bletl - A Python Package for Integrating Microbioreactors in the Design-Build-Test-Learn Cycle

Osthege, Michael; Tenhaef, Niklas; Zyla, Rebecca; Mueller, C.; Hemmerich, Johannes; Wiechert, Wolfgang; Noack, Stephan; Oldiges, Marco

doi:10.1101/2021.08.24.457462

Cited by 8 publications

(11 citation statements)

References 22 publications

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“…The result file was parsed with the bletl Python package [27] to extract backscatter measurements made at 1400 rpm shaking frequency. A log(independent) asymmetric logistic calibration model was fitted as described in Section 4.1.2.…”

Section: Biomass Calibrationmentioning

confidence: 99%

Bayesian calibration, process modeling and uncertainty quantification in biotechnology

et al. 2022

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High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and specifically the degree to which random effects in the experimental data-generating process are quantified and accounted for. Accordingly calibration, i.e. the quantitative association between observed quantities and measurement responses, is a core element of many workflows in experimental sciences. Particularly in life sciences, univariate calibration, often involving non-linear saturation effects, must be performed to extract quantitative information from measured data. At the same time, the estimation of uncertainty is inseparably connected to quantitative experimentation. Adequate calibration models that describe not only the input/output relationship in a measurement system but also its inherent measurement noise are required. Due to its mathematical nature, statistically robust calibration modeling remains a challenge for many practitioners, at the same time being extremely beneficial for machine learning applications. In this work, we present a bottom-up conceptual and computational approach that solves many problems of understanding and implementing non-linear, empirical calibration modeling for quantification of analytes and process modeling. The methodology is first applied to the optical measurement of biomass concentrations in a high-throughput cultivation system, then to the quantification of glucose by an automated enzymatic assay. We implemented the conceptual framework in two Python packages, calibr8 and murefi, with which we demonstrate how to make uncertainty quantification for various calibration tasks more accessible. Our software packages enable more reproducible and automatable data analysis routines compared to commonly observed workflows in life sciences. Subsequently, we combine the previously established calibration models with a hierarchical Monod-like ordinary differential equation model of microbial growth to describe multiple replicates of Corynebacterium glutamicum batch cultures. Key process model parameters are learned by both maximum likelihood estimation and Bayesian inference, highlighting the flexibility of the statistical and computational framework.

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Section: Biomass Calibrationmentioning

confidence: 99%

Bayesian calibration, process modeling and uncertainty quantification in biotechnology

et al. 2022

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“…1 ) are defined in separate configuration files and can be adjusted even during the cultivation. In the ECS, the bletl Python package [ 44 ] is employed to parse current BioLector data for a given measurement cycle which may be plotted and sent to the practitioner via Slack, e.g., in the format of a heat map portraying the backscatter values of all wells or as line diagrams focusing specifically on selected wells and parameters.…”

Section: Resultsmentioning

confidence: 99%

Hot isopropanol quenching procedure for automated microtiter plate scale 13C-labeling experiments

et al. 2022

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Background Currently, the generation of genetic diversity for microbial cell factories outpaces the screening of strain variants with omics-based phenotyping methods. Especially isotopic labeling experiments, which constitute techniques aimed at elucidating cellular phenotypes and supporting rational strain design by growing microorganisms on substrates enriched with heavy isotopes, suffer from comparably low throughput and the high cost of labeled substrates. Results We present a miniaturized, parallelized, and automated approach to 13C-isotopic labeling experiments by establishing and validating a hot isopropanol quenching method on a robotic platform coupled with a microbioreactor cultivation system. This allows for the first time to conduct automated labeling experiments at a microtiter plate scale in up to 48 parallel batches. A further innovation enabled by the automated quenching method is the analysis of free amino acids instead of proteinogenic ones on said microliter scale. Capitalizing on the latter point and as a proof of concept, we present an isotopically instationary labeling experiment in Corynebacterium glutamicum ATCC 13032, generating dynamic labeling data of free amino acids in the process. Conclusions Our results show that a robotic liquid handler is sufficiently fast to generate informative isotopically transient labeling data. Furthermore, the amount of biomass obtained from a sub-milliliter cultivation in a microbioreactor is adequate for the detection of labeling patterns of free amino acids. Combining the innovations presented in this study, isotopically stationary and instationary automated labeling experiments can be conducted, thus fulfilling the prerequisites for 13C-metabolic flux analyses in high-throughput.

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“…Because the choice of the smoothing hyperparameter strongly influences the final result we automatically apply stratified k‐fold cross‐validation for determining its optimal value. The implementation can be found in the code repository of the bletl project [16].…”

Section: Methodsmentioning

confidence: 99%

“…The results are returned as a DataFrame for maximal compatibility with downstream analysis operations. For details on the implementation we refer to the code and documentation [16, 25].…”

Section: Methodsmentioning

confidence: 99%

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bletl ‐ A Python package for integrating BioLector microcultivation devices in the Design‐Build‐Test‐Learn cycle

Osthege

Tenhaef

Zyla

et al. 2022

Engineering in Life Sciences

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Microbioreactor (MBR) devices have emerged as powerful cultivation tools for tasks of microbial phenotyping and bioprocess characterization and provide a wealth of online process data in a highly parallelized manner. Such datasets are difficult to interpret in short time by manual workflows. In this study, we present the Python package bletl and show how it enables robust data analyses and the application of machine learning techniques without tedious data parsing and preprocessing. bletl reads raw result files from BioLector I, II and Pro devices to make all the contained information available to Python‐based data analysis workflows. Together with standard tooling from the Python scientific computing ecosystem, interactive visualizations and spline‐based derivative calculations can be performed. Additionally, we present a new method for unbiased quantification of time‐variable specific growth rate trueμ⃗t$\overrightarrow{\mu }_t$ based on unsupervised switchpoint detection with Student‐t distributed random walks. With an adequate calibration model, this method enables practitioners to quantify time‐variable growth rate with Bayesian uncertainty quantification and automatically detect switch‐points that indicate relevant metabolic changes. Finally, we show how time series feature extraction enables the application of machine learning methods to MBR data, resulting in unsupervised phenotype characterization. As an example, Neighbor Embedding (t‐SNE) is performed to visualize datasets comprising a variety of growth/DO/pH phenotypes.

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bletl - A Python Package for Integrating Microbioreactors in the Design-Build-Test-Learn Cycle

Cited by 8 publications

References 22 publications

Bayesian calibration, process modeling and uncertainty quantification in biotechnology

Bayesian calibration, process modeling and uncertainty quantification in biotechnology

Hot isopropanol quenching procedure for automated microtiter plate scale 13C-labeling experiments

bletl ‐ A Python package for integrating BioLector microcultivation devices in the Design‐Build‐Test‐Learn cycle

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