Background/Objectives:Biofilms and specifically urea-hydrolysing biofilms are of interest to the medical community (for example, urinary tract infections), scientists and engineers (for example, microbially induced carbonate precipitation). To appropriately model these systems, biofilm-specific reaction rates are required. A simple method for determining biofilm-specific reaction rates is described and applied to a urea-hydrolysing biofilm.Methods:Biofilms were grown in small silicon tubes and influent and effluent urea concentrations were determined. Immediately after sampling, the tubes were thin sectioned to estimate the biofilm thickness profile along the length of the tube. Urea concentration and biofilm thickness data were used to construct an inverse model for the estimation of the urea hydrolysis rate.Results/Conclusions:It was found that urea hydrolysis in Escherichia coli MJK2 biofilms is well approximated by first-order kinetics between urea concentrations of 0.003 and 0.221 mol/l (0.186 and 13.3 g/l). The first-order rate coefficient (k1) was estimated to be 23.2±6.2 h−1. It was also determined that advection dominated the experimental system rather than diffusion, and that urea hydrolysis within the biofilms was not limited by diffusive transport. Beyond the specific urea-hydrolysing biofilm discussed in this work, the method has the potential for wide application in cases where biofilm-specific rates must be determined.

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 it 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 on 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.

The maximal binding capacity (MBC) of hepatic T3 nuclear receptors was decreased in uremic rats (132 +/- 37 fmol/mg DNA) compared to sham-operated controls (212 +/- 44 fmol/mg DNA; P < 0.025), while the equilibrium affinity constants (Ka) remained unaltered (1.8 +/- 0.4 and 1.5 +/- 0.3 X 10(9) M-1 in the uremic and control rats, respectively, P = NS). There was also a reduction in the MBC of the kidney T3 receptors, from 73 +/- 14 fmol/mg DNA in the control animals to 32 +/- 7 fmol/mg DNA in the uremic rats (P < 0.10), while the Ka values were identical in both groups (1.9 +/- 0.5 X 10(9) M-1). In addition, there were significant reductions in serum T4 (1.5 +/- 0.7 microgram/dl) and T3 (92 +/- 10 ng/dl) in the uremic rats compared to control rats, whose T4 levels averaged 4.4 +/- 0.1 microgram/dl (P < 0.005) and whose T3 levels averaged 140 +/- 13 ng/dl (P < 0.005). Further, insulin levels averaged 83 +/- 21 microU/ml in uremic rats and 38 +/- 7 microU/ml in control rats (P < 0.025), while glucagon levels averaged 457 +/- 114 pg/ml in the uremic rats and 101 +/- 30 pg/ml in the control animals (P < 0.0125). These data suggest that 1) in addition to starvation and hepatectomy, uremia is another pathological condition associated with the modification of the number of T3 receptors, 2) the reduction in MBC observed may be generalized rather than organ specific for hepatic nuclear receptors, and 3) elevated glucagon levels are associated with reduced MBC in uremia, but it is indeterminate whether hyperglucagonemia is the etiology of the decrease.