Flexible and Scalable Automation

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.

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.

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.

• Unlocking the power of lab automation. Using the no-code workflow builder and automatically generated instructions for in-lab setup, from labware and reagent details to deck layout, Synthace provides Syngenta with both a biological language for expressing automation needs and a tool to visualize the outcomes without stepping foot inside the lab. This removed the “black box” of automation scripts for their first time users who can understand what happens to their samples. Synthace’s easy to use interface hides the complexity of the workflows, allowing the end users to become more independent in their automation use.
• Rapidly iterating automated workflows - in the office or remotely. Using Synthace’s detailed experimental previews that reduce the need for dry and wet lab testing, Syngenta’s automation engineers use core platform functionality to rapidly iterate on protocols in silico. They can now develop robust processes to meet the ever-increasing demands of their team’s scientists. Automation engineers were able to build complex workflows in days, not weeks or months, and their time was freed to improve other processes.
• Automatically capturing, processing and storing all experimental results and metadata. Synthace minimizes manual data handling, and encourages organization-wide sharing of contextualized data sets. The Synthace platform knows how robots move samples and what biological meaning they have to the scientists, meaning Syngenta can track information with minimal effort. Syngenta has integrated the data tracking capabilities of Synthace with other solutions to build a mature ecosystem of lab tools that promote productive experimental design and execution. The Synthace platform has helped Syngenta achieve many drastic improvements to the accessibility, flexibility, and scalability of the Biologicals Group’s automation solutions. Syngenta’s scientists are now empowered to focus on the science rather than spending hours and days working out practical details for experimental execution and manual handling of experimental data.

• Automation engineers were able to build complex workflows in days, not weeks or months.
• The Synthace platform reduces manual data wrangling, giving scientists more time to focus on critical tasks.