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At the intersection of artificial intelligence (AI) and machine learning (ML) with synthetic biology, scientists are making groundbreaking strides. At the forefront of this exciting journey is gene synthesis, now experiencing a revolutionary transformation thanks to AI and ML. But what exactly is the role of these technologies in pushing the boundaries of synthetic biology?
Leverage Advanced AI Technologies at Synbio Technologies
As a leader in synthetic biology technology, Synbio Technologies harnesses state-of-the-art algorithms and manufacturing processes in DNA synthesis. Our suite of bio-intelligent analysis tools, like Syno Ab, NG Codon, CI, and AI-TAT, combined with a massive synthesis capacity, is propelling DNA synthesis toward greater intelligence and efficiency.
Advanced Sequence Design | NG Codon Optimization: Our NG Codon technology marries deep learning with gene expression characteristics, making codon optimization more reliable and efficient.
The following illustrates the impact of our codon optimization technology; showing a significant increase in protein expression and solubility after leveraging our proprietary NG Codon software.
Rethink Complex Gene Synthesis | CI System: Our CI system, created with advanced algorithms, excels at synthesizing complex sequences with 100% accuracy, outperforming industry competitors.
The following analysis results presents Synbio Technologies compared with two other suppliers for 2000 synthetic genes. Our expert teams can synthesize diverse complex sequences with 100% accuracy, encompassing repetitive sequences, intricate hairpin structures, sequences with high GC content, poly structures, and beyond.
At Synbio Technologies, we offer accurate gene sequences in any vector of your choice. Our intelligent platform, based on Design–Build–Test–Learn cycles, is committed to automated, intelligent, and standardized production processes. We’re not just enhancing efficiency; we’re revolutionizing data quality and stability, providing technical solutions for the biopharmaceutical industry and beyond.
References
[1] Carbonell, P., Radivojevic, T., & García Martín, H. (2019). Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation. ACS Synthetic Biology, 8(7), 1474–1477.
[2] Kim, G. B., Kim, W. J., Kim, H. U., & Lee, S. Y. (2020). Machine learning applications in systems metabolic engineering. Current Opinion in Biotechnology, 64, 1–9.
[3] Mohammed, E., A. A., Rajmonda S. C., Joshua G. D., Nancy K. L., Vanessa A. V., & Hector G. M. (2022). Artificial Intelligence for Synthetic Biology. Communications of the ACM, 65(5), 88-97.
