ECE Calendar

A joint work with Janis Dingel (Technisches Universitat, Munchen) and Narayan Prasad Santhanam (University of Hawaii, Manoa)

Abstract:
One of the most important problems in systems biology is to infer the complex interaction patterns of genes and gene products, such as RNA molecules and proteins, within regulatory networks. In most practical settings, inference is performed using a small number of potentially erroneous observations, generated by DNA microarrays, ChIP-chip systems, or via PCR methods. Consequently, most reverse engineering approaches tend to produce inaccurate models that do not capture biologically important properties of regulatory networks.

We propose two new techniques for the inference of the topology and the dynamics of gene regulatory networks, based on methods from statistical physics, information and coding theory. In particular, we use a statistical physics framework to construct simple tree-like models for gene interaction networks. We also quantify ''the cost'' of adding new edges in the interaction tree in terms of the number of samples used for inference. Furthermore, we develop a "probabilistic" network dynamics model that can accommodate both observation errors and small number of measurements. These probabilistic network models are generated via list decoding techniques applied on collections of quantized DNA expression profiles.

Our methods are tested on both synthetic data and gene expression profiles of the SOS repair system in E. coli.

Biography:
Olgica Milenkovic received her M.S. degree in mathematics and Ph.D. in electrical engineering from the University of Michigan, Ann Arbor, in 2001 and 2002, respectively. She was an assistant professor at ECE Department of the University of Colorado from 2002 until 2006, and visiting professor at the University of California from 2006-2007. She joined the Department of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign in 2007. In 2007, she was awarded the NSF Career Award and the DARPA Young Faculty Award.