Biomedical subjects

Zoltán Kutalik

Publications and source records attributed to Zoltán Kutalik.

3 recordsLinked to original sources

PubMed2005

Stochastic modelling of individual cell growth using flow chamber microscopy images.

In this paper, we analysed individual cell growth images obtained by flow chamber microscopy system. We used replicate flow chamber experiment data as reported by [Elfwing, A., Le Marc, Y., Baranyi, J., Ballagi, A., 2004. Observing the growth and division of large number of individual bacteria using image analysis. Applied and Environmental Microbiology 70, 675-678] involving both unstressed and heat shocked Listeria innocua cells. After observing the kinetics of a large number of cells, we propose a new stochastic model for their individual growth. By comparing our model with other existing models in the literature, we demonstrate that ours can accurately describe the growth of both stressed and unstressed cells. Our results indicate that the lag period, in terms of cell division, coincides dominantly with a lag period in terms of cell size. We also reveal various connections between cell length, lag time and cell division models. Finally, we present the results of our investigation on the effect of the duration of sublethal heat shock on the found growth properties.

Biomass↗

PubMed2005

Connection between stochastic and deterministic modelling of microbial growth.

We present in this paper various links between individual and population cell growth. Deterministic models of the lag and subsequent growth of a bacterial population and their connection with stochastic models for the lag and subsequent generation times of individual cells are analysed. We derived the individual lag time distribution inherent in population growth models, which shows that the Baranyi model allows a wide range of shapes for individual lag time distribution. We demonstrate that individual cell lag time distributions cannot be retrieved from population growth data. We also present the results of our investigation on the effect of the mean and variance of the individual lag time and the initial cell number on the mean and variance of the population lag time. These relationships are analysed theoretically, and their consequence for predictive microbiology research is discussed.

Bacteria↗

PubMed2004

Optimal sampling time selection for parameter estimation in dynamic pathway modeling.

Systems Biology is an emerging research area, which considers mathematical representations of inter- and intra-cellular dynamics. Among the many research problems that have been addressed, dynamic modeling of signal transduction pathways has received increasing attention. The usual approach to represent intra-cellular dynamics are nonlinear, usually ordinary, differential equations. The purpose of the models is to test and generate hypothesis of specific pathways and it is therefore required to estimate model parameters from experimental data. The experiments to generate data are complex and expensive, as a consequence of which the time series available are usually rather short, with few if any replicates. Almost certainly, not all variables one would like to include in a model can be measured. Parameter estimation is therefore an important research problem in Systems Biology and the focus of this paper. In particular, we are interested in optimizing the sampling time selection in order to minimize the variance of the parameter estimation error. With few sampling time points feasible, their selection is of practical importance in experimental design. Finally, the theoretical results are supported with an application.

Computer Simulation↗