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The complex fluctuations of probabilistic Boolean networks

机译:概率布尔网络的复杂涨落

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摘要

Probabilistic Boolean networks (PBNs) are extensions of Boolean networks (BNs), and both have been widely used to model biological systems. In this paper, we study the long-range correlations of PBNs based on their corresponding Markov chains. PBN states are quantified by the deviation of their steady-state distributions. The results demonstrate that, compared with BNs, PBNs can exhibit these dynamics over a wider and higher noise range. In addition, the constituent BNs significantly impact the generation of 1/. f dynamics of PBNs, and PBNs with homogeneous steady-state distributions tend to sustain the 1/. f dynamics over a wider noise range.
机译:概率布尔网络(PBN)是布尔网络(BN)的扩展,并且都已广泛用于对生物系统进行建模。在本文中,我们基于PBN的相应马尔可夫链研究了PBN的远程相关性。 PBN状态通过其稳态分布的偏差来量化。结果表明,与BN相比,PBN可以在更宽和更高的噪声范围内表现出这些动态。此外,组成的BN会显着影响1 /的生成。 PBN的动态变化,以及具有均匀稳态分布的PBN倾向于维持1 /。 f在更宽的噪声范围内的动态。

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