scholarly journals Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect

PLoS Biology ◽  
2019 ◽  
Vol 17 (8) ◽  
pp. e3000399 ◽  
Author(s):  
Kaitlyn E. Johnson ◽  
Grant Howard ◽  
William Mo ◽  
Michael K. Strasser ◽  
Ernesto A. B. F. Lima ◽  
...  
Keyword(s):  
Population Growth ◽  
Cancer Cell ◽  
Cell Population ◽  
Growth Model ◽  
Growth Kinetics ◽  
Exponential Growth ◽  
Allee Effect ◽  
Cell Population Growth ◽  
Cancer Cell Population ◽  
Exponential Growth Model

scholarly journals Finsler-type estimators for the cancer cell population dynamics

2015 ◽  
Vol 98 (112) ◽  
pp. 53-69
Author(s):  
Vladimir Balan ◽  
Jelena Stojanov
Keyword(s):  
Dynamical System ◽  
Population Growth ◽  
Cancer Cell ◽  
Cell Population ◽  
Biological Models ◽  
Cell Population Dynamics ◽  
Finsler Structure ◽  
Cell Population Growth ◽  
Cancer Cell Population ◽  
The Difference

We introduce a Finslerian model related to the classical Garner dynamical system, which models the cancer cell population growth. The Finsler structure is determined by the energy of the deformation field-the difference of the fields, which describe the reduced and the proper biological models. It is shown that a certain locally-Minkowski anisotropic Randers structure, obtained by means of statistical fitting, is able to provide a Zermelo-type drift of the overall cancer cell population growth, which occurs due to significant changes within the cancerous process. The geometric background, the applicative advantages and perspective openings of the constructed geometric structure are discussed.

scholarly journals Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect

2019 ◽  
Author(s):  
Kaitlyn E. Johnson ◽  
Grant Howard ◽  
William Mo ◽  
Michael K. Strasser ◽  
Ernesto A. B. F. Lima ◽  
...  
Keyword(s):  
Cell Growth ◽  
Cancer Cells ◽  
Tumor Cells ◽  
Cancer Cell ◽  
Growth Kinetics ◽  
Exponential Growth ◽  
Allee Effect ◽  
Growth Models ◽  
Cooperative Behavior ◽  
Cell Densities

AbstractModels of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations from exponential growth only at higher densities due to limited resources such as space and nutrients. However, recent pre-clinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models however, tumor growth data is limited by the lower limit of detection (i.e. a measurable lesion) and confounding variables, such as tumor microenvironment and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlledin vitrosetting. We propose a stochastic modeling framework to consider the small number of cells in this low-density regime and use the moment approach for stochastic parameter estimation to calibrate the stochastic growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells depends on cell number. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging tumors and relapse.Author SummaryThe growth kinetics of cancer cells at very low cell densities are of utmost clinical importance as the ability of a small number of newly transformed or surviving cells to grow exponentially and thus, to “take off” underlies tumor formation and relapse after treatment. Mathematical models of stochastic tumor cell growth typically assume a stochastic birth-death process of cells impacted by limited nutrients and space when cells reach high density, resulting in the widely accepted logistic growth model. Here we present an in-depth investigation of alternate growth models adopted from ecology to describe potential deviations from a simple cell autonomous birth-death model at low cell densities. We show that our stochastic modeling framework is robust and can be used to identify the underlying structure of stochastic growth trajectories from both simulated and experimental data taken from a controlledin vitrosetting in which we can capture data from the relevant low cell density regime. This work suggests that the assumption of cell autonomous proliferation via a constant exponential growth rate at low cell densities may not be appropriate for all cancer cell growth dynamics. Consideration of cooperative behavior amongst tumor cells in this regime is critical for elucidating strategies for controlling tumor cell growth.

scholarly journals clonealign: statistical integration of independent single-cell RNA & DNA-seq from human cancers

2018 ◽  
Author(s):  
Kieran R Campbell ◽  
Adi Steif ◽  
Emma Laks ◽  
Hans Zahn ◽  
Daniel Lai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cancer Cell ◽  
Cell Population ◽  
Rna Seq ◽  
Dna And Rna ◽  
Ovarian Cancer Cell Lines ◽  
Cell Population Growth ◽  
Cancer Cell Population ◽  
Scalable Methods

