Table of Contents
ISRN Biomathematics
Volume 2012, Article ID 613174, 7 pages
Research Article

Inferring Biologically Relevant Models: Nested Canalyzing Functions

1Mathematical Biosciences Institute, Ohio State University, Columbus, OH 43210, USA
2Department of Mathematics and Statistics, American University of Sharjah, P.O. Box 26666, Sharjah, United Arab Emirates

Received 9 March 2012; Accepted 9 April 2012

Academic Editors: J. Chow and J. Fellman

Copyright © 2012 Franziska Hinkelmann and Abdul Salam Jarrah. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Inferring dynamic biochemical networks is one of the main challenges in systems biology. Given experimental data, the objective is to identify the rules of interaction among the different entities of the network. However, the number of possible models fitting the available data is huge, and identifying a biologically relevant model is of great interest. Nested canalyzing functions, where variables in a given order dominate the function, have recently been proposed as a framework for modeling gene regulatory networks. Previously, we described this class of functions as an algebraic toric variety. In this paper, we present an algorithm that identifies all nested canalyzing models that fit the given data. We demonstrate our methods using a well-known Boolean model of the cell cycle in budding yeast.