A Robust Network Model for Studying Microbiomes in Precision Agriculture Applications

Recent rapid advancements in high-throughput sequencing technologies have made it possible for researchers to explore the microbial universe in high degrees of depth that were not possible even few years ago. Microbial communities occupy numerous environments everywhere and significantly impact the health of the living organisms in their environments. With the availability of microbiome data, advanced computational tools are critical to conduct the needed analysis and allow researchers to extract meaningful knowledge leading to actionable decisions. However, despite many attempts to develop tools to analyze the heterogeneous datasets associated with various microbiomes, such attempts lack the sophistication and robustness needed to efficiently analyze these complex heterogeneous datasets and produce accurate results. In addition, almost all current methods employ heuristic concepts that do not guarantee the robustness and reproducibility needed to provide the biomedical community with trusted analysis that lead to precise data-driven decisions. In this study, we present a network model that attempts to overcome these challenges by utilizing graph-theoretic concepts and employing multiple computational methods with the goal of conducting robust analysis and produce accurate results. To test the proposed model, we performed the analysis on plant microbiome datasets to obtain distinctive functional modules based on key microbial interrelationships in a given host environment. Our findings establish a framework for a new understanding of the association between functional modules based on microbial community structure.