Browsing by Author "Wu, Feilun"

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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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    General principles of microbial community structure
    (2020) Wu, Feilun

    Microbial communities are an integral and indispensable part of biogeochemical processes, health of plants and animals, and human activities [1-3]. The community structure (the members and their relative abundance) is a key variable that determines the dynamics, stability, functions, and evolution of a microbial community [4]. However, our capability of interpreting, predicting, and controlling microbial community structure is still lacking. This is manifested in the challenges we face in maintaining the stable colonization of beneficial strains [5], reducing the colonization of pathogenic strains [6], and controlling the community structure for engineering purposes [7].

    To solve this problem, one approach is to develop methods and tools specific to each system. However, due to the diversity and complexity of microbial communities, these system-specific approaches can be labor intensive and ad hoc. The opposite approach is to distill general principles that can be widely applicable to any microbial communities without characterizing low-level details, such as at the metabolic level. If there exist such general rules, we can then build a toolbox to solve classes of problems related to microbial community structure in an efficient manner.

    In this dissertation, I lay out the reasoning and steps to distill general principles that underlie microbial community structure. Using this approach, I then study two types of factors that have significant impacts on microbial community structures. One is a study on mutualism, where I established a general mathematical criterion that underlies the coexistence of mutualistic systems and developed a machine learning method to apply the criterion to diverse experimental systems for interpretation and prediction of system outcomes. The other study is on spatial partitioning, where I established that spatial partitioning increases biodiversity for communities dominated by negative interactions and decreases biodiversity for those dominated by positive interactions. When strong positive and negative interactions are present, biodiversity peaks at an intermediate partitioning level. This general principle can be used to interpret, predict, and control microbial community structure in a robust manner.

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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.