A Flexible System for Stepwise Automation of Microbial Testing of Drinking and Process Water.

Ambitions to improve the connectivity of devices to enable automation and digital representation of processes have been around for some time. Nevertheless, some companies, especially in life science and analytical science, tend to adopt these developments rather slowly. In the field of microbial analysis of drinking and process water, for example, a large part of the work is still carried out manually, although the high number of samples per day and the low fluctuation in work processes would predestine water analysis for a higher degree of automation. Obstacles such as the risk of bottlenecks and possible downtimes after machine failure, the spatial conditions together with the low flexibility of the automated system, a lack of trained personnel, and the high acquisition costs hinder this development, however.To lower these barriers, we have developed a system for the generation of flexibly expandable automated process lines, which handles sample handling and sample transport as a decisive step in the networking of several devices. The system allows the connection of devices that are distributed over the entire laboratory or close to each other, as well as those with a combination of both spatial situations. A functional or throughput expansion of the process can be realized by adding additional devices or storage areas to the network.With this concept, we have established a system for the automatic processing of defined steps of a routine Legionella pneumophila screening in drinking water testing. From this starting point, the process can be extended to cover further steps, such as concentrating or plating, up to the full analytical workflow.

Development of a Gold Nanoparticle-Based Colorimetric Sensor for Water for Injection At-Line Impurity Testing.

The best-known rapid test using gold nanoparticles (AuNPs) is the human chorionic gonadotropin pregnancy test. AuNPs are a powerful tool in point-of-care testing because of their flexibility, modifiability, and visibility. Here, we report a method to detect impurities for at-line process control in water-for-injection (WFI) manufacturing through the example of endotoxins. If a distinct concentration of these amphipathic molecules, originated from gram-negative bacteria, enters the human body, it will result in septic shock, followed by organ failure and possibly death. Every fluid given parenterally is subject to strict regulatory requirements and therefore endotoxin testing. Through use of traditional methods like the limulus amebocyte lysate (LAL) test, it takes more than 2 h to complete. With the presented method, one-fifth of the sample volume is sufficient compared with the LAL test. Once the assay components have been mixed, the result can be interpreted visually within 2 min without the use of further instruments.

A new method for non-invasive biomass determination based on stereo photogrammetry.

A novel, non-destructive method for the biomass estimation of biological samples on culture dishes was developed. To achieve this, a photogrammetric system, which consists of a digital single-lens reflex camera (DSLR), an illuminated platform where the culture dishes are positioned and an Arduino board which controls the capturing process, was constructed. The camera was mounted on a holder which set the camera at different title angles and the platform rotated, to capture images from different directions. A software, based on stereo photogrammetry, was developed for the three-dimensional (3D) reconstruction of the samples. The proof-of-concept was demonstrated in a series of experiments with plant tissue cultures and specifically with calli cultures of Salvia fruticosa and Ocimum basilicum. For a period of 14 days images of these cultures were acquired and 3D-reconstructions and volumetric data were obtained. The volumetric data correlated well with the experimental measurements and made the calculation of the specific growth rate, µ max, possible. The µ max value for S. fruticosa samples was 0.14 day-1 and for O. basilicum 0.16 day-1. The developed method demonstrated the high potential of this photogrammetric approach in the biological sciences.

PetriJet Platform Technology: An Automated Platform for Culture Dish Handling and Monitoring of the Contents.

Due to the size of the required equipment, automated laboratory systems are often unavailable or impractical for use in small- and mid-sized laboratories. However, recent developments in automation engineering provide endless possibilities for incorporating benchtop devices. Here, the authors describe the development of a platform technology to handle sealed culture dishes. The programming is based on the Petri net method and implemented via Codesys V3.5 pbF. The authors developed a system of three independent electrical driven axes capable of handling sealed culture dishes. The device performs two difference processes. First, it automatically obtains an image of every processed culture dish. Second, a server-based image analysis algorithm provides the user with several parameters of the cultivated sample on the culture dish. For demonstration purposes, the authors developed a continuous, systematic, nondestructive, and quantitative method for monitoring the growth of a hairy root culture. New results can be displayed with respect to the previous images. This system is highly accurate, and the results can be used to simulate the growth of biological cultures. The authors believe that the innovative features of this platform can be implemented, for example, in the food industry, clinical environments, and research laboratories.

Ramified Challenges: Monitoring and Modeling of Hairy Root Growth in Bioprocesses--A Review.

The review presents a comprehensive overview on available solutions for the monitoring and modeling of various aspects of hairy root growth processes. Several online and offline measurement principles get explained exemplary and are being compared. It was found that no direct online measurement principle for hairy root biomass in submerged and solid-state culturing environment is available. However, certain indirect methods involving one or more measurement values have been developed for biomonitoring of hairy roots especially in bioreactors. In the field of modeling of hairy root growth processes, four independent architectures (continuous models, metabolic flux analysis, agent-based models, and artificial neural networks) are described and compared including literature references. The discussion is structured into microscopic model approaches, addressing only certain aspects of growth, and macroscopic model approaches, describing the hairy root network as a whole. An agent-based macroscopic model based on phenomenological data acquired with systematic imaging of hairy roots on culture dishes together with a 3D visualization of simulation results is presented in detail.

Modeling hairy root tissue growth in in vitro environments using an agent-based, structured growth model.

An agent-based model for simulating the in vitro growth of Beta vulgaris hairy root cultures is described. The model fitting is based on experimental results and can be used as a virtual experimentator for root networks. It is implemented in the JAVA language and is designed to be easily modified to describe the growth of diverse biological root networks. The basic principles of the model are outlined, with descriptions of all of the relevant algorithms using the ODD protocol, and a case study is presented in which it is used to simulate the development of hairy root cultures of beetroot (Beta vulgaris) in a Petri dish. The model can predict various properties of the developing network, including the total root length, branching point distribution, segment distribution and secondary metabolite accumulation. It thus provides valuable information that can be used when optimizing cultivation parameters (e.g., medium composition) and the cultivation environment (e.g., the cultivation temperature) as well as how constructional parameters change the morphology of the root network. An image recognition solution was used to acquire experimental data that were used when fitting the model and to evaluate the agreement between the simulated results and practical experiments. Overall, the case study simulation closely reproduced experimental results for the cultures grown under equivalent conditions to those assumed in the simulation. A 3D-visualization solution was created to display the simulated results relating to the state of the root network and its environment (e.g., oxygen and nutrient levels).