Numerical modelling of label-structured cell population growth using CFSE distribution data

<p>Abstract</p> <p>Background</p> <p>The flow cytometry analysis of CFSE-labelled cells is currently one of the most informative experimental techniques for studying cell proliferation in immunology. The quantitative interpretation and understanding of such heterogenous cell population data requires the development of distributed parameter mathematical models and computational techniques for data assimilation.</p> <p>Methods and Results</p> <p>The mathematical modelling of label-structured cell population dynamics leads to a hyperbolic partial differential equation in one space variable. The model contains fundamental parameters of cell turnover and label dilution that need to be estimated from the flow cytometry data on the kinetics of the CFSE label distribution. To this end a maximum likelihood approach is used. The Lax-Wendroff method is used to solve the corresponding initial-boundary value problem for the model equation. By fitting two original experimental data sets with the model we show its biological consistency and potential for quantitative characterization of the cell division and death rates, treated as continuous functions of the CFSE expression level.</p> <p>Conclusion</p> <p>Once the initial distribution of the proliferating cell population with respect to the CFSE intensity is given, the distributed parameter modelling allows one to work directly with the histograms of the CFSE fluorescence without the need to specify the marker ranges. The label-structured model and the elaborated computational approach establish a quantitative basis for more informative interpretation of the flow cytometry CFSE systems.</p

Asymmetry of Cell Division in CFSE-Based Lymphocyte Proliferation Analysis

Flow cytometry-based analysis of lymphocyte division using carboxyfluorescein succinimidyl ester (CFSE) dye dilution permits acquisition of data describing cellular proliferation and differentiation. For example, CFSE histogram data enable quantitative insight into cellular turnover rates by applying mathematical models and parameter estimation techniques. Several mathematical models have been developed using different types of deterministic or stochastic approaches. However, analysis of CFSE proliferation assays is based on the premise that the label is halved in the two daughter cells. Importantly, asymmetry of protein distribution in lymphocyte division is a basic biological feature of cell division with the degree of the asymmetry depending on various factors. Here, we review the recent literature on asymmetric lymphocyte division and CFSE-based lymphocyte proliferation analysis. We suggest that division- and label-structured mathematical models describing CFSE-based cell proliferation should take into account asymmetry and time-lag in cell proliferation. Utilization of improved modeling algorithms will permit straightforward quantification of essential parameters describing the performance of activated lymphocytes

A stochastic model for CD4+ T cell proliferation and dissemination network in primary immune response

The study of the initial phase of the adaptive immune response after first antigen encounter provides essential information on the magnitude and quality of the immune response. This phase is characterized by proliferation and dissemination of T cells in the lymphoid organs. Modeling and identifying the key features of this phenomenon may provide a useful tool for the analysis and prediction of the effects of immunization. This knowledge can be effectively exploited in vaccinology, where it is of interest to evaluate and compare the responses to different vaccine formulations. The objective of this paper is to construct a stochastic model based on branching process theory, for the dissemination network of antigen-specific CD4+ T cells. The devised model is validated on in vivo animal experimental data. The model presented has been applied to the vaccine immunization context making references to simple proliferation laws that take into account division, death and quiescence, but it can also be applied to any context where it is of interest to study the dynamic evolution of a population. Copyright:© 2015 Boianelli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Label Structured Cell Proliferation Models.

International audienceWe present a general class of cell population models that can be used to track the proliferation of cells which have been labeled with a fluorescent dye. The mathematical models employ fluorescence intensity as a structure variable to describe the evolution in time of the population density of proliferating cells. While cell division is a major component of changes in cellular fluorescence intensity, models developed here also address overall label degradation

Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Modeling cell proliferation in human acute myeloid leukemia xenografts

