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Flexible and Scalable Automation
Technology Category
- Platform as a Service (PaaS) - Application Development Platforms
- Functional Applications - Remote Monitoring & Control Systems
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Agriculture
- Life Sciences
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Predictive Maintenance
- Remote Collaboration
- Remote Control
- Digital Twin
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
- System Integration
The Challenge
Syngenta was looking to improve their use of automation, and methods to make their protein production process more reproducible. Their three main pain points within their existing process were: Low uptake of automation for scientists across the Biologicals team. Automation is a powerful tool, but is difficult to use without considerable expertise. Think of trimming the edges of a poster but only being given a chainsaw; this is the feeling new automation users face, and one that Syngenta’s automation team wants to mitigate. Time-consuming processes refactoring complex liquid handler scripts. Even for expert users on Syngenta’s automation team, scaling automation for different numbers of samples is time intensive and requires careful validation in the lab to ensure the intended actions are scripted properly. This often results in reticence from scientists to invest their time in automation as they can do it quicker manually, which ultimately results in lower productivity over time. Insufficient traceability and reproducibility of experiments and data analyses. Machines improve reproducible execution of experiments, but they also increase throughput. This moved the bottleneck for Syngenta to the ability to track what has happened to each and every sample. While custom solutions were made in-house, their long term support persistence was low due to disparate documentation of the processes.
About The Customer
Syngenta is a world leading agricultural company that develops novel solutions to support farmers worldwide, helping them feed the planet and enabling them to establish sustainable, safe, and consistent agricultural practices. Scientific research forms the foundation of Syngenta, employing 5000+ scientists across chemistry, chemical engineering, biochemistry, and biology. The Biologicals Group at Syngenta develops proteins for use as biological control agents and for regulatory testing of novel chemistries. These proteins are often diverse and challenging to produce, highlighting the need for a more flexible, adaptable, and rapid approach to their production.
The Solution
Building adaptable and scalable experimental workflows. Syngenta Biologicals team used Synthace’s no-code and device-agnostic Core Elements as building blocks to construct an automated bacterial transformation pipeline. The workflow automatically scales to accommodate changes in sample numbers and other process-specific parameters such as the antibiotic to use, making automation more user-friendly and accessible. Achieving routine, data-driven decision-making. The resulting data and sample metadata were used to automatically drive decision points within workflows. Using a combination of Synthace’s Core and Data Elements, Syngenta performed in-line data processing and data-driven decision making, to automate routine analytical tasks that would have otherwise used up the precious time of expert scientists. By automating this routine data processing, the Synthace platform reduces manual data wrangling and gives scientists more time to focus on more critical and sophisticated tasks. An example workflow that incorporates automated data aggregation, in-line data processing and decision making for control of liquid handling actions and outcomes. Syngenta used Synthace to build sophisticated workflows that describe their end-to-end bacterial transformation and cryo preparation processes. The processed data was used in data-driven decision making, prompting different experimental paths based on the success or failure of the transformation process. The above example workflow encodes the following steps (1) the selection of bacterial cultures that have grown, (2) the aliquoting of the cultures into barcoded cryotubes, (3) the addition of a cryopreservative, (4) the addition of barcode information, (5) the export of cryostock information, (6) the export of information about failed transformations, and (7) the return of flagged failed transformations to the user along with information about the DNA used in the failed transformations–allowing for rapid iteration and downstream optimization.
Operational Impact
Quantitative Benefit
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