Browsing by Author "Lee, Charlotte T"

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    A unifying framework for interpreting and predicting mutualistic systems.
    (Nature communications, 2019-01) Wu, Feilun; Lopatkin, Allison J; Needs, Daniel A; Lee, Charlotte T; Mukherjee, Sayan; You, Lingchong
    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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    Modulation of microbial community dynamics by spatial partitioning.
    (Nature chemical biology, 2022-04) Wu, Feilun; Ha, Yuanchi; Weiss, Andrea; Wang, Meidi; Letourneau, Jeffrey; Wang, Shangying; Luo, Nan; Huang, Shuquan; Lee, Charlotte T; David, Lawrence A; You, Lingchong
    Microbial communities inhabit spatial architectures that divide a global environment into isolated or semi-isolated local environments, which leads to the partitioning of a microbial community into a collection of local communities. Despite its ubiquity and great interest in related processes, how and to what extent spatial partitioning affects the structures and dynamics of microbial communities are poorly understood. Using modeling and quantitative experiments with simple and complex microbial communities, we demonstrate that spatial partitioning modulates the community dynamics by altering the local interaction types and global interaction strength. Partitioning promotes the persistence of populations with negative interactions but suppresses those with positive interactions. For a community consisting of populations with both positive and negative interactions, an intermediate level of partitioning maximizes the overall diversity of the community. Our results reveal a general mechanism underlying the maintenance of microbial diversity and have implications for natural and engineered communities.