Biology subjects

Noack, S.

Publications and source records attributed to Noack, S..

6 recordsLinked to original sources

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

Growth-rate dependency of ribosome abundance and translation elongation rate in Corynebacterium glutamicum differs from Escherichia coli

The growth rate {micro} of bacteria depends on the protein synthesis capacity of the cell and thus on the number of active ribosomes and their translation elongation rate. The relationship between these fundamental growth parameters have only been described for a few bacterial species, in particular Escherichia coli, but are missing for most bacterial phyla. In this study, we systematically analysed the growth-rate dependency of ribosome abundance and translation elongation rate for Corynebacterium glutamicum, a gram-positive model species differing from E. coli by a lower growth temperature optimum and a lower {micro}max. Ribosomes were quantified via single-molecule localization microscopy (SMLM) using fluorescently tagged ribosomal proteins and via RNA/protein ratio. Both methods revealed a non-linear relationship with little change in ribosome abundance below {micro} = 0.4 h-1 and a steep increase at higher {micro}. Unlike E. coli, C. glutamicum keeps a large pool of active ribosomes at low {micro}, but the translation elongation rate declines from [~]9 amino acids s-1 at {micro}max to <2 aa s-1 at {micro} < 0.1 h-1. A model-based approach shows that depletion of translation precursors at low growth rates can explain the observed decrease in translation elongation rate. Nutrient up-shift experiments support the hypothesis that maintenance of excess ribosomes during poor nutrient conditions enables C. glutamicum to quickly restart growth when conditions improve.

microbiology↗

bioRxiv2021

Metabolic and Process Engineering for Microbial Production of Protocatechuate from Xylose with Corynebacterium glutamicum

3,4-Dihydroxybenzoate (protocatechuate, PCA) is a phenolic compound naturally found in edible vegetables and medicinal herbs. PCA is of interest in the chemical industry as a building block for novel polymers and has wide potential for pharmaceutical applications due to its antioxidant, anti-inflammatory, and antiviral properties. In the present study, we designed and constructed a novel Corynebacterium glutamicum strain to enable the efficient utilization of O_SCPLOWDC_SCPLOW-xylose for microbial production of PCA. The engineered strain showed a maximum PCA titer of 62.1 {+/-} 12.1 mM (9.6 {+/-} 1.9 g L-1) from O_SCPLOWDC_SCPLOW-xylose as the primary carbon and energy source. The corresponding yield was [Formula], which corresponds to 38 % of the maximum theoretical yield and is 14-fold higher compared to the parental producer strain on O_SCPLOWDC_SCPLOW-glucose. By establishing a one-pot bioreactor cultivation process followed by subsequent process optimization, the same maximum titer and a total amount of 16.5 {+/-} 1.1 g was reached. Downstream processing of PCA from this fermentation broth was realized via electrochemically induced crystallization by taking advantage of the pH-dependent properties of PCA. Since PCA turned out to be electrochemically unstable in combination with several anode materials, a threechamber electrolysis setup was established to crystallize PCA and to avoid direct anode contact. This resulted in a maximum final purity of 95.4 %. In summary, the established PCA production process represents a highly sustainable approach, which will serve as a blueprint for the bio-based production of other hydroxybenzoic acids from alternative sugar feedstocks.

bioengineering↗

bioRxiv2021

Metabolic flux partitioning between the TCA cycle and glyoxylate shunt combined with a reversible methyl citrate cycle provide nutritional flexibility for Mycobacterium tuberculosis

The utilisation of multiple host-derived carbon substrates is required by Mycobacterium tuberculosis (Mtb) to successfully sustain a tuberculosis infection thereby identifying the Mtb specific metabolic pathways and enzymes required for carbon co-metabolism as potential drug targets. Metabolic flux represents the final integrative outcome of many different levels of cellular regulation that contribute to the flow of metabolites through the metabolic network. It is therefore critical that we have an in-depth understanding of the rewiring of metabolic fluxes in different conditions. Here, we employed 13C-metabolic flux analysis using stable isotope tracers (13C and 2H) and lipid fingerprinting to investigate the metabolic network of Mtb growing slowly on physiologically relevant carbon sources in a steady state chemostat. We demonstrate that Mtb is able to efficiently co-metabolise combinations of either cholesterol or glycerol along with C2 generating carbon substrates. The uniform assimilation of the carbon sources by Mtb throughout the network indicated no compartmentalization of metabolism in these conditions however there were substrate specific differences in metabolic fluxes. This work identified that partitioning of flux between the TCA cycle and the glyoxylate shunt combined with a reversible methyl citrate cycle as the critical metabolic nodes which underlie the nutritional flexibility of Mtb. These findings provide new insights into the metabolic architecture that affords adaptability of Mtb to divergent carbon substrates. ImportanceEach year more than 1 million people die of tuberculosis (TB). Many more are infected but successfully diagnosed and treated with antibiotics, however antibiotic-resistant TB isolates are becoming ever more prevalent and so novel therapies are urgently needed that can effectively kill the causative agent. Mtb specific metabolic pathways have been identified as an important drug target in TB. However the apparent metabolic plasticity of this pathogen presents a major obstacle to efficient targeting of Mtb specific vulnerabilities and therefore it is critical to define the metabolic fluxes that Mtb utilises in different conditions. Here, we used 13C-metabolic flux analysis to measure the metabolic fluxes that Mtb uses whilst growing on potential in vivo nutrients. Our analysis identified selective use of the metabolic network that included the TCA cycle, glyoxylate shunt and methyl citrate cycle. The metabolic flux phenotypes determined in this study improves our understanding about the co-metabolism of multiple carbon substrates by Mtb identifying a reversible methyl citrate cycle and the glyoxylate shunt as the critical metabolic nodes which underlie the nutritional flexibility of Mtb.

systems biology↗

bioRxiv2020

Automated Rational Strain Construction Based on High-Throughput Conjugation

Molecular cloning is the core of Synthetic Biology, as it comprises the assembly of DNA and its expression in target hosts. At present, however, cloning is most often a manual, time-consuming and repetitive process that highly benefits from automation. The automation of a complete rational cloning procedure, i.e., from DNA part creation to expression in the target host, involves the integration of different operations and machines. Examples of such workflows are sparse, especially when the design is rational (i.e., the DNA sequence design is fixed, and not based on randomized libraries) and the target host is less genetically tractable (e.g., not sensitive to heat-shock transformation). In this study, an automated workflow for the rational construction of plasmids and their subsequent conjugative transfer into the biotechnological platform organism Corynebacterium glutamicum is presented. The whole workflow is accompanied by a custom-made software tool. As an application example, a rationally designed library of transcription factor biosensors based on the regulator Lrp was constructed and characterized. A sensor with an improved dynamic range was obtained, and insights from the screening provided evidence for a dual regulator function of C. glutamicum Lrp.

synthetic biology↗

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↗