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SPARCED is a simple and efficient pipeline for constructing, merging, expanding and simulating large-scale, single-cell mechanistic models.
- With minimal set-up, users can configure small-scale experiments on their local machines, is it through pure Python scripts or Jupyter Notebooks.
- Both Docker and Singularity containers are provided.
- SPARCED is also compatible with High Performance Computing and parallelization.
The acronym SPARCED stands for SBML, Proliferation, Apoptosis, Receptor signaling, Cell cycle, Expression & DNA damage, which are sub-models of the large-scale mechanistic model.
The SPARCED pipeline can be run with few to no previous coding experience. To do so, we strongly encourage you to use the Docker / Singularity containers we provide.
A complete installation guide is available here.
We recommend to use Anaconda and create a conda environment based on the environment.yml file we provide. Otherwise, you may base yourself on the requirements.txt file we provide for the minimal required versions.
A detailed installation guide is available here.
You will find specific instructions on how to run the model (including previousversions) as described in each of our published papers here.
The SPARCED model can be used to create and run small to large-scale mechanistic models.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the GNU General Public License v2.0. See LICENSE.txt for more information.
SPARCED is a product of the Birtwistle Lab and the Erdem Lab.
We greatly appreciate the help from multiple colloborators, including the Hasenauer Lab.
This material is based on work supported by the National Science Foundation under Grant Nos. MRI# 2024205, MRI# 1725573, and CRI# 2010270.
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Clemson University is acknowledged for their generous allotment of compute time on the Palmetto Cluster.
