A Study of the Community Relationships between Methanotrophs and Their Satellites Using Constraint-Based Modeling Approach

Biotechnology continues to drive innovation in the production of pharmaceuticals, biofuels, and other valuable compounds, leveraging the power of microbial systems for enhanced yield and sustainability. Genome-scale metabolic (GSM) modeling has become an essential approach in this field that enables a guide for target genetic modifications and optimization of metabolic pathways for various industrial applications. While single-species GSM models have traditionally been employed to optimize strains like Escherichia coli and Lactococcus lactis, the integration of these models into community-based approaches is gaining momentum. Constraint-based metabolic community (CBMc) models, which simulate the interactions between multiple microbial species, offer a more comprehensive understanding of metabolic networks and their potential for improvement of biotechnological processes on pilot- and industrial scales. However, there are few platforms that effectively support both single-species GSM and CBMc models, limiting the accessibility and application of these advanced modeling techniques. In this study, we demonstrate the use of the BioUML platform, one of the few tools that provides simultaneous investigation of both single-species and community models. We harnessed the platform to reconstruct and analyze microbial interactions within a synthetic consortium of widely used biotechnology microorganisms: methanotroph, Methylococcus capsulatus Bath and engineered Escherichia coli strains under oxygen- and nitrogen- limited conditions. Our research highlights the potential of E. coli to reduce the accumulation of inhibitory by-products like acetate, thereby enhancing the metabolic efficiency and growth of M. capsulatus. The study underscores the utility of CBMc models in optimization of microbial consortia for biotechnological applications, offering new strategies for the sustainable production of biofuels, single-cell proteins, and other high-value compounds.