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Description

Boolean networks are systems of variables that are either ON/1 or OFF/0, and a set of rules/functions that describe how these variables interact. These networks can represent biological systems and their regulation/gates. An important feature of Boolean networks is its steady states, which represent values of the system that remain stable over time and correspond to biologically stable patterns. Finding the number of steady states is very difficult, so theoretical results that predict the number of steady states are fundamental for the understanding of Boolean networks and their applications. Thus, we focus on a specific class of Boolean networks: AND-OR gate networks with chain structure, where activation is either synergistic or independent, and which resemble the structure of signal transduction networks. We find closed formulas for subclasses of these networks and recursive formulas in the general case. Our results allow for an effective computation of the number of steady states.

Publication Date

4-24-2019

Project Designation

Honors Thesis

Primary Advisor

Alan A Veliz-Cuba

Primary Advisor's Department

Mathematics

Keywords

Stander Symposium poster

Comments

Presenter: Nikki Nicole Geiser

Steady States of Gene Networks with AND/OR Gates

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