Case Study: Enabling Right-First-Time Scale Up Decisions


Laura Jennings-Antipov, Riffyn’s Director of Science, speaks with John Cumbers, CEO of SynBioBeta, about how Riffyn SDE is used to detect unforeseen process variations that impact process yields.

Finding the best yeast strains

This video illustrates how the Riffyn Scientific Development Environment enabled a customer to select the right strain for scale-up the first time, saving months of wasted time and effort and accelerating product development cycles.

In this case, a customer was engineering yeast strains for maximum succinate production, then testing yield in small scale fermentation experiments, and then using these data to choose strains for scale-up. Top strains were being identified by manual inspection plots of yield vs. time, and initially all observed changes in yield were assumed to be due to the changes in strain from sample to sample. However, the selected top performing strains were not repeating their performance in downstream runs, creating frustration for the team on both sides of the selection decision.


Paper batch records contain important data within them - such as lot numbers and operating parameters - which can affect key outcomes such as yield. Keeping them in an unstructured, inaccessible format, prevents them from being incorporated into statistical analysis. 

Standard time-course analysis does not control for process-related variables that may impact the measured yields. 

The customer recognized that there may be other variables contributing to succinate yield, and wanted to identify those variables, but the data were fragmented; strain engineering data were in lab notebooks, fermentation data were stored in on-board instrument databases, and analysis data were stored in Excel spreadsheets. Datasets were hard to find, even harder to interpret, and deciphering connections between datasets was almost impossible.

Laura shows how the Riffyn SDE was used to pull all these data together into a globally accessible system, where data were contextualized and connected automatically into a single comprehensive data table ready for analysis. Using this Riffyn SDE data table, the customer was able to find an unexpected correlation between temperature and succinate yield, which changed the interpretation of their data. When the results were corrected for temperature variations, the true strain effect was revealed. The data analysis enabled by Riffyn SDE allowed the customer to select reliably improved strains for production, saving the customer months of wasted time and effort.

So what Riffyn’s allowed you to do is look at everything visually, collect all the data, tag it, annotate it; collect all the instrument data, all the strain data, all the metadata and associated it in one place. You wouldn’t have been able to do that before.
— John Cumbers, CEO SynBioBeta

By incorporating data and parameters from each process step, Riffyn can generate structured data sets that can be used in more in-depth data analysis.  Using this Riffyn SDE-generated data table, the customer was able to find an unexpected correlation between temperature and succinate yield, which changed the interpretation of their data.

Key Features

Process design

Data collection & joining

Connection to data analysis applications

Statistical analysis with JMP




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