Modeling ecological dynamics via multi-agent systems of microbial metabolism

Metabolic interactions govern the assembly and dynamics of microbial communities. These interactions are studied computationally through genome-scale metabolic reconstructions in which an organism's biochemical reaction network is assembled from annotated genomes and curated databases. The capabilities of each organism are then predicted by optimizing flux through the resulting metabolic network using flux balance analysis (FBA) over a particular phenotype (growth, for example). In this work, we asked whether a language model can replicate the predictions of a genome-scale metabolic model, with each organism acting as an agent that chooses its exchange with its environment, which represented by the model context. We first show that prompted single microbial agents were able to recover key metabolic capabilities quantitatively, albeit at a lesser precision than dynamic flux balance analysis (dFBA), over multiple query rounds. Subsequently, by fine-tuning an open-source model on flux envelopes drawn from 897 reconstructions, microbial agents outperformed metabolic models in predicting metabolite concentrations. Our analysis showed that fine-tuning produces valid flux distributions that respect the underlying stoichiometry and effectively turned the language model into a reliable solver of metabolic fluxes under constraints. Then, by scaling to bacterial communities, multi-agent systems correctly captured competition as the main driver of interactions in communities, and correctly modeled smooth network dynamics that transition through building, converging, and stabilizing of the community structure over time, which DyMMM (Dynamic Multispecies Metabolic Modeling), a framework based on dFBA, fails to capture. To do so, we built an observability and evaluation system that was derived from ecological theory and adapted to the multi-agent model setting. Taken together, we present a new framework for modeling microbial communities relevant to bioengineering and biomedicine by using multi-agent large language model systems constructed with domain-specific harnesses. We also propose fine-tuning on metabolic knowledgebases as a strategy to build accurate modeling tools for the prediction of intervention effects in microbial communities and for the design of synthetic communities.