Strengthening Insights in Microbial Ecological Networks from Theory to Applications.

Xiaofei Lv,Ran Xue,Yuanhui Liu,Jianming Xu,Bin Ma,Kankan Zhao

doi:10.1128/msystems.00124-19

Xiaofei Lv, Ran Xue + Show 4 more

Open Access

https://doi.org/10.1128/msystems.00124-19

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Journal: mSystems	Publication Date: May 21, 2019
Citations: 29	License type: CC BY 4.0

Affiliation: Zhejiang University, China Jiliang University

Abstract

Networks encode the interactions between the components in complex systems and play an essential role in understanding complex systems. Microbial ecological networks provide a system-level insight for comprehensively understanding complex microbial interactions, which play important roles in microbial community assembly. However, microbial ecological networks are in their infancy of both network inference and biological interpretation. In this perspective, we articulate the theory gaps and limitations in building and interpreting microbial ecological networks, emphasize developing tools for evaluating the predicted microbial interaction relationships, and predict the potential applications of microbial ecological networks in the long run.

Highlights

Networks encode the interactions between the components in complex systems and play an essential role in understanding complex systems
Given the important role complex systems play in every aspect of the world, it is one of the major scientific challenges in understanding, describing, predicting, and controlling complex systems (4)
The bellwether of the network science, claimed that “we will never understand complex systems unless we develop a deep understanding of the networks behind them” (4)

Summary

Introduction

Networks encode the interactions between the components in complex systems and play an essential role in understanding complex systems. Reconstruction of microbial ecological networks representing these interactions can advance our understanding of the complex behaviors in microbial communities, predict the effects of perturbations on community dynamics, and help with the engineering of complex microbial communities (6). Limitations in measuring microbial interactions, identifying indirect edges, understanding biological implications, and describing network evolution (Fig. 1). We look ahead to the applications of microfluidics, high-throughput culturing methods, and verified interaction databases for evaluating the predicted microbial interaction relationships.

Results

Conclusion