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Despite traditionally regarded as identical, cells in a microbial cultivation present a distribution of phenotypic
traits, forming a heterogeneous cell population. Moreover, the degree of heterogeneity is notably enhanced by
changes in micro-environmental conditions. A major development in experimental single-cell studies has taken place in the last decades. It has however not been fully accompanied by similar contributions within data analysis and mathematical modeling. Indeed, literature reporting, for example, quantitative analyses of experimental single-cell observations and validation of model predictions for cell property distributions against experimental data is scarce. This study focuses on the experimental and mathematical description of the dynamics of cell size and cell cycle position distributions, of a population of Saccharomyces cerevisiae, in response to the substrate consumption observed during batch cultivation. The good agreement between the proposed multi-scale model (a population balance model [PBM] coupled to an unstructured model) and experimental data (both the overall physiology and cell size and cell cycle distributions) indicates that a mechanistic model is a suitable tool for describing the microbial population dynamics in a bioreactor. This study therefore contributes towards the understanding of the development of heterogeneous populations during microbial cultivations. More generally, it consists of a step towards a paradigm change in the study and description of cell cultivations, where average cell behaviors observed experimentally now are interpreted as a potential joint result of various co-existing single-cell behaviors, rather than a unique response common to all cells in the cultivation.
Original languageEnglish
JournalBiotechnology and Bioengineering (Print)
Publication date2013
Volume110
Issue3
Pages812-826
ISSN0006-3592
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 1

Keywords

  • Population balance model (PBM), Multiscale modeling, Flow cytometry, Standardized data analysis, Saccharomyces cerevisiae, Total protein content, Cell cycle
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