基于小样本数据的高维生物系统重构理论及算法与应用

项目名称： 基于小样本数据的高维生物系统重构理论及算法与应用

项目编号： No.91230204

项目类型： 重大研究计划

立项/批准年度： 2013

项目学科： 数理科学和化学

项目作者： 周天寿

作者单位： 中山大学

项目金额： 300万元

中文摘要： 基于生物、医学数据建立数学模型是揭示生命现象以及理解人类复杂疾病机理的重要一步，相关研究已成为国际研究热点。然而，如何从小样本高通量数据中挖掘出有规律性的知识，并整合这些知识形成对系统的整体认识，需要解决诸如系统重构与可计算建模等问题。我们在前期研究中发现，小样本高维数据并不是缺陷，而是可以对有关系统提供全局有用的信息，并能反映出系统长时间动力行为的积累效果。本项目将研究基于小样本、非线性、噪声数据的高维动力系统重构理论、方法与算法，包括相应动态分子网络的构建与推断、可计算模型与算法、运行机制分析与阐明等，并应用理论方法与结果构建慢性阻塞性肺病状态(正常，COPD，治疗后)转化过程的动态分子调控网络，研究有关数学模型的动力学性质和有关网络的调节控制机理，确立动态网络标记物等。本项目的理论与应用研究将为基于小样本实验数据解释复杂生命现象和探索复杂疾病机理奠定理论基础并提供方法论。

中文关键词： 小样本数据；系统重构；分子网络；复杂疾病；

英文摘要： Mathematical modeling based on biological and medical data is an important step forwards revealing life phenomena and understanding human complex diseases while the related research has become an international frontier. However, how to mine disciplinary knowledge from high-through data with small samples and how to integrate it into the complete information on systems of interest need to solve such issues as system reconstruction and computational modeling. Our preliminary results indicate that rather than being a drawback, high dimensional data with small samples may provide a global view with rich information on the system of interest and also reflect insight on the accumulated effects of long time dynamics. We will focus on theory, method and algorithm for reconstruction of high-dimensional systems based on nonlinear noisy data with small samples, including building and inference, computational modeling and algorithms, analysis and elucidation of the mechanisms of dynamic molecular networks. Furthermore, we will use the theoretical methods and results to study the nature progression of COPD and the novel recovery process after bypass surgery, including building the related dynamical molecular networks, studying dynamical properties of the related mathematical models, describing the mechanism of regulation and

英文关键词： Small sample data；system reconstruction；molecular network；complex disease；