New addition to the sallea platform: AI-Driven Culture Optimization
We’re excited to announce that, in collaboration with LABMaiTE, the sallea platform is expanding with a validated AI model trained on data generated specifically on our scaffolds. This project explored how artificial intelligence can be used to better understand and optimize biomass development on sallea’s proteinPURE scaffolds and the results speak for themselves.
🔬 What does this mean for our customers?
- An AI model tailored to scaffold-specific cell culture data
- Faster optimization of culture strategies
- Reduced experimental time and costs
- Accelerated project and process development
📊 Key insight from the model
By analyzing a subset of our data, the AI identified cell number as the most influential parameter for biomass development on the scaffold. This insight provides customers with a strong, data-driven starting point when working with Sallea scaffolds.
This milestone strengthens our platform with intelligent, data-driven tools, helping customers move from experimentation to results faster. 👏 A big thank you to LABMaiTE for the successful collaboration.
The goal of this collaboration was to train an AI model on scaffold culture data to identify the most important parameters required to achieve desired culture outcomes. LABMaite successfully validated a subset of sallea scaffold culture data using their AI model, identifying an optimal seeding density for proteinPURE scaffolds to achieve target cell density on the scaffold.
sallea’s input data included seeding efficiency data (as depicted above) and growth curves. These datasets enabled LABMaiTE to generate output data correlating cell density with seeded cell number, revealing an optimal seeding density of 1 million cells per scaffold cultured in food-grade culture medium.
We are excited to see the strong compatibility of our scaffold culture data with AI optimization tools and look forward to further improving scaffold cultures, bringing commercially relevant structured cultivated meat products closer to reality.
