期刊
NATURE COMMUNICATIONS
卷 10, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41467-018-08188-5
关键词
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资金
- US National Institutes of Health [R01GM098642, R01GM110494]
- National Science Foundation [MCB-1412459, DEB 1257882, DMS 17-13012, ABI 16-61386, DMS 16-13261]
- Office of Naval Research [N00014-12-1-0631]
- Army Research Office [W911NF-14-1-0490]
- Human Frontier Science Program [RGP0051]
- David and Lucile Packard Fellowship
Coarse-grained rules are widely used in chemistry, physics and engineering. In biology, however, such rules are less common and under-appreciated. This gap can be attributed to the difficulty in establishing general rules to encompass the immense diversity and complexity of biological systems. Furthermore, even when a rule is established, it is often challenging to map it to mechanistic details and to quantify these details. Here we report a framework that addresses these challenges for mutualistic systems. We first deduce a general rule that predicts the various outcomes of mutualistic systems, including coexistence and productivity. We further develop a standardized machine-learning-based calibration procedure to use the rule without the need to fully elucidate or characterize their mechanistic underpinnings. Our approach consistently provides explanatory and predictive power with various simulated and experimental mutualistic systems. Our strategy can pave the way for establishing and implementing other simple rules for biological systems.
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