Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses. Issue 7 (24th May 2017)

• Record Type: Journal Article
• Authors: Delvigne, Frank; Baert, Jonathan; Sassi, Hosni; Fickers, Patrick; Grünberger, Alexander; Dusny, Christian
• Is Part Of: Biotechnology journal. Volume 12:Issue 7(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-05-24
• Subjects: Flow cytometry -- Microbial stress -- Microfluidics -- Phenotypic heterogeneity -- Single cell; Biotechnology -- Periodicals; 660.605
• DOI: 10.1002/biot.201600549
• Languages: English
• ISSNs: 1860-6768

Abstract

Abstract: Phenotypic plasticity of microbial cells has attracted much attention and several research efforts have been dedicated to the description of methods aiming at characterizing phenotypic heterogeneity and its impact on microbial populations. However, different approaches have also been suggested in order to take benefit from noise in a bioprocess perspective, e.g. by increasing the robustness or productivity of a microbial population. This review is dedicated to outline these controlling methods. A common issue, that has still to be addressed, is the experimental identification and the mathematical expression of noise. Indeed, the effective interfacing of microbial physiology with external parameters that can be used for controlling physiology depends on the acquisition of reliable signals. Latest technologies, like single cell microfluidics and advanced flow cytometric approaches, enable linking physiology, noise, heterogeneity in productive microbes with environmental cues and hence allow correctly mapping and predicting biological behavior via mathematical representations. However, like in the field of electronics, signals are perpetually subjected to noise. If appropriately interpreted, this noise can give an additional insight into the behavior of the individual cells within a microbial population of interest. This review focuses on recent progress made at describing, treating and exploiting biological noise in the context of microbial populations used in various bioprocess applications. Abstract : Biological noise has multiple sources and induces cell to cell variation in phenotypic efficiency (i.e., cell productivity, robustness…). Biological noise can now be captured through mechanistic models. These models can be used for increasing the efficiency of the single cell toolbox aiming at controlling bioprocesses on the basis of population heterogeneity.