Model-Based Assessment of the Role of Uneven Partitioning of Molecular Content on Heterogeneity and Regulation of Differentiation in CD8 T-Cell Immune Responses

Activation of naive CD8 T-cells can lead to the generation of multiple effector and memory subsets. Multiple parameters associated with activation conditions are involved in generating this diversity that is associated with heterogeneous molecular contents of activated cells. Although naive cell polarisation upon antigenic stimulation and the resulting asymmetric division are known to be a major source of heterogeneity and cell fate regulation, the consequences of stochastic uneven partitioning of molecular content upon subsequent divisions remain unclear yet. Here we aim at studying the impact of uneven partitioning on molecular-content heterogeneity and then on the immune response dynamics at the cellular level. To do so, we introduce a multiscale mathematical model of the CD8 T-cell immune response in the lymph node. In the model, cells are described as agents evolving and interacting in a 2D environment while a set of differential equations, embedded in each cell, models the regulation of intra and extracellular proteins involved in cell differentiation. Based on the analysis of in silico data at the single cell level, we show that immune response dynamics can be explained by the molecular-content heterogeneity generated by uneven partitioning at cell division. In particular, uneven partitioning acts as a regulator of cell differentiation and induces the emergence of two coexisting sub-populations of cells exhibiting antagonistic fates. We show that the degree of unevenness of molecular partitioning, along all cell divisions, affects the outcome of the immune response and can promote the generation of memory cells

BM63B versus Immgen data

Comparison between Immgen project mRNA data and experimental data from CIR

Modélisation de la Réponse Immunitaire T-CD8 : Analyse Mathématique et Modèles Multiéchelles

Infection of an organism by a pathogen triggers the activation of the CD8 T-cells and the initiation of the immune response. The result is a complex program of proliferation and differentiation of the CD8 T-cells, controlled by the evolution of their molecular content. In this manuscript, we present two mathematical models of the CD8 T-cell response. The first one is presented as an impulsive differential equation by which we study the effect of unequal molecular partitioning at cell division on the regulation of molecular heterogeneity. The second one is an agent-based-model that couples the description of a discrete population of CD8 T-cells and that of their molecular content. This model can reproduce the different typical phases of the CD8 T-cell response at both the cellular and the molecular scales. These two studies support the hypothesis that the cell dynamics observed in vivo is a consequence of the molecular heterogeneity structuring the CD8 T-cell population.L’infection d’un organisme par un agent pathogène déclenche l’activation des lymphocytes T-CD8 et l’initiation de la réponse immunitaire. Il s’ensuit un programme complexe de prolifération et de différenciation des lymphocytes T-CD8, contrôlé par l’évolution de leur contenu moléculaire. Dans ce manuscrit, nous présentons deux modèles mathématiques de la réponse T-CD8. Le premier se présente comme une équation différentielle à impulsions grâce à laquelle nous étudions l’effet du partage inégal des protéines lors des divisions cellulaires sur la régulation de l’hétérogénéité moléculaire. Le second est un modèle à base d’agents couplant la description d’une population discrète de lymphocytes T-CD8 à celle du contenu moléculaire de ces derniers. Ce modèle s’avère capable de reproduire les différentes phases caractéristiques de la réponse T-CD8 aux échelle cellulaire et moléculaire. Ces deux travaux supportent l’hypothèse que la dynamique cellulaire observée in vivo est le reflet de l’hétérogénéité moléculaire qui structure la population de lymphocytes T-CD8

Modeling the CD8 T-cell Immune Response : Mathematical Analysis and Multiscale Models

L'infection d'un organisme par un agent pathogène déclenche l'activation des lymphocytes T-CD8 et l'initiation de la réponse immunitaire. Il s'ensuit un programme complexe de prolifération et de différenciation des lymphocytes T-CD8, contrôlé par l'évolution de leur contenu moléculaire. Dans ce manuscrit, nous présentons deux modèles mathématiques de la réponse T-CD8. Le premier se présente comme une équation différentielle à impulsions grâce à laquelle nous étudions l'effet du partage inégal des protéines lors des divisions cellulaires sur la régulation de l'hétérogénéité moléculaire. Le second est un modèle à base d'agents couplant la description d'une population discrète de lymphocytes T-CD8 à celle du contenu moléculaire de ces derniers. Ce modèle s'avère capable de reproduire les différentes phases caractéristiques de la réponse T-CD8 aux échelle cellulaire et moléculaire. Ces deux travaux supportent l'hypothèse que la dynamique cellulaire observée in vivo est le reflet de l'hétérogénéité moléculaire qui structure la population de lymphocytes T-CD8Infection of an organism by a pathogen triggers the activation of the CD8 T-cells and the initiation of the immune response. The result is a complex program of proliferation and differentiation of the CD8 T-cells, controlled by the evolution of their molecular content. In this manuscript, we present two mathematical models of the CD8 T-cell response. The first one is presented as an impulsive differential equation by which we study the effect of unequal molecular partitioning at cell division on the regulation of molecular heterogeneity. The second one is an agent-based-model that couples the description of a discrete population of CD8 T-cells and that of their molecular content. This model can reproduce the different typical phases of the CD8 T-cell response at both the cellular and the molecular scales. These two studies support the hypothesis that the cell dynamics observed in vivo is a consequence of the molecular heterogeneity structuring the CD8 T-cell populatio

Existence and stability of periodic solutions of an impulsive differential equation and application to CD8 T-cell differentiation

