期刊
ECOLOGY LETTERS
卷 24, 期 12, 页码 2763-2774出版社
WILEY
DOI: 10.1111/ele.13897
关键词
dynamical stability; interaction network; microbial community; network topology
类别
资金
- National Taiwan University, National Center for Theoretical Sciences, Foundation for the Advancement of Outstanding Scholarship
- Ministry of Science and Technology, Taiwan
A novel approach for reconstructing high-dimensional interaction Jacobian networks without specific model assumptions was proposed in this study, which successfully identified important species and revealed mechanisms governing the dynamical stability of a bacterial community. The method overcame the challenge of high dimensionality in large natural dynamical systems.
Reconstructing interactions from observational data is a critical need for investigating natural biological networks, wherein network dimensionality is usually high. However, these pose a challenge to existing methods that can quantify only small interaction networks. Here, we proposed a novel approach to reconstruct high-dimensional interaction Jacobian networks using empirical time series without specific model assumptions. This method, named multiview distance regularised S-map, generalised the state space reconstruction to accommodate high dimensionality and overcome difficulties in quantifying massive interactions with limited data. When evaluating this method using time series generated from theoretical models involving hundreds of interacting species, estimated strengths of interaction Jacobians were in good agreement with theoretical expectations. Applying this method to a natural bacterial community helped identify important species from the interaction network and revealed mechanisms governing the dynamical stability of a bacterial community. The proposed method overcame the challenge of high dimensionality in large natural dynamical systems.
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