Modelling the Growth of Moraxella sp. B on Monobromoacetic acid (MBA)

Main Article Content

Shukor M.S.

Abstract

Monohalogenated acetic acids have been widely used in industry and agriculture as precursor tochemicals and as herbicides, respectively. Bioremediation of xenobiotics have been touted as amore economical and feasible method compared to physical and chemical approaches. In thiswork, we model the growth of Moraxella sp. B on monobromoacetic acid (MBA) from publishedliterature to obtain vital growth constants. These growth constants can only be accuratelyobtained from mathematical modelling of the growth curves using various available primarymodels such as logistic, Gompertz, Richards, Schnute, Baranyi-Roberts, Von Bertalanffy,Buchanan three-phase and more recently Huang models. The Buchanan three-phase model waschosen as the best model based on statistical tests such as root-mean-square error (RMSE),adjusted coefficient of determination (R2), bias factor (BF), accuracy factor (AF) and correctedAICc (Akaike Information Criterion). The constants obtained indicated that the lag period wasincreased as the concentrations of MAB was increased. In addition, the growth rate was severely reduced at high concentrations of MAB indicating substrate inhibition. Novel constants obtained from the modelling exercise would be useful for further secondary modelling implicating the effect of media conditions and other factors on the growth of this bacterium on MAB.

Article Details

How to Cite
M.S., Shukor. Modelling the Growth of Moraxella sp. B on Monobromoacetic acid (MBA). Bulletin of Environmental Science and Management, [S.l.], v. 3, n. 1, p. 1-6, dec. 2015. ISSN 2289-5876. Available at: <http://journal.hibiscuspublisher.com/index.php/BESM/article/view/258>. Date accessed: 19 feb. 2018.
Section
Articles

