In this Commentary, we will discuss some of the current trends and challenges in modeling microbiome metabolism. A focus will be the state of the art in the integration of metabolic networks, ecological and evolutionary principles, and spatiotemporal considerations, followed by envisioning integrated frameworks incorporating different principles and data to generate predictive models in the future.

Microbial genome annotation is the process of identifying structural and functional elements in DNA sequences and subsequently attaching biological information to those elements. DRAM is a tool developed to annotate bacterial, archaeal, and viral genomes derived from pure cultures or metagenomes. DRAM goes beyond traditional annotation tools by distilling multiple gene annotations to genome level summaries of functional potential. Despite these benefits, a downside of DRAM is the requirement of large computational resources, which limits its accessibility. Further, it did not integrate with downstream metabolic modeling tools that require genome annotation. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated in the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point and click interface, as well as generate genome scale metabolic models from DRAM annotations. Availability Supplementary information Supplementary data are available at Bioinformatics online.

Envisioning value chains inspired by environmental sustainability and circularity in economic models is essential to counteract the alterations in the global natural carbon cycle induced by humans. Recycling carbon-based waste gas streams into chemicals by devising gas fermentation bioprocesses mediated by acetogens of the genus Clostridium is one component of the solution.

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).

Summary: Annotation is predicting the location of and assigning function to genes in a genome. DRAM is a tool developed to annotate bacterial, archaeal and viral genomes derived from pure cultures or metagenomes. DRAM distills multiple gene annotations to summaries of functional potential. Despite these benefits, a downside of DRAM is processing requires large computation resources, which limits its accessibility, and it did not integrate with downstream metabolic modelling tools. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated in the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point and click interface, as well as generate genome scale metabolic models from these DRAM annotations. Availability and Implementation: The kb_DRAM software is available at https://github.com/shafferm/kb_DRAM. The kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. A narrative with examples of running all KBase apps is available at https://kbase.us/n/88325/84/.