Biology subjects

Osthege, M.

Publications and source records attributed to Osthege, M..

3 recordsLinked to original sources

bioRxiv2024

PeakPerformance - A software tool for fitting LC-MS/MS peaks including uncertainty quantification by Bayesian inference

A major bottleneck of chromatography-based analytics has been the elusive fully automated identification and integration of peak data without the need of extensive human supervision. The presented Python package PeakPerformance applies Bayesian inference to chromatographic peak fitting, and provides an automated approach featuring model selection and uncertainty quantification. Currently, its application is focused on data from targeted liquid chromatography tandem mass spectrometry (LC-MS/MS), but its design allows for an expansion to other chromatographic techniques. PeakPerformance is implemented in Python and the source code is available on GitHub. It is unit-tested on Linux and Windows and accompanied by general introductory documentation, as well as example notebooks. Practical applicationThe presented PeakPerformance tool performs automated chromatographic peak data fitting using Bayesian methodology. Accordingly, it innovates by delivering built-in uncertainty quantification for each peak, thus taking the measurement noise into account. Using a convergence statistic and based on the determined peak uncertainties, the differentiation of signals into peak and noise was improved and false positives or negatives were largely eliminated. The provided documentation and the implemented convenience functions are meant to lower the barrier of entry for users with little programming experience. Lastly, the modular design of the software enables modification and expansion to data from different chromatographic methods.

systems biology↗

bioRxiv2021

bletl - A Python package for integrating microbioreactors in the design-build-test-learn cycle

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 [Formula] 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 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, t-distributed Stochastic Neighbor Embedding (t-SNE) is performed to visualize datasets comprising a variety of growth/DO/pH phenotypes. Practical ApplicationThe bletl package can be used to analyze microbioreactor datasets in both data analysis and autonomous experimentation workflows. Using the example of BioLector datasets, we show that loading such datasets into commonly used data structures with one line of Python code is a significant improvement over spreadsheet or hand-crafted scripting approaches. On top of established standard data structures, practitioners may continue with their favorite data analysis routines, or make use of the additional analysis functions that we specifically tailored to the analysis of microbioreactor time series. Particularly our function to fit cross-validated smoothing splines can be used for on-line signals from any microbioreactor system and has the potential to improve robustness and objectivity of many data analyses. Likewise, our random walk based [Formula] method for inferring growth rates under uncertainty, but also the time-series feature extraction may be applied to on-line data from other cultivation systems as well.

bioengineering↗

bioRxiv2021

Bayesian calibration, process modeling and uncertainty quantification in biotechnology

AO_SCPLOWBSTRACTC_SCPLOWHigh-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 with 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, with which we demonstrate how it makes 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 differential equation model of microbial growth to describe multiple replicates of Corynebacterium glutamicum batch microbioreactor cultures. Key process model parameters are learned by both maximum likelihood estimation and Bayesian inference, highlighting the flexibility of the statistical and computational framework.

bioengineering↗