Optimization of Cdx Transcription Factors Characteristics

Author(s): Javanbakht T.

Affiliation(s): Department of Computer Science, University of Quebec in Montreal, 201, President Kennedy St., (QC) H2X 3Y7 Montreal, Canada

*Corresponding Author’s Address: [email protected]

Issue: Volume 10, Issue 2 (2023)

Submitted: April 19, 2023
Received in revised form: June 16, 2023
Accepted for publication: August 13, 2023
Available online: August 16, 2023

Javanbakht T. (2023). Optimization of Cdx transcription factors characteristics. Journal of Engineering Sciences (Ukraine), Vol. 10(2), pp. E1–E7. DOI: 10.21272/jes.2023.10(2).e1

DOI: 10.21272/jes.2023.10(2).e1

Research Area:  MECHANICAL ENGINEERING: Computational Mechanics

Abstract. This study presents a new application of TOPSIS for the optimization of transcription factors characteristics. This application is essential as it can help compare the characteristics of these proteins and determine the optimized output of their comparison with this decision-making method. The hypothesis in this article was that according to the previous study of the Cdx transcription factors, as the Cdx2 transcription factor showed more robust characteristics than Cdx1 and Cdx4, the TOPSIS method would show a better rank position of these first proteins in comparison with the two other ones. Moreover, the engrailed repressor domain EnRCdx1 used in the plasmid showed the reduction of the pax3 gene expression in comparison with the induced regulation of the gene expression with the production of the Cdx1, Cdx2, and Cdx4 transcription factors using the corresponding plasmids, the worst rank position with TOPSIS was expected for this repressor domain. The results obtained with this ranking method showed that the rank positions of the transcription factors and the repressor domain corresponded to their compared properties. Moreover, the change in the weight values of the candidates showed the modification of their distances from the best and worst alternatives and closeness coefficients. However, as expected, the candidates’ rank positions were unchanged, and the Cdx2 transcription factor was still the best candidate. The results of this article can be used in computer engineering to improve biological applications of these proteins.

Keywords: decision-making process, TOPSIS, algorithm, transcription factor, optimization.


