Gene Regulatory Networks with Hidden-Delayed Parameters
Dr. Abdollah Homaifar
Duke Energy Eminent Professor
North Carolina A&T State University
Greensboro, NC, 27411
Reverse Engineering of Gene Regulatory Networks (GRN) (i.e., finding appropriate mathematical models to understand complex cellular systems) can be used in disease diagnosis, treatment, and drug design. There are fundamental gaps in the construction of GRN in relation to modeling of hidden/delayed interactions. Addressing these deficiencies is critical to understanding complex intracellular processes and enabling full use of the vast and ever-growing amount of available genomic data. Current modeling strategies either ignore or oversimplify time delays resulting from transcription and translation processes during gene expression. In addition, many research works do not account for hidden variables such as transcription factors, repressors, small metabolites, DNA, or microRNA species that self-regulate and other genes but are not readily detectable on microarray experiments. To capture the effect of these parameters, we utilized our developed Partially Connected Artificial Neural Networks with Evolvable Topology (PANNET) to find a more comprehensive model of GRN by considering the effects of unknown hidden variables and different time delays. This method is innovative, since the structure of the network has memory and internal states, which can model the unknown hidden variables and time delays. Furthermore, we use a new evolutionary optimization based on variable-length Genetic Algorithm (VGA) to find a sparse structure of PANNET to predict the gene expression levels accurately. Finally, we demonstrate the capability of PANNET in constructing the GRN, including the effect of different delays and unknown hidden variables through modeling the E. coli SOS inducible DNA repair system.
Abdollah Homaifar received his B.S. and M.S. degrees from the State University of New York at Stony Brook in 1979 and 1980, respectively, and his Ph.D. degree from the University of Alabama in 1987, all in electrical engineering. He is currently the Duke Energy Eminent professor in the Department of Electrical and Computer Engineering at North Carolina A&T State University (NCA&TSU). He is also the director of the Autonomous Control and Information Technology center at NCA&TSU. His research interests include machine learning, climate data processing, optimization, optimal control, flexible robotics, signal processing, soft computing and modeling. He is the author and co-author of over 200 articles in journals and conference proceedings, one book, and three chapters of books. He has participated in six short courses, serves as an associate editor of the Journal of Intelligent Automation and Soft Computing, and is a reviewer for IEEE Transactions on Fuzzy Systems, Man Machines & Cybernetics, and Neural Networks. He is a member of the IEEE Control Society, Sigma Xi, Tau Beta Pi, and Eta Kapa Nu.
Thursday, November 13, 2014 at 2:00pm to 3:00pm
Earle Hall, 100
206 S. Palmetto Blvd., Clemson, SC 29634