Microbial Interactions from a New Perspective: Reinforcement Learning Reveals New Insights into Microbiome Evolution

Abstract

Microbes play a vital role in diverse ecosystems, influencing material flow and shaping the dynamics of their surroundings. Understanding the function of microbial life within ecosystems is crucial for tackling modern challenges. Metagenomics studies provide valuable insights into the potential functions of microbial communities but predicting the phenotype of these communities from on their genotype remains a complex endeavor. Trophic interactions between microbes further complicate the prediction of emergent properties in microbiomes. Mathematical modeling, particularly Flux Balance Analysis (FBA) approaches, have been employed to forecast phenotypes and explain experimental findings. However, FBA solutions often lack uniqueness and assume a steady-state condition. While Dynamic Flux Balance Analysis (DFBA) addresses some of these limitations, it still relies on instantaneous biomass maximization assumptions and faces challenges related to non-uniqueness solutions of linear programming problems. In this article, a novel modeling approach is proposed that integrates deep reinforcement learning into DFBA. This framework views microbial metabolism as a decision-making process, where each microbial agent evolves by learning and adapting metabolic strategies to enhance long-term fitness. Reinforcement learning algorithms facilitate the discovery of optimal strategies through iterative trial and error, while considering the consequences of actions within a dynamic context. This approach diverges from traditional FBA assumptions and provides insights into evolutionary stable strategies, requiring minimal reliance on predefined strategies. The proposed method holds promise for elucidating the behavior and mechanisms of microbial systems, including phenomena like quorum sensing and other interactions that can be explained by considering the long-term consequences of metabolic regulation strategies. The modeling algorithm demonstrates success in predicting microbial interactions in simple communities, surpassing the capabilities of existing models, and exhibits potential for scalability when applied to Genome-scale Metabolic Models (GEMs).

ADToolbox: Incorporating Metagenomics Data for Improved Prediction of Anaerobic Digestion Dynamics

“…FBA also can be extended to dynamic simulation of heterogenous microbial systems [25]. There are several inherent limitations in using FBA and DFBA, for example the instantaneous biomass maximization assumption that does not always describe observed microbial interactions [26], [27], [28]. Additionally, parameters like temperature and pH are shown to be critical in determining the performance of AD reactors which are often ignored in FBA-based approaches.…”