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Biofilm Models (biofilm + models)
Selected AbstractsAn in vitro biofilm model of subgingival plaqueMOLECULAR ORAL MICROBIOLOGY, Issue 3 2007C. Walker Introduction:, Numerous biofilm models have been described for the study of bacteria associated with the supragingival plaque. However, there are fewer models available for the study of subgingival plaque. The purpose of this study was to develop and validate a model that closely mimicked the composition of the subgingival flora. Methods:, The model was developed as follows: calcium hydroxyapatite disks were coated overnight with 10% sterile saliva, placed in flat-bottomed tissue culture plates containing trypticase-soy broth, directly inoculated with a small aliquot of dispersed subgingival plaque, incubated anaerobically, and transferred to fresh medium at 48-h intervals until climax (steady-state) biofilms were formed (,10 days). Results:, The model, based on samples from eight periodontitis patients and eight healthy subjects, yielded a multi-species, heterogeneous biofilm, consisting of both gram-positive and gram-negative species, and comprising 15,20 cultivable species associated with the subgingival flora. The species present and their proportions were reflective of the initial cultivable subgingival flora. Comparisons of the initial plaque samples from healthy subjects and the mature biofilms showed 81% similarity in species and 70% similarity in the proportions present. Biofilms formed from samples obtained from periodontally diseased subjects were 69% similar in species and 57% similar in the proportions present. Conclusions:, The biofilm model described here closely reproduces the composition of the cultivable subgingival plaque both in the species present and in their relative proportions. Differences existed between biofilms grown from diseased and non-diseased sites with the former being characterized by the presence of periodontal pathogens at microbially significant levels. [source] In situ effective diffusion coefficient profiles in live biofilms using pulsed-field gradient nuclear magnetic resonanceBIOTECHNOLOGY & BIOENGINEERING, Issue 6 2010Ryan S. Renslow Abstract Diffusive mass transfer in biofilms is characterized by the effective diffusion coefficient. It is well documented that the effective diffusion coefficient can vary by location in a biofilm. The current literature is dominated by effective diffusion coefficient measurements for distinct cell clusters and stratified biofilms showing this spatial variation. Regardless of whether distinct cell clusters or surface-averaging methods are used, position-dependent measurements of the effective diffusion coefficient are currently: (1) invasive to the biofilm, (2) performed under unnatural conditions, (3) lethal to cells, and/or (4) spatially restricted to only certain regions of the biofilm. Invasive measurements can lead to inaccurate results and prohibit further (time-dependent) measurements which are important for the mathematical modeling of biofilms. In this study our goals were to: (1) measure the effective diffusion coefficient for water in live biofilms, (2) monitor how the effective diffusion coefficient changes over time under growth conditions, and (3) correlate the effective diffusion coefficient with depth in the biofilm. We measured in situ two-dimensional effective diffusion coefficient maps within Shewanella oneidensis MR-1 biofilms using pulsed-field gradient nuclear magnetic resonance methods, and used them to calculate surface-averaged relative effective diffusion coefficient (Drs) profiles. We found that (1) Drs decreased from the top of the biofilm to the bottom, (2) Drs profiles differed for biofilms of different ages, (3) Drs profiles changed over time and generally decreased with time, (4) all the biofilms showed very similar Drs profiles near the top of the biofilm, and (5) the Drs profile near the bottom of the biofilm was different for each biofilm. Practically, our results demonstrate that advanced biofilm models should use a variable effective diffusivity which changes with time and location in the biofilm. Biotechnol. Bioeng. 2010;106: 928,937. © 2010 Wiley Periodicals, Inc. [source] Estimating biokinetic parameters for biofilm modelsBIOTECHNOLOGY & BIOENGINEERING, Issue 3 2008Article first published online: 26 AUG 200 No abstract is available for this article. [source] Practical identifiability of biokinetic parameters of a model describing two-step nitrification in biofilmsBIOTECHNOLOGY & BIOENGINEERING, Issue 3 2008D. Brockmann Abstract Parameter estimation and model calibration are key problems in the application of biofilm models in engineering practice, where a large number of model parameters need to be determined usually based on experimental data with only limited information content. In this article, identifiability of biokinetic parameters of a biofilm model describing two-step nitrification was evaluated based solely on bulk phase measurements of ammonium, nitrite, and nitrate. In addition to evaluating the impact of experimental conditions and available measurements, the influence of mass transport limitation within the biofilm and the initial parameter values on identifiability of biokinetic parameters was evaluated. Selection of parameters for identifiability analysis was based on global mean sensitivities while parameter identifiability was analyzed using local sensitivity functions. At most, four of the six most sensitive biokinetic parameters were identifiable from results of batch experiments at bulk phase dissolved oxygen concentrations of 0.8 or 5 mg O2/L. High linear dependences between the parameters of the subsets and resulted in reduced identifiability. Mass transport limitation within the biofilm did not influence the number of identifiable parameters but, in fact, decreased collinearity between parameters, especially for parameters that are otherwise correlated (e.g., µAOB and , or µNOB and ). The choice of the initial parameter values had a significant impact on the identifiability of two parameter subsets, both including the parameters µAOB and . Parameter subsets that did not include the subsets µAOB and or µNOB and were clearly identifiable independently of the choice of the initial parameter values. Biotechnol. Bioeng. 2008;101: 497,514. © 2008 Wiley Periodicals, Inc. [source] Genetics and genomics of Candida albicans biofilm formationCELLULAR MICROBIOLOGY, Issue 9 2006Clarissa J. Nobile Summary Biofilm formation by the opportunistic fungal pathogen Candida albicans is a complex process with significant consequences for human health: it contributes to implanted medical device-associated infections. Recent advances in gene expression profiling and genetic analysis have begun to clarify the mechanisms that govern C. albicans biofilm development and acquisition of unique biofilm phenotypes. Such studies have identified candidate adhesin genes, and have revealed that biofilm drug resistance is multifactorial. Newly defined cell,cell communication pathways also have profound effects on biofilm formation. Future challenges include the elucidation of the structure and function of the extracellular exopolymeric substance that surrounds biofilm cells, and the extension of in vitro biofilm observations to newly developed in vivo biofilm models. [source] | ||||||||||