Predictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models

Simplicity renders shake flasks ideal for strain selection and substrate optimization in biotechnology. Uncertainty during initial experiments may, however, cause adverse growth conditions and mislead conclusions. Using growth models for online predictions of future biomass (BM) and the arrival of critical events like low dissolved oxygen (DO) levels or when to harvest is hence important to optimize protocols. Established knowledge that unfavorable metabolites of growing microorganisms interfere with the substrate suggests that growth dynamics and, as a consequence, the growth model parameters may vary in the course of an experiment. Predictive monitoring of shake flask cultures will therefore benefit from estimating growth model parameters in an online and adaptive manner. This paper evaluates a newly developed particle filter (PF) which is specifically tailored to the requirements of biotechnological shake flask experiments. By combining stationary accuracy with fast adaptation to change the proposed PF estimates time-varying growth model parameters from iteratively measured BM and DO sensor signals in an optimal manner. Such proposition of inferring time varying parameters of Gompertz and Logistic growth models is to our best knowledge novel and here for the first time assessed for predictive monitoring of Escherichia. coli (E. coli) shake flask experiments. Assessments that mimic real-time predictions of BM and DO levels under previously untested growth conditions demonstrate the efficacy of the approach. After allowing for an initialization phase where the PF learns appropriate model parameters, we obtain accurate predictions of future BM and DO levels and important temporal characteristics like when to harvest. Statically parameterized growth models that represent the dynamics of a specific setting will in general provide poor characterizations of the dynamics when we change strain or substrate. The proposed approach is thus an important innovation for scientists working on strain characterization and substrate optimization as providing accurate forecasts will improve reproducibility and efficiency in early-stage bioprocess development.

Biotechnology data analysis training with Jupyter Notebooks

Biotechnology has experienced innovations in analytics and data processing. As the volume of data and its complexity grows, new computational procedures for extracting information are developed. However, the rate of change outpaces the adaptation of biotechnology curricula, necessitating new teaching methodologies to equip biotechnologists with data analysis abilities. To simulate experimental data, we created a virtual organism simulator (silvio) by combining diverse cellular and sub-cellular microbial models. silvio was utilized to construct a computer-based instructional workflow with important steps during strain characterization and recombinant protein expression. The instructional workflow is provided as a Jupyter Notebook with comprehensive explanatory text of biotechnological facts and experiment simulations using silvio tools. The students conduct data analysis in Python or Excel. This instructional workflow was separately implemented in two distance courses for Master's students in biology and biotechnology. The concept of using virtual organism simulations that generate coherent results across different experiments can be used to construct consistent and motivating case studies for biotechnological data literacy.

Delivery of Metabolically Neuroactive Probiotics to the Human Gut

The human microbiome is a rich factory for metabolite production and emerging data has led to the concept that orally administered microbial strains can synthesize metabolites with neuroactive potential. Recent research from ex vivo and murine models suggests translational potential for microbes to regulate anxiety and depression through the gut-brain axis. However, so far, less emphasis has been placed on the selection of specific microbial strains known to produce the required key metabolites and the formulation in which microbial compositions are delivered to the gut. Here, we describe a double-capsule technology to deliver high numbers of metabolically active cells derived from the 24-strain probiotic product SH-DS01 to the gastrointestinal tract, including the small intestine, where immune responses and adsorption of metabolites into the bloodstream occur. Based on its genome sequence, Limosilactobacillus reuteri SD-LRE2-IT was predicted to have the genetic capacity to de novo produce a specific metabolite of interest to brain health, vitamin B12, which could be confirmed in vitro. Taken together, our data conceptualizes the importance of rationally defined microbial strain characterization based on genomics and metabolomics data, combined with carefully designed capsule technology for delivery of live cells and concomitant functionality in and beyond the gut ecosystem.

Strain Characterization in Two-Dimensional Crystals

Two-dimensional (2D) crystals provides a material platform to explore the physics and chemistry at the single-atom scale, where surface characterization techniques can be applied straightforwardly. Recently there have been emerging interests in engineering materials through structural deformation or transformation. The strain field offers crucial information of lattice distortion and phase transformation in the native state or under external perturbation. Example problems with significance in science and engineering include the role of defects and dislocations in modulating material behaviors, and the process of fracture, where remarkable strain is built up in a local region, leading to the breakdown of materials. Strain is well defined in the continuum limit to measure the deformation, which can be alternatively calculated from the arrangement of atoms in discrete lattices through methods such as geometrical phase analysis from transmission electron imaging, bond distortion or virial stress from atomic structures obtained from molecular simulations. In this paper, we assess the accuracy of these methods in quantifying the strain field in 2D crystals through a number of examples, with a focus on their localized features at material imperfections. The sources of errors are discussed, providing a reference for reliable strain mapping.

European Population of Pectobacterium punjabense: Genomic Diversity, Tuber Maceration Capacity and a Detection Tool for This Rarely Occurring Potato Pathogen

Enterobacteria belonging to the Pectobacterium and Dickeya genera are responsible for soft rot and blackleg diseases occurring in many crops around the world. Since 2016, the number of described species has more than doubled. However, some new species, such as Pectobacterium punjabense, are often poorly characterized, and little is known about their genomic and phenotypic variation. Here, we explored several European culture collections and identified seven strains of P. punjabense. All were collected from potato blackleg symptoms, sometimes from a long time ago, i.e., the IFB5596 strain isolated almost 25 years ago. We showed that this species remains rare, with less than 0.24% of P. punjabense strains identified among pectinolytic bacteria present in the surveyed collections. The analysis of the genomic diversity revealed the non-clonal character of P. punjabense species. Furthermore, the strains showed aggressiveness differences. Finally, a qPCR Taqman assay was developed for rapid and specific strain characterization and for use in diagnostic programs.