AbstractMeasuring gene expression of genomically defined tumour clones at single cell resolution would associate functional consequences to somatic alterations, as a prelude to elucidating pathways driving cell population growth, resistance and relapse. In the absence of scalable methods to simultaneously assay DNA and RNA from the same single cell, independent sampling of cell populations for parallel measurement of single cell DNA and single cell RNA must be computationally mapped for genome-transcriptome association. Here we presentclonealign, a robust statistical framework to assign gene expression states to cancer clones using single-cell RNA-seq and DNA-seq independently sampled from an heterogeneous cancer cell population. We applyclonealignto triple-negative breast cancer patient derived xenografts and high-grade serous ovarian cancer cell lines and discover clone-specific dysregulated biological pathways not visible using either DNA-Seq or RNA-Seq alone.

scholarly journals Estimating COVID-19 cases infected with the variant alpha (VOC 202012/01): an analysis of screening data in Tokyo, January-March 2021

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Hiroaki Murayama ◽  
Taishi Kayano ◽  
Hiroshi Nishiura
Keyword(s):  
Confidence Interval ◽  
Growth Model ◽  
Exponential Growth ◽  
Reproduction Number ◽  
Hypergeometric Distribution ◽  
Actual Number ◽  
Growth Data ◽  
Exponential Growth Model

Abstract Background In Japan, a part of confirmed patients’ samples have been screened for the variant of concern (VOC), including the variant alpha with N501Y mutation. The present study aimed to estimate the actual number of cases with variant alpha and reconstruct the epidemiological dynamics. Methods The number of cases with variant alpha out of all PCR confirmed cases was estimated, employing a hypergeometric distribution. An exponential growth model was fitted to the growth data of variant alpha cases over fourteen weeks in Tokyo. Results The weekly incidence with variant alpha from 18–24 January 2021 was estimated at 4.2 (95% confidence interval (CI): 0.7, 44.0) cases. The expected incidence in early May ranged from 420–1120 cases per week, and the reproduction number of variant alpha was on the order of 1.5 even under the restriction of contact from January-March, 2021, Tokyo. Conclusions The variant alpha was predicted to swiftly dominate COVID-19 cases in Tokyo, and this has actually occurred by May 2021. Devising the proposed method, any country or location can interpret the virological sampling data.

scholarly journals Simulation framework for generating intratumor heterogeneity patterns in a cancer cell population

PLoS ONE ◽  
2017 ◽  
Vol 12 (9) ◽  
pp. e0184229 ◽  
Author(s):  
Watal M. Iwasaki ◽  
Hideki Innan
Keyword(s):  
Cancer Cell ◽  
Cell Population ◽  
Intratumor Heterogeneity ◽  
Simulation Framework ◽  
Cancer Cell Population

Population growth and density dependence

2019 ◽  
pp. 63-80
Author(s):  
Gary G. Mittelbach ◽  
Brian J. McGill
Keyword(s):  
Population Growth ◽  
Density Dependence ◽  
Growth Model ◽  
Species Interactions ◽  
Single Species ◽  
Positive Density ◽  
Allee Effects ◽  
Species Population ◽  
Exponential Growth Model ◽  
Single Species Population

This chapter reviews the basic mathematics of population growth as described by the exponential growth model and the logistic growth model. These simple models of population growth provide a foundation for the development of more complex models of species interactions covered in later chapters on predation, competition, and mutualism. The second half of the chapter examines the important topic of density-dependence and its role in population regulation. The preponderance of evidence for negative density-dependence in nature is reviewed, along with examples of positive density dependence (Allee effects). The study of density dependence in single-species populations leads naturally to the concept of community-level regulation, the idea that species richness or the total abundance of individuals in a community may be regulated just like abundance in a single-species population. The chapter concludes with a look at the evidence for community regulation in nature and a discussion of its importance.

scholarly journals Tumour cell population growth inhibition and cell death induction of functionalized 6-aminoquinolone derivatives

2015 ◽  
Vol 48 (6) ◽  
pp. 705-717 ◽  
Author(s):  
G. Franci ◽  
G. Manfroni ◽  
R. Cannalire ◽  
T. Felicetti ◽  
O. Tabarrini ◽  
...  
Keyword(s):  
Cell Death ◽  
Population Growth ◽  
Growth Inhibition ◽  
Cell Population ◽  
Tumour Cell ◽  
Cell Population Growth ◽  
Tumour Cell Population

scholarly journals Circadian rhythm and cell population growth

2011 ◽  
Vol 53 (7-8) ◽  
pp. 1558-1567 ◽  
Author(s):  
Jean Clairambault ◽  
Stéphane Gaubert ◽  
Thomas Lepoutre
Keyword(s):  
Circadian Rhythm ◽  
Population Growth ◽  
Cell Population ◽  
Cell Population Growth
Sign in / Sign up

Export Citation Format

Share Document