دوره 17، شماره 2 - ( 6-1399 )                   جلد 17 شماره 2 صفحات 112-101 | برگشت به فهرست نسخه ها


XML English 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-fa.html
بیات رضا، صادقی مهدی، عارف محمد رضا. مدل‌‌سازی شبکه‌‌های تنظیم ژنی: مدل‌های کلاسیک، اختلال بهینه برای شناسایی شبکه. پردازش علائم و داده‌ها. 1399; 17 (2) :112-101

URL: http://jsdp.rcisp.ac.ir/article-1-918-fa.html


دانشگاه یزد
چکیده:   (137 مشاهده)
ارتقای عمق و گستره درک ما از دانش زیستشناسی ملکولی، از یک سو امکان بهره‌برداری از آن را در توسعه فناوری­هایی مانند رمزگشایی فراهم ساخته است و از سوی دیگر، مداخله در سیستم ژنتیکی را امکان‏‌پذیر میسازد که نویدبخش آیندهای روشن برای علوم زیستی و پزشکی است. دست‌‌یابی به این هدف با مداخله در شبکه تنظیم ژنی (GRN) امکانپذیر می­شود؛ زیرا GRN کنترل‌کننده فعالیتهای زیستی در سطح ملکولی است. در این مسیر، شناسایی GRN، شامل شناسایی مرز، ساختار و گره‌­های شبکه اهمیت بهسزایی دارد. در این مقاله به دو جنبه ساختار و گره در مدلسازی و شناسایی GRN در شبکههای بزرگ (با بیش از پنجاه گره) پرداخته میشود. نخست محدودیت‏‌های کاربست مدلهای احتمالاتی برای گره (ژن) مورد بررسی قرار می‏‌گیرد. همچنین محدودیت‏‌های کاربست مدل چند-درختی برای ساختار GRN مورد بررسی قرار می‏‌گیرد. در بخش اصلی مقاله، مسأله شناسایی GRN با مدل بولی مورد بحث قرار گرفته و نشان داده می‏‌شود که بر‌خلاف تصور معمول، آزمایش بهینه از دید شناسایی ساختار GRN، آزمایش تک‌اختلال است.
متن کامل [PDF 3633 kb]   (49 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: مقالات گروه علائم حیاتی ( مرتبط با مهندسی پزشکی)
دریافت: 1397/8/1 | پذیرش: 1397/11/6 | انتشار: 1399/6/24 | انتشار الکترونیک: 1399/6/24

فهرست منابع
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.

ارسال نظر درباره این مقاله : نام کاربری یا پست الکترونیک شما:
CAPTCHA

ارسال پیام به نویسنده مسئول


کلیه حقوق این تارنما متعلق به فصل‌نامة علمی - پژوهشی پردازش علائم و داده‌ها است.