LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data

Abstract Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network...

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Vydáno v:	Briefings in bioinformatics Ročník 25; číslo 4
Hlavní autoři:	Zhang, Dezhen, Gao, Shuhua, Liu, Zhi-Ping, Gao, Rui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Oxford University Press 23.05.2024 Oxford Publishing Limited (England)
Témata:	Algorithms Boolean Boolean algebra Boolean functions Computational Biology - methods Dynamic characteristics Functionals Gene expression Gene Expression Profiling - methods Gene Regulatory Networks Humans Inference Multiple objective analysis Network topologies Neural networks Neural Networks, Computer Problem Solving Protocol Software Structure-function relationships Time series Topology Topology optimization Transcriptome Transcriptomics single-cell RNA sequencing network inference Boolean network symbolic regression gene regulatory network
ISSN:	1467-5463, 1477-4054, 1477-4054
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Shrnutí:	Abstract Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network (BN) framework, which qualitatively indicates how multiple regulators aggregated to affect a common target gene. However, inferring both the network topology and gene interaction dynamics simultaneously is still a challenging problem since gene expression data are typically noisy and data discretization is prone to information loss. We propose a new method for BN inference from time-series transcriptional profiles, called LogicGep. LogicGep formulates the identification of Boolean functions as a symbolic regression problem that learns the Boolean function expression and solve it efficiently through multi-objective optimization using an improved gene expression programming algorithm. To avoid overly emphasizing dynamic characteristics at the expense of topology structure ones, as traditional methods often do, a set of promising Boolean formulas for each target gene is evolved firstly, and a feed-forward neural network trained with continuous expression data is subsequently employed to pick out the final solution. We validated the efficacy of LogicGep using multiple datasets including both synthetic and real-world experimental data. The results elucidate that LogicGep adeptly infers accurate BN models, outperforming other representative BN inference algorithms in both network topology reconstruction and the identification of Boolean functions. Moreover, the execution of LogicGep is hundreds of times faster than other methods, especially in the case of large network inference.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1467-5463 1477-4054 1477-4054
DOI:	10.1093/bib/bbae286