For decades, biology has been focused on decoding cellular processes one gene at a time, but it is clear that many of the most pressing questions and diseases (e.g., cancer, heart disease, obesity) are related to the interaction of hundreds, or even thousands, of gene products. How can we begin to understand this complexity? Thanks to a variety of technological developments, we now have ways to assay the activity of thousands of gene products at a time, but the methods for assimilating this data and teasing out the most important features of a biological network are still very much in development. Systems biology is the exciting field that has arisen to address this need, at the intersection of computational modeling and molecular biology.
- Implement fundamental approaches to modeling biological systems, including
- boolean representations
- analytical solutions
- graphical analysis
- numerical integration
- stochastic simulations
- Identify strengths and weaknesses of each approach
- Apply these concepts to metabolic, signaling, and regulatory networks
- Integrate different modeling techniques together into a unified model
Note: This is the most friendly approach for users from non-coding backgrounds. The online textbook is organized by chapters under Notebook_by_Chapters folder.
git clone https://github.com/CovertLab/BIOE101-Online-Companion.git
Step 1: Upload .ipynb files to a Google Drive
Step 2: Open in Google Colab Double-clicking on the file will direct you to Google Colab. All the figures are already rendered but feel free to run the code segments at your convenience. Enjoy!
Note The notebooks only render well in PyCharm and are not compatible with Visual Studio Code or Jupyter Notebook.