复杂疾病高通量数据的多尺度建模与网络分析

This project tries to combine multiscale mathematical modeling and molecular network analysis to study in depth the dynamic molecular mechanism of apoptosis and autophagy in the process of complex diseases caused by virus infection. (1) Construct multiscale mathematical models based on virus infection triggered cell apoptosis and autophagy signaling network, and try to optimize parameters of the model by using evolutionary algorithms; (2) The methods such as numerical simulation, dynamic analysis as well as sensitivity analysis, etc are applied to qualitative and quantitative study of the system. Try to reveal the main molecular regulatory mechanisms underlying the choice of cell apoptosis and autophagy, and analyze the effect of noise and cross-talk between the two signaling pathways on the regulation of cell apoptosis and autophagy. (3) Based on the high-throughput data, we focus on construct dynamic network for virus infection and design new algorithm for mining key features underlying the dynamic network, we try to reveal the dynamic molecular regulatory mechanisms underlying the process of complex diseases caused by virus infection from the network level. The implementation of this project will help to elucidate the dynamic molecular regulatory mechanisms underlying virus triggered type 1 interferon and endoplasmic reticulum stress signal transduction network at both overall and systemic levels, and the research results provide theoretical basis and scientific guidance for developing of highly effective antiviral drugs and treating complex diseases related to virus infection.

本项目拟结合多尺度数学建模和分子网络分析方法, 对病毒感染引起复杂疾病过程中调控细胞凋亡和自噬的动态分子机制进行深入研究：(1)构建病毒感染引发细胞凋亡和自噬信号网络的多尺度数学模型，利用演化算法来优化模型中的参数；(2)采用数值模拟、动力学分析和敏感性分析等方法对模型进行定性、定量的分析，研究导致细胞自噬和凋亡命运抉择的主要分子机制，并分析噪声及通路之间的交谈机制对调控细胞凋亡和自噬的影响；(3)基于高通量数据，构建病毒感染后的动态分子网络，设计算法挖掘动态网络中关键特征，从分子网络层面分析病毒感染引起复杂疾病发生、发展过程中动态分子调控机制。 本项目的实施有助于整体和系统层面上揭示病毒感染引起复杂疾病发生、发展过程中1型干扰素和内质网应激信号传导网络的动态分子调控机制。其研究成果为高效抗病毒药物研发和病毒感染引起复杂疾病的治疗提供非常重要的理论依据和科学指导。

在本基金的资助下，课题组在生物高通量数据的建模和数据分析方面进行了深入的研究，并取得了较好的研究进展。1.提出了一系列结合不同类型生物学数据，通过有效挖掘不同数据内在特性来设计预测分子网络中关键信息如蛋白质和蛋白质复合物的算法等；2.为了阐明胚胎干细胞在分化过程中分子调控机制，建立了基于干细胞分化过程中核心调控模块的确定性和随机性的数学模型，对干细胞分化过程中可能产生的分子调控机制做了详实的动力学分析，为定量理解分化过程中分子调控机制提供了一定的理论依据；3.为了阐明复杂疾病如肿瘤异质性的分子机制，在细胞类型识别方面结合数据降维、低秩理论和矩阵分解方法提出了两种基于单细胞测序数据的聚类算法，可为肿瘤细胞亚型鉴定提供理论依据。在IEEE Journal of Biomedical and Health Informatics， IEEE/ACM Transactions on Computational Biology and Bioinformatics，Interdisciplinary Sciences: Computational Life Sciences，MATHEMATICAL BIOSCIENCES AND ENGINEERING，Journal of Bioinformatics and Computational Biology等计算生物学领域重要期刊发表学术论文7篇，申请发明专利两项，已获得授权发明专利一项。