Progenitor cell self‐renewal and cyclic neutropenia

Dingli, David; Antal, Tibor; Traulsen, Arne; Pacheco, Jorge M.

doi:10.1111/j.1365-2184.2009.00598.x

Cited by 25 publications

(31 citation statements)

References 55 publications

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“…In most patients, the period of the oscillations is 19-21 days and remains stable, which in our model allows us to fix the compartment number (see Fig. 1) [10]. Here we combine (i) the insight from Ref.…”

Section: Resultsmentioning

confidence: 85%

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Cyclic neutropenia in mammals

Pacheco¹,

Traulsen

Antal

et al. 2008

American J Hematol

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Cyclic neutropenia (CN) has been well documented in humans and the gray collie. A recent model of the architecture and dynamics of hematopoiesis has been used to provide insights into the mechanism of cycling of this disorder. It provides a link between the cycling period and the cells where the mutated ELA2 is expressed. Assuming that the biologic defect in CN is the same in dogs, and the observation that the structure of hematopoiesis is invariant across mammals, we use allometric scaling techniques to correctly predict the period of cycling in the gray collie and extend it to other mammals from mice to elephants. This work provides additional support for the relevance of animal models to understand disease but cautions that disease dynamics in model animals are different and this has to be taken into consideration when planning experiments. Am. J. Hematol. 83:920-921, 2008. V

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Section: Resultsmentioning

confidence: 85%

“…1). Since the period T of oscillations is inversely proportional to the replication rate (T 1/R) [10], it scales with the mass of the adult mammal as T 5 T 0 M 1/4 . The constant T 0 can be obtained by calibrating the formula for humans (M 70 kg and T 21 days):T 0 7.25.…”

Section: Resultsmentioning

confidence: 99%

Cyclic neutropenia in mammals

Pacheco¹,

Traulsen

Antal

et al. 2008

American J Hematol

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“…Stem cells are the pluripotential undifferentiated cells that are found in all renewing tissues, in very small numbers, from which proliferating cell populations arise [266]. They have been particularly studied in the intestinal crypt [221,222] for mucosa proliferation, and in the haematopoietic system [68,69,70,72,139,140,173,192,233]. Stem cells are an object of active research, not only for cancer, but also for other diseases in which tissue reconstruction by young cells is needed.…”

Section: Cancer As An Evolutionary Dynamic Process; Stem Cellsmentioning

confidence: 99%

“…More generally, stem cells have been studied by probabilistic modelling [68,70,72], by cellular automata [233], by ODEs [192], by difference equations [139] and by PDEs [140] or delay differential equations [183,186], with applications mainly in haematology and haematological diseases (in particular chronic myelogenous leukemia and cyclical neutropenia), where observations of the resulting mature cells populations are easy to perform in the clinic. The interested reader is invited to refer to the rich literature mentioned in the references.…”

Section: Models Involving Stem Cell Dynamics; Maturity-structured Modelsmentioning

confidence: 99%

Modelling Physiological and Pharmacological Control on Cell Proliferation to Optimise Cancer Treatments

Clairambault

2009

Math. Model. Nat. Phenom.

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Abstract. This review aims at presenting a synoptic, if not exhaustive, point of view on some of the problems encountered by biologists and physicians who deal with natural cell proliferation and disruptions of its physiological control in cancer disease. It also aims at suggesting how mathematicians are naturally challenged by these questions and how they might help, not only biologists to deal theoretically with biological complexity, but also physicians to optimise therapeutics, on which last point the focus will be set here. To this purpose, mathematical modelling should represent proliferating cell population dynamics with natural built-in control targets (which implies modelling the cell division cycle), together with the distribution of drugs in the organism and their molecular actions on different targets at the cell level on proliferation, i.e., molecular pharmacokinetics-pharmacodynamics of antiproliferative drugs. This should make possible optimal control of drug delivery with constraints to be determined according to the main pharmacological issues encountered in the clinic: unwanted toxic side-effects, occurrence of drug resistance. Mathematical modelling should also take into account physiological determinants of cell and tissue proliferation, such as intervention of the immune system, circadian control on cell cycle checkpoint proteins, and activity of intracellular drug processing enzymes together with individual variations in the activities of these proteins (genetic polymorphism). Taking these points into account will add to the rich scenery of normal or disrupted cell and tissue regulations, and their corrections by drugs, a natural environmental, whole body physiological, frame. It is necessary indeed to consider such a framework if one wants to eventually be actually helpful to clinicians who routinely treat by combinations of drugs living Humans with their complex whole body regulations, often dependent on genotypic variations, and not isolated cells or tissues.Key words: cell proliferation, population dynamics, physiologically structured partial differential equations, pharmacological control, pharmacokinetics-pharmacodynamics, physiological modelling, cancer treatments, therapeutic optimisation, individualised medicine AMS subject classification: 92-02, 92B05 Biological common knowledge on cancerThis section shortly presents the general scenery of cancer evolution and features that must be included in models designed to describe and control it, bearing in mind a more descriptive (physicist's) than a therapeutic (physician's) point of view, only to reverse this perspective in the following sections, in which will be stressed the interest of modelling from the point of view of control, at least if one wants to design models for therapeutic applications. This biological section will be only a short sketch, if one considers that on this subject many books have already been written, e.g. [24,78,96,97,223]. Cancer: a challenging public health problemCardiovascular diseases and cancer are by fa...

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“…Many mutations are indeed neutral [6,7] but the impact of a mutation is cell-context-dependent. In fact, the same mutation may be neutral in one microenvironment but exhibit a fitness difference in another [8][9][10]. Fitness may also fluctuate in time [11], reflecting changes in the environment of the cells.…”

Section: Introductionmentioning

confidence: 99%

On the dynamics of neutral mutations in a mathematical model for a homogeneous stem cell population

Traulsen

Lenaerts

Pacheco

et al. 2013

J. R. Soc. Interface.

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The theory of the clonal origin of cancer states that a tumour arises from one cell that acquires mutation(s) leading to the malignant phenotype. It is the current belief that many of these mutations give a fitness advantage to the mutant population allowing it to expand, eventually leading to disease. However, mutations that lead to such a clonal expansion need not give a fitness advantage and may in fact be neutral—or almost neutral—with respect to fitness. Such mutant clones can be eliminated or expand stochastically, leading to a malignant phenotype (disease). Mutations in haematopoietic stem cells give rise to diseases such as chronic myeloid leukaemia (CML) and paroxysmal nocturnal haemoglobinuria (PNH). Although neutral drift often leads to clonal extinction, disease is still possible, and in this case, it has important implications both for the incidence of disease and for therapy, as it may be more difficult to eliminate neutral mutations with therapy. We illustrate the consequences of such dynamics, using CML and PNH as examples. These considerations have implications for many other tumours as well.

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Progenitor cell self‐renewal and cyclic neutropenia

Cited by 25 publications

References 55 publications

Cyclic neutropenia in mammals

Cyclic neutropenia in mammals

Modelling Physiological and Pharmacological Control on Cell Proliferation to Optimise Cancer Treatments

On the dynamics of neutral mutations in a mathematical model for a homogeneous stem cell population

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