Exploring microbial community structure and resilience through visualization and analysis of microbial co-occurrence networks

Cultivation independent microbial ecology research relies on high throughput sequencing technologies and analytical methods to resolve the infinite diversity of microbial life on Earth. Microorganisms live in communities driven by genetic and metabolic processes as well as symbiotic relationships. Interconnected communities of microorganisms provide essential functions in natural and human engineered ecosystems. Modelling the community as an inter-connected system can give insight into the community's functional characteristics related to the biogeochemical processes it performs. Network science resolves associations between elements of structure to notions of function in a system and has been successfully applied to the study of microbial communities and other complex biological systems. Microbial co-occurrence networks are inferred from community composition data to resolve structural patterns related to ecological properties such as community resilience to disturbance and keystone species. However, the interpretation of global and local network properties from an ecological standpoint remains difficult due to the complexity of these systems creating a need for quantitative analytical methods and visualization techniques for co-occurrence networks. This thesis tackles the visualization and analytical challenges of modelling microbial community structure from a network science approach. First, Hive Panel Explorer, an interactive visualization tool, is developed to permit data driven exploration of topological and data association patterns in complex systems. The effectiveness of Hive Panel Explorer is validated by resolving known and novel patterns in a model biological network, the C. elegans connectome. Second, network structural robustness analysis methods are applied to study microbial communities from timber harvested forest soils from a North American longterm soil productivity study. Analyzing these geographically dispersed soils reveals biogeographic patterns of diversity and enables the discovery of conserved organizing principles shaping microbial community structure. The capacity of robustness analysis to identify key microbial community members as well as model shifts in community structure due to environmental change is demonstrated. Finally, this work provides insight into the relationship between microbes and their ecosystem, and characterizing this relationship can help us understand the organization of microbial communities, survey microbial diversity and harness its potential.Science, Faculty ofGraduat