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

ABSTRACT: Microbial communities often exhibit apparently complex dynamics driven by myriad interactions among community members and with their environments. Yet, practical modeling and control are often based on limited number of observables, raising a fundamental question: to what extent are these observed dynamics predictable given unobserved background complexity? Here, we report an emergent simplicity that the temporal dynamics of observable microbial populations can be captured by low-dimensional representations. Using variational autoencoders (VAEs), we define a critical latent dimension (E_c ) that quantifies the minimal number of variables required to represent observable microbial dynamics. We find that E_c scales linearly with the number of observab

SUBMITTER: Zhou Z 

PROVIDER: S-EPMC12262697 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature