SACS: A Reproducible, Configuration-Driven Software Framework for Schematic Multi-Region Circuit Simulation and Integrated Analytics.

Computational neuroscience projects often combine simulation code, configuration files, analysis scripts, plotting utilities, and provenance records through loosely coupled and difficult-to-replay workflows. SACS is presented as a configuration-driven research-software framework for reproducible schematic multi-region circuit simulation with integrated analytics, validation checks, deterministic execution, deterministic replay, artifact replay, and graphical inspection. Implemented as the Python package brain_sim, the framework converts declarative model and scenario specifications into standardized run directories containing numerical outputs, machine-readable summaries, figure-generation recipes, validation reports, and provenance metadata. The contribution is methodological and neuroinformatics-oriented. SACS is not presented as a biologically validated model of anxiety, brain function, or treatment response, and its built-in circuit materials are used as configurable demonstration components rather than as evidence of clinical or biological validity. Instead, the framework is evaluated as software for reproducible computational experimentation: hypotheses are encoded as explicit configurations, executed under recorded seeds, inspected through analytics and validation layers, and then used to inform subsequent configurations in an iterative workflow. The manuscript describes the software architecture, configuration and execution model, artifact structure, replay mechanisms, and graphical/programmatic interfaces that support this workflow. The central claim is that SACS improves transparency, inspectability, and reuse for schematic circuit experiments by binding configuration, execution, analytics, provenance, and replay within a single software environment.