Review and assessment of Boolean approaches for inference of gene regulatory networks

• Published in: Heliyon Vol. 8; no. 8; p. e10222
• Main Authors: Pušnik, Žiga, Mraz, Miha, Zimic, Nikolaj, Moškon, Miha
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2022; Elsevier
• Subjects: Boolean network inference; Dynamic validation; Gene regulatory networks; Review; Static validation; Systems biology; Boolean network inference; Gene regulatory networks; Systems biology; Dynamic validation; Static validation
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2022.e10222

Boolean descriptions of gene regulatory networks can provide an insight into interactions between genes. Boolean networks hold predictive power, are easy to understand, and can be used to simulate the observed networks in different scenarios. We review fundamental and state-of-the-art methods for inference of Boolean networks. We introduce a methodology for a straightforward evaluation of Boolean inference approaches based on the generation of evaluation datasets, application of selected inference methods, and evaluation of performance measures to guide the selection of the best method for a given inference problem. We demonstrate this procedure on inference methods REVEAL (REVerse Engineering ALgorithm), Best-Fit Extension, MIBNI (Mutual Information-based Boolean Network Inference), GABNI (Genetic Algorithm-based Boolean Network Inference) and ATEN (AND/OR Tree ENsemble algorithm), which infers Boolean descriptions of gene regulatory networks from discretised time series data. Boolean inference approaches tend to perform better in terms of dynamic accuracy, and slightly worse in terms of structural correctness. We believe that the proposed methodology and provided guidelines will help researchers to develop Boolean inference approaches with a good predictive capability while maintaining structural correctness and biological relevance. Boolean network inference; Gene regulatory networks; Static validation; Dynamic validation; Systems biology