Rita Lencastre Fernandes

Physiological heterogeneities in microbial populations and implications for physical stress tolerance.

BackgroundTraditionally average values of the whole population are considered when analysing microbial cell cultivations. However, a typical microbial population in a bioreactor is heterogeneous in most phenotypes measurable at a single-cell level. There are indications that such heterogeneity may be unfavourable on the one hand (reduces yields and productivities), but also beneficial on the other hand (facilitates quick adaptation to new conditions - i.e. increases the robustness of the fermentation process). Understanding and control of microbial population heterogeneity is thus of major importance for improving microbial cell factory processes.ResultsIn this work, a dual reporter system was developed and applied to map growth and cell fitness heterogeneities within budding yeast populations during aerobic cultivation in well-mixed bioreactors. The reporter strain, which was based on the expression of green fluorescent protein (GFP) under the control of the ribosomal protein RPL22a promoter, made it possible to distinguish cell growth phases by the level of fluorescence intensity. Furthermore, by exploiting the strong correlation of intracellular GFP level and cell membrane integrity it was possible to distinguish subpopulations with high and low cell membrane robustness and hence ability to withstand freeze-thaw stress. A strong inverse correlation between growth and cell membrane robustness was observed, which further supports the hypothesis that cellular resources are limited and need to be distributed as a trade-off between two functions: growth and robustness. In addition, the trade-off was shown to vary within the population, and the occurrence of two distinct subpopulations shifting between these two antagonistic modes of cell operation could be distinguished.ConclusionsThe reporter strain enabled mapping of population heterogeneities in growth and cell membrane robustness towards freeze-thaw stress at different phases of cell cultivation. The described reporter system is a valuable tool for understanding the effect of environmental conditions on population heterogeneity of microbial cells and thereby to understand cell responses during industrial process-like conditions. It may be applied to identify more robust subpopulations, and for developing novel strategies for strain improvement and process design for more effective bioprocessing.

Cell mass and cell cycle dynamics of an asynchronous budding yeast population: experimental observations, flow cytometry data analysis, and multi-scale modeling.

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. Biotechnol. Bioeng. 2013; 110: 812–826.

Topology optimized microbioreactors

This article presents the fusion of two hitherto unrelated fields—microbioreactors and topology optimization. The basis for this study is a rectangular microbioreactor with homogeneously distributed immobilized brewers yeast cells (Saccharomyces cerevisiae) that produce a recombinant protein. Topology optimization is then used to change the spatial distribution of cells in the reactor in order to optimize for maximal product flow out of the reactor. This distribution accounts for potentially negative effects of, for example, by‐product inhibition. We show that the theoretical improvement in productivity is at least fivefold compared with the homogeneous reactor. The improvements obtained by applying topology optimization are largest where either nutrition is scarce or inhibition effects are pronounced. Biotechnol. Bioeng. 2011; 108:786–796.

Applying Mechanistic Models in Bioprocess Development

The available knowledge on the mechanisms of a bioprocess system is central to process analytical technology. In this respect, mechanistic modeling has gained renewed attention, since a mechanistic model can provide an excellent summary of available process knowledge. Such a model therefore incorporates process-relevant input (critical process variables)-output (product concentration and product quality attributes) relations. The model therefore has great value in planning experiments, or in determining which critical process variables need to be monitored and controlled tightly. Mechanistic models should be combined with proper model analysis tools, such as uncertainty and sensitivity analysis. When assuming distributed inputs, the resulting uncertainty in the model outputs can be decomposed using sensitivity analysis to determine which input parameters are responsible for the major part of the output uncertainty. Such information can be used as guidance for experimental work; i.e., only parameters with a significant influence on model outputs need to be determined experimentally. The use of mechanistic models and model analysis tools is demonstrated in this chapter. As a practical case study, experimental data from Saccharomyces cerevisiae fermentations are used. The data are described with the well-known model of Sonnleitner and Käppeli (Biotechnol Bioeng 28:927-937, 1986) and the model is analyzed further. The methods used are generic, and can be transferred easily to other, more complex case studies as well.

Multi-scale modeling for prediction of distributed cellular properties in response to substrate spatial gradients in a continuously run microreactor

Abstract In large-scale fermentors, non-ideal mixing leads to the development of heterogeneous cell populations. This cell-to-cell variability may explain the differences in e.g. yields for large- and lab-scale cultivations. In this work the anaerobic growth of Saccharomyces cerevisiae in a continuously run microbioreactor is simulated. A multi- scale model consisting of the coupling of a population balance model, a kinetic model and a flow model was developed in order to predict simultaneously local concentrations of substrate (glucose), product (ethanol) and biomass, as well as the local cell size distributions.

Fulgur: MATLAB GUI application for working with near infrared calibration sets. Part 1: The Fulgur application

NIR instruments in combination with multivariate data calibration models are useful tools in process monitoring. The calibration models often need to overcome matrix effects and must be re-calibrated over time to compensate for, e.g., changes in process parameters or raw materials, a task often involving manual expert assistance, which is a bottleneck for more widespread application of the technique. To ease the re-calibration, the BIOPRO consortium launched the “Hands free” project to research and develop automated methods for handling data sets and calibration of partial least squares (PLS) models. The intention was to find methods and algorithms which could be operated either by non-experts or fully automatically. Part of this effort was to develop a graphical user interface (GUI) application in MATLAB to enable rapid dissemination of methods to partners in the consortium and early testing by non-programmers. The resulting application, called Fulgur, can assist in handling near infrared (NIR) instrument calibration datasets, identifying outliers and developing and assessing PLS model performance with standard plots. Plots are focused on performance over time as it is usual to collect NIR data over longer time spans when monitoring industry processes. In this article, we present an overview of the application for end-users and explain the outlier detection and model performance plotting in more depth. Source code for the application together with a more in-depth user guide is available for download from models.life.ku.dk. In the next article, we will introduce the source code for potential developers and the principles behind the programming architecture used to develop the application.

Fulgur: MATLAB GUI application for working with near infrared calibration sets. Part 2: the source code

Near infrared (NIR) instruments in combination with multivariate data calibration models are useful tools in process monitoring. However, the need for expert assistance with calibration and maintenance of the models is a bottleneck limiting their more widespread use. In the previous article [NIR news 26(8), 13–16 (2015)], the MATLAB graphical application called Fulgur was introduced. Fulgur was developed as part of the BIOPRO “Hands Free” project with the goal of developing automated methods for handling data sets and calibrations of partial least squares models for NIR instruments. In this article, the Model-View-Controller programming design principles needed for developing the application are described in more depth as an introduction for potential developers who may want to use and expand the Fulgur framework. The programing design principles may also be of interest to chemometricians who want to program and develop their own GUI applications in MATLAB. The source code for the application together with more in-depth user and developer guides and tutorials for programming plugins is available for download from models.life.ku.dk.