ArticleMathematical biosciences2012
Stochastic modeling of stem-cell dynamics with control.
Article in Mathematical biosciences, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Mathematical modeling ofProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Clonal dynamics deviate from neutral drift in zebrafish spermatogenesis.bioRxiv : the preprint server for biology · 2026Article
- Clonal abundance patterns in hematopoiesis: Mathematical modeling and parameter estimation.Frontiers in systems biology · 2023Article
- Article
- A General Theoretical Framework to Study the Influence of Electrical Fields on Mesenchymal Stem Cells.Frontiers in bioengineering and biotechnology · 2020Article
- Modeling large fluctuations of thousands of clones during hematopoiesis: The role of stem cell self-renewal and bursty progenitor dynamics in rhesus macaque.PLoS computational biology · 2018Article
- Modeling of Interactions between Cancer Stem Cells and their Microenvironment: Predicting Clinical Response.Methods in molecular biology (Clifton, N.J.) · 2018Review
- Dynamic heterogeneity as a strategy of stem cell self-renewal.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Stochastic control of proliferation and differentiation in stem cell dynamics.Journal of mathematical biology · 2015Article
- Analysis of stochastic stem cell models with control.Mathematical biosciences · 2015Article
- Cell lineage branching as a strategy for proliferative control.BMC biology · 2015Article
- Stochastic dynamics of interacting haematopoietic stem cell niche lineages.PLoS computational biology · 2014Article
- Robustness of differentiation cascades with symmetric stem cell division.Journal of the Royal Society, Interface · 2014Article
- Beyond boundaries--Eph:ephrin signaling in neurogenesis.Cell adhesion & migration · 2014Review
- Age-dependent stochastic models for understanding population fluctuations in continuously cultured cells.Journal of the Royal Society, Interface · 2013Article
- Stem cell modeling: From gene networks to cell populations.Current opinion in chemical engineering · 2013Article
- Spatial pattern dynamics of 3D stem cell loss of pluripotency via rules-based computational modeling.PLoS computational biology · 2013Article
- Principles of regulation of self-renewing cell lineages.PloS one · 2013Article
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2 authors.
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Abstract
Tissue development and homeostasis are thought to be regulated endogenously by control loops that ensure that the numbers of stem cells and daughter cells are maintained at desired levels, and that the cell dynamics are robust to perturbations. In this paper we consider several classes of stochastic models that describe stem/daughter cell dynamics in a population of constant size, which are generalizations of the Moran process that include negative control loops that affect differentiation probabilities for stem cells. We present analytical solutions for the steady-state expectations and variances of the numbers of stem and daughter cells; these results remain valid for non-constant cell populations. We show that in the absence of differentiation/proliferation control, the number of stem cells is subject to extinction or overflow. In the presence of linear control, a steady state may be maintained but no tunable parameters are available to control the mean and the spread of the cell population sizes. Two types of nonlinear control considered here incorporate tunable parameters that allow specification of the expected number of stem cells and also provide control over the size of the standard deviation. We show that under a hyperbolic control law, there is a trade-off between minimizing standard deviations and maintaining the system robustness against external perturbations. For the Hill-type control, the standard deviation is inversely proportional to the Hill coefficient of the control loop. Biologically this means that ultrasensitive response that is observed in a number of regulatory loops may have evolved in order to reduce fluctuations while maintaining the desired population levels.
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