Data-driven models of hematological cell fate decision and differentiation

该项目是在数学和生物医学科学方面具有互补专业知识的科学家之间的合作努力。该项目有三个目标：(i)开发一个能够再现血统分化的数学信号模型，使用联想记忆来表示单细胞状态。该模型将能够预测受体配体和药物的特定组合对分化的影响。（ii）在共培养条件下，为不同血液学细胞的集合建立数学模型。该模型将把细胞的动力学描述为相互作用的吸引子。（iii）使用体外实验验证数学模型的预测，以检测分化标记，通过流式细胞术评估细胞分化，并通过对细胞池和单个细胞进行RNA-seq。细胞将作为纯群体和共培养条件进行研究。不同类型细胞的基因表达数据的迅速增加为将这些数据集整合到数学模型中以进行实验验证的预测创造了新的机会。所提出的模型将捕捉调节细胞分化的复杂细胞信号网络中的多稳态非线性动力学。这将通过在池细胞样本和单个细胞上使用RNA-seq数据来实现。该模型将预测转录因子或受体配体的组合，从而诱导特定的细胞谱系。通过与我们计划的实验进行比较，该模型将阐明特定受体配体在单细胞或细胞群体的细胞命运决定中的作用。提出的方法将增强我们对细胞分化起关键作用的生物过程和疾病的一般理解。特别是，该项目可以为干细胞生物学、免疫学、血液学和人类发育提供新的生物医学见解。

This project is a collaborative effort between scientists with complementary expertise in mathematics and biomedical sciences. The project has three aims: (i) Develop a mathematical signaling model able to reproduce blood lineage differentiation, using associative memories to represent single cell states. The model will be able to make predictions on the effect on differentiation of specific combinations of receptor ligands and drugs. (ii) Develop a mathematical model for an ensemble of different hematological cells, under co-culture conditions. The model will describe the dynamics of cells as interacting attractors. (iii) Verify the predictions of the mathematical modeling using in vitro experiments to detect markers of differentiation, to assess cellular differentiation by flow cytometry, and by performing RNA-seq on pools of cells and on single cells. Cells will be studied as pure populations and in co-culture conditions. The rapidly increasing availability of gene expression data of different types of cells has created new opportunities for integrating these datasets into mathematical models to make experimentally verifiable predictions. The proposed model will capture the multistable nonlinear dynamics in complex cell signaling networks regulating cell differentiation. This will be realized by using RNA-seq data on pooled cell samples and single cells. The model will make predictions on combinations of transcription factors or receptor ligands that could induce a specific cell lineage. By comparison with our planned experiments, the model will clarify the role of specific receptor ligands in cell fate decision of single cells or a population of cells. The proposed methodology will enhance our general understanding of biological processes and diseases where cell differentiation plays a key role. In particular, this project could provide new biomedical insight in stem cell biology, immunology, hematology, and human development.