High-throughput workflow for monitoring and mining bioprocess data and its application to inferring the physiological response of Escherichia coli to perturbations.

Miniaturization and high-throughput screening are currently the focus of emerging research areas such as systems biology and systems biotechnology. A fluorescence-based screening assay for the online monitoring of oxygen and pH and a numerical method to mine the resulting online process data are described. The assay employs commercial phosphorescent oxygen- and pH-sensitive probes in standard 48- or 96-well plates on a plate reader equipped with a shaker. In addition to dual parametric analysis of both pH and oxygen in a single well, the assay allows monitoring of growth, as measured by absorbance. Validation of the assay is presented and compared with commercially available plates equipped with optical sensors for oxygen and pH. By using model-free fitting to the readily available online measurements, the length and rate of each phase such as the duration of lag and transition phase or acidification, growth, and oxygen consumption rates are automatically detected. In total, nine physiological descriptors, which can be used for further statistical and comparison analysis, are extracted from the pH, oxygen partial pressure (pO(2)), and optical density (OD) profiles. The combination of a simple mix-and-measure procedure with an automatic data mining method allows high sample throughput and good reproducibility while providing a physiological state identification and characterization of test cells. As a proof of concept, the utility of the workflow in assessing the physiological response of Escherichia coli to environmental and genetic perturbations is demonstrated.