Blood Cell Dynamics: Half of a Century of Modelling

Praly, Laurent

doi:10.1051/mmnp/201611106

Cited by 49 publications

(47 citation statements)

References 261 publications

(251 reference statements)

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“…Branches represent distinct cell lineages (neutrophils, monocytes, platelets, erythrocytes), and form our model subunits. Note that we did not consider the lymphoid branch because the proliferation, activation, and regulation of lymphoid cells occur mostly in lymphoid organs . Within lineages, compartments correspond to specific haematopoietic cell stages (progenitors, transit cells, mature cells), and model variables describe the cell count in each of these compartments .…”

Section: Methodsmentioning

confidence: 99%

“…Note that we did not consider the lymphoid branch because the proliferation, activation, and regulation of lymphoid cells occur mostly in lymphoid organs. 16 Within lineages, compartments correspond to specific haematopoietic cell stages (progenitors, transit cells, mature cells), and model variables describe the cell count in each of these compartments. 17 Our model describes a dynamic system of cells continuously proliferating and differentiating, able to maintain and recover homoeostasis after perturbations by balanced cell flows across the connected compartments (predecessor-successor populations).…”

Section: Theoretical Description Of Carboplatin-induced Myelosuppressionmentioning

confidence: 99%

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Quantifying Drug‐Induced Bone Marrow Toxicity Using a Novel Haematopoiesis Systems Pharmacology Model

Fornari

O’Connor

et al. 2019

CPT Pharmacom & Syst Pharma

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Haematological toxicity associated with cancer therapeutics is monitored by changes in blood cell count, and their primary effect is on proliferative progenitors in the bone marrow. Using observations in rat bone marrow and blood, we characterize a mathematical model that comprises cell proliferation and differentiation of the full haematopoietic phylogeny, with interacting feedback loops between lineages in homeostasis as well as following carboplatin exposure. We accurately predicted the temporal dynamics of several mature cell types related to carboplatin‐induced bone marrow toxicity and identified novel insights into haematopoiesis. Our model confirms a significant degree of plasticity within bone marrow cells, with the number and type of both early progenitors and circulating cells affecting cell balance, via feedback mechanisms, through fate decisions of the multipotent progenitors. We also demonstrated cross‐species translation of our predictions to patients, applying the same core model structure and considering differences in drug‐dependent and physiology‐dependent parameters.

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Section: Methodsmentioning

confidence: 99%

Section: Theoretical Description Of Carboplatin-induced Myelosuppressionmentioning

confidence: 99%

Quantifying Drug‐Induced Bone Marrow Toxicity Using a Novel Haematopoiesis Systems Pharmacology Model

Fornari

O’Connor

et al. 2019

CPT Pharmacom & Syst Pharma

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“…Since the 1970s, mathematical models have been used to study the dynamics of RBC metabolism [42]. Other modeling formalisms, like constraint-based modeling methods [51], have been used to study mechanisms underlying cellular metabolism [8,38,53].…”

Section: Introductionmentioning

confidence: 99%

Systems-level physiology of the human red blood cell is computed from metabolic and macromolecular mechanisms

Yurkovich

Yang

Kim³

2019

Preprint

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The human red blood cell has served as a starting point for the application and development of systems biology approaches due to its simplicity, intrinsic experimental accessibility, and importance in human health applications. Here, we present a multi-scale computational model of the human red blood cell that accounts for the full metabolic network, key proteins (>95% of proteome mass fraction), and several macromolecular mechanisms. Proteomics data are used to place quantitative constraints on individual protein complexes that catalyze metabolic reactions, as well as a total proteome capacity constraint. We explicitly describe molecular mechanisms-such as hemoglobin binding and the formation and detoxification of reactive oxygen species-and takes standard hematological variables (e.g., hematocrit, hemoglobin concentration) as input, allowing for personalized physiological predictions. This model is built from first principles and allows for direct computation of physiologically meaningful quantities such as the oxygen dissociation curve and an accurate computation of the flux state of the metabolic network. More broadly, this work represents an important step toward including the proteome and its function in whole-cell models of human cells. KEYWORDS Systems biology MetabolismHuman red blood cell Hemoglobin binding ROS detoxSubmission to bioRxiv Research Article