References

[1] McRae BM, Lapara TM, Hozalski RM. Biodegradation of
haloacetic acids by bacterial enrichment cultures. Chemosphere.
2004;55(6):915–25.
[2] Torz MS, Yankov DS, Beschkov DN. Biodegradation of monoand
dihaloacetic acids by Moraxella sp. B. Comptes Rendus
L’Academie Bulg Sci. 2006;59(3):295–300.
[3] Kühn R, Pattard M. Results of the harmful effects of water
pollutants to green algae (Scenedesmus subspicatus) in the cell
multiplication inhibition test. Water Res. 1990;24(1):31–8.
[4] Hanson ML, Sibley PK, Ellis DA, Mabury SA, Muir DCG,
Solomon KR. Evaluation of monochloroacetic acid (MCA)
degradation and toxicity to Lemna gibba, Myriophyllum
spicatum, and Myriophyllum sibiricum in aquatic microcosms.
Aquat Toxicol. 2002;61(3-4):251–73.
[5] Müller SR, Zweifel H-R, Kinnison DJ, Jacobsen JA, Meier MA,
Ulrich MM, et al. Occurrence, sources, and fate of trichloroacetic
acid in Swiss waters. Environ Toxicol Chem. 1996;15(9):1470–8.
[6] Berg M, Müller SR, Mühlemann J, Wiedmer A, Schwarzenbach
RP. Concentrations and mass fluxes of trifluoroacetic acid in rain
and natural waters in Switzerland. Environ Sci Technol.
2000;34(13):2675–83.
[7] Scott BF, Mactavish D, Spencer C, Strachan WMJ, Muir DCG.
Haloacetic acids in Canadian lake waters and precipitation.
Environ Sci Technol. 2000;34(20):4266–72.
[8] Hamid AAA, Tengku Abdul Hamid TH, Wahab RA, Huyop F.
Identification of functional residues essential for dehalogenation
by the non-stereospecific -haloalkanoic acid dehalogenase from
Rhizobium sp. RC1. J Basic Microbiol. 2015;55(3):324–30.
[9] Weightman AL, Weightman AJ, Slater JH. Microbial
dehalogenation of trichloroacetic acid. World J Microbiol
Biotechnol. 1992;8(5):512–8.
[10] Yu P, Welander T. Growth of an aerobic bacterium with
trichloroacetic acid as the sole source of energy and carbon. Appl
Microbiol Biotechnol. 1995;42(5):769–74.
[11] Olaniran AO, Babalola GO, Okoh AI. Aerobic dehalogenation
potentials of four bacterial species isolated from soil and sewage
sludge. Chemosphere. 2001;45(1):45–50.
[12] Olaniran AO, Pillay D, Pillay B. Haloalkane and haloacid
dehalogenases from aerobic bacterial isolates indigenous to
contaminated sites in Africa demonstrate diverse substrate
specificities. Chemosphere. 2004;55(1):27–33.
[13] Alomar D, Abdul Hamid AA, Khosrowabadi E, Gicana RG,
Lamis RJ, Huyop F, et al. Molecular characterization of
monochloroacetate-degrading Arthrobacter sp. Strain d2 isolated
from Universiti Teknologi Malaysia agricultural area.
Bioremediation J. 2014;18(1):12–9.
[14] Tonomura K, Kawasaki H, Tone N, Yahara H. Plasmid encoding
mercury reductase and haloacetate halidohydrolase in drugresistant
bacteria. J Pharmacobiodyn. 1981;4(4).
[15] Liu J-Q, Kurihara T, Ichiyama S, Miyagi M, Tsunasawa S,
Kawasaki H, et al. Reaction mechanism of fluoroacetate
dehalogenase from Moraxella sp. B. J Biol Chem.
1998;273(47):30897–902.
[16] Zwietering MH, Jongenburger I, Rombouts FM, Van’t Riet K.
Modeling of the bacterial growth curve. Appl Environ Microbiol.
1990;56(6):1875–81.
[17] Baranyi J, Roberts TA. A dynamic approach to predicting
bacterial growth in food. Int J Food Microbiol. 1994;23(3-
4):277–94.
[18] Cloern JE, Nichols FH. A von Bertalanffy growth model with a
seasonally varying coefficient. J Fish Res Board Can.
1978;35(11):1479–82.
[19] Darmani Kuhi H, Kebreab E, Lopez S, France J. A derivation
and evaluation of the von Bertalanffy equation for describing
growth in broilers over time. J Anim Feed Sci. 2002;11(1):109–
25.
[20] Buchanan RL, Whiting RC, Damert WC. When is simple good
enough: A comparison of the Gompertz, Baranyi, and threephase
linear models for fitting bacterial growth curves. Food
Microbiol. 1997;14(4):313–26.
[21] Huang L. Growth kinetics of Escherichia coli O157:H7 in
mechanically-tenderized beef. Int J Food Microbiol.
2010;140(1):40–8.
[22] Rohatgi, A. WebPlotDigitizer.
http://arohatgi.info/WebPlotDigitizer/app/ Accessed June 2
2014.;
[23] Halmi MIE, Shukor MS, Johari WLW, Shukor MY. Evaluation
of several mathematical models for fitting the growth of the algae
Dunaliella tertiolecta. Asian J Plant Biol. 2014;2(1):1–6.
[24] 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;1(5):365–74.
[25] Akaike H. Statistical information processing system for
prediction and control. AAPG Bull Am Assoc Pet Geol.
1981;237–41.
[26] Ross T. Indices for performance evaluation of predictive models
in food microbiology. J Appl Bacteriol. 1996;81(5):501–8.
[27] Abou-Zeid KA, Yoon KS, Oscar TP, Schwarz JG, Hashem FM,
Whiting RC. Survival and growth of Listeria monocytogenes in
broth as a function of temperature, pH, and potassium lactate and
sodium diacetate concentrations. J Food Prot. 2007;70(11):2620–
5.
[28] Baranyi J. Comparison of stochastic and deterministic concepts
of bacterial lag. J Theor Biol. 1998;192(3):403–8.
[29] Caleb OJ, Mahajan PV, Al-Said FA-J, Opara UL. Modified
atmosphere packaging technology of fresh and fresh-cut produce
and the microbial consequences-A Review. Food Bioprocess
Technol. 2013;6(2):303–29.
[30] Singh Y, Srivastava SK. Performance improvement of Bacillus
aryabhattai ITBHU02 for high-throughput production of a tumorinhibitory
L-asparaginase using a kinetic model based approach.
J Chem Technol Biotechnol. 2014;89(1):117–27.
[31] Collins S. Competition limits adaptation and productivity in a
photosynthetic alga at elevated CO2. Proc R Soc B Biol Sci.
2011;278(1703):247–55.
[32] Nieminen JK. Enchytraeid population dynamics: Resource
limitation and size-dependent mortality. Ecol Model.
2009;220(11):1425–30.
[33] Li H, Xie G, Edmondson A. Evolution and limitations of primary
mathematical models in predictive microbiology. Br Food J.
2007;109(8):608–26.