Identifiability of metabolic resilience from sparse longitudinal metabolomics

Observational data collected from sporadic, intermittent metabolomic sampling of the gut microbiome severely limits the ability to accurately infer certain dynamic properties, according to a new study. Researchers have created a landscape inference framework to shed light on these constraints while still identifying elements of metabolic resilience that can be recovered even with such limited sampling.

By employing stochastic simulations with known outcomes, scientists were able to pinpoint the boundaries of inferring multistability in metabolic systems when data is sparse, revealing that complex dynamics can sometimes be mistaken for simple ones when sampled at typical rates found in human studies. The framework was then applied to a small group of four well-controlled stool metabolomic time series, demonstrating that even with sparse data, the curvature of the data landscape - a measure of how well local data can be used to reconstruct global patterns - can still be discerned.

This curvature offers a preliminary indication that different individuals may have varying capacities for recovering from metabolic disturbances, as suggested by the recovery dynamics of certain metabolites. Notably, the study suggests that bile acids and fermentation byproducts could be key factors influencing the restoration of butyrate levels, a beneficial short-chain fatty acid produced by gut bacteria.