  1. Kobryń, A., Prystrom, J. (2016). A data pre-processing model for the TOPSIS method. Folia Oeconomica Stetinensia, Vol. 6(20), pp. 219–235. https://doi.org/10.1515/foli-2016-0036.
  2. Rahim, R., Supyiandi, S., Siahaan, A. P. U., Listyorini, T., Utomo, A. P., Triyanto, W. A., Irawan, Y., Aisyah, S., Khairani, M., Sundari, S., Khairunnisa, K. (2018). TOPSIS method application for decision support system in internal control for selecting best employees. Journal of Physics: Conference Series, Vol. 1028, 012052. https://doi.org/10.1088/1742-6596/1028/1/012052
  3. Vavrek, R. Bečica, J. (2022). Similarity of TOPSIS results based on criterion variability: Case study on public economic. Plos One, Vol. 17(8), e0271951. https://doi.org/10.1371/journal.pone.0271951
  4. Houska, M., Domeova, L., Berankova, M. (2012). The conversion of cost and benefit criteria in the TOPSIS method. International Journal of Applied Decision Sciences, Vol. 5(4), 050021. https://doi.org/10.1504/IJADS.2012.050021
  5. Baczkiewicz, A. (2021). MCDM based e-commerce consumer decision support tool. Procedia Computer Science, Vol. 192, pp. 4991–5002. https://doi.org/10.1016/j.procs.2021.09.277
  6. Wu, Z., Abdul-Nour, G. (2020). Comparison of multi-criteria group decision-making methods for urban sewer network plan selection. CivilEng, Vol. 1(1), pp. 26–48. https://doi.org/doi:10.3390/civileng1010003
  7. De Farias Aires, R. F., Ferreira, L. (2022). A new multi-criteria approach for sustainable material selection problem. Sustainability, Vol. 14(8), 11191. https://doi.org/10.3390/141811191
  8. Sałabun, W. (2013). The mean error estimation of TOPSIS method using a fuzzy reference models. Journal of Theoretical and Applied Computer Science, Vol. 7(3), pp. 40–50.
  9. Boija, A., Klein, I. A., Sabari, B. R., Dall’Agnese, A., Coffey, E. L., Zamudio, A. V., Li, C. H., Shrinivas, K., Manteiga, J. C., Hannett, N. M., Abraham, B. J., Afeyan, L. K., Guo, Y. E., Rimel, J. K., Fant, C. B., Schuijers, J. S., Lee, T. I., Taatjes, D. J., Young, R. A. (2018). Transcription factors activate genes through the phase-separation capacity of their activation domains. Cell, Vol. 175, pp. 1842–1855. https://doi.org/10.1016/j.cell.2018
  10. Wu, Z., Nicoll, M., Ingham, R. J. (2021). AP-1 family transcription factors: a diverse family of proteins that regulate varied cellular activities in classical Hodgkin lymphoma and ALK+ ALCL. Experimental Hematology and Oncology, Vol. 10, 4. https://doi.org/10.1186/s40164-020-00197-9
  11. Lee, T. I., Young, R. A. (2014). Transcriptional regulation and its misregulation in disease. Cell, Vol. 152(6), pp. 1237–1251. https://doi.org/10.1016/j.cell.2013.02.014
  12. Odame, E., Chen, Y., Zheng, S., Dai, D., Kyei, B., Zhan, S., Cao, J., Guo, J., Zhong, T., Wang, L., Li, L., Zhang, H. (2021). Enhancer RNAs: transcriptional regulators and workmates of NamiRNAs in myogenesis. Cellular and Molecular Biology Letters, Vol. 26, 4. https://doi.org/10.1186/s11658-021-00248-x
  13. Wyrwicz, L., Gaj, P., Hoffmann, M., Rychlewski, L., Ostrowski, J. (2007). A common cis-element in promoters of protein synthesis and cell cycle genes. Acta Biochimica Polonica, Vol. 54(1), pp. 89–98. https://doi.org/10.18388/abp.2007_3273
  14. Yamamoto, T., Sakaue, T., Schiessel, H. (2021). Slow chromatin dynamics enhances promoter accessibility to transcriptional condensates. Nucleic Acids Res, Vol. 49(9), pp. 5017–5027. https://doi.org/10.1093/nar/gkab275
  15. Patra, P. (2015). Regulation Expression Pathway Analysis (REPA): A Novel Method to Facilitate Biological Interpretation of High Throughput Expression Profiling Data. M.Sc. Thesis, Memorial University of Newfoundland, Canada. Available online: https://research.library.mun.ca/11644/1/thesis.pdf
  16. English, B. P., Singer, R. H. (2015). A three-camera imaging microscope for high-speed single-molecule tracking and super resolution imaging in living cells. Proc SPIE Int Soc Opt Eng, Vol. 9550, 955008. https://doi.org/10.1117/12.2190246
  17. Janiak, A., Kwaśniewski, M., Szarejko, I. (2016). Gene expression regulation in roots under drought. J Exp Bot, Vol. 67(4), pp. 1003–1014. https://doi.org/10.1093/jxb/erv512
  18. Cheng, C., Shi, X., Zhang, Y., Wang, B., Wu, J., Yang, S., Wang, S. (2022). Identification, characterization and comparison of the genome-scale UTR introns from six citrus species. Horticulturae, Vol. 8(5), 434. https://doi.org/10.3390/horticulturae8050434
