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

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2763-2774
Number of pages12
JournalEcology Letters
Volume24
Issue number12
DOIs
Publication statusPublished - 2021 Dec

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics

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