A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia

Abstract

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 byC. autoethanogenumandC. kluyverifor the static tools, b) glucose/xylose fermentation with engineeredE. coliandS. cerevisiaefor the dynamic tools, and c) a Petri dish ofE. coliandS. entericafor 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. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.

Flux sampling in genome-scale metabolic modeling of microbial communities

“…To our knowledge, one paper has used sampling to study cell–cell metabolic interactions [ 62 ]. This gap has been identified by other researchers, and our work motivates future studies to more earnestly utilize and leverage the technique [ 63 ].…”

Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities

“…To our knowledge, one paper has used sampling to study cell-cell metabolic interactions 62 . Other researchers have identified this gap, and future work can more earnestly utilize and leverage the technique 63 .…”

Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

“…In an IBM model, each individual can be provided with a GEM, which can be used to predict its metabolism with FBA. Such a framework has been already included in platforms like BacArena (Bauer et al., 2017), ACBM (Karimian & Motamedian, 2020), IndiMeSH (Borer et al., 2019) or CROMICS (Angeles‐Martinez & Hatzimanikatis, 2021), that were recently compared (Scott et al., 2023). These frameworks allow spatialisation: the IBM can include movement functions modelling microbial motility, and complex multi‐physics features, for example, the complex diffusive pattern in the rhizospheric area for IndiMesh.…”