:10.1109/TCBB.2018.2805686] [PMID]
14. [14] Y. Li, "The max-min high-order dynamic Bayesian network for learning gene regulatory networks with time-delayed regulations," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 13, no. 4, pp. 792-803, July 2016. [DOI:10.1109/TCBB.2015.2474409] [PMID]
15. [15] H. Chen, et al, "Bayesian data fusion of gene expression and histone modification profiles for inference of gene regulatory network," IEEE/ACM Trans. Computational Biology and Bioinformatics, doi: 10.1109/TCBB.2018.2869590, early access, Sep. 2018. [DOI:10.1109/TCBB.2018.2869590] [PMID]
16. [16] M. Shi, et al, "Adaptive modelling of gene regulatory network using Bayesian information criterion-guided sparse regression approach," IET Systems Biology, vol. 10, no. 6, pp. 252-59, June 2016. [DOI:10.1049/iet-syb.2016.0005] [PMID]
17. [17] S. Chan, et al, "Maximum a posteriori probability and time-varying approach for inferring gene regulatory networks from time course gene microarray data," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 12, no. 1, pp. 123 - 135, Jan. 2015. [DOI:10.1109/TCBB.2014.2343951] [PMID]
18. [18] H. Xu, et al, "Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells," PLoS One Computational Biology, vol. 10, no. 8, pp. 1-14, Aug. 2014. [DOI:10.1371/journal.pcbi.1003777] [PMID] [PMCID]
19. [19] S. Mehra, W. Hu, G. Karypis, "A Boolean algorithm for reconstructing the structure of regulatory networks," Metabolic Engineering, vol. 6, no. 4, pp. 326-39, Nov. 2004. [DOI:10.1016/j.ymben.2004.05.002] [PMID]
20. [20] S. A. Kauffman, "Metabolic stability and epigenesis in randomly constructed genetic nets, J. of Theoretical Biology, vol. 22, no. 3, pp. 437-67, March 1969. [DOI:10.1016/0022-5193(69)90015-0]
21. [21] S. A. Kauffman, "The large-scale structure and dynamics of gene control circuits: an ensemble approach," J. of Theoretical Biology, vol. 44, no. 1, pp. 167-90, March 1974. [DOI:10.1016/S0022-5193(74)80037-8]
22. [22] J. I. Joo, et al, "Determining relative dynamic stability of cell states using Boolean network mode," Nature Scientific Reports, vol. 8, no. 1, online, Aug. 2018. [DOI:10.1038/s41598-018-30544-0] [PMID] [PMCID]
23. [23] www.humancellatlas.org, accessed Sep. 2018.
24. [24] H. P. Yockey, Information theory, evolution and origin of life, 2nd edition, Cambridge University Press, New York, 2011.
25. [25] S. L. Salzberg, et al, "Open questions: How many genes do we have?" BMC Biology, vol. 16, no. 1, online, Aug. 2018. [DOI:10.1186/s12915-018-0564-x] [PMID] [PMCID]
26. [26] Y. Lee, Qing Zhou, "Co-regulation in embryonic stem cells via context-dependent binding of transcription factors," Bioinformatics, vol. 29, no. 17, pp. 2162-68, Sept. 2013. [DOI:10.1093/bioinformatics/btt365] [PMID]
27. [27] A. J. M. Walhout, "What does biologically meaningful mean? A perspective on gene regulatory network validation," Genome Biology, vol. 12, no. 4, online, April 2011. [DOI:10.1186/gb-2011-12-4-109] [PMID] [PMCID]
28. [28] M. Hecker, et al, "Gene regulatory network inference: Data integration in dynamic models - a review," BioSystems, vol. 96, no. 1, pp. 86-103, April 2009. [DOI:10.1016/j.biosystems.2008.12.004] [PMID]
29. [29] W. Lee, W.S. Tzou, "Computational methods for discovering gene networks from expression data," Briefings in Bioinformatics, vol. 10, no. 4, pp. 408-23, July 2009. [DOI:10.1093/bib/bbp028] [PMCID]
30. [30] Q. Zhang et al, "Using single-index ODEs to study dynamic gene regulatory networks," PLoS One Computational Biology, vol. 13, no. 2, online, Feb. 2018. [DOI:10.1371/journal.pone.0192833] [PMID] [PMCID]
31. [31] T. M. Cover, J. A. Thomas, Elements of information theory, 2nd edition, Wiley, New York, 2006.
32. [32] K. Do, P. Muller, M. Vannucci, Bayesian inference for gene expression and proteomics, Cambridge University Press, New York, 2006.
33. [33] P. Lin and S. P. Khatri, "Determining gene function in Boolean networks using Boolean satisfiability," IEEE Int'l Workshop on Genomic Signal Processing and Statistics (GENSIPS), San Antonio, Dec. 2012. [DOI:10.1109/GENSIPS.2012.6507757]
34. [34] T. E. Ideker, V. Thorsson, R. M. Karp, "Discovery of regulatory interactions through perturbation: Inference and experimental design," Proc. Pacific Symposiums on Biocomputing, pp. 305-316, Hawaii, Jan. 2000.
35. [35] A. Kaufman, M. Kupiec, E. Ruppin, "Multi-knockout genetic network analysis: the Rad6 example, Proc. IEEE Computational Systems and Bioinformatics (CSB) Conference, pp. 332-340, Stanford, California, Feb 2004.
36. [36] A. Kaufman, et al, "Quantitative analysis of genetic and neuronal multi-perturbation experiments," PLoS One Computational Biology, vol. 1, no. 6, online, Nov. 2005. [DOI:10.1371/journal.pcbi.0010064.eor]
37. [37] R. Dehghannasiri, B. Yoon, Edward R. Dougherty, "Optimal experimental design for gene regulatory networks in the presence of uncertainty," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 12, no. 4, pp. 938-950, July 2015. [DOI:10.1109/TCBB.2014.2377733] [PMID]
38. [38] A. R. Alizad-Rahvar, M. Sadeghi, "Integrative perturbation analysis of logic-based models of gene regulatory networks," PLoS One Computational Biology, Accepted, Oct. 2018.

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


© 2015 All Rights Reserved | Signal and Data Processing