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Biodegradation kinetics of aromatic hydrocarbon mixtures by pure and mixed bacterial cultures.

Microbial growth on pollutant mixtures is an important aspect of bioremediation and wastewater treatment. However, efforts to develop mathematical models for mixed substrate kinetics have been limited. Nearly all models group either the microbial population (as "biomass") or the chemical s...

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Detalles Bibliográficos
Autores principales: Reardon, Kenneth F, Mosteller, Douglas C, Rogers, Julia Bull, DuTeau, Nancy M, Kim, Kee-Hong
Formato: Texto
Lenguaje:English
Publicado: 2002
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1241285/
https://www.ncbi.nlm.nih.gov/pubmed/12634132
Descripción
Sumario:Microbial growth on pollutant mixtures is an important aspect of bioremediation and wastewater treatment. However, efforts to develop mathematical models for mixed substrate kinetics have been limited. Nearly all models group either the microbial population (as "biomass") or the chemical species (e.g., as biological oxygen demand). When individual chemical species are considered, most models assume either no interaction or that the nature of the interaction is competition for the same rate-limiting enzyme. And when individual microbial species are considered, simple competition for the growth substrate is the only interaction included. Here, we present results using Pseudomonas putida F1 and Burkholderia sp. strain JS150 growing individually and together on benzene, toluene, phenol, and their mixtures and compare mathematical models to describe these results. We demonstrate that the simple models do not accurately predict the outcome of these biodegradation experiments, and we describe the development of a new model for substrate mixtures, the sum kinetics with interaction parameters (SKIP) model. In mixed-culture experiments, the interactions between species were substrate dependent and could not be predicted by simple competition models. Together, this set of experimental and modeling results presents our current state of work in this area and identifies challenges for future modeling efforts.