Machine learning model foretells whether symbiotic relationships will thrive or collapse

Biomedical engineers at Duke University have used dynamic modeling and machine learning to construct simple rules for complex biology.
The researchers have devised a framework to accurately interpret and predict the behavior of mutually beneficial biological systems, such as human gut bacteria, plants and pollinators, or algae and corals, according to a university article.

The research appears on January 16, 2019 in the journal Nature Communications.
“In a perfect world, you'd be able to follow a simple set of molecular rules to understand how every biological system operated,” said Lingchong You, a professor in Duke's Department of Biomedical Engineering. “But in reality, it's difficult to establish general rules that encompass the immense diversity and complexity of biological systems. Even when we do establish general rules, it's still challenging to use them to explain and quantify various physical properties,” Lingchong, a BMES member, said in the article.
You and Feilun Wu, a graduate student and first author of the paper, addressed these challenges by examining the behavior of mutualistic systems, according to the article. These symbiotic systems are made of two or more populations that provide reciprocal benefit, such as monarch butterflies and milkweed plants. Wu is also a BMES member.

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