Contrary to the belief that glycosylation is only controlled externally by the cell's state, new research shows a "code" within the protein sequence offers strong, "inside-out" control over product quality attributes, changing a long-held paradigm.
A digital twin trained only on a single lab's limited data is not extendable or robust. The future is building on "foundation models" trained on massive, public consortium data, which allows you to transfer learning and strengthen specific models.
Don't just generate data. Strategically choose omics types based on a clear trade-off: cheap but less actionable (genomics) vs. expensive but highly actionable (fluxomics) or even interventional (CRISPR screens). This provides a practical framework for R&D.
Pure AI models can't obey physical laws like thermodynamics. The most powerful approach is a hybrid model: embed decades of trusted biochemical knowledge mechanistically, then use machine learning to fill in unknown parameters and discover new mechanisms.
Most experiments are designed for a single purpose. A more powerful approach is prospective study design: use bridging samples and balance for covariates so new datasets can be stacked on old ones, avoiding batch effects and creating larger, more valuable datasets over time.
AI agents can parse old electronic lab notebooks and spreadsheets, infer data structures from user notes, and populate modern databases. This makes previously siloed, unstructured legacy data AI-ready and queryable without massive manual effort.