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Linear scaling reveals low-dimensional structure in observable microbial dynamics.

Zhengqing Zhou1,2, Xiaoli Chen1,2, Emrah Şimşek1,2

  • 1Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.

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Summary
This summary is machine-generated.

Microbial community dynamics are predictable using low-dimensional models. Even with unobserved complexity, observable microbial populations can be captured by a minimal set of variables for effective prediction and control.

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Area of Science:

  • Microbiology
  • Systems Biology
  • Ecological Modeling

Background:

  • Microbial communities display complex dynamics due to numerous interactions.
  • Predicting and controlling these communities is challenging with limited observable data.
  • A key question is the predictability of observed dynamics amidst unobserved complexity.

Purpose of the Study:

  • To investigate the extent to which observed microbial community dynamics are predictable.
  • To identify a method for quantifying the minimal variables needed to represent observable dynamics.
  • To establish a scaling law for microbial community dynamics.

Main Methods:

  • Utilized variational autoencoders (VAEs) to analyze microbial population dynamics.
  • Defined a critical latent dimension (Ec) to quantify the minimal required variables.
  • Applied methods across diverse simulations and experimental microbial communities.

Main Results:

  • Observable microbial population dynamics can be represented by low-dimensional models.
  • The critical latent dimension (Ec) scales linearly with the number of observables.
  • This principle was validated across ecological, spatial, gene-transfer models, and human microbiomes.

Conclusions:

  • Microbial community dynamics exhibit emergent simplicity.
  • Observable dynamics alone contain sufficient information for prediction and control.
  • A universal scaling law for microbial community dynamics has been established.