Discrimination of attractors with noisy nodes in Boolean networks

TL;DR

This paper addresses the challenge of identifying the minimum number of sensor nodes needed to distinguish attractors in Boolean networks with noisy nodes, providing algorithms and experimental validation.

Contribution

It introduces exact and approximation algorithms for minimizing sensor nodes in noisy Boolean networks, with demonstrated effectiveness on synthetic and biological data.

Findings

Algorithms successfully discriminate attractors with noisy nodes.

Computational experiments validate algorithm effectiveness.

Applicable to biological network analysis.

Abstract

Observing the internal state of the whole system using a small number of sensor nodes is important in analysis of complex networks. Here, we study the problem of determining the minimum number of sensor nodes to discriminate attractors under the assumption that each attractor has at most K noisy nodes. We present exact and approximation algorithms for this minimization problem. The effectiveness of the algorithms is also demonstrated by computational experiments using both synthetic data and realistic biological data.