Simple rules predict and explain biological mutualism

Phys.org | January 16, 2019

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 at Duke University 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. 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."

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