Primary Mathematical Modeling of the Growth of SDS by a bacterium Isolated From a Paddy Field
DOI:
https://doi.org/10.54987/jemat.v12i1.1062Keywords:
Primary models, Biodegradation, SDS, MMF model, Pseudomonas sp.Abstract
The maximum specific growth rate and other critical parameters can be obtained from the mathematical modelling of microbial growth on toxic cubstances using nonlinear regression of several models. These models form the basis for secondary modelling in predictive microbiology. NumerousiImportant biotechnological applications like bioremediation and wastewater treatment rely on models that explain how substrates affect bacterial growth and biotransformation processes. These models include the modified Gompertz, modified Logistic, modified Richards, Buchanan-3-phase, Baranyi-Roberts, modified Schnute, von Bertalanffy, Morgan-Mercer-Flodin (MMF), and Huang. A previously isolated SDS-degrading Pseudomonas sp. strain Maninjau1 was studied for its growth on SDS using these primary models. Experimental data showed that SDS concentrations from 0.25 to 2.2 g/L (250 to 2200 mg/L) were toxic, slowing bacterial growth. As SDS concentrations increased, toxicity was evident, marked by an increase in the lag phase from 5.9 to 6.9 hours and a decline in biomass. The highest growth rate was observed at 1000 mg/L SDS. The bet model based on error function discriminatory analysis is MMF, which outperformed other models that were tested. The reasons for this include a favorable accuracy (AF) and bias factor (BF), a low root-mean-square (RMSE) and AICc value, and a high adjusted coefficient of determination. The validity of the MMF model for simulating bacterial development in toxic environments is supported by its high reliability. Insights into how bacteria adapt and grow under stressful circumstances can greatly improve biotechnological processes.
References
Liwarska-Bizukojc E, Miksch K, Malachowska-Jutsz A, Kalka J. Acute toxicity and genotoxicity of five selected anionic and nonionic surfactants. Chemosphere. 2005 Mar;58(9):1249-53.
Chukwu LO, Odunzeh CC. Relative toxicity of spent lubricant oil and detergent against benthic macro-invertebrates of a west African estuarine lagoon. J Environ Biol. 2006 Jul;27(3):479-84.
Kumar M, Trivedi SP, Misra A, Sharma S. Histopathological changes in testis of the freshwater fish, Heteropneustes fossilis (Bloch) exposed to linear alkyl benzene sulphonate (LAS). J Environ Biol. 2007 Jul;28(3):679-84.
Pettersson A, Adamsson M, Dave G. Toxicity and detoxification of Swedish detergents and softener products. Chemosphere. 2000;41(10):1611-20.
Cserháti T, Forgács E, Oros G. Biological activity and environmental impact of anionic surfactants. Environ Int. 2002;28(5):337-48.
Rosety M, Ribelles A, Rosety-Rodriguez M, Carrasco C, Ordonez FJ, Rosety JM, et al. Morpho-histochemical study of the biological effects of sodium dodecyl sulphate on the digestive gland of the Portuguese oyster. Histol Histopathol. 2000;15(4):1137-43.
Najim AA, Ismail ZZ, Hummadi KK. Biodegradation potential of sodium dodecyl sulphate (SDS) by mixed cells in domestic and non-domestic actual wastewaters: Experimental and kinetic studies. Biochem Eng J. 2022 Mar 1;180:108374.
Syed MA, Mahamood M, Shukor MY, Shamaan NA. Isolation and characterization of SDS-degrading Pseudomonas aeruginosa sp. strain D1. Aust J Basic Appl Sci. 2010;4(10):5000-11.
Payne WJ, Feisal VE. Bacterial utilization of dodecyl sulfate and dodecyl benzene sulfonate. Appl Microbiol. 1963;11:339-44.
Asok AK, Jisha MS. Molecular characterization of linear alkylbenzene sulphonate degrading pseudomonas nitroreducens (MTCC 10463) and P. Aeruginosa (MTCC 10462). Indian J Biotechnol. 2013;12(4):514-22.
Chaturvedi V, Kumar A. Metabolism dependent chemotaxis of Pseudomonas aeruginosa N1 towards anionic detergent Sodium Dodecyl Sulfate. Indian J Microbiol. 2013;1-5.
