Register now After registration you will be able to apply for this opportunity online.
This opportunity is not published. No applications will be accepted.
Master Thesis (6mos): Gas analysis for non-invasive detection of bacterial contamination in cell cultures
Organs-on-a-Chips (OoaC) are essential to ensure pharmaceutical pre-clinical safety. OoaCs are
in vitro miniaturized simplified model systems of organs. Most OoaC engineering in academic research is
carried out manually and is labour-intensive. We currently perform research to build an automated OoaC
bioreactor that includes necessary functions to care for OoaC 24/7 (see Figure 1).
Usually, bacterial contamination is detected by humans in microscopy imaging, which is not automated,
or by pH monitoring, which is not accurate and is invasive, as sensors should be added directly in contact
with the OoaC perfusion medium. We aim to automatically detect bacterial contamination inside the
automated OoaC bioreactor with compact and non-invasive sensors. It has been demonstrated that bacterial
contamination can be detected in a non-invasive fashion using chromatography-mass spectrometry (GC-MS);
however, GC-MS is expensive, bulky, and therefore, cannot be integrated inside bioreactors.
The question of whether compact, non-invasive, and automatic detection of bacterial contamination is
feasible and effective to detect bacterial contamination in an OoaC automation context is your opportunity
to work on an exciting project that impacts research and the pharmaceutical industry.
Task description: You will be responsible for selecting non-invasive, automation-friendly bacterial detection
strategies and sensors. Your education in microbiology will be an asset to define and test a range
of potential sensors (fig. 2) that deliver the bacterial contamination probability information to the control
system.
Task description: You will be responsible for selecting non-invasive, automation-friendly bacterial detection strategies and sensors. Your education in microbiology will be an asset to define and test a range of potential sensors (fig. 2) that deliver the bacterial contamination probability information to the control system.
Workpackages:
• review of the relevant literature on bacterial exometabolomics, bacterial detection using gas sensors,
and volatile organic compounds (VOC) sensors
• design an array of solutions and assess their integration feasibility with the existing device
• test and compare 2 to 3 solutions on detection of non-pathogenic bacteria
• setup the bacterial contamination probability information into the existing control system
Workpackages: • review of the relevant literature on bacterial exometabolomics, bacterial detection using gas sensors, and volatile organic compounds (VOC) sensors • design an array of solutions and assess their integration feasibility with the existing device • test and compare 2 to 3 solutions on detection of non-pathogenic bacteria • setup the bacterial contamination probability information into the existing control system
MT MoLiBB 6 non-invasive detection of bacterial contamination.pdf
