How to Characterize Stem Cells? Contributions from Mathematical Modeling

Stiehl, Thomas; Marciniak–Czochra, Anna

doi:10.1007/s40778-019-00155-0

Cited by 10 publications

(4 citation statements)

References 56 publications

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“…If progeny are of the same cell type as the parent, e.g., a progeny of a stem cell is again a stem cell, this process is referred to as self-renewal. The alternative scenario, where progeny are of a more mature cell type compared to their parent cell is referred to as differentiation [64,65].…”

Section: Model Motivation and Formulationmentioning

confidence: 99%

Oscillations in a white blood cell production model with multiple differentiation stages

2019

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In this work we prove occurrence of a super-critical Hopf bifurcation in a model of white blood cell formation structured by three maturation stages. We provide an explicit analytical expression for the bifurcation point depending on model parameters. The Hopf bifurcation is a unique feature of the multi-compartment structure as it does not exist in the corresponding two-compartment model. It appears for a parameter set different from the parameters identified for healthy hematopoiesis and requires changes in at least two cell properties. Model analysis allows identifying a range of biologically plausible parameter sets that can explain persistent oscillations of white blood cell counts observed in some hematopoietic diseases. Relating the identified parameter sets to recent experimental and clinical findings provides insights into the pathological mechanisms leading to oscillating blood cell counts.

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Section: Model Motivation and Formulationmentioning

confidence: 99%

Oscillations in a white blood cell production model with multiple differentiation stages

2019

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“…We then use the quantified cell properties to assign an individual patient to a risk group. Recent literature demonstrates that mathematical modeling is a powerful tool to quantify processes that are not accessible to direct measurements (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28).…”

Section: Introductionmentioning

confidence: 99%

“…In case of a shortage of healthy mature cells, healthy progenitors and precursors divide faster and perform more divisions before terminal differentiation (5,(25)(26)(27)(30)(31)(32)(33).…”

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confidence: 99%

Mathematical Modeling Provides Evidence for Niche Competition in Human AML and Serves as a Tool to Improve Risk Stratification

et al. 2020

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Acute myeloid leukemia (AML) is a stem cell-driven malignant disease. There is evidence that leukemic stem cells (LSC) interact with stem cell niches and outcompete hematopoietic stem cells (HSC). The impact of this interaction on the clinical course of the disease remains poorly understood. We developed and validated a mathematical model of stem cell competition in the human HSC niche. Model simulations predicted how processes in the stem cell niche affect the speed of disease progression. Combining the mathematical model with data of individual patients, we quantified the selective pressure LSCs exert on HSCs and demonstrated the model's prognostic significance. A novel model-based riskstratification approach allowed extraction of prognostic informa-tion from counts of healthy and malignant cells at the time of diagnosis. This model's feasibility was demonstrable based on a cohort of patients with ALDH-rare AML and shows that the modelbased risk stratification is an independent predictor of disease-free and overall survival. This proof-of-concept study shows how model-based interpretation of patient data can improve prognostic scoring and contribute to personalized medicine.Significance: Combining a novel mathematical model of the human hematopoietic stem cell niche with individual patient data enables quantification of properties of leukemic stem cells and improves risk stratification in acute myeloid leukemia.

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“…Stem-cell based models for myeloid leukaemia are based on mathematical models of the normal blood generation process, called haematopoiesis. The role of stem cells in cancer was recently reviewed in [111] in terms of mathematical models which can characterise cell behaviour in normal cell development. For blood cells, an important haematopoiesis model was proposed by Marciniak et al [65].…”

Section: Stem-cell Based Models Of Myeloid Leukaemiasmentioning

confidence: 99%

Mathematical models of leukaemia and its treatment: a review

Chulián

Rubio

Rosa

et al. 2022

SeMA

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Leukaemia accounts for around 3% of all cancer types diagnosed in adults, and is the most common type of cancer in children of paediatric age (typically ranging from 0 to 14 years). There is increasing interest in the use of mathematical models in oncology to draw inferences and make predictions, providing a complementary picture to experimental biomedical models. In this paper we recapitulate the state of the art of mathematical modelling of leukaemia growth dynamics, in time and response to treatment. We intend to describe the mathematical methodologies, the biological aspects taken into account in the modelling, and the conclusions of each study. This review is intended to provide researchers in the field with solid background material, in order to achieve further breakthroughs in the promising field of mathematical biology.

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How to Characterize Stem Cells? Contributions from Mathematical Modeling

Cited by 10 publications

References 56 publications

Oscillations in a white blood cell production model with multiple differentiation stages

Oscillations in a white blood cell production model with multiple differentiation stages

Mathematical Modeling Provides Evidence for Niche Competition in Human AML and Serves as a Tool to Improve Risk Stratification

Mathematical models of leukaemia and its treatment: a review

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