From prior knowledge to data-informed models: a review of Boolean network inference

Molecular mechanisms are highly complex and involve numerous components in non-linear interactions, imposing challenges in analysis and understanding. Networks provide intuitive representations of these systems, enabling visualisation of experimental data and the aggregation of prior knowledge. However, static networks cannot account for key temporal dynamics. Boolean networks address this limitation as discrete dynamical models representing temporal properties of biological regulation through binary variables connected by logical functions, offering scalable frameworks for hypothesis generation while avoiding parameterisation challenges of quantitative modelling approaches. Construction of (Boolean) networks is a mostly manual, labour-intensive and bias-prone process, relying on the review of a large corpora of prior knowledge. This review surveys methods for Boolean network inference that integrate experimental data with or without prior knowledge to automatically construct context-specific, data-informed models. We begin by examining network curation methods from text mining to manual curation given their use as backbones of Boolean networks, then analyse several inference algorithms categorised as heuristic (MIBNI, ATEN, LogicGep, CANTATA, and CellNetOptimizer) and exact (BoNesis, caspo, Sketchbook, RE:IN, and Griffin) methods. For each method, we discuss computational representation, inputs, inference algorithms, and benchmarking. We identify key considerations for method selection, including handling of uncertainty, choice of updating scheme, scalability, and data discretisation challenges. We advise that method selection be guided by the type and extent of available prior knowledge and experimental data, and modelling objectives.