  19. Galganski, L., Urbanek, M. O., Krzyzosiak, W. J. (2017). Nuclear speckles: Molecular organization, biological function and role in disease. Nucleic Acids Research, Vol. 45(18), pp. 10350–10368. https://doi.org/10.1093/nar/gkx759
  20. Patenge, N., Pappesch, R., Khani, A., Kreikemeyer, B. (2015). Genome-wide analyses of small non-coding RNAs in streptococci. Front. Genet., Sec. RNA, Vol. 6, 189. https://doi.org/10.3389/fgene.2015.00189
  21. Sanchez-Ferras, O., Coutaud, B., Samani, T. J., Tremblay, I., Souchkova, O., Pilon, N. (2012). Caudal-related homeobox (Cdx) protein-dependent integration of canonical Wnt signaling on Paired-box 3 (Pax3) neural crest enhancer. Journal of Biological Chemistry, Vol. 287(20), pp. 16623–16635. https://doi.org/10.1074/jbc.M112.356394
  22. Samani, T. J. (2011). Régulation de l’Expression du Gène Pax-3 par les Facteurs de Transcription Cdx. Ph.D. Thesis, University of Quebec in Montreal, Canada. Available online: https://archipel.uqam.ca/3948/1/M11984.pdf
  23. Monsoro-Burq, A. H. (2015). PAX transcription factors in neural crest development. Seminars in Cell and Developmental Biology, Vol. 44, pp. 87–96. https://doi.org/10.1016/j.semcdb.2015.09.015
  24. Rogers, C. D., Nie, S. (2018). Specifying neural crest cells: From chromatin to morphogens and factors in between. Wiley Interdiscip Rev Dev Biol., Vol. 7(5), e322. https://doi.org/10.1002/wdev.322
  25. Angelopoulou, E., Paudel, Y.N., Piperi, C. (2019). Emerging pathogenic and prognostic significance of paired Box 3 (PAX3) protein in adult gliomas. Translational Oncology, Vol. 12(10), pp. 1357–1363. https://doi.org/10.1016/j.tranon.2019.07.001
  26. Javanbakht, T., Chakravorty, S. (2022). Prediction of human behavior with TOPSIS. Fuzzy Extension and Applications, Vol. 3(2), pp. 109–125. https://doi.org/10.22105/jfea.2022.326185.1197
  27. Pavić, Z., Novoselac, V. (2013). Notes on TOPSIS method. International Journal of Engineering Research and General Science, Vol. 1(2), pp. 5–12.
  28. Javanbakht, T. (2022). Modélisation et Traitement Informatique de l’Inconsistance des Croyances Épistémiques. Ph.D. Thesis, University of Quebec in Montreal, Canada.
  29. Javanbakht, T. (2022). Optimization of physical instruments’ characteristics with TOPSIS, Ukrainian Journal of Mechanical Engineering and Materials Science, Vol. 8(3), pp. 1–9. https://doi.org/10.23939/ujmems2022.03.001
  30. Heera, P., Shanmugam, S. (2015). Nanoparticle characterization and application: An overview. Int. J. Curr. Microbiol. App. Sci, Vol. 4(8), pp. 379–386.
  31. Javanbakht, T., David, E. (2020). Rheological and physical properties of a nanocomposite of graphene oxide nanoribbons with polyvinyl alcohol. Journal of Thermoplastic Composite Materials, Vol. 35(5), pp. 651–664. https://doi.org/10.1177/0892705720912767
  32. Javanbakht, T., Laurent, S., Stanicki, D., David, E. (2019). Related physicochemical, rheological, and dielectric properties of nanocomposites of superparamagnetic iron oxide nanoparticles with polyethyleneglycol. Journal of Applied Polymer Science, Vol. 137, pp. 48280–48290. https://doi.org/10.1002/app.48280
  33. Javanbakht, T., Sokolowski, W. (2015). Thiol-ene/acrylate systems for biomedical shape-memory polymers. Shape Memory Polymers for Biomedical Applications, Vol. 2015, pp. 157–166. https://doi.org/10.1016/B978-0-85709-698-2.00008-8
  34. Djavanbakht, T., Carrier, V., André, J. M., Barchewitz, R., Troussel, P. (2000). Effets d’un chauffage thermique sur les performances de miroirs multicouches Mo/Si, Mo/C et Ni/C pour le rayonnement X mou. Journal de Physique IV, Vol. 10, pp. 281–287. https://doi.org/10.1051/jp4:20001031
  35. Ahmed, H. M., Roy, A., Wahab, M., Ahmed, M., Othman-Qadir, G., Elesawy, B. H., Khandaker, M. U., Islam, M. N., Emran, T. B. (2021). Applications of nanomaterials in agrifood and pharmaceutical industry. Journal of Nanomaterials, Vol. 2021, 1472096. https://doi.org/10.1155/2021/1472096
  36. Ali, A. S. (2020). Application of Nanomaterials in Environmental Improvement. In: Sen, M. (eds.) Nanotechnology and the Environment. Intechopen. https://doi.org/10.5772/intechopen.91438
  37. Javanbakht, T., Laurent, S., Stanicki, D., Frenette, M. (2020). Correlation between physicochemical properties of superparamagnetic iron oxide nanoparticles and their reactivity with hydrogen peroxide. Canadian Journal of Chemistry, Vol. 98, pp. 601–608. https://doi.org/10.1139/cjc-2020-0087
