Exponentially modified Gaussian (EMG) relevance to distributions related to cell proliferation and differentiation

DOI10.1016/j.jtbi.2009.10.005zbMath1403.92026OpenAlexW2014107103WikidataQ35007738 ScholiaQ35007738MaRDI QIDQ1629009

Y. Aharonov

Publication date: 11 December 2018

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2009.10.005

zbMATH Keywords

cell differentiationcell proliferationlognormal distributiongene expressioncell cycleexponentially modified Gaussiantheoretical modelrestriction point

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Exact distribution theory in statistics (62E15) Biochemistry, molecular biology (92C40) Developmental biology, pattern formation (92C15) Cell biology (92C37)

Related Items (11)

Threshold behavior of an age-structured tumor immune model ⋮ The contribution of age structure to cell population responses to targeted therapeutics ⋮ A comparison of different parameter estimation methods for exponentially modified Gaussian distribution ⋮ Derivation and experimental comparison of cell-division probability densities ⋮ A multi-stage representation of cell proliferation as a Markov process ⋮ Exponentially modified peak functions in biomedical sciences and related disciplines ⋮ A Bayesian statistical analysis of stochastic phenotypic plasticity model of cancer cells ⋮ Random discrete competing events vs. dynamic bistable switches in cell proliferation in differentiation ⋮ The impact of maturation time distributions on the structure and growth of cellular populations ⋮ Positive semigroups and perturbations of boundary conditions ⋮ A new regression model for bimodal data and applications in agriculture

Cites Work

• Selection between Weibull and lognormal distributions: a comparative simulation study
• Cell volume distributions reveal cell growth rates and division times
• An in silico investigation into the causes of telomere length heterogeneity and its implications for the Hayflick limit
• Modeling T cell proliferation and death in vitro based on labeling data: generalizations of the Smith-Martin cell cycle model
• A STOCHASTIC MODEL OF STEM CELL PROLIFERATION, BASED ON THE GROWTH OF SPLEEN COLONY-FORMING CELLS

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