Comprehensive understanding of finite-data errors in lineage-based growth rate inference

Develop a comprehensive understanding of the systematic errors introduced when estimating the long-term population growth rate Λ from finite single-cell lineage data, particularly those arising from sampling large deviations of generation times and division counts in lineage-based methods that map single-cell statistics to population growth.

Background

The paper studies how population growth rate Λ can be inferred from single-cell lineage statistics, noting that growth selects for exponentially rare phenotypes and inference necessarily relies on sampling large deviations from finite datasets. This introduces systematic errors whose full characterization has been difficult.

The authors analyze bias and variance in two lineage-based estimators and decompose bias into finite-time and nonlinear averaging components, providing tools to mitigate and understand these errors. The abstract frames the need for a comprehensive understanding of finite-data errors as an open issue motivating the work.

References

A comprehensive understanding of these errors in the context of finite data remains elusive.

— Extremal events dictate population growth rate inference  (2501.08404 - GrandPre et al., 14 Jan 2025) in Abstract

Several directions remain open. First, the present work focuses primarily on systems with a stable balanced-growth composition. Systems with multiple balanced-growth states would require a global quasipotential constructed from least-action transitions between competing growth modes . Second, although genetically engineered cells can in some cases be maintained for prolonged periods in a balanced-growth regime, natural cells generally undergo repeated cycles of growth and division. Incorporating division would connect the present framework to lineage and population statistics and could provide more systematic null models for single-cell variability .

— Large-deviations theory for growing chemical reaction networks  (2609.30970 - Gagrani et al., 25 Sep 2026) in Section Discussion