Automated growth rate determination in high-throughput microbioreactor systems

Abstract

Objective: The calculation of growth rates provides basic metric for biological fitness and is standard task when using microbioreactors (MBRs) in microbial phenotyping. MBRs easily produce huge data at high frequency from parallelized high-throughput cultivations with online monitoring of biomass formation at high temporal resolution. Resulting high-density data need to be processed efficiently to accelerate experimental throughput.

Results: A MATLAB code is presented that detects the exponential growth phase from multiple microbial cultivations in an iterative procedure based on several criteria, according to the model of exponential growth. These were obtained with Corynebacterium glutamicum showing single exponential growth phase and Escherichia coli exhibiting diauxic growth with exponential phase followed by retarded growth. The procedure reproducibly detects the correct biomass data subset for growth rate calculation. The procedure was applied on data set detached from growth phenotyping of library of genome reduced C. glutamicum strains and results agree with previously reported results where manual effort was needed to pre-process the data. Thus, the automated and standardized method enables a fair comparison of strain mutants for biological fitness evaluation. The code is easily parallelized and greatly facilitates experimental throughout in biological fitness testing from strain screenings conducted with MBR systems.

Keywords: Exponential growth model; Growth rate; Microbioreactor; Online biomass monitoring; Quantitative microbial phenotyping.

Codebox 1

Pseudocode for automated calculation of growth rates, using backscatter (BS) signal as example biomass signal. The procedure starts with the calculation of the blank (zero) value from first BS signals, where the cell concentration is below the limit of detection LOD (lines 1–3). These first BS signals below the LOD are also used to calculate the BS measurement error which is considered to be additive for all BS signals (lines 4, 5). Next, the measurement cycle is identified where the BS signal exceeds the user defined limit of quantification LOQ (lines 6–13). To detect the exponential growth phase from the complete BS data set, a BS subset is extracted ranging from measurement cycles where the BS signal reaches the LOQ to the last one. For this BS subset, the growth rate is calculated by a weighted linear regression and several stopping criteria are evaluated. If these criteria are not fulfilled, a new BS subset is evaluated from which the last measurement is removed. This procedure is repeated until the stopping criteria are fulfilled. Please note: this is implemented as for-loop in the MATLAB function (lines 14–28). Three stopping criteria are defined: A certain adjusted R² from the regression has to be reached. The last biomass increase has to be higher than the previous one, and these two increases need to have a positive sign (lines 29–34)

Fig. 1

Growth kinetics of C. glutamicum and E. coli from BioLector cultivations and depiction of data processing for automated growth rate calculation. a Online monitored backscatter and dissolved oxygen (DO) signal for C. glutamicum in CgXII medium with 10 g/L glucose, a filling volume of 1000 µL and a shaking frequency of 1000 rpm. The panel on the right shows the processed biomass data, i.e. blanked backscatter and data points determined automatically for calculation of growth rate. Propagated measurement error for blanked BS signal was calculated to 0.39 a.u. b Online monitored signals like in part a, but for E. coli in M9 medium with 20 g/L glucose, a filling volume of 1000 µL and a shaking frequency of 1400 rpm. Propagated measurement error for blanked BS signal was calculated to 0.34 a.u. Right panel analogous to the one in part A. Insets in part B magnify the first 18 h of cultivation. Measurement cycle time for recording backscatter and DO signals was set to 9 min for both C. glutamicum and E. coli cultivations