Microbial consortia drive essential processes, ranging from nitrogen fixation in soils to providing metabolic breakdown products to animal hosts. However, it is challenging to translate the composition of microbial consortia into their emergent functional capacities. Community-scale metabolic models hold the potential to simulate the outputs of complex microbial communities in a given environmental context, but there is currently no consensus for what the fitness function of an entire community should look like in the presence of ecological interactions and whether community-wide growth operates close to a maximum. Transitioning from single-taxon genome-scale metabolic models to multitaxon models implies a growth cone without a well-specified growth rate solution for individual taxa. Here, we argue that dynamic approaches naturally overcome these limitations, but they come at the cost of being computationally expensive. Furthermore, we show how two nondynamic, steady-state approaches approximate dynamic trajectories and pick ecologically relevant solutions from the community growth cone with improved computational scalability.
Metabolic models of microbial communities can be awesome tools to generate hypotheses about metabolism and ecology, but getting them to perform well is hard. See our minireview/pespective on one of the challenges now out at https://t.co/Un6So36o9A #noxp
— Christian Diener (@thaasophobia) March 22, 2023
Check out our minireview in @mSystemsJ on how 'more is different' when it comes to metabolic modeling of microbial communities.
— Sean Gibbons (@gibbological) March 22, 2023
See @thaasophobia's excellent 🧵 here.@isbsci https://t.co/aogBiZfugp