感染性疾病调控网络的动力学模型与并行算法

Infectious diseases are a public health threat worldwide, and viral and bacterial infection induced inflammatory regulatory networks are extremely complex and dynamical regulatory networks. The project aims at developing predictive computational models to meet the actual precision, and exploring high-performance parallel evolutionary algorithms and their theoretical basis through the research of computational modeling and high-dimensional optimization problems extracted from the construction and analysis of dynamic regulatory networks for infectious diseases. Specifically, based on the high-throughput multi-omics data, the construction of the interaction networks of inflammatory cytokines will be converted into solving a single-objective optimization problem that the correlation of protein expression as the objective function and the specificity and the relevance for gene expression as constraints. Nonlinear dynamic modeling of the inflammation regulatory networks is studied, and the parallel algorithms for high-dimensional multiobjective optimization problem with a mixture of integer and real variables to identify the parameters of the networks are designed. The quantitative method for assessing the relationship between the dynamical behavior of regulatory networks and the disease phenotypes are investigated. Furthermore, the theoretical methods and results will be used to identify the inflammatory regulatory network and the molecular mechanisms of influenza A virus infection, which will be validation by the biological experiments. These results will provide innovative ideas and rationale for revealing the pathogenesis of infectious diseases. Those theoretical results and computational techniques from this project can also be more widely used in other complex biological systems.

感染性疾病对全球公共健康构成巨大威胁，病毒和细菌感染诱导机体炎症反应的调控网络是极其复杂的动态调控网络。本项目旨在通过对感染性疾病动态调控网络的构建与分析，提炼出可计算模型以及高维优化问题进行研究，开发满足实际精度要求的动力学模型，探索高性能的并行进化算法及其理论。具体地，以高通量的多组学数据为基础，将构建炎症因子相互作用网络转化为以蛋白表达相关性为目标函数,以基因表达特异性与关联性为约束条件的单目标优化问题求解；建立炎症因子调控网络的非线性动力学模型，探索用整数与实数变量混合的高维多目标优化问题的并行算法来识别网络的参数；研究调控网络的动力学性态与疾病表型关联的定量评估方法等。将这些方法和结果应用于识别A型流感病毒感染诱导细胞炎症反应的复杂调控网络及其分子机制，并进行生物学实验验证，为揭示感染性疾病的致病机制提供新思路。本项目形成的理论成果和计算技术可更广泛应用于其它复杂的生物学系统。

本项目通过对感染性疾病等复杂疾病的动态调控网络的构建与分析，提炼出可计算模型以及高维优化问题进行研究，开发满足实际精度要求的动力学模型，探索高性能的并行进化算法及其理论基础。具体地，整合高通量的多组学数据，设计了在计算精度和时间复杂性方面均优的构建复杂疾病动态调控网络的高效算法；开发了在动态网络的框架下识别动态网络标记物的新方法；基于优化算法建立了疾病调控网络的非线性动力学模型，结合基于熵的定量分析与动力学性态分析，识别了流感病毒感染诱导的炎症网络中的关键蛋白及其复合物；研究了复杂网络系统的可控性，提出了复杂疾病的区域控制的新概念及其可控性证明，并从控制论的角度去揭示疾病的致病机制和识别药物靶标的新思路。这些结果为重大疾病的早期诊断、治疗、预防和控制提供方法论，并为相关新型药物的靶标识别奠定理论基础。本项目形成的理论成果和计算技术可更广泛应用于其它复杂的生物学系统。. 本项目取得了创造性的研究成果，在“Scientific Reports” ,“Mathematical Biosciences”,“International Journal of Bifurcation and Chaos”, “IEEE/ACM Transactions on Computational Biology and Bioinformatics”等国际重要学术刊物上发表SCI学术论文20余篇,其中大部分论文被引用多次。通过本项目的实施，培养毕业了博士研究生4人，硕士研究生4人，参与本项目的硕士和博士研究生均已具有扎实科学计算与系统生物学理论知识和研究能力。

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