Chaturvedi V, Kumar A. Presence of SDS-degrading enzyme, alkyl sulfatase (SdsA1) is specific to different strains of Pseudomonas aeruginosa. Process Biochem. 2013;48(4):688-93.
Cortés-Lorenzo C, Sánchez-Peinado MDM, Oliver-Rodríguez B, Vílchez JL, González-López JJ, Rodríguez-Díaz M. Two novel strains within the family Caulobacteraceae capable of degradation of linear alkylbenzene sulfonates as pure cultures. Int Biodeterior Biodegrad. 2013;85:62-5.
Dhouib A, Hamad N, Hassaïri I, Sayadi S. Degradation of anionic surfactants by Citrobacter braakii. Process Biochem. 2003;38(8):1245-50.
Halmi MIE, Hussin WSW, Aqlima A, Syed MA, Ruberto L, MacCormack WP, et al. Characterization of a sodium dodecyl sulphate-degrading Pseudomonas sp. strain DRY15 from Antarctic soil. J Environ Biol. 2013;34(6):1077-82.
Ke N, Xiao C, Ying Q, Ji S. A new species of the genus Phenylobacterium for the degradation of LAS (linear alkylbenzene sulfonate). Wei Sheng Wu Xue Bao. 2003;43(1):1-7.
Khleifat KM, Halasah RA, Tarawneh KA, Halasah Z, Shawabkeh R, Wedyan MA. Biodegradation of linear alkylbenzene sulfonate Byburkholderia sp.: Effect of some growth conditions. Int J Agric Biol. 2010;12(1):17-25.
Kostal J, Suchanek M, Klierova H, Demnerova K, Kralova B, McBeth DL. Pseudomonas C12B, an SDS degrading strain, harbours a plasmid coding for degradation of medium chain length n-alkanes. Int Biodeterior Biodegrad. 1998;42(4):221-8.
Lee C, Russell NJ, White GF. Modelling the kinetics of biodegradation of anionic surfactants by biofilm bacteria from polluted riverine sites: A comparison of five classes of surfactant at three sites. Water Res. 1995;29(11):2491-7.
Roig MG, Pedraz MA, Sanchez JM, Huska J, Tóth D. Sorption isotherms and kinetics in the primary biodegradation of anionic surfactants by immobilized bacteria: II. Comamonas terrigena N3H. J Mol Catal - B Enzym. 1998;4(5-6):271-81.
Thomas ORT, White GF. Immobilization of the surfactant-degrading bacterium Pseudomonas C12B in polyacrylamide gel. II. Optimizing SDS-degrading activity and stability. Enzyme Microb Technol. 1990;12(12):969-75.
Venkatesh C. A plate assay method for isolation of bacteria having potent Sodium Dodecyl Sulfate (SDS) degrading ability. Res J Biotechnol. 2013;8(2):27-31.
Wu C. Isolation and characterization of a sodium dodecyl benzene sulfonate degrading bacterial strain. Wei Sheng Wu Xue Bao. 2006;46(6):988-93.
Yilmaz F, Icgen B. Characterization of SDS-degrading Delftia acidovorans and in situ monitoring of its temporal succession in SDS-contaminated surface waters. Environ Sci Pollut Res. 2014;21(12):7413-24.
Yin J, Li J, Wang X, Wang F, Wang J, Zhang B, et al. Isolation of linear alkylbenzene sulfonate(LAS) -degrading bacterium and its degrading characters. Chin J Appl Environ Biol. 2005;11(4):483-5.
Rusnam, Syafrawati S, Rahman MF, Nasution FI, Othman AR. SDS-degrading Bacterium Isolated from a Paddy Field. Asian J Plant Biol. 2022 Dec 31;4(2):38-44.
Zubkov IN, Nepomnyshchiy AP, Kondratyev VD, Sorokoumov PN, Sivak KV, Ramsay ES, et al. Adaptation of Pseudomonas helmanticensis to fat hydrolysates and SDS: fatty acid response and aggregate formation. J Microbiol. 2021 Dec 1;59(12):1104-11.
Rusnam, Gusmanizar N, Shukor MY, Dan-Iya BI. Modelling the Effect of Copper on the Growth Rate of Enterobacter sp. strain Neni-13 on SDS. J Environ Microbiol Toxicol. 2021 Jul 31;9(1):10-5.
