This repository, CAAIL (Cellular Agriculture AI Library), is a curated, open-source collection of resources dedicated to the intersection of Cellular Agriculture and AI.
Cellular Agriculture faces major technical barriers to wide-spread adoption, particularly around cost, media formulation, and scaling. We believe that AI can provide crucial insights to address these challenges.
This project exists to collect all relevant published materials in one place, serving as a centralized library for researchers, engineers, and students looking to apply AI to cell-ag challenges.
We organize and host links to public resources across key areas of research:
- Papers & Preprints: Core research literature focusing on computational biology, modeling, and machine learning applied to cell culture, bioprocessing, and tissue engineering.
- Software & Code: Links to open-source tools, code repositories, and specialized algorithms used for media optimization, bioreactor modeling, and image analysis.
- Datasets: References and links to publicly available datasets (e.g., proteomics, genomics, high-throughput screening data) relevant to cellular agriculture.
- Other Educational Materials: Videos and other resources relevant to the use of AI in cellular agriculture.