Lauchnor E G, Topp D M, Parker A E, Gerlach R
Center for Biofilm Engineering, Montana State University, Bozeman, MT, USA.
Department of Civil Engineering, Montana State University, Bozeman, MT, USA.
J Appl Microbiol. 2015 Jun;118(6):1321-32. doi: 10.1111/jam.12804. Epub 2015 Apr 21.
Ureolysis drives microbially induced calcium carbonate precipitation (MICP). MICP models typically employ simplified urea hydrolysis kinetics that do not account for cell density, pH effect or product inhibition. Here, ureolysis rate studies with whole cells of Sporosarcina pasteurii aimed to determine the relationship between ureolysis rate and concentrations of (i) urea, (ii) cells, (iii) NH4+ and (iv) pH (H(+) activity).
Batch ureolysis rate experiments were performed with suspended cells of S. pasteurii and one parameter was varied in each set of experiments. A Michaelis-Menten model for urea dependence was fitted to the rate data (R(2) = 0·95) using a nonlinear mixed effects statistical model. The resulting half-saturation coefficient, Km , was 305 mmol l(-1) and maximum rate constant, Vmax , was 200 mmol l(-1) h(-1) . However, a first-order model with k1 = 0·35 h(-1) fit the data better (R(2) = 0·99) for urea concentrations up to 330 mmol l(-1) . Cell concentrations in the range tested (1 × 10(7) -2 × 10(8) CFU ml(-1) ) were linearly correlated with ureolysis rate (cell dependent Vmax' = 6·4 × 10(-9) mmol CFU(-1) h(-1) ).
Neither pH (6-9) nor ammonium concentrations up to 0·19 mol l(-1) had significant effects on the ureolysis rate and are not necessary in kinetic modelling of ureolysis. Thus, we conclude that first-order kinetics with respect to urea and cell concentrations are likely sufficient to describe urea hydrolysis rates at most relevant concentrations.
These results can be used in simulations of ureolysis driven processes such as microbially induced mineral precipitation and they verify that under the stated conditions, a simplified first-order rate for ureolysis can be employed. The study shows that the kinetic models developed for enzyme kinetics of urease do not apply to whole cells of S. pasteurii.
尿素分解驱动微生物诱导碳酸钙沉淀（MICP）。MICP模型通常采用简化的尿素水解动力学，未考虑细胞密度、pH值影响或产物抑制作用。在此，利用巴氏芽孢杆菌的全细胞进行尿素分解速率研究，旨在确定尿素分解速率与以下因素浓度之间的关系：（i）尿素、（ii）细胞、（iii）NH₄⁺和（iv）pH值（H⁺活性）。
用巴氏芽孢杆菌的悬浮细胞进行间歇尿素分解速率实验，每组实验中改变一个参数。使用非线性混合效应统计模型，将尿素依赖性的米氏模型拟合到速率数据（R² = 0.95）。所得的半饱和系数Km为305 mmol·l⁻¹，最大速率常数Vmax为200 mmol·l⁻¹·h⁻¹。然而，对于高达330 mmol·l⁻¹的尿素浓度，k1 = 0.35 h⁻¹的一级模型对数据拟合得更好（R² = 0.99）。测试范围内的细胞浓度（1×10⁷ - 2×10⁸ CFU·ml⁻¹）与尿素分解速率呈线性相关（细胞依赖性Vmax' = 6.4×10⁻⁹ mmol·CFU⁻¹·h⁻¹）。
pH值（6 - 9）和高达0.19 mol·l⁻¹的铵浓度对尿素分解速率均无显著影响，在尿素分解动力学建模中并非必需。因此，我们得出结论，对于尿素和细胞浓度而言，一级动力学可能足以描述大多数相关浓度下的尿素水解速率。
这些结果可用于模拟尿素分解驱动的过程，如微生物诱导的矿物沉淀，并且验证了在所述条件下，可采用简化的尿素分解一级速率。该研究表明，为脲酶酶动力学开发的动力学模型不适用于巴氏芽孢杆菌的全细胞。
