THIS STORY HAS BEEN APPROVED BY THE CUSTOMER FOR EXTERNAL PUBLICATION. PLEASE FEEL FREE TO SHARE!
Read it here: jmp.com/uniofoxford. The recording is also available on JMP's YouTube channel.
Reversing bacterial resistance with accelerated assay development
We’re excited to share a new academic success story from The University of Oxford in collaboration with JMP and Synthace.
Video Summary
Scientists from the Lanyon-Hogg Group at the University of Oxford are tackling drug-resistant bacteria by developing a new assay targeting the RecBCD enzyme. Using JMP alongside Synthace, they transformed manual, low-throughput experimentation into high-efficiency, high-dimensional screening—enabling faster, more robust drug discovery.
Key Takeaways
- Drug resistance challenge: Drug-resistant bacteria threaten modern medicine; new solutions are urgently needed.
- New target enzyme: RecBCD is identified as a key enzyme involved in bacterial resistance.
- Innovation through assay development: Adam initially developed a manual assay, but it evolved through advanced tools.
- High-dimensional experimentation: 11 variables required optimization across a complex design space.
Returns on Investment
- Time savings: Manual experiments took weeks to months. Automated, multidimensional experiments were completed within a day.
- Data quality and insight: JMP provided immediate modeling and analysis, reducing data processing time from days to minutes.
- Experimental throughput: Increased from 1 variable at a time to 11 variables simultaneously across hundreds of conditions.
- Scientific acceleration: Rapid iteration and improved assay quality now enable drug screening against RecBCD.
Success Quotes
Adam Winnifrith, University of Oxford:
- “We were able to do those experiments within a day. Without JMP and Synthace, we wouldn't have been able to do it at all, hand pipetting those kinds of experiments. Even if I just did maybe one variable at a time, it would've taken weeks, maybe months.”
- “The amazing thing about JMP is that you can very, very quickly take your data and you can generate a number of different models to analyze the data. If I was to write the code to do that myself, that would've taken hours, maybe days. Within 20 minutes almost of getting my data, I was already able to get a really good feel for everything that had happened despite having a very complex, multidimensional design space.”
