SAILoR: Structure-Aware Inference of Logic Rules

• Published in: PloS one Vol. 19; no. 6; p. e0304102
• Main Authors: Pušnik, Žiga, Mraz, Miha, Zimic, Nikolaj, Moškon, Miha
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.06.2024
• Subjects: Accuracy; Algorithms; Analysis; Animals; Biology and Life Sciences; Boolean; Boolean algebra; Boolean functions; Censuses; Computational Biology - methods; Computer and Information Sciences; Descriptions; Drosophila melanogaster - genetics; Gene expression; Gene Regulatory Networks; Genes; Genetic algorithms; Inference; Insects; Knowledge; Logic; Machine learning; Methods; Models, Genetic; Mutation; Networks; Research and Analysis Methods; Time series; Topology
• DOI: 10.1371/journal.pone.0304102
• ISSN: 1932-6203

Boolean networks provide an effective mechanism for describing interactions and dynamics of gene regulatory networks (GRNs). Deriving accurate Boolean descriptions of GRNs is a challenging task. The number of experiments is usually much smaller than the number of genes. In addition, binarization leads to a loss of information and inconsistencies arise in binarized time-series data. The inference of Boolean networks from binarized time-series data alone often leads to complex and overfitted models. To obtain relevant Boolean models of gene regulatory networks, inference methods could incorporate data from multiple sources and prior knowledge in terms of general network structure and/or exact interactions. We propose the Boolean network inference method SAILoR (Structure-Aware Inference of Logic Rules). SAILoR incorporates time-series gene expression data in combination with provided reference networks to infer accurate Boolean models. SAILoR automatically extracts topological properties from reference networks. These can describe a more general structure of the GRN or can be more precise and describe specific interactions. SAILoR infers a Boolean network by learning from both continuous and binarized time-series data. It navigates between two main objectives, topological similarity to reference networks and correspondence with gene expression data. By incorporating the NSGA-II multi-objective genetic algorithm, SAILoR relies on the wisdom of crowds. Our results indicate that SAILoR can infer accurate and biologically relevant Boolean descriptions of GRNs from both a static and a dynamic perspective. We show that SAILoR improves the static accuracy of the inferred network compared to the network inference method dynGENIE3. Furthermore, we compared the performance of SAILoR with other Boolean network inference approaches including Best-Fit, REVEAL, MIBNI, GABNI, ATEN, and LogBTF. We have shown that by incorporating prior knowledge about the overall network structure, SAILoR can improve the structural correctness of the inferred Boolean networks while maintaining dynamic accuracy. To demonstrate the applicability of SAILoR, we inferred context-specific Boolean subnetworks of female Drosophila melanogaster before and after mating.