General principles of microbial community structure

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.