KGBN: Augmenting and optimizing logical gene regulatory networks using knowledge graphs.
Publication Title
bioRxiv
Document Type
Article
Publication Date
1-30-2026
Keywords
acute myeloid leukemia; gene regulatory networks; knowledge graphs; logical models; systems biology.; washington; isb
Abstract
MOTIVATION: Logical gene regulatory network (GRN) models provide interpretable, mechanistic representations of cellular regulation and are widely used in systems biology. However, most existing models remain incomplete, context-specific, and difficult to extend to comprehensive GRNs, limiting their broader applicability to tasks such as drug-response prediction and precision medicine.
RESULTS: We present KGBN (Knowledge Graph-augmented Boolean Network modeling), a computational workflow for systematically augmenting logical GRN models. KGBN incorporates regulatory interactions derived from curated knowledge graphs as alternative logical rules while preserving the validated structure of existing models. Rule probabilities are optimized against experimental data to represent regulatory uncertainty and achieve data-driven calibration. Applying KGBN to acute myeloid leukemia, we show that extending an existing GRN with drug-target pathways and training against
AVAILABILITY AND IMPLEMENTATION: KGBN is available at https://github.com/IlyaLab/KGBN.
Area of Special Interest
Cancer
Specialty/Research Institute
Oncology
Specialty/Research Institute
Institute for Systems Biology
Specialty/Research Institute
Pharmacy
DOI
10.64898/2026.01.29.702644
