From Signals to Measurands: A Measurement-Science Roadmap for Reproducible Analytical Biochemistry.
- 1Department of Mathematics, Dartmouth College, Hanover, New Hampshire 03755, United States.
- 2Departments of Biochemistry & Cell Biology and Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, United States.
Abstract
Analytical biochemistry often reports kinetic or binding constants as though they were observed directly. Each result is instead inferred from an instrumental signal. Calibration establishes the scale, biochemical and observation models connect the signal to a target, and statistical analysis produces the estimate and its uncertainty. This Perspective treats reproducibility as the recovery of a specified measurand, with an uncertainty statement, through an auditable measurement chain. A Michaelis-Menten substrate-depletion example provides the organizing case. Two progress-curve experiments generated with the same parameters and noise model both fit well, yet only the design that samples substrate concentrations around the Michaelis constant yields a bounded interval for K M. In that design, uncertainty in active-site concentration dominates the uncertainty of k cat even though the curve fit is precise. Ligand-receptor binding shows parallel effects of depletion, signal modeling, and parameter confounding. The framework connects established metrological and chemical-measurement concepts with enzymology reporting standards, machine-readable data formats, and modern identifiability analysis. It distinguishes repeatability, transfer to another laboratory, and agreement across independent measurement routes (or techniques), and explains what interlaboratory studies can reveal. The recommendations are prioritized as minimum practice, stronger support for parameter estimation, and reference-grade validation. The same logic applies whenever a biochemical quantity is inferred from a measured signal.
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BibTeX
@article{schnell2026from,
title = {{From Signals to Measurands: A Measurement-Science Roadmap for Reproducible Analytical Biochemistry.}},
author = {Santiago Schnell},
journal = {ACS measurement science au},
year = {2026},
month = sep,
doi = {10.1021/acsmeasuresciau.6c00198},
url = {https://europepmc.org/article/MED/42921108},
note = {CC BY},
}
RIS
TY - JOUR
TI - From Signals to Measurands: A Measurement-Science Roadmap for Reproducible Analytical Biochemistry.
AU - Santiago Schnell
PY - 2026
DA - 2026/09/07
JO - ACS measurement science au
DO - 10.1021/acsmeasuresciau.6c00198
UR - https://europepmc.org/article/MED/42921108
AB - Analytical biochemistry often reports kinetic or binding constants as though they were observed directly. Each result is instead inferred from an instrumental signal. Calibration establishes the scale, biochemical and observation models connect the signal to a target, and statistical analysis produces the estimate and its uncertainty. This Perspective treats reproducibility as the recovery of a specified measurand, with an uncertainty statement, through an auditable measurement chain. A Michaelis-Menten substrate-depletion example provides the organizing case. Two progress-curve experiments generated with the same parameters and noise model both fit well, yet only the design that samples substrate concentrations around the Michaelis constant yields a bounded interval for K M. In that design, uncertainty in active-site concentration dominates the uncertainty of k cat even though the curve fit is precise. Ligand-receptor binding shows parallel effects of depletion, signal modeling, and parameter confounding. The framework connects established metrological and chemical-measurement concepts with enzymology reporting standards, machine-readable data formats, and modern identifiability analysis. It distinguishes repeatability, transfer to another laboratory, and agreement across independent measurement routes (or techniques), and explains what interlaboratory studies can reveal. The recommendations are prioritized as minimum practice, stronger support for parameter estimation, and reference-grade validation. The same logic applies whenever a biochemical quantity is inferred from a measured signal.
SN - 2694-250X
ER -