Manogaran M, Othman AR, Shukor MY, Halmi MIE. Modelling the Effect of Heavy Metal on the Growth Rate of an SDS-degrading Pseudomonas sp. strain DRY15 from Antarctic soil. Bioremediation Sci Technol Res. 2019 Jul 31;7(1):41-5.
Icgen B, Salik SB, Goksu L, Ulusoy H, Yilmaz F. Higher alkyl sulfatase activity required by microbial inhabitants to remove anionic surfactants in the contaminated surface waters. Water Sci Technol J Int Assoc Water Pollut Res. 2017 Nov;76(9-10):2357-66.
Rahman MF, Rusnam M, Gusmanizar N, Masdor NA, Lee CH, Shukor MS, et al. Molybdate-reducing and SDS-degrading Enterobacter sp. Strain Neni-13. Nova Biotechnol Chim. 2016 Dec 1;15(2):166-81.
Margesin R, Schinner F. Biodegradation of the anionic surfactant sodium dodecyl sulfate at low temperatures. Int Biodeterior Biodegrad. 1998;41(2):139-43.
George AL. Seasonal factors affecting surfactant biodegradation in Antarctic coastal waters: Comparison of a polluted and pristine site. Mar Environ Res. 2002;53(4):403-15.
Arora J, Chauhan A, Ranjan A, Rajput VD, Minkina T, Zhumbei AI, et al. Degradation of SDS by psychrotolerant Staphylococcus saprophyticus and Bacillus pumilus isolated from Southern Ocean water samples. Braz J Microbiol [Internet]. 2024 Mar 12 [cited 2024 May 21]; Available from: https://doi.org/10.1007/s42770-024-01294-1
Yahuza S, Dan-Iya BI, Sabo IA. Modelling the Growth of Enterobacter sp. on Polyethylene. J Biochem Microbiol Biotechnol. 2020 Jul 31;8(1):42-6.
Rusnam, Yakasai HM, Rahman MF, Gusmanizar N, Shukor MY. Mathematical Modeling of Molybdenum-Blue Production from Bacillus sp. strain Neni-. Bioremediation Sci Technol Res. 2021 Jul 31;9(1):7-12.
Yakasai MH, Manogaran M. Kinetic Modelling of Molybdenum-blue Production by Bacillus sp. strain Neni-10. J Environ Microbiol Toxicol. 2020 Jul 31;8(1):5-10.
López S, Prieto M, Dijkstra J, Dhanoa MS, France J. Statistical evaluation of mathematical models for microbial growth. Int J Food Microbiol. 2004;96(3):289-300.
McKellar RC, Knight K. A combined discrete-continuous model describing the lag phase of Listeria monocytogenes. Int J Food Microbiol. 2000;54(3):171-80.
Kim HW, Lee SA, Yoon Y, Paik HD, Ham JS, Han SH, et al. Development of kinetic models describing kinetic behavior of Bacillus cereus and Staphylococcus aureus in milk. Korean J Food Sci Anim Resour. 2013;33(2):155-61.
Li MY, Sun XM, Zhao GM, Huang XQ, Zhang JW, Tian W, et al. Comparison of Mathematical Models of Lactic Acid Bacteria Growth in Vacuum-Packaged Raw Beef Stored at Different Temperatures. J Food Sci. 2013;78(4):M600-4.
Zwietering MH, Jongenburger I, Rombouts FM, Van't Riet K. Modeling of the bacterial growth curve. Appl Environ Microbiol. 1990;56(6):1875-81.
Buchanan RL, Whiting RC, Damert WC. When is simple good enough: A comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves. Food Microbiol. 1997;14(4):313-26.
Bratbak G. Bacterial Biovolume and Biomass Estimations. Appl Environ Microbiol. 1985 Jun;49(6):1488-93.
Motulsky HJ, Ransnas LA. Fitting curves to data using nonlinear regression: a practical and nonmathematical review. FASEB J Off Publ Fed Am Soc Exp Biol. 1987 Nov;1(5):365-74.
Halmi, MIE, ,Shukor MS, Johari W.L.W WLW, Shukor MY. Mathematical Modeling of the Growth Kinetics of Bacillus sp . on Tannery Effluent Containing Chromate. J Environ Bioremediation Toxicol. 2014;2(1):6-10.
