TRIPLE OBJECTIVE OPTIMIZATION OF CYTOTOXIC POTENCY OF HUMAN CARCINOMA CELL LINES OF A MARINE MACROALGAE USING NON-SORTING GENETIC ALGORITHMâ€“A THEORETICAL STUDY
Objective: The main purpose of our work was to arrive at an acceptable model for optimizing the cytotoxic potency of Ulva fasciata Delile extract on human carcinoma cell lines of which can provide believable indications as compared to experimental results.
Methods: The experimental result for cytotoxic potency of a methanolic extract of the Ulva fasciata Delile (MEUF) with a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay against human colon carcinoma (HT-29), human hepatocyte carcinoma (Hep-G2), and human breast carcinoma (MCF-7) cell lines was used to carry out a multi-objective (triple objective) optimization. Thirty non-dominating solutions were considered for analyses of absorbance (y1), % cell survival (y2) and% cell inhibition (y3) data.
Results: The model developed using non-dominated sorting genetic algorithm (NSGA) was compared with data obtained experimentally and the results were found to be significant. This method has distinct advantages over other methods which relied heavily on statistical-regression-models, in the sense that it does triple-objective optimization. The resulted in obtaining solutions which were not only significant or believable, but it also corroborated well with experimental results. Thus the solutions obtained during optimization provided the necessary data for generating a successful model.Conclusion: The solutions obtained by NSGA method helped to build an acceptable model for optimizing the cytotoxic potency of Ulva fasciata Delile on human carcinoma cell lines.
2. Hanson PC, Bade DL, Carpenter SR. Lake metabolism: relationships with dissolved organic carbon and phosphorus. Limnol Oceanogr 2003;48:1112-9.
3. Bhat ZA, Ansari SH, Mukhtar HM, Naved T, Siddiqui J, Khan N. Effect of Aralia cachemirica decne root extracts on blood glucose level in normal and glucose-loaded rats. Pharmazie 2005;60:712-3.
4. Thornburg CC, Zabriskie TM, McPhail KL. Deep-sea hydrothermal vents: potential hot spots for natural products discovery? J Nat Prod 2010;73:489-99.
5. Niehaus F, Bertoldo C, KÃ¤hler M, Antranikian G. Extremophiles as a source of novel enzymes for industrial application. Appl Microbiol Biotechnol 1999;51:711-29.
6. Vijayavel K, Martinez JA. In vitro antioxidant and antimicrobial activities of two Hawaiian marine Limu: Ulva fasciata (Chlorophyta) and Gracilaria salicornia (Rhodophyta). J Med Food 2010;13:1494-9.
7. Kolanjinathan K, Saranraj P. Pharmacological efficacy of marine seaweed Gracilaria edulis extracts against clinical pathogens. Global J Pharmacol 2014;8:268-74.
8. Sujatha S, Rajasree SR, Sowmya JD, Donatus M. Imminent intriguing acquired potential biological effect of marine sea weeds: a review. World J Pharma Res 2015;4:524-41.
9. Samuelsson G. Drugs of natural origin: a textbook of pharmacognosy, Swedish Pharmaceutical Press. First ed. Stockholm, 1999.10. Hughes CC, Fenical W. Antibacterial from the sea. Chemistry 2010;16:12512-25.
10. Yasuhara-Bell J, Lu Y. Marine compounds and their antiviral activities. Antiviral Res 2010;86:231-40.
11. Smith VJ, Desbois AP, Dyrynda EA. Conventional and nonconventional antimicrobials from fish, marine invertebrates and micro-algae. Mar Drugs 2010;8:1213-62.
12. Das MK, Sahu PK, Rao GS, Mukkanti K. Application of response surface method to evaluate the cytotoxic potency of Ulva fasciata Delile, a marine macro alga. Saudi J Biol Sci 2014;21:539-46.
13. Satya EJ, Anand M, Venkateswarlu M. Optimum culture medium composition for Rhamnolipid production by Pseudomonas aeruginosa AT10 using a novel multi-objective optimization method. J Chem Technol Biotechnol 2013;88:271-9.
14. Satya EJ, Venkateswarlu C. Evaluation of anaerobic biofilm reactor kinetic parameters using ant colony optimization. Environ Eng Sci 2013;30:527-35.
15. Satya EJ, Anand P, Venkateswarlu C. Optimal state estimation and on-line optimization of a biochemical reactor. Chem Proc Eng 2013;34:449-62.
16. Satya EJ, Venkateswarlu C. Optimization of culture conditions for chinese hamster ovary (CHO) cells production using differential evolution. Int J Pharm Sci 2012;4:465-70.
17. Ghosh A, Saha PD. Optimization of copper reduction from solution using Bacillus pumilus PD3, isolated from Marine water. Pollution 2013;55:12910-4.
18. Kiran GS, Lipton AN, Priyadharshini S, Anitha K, SuÃ¡rez LE, Arasu MV, et al. Antiadhesive activity of polyhydroxy butyrate biopolymer from a marine Brevibacterium casei MSI04 against shrimp pathogenic vibrios. Microb Cell Fact 2014;13:114.
19. Chen XP, Wang WX, Li SB, Xue JL, Fan LJ, Sheng ZJ, et al. Optimization of ultrasound-assisted extraction of Lingzhi polysaccharides using response surface methodology and its inhibitory effect on cervical cancer cells. Carbohydr Polym 2010;80:944-8.
