Automated growth rate determination in high-throughput microbioreactor systems

Area of Science:

• Microbiology
• Biotechnology
• Computational Biology

Background:

• Microbial growth rate calculation is crucial for assessing biological fitness.
• Microbioreactors (MBRs) generate high-frequency, high-density data for microbial phenotyping.
• Efficient data processing is essential to keep pace with high-throughput cultivation.

Purpose of the Study:

• To develop an automated method for detecting microbial exponential growth phases.
• To enable accurate and efficient calculation of growth rates from MBR data.
• To facilitate high-throughput biological fitness evaluation of microbial strains.

Main Methods:

• A MATLAB code was developed using an iterative procedure based on exponential growth models.
• The code analyzes biomass formation data from MBR cultivations.
• The method was validated using Corynebacterium glutamicum and Escherichia coli.

Main Results:

• The code accurately detects exponential growth phases in both single and diauxic growth conditions.
• It reproducibly identifies the correct biomass data subset for growth rate calculation.
• Application to genome-reduced C. glutamicum strains yielded results consistent with manual pre-processing.

Conclusions:

• The automated MATLAB code standardizes and accelerates microbial growth rate calculation.
• This facilitates fair comparison of strain mutants for biological fitness evaluation.
• The parallelizable code significantly enhances experimental throughput in MBR-based strain screenings.