Electrical & Computer Engineering     |     Carnegie Mellon

Tuesday, April 6, 1:30-2:30 p.m. HH-1112


Natasa Miskov-Zivanov
Natasa Miskov-Zivanov
University of Pittsburgh

Logical Approach to Modeling Biological Networks

The cell is an adaptive system whose emergent behavior is still understood only poorly. One reason for our lack of understanding is the complexity of cellular decision making, which is often mediated by a system of interacting proteins. Such systems are particularly prominent in signal transduction. Modeling a system marked by combinatorial complexity is problematic because of the large number of chemical species and reactions. Manually writing a mass-balance equation for each of the chemical species in a large reaction network is far too time consuming and error prone. This barrier to modeling signal transduction, and other biological networks, has been recognized by a number of researchers and there have been some attempts to overcome it.

In this talk, I will present several approaches that have been suggested for addressing the complexity of biological systems, one of them being the logical modeling approach. I will then present the model of a system that is important in the process of differentiation of peripheral naive T cells (T lymphocytes) into regulatory and helper cells. The relative numbers produced of each cell type are critical for immune tolerance and affect tumor and immune-related pathologies. To better understand this intricate system, we have constructed a logical model in which each molecule type is treated as a Boolean variable, permitting all known intracellular players to be modeled simultaneously without adjustable parameters. As a preliminary result, the logical model reproduces the experimental observation and the construction of this model has already suggested novel experimental avenues and identified key components requiring theoretical attention.


Natasa Miskov-Zivanov is a Postdoctoral Associate at the Department of Computational Biology, School of Medicine, University of Pittsburgh. She received her Ph.D. and M.S. degrees in the Electrical and Computer Engineering Department at Carnegie Mellon University, in 2008 and 2005, respectively, and a B.S. degree in Electrical Engineering and Computer Science from University of Novi Sad, Serbia, in 2003. Her research interests include computational methods for modeling and analysis of biological systems, reliability and fault-tolerance in nanoscale designs and emerging technologies.