基于高维短序列生物数据的系统重构研究

项目名称： 基于高维短序列生物数据的系统重构研究

项目编号： No.11301366

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 数理科学和化学

项目作者： 马欢飞

作者单位： 苏州大学

项目金额： 22万元

中文摘要： 系统生物学的一个重要目标是从系统的角度来认识各个层次的生物学现象，而探索和理解生物系统的一个重要手段是通过分析海量的各层次多种类的实验数据特别是生物系统的时间序列数据来重构系统的各种动力学行为。目前已有的数理方法主要集中于对低维长时间序列数据的动态行为研究，而对于以基因芯片等高通量生物数据为代表的高维短时间序列数据还缺乏相应的分析和重构手段。本项目通过理论研究、算法设计及结合实验结果，旨在获得针对高维短时间序列数据的系统重构方法，主要包括研究高维短时间序列数据的可靠预测和历史追溯以实现数据的动力学重构；研究短时间序列变量间的因果关系检测以实现系统调控网络的重构；研究基于短测量数据的大规模参数识别算法以实现系统模型的重构；并应用以上理论算法来研究II型糖尿病高通量数据的信息挖掘以验证和探索疾病的机制和治疗方法。

中文关键词： 高维数据；短序列数据；动力学重构；因果网络重构；参数辨识

英文摘要： One of systems biology's main purposes is to understand the mechanism of various biological phenomenon from a systematic view. Thus how to use the large amount of experimental data especially the time evolution data to reconstruct the biological system is an important method to understand biological systems. Though various kinds of time series analysis methods have been fruitfully proposed, the traditional methods mainly focus on the analysis of low dimensional long-term data, and several algorithms such as prediction, causality detection and system reconstruction has successfully proposed. However, there are still few effective methods to handle high dimensional short-term data such as high throughput data developed these years. Therefore, this proposal will aim to develop algorithms particularly effective for high dimensional short-term time series data. We will include theoretical analysis, algorithm design and validation by biological experiments. It is expected to investigate how to transfer the information embedded in high dimensional data into time domain such that we can reconstruct the dynamics of the system. Also it is expected to investigate the causality theory based on the map between reconstructed attractors so that we can reconstruct the regulation network. And it is expected to investigate the

英文关键词： high dimensional data；short-term；dynamics-reconstruction；causality detection；parameter identification