Phenotypic variability in bioprocessing conditions can be tracked on the basis of on‐line flow cytometry and fits to a scaling law

Abstract

Noise in gene and protein expression is a major cause for bioprocess deviation. However, this phenomenon has been only scarcely considered in real bioprocessing conditions. In this work, a scaling-law derived from genome-scale studies based on GFP reporter systems has been calibrated to an on-line flow cytometry device, allowing thus to get an insight at the level of promoter activity and associated noise during a whole microbial culture carried out in bioreactor. We show that most of the GFP reporter systems investigated and thus corresponding genes could be included inside the area covered by the scaling-law. The experimental results suggest that this scaling-law could be used to predict the dynamics of promoter activity, as well as the associated noise, in bioprocessing conditions. The knowledge acquired throughout this work could be used for the design of more robust expression systems.

The effect of acetate on population heterogeneity in different cellular characteristics of Escherichia coli in aerobic batch cultures

“…Noise was found to be minimized in the expression of genes that are essential and contribute to cellular growth whereas it was elevated in genes that mediate the response to environmental changes, for example, the stress response, energy metabolism, and carbon utilization . Additionally, an inverse correlation between noise and gene expression could be established; highly expressed genes exhibit low levels of noise whereas the opposite was found for weakly expressed genes . Therefore, it was suspected that noise in gene expression is used as a regulatory strategy to increase, respectively, decrease the level of population heterogeneity depending on what is beneficial for the cell population …”

“…Additionally, an inverse correlation between noise and gene expression could be established; highly expressed genes exhibit low levels of noise whereas the opposite was found for weakly expressed genes . Therefore, it was suspected that noise in gene expression is used as a regulatory strategy to increase, respectively, decrease the level of population heterogeneity depending on what is beneficial for the cell population …”

Bioprocess scale‐up/down as integrative enabling technology: from fluid mechanics to systems biology and beyond

“…Linking experimental single-cell studies with in silico approaches allows for a better interpretation of microbial individuality in bioprocesses. As an example, automated flow cytometry has been used for validating a biological scaling law, linking protein expression to promoter noise in process-related conditions (Baert et al, 2015). Stochastic simulation of intracellular biochemical reactions can be achieved through the use of the Gillespie algorithm (Gillespie, 1977), in a way that can be assimilated to an agent-based model (ABM).…”

Segregostat: A novel concept to control phenotypic diversification dynamics on the example of Gram-negative bacteria

“…Ultimately, the superimposition of these three mechanisms lead to population phenotypic heterogeneity that can in turn confer interesting functionalities to the whole population (e.g., bet-hedging, division of labor…) [10]. Most of the works focused on phenotypic diversification of microbial populations have been carried out based on single cell proxies involving either GFP expression (used as a proxy for noise in gene expression) [11] [12] or growth rate [13] [14]. In the context of this work, we propose to focus on another relevant single cell proxy, i.e.…”