open access

https://hdl.handle.net/2115/90943

Optimal reconstruction of noisy dynamics and selection probabilities in Boolean networks

英語

Systems biology

2022-02

Elsevier

Automatica

136

110094

In the analysis and control of complex systems, including gene regulatory networks, it is important to reconstruct a mathematical model from a priori information and noisy experimental data. A Boolean network (BN) is well known as a mathematical model of gene regulatory networks. Each state of BNs takes a binary value (0 or 1), and its update rule is given by a set of Boolean functions. In this paper, we consider the optimal reconstruction problem of finding a probabilistic BN consisting of the main dynamics and the noisy dynamics, by giving the main dynamics and the sample mean of the state obtained from noisy experimental data. In the proposed method, the selection probability of the main dynamics is maximized. We show that the optimal Boolean function of the noisy dynamics is a constant (0 or 1) map under no assumption on the structure of noisy dynamics. Finally, as a biological application, the reconstruction of a PBN model of the lac operon networks of Escherichia coli bacterium is addressed using the proposed approach.

学術雑誌論文

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