In 1978, scientists from Kelco, then a division of Merck, collected samples of Elodea plants from a pond in Pennsylvania. From tissue of those plants, often known simply as waterweeds, Kelco scientists discovered bacteria that secreted a polysaccharide now known as gellan gum (GG). This water-soluble exopolysaccharide was soon marketed as a replacement for agar in culturing microorganisms. Over the years, companies have used GG in cosmetics, foods, and pharmaceuticals.
Despite decades of using GG, Nageswar Sahu, PhD, a research scholar at the Karunya Institute of Technology and Sciences in India, and his colleagues recently reported: “In spite of the commercial importance and expanding application horizon of GG, little attention has been directed toward the exploration of novel microbial cultures, development of advanced screening protocols, strain engineering, and robust upstream or downstream processes.”
In a GEN interview, Sahu provided some additional information.
“The major challenges in GG production include cost-effective production and improving fermentation processes,” he says. “However, the most critical challenge lies in the screening and isolation of gellan-producing microbes, as there are currently no standardized methods for this. To address these issues, alternative approaches—such as development of specific molecular markers, integration of droplet-based microfluidics, automated imaging, and robotic handling—can help.”
One way to improve the fermentation-based production of GG is with kinetic modeling, according to Sahu, who explains that “Kinetic study can help to understand the relation between GG production and substrate consumption, and it will help to make a model equation for future study.”
Like many bioprocesses, maximizing the production of GG requires a wholistic approach. “A process-optimization strategy can improve GG’s productivity,” Sahu says. “Applying machine-learning approaches can solve this problem, and statistical design can provide good results and progress in GG production.”
