Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions.

ABSTRACT: Microbiomes interact dynamically with their environment to perform exploitable functions such as production of valuable metabolites and degradation of toxic metabolites for a wide range of applications in human health, agriculture, and environmental cleanup. Developing computational models to predict the key bacterial species and environmental factors to build and optimize such functions are crucial to accelerate microbial community engineering. However, there is an unknown web of interactions that determine the highly complex and dynamic behavior of these systems, which precludes the development of models based on known mechanisms. By contrast, entirely data-driven machine learning models can produce physically unrealistic predictions and often require significant amounts of experimental

SUBMITTER: Thompson JC 

PROVIDER: S-EPMC10540976 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature