干细胞增殖的计算建模及其在癌症演变动力学的应用

Cancer is a disease of uncontrolled somatic stem cell regeneration. With increasing incidence and mortality, cancer is the leading cause of death in China and is a major public health problem. Currently, cancer therapy faces a major problem in early diagnose and prognosis, and is crying for theoretical study from the perspective of quantitative modeling. It is of great significant to develop data and mechanisms driven predictable computational models in order to investigate the progress of cancer evolution. This integrating project carries out the promoting study of multi-scale modeling for stem cell regeneration dynamics with application to cancer development on the basis of previous studies in data analysis, model development, and high-performance computing, etc. The main studies focus on establishment of quantitative relationships between cellular dynamical process and gene expression differences, hybrid modeling from molecular to population dynamics of heterogeneous stem cell regeneration under complex niche, and application of the model to the evolutionary dynamics of chronic myeloid leukemia. Through the study of this project, we aim at discovering methods of establishing multi-scale computational models of cancer development, founding the base of investigating cancer development through both data and mechanism driven computational modeling and high-performance computing, providing theoretical guidance to cancer early diagnose and methods for the combination of data and model analysis.

癌症是由成体干细胞不可控增殖引起的疾病。随着发病率和死亡率的增加，癌症已成为中国首要的死亡原因和公共卫生问题。目前的癌症治疗主要面临早期诊断率低和病情复杂的困难，迫切需要定量化研究的新理论和新方法。基于数据和机理驱动建立癌症发生发展的可预测计算模型具有重要意义。本集成项目在前期工作的基础上，整合数据分析、模型建立、高性能计算等方面的工作，开展干细胞增殖过程多尺度可计算建模及癌症演变动力学机制的研究。主要研究内容包括：基于数据建立基因表达差异与细胞动力学行为变化的定量关系；基于单细胞行为的异质性构建多细胞群体动力学模型；作为模型应用研究慢性髓性白血病发生发展的动力学过程。通过本项目的研究，探索建立癌症演变过程跨尺度计算模型的方法，建立数据驱动与机理驱动下可计算建模和高性能计算研究癌症演变机制的基础，为疾病的早期诊断提供理论指导并建立数据与模型相结合的方法论。

本项目主要构建可计算模型对癌症演变的动力学过程进行研究。主要研究慢性髓性白血病的发生发展过程和B-细胞急性淋巴瘤在CAR-T免疫治疗的响应过程的可计算模型. 通过建立的随机动力学模型研究了肿瘤微环境的相互作用对髓性白血病发生的动力学过程影响及其在激酶抑制剂治疗下的复发机制，并建立基于单细胞异质性和可塑性的多尺度计算模型探讨B-细胞急性淋巴瘤在免疫治疗下的复发机制。通过对所建立的基于单细胞变化的多尺度可计算模型进行提升，提出了统一的异质性多细胞体系增殖过程的数学模型框架，为今后对一般性的癌症演变动力学研究奠定了基础。