A unifying framework for interpreting and predicting mutualistic systems.

Wu, Feilun

Lopatkin, Allison J

Needs, Daniel A

Lee, Charlotte T

Mukherjee, Sayan

You, Lingchong

2023-03-06T14:16:39Z

2023-03-06T14:16:39Z

2019-01

2023-03-06T14:16:36Z

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.

10.1038/s41467-018-08188-5

2041-1723

2041-1723

https://hdl.handle.net/10161/26726

eng

Springer Science and Business Media LLC

Nature communications

10.1038/s41467-018-08188-5

Calibration

Probability

Feasibility Studies

Food Chain

Symbiosis

Models, Biological

Support Vector Machine

A unifying framework for interpreting and predicting mutualistic systems.

Journal article

Lee, Charlotte T|0000-0002-5863-735X

242

1

Duke

Pratt School of Engineering

School of Medicine

Trinity College of Arts & Sciences

Basic Science Departments

Molecular Genetics and Microbiology

Biomedical Engineering

Biology

Published

10