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“…It occurs within the broader frame of haematopoiesis, the process which generates all blood cells. The dynamics of haematopoiesis has been extensively modelled mathematically in the past decades, with the first historical models published as early as fifty years ago (1,2) (for a review of the history of haematopoiesis modelling in general, see (3) ). Models of haematopoiesis have improved the understanding of both the processes they describe (4) , and the mathematical tools they use.…”

Section: Introductionmentioning

confidence: 99%

Calibration, Selection and Identifiability Analysis of a Mathematical Model of the in vitro Erythropoiesis in Normal and Perturbed Contexts

Duchesne

Guillemin

Gandrillon

et al. 2018

Preprint

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The in vivo erythropoiesis, which is the generation of mature red blood cells in the bone marrow of whole organisms, has been described by a variety of mathematical models in the past decades. However, the in vitro erythropoiesis, which produces red blood cells in cultures, has received much less attention from the modelling community. In this paper, we propose the first mathematical model of in vitro erythropoiesis. We start by formulating different models and select the best one at fitting experimental data of in vitro erythropoietic differentiation. It is based on a set of linear ODE, describing 3 hypothetical populations of cells at different stages of differentiation. We then compute confidence intervals for all of its parameters estimates, and conclude that our model is fully identifiable. Finally, we use this model to compute the effect of a chemical drug called Rapamycin, which affects all states of differentiation in the culture, and relate these effects to specific parameter variations. We provide the first model for the kinetics of in vitro cellular differentiation which is proven to be identifiable. It will serve as a basis for a model which will better account for the variability which is inherent to experimental protocol used for the model calibration.vitro context, i.e. the process that takes place in cells grown in culture, is much simpler to characterize experimentally. Yet, to our knowledge, no modeling study has focused on it so far. Since the in vitro differentiation is an experimental tool of choice for the study of cellular decision-making (17,18,19) , we propose to develop a model for the dynamics of the in vitro erythropoiesis.Moreover, the current models of erythropoiesis suffer from one major drawback: the weakness of their parameterization, which can fall within three categories.A vast majority of the existing models of erythropoiesis are based on experimental parameter values from the litterature. In some cases these values are used in other contexts that those in which they were obtained (typically, in other species (12) ).In other cases, the parameter values of a model are chosen arbitrarily to reproduce a qualitative behaviour. Apart from this qualitative fit, such approaches do not provide any information regarding the validity of the values (16) .Finally, when the parameters of a model are estimated to reproduce a dataset, the precision of this estimation is seldom investigated (20) . By this, we mean that depending on the algorithmic details of the estimation, it is possible that several values of the parameters might render the same fit to the data. In this case the model is said to be unidentifiable.A model is said to be identifiable if and only if it is possible to infer a unique value for each of its parameter by comparing its output to experimental data. Otherwise it is unidentifiable. A model can be non-identifiable for several reasons (21,22) .Structural identifiability is related to the structure of the model, and the observed variables. A model is structurally unidentifi...

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Blood Cell Dynamics: Half of a Century of Modelling

Cited by 49 publications

References 261 publications

Quantifying Drug‐Induced Bone Marrow Toxicity Using a Novel Haematopoiesis Systems Pharmacology Model

Quantifying Drug‐Induced Bone Marrow Toxicity Using a Novel Haematopoiesis Systems Pharmacology Model

Systems-level physiology of the human red blood cell is computed from metabolic and macromolecular mechanisms

Calibration, Selection and Identifiability Analysis of a Mathematical Model of the in vitro Erythropoiesis in Normal and Perturbed Contexts

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