20. Deb K. Multi-objective optimization using evolutionary algorithms, John Wiley and Sons Limited, New York; 2001.
21. Rajesh JK, Gupta SK, Rangaiah GP, Ray AK. Multi-objective optimization of industrial hydrogen plants. Chem Eng Sci 2001;56:999â€“1010.
22. Cheng SH, Chen HJ, Chang H, Chang CK, Chen YM. Multi-objective optimization for two catalytic membrane reactors-Methanol synthesis and hydrogen production. Chem Eng Sci 2008;63:1428-37.
23. Burger J, Hasse H. Multi-objective optimization using reduced models in the conceptual design of a fuel additive production process. Chem Eng Sci 2015;99:118-26.
24. Oh PP, Rangaiah GP, Ray AK. Simulation and multi-objective optimization of an industrial hydrogen plant based on refinery off-gas. Ind Eng Chem Res 2002;41:2248-61.
25. Vandervoort A, Thibault J, Gupta Y. In: Multi-objective optimization in chemical engineering: Developments and applications, Eds. Rangaiah GP, Bonilla-Petriciolet A, John Wiley and Sons Ltd, Oxford, UK; 2013.
26. Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q. Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evolutionary Computation 2011;1:32-49.
27. Vallerio M, Hufkens J, Impe JV, Logist F. Interactive multi-objective decision support for the optimization of nonlinear dynamic (Bio) chemical processes with Uncertainty. 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering, Elsevier; 2015. p. 2585.
28. Box GEP, Wilson KB. On the experimental attainment of optimum conditions. J Royal Statistical Soc: Series B 1951;13:1-45.
29. Draper NR. Introduction to box and Wilson on the experimental attainment of optimum conditions. In: Breakthroughs in statistics: methodology and distribution, Springer-Verlag New York; 1992. p. 267-9.
30. Myers RH, Montgomery DC, Anderson-Cook CM. In: Response Surface Methodology: Process and Product Optimization Using Designed Experiments. 3rd ed. John Wiley and Sons, NY; 2009.
31. Khuri AI, Mukhopadhyay S. Response surface methodology. WIREs Computational Statistics 2010;2:128-49.
32. Bradley N. The response surface methodology; 2007. Available from: https://www.iusb.edu/math-compsci/graduate-thesis. php. [Last accessed on 02 Mar 2016].
33. Oehlert GW. In: Design and analysis of experiments: Response surface design. W. H. Freeman and Company, New York; 2000.
34. Hilla WJ, Huntera WG. A review of response surface methodology: a literature survey. Technometrics 2010;8:571-90.
35. Zhang M, Das C, Vasquez H, Aguilera D, Zage P, Gopalakrishnan V, et al. Predicting tumor cell repopulation after response: mathematical modeling of cancer cell growth. Anticancer Res 2006;26:2933-6.
36. Mitchell M. In: An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press; 1996.
37. Mitchell M. Can evolution explain how the mind works? A review of the evolutionary psychology debates. Complexity 1999;3:17-24.
38. Reev C, Rowe JE. In: Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory. Springer; 2002.
39. Wang Y, Fan K. Applying genetic algorithms on pattern recognition: An analysis and survey. In: Pattern Recognition, International Conference on. IEEE Comput Soc 1996;2:740-0.
40. Cui FJ, Li Y, Xu ZH, Xu HY, Sun K, Tao WY. Optimization of the medium composition for production of mycelial biomass and exopolymer by Grifola frondosa GF9801 using response surface methodology. Bioresour Technol 2006;97:1209-16.
41. Duta FP, Franca FP, de Almeida Lopes LM. Optimization of culture conditions for epoxy saccharides production in rhizobeumsp using the response surface method. Electron J Biotechnol 2006;9:391-7.
42. Anjum MF, Tasadduq I, Al-Sultan K. Response surface methodology: a neural network approach. Eur J Opera Res 1996;101:65-73.
43. Xia YG, Yang BY, Di Wang JL, Yang Q, Kuang HX. Optimization of simultaneous ultrasonic-assisted extraction of water-soluble and fat-soluble characteristic constituents from Forsythiae Fructus Using response surface methodology and high-performance liquid chromatography. Pharmacogn Mag 2014;10:292-303.
44. Abraham A, Jain L, Goldberg R. Multi-objective evolutionary optimization: Theoretical advances and applications, Springer Science+Business Media; 2004.
45. Jena S. Multi-objective optimization of design parameters of a shell and tube type heat exchanger using a genetic algorithm. Int J Curr Eng Technol 2013;3:1379-86.
46. Jan-Christer Janson T. Optimization of large-scale chromatography of proteins. Korean J Chem Eng 2001;18:149-58.
47. Kyeongsook Kim, Kwang Sin Kim, Eun-Su Chung, Soon Hwan Son, Duk-Won Kang, Kyungwha Kim, et al. A manufacturing process of self luminou s glass tube utiliz ing tritium gas: optimization of phosphor coating conditions. Korean J Chem Eng 2005;22:899-904.
48. Yu-Jung Choi, Tae-In Kwon, Yeong-Koo Yeo. Optimization of the sulfolane extraction plant based on modeling and simulation. Korean J Chem Eng 2000;17:712-8.
49. Jung Heon Lee, Henry C Lim. Development of nonsingular optimization algorithm and its application to chemical engineering systems. Korean J Chem Eng 1999;16:118-27.