A benchmark suite of intracellular Boolean model variants and multiscale simulations for computational biology

2026-06-16

AI Summary

This work addresses a critical bottleneck in systems biology—the scarcity of standardized, executable Boolean regulatory network models accompanied by multiscale simulation data—by introducing PhysiBench, an open-source resource comprising 612 structurally diverse and behaviorally validated Boolean network variants. These models span key biological processes including the cell cycle, development, cancer, and immunity, and are paired with over 120,000 metadata-annotated stochastic simulation trajectories generated across multiple scales. Built upon the PhysiBoSS/PhysiCell framework, the project integrates mutational model generation, online behavioral filtering, offline sensitivity analysis, and systematic stimulus sampling to automate the entire pipeline from model construction to simulation. Comprehensive validation is achieved through graph-theoretic structural analysis, completeness checks, and assessment of behavioral heterogeneity.

Abstract

We present PhysiBench, an open resource for developing and evaluating computational methods in systems biology including a benchmark suite of 612 executable intracellular Boolean regulatory network variants and a dataset of 120,000 time-resolved multiscale stochastic simulations. The benchmark models are derived from seven published Boolean networks spanning cell-cycle control, developmental patterning, cancer signaling, immune response, and cell-fate decisions, and are executable in the PhysiBoSS/PhysiCell multiscale simulation framework. Model variants are generated through mutation-based model construction, online behavioral filtering, and offline sensitivity evaluation. The simulation dataset is produced from 60 selected models under systematically sampled stimulation protocols and fixed model-level initial configurations. Each trajectory is linked to its model identifier, input-parameter file, stochastic seed, and cell-level output file. PhysiBench supports direct simulation, surrogate modeling, data-driven inference, simulation-based optimization, and comparative benchmarking. Technical validation includes file-integrity and executability checks, graph-based structural diversity analyses, and behavioral heterogeneity assessment from multiscale simulation outputs.

Problem

• Boolean regulatory networks
• multiscale simulation
• benchmarking
• systems biology
• computational modeling

Innovation

• Boolean regulatory networks
• multiscale simulation
• benchmark suite
• stochastic modeling
• PhysiBoSS