A unifying framework for interpreting and predicting mutualistic systems.
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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.
Published Version (Please cite this version)
Wu, Feilun, Allison J Lopatkin, Daniel A Needs, Charlotte T Lee, Sayan Mukherjee and Lingchong You (2019). A unifying framework for interpreting and predicting mutualistic systems. Nature communications, 10(1). p. 242. 10.1038/s41467-018-08188-5 Retrieved from https://hdl.handle.net/10161/26726.
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The You lab uses a combination of mathematical modeling, machine learning, and quantitative experiments to elucidate principles underlying the dynamics of microbial communities in time and space and to control these dynamics for applications in computation, engineering, and medicine.
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