Stochastic simulation of growth curves of Acidithiobacillus ferrooxidans
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Abstract
To reveal the low growth rate of Acidithiobacillus ferrooxidans, a stochastic growth model was proposed to analyze growth curves of these bacteria in a batch culture. An algorithm was applied to simulate the bacteria population during lag and exponential phase. The results show that the model moderately fits the experimental data. Further, the mean growth constant (K) of growth curves is obtained by fitting the logarithm of the simulating population data versus the generation numbers with the different initial population number (N0) and initial mean activity of population (A0). When N0 is 300 and 700 respectively, the discrepancy of K value is only 0.91%, however, A0 is 0.34 and 0.38 respectively, the discrepancy of K value is 19.53%. It suggests that the effect of A0 on the lag phase exceeds N0, though both parameters could shorten the lag phase by increasing their values.
Key words
Acidithiobacillus ferrooxidans stochastic simulation growth curves lag phaseCLC number
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References
- [1]Kelly D P, Wood A P. Reclassification of some species of Thiobacillus to the newly designated genera Acidithiobacillus gen nov Halothiobacillus gen nov and Thermithiobacillus gen nov[J]. Int J Syst Evol Microbiol, 2000, 50(2): 489–500.CrossRefGoogle Scholar
- [2]YANG Yu, PENG Hong, HU Yue-hua, et al. Molecular diversity of the gene of Fe(II)-oxidizing enzyme of Acidithiobacillus ferrooxidans [J]. Hereditas, 2005, 27(5): 787–791.Google Scholar
- [3]Nestor D, Valdivia U, Chaves A P. Mechanisms of bioleaching of a refractory mineral of gold with Thiobacillus ferrooxidans[J]. International Journal of Mineral Processing, 2001, 62(1): 187–198.CrossRefGoogle Scholar
- [4]Sadowski Z, Jazdzyk E, Karas H. Bioleaching of copper ore flotation concentrates[J]. Minerals Engineering, 2003, 16(1): 51–53.CrossRefGoogle Scholar
- [5]Brierley J A, Brierley C L. Present and future commercial applications of biohydrometallurgy[J]. Hydrometallurgy, 2001, 59(2): 233–239.CrossRefGoogle Scholar
- [6]Prescott L M, Harley J P, Klein D A. Microbiology [M]. 4th ed. New York: McGraw-Hill, 1999: 115–117.Google Scholar
- [7]Ageeva S N, Kondrateva T F, Karavaiko G I. Phenotypic characteristics of thiobacillus ferrooxidant strains [J]. Microbiology, 2001, 70(2): 186–194.CrossRefGoogle Scholar
- [8]ZHANG Chuan-fu, MIN Xiao-bo, CHAI Li-yuan, et al. Influencing factors of lag phase in growth of Thiobacillus ferrooxidans [J]. Journal of Central South University of Technology: Natural Science, 1999, 30 (5): 489–492. (in Chinese)Google Scholar
- [9]LONG Zhong-er, CAI Zhao-ling, CONG Wei, et al. Advances in bioleaching kinetics of sulfide ores[J]. Mining and Metallurgy Engineering, 2002, 22(1): 6–10.Google Scholar
- [10]Kawabe Y, Inoue C, Suto K, et al. Inhibitory effect of high concentrations of ferric ions on the activity of Acidithiobacillus ferrooxidans [J]. Journal of Bioscience and Bioengineering, 2003, 96(4): 375–379.CrossRefGoogle Scholar
- [11]Meruane G, Vargas T. Bacterial oxidation of ferrous iron by Acidithiobacillus ferrooxidans in the pH range 2.5 – 7.0 [J]. Hydrometallurgy, 2003, 71(12): 149–158CrossRefGoogle Scholar
- [12]Gatti M N, Milocco R H, Giaveno A. Modeling the bacterial oxidation of ferrous iron with Acidithiobacillus ferroxidans using kriging interpolation [J]. Hydrometallurgy, 2003, 71(11): 89–96.CrossRefGoogle Scholar
- [13]Buchanan R L, Whiting R C, Damert W C. When is simple good enough: a comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves[J]. Food Microbiology, 1997, 14(4): 313–326.CrossRefGoogle Scholar
- [14]PENG Hong, YANG Yu, HU Yue-hua, et al. Structure Analysis of 16S rDNA Sequences from Strains of Acidithiobacillus ferrooxidans[J]. Journal of Biochemistry and Molecular Biology, 2006, 39(2): 178–182.Google Scholar