A Computational Algebra Approach to Reverse Engineering
Mathematical Biosciences Institute (MBI), The Ohio State University
(October 13, 2005 10:30 AM - 11:30 AM)
The emerging field of systems biology is focused on the integration of biological information into predictive mathematical models. One primary approach in the systems-biology paradigm is to build models from time series of experimental data, obtained by measuring the response of a biological system to perturbations. Referred to as reverse engineering, this approach is used to elucidate features of such systems, including their structure and dynamics. Of relevance for reverse engineering is to design biological experiments that are suitable for modeling and to identify perturbations that will reveal salient features of the system.
In this talk I will introduce a collaborative project, in which one objective is to generate appropriate time series data for reverse engineering a stress-response network in yeast. I will present a modeling approach that uses algorithmic tools from computational algebra to build the set of all possible discrete models that fit time series data and to select minimal models from this set. In this setting, discrete models are given by systems of polynomial functions over a finite field. As it is important to identify which perturbations are best suited to build accurate models, properties of the data that make them appropriate for the discrete modeling method will be discussed.