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Detecting small attractors of large Boolean networks by function-reduction-based strategy.

Qiben Zheng¹, Liangzhong Shen¹, Xuequn Shang²

¹Department of Physics and Electronic information engineering, Wenzhou University, Wenzhou 325035, Zhejiang, People's Republic of China.

IET Systems Biology

|March 22, 2016

Summary

This study introduces a novel algorithm for Boolean networks (BNs) to efficiently identify cellular states by finding attractors. The method speeds up the analysis of gene regulatory networks, even for large systems.

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Area of Science:

Systems Biology
Computational Biology
Bioinformatics

Background:

Boolean networks (BNs) are crucial for modeling gene regulatory networks and designing interventions.
Identifying attractors in BNs is essential for understanding cellular states like cell types and differentiation.
This attractor identification problem is computationally challenging (NP-hard).

Purpose of the Study:

To develop an efficient algorithm for finding attractors in Boolean networks.
To improve the analysis of gene regulatory networks and cellular dynamics.

Main Methods:

The study proposes an algorithm that reduces the truth table of Boolean functions by extending partial gene activity profiles (GAPs).
This method prunes inconsistent subsequences and determines node values early.
It allows for the early abandonment of GAPs that cannot lead to attractors.

Main Results:

The algorithm successfully detects small attractors (length p=4) in BNs up to 200 nodes.
It demonstrates efficiency with an average indegree (K=2).
Simulations confirm the algorithm's capability in analyzing complex BNs.

Conclusions:

The developed algorithm offers an efficient approach to attractor detection in Boolean networks.
This method enhances the analysis of gene regulatory networks and aids in understanding cellular states.
The findings are significant for computational biology and therapeutic strategy design.