Heterogeneous Microbial Populations: Using Flow Cytometric Data for Building Dynamic Distributed Models

Tuesday, October 18, 2011: 1:18 PM
Conrad C (Hilton Minneapolis)
Rita Lencastre Fernandes1, Magnus Carlquist2, Luisa Lundin3, Anna-Lena Heins4, Abhishek Dutta5, Ingmar Nopens5, Anker D. Jensen1, Søren J. Johansen3, Anna Eliasson Lantz4 and Krist V. Gernaey1, (1)Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark, (2)Department of Chemistry, Lund University, Lund, Sweden, (3)Department of Biology, University of Copenhagen, Copenhagen, Denmark, (4)Department of Systems Biology, Technical University of Denmark, Kgs. Lyngby, Denmark, (5)Biomath, University of Ghent, Ghent, Belgium

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.

[1] Avery SV. Microbial cell individuality and the underlying sources of heterogeneity. Nat Rev Microbiol 2006; 4:577-587.

[2] Enfors SO, Jahic M, Rozkov A, Xu B, Hecker M, JŸrgen B et al. Physiological responses to mixing in large scale bioreactors. J Biotechnol 2001; 85:175-185.

[3] MŸller 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. Microbiology 2002M; 148:345-351.

[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

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See more of this Session: Modeling Approaches In the Life Sciences I
See more of this Group/Topical: Topical A: Systems Biology