Reconstructing large interaction networks from empirical time series data

Chun Wei Chang, Takeshi Miki, Masayuki Ushio, Po Ju Ke, Hsiao Pei Lu, Fuh Kwo Shiah, Chih hao Hsieh

研究成果: Article同行評審

23 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)2763-2774
頁數12
期刊Ecology Letters
24
發行號12
DOIs
出版狀態Published - 2021 12月

All Science Journal Classification (ASJC) codes

  • 生態學、進化論、行為學與系統學

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