Discrete Modeling Methods

One of the central problems in systems biology is the design of global models for biochemical networks from experimental data, such as gene regulatory networks from DNA microarray data. We have developed a new modeling approach for such networks, based on the framework of discrete dynamical systems in which each variable can take on a finite number of states. The most basic examples of such systems are cellular automata and Boolean networks. Using methods from computational algebra, we have constructed a method to reverse-engineer biochemical networks. This tool, combined with tools developed by the Mendes group, is being used in this project, in collaboration with the Shulaev Laboratory, with support from a grant by the National Institutes of Health. Other collaborators include Ed Green, Dept. of Math., Virginia Tech, and Michael Stillman, Dept. of Math., Cornell University. This work has been partially supported by an NSF Biocomplexity grant.

---

Up to Modeling