Experimental kSk_S estimation: A comparison of methods for Corynebacterium glutamicum from lab to microfluidic scale

Knowledge about the specific affinity of whole cells toward a substrate, commonly referred to as $k_S$, is a crucial parameter for characterizing growth within bioreactors. State-of-the-art methodologies measure either uptake or consumption rates at different initial substrate concentrations. Alternatively, cell dry weight or respiratory data like online oxygen and carbon dioxide transfer rates can be used to estimate $k_S$. In this work, a recently developed substrate-limited microfluidic single-cell cultivation (sl-MSCC) method is applied for the estimation of $k_S$ values under defined environmental conditions. This method is benchmarked with two alternative microtiter plate methods, namely high-frequency biomass measurement (HFB) and substrate-limited respiratory activity monitoring (sl-RA). As a model system, the substrate affinity $k_S$ of Corynebacterium glutamicum ATCC 13032 regarding glucose was investigated assuming a Monod-type growth response. A $k_S$ of <70.7 mg/L (with 95% probability) with HFB, 8.55 ± 1.38 mg/L with sl-RA, and 2.66 ± 0.99 mg/L with sl-MSCC was obtained. Whereas HFB and sl-RA are suitable for a fast initial $k_S$ estimation, sl-MSCC allows an affinity estimation by determining $t_D$ at concentrations less or equal to the $k_S$ value. Thus, sl-MSCC lays the foundation for strain-specific $k_S$ estimations under defined environmental conditions with additional insights into cell-to-cell heterogeneity

Development of a Modular Biosensor System for Rapid Pathogen Detection

Progress in the field of pathogen detection relies on at least one of the following three qualities: selectivity, speed, and cost-effectiveness. Here, we demonstrate a proof of concept for an optical biosensing system for the detection of the opportunistic human pathogen Pseudomonas aeruginosa while addressing the abovementioned traits through a modular design. The biosensor detects pathogen-specific quorum sensing molecules and generates a fluorescence signal via an intracellular amplifier. Using a tailored measurement device built from low-cost components, the image analysis software detected the presence of P. aeruginosa in 42 min of incubation. Due to its modular design, individual components can be optimized or modified to specifically detect a variety of different pathogens. This biosensor system represents a successful integration of synthetic biology with software and hardware engineering

Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany

The effective reproductive number R$_t$ has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of R$_t$ may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates

Automated Processing of Pipelines Managing Now- and Forecasting of Infectious Diseases

When faced with the challenge of now- and forecasting infectious diseases, multiple data sources and state-of-the-art models have to be considered. Automatic aggregation, processing, and publishing to relevant data sinks is paramount to achieving consistent, reproducible, and timely results given daily-reported data. To facilitate scientific collaboration and reproducibility of workflows, open and extensible architectures for compute pipelines are required. In this research, we devise an architecture realizing the seamless management and processing of reproducible pipelines. Our case-study is a daily pipeline for nowcasting the state of SARS-CoV-2 in Germany based on public data and state-of-the-art models implemented in the simulation software MEmilio. The results of our pipeline are pushed to ESID (Epidemiological Scenarios for Infectious Diseases), a user interface to epidemiological simulations. To realize the given pipeline, a workflow management system is required to ensure pipeline processing and secure access to multiple heterogeneous data storages. For this purpose, we based our work on an open-source workflow management system - Apache Airflow, which provides the orchestration, coordination and management of complex connected tasks. S3 is utilized as an intermediate data storage service for sharing data between workflow steps and persisting experiment output. We provide a comprehensive view on our work on automated, end-to-end and reproducible pipelines, with detailed commentary on use case, and its realization

michaelosthege/pyrff: v2.0.0 - pyrff: Approximating Gaussian Process Samples with Random Fourier Features

Changes & new features for discrete Thompson sampling: signature of sample_batch changed: samples must be passed as (C, S) or (C, ?) instead of (S, C). This is to permit unequal sample sizes. a new kwarg correlated:bool must be specified to choose between jointly or independently sampling from the candidate posterior samples brute-force probability calculation get_probabilities is replaced with sampling_probabilities that calculates the Thompson sampling probability for each candidate exactly

michaelosthege/calibr8: v5.0.1calibr8: Toolbox for non-linear calibration and error modeling

Release for Zenod