KAUST researchers have developed a new synthetic biology process using metabolically engineered algae to produce fragrant sesquiterpenoids, the core compounds in agarwood and other perfumes. The process, developed by the Lauersen and Szekely groups, achieved yields 25 times higher than previous methods and allows for the synthesis of 103 types of fragrant sesquiterpenoids. It also incorporates an energy-efficient nanofiltration step and operates at room temperature with minimal waste. Why it matters: This sustainable bioprocess offers a green alternative to environmentally damaging harvesting of natural resources for the $44 billion fragrance industry, with potential applications in drug development.
KAUST researchers have developed a green synthetic biology approach using engineered algae to replicate the complex fragrances of agarwood, also known as oudh. They catalogued the chemical diversity of sesquiterpenes (STPs) in 58 agarwood samples and reproduced some of the chemical complexity of agarwood STPs in algae using synthetic biology. The team used the green alga Chlamydomonas reinhardtii to produce nine distinct STP chemical products widely found in agarwood, offering a sustainable alternative to harvesting endangered trees. Why it matters: This research provides a sustainable route for producing sought-after fragrances, reducing pressure on endangered agarwood tree populations and promoting green chemistry in the region.
The Technology Innovation Institute (TII) in Abu Dhabi has launched a Biofoundry to advance R&D in synthetic biology, focusing on genetic engineering, metabolic engineering, and bioinformatics. The facility features high-throughput robotic systems, next-generation sequencing, and advanced computational tools. TII's Biofoundry is now part of the Global Biofoundry Alliance (GBA) to foster partnerships and address shared challenges. Why it matters: This initiative positions the UAE as a key player in synthetic biology, with potential breakthroughs across healthcare, agriculture, and environmental sustainability.
Researchers at the Rosalind Franklin Institute are using generative AI, including GANs, to augment limited biological datasets, specifically mirtron data from mirtronDB. The synthetic data created mimics real-world samples, facilitating more comprehensive training of machine learning models, leading to improved mirtron identification tools. They also plan to apply Large Language Models (LLMs) to predict unknown patterns in sequence and structure biology problems. Why it matters: This research explores AI techniques to tackle data scarcity in biological research, potentially accelerating discoveries in noncoding RNA and transposable elements.