Stem cell biology is population biology: differentiation of hematopoietic multipotent progenitors to common lymphoid and myeloid progenitors

Mangel, Marc; Bonsall, Michael B.

doi:10.1186/1742-4682-10-5

Cited by 25 publications

(39 citation statements)

References 71 publications

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“…Here it is shown that stability properties depend crucially on the type of model that is considered, and that great care must be taken in drawing conclusions regarding the stability of systems without reference to a particular model. It has also been shown for models of stem cell differentiation that the niche confers stability onto stem cell systems with remarkably high probability , in contrast to previous results. Thus, despite the time that has passed since the work of Lotka and Volterra, questions regarding the stability of ecosystems remain very topical today, and the debate rumbles on .…”

Section: Theoretical Population Biology—an Introduction For Cell Biolcontrasting

confidence: 65%

Concise Review: Stem Cell Population Biology: Insights from Hematopoiesis

2016

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Stem cells are fundamental to human life and offer great therapeutic potential, yet their biology remains incompletely-or in cases even poorly-understood. The field of stem cell biology has grown substantially in recent years due to a combination of experimental and theoretical contributions: the experimental branch of this work provides data in an ever-increasing number of dimensions, while the theoretical branch seeks to determine suitable models of the fundamental stem cell processes that these data describe. The application of population dynamics to biology is amongst the oldest applications of mathematics to biology, and the population dynamics perspective continues to offer much today. Here we describe the impact that such a perspective has made in the field of stem cell biology. Using hematopoietic stem cells as our model system, we discuss the approaches that have been used to study their key properties, such as capacity for self-renewal, differentiation, and cell fate lineage choice. We will also discuss the relevance of population dynamics in models of stem cells and cancer, where competition naturally emerges as an influential factor on the temporal evolution of cell populations. STEM CELLS 2017;35:80-88 SIGNIFICANCE STATEMENTAdult stem cells engage in complex interactions with their environment and progeny; these are believed to maintain their stemness. Here we put these observations into a population biology framework which allows us to take ideas from ecology and shed light on the dynamics within the stem cell niche. Using the particular example of hematopoietic stem cells, we show that this perspective helps to understand stem cell dynamics both in cases of health and disease.

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Section: Theoretical Population Biology—an Introduction For Cell Biolcontrasting

confidence: 65%

Concise Review: Stem Cell Population Biology: Insights from Hematopoiesis

2016

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“…In [27], we used this model to examine the behaviour of the haematopoietic system from an evolutionary perspective. Treating it as a demand control system, where the demand comes from the entire organism, we showed that there is varying selection on organisms with different MPCR parameters and .…”

Section: Methodsmentioning

confidence: 99%

Stochastic Dynamics of Interacting Haematopoietic Stem Cell Niche Lineages

et al. 2014

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Since we still know very little about stem cells in their natural environment, it is useful to explore their dynamics through modelling and simulation, as well as experimentally. Most models of stem cell systems are based on deterministic differential equations that ignore the natural heterogeneity of stem cell populations. This is not appropriate at the level of individual cells and niches, when randomness is more likely to affect dynamics. In this paper, we introduce a fast stochastic method for simulating a metapopulation of stem cell niche lineages, that is, many sub-populations that together form a heterogeneous metapopulation, over time. By selecting the common limiting timestep, our method ensures that the entire metapopulation is simulated synchronously. This is important, as it allows us to introduce interactions between separate niche lineages, which would otherwise be impossible. We expand our method to enable the coupling of many lineages into niche groups, where differentiated cells are pooled within each niche group. Using this method, we explore the dynamics of the haematopoietic system from a demand control system perspective. We find that coupling together niche lineages allows the organism to regulate blood cell numbers as closely as possible to the homeostatic optimum. Furthermore, coupled lineages respond better than uncoupled ones to random perturbations, here the loss of some myeloid cells. This could imply that it is advantageous for an organism to connect together its niche lineages into groups. Our results suggest that a potential fruitful empirical direction will be to understand how stem cell descendants communicate with the niche and how cancer may arise as a result of a failure of such communication.

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“…Optimal controls of proliferation based on our model lead to the negative feedback regulation of proliferation through the cell population. Similar regulatory mechanisms with negative feedback have been explicitly introduced in many stem cell population models (17,27,36,46) by use of a Hill function (strategy C) such as…”

Section: An Optimal Control For Proliferation During Each Cell Cycle mentioning

confidence: 99%

“…The exploration and analysis of models that include transit-amplifying progenitor cells and terminally differentiated cells have suggested that multiple feedback mechanisms at different lineage stages can influence the speed of tissue regeneration for better performance (17,(26)(27)(28). These population dynamic models could include age structure (29), evolution (27,30), and stochasticity (30,31); and these models could also be applied to the regulation of cancer (32). Studies based on spatial modeling have found that diffusive and regulatory molecules involved in feedback mechanisms regulating the differentiation capabilities of the cells are important in maintaining the stem cell niche and shaping tissue stratification (33,34).…”

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confidence: 99%

Mathematical model of adult stem cell regeneration with cross-talk between genetic and epigenetic regulation

Lei

Levin

Nie

2014

Proc. Natl. Acad. Sci. U.S.A.

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Adult stem cells, which exist throughout the body, multiply by cell division to replenish dying cells or to promote regeneration to repair damaged tissues. To perform these functions during the lifetime of organs or tissues, stem cells need to maintain their populations in a faithful distribution of their epigenetic states, which are susceptible to stochastic fluctuations during each cell division, unexpected injury, and potential genetic mutations that occur during many cell divisions. However, it remains unclear how the three processes of differentiation, proliferation, and apoptosis in regulating stem cells collectively manage these challenging tasks. Here, without considering molecular details, we propose a genetic optimal control model for adult stem cell regeneration that includes the three fundamental processes, along with cell division and adaptation based on differential fitnesses of phenotypes. In the model, stem cells with a distribution of epigenetic states are required to maximize expected performance after each cell division. We show that heterogeneous proliferation that depends on the epigenetic states of stem cells can improve the maintenance of stem cell distributions to create balanced populations. A control strategy during each cell division leads to a feedback mechanism involving heterogeneous proliferation that can accelerate regeneration with less fluctuation in the stem cell population. When mutation is allowed, apoptosis evolves to maximize the performance during homeostasis after multiple cell divisions. The overall results highlight the importance of cross-talk between genetic and epigenetic regulation and the performance objectives during homeostasis in shaping a desirable heterogeneous distribution of stem cells in epigenetic states.fitness function | optimization | robustness | dynamic programming | systems biology

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Stem cell biology is population biology: differentiation of hematopoietic multipotent progenitors to common lymphoid and myeloid progenitors

Cited by 25 publications

References 71 publications

Concise Review: Stem Cell Population Biology: Insights from Hematopoiesis

Concise Review: Stem Cell Population Biology: Insights from Hematopoiesis

Stochastic Dynamics of Interacting Haematopoietic Stem Cell Niche Lineages

Mathematical model of adult stem cell regeneration with cross-talk between genetic and epigenetic regulation

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