Cytokinesis machinery promotes cell dissociation from collectively migrating strands in confinement
Creators
- 1. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD 21218, USA.
- 2. Departments of Physics and Astronomy and Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.
- 3. Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
- 4. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD 21218, USA.
- 5. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD 21218, USA. Department of Chemical Engineering, Auburn University, Auburn, AL 36849, USA.
- 6. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
- 7. Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Department of Cell Biology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands. Cancer Genomics Center, 3584 Utrecht, Netherlands.
- 8. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD 21218, USA. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. Department of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA.
Description
Supplementary code - Cytokinesis machinery promotes cell dissociation from collectively migrating strands in confinement
- Robert A. Law, Alexander Kiepas, Habben E. Desta, Emiliano Perez Ipiña**, Maria Parlani, Se Jong Lee, Chris L. Yankaskas, Runchen Zhao, Panagiotis Mistriotis, Nianchao Wang, Zhizhan Gu, Petr Kalab1, Peter Friedl, Brian A. Camley**, Konstantinos Konstantopoulos.
**Corresponding authors for codes and model
These codes are shared to allow replication of our results and further exploration. Our codes were not developed with the goal of being efficient or easy to read, nor do they follow standard notations and conventions. We cannot guarantee that they will work as predicted if you try to change things without understanding them. In this sense, they are released in the spirit of the CRAPL license.
Requirements:
- All python code runs in python 3, specifically we used Python 3.9.5.
- The Jupyter notebook version is 6.4.6 and was installed using the default Anaconda installation.
- To install all required packages run 'pip install -r requirements.txt'.
- Stan library requires c++ installed.
NOTE: If the versions of the libraries are different from those used in our study, we cannot guarantee that the software will operate successfully.
We advise utilizing conda (https://conda.io/), setting up a fresh environment, and installing the versions of the study's libraries.
Files
Model_fitting.zip
Files
(2.2 MB)
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