Lall Raman, Mitchell Julie
BACTER Institute, University of Wisconsin-Madison, Wisconsin 53706, USA.
Bioinformatics. 2007 Oct 15;23(20):2754-9. doi: 10.1093/bioinformatics/btm400. Epub 2007 Aug 25.
Metal reduction kinetics have been studied in cultures of dissimilatory metal reducing bacteria which include the Shewanella oneidensis strain MR-1. Estimation of system parameters from time-series data faces obstructions in the implementation depending on the choice of the mathematical model that captures the observed dynamics. The modeling of metal reduction is often based on Michaelis-Menten equations. These models are often developed using initial in vitro reaction rates and seldom match with in vivo reduction profiles.
For metal reduction studies, we propose a model that is based on the power law representation that is effectively applied to the kinetics of metal reduction. The method yields reasonable parameter estimates and is illustrated with the analysis of time-series data that describes the dynamics of metal reduction in S.oneidensis strain MR-1. In addition, mixed metal studies involving the reduction of Uranyl (U(VI)) to the relatively insoluble tetravalent form (U(IV)) by S. alga strain (BR-Y) were studied in the presence of environmentally relevant iron hydrous oxides. For mixed metals, parameter estimation and curve fitting are accomplished with a generalized least squares formulation that handles systems of ordinary differential equations and is implemented in Matlab. It consists of an optimization algorithm (Levenberg-Marquardt, LSQCURVEFIT) and a numerical ODE solver. Simulation with the estimated parameters indicates that the model captures the experimental data quite well. The model uses the estimated parameters to predict the reduction rates of metals and mixed metals at varying concentrations.
Supplementary data are available at Bioinformatics online.
已在异化金属还原细菌（包括希瓦氏菌属MR-1菌株）的培养物中研究了金属还原动力学。根据捕捉观测动态的数学模型的选择，从时间序列数据估计系统参数在实施过程中面临障碍。金属还原的建模通常基于米氏方程。这些模型通常使用初始体外反应速率开发，很少与体内还原曲线匹配。
对于金属还原研究，我们提出了一个基于幂律表示的模型，该模型有效地应用于金属还原动力学。该方法产生了合理的参数估计，并通过对描述希瓦氏菌属MR-1菌株中金属还原动态的时间序列数据的分析进行了说明。此外，在存在与环境相关的铁水合氧化物的情况下，研究了混合金属研究，即海藻菌株（BR-Y）将铀酰（U(VI)）还原为相对不溶性的四价形式（U(IV)）。对于混合金属，参数估计和曲线拟合通过处理常微分方程组并在Matlab中实现的广义最小二乘公式来完成。它由一个优化算法（列文伯格-马夸尔特，LSQCURVEFIT）和一个数值常微分方程求解器组成。用估计参数进行的模拟表明该模型能很好地捕捉实验数据。该模型使用估计参数预测不同浓度下金属和混合金属的还原速率。
补充数据可在《生物信息学》在线获取。
