Fast Simulation of Probabilistic Boolean Networks (Technical Report)

TL;DR

This paper introduces a structure-based method that significantly accelerates the simulation of Probabilistic Boolean Networks, enabling faster steady-state analysis of large biological systems.

Contribution

The paper presents a novel network reduction and parallel simulation approach that improves simulation speed for PBNs compared to existing methods.

Findings

Achieves approximately 10 times speedup in steady-state probability computation.

Effective for large biological networks with high estimation precision.

Demonstrates practical utility through experiments on real-life biological data.

Abstract

Probabilistic Boolean networks (PBNs) is an important mathematical framework widely used for modelling and analysing biological systems. PBNs are suited for modelling large biological systems, which more and more often arise in systems biology. However, the large system size poses a~significant challenge to the analysis of PBNs, in particular, to the crucial analysis of their steady-state behaviour. Numerical methods for performing steady-state analyses suffer from the state-space explosion problem, which makes the utilisation of statistical methods the only viable approach. However, such methods require long simulations of PBNs, rendering the simulation speed a crucial efficiency factor. For large PBNs and high estimation precision requirements, a slow simulation speed becomes an obstacle. In this paper, we propose a structure-based method for fast simulation of PBNs. This method first performs a network reduction operation and then divides nodes into groups for parallel simulation. Experimental results show that our method can lead to an approximately 10 times speedup for computing steady-state probabilities of a real-life biological network.