Talk Title :
Deriving Executable Models of Biochemical Network Dynamics from
Qualitative and Semi-Quantitative Data.
Date / Time / Location:
Thursday March 11th, 2010 – 6:00 pm
Room S1-151 at IRIC
Progress in advancing our understanding of biological systems is limited by their sheer complexity, the cost of laboratory materials and equipment, and limitations of current laboratory technology. Computational and mathematical modeling provides ways to address these limitations through hypothesis generation and testing without experimentation – allowing researchers to analyze system structure and dynamics in silico and, then, design lab experiments that yield desired information about phenomena of interest.
These models, however, are only as accurate and complete as the data used to build them. Currently most models are constructed from quantitative experimental data. However, since accurate quantitative measurements are hard to obtain and difficult to adapt from literature and online databases, new sources of data for building models need to be explored. In my research, I design methods for building and executing computational models of cellular networks based on qualitative experimental data, which is more abundant, easier to obtain, and reliably reproducible. Such executable models allow for in silico perturbation, simulation, and exploration of biological systems.
In this talk, I will present two general strategies for building and executing Petri net-based models of biochemical networks. Both have been successfully used to model and predict the dynamics of signaling networks in normal and cancer cell lines, rivaling the accuracy of existing methods trained on quantitative data.