Year: 2012
Type: article
Abstract: 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.
Source: ISRN Biomathematics
DOI: 10.5402/2012/613174
Authors: Franziska Hinkelmann, Abdul Salam Jarrah
Institutions: The Ohio State University, American University of Sharjah
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: diamond
Funder: Army Research Office
OpenAlex ID: W2082789703