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Inferring Biologically Relevant Models : Nested Canalyzing Functions
Joint Authors
Jarrah, Abdul Salam
Hinkelmann, Franziska
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-06-12
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Hinkelmann, Franziska& Jarrah, Abdul Salam. 2012. Inferring Biologically Relevant Models : Nested Canalyzing Functions. ISRN Biomathematics،Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-485124
Modern Language Association (MLA)
Hinkelmann, Franziska& Jarrah, Abdul Salam. Inferring Biologically Relevant Models : Nested Canalyzing Functions. ISRN Biomathematics No. 2012 (2012), pp.1-7.
https://search.emarefa.net/detail/BIM-485124
American Medical Association (AMA)
Hinkelmann, Franziska& Jarrah, Abdul Salam. Inferring Biologically Relevant Models : Nested Canalyzing Functions. ISRN Biomathematics. 2012. Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-485124
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-485124