Computer models help microbes produce plant medicines more efficiently
Cheng M, Gao X, Shen R, Liang X, Dai Y
Medicinal Plants
Many of the plant compounds in your herbal teas, essential oils, and natural remedies take years to grow and acres of land to harvest at scale, but these computational tools could let microbes brew them in tanks, easing pressure on wild plant populations and farms.
Plants make thousands of useful compounds called terpenoids, including things like menthol, taxol, and the scents in lavender and cannabis. Scientists want microbes like yeast and bacteria to make these compounds instead, but getting the biology right is tricky. This review looks at how detailed computer maps of microbial metabolism are helping researchers pinpoint exactly where to tweak the chemistry to get higher yields.
Key Findings
Genome-scale metabolic models (GSMMs) were reviewed across four microbial hosts: E. coli, S. cerevisiae, cyanobacteria, and Yarrowia lipolytica, covering both model and non-model organisms.
Key production bottlenecks identified include metabolic flux competition, cofactor imbalance, and product toxicity, all of which GSMMs can computationally target.
Future directions include multiconstraint models and integration of machine learning with synthetic biology to accelerate terpenoid cell factory design.
chevron_right Technical Summary
Scientists are using computer models of entire microbial genomes to figure out how to make bacteria and yeast produce terpenoids more efficiently. Terpenoids are plant compounds used in medicines, fragrances, and fuels, and these computational tools could make lab-grown versions far cheaper and cleaner than extracting them from plants.
Abstract Preview
Original paper
Genome-Scale Metabolic Modeling of Terpenoid Biosynthesis: Advances and Perspectives.
Terpenoids are valuable natural products that are widely used in medicine, agriculture, energy, and food. Traditional production by plant extraction or chemical synthesis is inefficient, costly, an...
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