svGrowth is a modular computational framework for simulating growth and remodeling (G&R) in biological tissues based on the constrained mixture theory.

🚧 svGrowth is currently in early development. API is subject to change. 🚧

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Background

svGrowth is based on the constrained mixture theory developed by Humphrey & Rajagopal (2002), where tissues adapt to mechanobiological stimuli through continuous turnover of their structurally-significant constituents:

Humphrey, J. D., & Rajagopal, K. R. (2002)
A constrained mixture model for growth and remodeling of soft tissues
Mathematical Models and Methods in Applied Sciences, 12(03), 407-430.
DOI: 10.1142/S0218202502001714

Key Features

✅ Scalable Multi-Layer Architecture
Single-layer to multi-layer geometries with unlimited constituents per layer, including multi-fiber families with distinct fiber orientations.

✅ Geometry-Agnostic Framework (in dev)
Built-in support for axisymmetric shapes (cylinders, spheres, ellipsoids) with thin/thick wall assumptions. Designed for future integration with arbitrary 3D geometries and finite element coupling.

✅ Extensive Constitutive Library
Pre-built models for constituent mechanics (neo-Hookean, Fung exponential) and turnover kinetics (stress-mediated, shear-mediated, inflammation-mediated), with flexible support for user-defined models.

✅ End-to-End Simulation Pipeline (in dev)
Complete workflow from experimental data and G&R parameter fitting to post-processing analysis and figures.

✅ Comprehensive Testing Framework
Dedicated unit, integration, and end-to-end testing framework to validate individual components, coupled subsystems, and full pipeline behavior. Continuous integration ensures cross-platform reproducibility.

✅ Developer-Friendly
Designed for researchers interested in extending the framework. Modular architecture with small, readable functions and integrated testing/profiling tools promote long-term, sustainable development.

Installation

Prerequisites

• Python 3.9+

Install from Source

git clone https://github.com/StanfordCBCL/svGrowth.git

# Install dependencies
cd svGrowth
pip install -r requirements.txt

# For developers
pip install -r requirements-dev.txt

Quick Start

Running Your First Simulation

All you need to launch a G&R simulation is a YAML parameter file. Documentation for available configuration options is currently under development.

An example parameter file is provided in examples/latorre2018.yaml, which reproduces the cerebral artery model from:

Latorre, M., & Humphrey, J. D. (2018)
A mechanobiologically equilibrated constrained mixture model for growth and remodeling of soft tissues
ZAMM-Journal of Applied Mathematics and Mechanics, 98, 2048–2071.
DOI: 10.1002/zamm.201700302

Run the Example

cd svGrowth/src

# Run simulation with example parameter file
python main.py --input ../examples/latorre2018.yaml --output ../sim_results

Documentation

Resource	Description	Status
Architecture Breakdown	Introductory guide to svGrowth architecture and data flow	✅ Available
API Reference	Class-level function summaries	🚧 Partial
User Guide	Tutorials and examples	📋 Planned
Theory Guide	Mathematical background	📋 Planned

Contributing

Contributions are welcome! We are currently finalizing contribution guidelines. In the meantime:

Request features or report bugs

1. Search existing issues — Your problem may already be documented.
2. Open a new issue — Use the issue templates for "Feature request" or "Bug report". See previous issues for styling and format.

Citation

If you use svGrowth in your research, please cite:

@software{svGrowth,
  author = {TBD},
  title = {TBD},
  year = {TBD},
}