# From Single Organisms to Communities: Modeling Methanotrophs and Their Satellites

## TL;DR

This paper reviews how methanotroph bacteria and their associated microbes work together to oxidize methane and how better models are needed to understand these interactions.

## Contribution

The paper highlights the need for targeted reconstruction of satellite metabolism and integration of computational methods to improve predictive models of methanotrophic communities.

## Key findings

- Current genome-scale metabolic models are limited for methanotroph satellites.
- Community-level reconstructions remain scarce and hinder predictive power.
- An integrative strategy combining models and experiments is essential for understanding these microbial consortia.

## Abstract

Aerobic methanotrophs mediate methane oxidation contributing to a major biological sink that limits CH4 release to the atmosphere in oxygenated environments and serve as promising platforms for biotechnological applications. In natural and engineered environments, these bacteria rarely exist in isolation but form stable associations with heterotrophic satellites that utilize methanotrophic metabolites, remove inhibitory intermediates, and provide essential growth factors. Such interactions enhance methane oxidation efficiency and community stability, yet the metabolic mechanisms underlying them remain poorly resolved. This review summarizes current knowledge on both natural and synthetic aerobic methanotrophic consortia, focusing on the composition, functions, and biotechnological relevance of satellite microorganisms. We systematically examine available mathematical frameworks—from ecological and statistical models to genome-scale metabolic reconstructions and dynamic flux balance analysis—applied to methanotrophs and their satellites. Our analysis reveals that while genome-scale metabolic models have been developed for model heterotrophic species, only a few correspond to experimentally identified methanotroph satellites, and community-level reconstructions remain limited. The lack of curated and experimentally validated models restricts the predictive power of current approaches. Addressing these limitations will require not only targeted reconstruction of satellite metabolism, but also the combined use of complementary computational methods followed by experimental verification. Such an integrative strategy will be essential for understanding methanotrophic community organization and function and, more broadly, other microbial consortia with complex metabolic interactions. Addressing these limitations through targeted reconstruction of satellite metabolism and integration of existing models will be key to advancing quantitative understanding of methanotrophic community organization and function.