Stabilization and Reconstruction of Sampled-Data Boolean Control Networks Under Noisy Sampling Interval

• 成果类型: Article
• 署名作者: Sun, Liangjie; Ching, Wai-Ki
• 署名单位: University of Hong Kong
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3173942
• 发表日期: 2023
• 页码: 2444-2451
• 关键词: Noise measurement; Probabilistic logic; BOOLEAN FUNCTIONS; Stability criteria; Random variables; mathematical models; proteins; Boolean control networks (BCNs); large-scale Boolean control networks (BCNs); Linear programming (LP); noisy sampling interval; probabilistic Boolean networks (PBNs)

摘要

In this article, we consider stabilization and reconstruction of sampled-data Boolean control networks (BCNs) under noisy sampling interval. A sampled-data BCN under noisy sampling interval is first converted into a probabilistic Boolean network (PBN). We then obtain some necessary and sufficient conditions for global stochastic stability of the considered sampled-data BCN under two types of noisy sampling intervals. However, in analyzing the stochastic stability of large-scale sampled-data BCNs under noisy sampling interval, using the abovementioned necessary and sufficient conditions, leads to huge computational cost. Therefore, for a large-scale sampled-data BCN, we have to transform it into a size-reduced probabilistic logical network. Then, by studying the stochastic stability of the probabilistic logical network, some sufficient conditions for global stochastic stability of the large-scale sampled-data BCN are obtained. Moreover, based on the given steady-state probabilities of the transformed PBN, the reconstruction problem of sampled-data BCNs under noisy sampling interval can be well-solved as a linear programming problem. Notably, the reconstruction method we presented here is also applicable to large-scale sampled-data BCNs.