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Better Understanding of Cellular Metabolism With AI

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metabolic process modeling, illustration

An EPFL framework that leverages deep learning paves the way for the efficient and accurate modeling of metabolic processes.

Credit: Subham Choudhury / EPFL

Scientists at EPFL, the Swiss Federal Institute of Technology, Lausanne, have developed REKINDLE, a deep learning-based computational framework that replicates dynamic metabolism in cells.

"REKINDLE will allow the research community to reduce computational efforts in generating kinetic models by several orders of magnitude," says EPFL's Ljubisa Miskovic. "It will also help in postulating new hypotheses by integrating biochemical data in these models, elucidating experimental observations, and steering new therapeutic discoveries and biotechnology designs."

Researchers envision the framework optimizing the metabolic network of microbes to generate industrial-scale chemical compounds, as well as unifying the use of kinetic modeling in the scientific community. To that end, EPFL's Subham Choudhury said REKINDLE uses popular Python libraries to promote accessibility and ease of use.

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Abstracts Copyright © 2022 SmithBucklin, Washington, DC, USA


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