ABSTRACT

Urea-hydrolysing biofilms are crucial to applications in medicine, engineering, and science. Quantitative information about ureolysis rates in biofilms is required to model these applications. We formulate a novel model of urea consumption in a biofilm that allows different kinetics, for example either first order or Michaelis–Menten. The model is fit to synthetic data to validate and compare two approaches, Bayesian and nonlinear least squares (NLS), commonly used by biofilm practitioners. The shortcomings of NLS motivate the Bayesian approach where a simple Markov Chain Monte Carlo (MCMC) sampler is applied. The model is then fit to real data of influent and effluent urea concentrations from experiments with biofilms of Escherichia coli. Results from synthetic data aid in interpreting results from real data, where first-order and Michaelis–Menten kinetic models are compared. The method shows potential for general applications requiring biofilm kinetic information.

摘要

尿素水解生物膜对于医学、工程和科学的应用至关重要。需要关于生物膜中尿素分解率的定量信息来模拟这些应用。我们在生物膜中制定了一种新的尿素消耗模型，该模型允许不同的动力学，例如一级或 Michaelis-Menten。该模型适用于合成数据，以验证和比较生物膜从业者常用的两种方法，即贝叶斯和非线性最小二乘法 (NLS)。NLS 的缺点激发了贝叶斯方法，其中应用了简单的马尔可夫链蒙特卡罗 (MCMC) 采样器。然后将该模型拟合到来自大肠杆菌生物膜实验的进水和出水尿素浓度的真实数据. 合成数据的结果有助于解释真实数据的结果，其中比较了一阶和 Michaelis-Menten 动力学模型。该方法显示出需要生物膜动力学信息的一般应用的潜力。