Biomedical subjects

F Baty

Publications and source records attributed to F Baty.

3 recordsLinked to original sources

PubMed2004

Modelling the effect of a temperature shift on the lag phase duration of Listeria monocytogenes.

The aim of this work is to study and model the effect of a temperature shift on h(0), the product of the growth rate by the lag phase duration (mulambda). Our work is based on the data of Whiting and Bagi [Int. J. Food Microbiol. 73 (2002) 291], who studied the influence of both the pre-incubation temperature (T(prior)) and the growth temperature (T(growth)) on lambda values of Listeria monocytogenes. We introduce a new model to describe the evolution of the parameter h(0) as a function of T(prior) and T(growth), and compare it to Whiting and Bagi's published polynomial model that describes the influence of T(prior) and T(growth) on lambda independently of mu. For exponential as well as stationary phase cells, h(0) increases almost linearly with the magnitude of the temperature shift. A simple linear model of h(0) turns out to be more suitable to predict lambda values than a polynomial model of lambda.

Food Microbiology↗

PubMed2003

BIBI, a bioinformatics bacterial identification tool.

BIBI was designed to automate DNA sequence analysis for bacterial identification in the clinical field. BIBI relies on the use of BLAST and CLUSTAL W programs applied to different subsets of sequences extracted from GenBank. These sequences are filtered and stored in a new database, which is adapted to bacterial identification.

Bacteria↗

PubMed2002

Modeling the lag time of Listeria monocytogenes from viable count enumeration and optical density data.

The following two factors significantly influence estimates of the maximum specific growth rate ( micro (max)) and the lag-phase duration (lambda): (i) the technique used to monitor bacterial growth and (ii) the model fitted to estimate parameters. In this study, nine strains of Listeria monocytogenes were monitored simultaneously by optical density (OD) analysis and by viable count enumeration (VCE) analysis. Four usual growth models were fitted to our data, and estimates of growth parameters were compared from one model to another and from one monitoring technique to another. Our results show that growth parameter estimates depended on the model used to fit data, whereas there were no systematic variations in the estimates of micro (max) and lambda when the estimates were based on OD data instead of VCE data. By studying the evolution of OD and VCE simultaneously, we found that while log OD/VCE remained constant for some of our experiments, a visible linear increase occurred during the lag phase for other experiments. We developed a global model that fits both OD and VCE data. This model enabled us to detect for some of our strains an increase in OD during the lag phase. If not taken into account, this phenomenon may lead to an underestimate of lambda.

Colony Count, Microbial↗