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Expanded Synthetic Promoter Library Introduced to Boost Biomanufacturing Operations

Developed de novo using the company’s proprietary transcriptional analysis and sequence engineering platform, SynGenSys’ CHO.SET synthetic promoters combine advanced computational design with empirical validation. The post Expanded Synthetic Promoter Library Introduced to Boost Biomanufacturing Operations appeared first on GEN - Genetic Engineering and Biotechnology News .

Expanded Synthetic Promoter Library Introduced to Boost Biomanufacturing Operations

SynGenSys, a biotechnology company based in Sheffield, U.K., has announced the introduction of CHO.SET ® 2.0, a synthetic gene promoter system designed to enhance protein production in Chinese Hamster Ovary (CHO) cells. Andy Racher, the CEO of SynGenSys, explained that the synthetic promoters were created de novo through the company's proprietary computational design and sequence engineering platform.

These promoters are designed to work across various CHO expression systems, catering to a broad range of biomanufacturing requirements, according to Racher.

Racher highlighted that experimental data demonstrate CHO.SET promoters provide a significant improvement in transcriptional activity and protein production metrics. Specific vectors have shown over 4 times higher antibody productivity, with titer levels exceeding 2 grams per liter in a simple fed-batch flask model. Furthermore, the synthetic promoter pools contain a higher proportion of high-expressing cells, increasing the chances of isolating top-performing clones.

Racher emphasized that CHO cells are the gold-standard cell factories in the pharmaceutical industry, and synthetic promoters offer a solution to productivity limitations. He expressed excitement about releasing this latest update and continuing to address the evolving needs of biomanufacturing.

The CHO.SET Promoter Library will be on display at BioProcess International next month as part of Biotech Week Boston.

Written by urgent.news from GEN Biotechnology's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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