pyFOOMB: Python framework for object oriented modeling of bioprocesses

Hemmerich, Johannes; Tenhaef, Niklas; Wiechert, Wolfgang; Noack, Stephan

doi:10.1002/elsc.202000088

Cited by 20 publications

(9 citation statements)

References 16 publications

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“…The prior for

\overrightarrow{\mu }

is a random walk of either a Normal or Students‐ t distribution, which pulls the neighboring entries in the growth rate vector closer to each other, resulting in a smooth drift of

\overrightarrow{\mu }_t

(Section 2.2.2). While this method makes few assumptions about the underlying process and therefore can be applied to many datasets, practitioners wanting to encode process knowledge should also consider differential‐equation based modeling approaches for which Python packages such as pyFOOMB or murefi can be applied [12, 32].…”

Section: Resultsmentioning

confidence: 99%

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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“…The prior for

\overrightarrow{\mu }

is a random walk of either a Normal or Students‐ t distribution, which pulls the neighboring entries in the growth rate vector closer to each other, resulting in a smooth drift of

\overrightarrow{\mu }_t

Section: Resultsmentioning

confidence: 99%

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

Self Cite

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show abstract

“…In addition to labeling data, the determination of extracellular flux rates constitutes a prerequisite for MFA. Since backscatter and dissolved oxygen are measured online in the BioLector and at-line assays are well established for further analysis of transient samples [ 54 ], the specific growth rate as well as substrate and (by)-product formation rates are readily accessible via bioprocess modeling [ 55 ]. Due to the lack of an off-gas analysis in this setup, the carbon dioxide evolution rate (CER) is not known the impact of which on the sensitivity of a MFA and statistical identifiability of the network needs to be investigated.…”

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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“…The prior for is a random walk of either a Normal or Students- t distribution, which pulls the neighboring entries in the growth rate vector closer to each other, resulting in a smooth drift of (Section 2.2.2). While this method makes few assumptions about the underlying process and therefore can be applied to many datasets, practitioners wanting to encode process knowledge should also consider differential-equation based modeling approaches for which Python packages such as or can be applied [19, 21].…”

Section: Resultsmentioning

confidence: 99%

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

Osthege

Tenhaef

Zyla

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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 μ(t) based on a novel method of 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 switchpoints 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, t-distributed Stochastic Neighbor Embedding (t-SNE) is performed to visualize datasets comprising a variety of growth/DO/pH phenotypes.

show abstract

pyFOOMB: Python framework for object oriented modeling of bioprocesses

Cited by 20 publications

References 16 publications

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

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

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

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

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