Inferring cell property distributions from high-throughput measurement data with cell-to-cell variability
Abstract
High-throughput measurements of cell populations produce broad distributions of observables due to inherent cell-to-cell variability. While these distributions contain valuable biological information, extracting the underlying distributions of cellular properties remains challenging. We introduce the likelihood functional, a statistical framework that infers cell property distributions by combining experimental data with deterministic computational models. The method first evaluates the model on a discrete parameter grid. Optimization of the likelihood functional is then possible without repeated model evaluations and yields best-fit property distributions which enable the direct prediction of observable distributions for comparison with experiments. As a demonstration, we apply the framework to real-time deformability cytometry data and infer cytosol and membrane viscosity distributions of human red blood cells.
Related
- Distributional signatures of computation: inferring mechanisms from computation time distributions
- Measurement of spectator fragment pseudorapidity distributions with the STAR Event Plane Detector
- Measurement of pseudorapidity distributions from Au+Au collisions with the STAR Event Plane Detector
- Toward dynamic Monte Carlo source modeling: measurement variability and primary electron source-width sensitivity in very small photon fields
- Inferring interactions between active particles using harmonic traps
BibTeX
@misc{gekle2026inferring,
title = {{Inferring cell property distributions from high-throughput measurement data with cell-to-cell variability}},
author = {Stephan Gekle},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.39772},
eprint = {2610.39772},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.39772},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Inferring cell property distributions from high-throughput measurement data with cell-to-cell variability
AU - Stephan Gekle
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.39772
UR - https://arxiv.org/abs/2610.39772
AB - High-throughput measurements of cell populations produce broad distributions of observables due to inherent cell-to-cell variability. While these distributions contain valuable biological information, extracting the underlying distributions of cellular properties remains challenging. We introduce the likelihood functional, a statistical framework that infers cell property distributions by combining experimental data with deterministic computational models. The method first evaluates the model on a discrete parameter grid. Optimization of the likelihood functional is then possible without repeated model evaluations and yields best-fit property distributions which enable the direct prediction of observable distributions for comparison with experiments. As a demonstration, we apply the framework to real-time deformability cytometry data and infer cytosol and membrane viscosity distributions of human red blood cells.
ER -