Exploring Microbial Robustness for a Sustainable and Efficient Bioproduction
Conference poster, 2020

Efficient microbial cell factories that produce valuable compounds are gaining increasing interest as one path towards a more sustainable economy. Therefore, there is an increasing need for robust microorganisms which can optimally perform even in harsh and challenging industrial conditions. The identification of robustness traits is crucial to improve the already-existing strains and develop new, better ones. Here, different approaches to study microbial robustness are presented. First, single-cell analysis in a cell population might give some insights on the development of more robust sub-populations. Physiological parameters (such as intracellular pH, fluxes, redox balance, etc.) and morphologic features were monitored with fluorescent biosensors and tagged proteins to study the single-cell status. Moreover, a barcoding technique will be used to discover and underline patterns in the development of population dynamics during the different industrial processes. Furthermore, an objective method to quantify robustness was developed for selection of useful strains and a large dataset was analysed to find predictive parameters for robustness. All together, these tools will give the possibility to identify robustness traits and understand robustness leading to improved industrial strains and processes.

cell status

bioeconomy

population dynamics

robustness

quantification

bioprocesses

Author

Luca Torello Pianale

Chalmers, Biology and Biological Engineering, Industrial Biotechnology

Cecilia Trivellin

Chalmers, Biology and Biological Engineering, Industrial Biotechnology

Peter Rugbjerg

Chalmers, Biology and Biological Engineering, Industrial Biotechnology

Lisbeth Olsson

Chalmers, Biology and Biological Engineering, Industrial Biotechnology

Microbial Stress 2020
Online, ,

Areas of Advance

Production

Energy

Life Science Engineering (2010-2018)

Subject Categories

Chemical Engineering

Microbiology

Bioinformatics (Computational Biology)

More information

Latest update

9/1/2022 2