Computation, including artificial intelligence (AI) and machine learning (ML), plays an increasing role in the basis of bioprocessing. “The stupendous demand for bioproducts necessitates the need for advanced computational technologies, such as ML and AI,” says Barkha Singhal, PhD, assistant professor in the School of Biotechnology at Gautam Buddha University in India.

Recently, Singhal and her colleague described ML’s role in the bioeconomy. “The need for transformation of the take-make-dispose concept of the linear economy to reduce, reuse, and recycle to the circular economy is urgently required to attain sustainability,” she says. “The ML tools will definitely reduce the time and enhance productivity in bioproducts in a cost-effective way, which will play a direct role in promoting a biobased economy and lead to circularity in the economic model.”

As Singhal explains, ML can improve bioprocessing in many ways, from expanding the design space explored in bioprocesses to designing manufacturing facilities. “All the facets of bioprocessing–including a selection of the right strain and its engineering, optimization of bioprocessing, as well as monitoring and control and downstream processing—can be done by ML approaches,” she says.

Implementing more ML in commercial bioprocessing depends on many elements. “Increased automation, data-driven development of bioprocesses, data availability, and exchange of data will be required to implement ML approaches at the industry level,” Singhal explains. “The existing industries need to understand the transformation that caters to the need for timely production and also paves the way for the prediction of bioproducts for futuristic applications.”

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