International audienceUnequal partitioning of the molecular content at cell division has been shown to be a source of heterogeneity in a cell population. We propose to model this phenomenon with the help of a scalar, nonlinear impulsive differential equation (IDE). In a first part, we consider a general autonomous IDE with fixed times of impulse and a specific form of impulse function. We establish properties of the solutions of that equation, most of them obtained under the hypothesis that impulses occur periodically. In particular, we show how to investigate the existence of periodic solutions and their stability by studying the flow of an autonomous differential equation. A second part is dedicated to the analysis of the convexity of this flow. Finally, we apply those results to an IDE describing the concentration of the protein Tbet in a CD8 T-cell, where impulses are associated to cell division, to study the effect of molecular partitioning at cell division on the effector/memory cell-fate decision in a CD8 T-cell lineage. We show that the degree of asymmetry in the molecular partitioning can affect the process of cell differentiation and the phenotypical heterogeneity of a cell population

Model-based assessment of the Role of Uneven Partitioning of Molecular Content on Heterogeneity and Regulation of Differentiation in CD8 T-cell Immune Responses

International audienceActivation of naive CD8 T-cells can lead to the generation of multiple effector and memory subsets. Multiple parameters associated with activation conditions are involved in generating this diversity that is associated with heterogeneous molecular contents of activated cells. Although naive cell polarisation upon antigenic stimulation and the resulting asymmetric division are known to be a major source of heterogeneity and cell fate regulation, the consequences of stochastic uneven partitioning of molecular content upon subsequent divisions remain unclear yet. Here we aim at studying the impact of uneven partitioning on molecular-content heterogeneity and then on the immune response dynamics at the cellular level. To do so, we introduce a multiscale mathematical model of the CD8 T-cell immune response in the lymph node. In the model, cells are described as agents evolving and interacting in a 2D environment while a set of differential equations, embedded in each cell, models the regulation of intra and extracellular proteins involved in cell differentiation. Based on the analysis of in silico data at the single cell level, we 1 show that immune response dynamics can be explained by the molecular-content heterogeneity generated by uneven partitioning at cell division. In particular, uneven partitioning acts as a regulator of cell differentiation and induces the emergence of two coexisting sub-populations of cells exhibiting antagonistic fates. We show that the degree of unevenness of molecular partitioning, along all cell divisions, affects the outcome of the immune response and can promote the generation of memory cells

Combined Biological and Modeling Approach of Hematopoiesis: From Native to Stressed Erythropoiesis

International audienc

Multistage hematopoietic stem cell regulation in the mouse: A combined biological and mathematical approach

International audienceWe have reconciled steady-state and stress hematopoiesis in a single mathematicalmodel based on murine in vivo experiments and with a focus on hematopoieticstem and progenitor cells. A phenylhydrazine stress was first applied tomice. A reduced cell number in each progenitor compartment was evidenced duringthe next 7 days through a drastic level of differentiation without proliferation,followed by a huge proliferative response in all compartments including longtermhematopoietic stem cells, before a return to normal levels. Data analysisled to the addition to the 6-compartment model, of time-dependent regulationthat depended indirectly on the compartment sizes. The resulting model wasfinely calibrated using a stochastic optimization algorithm and could reproducebiological data in silico when applied to different stress conditions (bleeding,chemotherapy, HSC depletion). In conclusion, our multi-step and time-dependentmodel of immature hematopoiesis provides new avenues to a better understandingof both normal and pathological hematopoiesis

Binocular rivalry in children with schizophrenia: the conscious and unconscious cognitive processing of interpersonal information

背景: 儿童期精神分裂是一种严重的精神障碍，有理论认为其认知功能无论是意识水平还是潜意识水平都存在异常。但目前尚无针对儿童期精神分裂的潜意识认知功能研究。 目的: 开发新的双眼竞争测验版本，用于评估个体在意识和潜意识状态下对人际交往信息的认知加工过程，并依此判断儿童期精神分裂症患者的社会认知功能是否受损。 方法: 选取3种不同类型的图片（图片中无人物、有2~3个人物、有4个及以上的人物）共30张，这些图片在双眼竞争测验不可见模式（存在双眼竞争性抑制，反映潜意识状态下认知加工）和可见模式（无双眼竞争性抑制，反映意识状态下）中展示。对15名年龄&le;16岁、以妄想为主要症状的精神分裂症患者及15名健康儿童进行双眼竞争测验，通过对目标图片出现后立即在其左侧或右侧出现光栅的方向的判别来比较两组儿童的正确率，并比较两组间的反应时间。 结果: 患者组对所有类型图片中光栅方向判断的正确率均低于对照组，但是12对比较中仅2对的差异有统计学意义。与对照组相比，无论是在可见模式还是非可见模式下，患者对人物图片的注意要比对无人图片的注意有所增加，但无显著性意义。我们并未发现精神病性症状的严重程度与对图像认知加工的受损程度之间存在任何关联。当要求被试对三组图片进行评分时，患者组对存在2~3个人物的图片报告的高兴程度明显高于对照组的评分，差异有统计学意义。 结论: 儿童期精神分裂症患者对描绘人际关系的图片注意一定程度的增加，提示该病与社会信息的认知处理过程受损相关，但目前结果尚不能证实这一关系。我们将双眼竞争范式应用于认知功能差异的研究，只是取得部分的成功，其主要原因是该测验中对不同类型图片的反应注意量的关键指标存在较大的变异。</p