A unifying framework for interpreting and predicting mutualistic systems.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Wu, F; Lopatkin, AJ; Needs, DA; Lee, CT; Mukherjee, S; You, L

Published Date

  • January 2019

Published In

Volume / Issue

  • 10 / 1

Start / End Page

  • 242 -

PubMed ID

  • 30651549

Pubmed Central ID

  • PMC6335432

Electronic International Standard Serial Number (EISSN)

  • 2041-1723

International Standard Serial Number (ISSN)

  • 2041-1723

Digital Object Identifier (DOI)

  • 10.1038/s41467-018-08188-5


  • eng