https://github.com/imperialcollegelondon/recode-stochastic-population-dynamics-in-changing-environments
ReCoDE Project - models stochastic population dynamics in time-varying environments, exploring adaptability and resilience under changing conditions. It combines randomness with environmental variability, offering insights relevant to ecology, evolution, and resource management.
Science Score: 13.0%
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○Scientific vocabulary similarity
Low similarity (10.7%) to scientific vocabulary
Keywords
Repository
ReCoDE Project - models stochastic population dynamics in time-varying environments, exploring adaptability and resilience under changing conditions. It combines randomness with environmental variability, offering insights relevant to ecology, evolution, and resource management.
Basic Info
- Host: GitHub
- Owner: ImperialCollegeLondon
- License: bsd-3-clause
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://imperialcollegelondon.github.io/ReCoDE-Stochastic-Population-Dynamics-in-Changing-Environments/
- Size: 1.1 MB
Statistics
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 9
- Releases: 0
Topics
Metadata Files
README.md
Cell Population Dynamics in Continuous Time Domain
Description
This project explores the mathematical modeling of cell population dynamics using the McKendrick–Von Foerster equation. The focus is on understanding the time-independent case where cell division and death rates depend on the cell's age.
Learning Outcomes
- Understand the McKendrick–Von Foerster equation for population dynamics.
- Apply separation of variables to solve partial differential equations.
- Derive and interpret the Euler-Lotka equation for population growth rates.
Requirements
Academic
- Basic knowledge of differential equations and mathematical modeling.
- Familiarity with population dynamics concepts.
System
- Python 3.11 or newer
- MkDocs for documentation generation
- Miniconda (recommended for managing dependencies)
Getting Started
- Clone the repository and navigate to the project directory.
- Set up a virtual environment and install the required dependencies:
sh conda create --name cell-population-dynamics python=3.11 conda activate cell-population-dynamics pip install -r requirements.txt
Project Structure
log
.
├── docs
├── notebooks
├── src
│ ├── __init__.py
| ├── cells_manager.py # Cell population manager
| ├── plots.py # Plotting functions
| ├── run.py # Main script to run the simulation
| ├── simulation.py # Core simulation logic
│ └── utils.py # Utility functions
├── mkdocs.yml
├── requirements.txt
└── README.md
MkDocs Documentation
To generate local documentation, run the following command:
sh
mkdocs serve
License
This project is licensed under the BSD-3-Clause license
Owner
- Name: Imperial College London
- Login: ImperialCollegeLondon
- Kind: organization
- Email: icgithub-support@imperial.ac.uk
- Location: Imperial College London
- Repositories: 311
- Profile: https://github.com/ImperialCollegeLondon
Imperial College main code repository
GitHub Events
Total
- Issues event: 5
- Delete event: 2
- Issue comment event: 1
- Push event: 14
- Pull request event: 2
- Pull request review event: 1
- Create event: 1
Last Year
- Issues event: 5
- Delete event: 2
- Issue comment event: 1
- Push event: 14
- Pull request event: 2
- Pull request review event: 1
- Create event: 1
Dependencies
- actions/checkout v4 composite
- actions/setup-python v5 composite
- actions/checkout v4 composite
- actions/setup-python v2 composite
- lycheeverse/lychee-action v2 composite
- matplotlib *
- mkdocs ==1.5.3
- mkdocs-include-markdown-plugin ==3.7.1
- mkdocs-jupyter ==0.24.5
- mkdocs-material ==9.4.6
- numpy *
- pandas *
- python-markdown-math ==0.8
- scikit-learn *
- seaborn *