Beyond boosting productivity, Novonesis employs genetic engineering as a safety tool. They modify production strains to remove any latent ability to become harmful, ensuring products for food and feed are exceptionally clean and safe, a key advantage over using wild-type strains.

The transition to an engineering discipline in drug discovery, analogous to aeronautics, means using powerful in silico models to get much closer to a final product before physical testing. This reduces reliance on iterative, expensive, and time-consuming wet lab experiments.

In biomanufacturing, purifying a product is a major cost. Using an organism that secretes the product directly into the media eliminates the need for cell lysis and reduces endotoxin concerns. This simplification of downstream processing can cut total production costs by 25-33%, a significant competitive advantage.

A key barrier to complex peptide-antibody drugs is manufacturing (CMC). Current methods require separate synthesis and conjugation steps. A fully genetically encoded system—where the entire hybrid molecule is produced in a single cell line—would dramatically lower the barrier to entry and simplify manufacturing, unlocking new drug designs.

The standard practice is to optimize for productivity (titer) first, then correct for quality (glycosylation) later. This is reactive and inefficient. Successful teams integrate glycan analysis into their very first screening experiments, making informed, real-time trade-offs between productivity and quality attributes.

The traditional method of engineering enzymes by making precise, knowledge-based changes (“rational design”) is largely ineffective. Publication bias hides the vast number of failures, creating a false impression of success while cruder, high-volume methods like directed evolution prove superior.

Frances Arnold, an engineer by training, reframed biological evolution as a powerful optimization algorithm. Instead of a purely biological concept, she saw it as a process for iterative design that could be harnessed in the lab to build new enzymes far more effectively than traditional methods.

Many innovative drug designs fail because they are difficult to manufacture. LabGenius's ML platform avoids this by simultaneously optimizing for both biological function (e.g., potency) and "developability." This allows them to explore unconventional molecular designs without hitting a production wall later.

For over a decade, slow growth rates and poor yields made cyanobacteria commercially unfeasible. The recent discovery of a faster-growing strain, combined with new genetic modification tools, has finally unlocked its industrial potential, closing the efficiency gap with established microbes like E. coli.