2016.07.27.Experimental design and model reduction for complex biological systems
题 目： Experimental design and model reduction for complex biological systems
报告人：Peng Qiu, Assistant Prof.
Department of Biomedical Engineering, Georgia Tech and Emory University
Mathematical modeling is a promising tool for studying complex systems. In systems biology, mathematical models are often constructed by including all known interactions among individual components (genes/proteins) in the system, leading to highly complex models with many unknown parameters. On the other hand, the amount of available experimental data is typically limited, not enough to constrain the model parameters. As a result, parameter estimation in this context can be a difficult and ill-posed problem. To fit complex models and limited data, we explored two simple intuitive ideas (1) perform additional experiments to get more data, (2) perform model reduction to derive simpler models. The challenges are how to design optimal experiments that are maximally informative, and how to write down reduced models in a systematic and automated way. Although these two problems are quite different, they can be tackled by one common mathematical framework. We consider a complex model as a high-dimensional manifold that lives in a higher-dimensional space defined by the experimental measurements, and examine the Jacobian, Fisher Information Matrix and geodesics around on the manifold locally at the observed data. Experiment design is to identify new experiments that remove the singularity, whereas model reduction is to identify the nearest extreme singularity which suggests appropriate form of reduced models.
Dr. Peng Qiu is the assistant professor in the department of biomedical engineering at Georgia Tech and Emory. He graduated from the University of Science and Technology of China (USTC) as a bachelor in 2003, and got his PhD degree at University of Maryland in 2007. His main research interests are in bioinformatics and computational biology, focusing on statistical signal processing, machine learning, control systems and optimization.