Attractor detection and enumeration algorithms for Boolean networks

Mori, Takahide; Akutsu, Tatsuya

doi:10.1016/j.csbj.2022.05.027

Cited by 4 publications

(1 citation statement)

References 78 publications

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“…The BN was first introduced by Kauffman (1969) to qualitatively describe gene regulatory interactions to account for a variety of complex biological processes. Over the past few decades, BNs have been attracting the great attention of many researchers ( Akutsu et al 1999 ; Sun et al 2021 ; Mori and Akutsu 2022 ; Sun and Ching 2023 ). BNs are not only a fundamental model for genetic systems that identify network structures from a systematic perspective ( Liu 2015 ), but also a powerful framework for studying and modeling the dynamics of GRNs ( Shi et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data

Sun

Chen

et al. 2023

Bioinformatics

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Motivation From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing (scRNA-seq) data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe the updated logic rules of regulation in GRNs to obtain their dynamics. Moreover, some inference methods also fail to deal with the over-fitting problem caused by the noise in time series data. Results In this paper, we propose a novel embedded Boolean threshold network method called LogBTF, which effectively infers GRN by integrating regularized logistic regression and Boolean threshold function. First, the continuous gene expression values are converted into Boolean values and the elastic net regression model is adopted to fit the binarized time series data. Then, the estimated regression coefficients are applied to represent the unknown Boolean threshold function of the candidate Boolean threshold network as the dynamical equations. To overcome the multi-collinearity and over-fitting problems, a new and effective approach is designed to optimize the network topology by adding a perturbation design matrix to the input data and thereafter setting sufficiently small elements of the output coefficient vector to zeros. In addition, the cross-validation procedure is implemented into the Boolean threshold network model framework to strengthen the inference capability. Finally, extensive experiments on one simulated Boolean value dataset, dozens of simulation datasets and three real scRNA-seq datasets demonstrate that the LogBTF method can infer GRNs from time series data more accurately than some other alternative methods for GRN inference. Availability and implementation The source data and code are available at https://github.com/zpliulab/LogBTF. Supplementary information Supplementary data are available at Bioinformatics online.

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