Identifiability of metabolic resilience from sparse longitudinal metabolomics
- 1UC Berkeley
Abstract
Sparse and irregular longitudinal metabolomic measurements can make dynamical quantities numerically estimable without making them scientifically identifiable. We formulate effective-landscape inference as an observation-timescale inverse problem and evaluate its operating characteristics using stochastic systems with known ground truth and longitudinal stool metabolomics from the Integrative Human Microbiome Project. Across simulation ensembles, recovery of a known double-well topology declined sharply as observations became sparse, whereas the tested monostable controls did not generate spurious secondary minima. Findings establish an identifiability hierarchy: global topology requires sufficient within-subject state-space coverage, whereas local return descriptors remain vulnerable to bias from the observation process. The framework therefore shifts the emphasis from assigning stability labels to determining what a sampling design can support. It provides a basis for designing denser longitudinal experiments and for validating clinical and machine-learning studies in which sparse observations might otherwise be overinterpreted as evidence about underlying dynamics.
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BibTeX
@misc{aminiandehkordi2026identifiability,
title = {{Identifiability of metabolic resilience from sparse longitudinal metabolomics}},
author = {J. Aminian-Dehkordi and M. Mofrad},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.01.742207},
url = {https://www.biorxiv.org/content/10.64898/2026.08.01.742207v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - Identifiability of metabolic resilience from sparse longitudinal metabolomics
AU - J. Aminian-Dehkordi
AU - M. Mofrad
PY - 2026
DA - 2026/08/01
PB - bioRxiv
DO - 10.64898/2026.08.01.742207
UR - https://www.biorxiv.org/content/10.64898/2026.08.01.742207v2
AB - Sparse and irregular longitudinal metabolomic measurements can make dynamical quantities numerically estimable without making them scientifically identifiable. We formulate effective-landscape inference as an observation-timescale inverse problem and evaluate its operating characteristics using stochastic systems with known ground truth and longitudinal stool metabolomics from the Integrative Human Microbiome Project. Across simulation ensembles, recovery of a known double-well topology declined sharply as observations became sparse, whereas the tested monostable controls did not generate spurious secondary minima. Findings establish an identifiability hierarchy: global topology requires sufficient within-subject state-space coverage, whereas local return descriptors remain vulnerable to bias from the observation process. The framework therefore shifts the emphasis from assigning stability labels to determining what a sampling design can support. It provides a basis for designing denser longitudinal experiments and for validating clinical and machine-learning studies in which sparse observations might otherwise be overinterpreted as evidence about underlying dynamics.
ER -