scholarly journals Stochastic Models for Population Dynamics

2015 ◽  
Author(s):  
Sean Sun
Keyword(s):  
Population Dynamics ◽  
Cell Population ◽  
Stochastic Models ◽  
Population Fluctuations ◽  
Computational Approaches ◽  
Stochastic Population Models ◽  
Cell Population Growth ◽  
Cancer Cell Population ◽  
Basic Concepts ◽  
Cell Variation

Cell growth and division are stochastic processes that exhibit significant amount of cell-to-cell variation and randomness. In order to connect single cell division dynamics with overall cell population, stochastic population models are needed. We summarize the basic concepts, computational approaches and discuss simple applications of this modeling approach to understanding cancer cell population growth as well as population fluctuations in experiments.

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 Implications of a simple mathematical model to cancer cell population dynamics

2006 ◽  
Vol 39 (1) ◽  
pp. 15-28 ◽  
Author(s):  
A. L. Garner ◽  
Y. Y. Lau ◽  
D. W. Jordan ◽  
M. D. Uhler ◽  
R. M. Gilgenbach
Keyword(s):  
Mathematical Model ◽  
Population Dynamics ◽  
Cancer Cell ◽  
Cell Population ◽  
Simple Mathematical Model ◽  
Cell Population Dynamics ◽  
Cancer Cell Population

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 Epithelial and mesenchymal prostate cancer cell population dynamics on a complex drug landscape

2017 ◽  
Vol 3 (4) ◽  
pp. 045001 ◽  
Author(s):  
Ke-Chih Lin ◽  
Gonzalo Torga ◽  
Amy Wu ◽  
Joshua D Rabinowitz ◽  
Wesley J Murray ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Population Dynamics ◽  
Cancer Cell ◽  
Cell Population ◽  
Prostate Cancer Cell ◽  
Cell Population Dynamics ◽  
Cancer Cell Population ◽  
Complex Drug

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.

Identification of Cancer Cell Population Dynamics Leveraging the Effect of Pre-Treatment for Drug Schedule Design

2021 ◽  
Author(s):  
Marius Wiggert ◽  
Megan Turnidge ◽  
Zoe Cohen ◽  
Ellen M. Langer ◽  
Rosalie C. Sears ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Cancer Cell ◽  
Cell Population ◽  
Cell Population Dynamics ◽  
Schedule Design ◽  
Cancer Cell Population ◽  
Pre Treatment

scholarly journals Mathematical Modeling Reveals the Factors Involved in the Phenomena of Cancer Stem Cells Stabilization

2018 ◽  
Author(s):  
N. Bessonov ◽  
G. Pinna ◽  
A. Minarsky ◽  
A. Harel-Bellan ◽  
N. Morozova
Keyword(s):  
Mathematical Model ◽  
Stem Cells ◽  
Population Dynamics ◽  
Cancer Stem Cells ◽  
Cancer Cells ◽  
Cancer Cell ◽  
Cell Population ◽  
Cell Population Dynamics ◽  
Cancer Cell Population ◽  
Population Stabilization

AbstractCancer Stem Cells (CSC), a subset of cancer cells resembling normal stem cells with self-renewal and asymmetric division capabilities, are present at various but low proportions in many tumors and are thought to be responsible for tumor relapses following conventional cancer therapies. In vitro, most intriguingly, when isolated, CSCs return to their original proportion level as shown by various investigators. This phenomenon still remains to be explained.We suggest a mathematical model of cancer cell population dynamics, based on the main parameters of cell population dynamics, including the proliferation rates, the rates of cell death and the frequency of symmetric and asymmetric cell divisions both in CSCs and in non-CSCs. This model should help elucidating some important factors underlying the dynamics of the two populations, first of all, the phenomena of cancer stem cell population stabilization.Author SummaryCancer Stem Cells (CSC) present a subset of cancer cells which is thought to be responsible for tumor growth. That is why CSC are also named “tumor initiation cells”. Additionally, it was shown that CSC are resistant to chemo- and radio-therapies which suggests that these cells can be responsible for tumor relapses after these treatments. Experimental data in cancer cell lines have shown the intriguing phenomena of CSC population stability, which means that isolated CSC population rapidly stabilizes at its characteristic level (the relative proportion of CSC in a whole cancer population). We suggest a mathematical model of cancer cell population dynamics, based on experimentally measured dynamics of CSC population stabilization and including main parameters of cell population growth.We have computationally predicted probability of different scenarios of cancer cell behavior for each experimental case with measurable growth parameters. Moreover, we provide an analytical tool for elucidating important biochemical factors responsible for a particular dynamics of CSC population.The results may have important implications in therapeutic, because the destroying of a set of factors underlying CSC stability may help to avoid tumor relapses.

scholarly journals Accounting for randomness in measurement and sampling in studying cancer cell population dynamics

2014 ◽  
Vol 8 (5) ◽  
pp. 230-241
Author(s):  
Siavash Ghavami ◽  
Olaf Wolkenhauer ◽  
Farshad Lahouti ◽  
Mukhtar Ullah ◽  
Michael Linnebacher
Keyword(s):  
Population Dynamics ◽  
Cancer Cell ◽  
Cell Population ◽  
Cell Population Dynamics ◽  
Cancer Cell Population

Cell population dynamics and somatic change

1958 ◽  
Vol 52 (S1) ◽  
pp. 337-369 ◽  
Author(s):  
Werner Braun
Keyword(s):  
Population Dynamics ◽  
Cell Population ◽  
Cell Population Dynamics
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