2011 Annual Meeting

(313d) Heterogeneous Microbial Populations: Using Flow Cytometric Data for Building Dynamic Distributed Models

Authors

Lencastre Fernandes, R. - Presenter, Technical University of Denmark
Gernaey, K. V. - Presenter, Technical University of Denmark
Heins, A. - Presenter, Technical University of Denmark
Eliasson Lantz, A. - Presenter, Technical University of Denmark
Carlquist, M. - Presenter, Lund University
Lundin, L. - Presenter, University of Copenhagen
Johansen, S. J. - Presenter, University of Copenhagen
Dutta, A. - Presenter, University of Ghent
Nopens, I. - Presenter, Ghent University


Traditionally, microbial populations
have been considered homogeneous in studies of fermentation processes. However,
research has shown that a typical microbial population in a fermentor is
heterogeneous [1-3].

Phenotypic heterogeneity arises as a result of the variability inherent
to the metabolic processes within single cells. Two dominant cell variables
responsible for differential gene expression are cell cycle and cell ageing [4].
Indeed, cells at different phases in the cell cycle, or with different ages,
have been observed to respond differently to stress conditions [1].
Although the number of experimental methods available for single-cell
analysis has boomed [5, 6], the knowledge acquired by such experimental studies
has not yet been integrated into a generally accepted modeling framework able
to account for distributed properties within a cell population [3].
In this work, focus was set on experimentally studying, as well as
modeling, the dynamics of phenotypic heterogeneous populations of Saccharomyces cerevisiae
during batch cultivations. Besides the common monitored variables (e.g. optical
density, glucose, ethanol), single-cell total protein content and DNA content
were measured by flow cytometry during the different
phases of batch cultivations. Aiming at establishing a population balance model
(PBM) which describes the dynamic behavior of the yeast
population (including the relative contribution of different subpopulations), a
systematic analysis of the flow cytometric data was
performed, and mathematical descriptions for the budding initiation and cell
division rates as functions of the available substrate concentration are
proposed.

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SV. Microbial cell individuality and the underlying sources of heterogeneity.
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[3] M?ller S, Harms H, Bley
T. Origin and analysis of microbial population heterogeneity in bioprocesses.
Curr Opin Biotechnol 2010; 21:100-113.

[4] Sumner ER, Avery SV.
Phenotypic heterogeneity: differential stress resistance among individual cells
of the yeast Saccharomyces cerevisiae.
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[5] Schmid A, Kortmann H,
Dittrich PS, Blank LM. Chemical and biological single cell analysis. Curr Opin
Biotechnol 2010; 21:12-20.

[6] Lencastre
Fernandes R, Nierychlo M, Lundin L, Pedersen AE,
Puentes Tellez PE, Dutta A et al. Experimental
methods and modeling techniques for description of cell population
heterogeneity. Biotechnol Adv
2011; doi:10.1016/j.biotechadv.2011.03.007