Kernel

Using our genetic system ontology and the first commercial-grade genetic schematic editor, we've published expression vector layouts as templates in Kernel. Try out the Genetic Compiler to get vector layout recommendations per antibody format.

Kernel’s Codon Optimizer uses machine learning trained on CHO codon usage statistics, and further refined with biophysical predictions, to boost titers across antibody formats and hosts.

We created a machine learning model trained on CHO genome statistics and further refined with biophysical predictions to boost titers across antibody formats.

Engineered vector layouts -- tailored to modality, molecule, and host -- are licensed for use and available as plasmid templates in Kernel. Composed with the schematic editor and modular genetic parts system, they provide flexible scaffolds for payload insertion and protein expression control.

Our next-generation transposase system shows excellent cell recovery curves and productivity gains. Paired with our model-guided expression vector design in Kernel, it shortens the path to functional data -- especially powerful for high-throughput discovery workflows.

CHO-K1 GS knock-out  host, suspension-adapted and GMP-banked under ICH Q5 guidelines. Licensed to partners scaling toward IND, it provides a proven foundation for CLD, process development, and CMC.