ArticleJournal of the Royal Society, Interface2015
A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity.
Article in Journal of the Royal Society, Interface, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 23 citations in OpenAlex.
- A multilevel hierarchical framework for quantification of experimental heterogeneity in population snapshot data.PLoS computational biology · 2026Article
- Unified tumor growth mechanisms from multimodel inference and dataset integration.PLoS computational biology · 2023Article
- A mathematical model for cell cycle control: graded response or quantized response.Cell cycle (Georgetown, Tex.) · 2022Article
- Modeling heterogeneous tumor growth dynamics and cell-cell interactions at single-cell and cell-population resolution.Current opinion in systems biology · 2019Article
- Non-cell-autonomous effects yield lower clonal diversity in expanding tumors.Scientific reports · 2017Article
- Functionalist Oncology to Model the Contextuality of Dynamics and Treatment in Acute Myeloid Leukaemia.IET systems biologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute myeloid leukaemia is characterized by marked inter- and intra-patient heterogeneity, the identification of which is critical for the design of personalized treatments. Heterogeneity of leukaemic cells is determined by mutations which ultimately affect the cell cycle. We have developed and validated a biologically relevant, mathematical model of the cell cycle based on unique cell-cycle signatures, defined by duration of cell-cycle phases and cyclin profiles as determined by flow cytometry, for three leukaemia cell lines. The model was discretized for the different phases in their respective progress variables (cyclins and DNA), resulting in a set of time-dependent ordinary differential equations. Cell-cycle phase distribution and cyclin concentration profiles were validated against population chase experiments. Heterogeneity was simulated in culture by combining the three cell lines in a blinded experimental set-up. Based on individual kinetics, the model was capable of identifying and quantifying cellular heterogeneity. When supplying the initial conditions only, the model predicted future cell population dynamics and estimated the previous heterogeneous composition of cells. Identification of heterogeneous leukaemia clones at diagnosis and post-treatment using such a mathematical platform has the potential to predict multiple future outcomes in response to induction and consolidation chemotherapy as well as relapse kinetics.
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Registered trials
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