The systems biology of stem cells

利用新的生物信息学方法，该项目旨在确定不同干细胞群体的新分类器，开发解决群体异质性的统计模型，并提供干细胞分化潜力的分子预测因子。理解、预测和指导分化过程是干细胞生物学、发育生物学、组织工程和再生医学学科的主要目标。分子图谱项目已经成功地揭示了基因组输出和调控的规则，通过挖掘在多种细胞类型和数据集上显而易见的模式。通过将这一理念应用于相关的、精心策划的干细胞实验，该项目旨在创建整合和询问较小个体数据集的新方法。这些方法应具有广泛的实用性，并使组织工程的新途径。

Using new bioinformatic methods, this project aims to identify new classifiers of different stem cell populations, develop statistical models that address population heterogeneity and provide molecular predictors of the differentiation potential of stem cells. Understanding, predicting and directing the processes of differentiation are major goals in the disciplines of stem cell biology, developmental biology, tissue engineering and regenerative medicine. Molecular atlas projects have successfully revealed rules of genome output and regulation, by mining patterns that are evident across multiple cell types and datasets. By applying this philosophy to relevant, well-curated stem cell experiments, this project aims to create new methods for the integration and interrogation of smaller individual datasets. These methods should have broad utility and enable new avenues in tissue engineering.