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Customer Success Story

Using The Cell Growth Quantifier (CGQ) For Screenings Of Xylose Utilizing Strains

Prof. Dr. Lars M. Blank, RWTH Aachen University, Institute of Applied Microbiology (iAMB), Aachen, Germany)

Background

Preliminary tests for (accepted) manuscript: “Comparison of Three Xylose Pathways in Pseudomonas putida KT2440 for the Synthesis of Valuable Products”
Pseudomonas putida KT2440 is a well-established chassis in industrial biotechnology. To increase the substrate spectrum, we implemented three alternative xylose utilization pathways, namely the Isomerase, Weimberg, and Dahms pathways. These different strains, which are able to consume xylose, were characterized in terms of growth and later synthesis of chemicals, especially mono-rhamnolipids and pyocyanin.

The strain characterization required information about their growth, which is determined by measuring the biomass, as well as substrate uptake rate, measured by HPLC. To ensure the availability of a researcher when samples had to be taken, the optimal time to start the differently growing strains had to be planned.
This required preliminary growth experiments, which were continuously monitored using a CGQ (aquila biolabs). The resulting growth curves showed the impact of the different xylose pathways and modifications. Further, they allowed a reliable planning of the following experiment with already fixed sampling times and thus minimized the effort considerably

chart Fig. 1 Growth curves of different genetically engineered P. putida KT2440 strains. Backscatter values were measured by the aquila biolabs’ Cell Growth Quantifier (CGQ). Displayed: mean from biological duplicates. Cultivation conditions: 30°C, 200 rpm, 50 mm shaking diameter, 500 mL shake flask, 50 mL filling volume. Medium: M9 + xylose.
cgq bay software

Testimonial

“The automated system CGQ provided accurate growth curves with a very dense sampling interval and gave us the opportunity to identify differences in the growth behaviors. Using these data, we were able to set up an efficient plan to start the experiments with already fixed sampling times to measure the substrate uptake of different strains. This planning minimized the effort considerably. The CGQ made this very easy and had a big impact in understanding the organisms and, eventually, the finalization of the publication.”

Prof. Dr. Lars Blank (RWTH Aachen)

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