EAGER: Systems Analysis of Signaling Pathway towards Robust Differentiation

1455800 Banerjee，Ipsita多能干细胞具有无限数量产生任何组织特定细胞类型的独特能力。这些突出的特性使它们成为研究、药物测试和细胞疗法的可再生人体组织来源的有前途的候选者。通过调节相关的信号通路获得器官特异性细胞类型的可行性已经被证明。然而，目前分化的细胞类型在产量和成熟功能方面仍然有限。实现干细胞潜力的下一个挑战是确保这些细胞的功能和同质成熟到所需的谱系。这一目标可以通过对信号转导和通过信号通路的噪声传播的定量了解来实现。这项急切的提议的主题是开发一种驱动干细胞分化的关键信号转导途径的定量和预测性表示。一旦知道了信号是如何传递的，以及在分化过程中不确定性是如何传播的，就有可能设计有针对性的干预措施，以实现同质和有效的分化。该项目将开发的预测平台将为设计有效和均匀的干细胞分化的靶向干预措施提供信息。在这项计划中，将结合定量实验、预测模型和系统分析，分析转化生长因子β(TGFβ)信号通路，以分化多潜能干细胞。转化生长因子β途径在诱导人多能干细胞向内胚层定向分化中起核心作用。实验平台的关键组成部分将是干细胞动力学单细胞水平定量分析技术的开发，以确保准确表示细胞-S的动态响应，不受种群中异质性的影响。具体地说，转化生长因子β效应器分子的动力学将使用绿色荧光蛋白融合效应器蛋白在单个细胞室中进行量化。这些蛋白质的核质穿梭将用光漂白后荧光恢复(FRAP)分析来定量。实验获得的单细胞动力学将用于训练该途径的数学模型。集成模型将被用来整合单个细胞的信息，以表示细胞群体的异质性。这种积分的计算效率将通过元建模技术来提高。系综模型的全局敏感性分析将使我们能够确定控制信号转导的关键机制。模型预测将在模型开发的每一步得到实验验证。经过验证的模型将通过(I)输入可变性的实验测量和(Ii)输入可变性到输出分子的传播的电子模拟来进一步分析不确定性在系统中的传播。拟议项目的最终结果将是一个经过实验验证的预测平台，它将允许设计有针对性的扰动，以提高分化效率，同时减少分化种群的异质性。

1455800 Banerjee, Ipsita Pluripotent stem cells have the unique capability of giving rise to any tissue-specific cell type at an unlimited quantity. These outstanding properties make them a promising candidate as a renewable source of human tissue for research, pharmaceutical testing and cell-based therapies. The feasibility of obtaining organ-specific cell types by modulation of associated signaling pathways has already been demonstrated. However, the differentiated cell types at present remain limited in their yield and mature functionality. The next challenge in realizing the potential of stem cells is to ensure functional and homogenous maturation of these cells to the desired lineage. This goal can be achieved through a quantitative understanding of signal transduction and noise propagation through the signaling pathway. The topic of this EAGER proposal is to develop a quantitative and predictive representation of a critical signal transduction pathway driving stem cell differentiation. Once it is known how signals are transduced and how uncertainty is propagated during differentiation, it will be possible to design targeted interventions to achieve homogenous and efficient differentiation. The predictive platform to be developed in this project will inform the design of targeted interventions for efficient and homogenous differentiation of stem cells.In this proposal, the Transforming Growth Factor beta (TGF beta) signaling pathway will be analyzed for differentiating pluripotent stem cells by integrating quantitative experiments with predictive modeling and systems analysis. The TGF beta pathway plays a central role in inducing definitive endoderm differentiation in human pluripotent stem cell. Key components of the experimental platform will be the development of techniques for quantitative single cell level analysis of stem cell dynamics, in order to ensure accurate representation of a cell¡¦s dynamic response, unbiased by heterogeneity in the population. Specifically, the dynamics of TGF beta effectors molecules will be quantified in individual cell compartment using Green Fluorescent Protein fused effector proteins. The nucleo-cytoplasmic shuttling of these proteins will be quantified using Fluorescence Recovery after Photobleaching (FRAP) assay. Experimentally obtained single cell dynamics will be used to train the mathematical model of the pathway. Ensemble modeling will be used to integrate the single cell information to represent heterogeneity in cell population. Computational efficiency of this integration will be enhanced by a meta-modeling technique. Global sensitivity analysis of the ensemble model will allow identification of key mechanisms governing signal transduction. The model predictions will be experimentally validated at every step of the model development. The validated model will be further analyzed for propagation of uncertainty through the system by (i) experimental measurement of input variability and (ii) in-silico simulation of propagation of input variability to output molecules. The final outcome of the proposed project will be an experimentally validated predictive platform which will allow design of targeted perturbations to enhance differentiation efficiency while reducing heterogeneity in the differentiated population.