题目： Characterizing complex diseases by big biological data in the forms of dynamics and network

报告人： 陈洛南教授

Key Laboratory of Systems Biology, Chinese Academy of Sciences

时间：2014-12-19（周五），13:00-14:00

地点：北京大学老化学楼东配楼102会议室

主持人：定量生物学中心，汤超教授

Abstract：

We described a few new network-based methodologies for solving bio-medical problems in a dynamic manner based on big biological data. (1) we developed a new concept, edge-biomarkers, which transforms‘node expression’ data into the ‘edge expression’ data and thus can classify the phenotype of each single sample in the form of network; (2) we proposed a path-consistent analysis method based on the measurements to reconstruct gene regulatory networks, which theoretically can infer the network structure without the approximation even with a small number of samples, which cannot be achieved by the traditional approaches; (3) we derive theoretical results based on a dynamical network biomarker (DNB) that serves as a general early-warning signal indicating an imminent sudden deterioration before the critical transition of a disease occurs; (4) When a system is constantly perturbed by big noise, it becomes a difficult task to identify the early-warning signals of a critical transition due to the strong fluctuations of the observed data. In this work, we present a new model-free computational method based on the observed time-series data even with big noise to detect such warning signals just before the critical transition. The key idea behind this method is a new strategy: “making big noise smaller”, which increases the dimensionality of the observed data and thus makes the noise smaller in the transformed higher-dimension data.

We adopted omics data of several diseases to demonstrate the effectiveness of our works.

个人简介:

陈洛南，中国科学院上海生命科学研究院研究员，博士生导师，中国科学院系统生物学重点实验室执行主任。 目前，在计算系统生物学领域的主要国际学术期刊都担任重要工作，在日本和美国从事科研教学工作等25年以上，近六年来，在系统生物学研究领域发表了100篇以上原创性研究论文。2009年，获中科院-诺和诺德长城教授奖。 主要研究工作为采用系统工程、动力学分析、优化和数学建模的方式，结合生物信息学和现代生命科学实验，以生物复杂网络和动态行为为主线来研究生命系统。其研究方向包括：（1）网络系统生物学（Network Systems Biology）；（2）合成系统生物学（Synthetic Systems Biology）；（3）计算系统生物学（Computational Systems Biology）。