Multi-type branching models to describe cell differentiation programs

Nordon, Robert E.; Ko, Kap-Hyoun; Odell, Ross A.; Schroeder, Timm

doi:10.1016/j.jtbi.2011.02.006

Cited by 33 publications

(33 citation statements)

References 30 publications

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“…In particular, the Smith-Martin model that takes into account progression of cells through the cell cycle, has been successfully used to predict and model population dynamics of in vitro erythropoiesis. [21][22][23][24][25] We have combined immunophenotyping, proliferation analysis and cytokine dependence to refine the analysis of early erythroid culture of CD34 + cells isolated from human peripheral blood (PB). Isolated PB CD34 + cells are CMP, and the appearance of detectable surface levels of the erythroid marker CD36 is the earliest identifier of progenitors restricted to the megakaryocyte/erythroid lineage.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling reveals differential effects of erythropoietin on proliferation and lineage commitment of human hematopoietic progenitors in early erythroid culture

et al. 2015

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Section: Introductionmentioning

confidence: 99%

Mathematical modeling reveals differential effects of erythropoietin on proliferation and lineage commitment of human hematopoietic progenitors in early erythroid culture

et al. 2015

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“…Additional model generalizations might also be considered. For instance, the Smith-Martin model has also been generalized to allow for division-linked differentiation and the inheritance of division times [65]. Branching process models (which can be thought of as generalizations of the probabilistic cyton model [51,77]) have been used to study the effects of correlations between the division characteristics of closely related cells (siblings, cousins, etc.)…”

Section: Discussionmentioning

confidence: 99%

Mathematical Models of Dividing Cell Populations: Application to CFSE Data

Banks

Thompson

2012

Math. Model. Nat. Phenom.

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Abstract. Flow cytometric analysis using intracellular dyes such as CFSE is a powerful experimental tool which can be used in conjunction with mathematical modeling to quantify the dynamic behavior of a population of lymphocytes. In this survey we begin by providing an overview of the mathematically relevant aspects of the data collection procedure. We then present an overview of the large body of mathematical models, along with their assumptions and uses, which have been proposed to describe the dynamics of proliferating cell populations. While much of this body of work has been aimed at modeling the generation structure (cells per generation) of the proliferating population, several recent models have considered the more fundamental task of modeling CFSE histogram data directly. Such models are analyzed and recent results are discussed. Finally, directions for future research are suggested.

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“…Qualitative examination of flow cytometric histograms of CFSE fluorescence is useful but does not allow a complete interpretation of time-series experiments. However, quantitative CFSE celldivision tracking consists of using computational tools and a working model of the dynamics of cell division to extract parameters that characterize the rates of cell activation, proliferation, and death from CFSE labeling experiments (8)(9)(10)(11)(12)(13).…”

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confidence: 99%

“…The ''transition probability'' model of the cell cycle developed by Smith and Martin (14) has been useful to develop mathematical models to extract proliferation rates from CFSE data (8)(9)(10)13,15,16) since it has made it possible to conceptualize the observed heterogeneity of intermitotic times within a cell population. The Smith-Martin model divides the cell cycling process into two fundamentally different stages: the A-phase which length is a random variable and a B-phase which length is fixed (deterministic).…”

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confidence: 99%

“…By fitting the CFSE data to this model, the authors were able to describe the population dynamics of a lymphocyte cell line (8). More recently, they developed multi-type branching models based on the Smith-Martin representation of the cell cycle that take into account cell proliferation, death, and differentiation and demonstrated the usefulness of this approach to describe the expansion of human cord blood CD34 1 cells in culture (13).…”

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Measurement of generation‐dependent proliferation rates and death rates during mouse erythroid progenitor cell differentiation

Akbarian¹,

Wang²,

Audet³

2012

Cytometry Pt A

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Herein, we describe an experimental and computational approach to perform quantitative carboxyfluorescein diacetate succinimidyl ester (CFSE) cell-division tracking in cultures of primary colony-forming unit-erythroid (CFU-E) cells, a hematopoietic progenitor cell type, which is an important target for the treatment of blood disorders and for the manufacture of red blood cells. CFSE labeling of CFU-Es isolated from mouse fetal livers was performed to examine the effects of stem cell factor (SCF) and erythropoietin (EPO) in culture. We used a dynamic model of proliferation based on the Smith-Martin representation of the cell cycle to extract proliferation rates and death rates from CFSE time-series. However, we found that to accurately represent the cell population dynamics in differentiation cultures of CFU-Es, it was necessary to develop a model with generation-specific rate parameters. The generation-specific rates of proliferation and death were extracted for six generations (G 0 -G 5 ) and they revealed that, although SCF alone or EPO alone supported similar total cell outputs in culture, stimulation with EPO resulted in significantly higher proliferation rates from G 2 to G 5 and higher death rates in G 2 , G 3 , and G 5 compared with SCF. In addition, proliferation rates tended to increase from G 1 to G 5 in cultures supplemented with EPO and EPO 1 SCF, while they remained lower and more constant across generations with SCF. The results are consistent with the notion that SCF promotes CFU-E self-renewal while EPO promotes CFU-E differentiation in culture. ' 2012 International Society for Advancement of Cytometry Key terms red blood cells; carboxyfluorescein diacetate succinimidyl ester; cell differentiation; cell division; erythropoiesis; cell division; CFSE CARBOXYFLUORESCEIN diacetate succinimidyl ester (CFSE) cell-division tracking is widely used to determine the number of divisions cells have undergone in cultures or in vivo. This flow cytometric assay is based on the redistribution and dilution of the CFSE fluorescent dye between mother and daughter cells during cell division, resulting in distinct fluorescence intensity for each cell generation (1-3). Originally developed for homogenous lymphocyte populations (1), Nordon et al. (4) extended this approach to track the divisions of primary hematopoietic progenitor cell populations that are composed of multiple cell subsets (5-7). Qualitative examination of flow cytometric histograms of CFSE fluorescence is useful but does not allow a complete interpretation of time-series experiments. However, quantitative CFSE celldivision tracking consists of using computational tools and a working model of the dynamics of cell division to extract parameters that characterize the rates of cell activation, proliferation, and death from CFSE labeling experiments (8-13).The ''transition probability'' model of the cell cycle developed by Smith and Martin (14) has been useful to develop mathematical models to extract proliferation rates from CFSE data (8-10,13,15,...

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Multi-type branching models to describe cell differentiation programs

Cited by 33 publications

References 30 publications

Mathematical modeling reveals differential effects of erythropoietin on proliferation and lineage commitment of human hematopoietic progenitors in early erythroid culture

Mathematical modeling reveals differential effects of erythropoietin on proliferation and lineage commitment of human hematopoietic progenitors in early erythroid culture

Mathematical Models of Dividing Cell Populations: Application to CFSE Data

Measurement of generation‐dependent proliferation rates and death rates during mouse erythroid progenitor cell differentiation

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