Biology subjects

Wiechert, W.

Publications and source records attributed to Wiechert, W..

7 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↗

bioRxiv2023

hopsy - a methods marketplace for convex polytope sampling in Python

SummaryEffective collaboration between developers of Bayesian inference methods and users is key to advance our quantitative understanding of biosystems. We here present hopsy, a versatile open source platform designed to provide convenient access to powerful Markov chain Monte Carlo sampling algorithms tailored to models defined on convex polytopes (CP). Based on the high-performance C++ sampling library HOPS, hopsy inherits its strengths and extends its functionalities with the accessibility of the Python programming language. A versatile plugin-mechanism enables seamless integration with domain-specific models, providing method developers with a framework for testing, benchmarking, and distributing CP samplers to approach real-world inference tasks. We showcase hopsy by solving common and newly composed domain-specific sampling problems, highlighting important design choices. By likening hopsy to a marketplace, we emphasize its role in bringing together users and developers, where users get access to state-of-the-art methods, and developers contribute their own innovative solutions for challenging domain-specific inference problems. Availability and ImplementationSources, documentation and a continuously updated list of sampling algorithms are available at https://jugit.fz-juelich.de/IBG-1/ModSim/hopsy, with Linux, Windows and MacOS binaries at https://pypi.org/project/hopsy/. Contactk.noeh@fz-juelich.de

systems biology↗

bioRxiv2022

CHRRT: boosting coordinate hit-and-run with rounding by thinning

Thinning is a sub-sampling technique to reduce the memory footprint of Markov chain Monte Carlo. Despite being commonly used, thinning is rarely considered efficient. For sampling convex polytopes uniformly, a highly relevant use-case in systems biology, we here demonstrate that thinning generally boosts computational and, thereby, sampling efficiencies of the widely used Coordinate Hit-and-Run with Rounding (CHRR) algorithm. We benchmark CHRR with thinning (CHRRT) with simplices and constrained-based metabolic networks with up to thousands of dimensions. With appropriate thinning, CHRRT offers a substantial increase in computational efficiency compared to unthinned CHRR, in our examples of up to three orders of magnitude, as measured by the effective sample size per time (ESS/t). Our experiments reveal that the performance gain of CHRRT by optimal thinning grows substantially with polytope (effective model) dimension. Based on our experiments, we provide practically useful advice for tuning thinning to efficient and effective use of compute resources. Besides allocating computational resources optimally to permit sampling convex polytopes uniformly to convergence in a fraction of time, exploiting thinning unlocks investigating hitherto intractable models under limited computational budgets. CHRRT thereby paves the way to keep pace with progressing model sizes within the existing constraint-based reconstruction and analysis (COBRA) tool set. Sampling and evaluation pipelines are available at https://jugit.fz-juelich.de/IBG-1/ModSim/fluxomics/chrrt.

systems biology↗

bioRxiv2022

CellSium - Versatile Cell Simulator for Microcolony Ground Truth Generation

SummaryTo train deep learning based segmentation models, large ground truth data sets are needed. To address this need in microfluidic live-cell imaging, we present CellSium, a flexibly configurable cell simulator built to synthesize realistic image sequences of bacterial microcolonies growing in monolayers. We illustrate that the simulated images are suitable for training neural networks. Synthetic time-lapse videos with and without fluorescence, using programmable cell growth models, and simulation-ready 3D colony geometries for computational fluid dynamics (CFD) are also supported. Availability and ImplementationCellSium is free and open source software under the BSD license, implemented in Python, available at https://github.com/modsim/cellsium (DOI: 10.5281/zenodo.6193033), along with documentation, usage examples and Docker images. Contactk.noeh@fz-juelich.de Supplementary informationSupplementary data are available online.

bioinformatics↗

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↗

bioRxiv2020

pyFOOMB: Python Framework for Object Oriented Modelling of Bioprocesses

Quantitative characterization of biotechnological production processes requires the determination of different key performance indicators (KPIs) such as titer, rate and yield. Classically, these KPIs can be derived by combining black-box bioprocess modelling with non-linear regression for model parameter estimation. The presented pyFOOMB package enables a guided and flexible implementation of bioprocess models in the form of ordinary differential equation systems (ODEs). By building on Python as powerful and multi-purpose programming language, ODEs can be formulated in an object-oriented manner, which facilitates their modular design, reusability and extensibility. Once the model is implemented, seamless integration and analysis of the experimental data is supported by various Python packages that are already available. In particular, for the iterative workflow of experimental data generation and subsequent model parameter estimation we employed the concept of replicate model instances, which are linked by common sets of parameters with global or local properties. For the description of multi-stage processes, discontinuities in the right-hand sides of the differential equations are supported via event handling using the freely available assimulo package. Optimization problems can be solved by making use of a parallelized version of the generalized island approach provided by the pygmo package. Furthermore, pyFOOMB in combination with Jupyter notebooks also supports education in bioprocess engineering and the applied learning of Python as scientific programming language. Finally, the applicability and strengths of pyFOOMB will be demonstrated by a comprehensive collection of notebook examples.

bioengineering↗