Hello everyone,
I’ve put together a demo video for using the Fit Curve and Curve DoE tool in JMP Pro 17 for the analysis of cell growth data. This is a great example that will quickly catch the interest of a wide range of scientists involved in cell growth measurements (bioprocess, genetics).
The example uses optical density readings from a plate reader measuring different cell lines (different genetically edited strains) on different percentages of maltose. The demo shows how quickly Scientists can get meaningful parameters (i.e. growth rate) and profiles within minutes from their data.
Some customers may ask why they wouldn’t just use the Functional Data Explorer; using the predetermined mechanistic models in Fit Curve will better fit the expected shape for cell growth – particularly in the ‘stationary phase’ of growth. With FDE this is not usually presented well – especially when the data is variable or noisy.
Overview
Who would find this useful?
- Groups focusing on microbial or mammalian culture – this can include bioprocess (mammalian, microbial, algae) or genetic groups – typically at the R+D stage.
- Look out for the mention of equipment (‘bioreactor’, ‘plate reader’, ‘microbioreactor’) such as the Ambr15 (Sartorius), Biolector/Robolector (Beckman Coulter), Shake Flask Reader (Kuhner).
Business value
- Increased process development/scale up speed – quicker identification of beneficial process parameters speeds up R+D without the need for additional runs.
Scientific value
- Quickly gaining valuable parameters in minutes rather than identifying ‘by eye’ or calculating the values by hand.
- Applying target profiles to explore parameters to reach goals.
- Guides additional experimentation for improved cell growth.
- Ease of communication with the profiler tool.
Excellent - nice example, nice presentation and great description! Thanks @Bingh94