Akaike H. Factor analysis and AIC. Psychometrika. 1987;52(3):317-32.
Ross T, McMeekin TA. Predictive microbiology. Int J Food Microbiol. 1994;23(3-4):241-64.
Syed M, Mahamood M, Shukor M, Shamaan NA, others. Isolation and characterization of SDS-degrading Pseudomonas aeruginosa sp. strain D1. Aust J Basic Appl Sci. 2010;4(10):5000-11.
Chaturvedi V, Kumar A. Presence of SDS-degrading enzyme, alkyl sulfatase (SdsA1) is specific to different strains of Pseudomonas aeruginosa. Process Biochem. 2013;48(4):688-93.
Shahbazi R, Kasra-Kermanshahi R, Gharavi S, Moosavi- Nejad Z, Borzooee F. Screening of SDS-degrading bacteria from car wash wastewater and study of the alkylsulfatase enzyme activity. Iran J Microbiol. 2013;5(2):153-8.
Chaturvedi V, Kumar A. Diversity of culturable sodium dodecyl sulfate (SDS) degrading bacteria isolated from detergent contaminated ponds situated in Varanasi city, India. Int Biodeterior Biodegrad. 2011;65(7):961-71.
Chaturvedi V, Kumar A. Isolation of a strain of Pseudomonas putida capable of metabolizing anionic detergent sodium dodecyl sulfate (SDS). Iran J Microbiol. 2011;3(1):47-53.
Shukor MY, Husin WSW, Rahman MFA, Shamaan NA, Syed MA. Isolation and characterization of an SDS-degrading Klebsiella oxytoca. J Environ Biol. 2009;30(1):129-34.
Halmi MIE, Wasoh H, Sukor S, Ahmad SA, Yusof MT, Shukor MY. Bioremoval of molybdenum from aqueous solution. Int J Agric Biol. 2014;16(4):848-50.
Baranyi J, Roberts TA. A dynamic approach to predicting bacterial growth in food. Int J Food Microbiol. 1994;23(3-4):277-94.
Agarry SE, Audu TOK, Solomon BO. Substrate inhibition kinetics of phenol degradation by Pseudomonas fluorescence from steady state and wash-out data. Int J Environ Sci Technol. 2009;6(3):443-50.
Othman AR, Bakar NA, Halmi MIE, Johari WLW, Ahmad SA, Jirangon H, et al. Kinetics of molybdenum reduction to molybdenum blue by Bacillus sp. strain A.rzi. BioMed Res Int. 2013;2013:Article number 371058.
Halmi MIE, Shukor MS, Masdor NA, Shamaan NA, Shukor MY. Testing the normality of residuals on regression model for the growth of Paracoccus sp. SKG on acetonitrile. J Environ Bioremediation Toxicol. 2015;3(1):15-7.
Benkhennouche-Bouchene H, Mahy JG, Lambert SD, Hayoun B, Deflaoui O, Bourouina M, et al. Statistical modeling and optimization of Escherichia coli growth parameters for the biological treatment of phenol. Biocatal Agric Biotechnol. 2021 Jul 1;34:102016.
Van Impe JF, Poschet F, Geeraerd AH, Vereecken KM. Towards a novel class of predictive microbial growth models. Int J Food Microbiol. 2005;100(1-3):97-105.
Zwietering MH, De Wit JC, Cuppers HGAM, Van't Riet K. Modeling of bacterial growth with shifts in temperature. Appl Environ Microbiol. 1994;60(1):204-13.
Zwietering MH, Rombouts FM, Van 't Riet K. Some aspects of modelling microbial quality of food. Food Control. 1993;4(2):89-96.
Baranyi J. Mathematics of predictive food microbiology. Int J Food Microbiol. 1995;26(2):199-218.
Ratkowsky DA, Ross T, McMeekin TA, Olley J. Comparison of Arrhenius-type and Belehradek-type models for prediction of bacterial growth in foods. J Appl Bacteriol. 1991;71(5):452-9.
Morgan PH, Mercer LP, Flodin NW. General model for nutritional responses of higher organisms. Proc Natl Acad Sci. 1975 Nov 1;72(11):4327-31.
Santos SA, Souza G da S e, Oliveira MR de, Sereno JR. Uso de modelos não-lineares para o ajuste de curvas de crescimento de cavalos pantaneiros. Pesqui Agropecuária Bras. 1999 Jul;34(7):1133-8.
