Data-driven models of hematological cell fate decision and differentiation

血液细胞命运决定和分化的数据驱动模型

• 批准号: 9247481
• 负责人: Carlo Piermarocchi
• 金额: $ 34.13万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2021-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9247481
• 关键词: Biological Process; Blood; Cell Differentiation process; Cell Lineage; Cells; Coculture Techniques; Complex; Data; Data Set; Development; Differentiation Antigens; Disease; Flow Cytometry; Gene Expression; Hematology; Human Development; Immunology; In Vitro; Ligands; Mathematics; Memory; Methodology; Modeling; Nonlinear Dynamics; Normal Cell; Pathology; Pharmaceutical Preparations; Play; Population; Role; Sampling; Science; Scientist; Signal Transduction; biological systems; cancer cell; cell type; insight; leukemia; mathematical model; receptor; research study; stem cell biology; transcription factor; transcriptome sequencing

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.

该项目是具有数学和生物医学科学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）开发一种能够再现血液谱系分化的数学信号模型，使用关联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，为不同血液细胞的集合建立数学模型。该模型将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建模的预测，以检测分化标志物，通过流式细胞术评估细胞分化，并对细胞池和单细胞进行RNA-seq。将在共培养条件下对纯细胞群进行研究。不同类型细胞的基因表达数据的快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多稳态的非线性动力学在复杂的细胞信号网络调节细胞分化。这将通过在合并的细胞样品和单细胞上使用RNA-seq数据来实现。该模型将预测可能诱导特定细胞谱系的转录因子或受体配体的组合。通过与我们计划的实验进行比较，该模型将阐明特异性受体配体在单个细胞或细胞群体的细胞命运决定中的作用。所提出的方法将提高我们对细胞分化起关键作用的生物过程和疾病的总体理解。特别是，该项目可以在干细胞生物学、免疫学、血液学和人类发育方面提供新的生物医学见解。