Speaker
Description
Estimating time-varying cellular growth rates from time-lapse microscopy remains computationally challenging \cite{1}. Image segmentation errors propagate into size measurements, and because traditional methods rely on finite-difference approximations, they amplify this noise. This obscures biological fluctuations and forces reliance on moving averages that artificially flatten true dynamics.
To address this, we propose a continuous state-space model uncoupling biological growth from observation noise. The unobservable cellular growth rate is modeled as a mean-reverting Ornstein-Uhlenbeck (OU) process. Cell area is modeled as the exponential of the integral of this growth rate, subject to multiplicative segmentation noise. We deployed a Bayesian inference pipeline, combining MCMC parameter estimation with a RTS Kalman Smoother, to retroactively extract the hidden time-varying growth rate from noisy area measurements \cite{2}.
Validation on simulated data demonstrates our Bayesian approach significantly outperforms moving-average techniques. By leveraging the exact cross-covariance between area and growth rate, the model successfully suppresses noise without sacrificing temporal resolution.
Currently being tested on raw experimental data, this model will subsequently be extended to jointly estimate cell-internal biochemical processes, specifically plasmid copy numbers, to quantify how internal process time-scales correlate with overall growth rate fluctuations.
Bibliography
@article{1,
title={RealTrace: Uncovering biological dynamics hidden under measurement noise in time-lapse microscopy data},
author={Kscheschinski, Bjoern and Fiori, Athos and Chauvin, Dany and Martin, Benjamin and Suter, David M and Towbin, Benjamin D and Julou, Thomas and van Nimwegen, Erik},
journal={bioRxiv},
year={2025},
publisher={Cold Spring Harbor Laboratory},
doi={10.1101/2025.09.12.675772}
}
@article{2,
title={Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level},
author={Davidovi{\'c}, An{\~d}ela and Chait, Remy and Batt, Gregory and Ruess, Jakob},
journal={PLOS Computational Biology},
volume={18},
number={3},
pages={e1009950},
year={2022},
publisher={Public Library of Science},
doi={10.1371/journal.pcbi.1009950}
}
