Simple rules predict and explain biological mutualism

Machine learning model foretells whether symbiotic relationships will thrive or collapse. Scientists have long employed relatively simple guidelines to help explain the physical world, from Newton’s second law of motion to the laws of thermodynamics.

Now, biomedical engineers in Dr. Lingchong You’s lab in the Duke Microbiome Center have used dynamic modeling and machine learning to construct similarly simple rules for complex biology. They 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.  Read more here.

CITATION: “A Unifying Framework for Interpreting and Predicting Mutualistic Systems,” Feilun Wu, Allison Lopatkin, Daniel Needs, Charlotte Lee, Sayan Mukherjee, and Lingchong You. Nature Communications, January 16, 2019. DOI: 10.1038/s41467-018-08188-5

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