A Predictive Batch Culture Growth and Biosynthesis for Bacillus cereus (ATCC 14579) using Response Surface Methodology

Authors

  • Mani Malam Ahmad Department of Biology, Faculty of Science, Kano University of Science and Technology (KUST), Wudil, 3244, Kano, Nigeria.
  • Abd.aziz Mohd Azoddein Faculty of Chemical and Natural Resources Engineering, Universiti Malaysia Pahang (UMP), Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang, Malaysia.
  • Olusegun Abayomi Olalere Analytical Biochemistry Research Centre (ABRC), Universiti Sains Malaysia (USM), 11800, Gelugor, Penang, Malaysia.
  • Shuaibu Isa Department of Microbiology, Faculty of Sciences, Gombe State University, P.M.B 127, Tudun Wada, Gombe State,Nigeria.

DOI:

https://doi.org/10.54987/jebat.v5i2.765

Keywords:

Optimization, Growth, Bacillus cereus, Batch mode, Central composite face-centred design (CCFD, RSM

Abstract

Optimization studies of Bacillus cereus (ATCC 14579) tolerance to stress phenomenon could serve as a basic prerequisite for its utilization in future biotechnological research. Successful cell culture at optimum parametric conditions was found to prepare the cells to withstand upper and lower range values depending on the need and purpose. This study was performed to develop a predictive optimum model for the growth of this mesophilic bacterium, B. cereus in batch culture shake flasks. The linear and mutual interactions effects of nutrient availability (4-16 g/L) and composition, temperature (30-40 oC) and agitation (140 - 200 rpm) collectively termed as growth regulators were sought using central composite face-centred design (CCFD) response surface methodology (RSM). The effectiveness of the independent variables on the dependent variable was weighted and validated using statistical and graphical indices which spelt a suitable predictive model for B. cereus growth and biosynthesis. This model provided an efficient and reliable approach for predicting the growth of B. cereus as a function of the growth-influencing markers. The results showed that the model term is highly significant at P >0.0001 and a well-correlated adjusted R2 and predicted R2 of less than 1.0 (0.9984). Moreover, the coefficient of determination value of only 1.45 % variability as well as agreed predictive (3.01) versus experimental (3.0) values depicted that the hidden (noise) effect was very minimal. Therefore, the model further confirmed the versatility of the isolate to simple growth nutrient within defined optimal physical operational parameters of simple shake flask culture.

References

Amaya OM, Barragán MT, Tapia FJ. Microbial biomass in batch and continuous systems. InBiomass Now-Sustainable Growth and Use 2013 Apr 30. IntechOpen.

de Lucena DK, Pühler A, Weidner S. The role of sigma factor RpoH1 in the pH stress response of Sinorhizobium meliloti. BMC Microbiol. 2010;10(1):1-7.

Maier RM. Bacterial Growth. Environ Microbiol. 2010;37-54.

Fajardo C, Mora M, Fernández I, Mosquera-Corral A, Campos JL, Méndez R. Cross effect of temperature, pH and free ammonia on autotrophic denitrification process with sulphide as electron donor. chemosphere 2014;97:10-15.

Munna MS, Tamanna S, Afrin MR, Sharif GA, Mazumder C, Kana KS, et al. Influence of aeration speed on bacterial colony forming unit (CFU) formation capacity. Am J Microbiol Res. 2014;2(1):47-51.

Siti MZ, Nurhaslina RC, Ku HK. Influence of agitation , pH and temperature on growth and decolorization of batik wastewater by bacteria Lactobacillus delbruckii. Int J Res Rev Appl Sci. 2013;14(2):269-275.

Bas D, Boyaci IH. Modeling and optimization I: Usability of response surface methodology. J Food Eng 2007;78(3):836-845.

Hamid Rashedi, Ali Izadi MEB. Optimization of operational parameters in rhamnolipid production by Pseudomonas aeruginosa MM1011 in a miniaturized shaken bioreactor. J Appl Biotechnol Reports. 2015;2(3):271-278.

Wang SJ, Loh KC. New cell growth pattern on mixed substrates and substrate utilization in cometabolic transformation of 4-chlorophenol. Water Res. 2000;34(15):3786-3794.

Durve A, Chandra N. FT-IR analysis of bacterial biomass in response to heavy metal stress. Int J. biotechol.. 2014;112:386-391.

Standbury, P.F., Whitaker, A. and Hall SJ. Principle of fermentation. 2nd ed. vol. 53, Principles of fermentation technology. oxford: Butterworth Heinemann. Oxford:1984. 351 p.

Roebuck K, Brundin A, Johns M. Response surface optimization of temperature and pH for the growth of Pachysolen tannophilus. Enzyme Microbial Technol. 1995;17(1):75-78.

Schultz D, Kishony R Optimization and control in bacterial lag phase. BMC Biol. 2013;11(1):120.

François F, Lombard C, Guigner JM, Soreau P, Brian-Jaisson F, Martino G, et al. Isolation and characterization of environmental bacteria capable of extracellular biosorption of mercury. Appl Environ Microbiol. 2012;78(4):1097-1106.

Momen SB, Siadat SD, Akbari N, Ranjbar B, Khajeh K. Applying central composite design and response surface methodology to optimize growth and biomass production of Haemophilus influenzae type B. Jundishapur J Microbiol. 2016;9(6)

Mani MA, Abd. Aziz MA, Mior Ahmad Khusairi Bin MZ, Mazrul Nizam Bin AS, Mohammed SJ. Screening of effective markers for mesophilic bacterium growth using factorial experimental design. Int J Biosci Biotechnol. 2017;9(3):59-74.

Khan N, Yahaya CM, Faizal M, Mohamed P, Abustan I. Process optimization for zn ( ii ) removal by activated carbon prepared from rice husk using chemical activation. Int J Basic Sppl sci IJBAS-IJENS. 2010;10(6):79-83.

Sahu JN, Acharya J, Meikap BC. Response surface modeling and optimization of chromium(vi) removal from aqueous solution using tamarind wood activated carbon in batch process. J Hazard Mater. 2009;172(2-3):818-25.

Bandari F, Safa F, Shariati S. Application of response surface method for optimization of adsorptive removal of eriochrome black t using magnetic multi-wall carbon nanotube nanocomposite. Rab J Sci Eng. 015;40(12):3363-72.

te Giffel MC, Zwietering MH. Validation of predictive models describing the growth of listeria monocytogenes. Int J Food Microbiol. 1999;46(2):135-149.

Dorn JG, Frye RJ, Maier RM. Effect of temperature , ph , and initial cell number on luxcdabe and nah gene expression during naphthalene and salicylate catabolism in the bioreporter organism Pseudomonas putida rb1353. Appl Environ Microbiol. 2003;69(4):2209-2216.

Periago, P. M., Schaik W V., Abee, T., Wouters JA. Identification of proteins involved in the heat stress response of bacillus cereus atcc 14579. Appl Environ Microbiol. 2002;68(7):3486-3495.

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Published

2022-12-31

How to Cite

Ahmad, M. M., Azoddein, A. M. ., Olalere, O. A. ., & Isa, S. (2022). A Predictive Batch Culture Growth and Biosynthesis for Bacillus cereus (ATCC 14579) using Response Surface Methodology. Journal of Environmental Bioremediation and Toxicology, 5(2), 40–45. https://doi.org/10.54987/jebat.v5i2.765

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