Accurate estimation of bioreaction parameters (e.g., the substrate reaction rate constant and the substrate half-saturation parameter in Monod or Monod-derived equations) is critical for successful modeling, reactor design, and scale-up in bioremediation. Conventional maximum likelihood estimation methods are not well suited to estimation of parameters associated with complex nonlinear biological reactions and small-scale experimental data. This paper demonstrates that Bayesian estimation, a standard approach for parameter estimation for physiologically based pharmacokinetic models, is viable for estimating model parameters for such dynamic biological systems. This approach is illustrated using reaction kinetic data from replicated batch experiments for toluene and trichloroethylene (TCE) biodegradation by the microorganism Pseudomonas putida F1. This paper evaluates the prediction capabilities of Bayesian estimation by comparing predicted and observed data and reports on goodness-of-fit statistics. The results demonstrate that Bayesian estimation methods can be particularly useful for bioreaction kinetic determination in the presence of small data.