Speaker
Description
How cells regulate their size remains an open question. Cell-size regulation is commonly characterized by the Pearson correlation between birth and division sizes, with the corresponding regulation parameter α defined as one minus this correlation coefficient. Single-cell experiments provide generation-resolved measurements of birth and division sizes along individual lineages, typically across many lineages of varying lengths. The parameter α is usually inferred either by estimating it
separately for each lineage and averaging across lineages, or by fitting a single effective parameter to the pooled data across lineages. These two approaches are affected by distinct systematic errors: the lineage-wise estimator is biased for finite lineages, whereas the pooled estimator is biased by lineage-to-lineage variability arising from extrinsic noise. Moreover, both estimators are affected by different kinds of measurement errors. Here, we quantify the above-mentioned effects and develop a Bayesian framework to overcome these biases. Applied to synthetic and experimental cell-size data, this approach yields more reliable estimates from short and heterogeneous lineages and provides a principled way to distinguish among different size regulation strategies.
