Dynamic carbon flux network of a diverse marine microbial community.

ABSTRACT: The functioning of microbial ecosystems has important consequences from global climate to human health, but quantitative mechanistic understanding remains elusive. The components of microbial ecosystems can now be observed at high resolution, but interactions still have to be inferred e.g., a time-series may show a bloom of bacteria X followed by virus Y suggesting they interact. Existing inference approaches are mostly empirical, like correlation networks, which are not mechanistically constrained and do not provide quantitative mass fluxes, and thus have limited utility. We developed an inference method, where a mechanistic model with hundreds of species and thousands of parameters is calibrated to time series data. The large scale, nonlinearity and feedbacks pose a challenging optimizat

SUBMITTER: Mayerhofer MM 

PROVIDER: S-EPMC9723560 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature