Detecting small attractors of large Boolean networks by function‐reduction‐based strategy. Issue 2 (1st April 2016)

• Record Type: Journal Article
• Authors: Zheng, Qiben; Shen, Liangzhong; Shang, Xuequn; Liu, Wenbin
• Is Part Of: IET systems biology. Volume 10:Issue 2(2016)
• Page Start: 49
• Page End: 56
• Publication Date: 2016-04-01
• Subjects: Boolean functions -- genetics -- cellular biophysics; detecting small attractors -- function‐reduction‐based strategy -- model gene regulatory networks -- therapeutic intervention strategies -- Boolean‐network analysis -- cellular states -- NP‐hard -- singleton attractor -- Boolean functions -- partial gene activity profile -- cell differentiation; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2015.0027
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : Boolean networks (BNs) are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long‐term behaviour of systems. A central aim of Boolean‐network analysis is to find attractors that correspond to various cellular states, such as cell types or the stage of cell differentiation. This problem is NP‐hard and various algorithms have been used to tackle it with considerable success. The idea is that a singleton attractor corresponds to n consistent subsequences in the truth table. To find these subsequences, the authors gradually reduce the entire truth table of Boolean functions by extending a partial gene activity profile (GAP). Not only does this process delete inconsistent subsequences in truth tables, it also directly determines values for some nodes not extended, which means it can abandon the partial GAPs that cannot lead to an attractor as early as possible. The results of simulation show that the proposed algorithm can detect small attractors with length p = 4 in BNs of up to 200 nodes with average indegree K = 2.