English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Density dependence is important in the ecology and evolution of microbial andcancer cells. Typically, we can only measure net growth rates, but theunderlying density-dependent mechanisms that give rise to the observed dynamicscan manifest in birth processes, death processes, or both. Therefore, weutilize the mean and variance of cell number fluctuations to separatelyidentify birth and death rates from time series that follow stochasticbirth-death processes with logistic growth. Our method provides a novelperspective on stochastic parameter identifiability, which we validate byanalyzing the accuracy in terms of the discretization bin size. We apply ourmethod to the scenario where a homogeneous cell population goes through threestages: (1) grows naturally to its carrying capacity, (2) is treated with adrug that reduces its carrying capacity, and (3) overcomes the drug effect torestore its original carrying capacity. In each stage, we disambiguate whetherit happens through the birth process, death process, or some combination of thetwo, which contributes to understanding drug resistance mechanisms. In the caseof limited data sets, we provide an alternative method based on maximumlikelihood and solve a constrained nonlinear optimization problem to identifythe most likely density dependence parameter for a given cell number timeseries. Our methods can be applied to other biological systems at differentscales to disambiguate density-dependent mechanisms underlying the same netgrowth rate.

Inferring Density-Dependent Population Dynamics Mechanisms through Rate Disambiguation for Logistic Birth-Death Processes