A benchmark suite of intracellular Boolean model variants and multiscale simulations for computational biology
2026-06-16 • Databases
Databases
AI summaryⓘ
The authors introduce PhysiBench, a free resource designed to help researchers create and test computer models that simulate how cells behave. It includes 612 versions of cell control networks and a large dataset from 120,000 detailed simulations showing how these cells react over time. These models come from real biological systems like cell cycles and immune responses and can be run using a special simulation platform. The authors also made sure the resource is reliable by checking files, model diversity, and how different simulations behave. PhysiBench aims to support various research methods, such as running simulations, building simpler models, and comparing different approaches.
Boolean regulatory networkssystems biologymultiscale simulationcell-cycle controlstochastic simulationmodel benchmarkingPhysiCellPhysiBoSSdata-driven inferencesimulation-based optimization
Authors
Marco Masera, Riccardo Smeriglio, Roberta Bardini, Alessandro Savino, Stefano Di Carlo
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.