Microbial interactions from a new perspective: reinforcement learning reveals new insights into microbiome evolution.

ABSTRACT:

Motivation

Microbes are essential part of all ecosystems, influencing material flow and shaping their surroundings. Metabolic modeling has been a useful tool and provided tremendous insights into microbial community metabolism. However, current methods based on flux balance analysis (FBA) usually fail to predict metabolic and regulatory strategies that lead to long-term survival and stability especially in heterogenous communities.

Results

Here, we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, which treats microbial metabolism as a decision-making process, allowing individual microbial agents to evolve by learning and adapting metabolic strategies for enhanced long-term fitness. This algorithm predicts what microbial flux regulatio

SUBMITTER: Ghadermazi P 

PROVIDER: S-EPMC10799744 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature