A stochastic model for mast cell proliferation in culture

Pharr, Pamela N.; Nedelman, Jerry; Downs, Heather; Ogawa, Makio; Gross, Alan J.

doi:10.1002/jcp.1041250304

Cited by 20 publications

(17 citation statements)

References 12 publications

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“…Analysis of the size of secondary mast cell colonies and computer simulation of a modification of the birth and death model of Till et al (14) also suggested that mast cell proliferation in culture is a stochastic process (29). Ibgether these data are consistent with the notion that both differentiation and proliferation of hemopoietic progenitors are separate stochastic processes.…”

Section: Differentiation Of Stem Cellssupporting

confidence: 55%

Hemopoietic stem cells: stochastic differentiation and humoral control of proliferation.

Ogawa

1989

Environ Health Perspect

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The central feature of hemopoiesis is life-long, stable cell renewal. This process is supported by hemopoietic stem cells which, in the steady state, appear to be dormant in cell cycling. The entry into cell cycle of the dormant stem cells may be promoted by such factors as interleukin-1, interleukin-6 (IL-6), and granulocyte colony-stimulating factor (G-CSF). Once the stem cells leave G0 and begin proliferation, the subsequent process is characterized by continued proliferation and differentiation. While several models of stem cell differentiation have been proposed, micromanipulation studies of individual progenitors suggest that the commitment of multipotential progenitors to single lineages is a random (stochastic) process. The proliferation of early hemopoietic progenitors requires the presence of interleukin-3 (IL-3), and the intermediate process appears to be supported by granulocyte/macrophage colony-stimulating factor (GM-CSF). Once the progenitors are committed to individual lineages, the subsequent maturation process appears to be supported by late-acting, lineage-specific factors such as erythropoietin and G-CSF. Synthesis of a hemopoietic factor may take place in different cell types and is regulated by multiple factors. The physiological regulator of erythropoiesis is erythropoietin, which, by a feedback mechanism, provides fine control of erythrocyte production. Feedback mechanisms for leukocyte production have not been identified. It is possible that there is no feedback regulator of leukopoiesis. In this model, leukocyte production in the steady state is maintained at a genetically determined level. When an infection occurs, the bacterial lipopolysaccharides may augment the production of interleukin 1 alpha and beta, tumor necrosis factor, macrophage colony-stimulating factor, etc.(ABSTRACT TRUNCATED AT 250 WORDS)

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Section: Differentiation Of Stem Cellssupporting

confidence: 55%

Hemopoietic stem cells: stochastic differentiation and humoral control of proliferation.

Ogawa

1989

Environ Health Perspect

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“…A simulation model was developed by Rittgen (1983). The stochastic model of mast cell proliferation developed by Pharr et al (1985) assumed a two-type Galton-Watson process including proliferative and nonproliferative cells. Each proliferative cell gives rise to either two proliferative progeny, or two nonproliferative progeny, or it may die.…”

Section: Hemopoiesis and Clonal Cell Populationsmentioning

confidence: 99%

Branching Processes in Biology

Kimmel

Axelrod

2015

Interdisciplinary Applied Mathematics

209

192

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Problems in engineering, computational science, and the physical and biological sciences are using increasingly sophisticated mathematical techniques. Thus, the bridge between the mathematical sciences and other disciplines is heavily traveled. The correspondingly increased dialog between the disciplines has led to the establishment of the series: Interdisciplinary Applied Mathematics.The purpose of this series is to meet the current and future needs for the interaction between various science and technology areas on the one hand and mathematics on the other. This is done, firstly, by encouraging the ways that mathematics may be applied in traditional areas, as well as point towards new and innovative areas of applications; and secondly, by encouraging other scientific disciplines to engage in a dialog with mathematicians outlining their problems to both access new methods as well as to suggest innovative developments within mathematics itself.The series will consist of monographs and high-level texts from researchers working on the interplay between mathematics and other fields of science and technology.More information about this series at

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“…[12][13][14] It has been previously suggested that cell lifecycle events may be referred to as stochastic events in theoretical modeling. 5,7,16,17,[23][24][25][26][27]29,32,33 For example, Pharr et al developed a stochastic model for mast cell proliferation in a culture. 23 Vogel et al similarly referred to stem cell development as stochastic events.…”

Section: Introductionmentioning

confidence: 98%

“…5,7,16,17,[23][24][25][26][27]29,32,33 For example, Pharr et al developed a stochastic model for mast cell proliferation in a culture. 23 Vogel et al similarly referred to stem cell development as stochastic events. 32 Gusella referred to the commitment to erythroid differentiation as a stochastic event as well.…”

Section: Introductionmentioning

confidence: 98%

Stochastic Modeling of Adipogenesis in 3T3-L1 Cultures to Determine Probabilities of Events in the Cell’s Life Cycle

Shoham

Gefen

2011

Ann Biomed Eng

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3T3-L1 preadipocytes are often being used in research of adipose-related diseases such as obesity, insulin resistance, and hyperlipidemia. We developed a stochastic model that simulated differentiation of four 3T3-L1 culture conditions distinct by the insulin concentration in the differentiation medium (2.5, 5, 7.5, or 10 μg/mL). The model simulated culture behavior and the accumulation of lipid droplets in the maturing cells from the day of induction of differentiation through 28 days after that. The cellular processes including cell adhesion, mitosis, growing after undergoing mitosis, commitment to the adipocyte lineage, and apoptosis were referred to as stochastic events in the modeling. By minimizing the error between our model and experimental results, we found that the probability for becoming committed to the adipocyte lineage in a single division and the probability for growing after undergoing mitosis were 0.02 and 0.8, respectively, regardless of the insulin concentration. The probability for undergoing mitosis was equal to 0.2 and 0.4 in cultures that had insulin concentrations of 2.5 and 5-10 μg/mL in the differentiation medium, respectively; hence the insulin concentration affected the probability for mitosis in the 3T3-L1 cells. The model and resulted probabilities now allow quantitative and visual predictions of adipogenesis in 3T3-L1 cultures, toward computational design of cell culturing protocols.

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A stochastic model for mast cell proliferation in culture

Cited by 20 publications

References 12 publications

Hemopoietic stem cells: stochastic differentiation and humoral control of proliferation.

Hemopoietic stem cells: stochastic differentiation and humoral control of proliferation.

Branching Processes in Biology

Stochastic Modeling of Adipogenesis in 3T3-L1 Cultures to Determine Probabilities of Events in the Cell’s Life Cycle

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