Motivation: Acute myeloid leukemia (AML) is one of the most common hematological malignancies, characterized by high relapse and mortality rates. The inherent intra-tumor heterogeneity in AML is thought to play an important role in disease recurrence and resistance to chemotherapy. Although experimental protocols for cell proliferation studies are well established and widespread, they are not easily applicable to in vivo contexts, and the analysis of related time-series data is often complex to achieve. To overcome these limitations, model-driven approaches can be exploited to investigate different aspects of cell population dynamics. Results: In this work, we present ProCell, a novel modeling and simulation framework to investigate cell proliferation dynamics that, differently from other approaches, takes into account the inherent stochasticity of cell division events. We apply ProCell to compare different models of cell proliferation in AML, notably leveraging experimental data derived from human xenografts in mice. ProCell is coupled with Fuzzy Self-Tuning Particle Swarm Optimization, a swarm-intelligence settings-free algorithm used to automatically infer the models parameterizations. Our results provide new insights on the intricate organization of AML cells with highly heterogeneous proliferative potential, highlighting the important role played by quiescent cells and proliferating cells characterized by different rates of division in the progression and evolution of the disease, thus hinting at the necessity to further characterize tumor cell subpopulations. Availability and implementation: The source code of ProCell and the experimental data used in this work are available under the GPL 2.0 license on GITHUB at the following URL: https://github.com/aresio/ProCell

Proliferation des Cellules T dans des Conditions Lymphopenique: Modelisation, Estimation des Parametres et Analyse Mathematique

T lymphocytes are a fundamental component of the immune system that can recognise and respond to foreign antigens by virtue of their clonally expressed T cell antigen receptor (TCR). T cells that have yet to encounter the antigen they recognise are termed 'naive' as they have not been activated to respond. Homeostatic mechanisms maintain the number of T cells at an approximately constant level by controling cell division and death. In normal replete hosts, cell turnover within the naive compartment is very low and naive cells are maintained in a resting state.However, disruption of the homeostatic balance can arise from a wide variety of causes (viral infection (e.g. HIV), or drugs used in peritransplant induction therapy or cancer chemotherapy) and can result in T cell deciency or T lymphopenia. Under conditions of T lymphopenia, naive T cells undergo cell division with a subtle change in the cell surface phenotype (CD44 expression), termed homeostatic proliferation or lymphopenia induced proliferation (LIP). In this thesis, our purpose is to understand the process of T cell homeostatic through mathematical approach. Atfirst, we build a new model that describes the proliferation of T cells in vitro under lymphopenic conditions. Our nonlinear model is composed of ordinary dierential equations and partial dierential equations structured by age (maturity of cell) and CD44 expression. To better understand the homeostasis of T cells, we identify the parameters that dene T cell division by using experimental data. Next, we consider an age-structured model system describing the T cell homeostatic in vivo, and we investigate its asymptotic behaviour. Finally, an optimal strategy is applied in thein vivo model to rebuild immunity under conditions of T lympopenia.Les lymphocytes T sont une composante essentielle du systeme immunitaire de l'organisme. Ils peuvent reconna^tre et repondre a un antigene etranger en vertu de leur recepteur d'antigene.Dans le cas normal, le renouvellement des cellules T naives est tres faible et ces derniers restent approximativement dans un etat de repos. Cependant, une perturbation de l'equilibre homeostatique peut resulter d'une grande variete de causes (infection virale, ou les traitements de chimiotherapie), et peut entrainer une lymphopenie (i.e. Une carence en lymphocytes T). Dans ces conditions lymphopeniques, les cellules T naives subissent la division cellulaire avec un changement de l'expression de CD44 sur leur surface cellulaire. Ce processus est appele "proliferation homeostatique" ou en anglais "lymphopenia induced proliferation" (LIP). Ainsi, le CD44 est un marqueur naturel qui caracterise la transition des cellules du phenotype naf (CD44-) au phenotype memoire (CD44+) durant LIP.L'objectif de cette these est de comprendre la relation complexe entre LIP et le passage du phenotype naif (CD44-) au phenotype memoire (CD44+) en utilisant des modeles mathematiques et des donnees experimentales. On s'interesse en plus au comportement asymptotique des cellules T durant le processus d'homeostasie in vivo

Équations différentielles à retard et leur application en hématopoïèse, avec étude du cas de la neutropénie cyclique

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal