Scientists studying lipid metabolism can add several distinguishable chemical labels to trace how lipids change over time, then measure the result with a single destructive mass‑spectrometry readout. More labels give more temporal information but also make the mathematical model much larger and slower to analyse. Using simulated and real data, the authors show that a middle ground — modelling three labels out of five introduced experimentally — often gives a good balance between scientific insight and computational cost.

In these experiments, labels are introduced one after another and act as passive tracers: they behave like the unlabelled molecules but can be told apart in the final measurement. Because a lipid molecule can carry several labels at once, the number of distinct labelled forms the model must track grows combinatorially. That increase produces many more state variables and higher simulation and fitting cost. Rule‑based tools can build these large networks automatically, but they do not remove the extra computing time.

To study the trade‑off, the researchers developed a model‑reduction strategy that maps data from a multi‑label experiment onto models that explicitly resolve fewer labels. They used synthetic data from a five‑label experiment to compare models that resolve different numbers of labels. They measured effects on parameter‑estimation accuracy, the ability to recover concentration trajectories over time, optimisation behaviour, and computational cost. They found diminishing returns from adding more labels: most of the improvement in parameter accuracy was reached at moderate label resolution, and further labels mainly aided optimisation convergence at substantial extra cost. For the systems they tested, modelling three of five labels gave a practical compromise between inferential power and tractability.