Affiliation:
1. Department of Chemical and Biological Engineering, Colorado State University , Fort Collins, CO 80521, United States
Abstract
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 regulation policies will stabilize in the dynamic ecosystem of interest in the presence of other microbes with minimal reliance on predefined strategies. Throughout this article, we present several scenarios wherein our algorithm outperforms existing methods in reproducing outcomes, and we explore the biological significance of these predictions.
Availability and implementation
The source code for this article is available at: https://github.com/chan-csu/SPAM-DFBA.
Funder
U.S. Army Research Office
U.S. Army Research Laboratory
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
Reference75 articles.
1. Bacterial quorum sensing and microbial community interactions;Abisado Rhea;mBio,2018
2. Microbial colonization and persistence in deep fractured shales is guided by metabolic exchanges and viral predation;Amundson;Microbiome,2022
3. Deep reinforcement learning: a brief survey;Arulkumaran;IEEE Signal Process Mag,2017
4. BacArena: individual-based metabolic modeling of heterogeneous microbes in complex communities;Bauer;PLoS Comput Biol,2017
5. Lotka-Volterra equation and replicator dynamics: A two-dimensional classification;Bomze,1983