Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed.

Optimizing bioreactor performance for organic matter removal can achieve sustainable and energy-efficient micropollutant removal in subsequent tertiary treatment. Bioreactor performance heavily depends on its resident microbial community; hence, a deeper understanding of community dynamics is essential. The microbial communities of three different bioreactors (biological activated carbon, moving bed biofilm reactor, sand filter), used for organic matter removal from wastewater treatment effluent, were characterized by 16S rRNA gene amplicon sequence analysis. An interdependency between bioreactor performance and microbial community profile was observed. Overall, Proteobacteria was the most predominant phylum, and Comamonadaceae was the most predominant family in all bioreactors. The relative abundance of the genus Roseococcus was positively correlated with organic matter removal. A generalized Lotka-Volterra (gLV) model was established to understand the interactions in the microbial community. By identifying microbial dynamics and their role in bioreactors, a strategy can be developed to improve bioreactor performance.