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

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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