Topal M, Bolukbasi ?C. Comparison of nonlinear growth curve models in broiler chickens. J Appl Anim Res. 2008 Dec 1;34(2):149-52.
Tariq M, Iqbal F, Eyduran E, Bajwa M, Huma Z, Waheed A. Comparison of non-linear functions to describe the growth in Mengali sheep breed of Balochistan. Pak J Zool. 2013 Jun 1;45:661-5.
Augustine A, Imelda J, Paulraj R, David NS. Growth kinetic profiles of Aspergillus niger S14 a mangrove isolate and Aspergillus oryzae NCIM 1212 in solid state fermentation. Indian J Fish. 2015;62(3):100-6.
Kemper CM. Growth and development of the brush-tailed rabbit-rat (Conilurus penicillatus), a threatened tree-rat from northern Australia. Aust Mammal. 2020 Jun 5;
Yahuza S, Sabo IA. Mathematical Modelling of the Growth of Bacillus cereus Strain wwcp1on Malachite Green Dye. J Biochem Microbiol Biotechnol. 2021 Dec 31;9(2):25-9.
Giacon TG, de Gois e Cunha GC, Eliodório KP, Oliveira RP de S, Basso TO. Homo- and heterofermentative lactobacilli are distinctly affected by furanic compounds. Biotechnol Lett. 2022 Dec 1;44(12):1431-45.
Tomulescu C, Moscovici M, Stoica R, Albu G, Sevcenco C, Vamanu A. Investigation of culture conditions by Response Surface Methodology and kinetic modeling for exopolysaccharide production by Klebsiella oxytoca ICCF 419 strain, using lactose as substrate. Romanian Biotechnol Lett. 2020 Aug 18;25:2033-44.
Carvalho ÂR, Genz Bazana LC, Ferrão MF, Fuentefria AM. Curve fitting and linearization of UV-Vis spectrophotometric measurements to estimate yeast in inoculum preparation. Anal Biochem. 2021 Jul 15;625:114216.
Khamis A, Ismail Z, Haron K, Mohammed AT. Nonlinear Growth Models for Modeling Oil Palm Yield Growth. J Math Stat. 2005 Sep 30;1(3):225-33.
Germec M, Turhan I. Ethanol production from acid-pretreated and detoxified tea processing waste and its modeling. Fuel. 2018 Nov 1;231:101-9.
Aisami A, Shukor MYA. Predictive Mathematical Modelling of the Total Number of COVID-19 Cases for the Kingdom of Saudi Arabia. J Environ Microbiol Toxicol. 2020 Jul 31;8(1):11-5.
Yahuza S, Sabo IA, Dan-Iya BI, Shukor MYY. Prediction of Cumulative Death Cases in Nigeria Due to COVID-19 Using Mathematical Models. Bull Environ Sci Sustain Manag. 2020 Jul 31;4(1):20-4.
Shukor MYA, Sabo IA, Yahuza S, Dan-Iya BI, Wada SA. Prediction of Cumulative Death Cases in The United States Due to COVID-19 Using Mathematical Models. J Environ Microbiol Toxicol. 2020 Jul 31;8(1):37-41.
Uba G, Yakasai HM, Abubakar A, Shukor MYY. Prediction of Cumulative Death Cases in Brazil Due to Covid-19 Using Mathematical Models. Bull Environ Sci Sustain Manag. 2020 Jul 31;4(1):13-9.
Yakasai HM, Shukor MYA. Predictive Mathematical Modelling of the Total Number of COVID-19 Cases for The United States. Bioremediation Sci Technol Res. 2020 Jul 31;8(1):11-6.
Aisami AB, Umar AM, Shukor MYA. Prediction of Cumulative Death Cases in Indonesia Due to COVID-19 Using Mathematical Models. Bioremediation Sci Technol Res. 2020 Jul 31;8(1):32-6.
Wijeratne AW, Karunaratne JA. Morgan-Mercer-Flodin model for long term trend analysis of currency exchange rates of some selected countries. Int J Bus Excell. 2013 Dec 2;7(1):76-87.
Bolker BM. Ecological Models and Data in R. Princeton, N.J: Princeton University Press; 2008. 408 p.
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