系统生物学的一个重要目标是从系统的层面上来认识各个层次的生物学机理，而一个重要的手段就是如何从海量的生物数据中提取有用的信息，帮助计算和重构生物系统的各个侧面。目前的数据处理手段主要集中于低维数据和大样本数据，而对于以基因芯片、脑影像为代表的高维小样本数据还缺乏相应的分析和重构手段。本项目将在高维数据的驱动下，通过理论研究、算法设计并结合实验数据，开发出一套生物系统的重构方法，并最终解决实际数据问题。主要包括高维数据的动力学重构和预测方法研究，以实现疾病预后和种群预测；非线性时滞因果和条件因果检测及网络重构研究，以实现优化基因调控网络模型，分析污染与疾病成因；高维系统不稳定不动点及不稳定周期震荡的重构研究，从而帮助研究细胞命运机制和节律震荡机制；以及人类大脑有向功能链接的非线性分析方法，从而在临床数据支持下获得精神类疾病的个体化异常神经环路定位。

One of systems biology's main purposes is to understand the mechanism of various biological phenomenon from a systematic view. Thus how to retrieve useful information from large amount of biological data is a key technique of systems biology. Though various kinds of time series analysis methods have been fruitfully proposed, the traditional methods mainly focus on the analysis of low dimensional data with long-term sampling. It is urgent to develop theory as well as methods for high-dimensional data with short-term sampling. .We will carry out research of biological systems reconstruction driven by high-dimensional data. Our research plan includes the prediction of high dimensional data, nonlinear causality analysis with time delay and conditional causality, unstable states in high dimensional systems and brain functional connections. We aim to use the developed methods to help solve problems driven by data: predicting disease process as well as species population; reconstructing gene network with directional regulation and analyzing disease caused by pollution; understanding cell fate and rhythm oscillations; detecting abnormal neural circuits in psychological patients.