ArticleeLife2022
A unified framework for measuring selection on cellular lineages and traits.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Extremal events dictate population growth rate inference.PLoS computational biology · 2026Article
- Studying adaptation at the invisible scale.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Coupling Bacterial Cell Size Regulation with Clonal Proliferation Dynamics Reveals Cell Division Based on Surface Area.bioRxiv : the preprint server for biology · 2025Article
- Observation of persister cell histories reveals diverse modes of survival in antibiotic persistence.eLife · 2025Article
- Diversification of single-cell growth dynamics under starvation influences subsequent reproduction in a clonal bacterial population.The ISME journal · 2025Article
- Feedback between stochastic gene networks and population dynamics enables cellular decision-making.Science advances · 2024Article
- Population dynamics models for various forms of adaptation.Biophysics and physicobiology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
Intracellular states probed by gene expression profiles and metabolic activities are intrinsically noisy, causing phenotypic variations among cellular lineages. Understanding the adaptive and evolutionary roles of such variations requires clarifying their linkage to population growth rates. Extending a cell lineage statistics framework, here we show that a population's growth rate can be expanded by the cumulants of a fitness landscape that characterize how fitness distributes in a population. The expansion enables quantifying the contribution of each cumulant, such as variance and skewness, to population growth. We introduce a function that contains all the essential information of cell lineage statistics, including mean lineage fitness and selection strength. We reveal a relation between fitness heterogeneity and population growth rate response to perturbation. We apply the framework to experimental cell lineage data from bacteria to mammalian cells, revealing distinct levels of growth rate gain from fitness heterogeneity across environments and organisms. Furthermore, third or higher order cumulants' contributions are negligible under constant growth conditions but could be significant in regrowing processes from growth-arrested conditions. We identify cellular populations in which selection leads to an increase of fitness variance among lineages in retrospective statistics compared to chronological statistics. The framework assumes no particular growth models or environmental conditions, and is thus applicable to various biological phenomena for which phenotypic heterogeneity and cellular proliferation are important.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.