Document 11311565

Department of Electrical and Computer Engineering
Spring 2015 Seminar Series
Seminar Title: Experiment design and model reduction for complex biological systems
Time: 3:00-4:00 PM, Friday, Feb 27, 2015
Location: PETRE 00121
Peng Qiu
Georgia Tech
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. These two ideas can be unified under one 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 on the manifold. We show that eigenvalues of local Fisher Information provide basis for optimal experiment design, and geodesics on the manifold identify its boundaries that correspond to appropriate reduced models. Speaker Bio:
Peng Qiu is currently an Assistant Professor in the Department of Biomedical Engineering at Georgia Institute of Technology and Emory University. He received the B.S. degree from the University of Science and Technology of China in 2003, and the Ph.D. degree from the University of Maryland College Park in 2007, both in electrical engineering. After 3 years as a postdoctoral fellow at Stanford, he worked as an assistant professor for 3 years in the Department of Bioinformatics and Computational Biology at the University of Texas MD Anderson Cancer Center before moving to Georgia Tech. His research interests include bioinformatics, signal processing, machine learning, and in particular, understanding biological progressions using high-­‐dimensional data.