  38. Javanbakht, T., Ghane-Motlagh, B., Sawan, M. (2020). Comparative study of antibiofilm activity and physicochemical properties of microelectrode arrays. Microelectronic Engineering, Vol. 229, 111305. https://doi.org/10.1016/j.mee.2020.111305
  39. Javanbakht, T., Hadian, H., Wilkinson, K. J. (2020). Comparative study of physicochemical properties and antibiofilm activity of graphene oxide nanoribbons. Journal of Engineering Sciences, Vol. 7(1), pp. C1–C8. https://doi.org/10.21272/jes.2020.7(1).c1
  40. Cary, M. P., Bader G. D., Sander, C. (2005). Pathway information for systems biology. FEBS Letters, Vol. 579(8), pp. 1815–1820. https://doi.org/10.1016/j.febslet.2005.02.005
  41. Mcswiggen, J., Beigelman, L., Chowrira, B., Pavco, P., Fosnaugh, K., Jamison, S. (2003). RNA Interference Mediated Inhibition of Gene Expression Using Short Interfering Nucleic Acid. Canadian Patent Application, CA 2455447 A1, Ottawa-Hull, Canada.
  42. Kwon, W.-W., Lee, C.H., Choi, D.-H., Jin, J.-I. (2009). Materials science of DNA. Journal of Materials Chemistry, Vol. 19, pp. 1353–1380. https://doi.org/10.1039/b808030e
  43. Ellner, S. P., Guckenheimer, J. (2006). Cellular Dynamics: Pathways of Gene Expression. In: Ellner, S. P., Guckenheimer, J. (eds.) Dynamic Models in Biology. Princeton University Press, DeGruyter. https://doi.org/10.1515/9781400840960-007
  44. García-Martínez, J., González-Candelas, F., Pérez-Ortín, J. E. (2007). Common gene expression strategies revealed by genome-wide analysis in yeast. Genome Biology, Vol. 8, R222. https://doi.org/10.1186/gb-2007-8-10-r222
  45. Pope, S. D., Medzhitov, R. (2018). Emerging principles of gene expression programs and their regulation. Molecular Cell, Vol. 71(3), pp. 389–397. https://doi.org/10.1016/j.molcel.2018.07.017
  46. Zien, A., Küffner, R., Zimmer, R., Lengauer, T. (2000). Analysis of gene expression data with pathway scores. International Conference on Intelligent Systems for Molecular Biology, Vol. 8, pp. 407–417. Available online: https://cdn.aaai.org/ISMB/2000/ISMB00-041.pdf
  47. Valencia, J. V., Mone, M., Zhang, J., Weetall, M., Buxton, F. P., Hughes, T. E. (2004). Divergent pathways of gene expression are activated by the RAGE ligands S100b and AGE-BSA. Diabetes, Vol. 53(3), pp. 743–751. https://doi.org/10.2337/diabetes.53.3.743
  48. Jackson, D. A. (2001). Regulation of Gene Expression. In: Chapman, K. E., Higgins, S. J. (eds.) Essays in Biochemistry, Vol. 77(3), Portland Press, London, UK.
  49. Scherrer, K. (2012). Regulation of gene expression and the transcription factor cycle hypothesis. Biochimie, Vol. 94(4), pp. 1057–1068. https://doi.org/10.1016/j.biochi.2011.12.010
  50. Qi, Y., Yan, L., Wei, W. (2021). Analysis of related pathways of gene expression difference in muscle tissue of Alzheimer’s disease mice. In: International Conference on Biomedicine, Medical Services & Specialties. Francis Academic Press, UK, pp. 130–135. https://doi.org/10.25236/bmmss.2021.024
  51. Khanna, D., Rana, P. S. (2019). Improvement in prediction of antigenic epitopes using stacked generalisation: An ensemble approach. IET Systems Biology, Vol. 14(1), pp. 1–7. https://doi.org/10.1049/iet-syb.2018.5083
  52. Sivakumar, G., Muthu, R. (2020). Decision making method for accessing the risk factors of blood pressure and cholesterol using Crisp Topsis method. Malaya Journal of Matematik, Vol. 8(4), pp. 1867–1871. https://doi.org/10.26637/MJM0804/0088
  53. Chang, M.-H., Liou, J. J. H., Lo, H.-W. (2019). A hybrid MCDM model for evaluating strategic alliance partners in the green pharmaceutical industry. Sustainability, Vol. 11(15), 4065. https://doi.org/10.3390/su11154065
  54. Nastiti, K. D., Rahman, A., Nasruddin (2021). Multiobjective optimization of synechocytis culture in flat-plate photobioreactor toward optimal growth and exergy. Journal of Physics: Conference Series, Vol. 1858, 012038. https://doi.org/10.1088/1742-6596/1858/1/012038
  55. Javanbakht, T. (2022). Automated decision-making with TOPSIS for water analysis. Journal of Engineering Sciences, Vol. 9(1), pp. H19–H24. https://doi.org/10.21272/jes.2022.9(1).h3
  56. Javanbakht, T., Chakravorty, S. (2022). Optimization of machine learning algorithms for proteomic analysis using TOPSIS. Journal of Engineering Sciences, Vol. 9(2), pp. E7–E11. https://doi.org/10.21272/jes.2022.9(2).e2

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