Synthetic Biology Mathematical Modeling Webinars and Course Resources

Welcome to the repository for the materials used in "Integrating Mathematical Modeling into Synthetic Biology Education: A Comprehensive Approach through Webinars and Syllabus Development". This repository contains all the educational materials, including PowerPoint presentations, MATLAB scripts, Python notebooks, and the survey used to evaluate the course's effectiveness.

Table of Contents

• Introduction
• Presentations
• MATLAB Scripts
• Python Notebooks
• Survey
• How to Use
• License

Introduction

This repository is designed to support the "Mathematical Modeling in Synthetic Biology" course, which was developed to enhance students' understanding of essential mathematical concepts in synthetic biology. The course materials include dynamic modeling, genetic circuit modeling, and the use of experimental data to refine models.

Presentations

The following PowerPoint presentations are included to guide you through the theoretical aspects of the course:

• Introduction to Mathematical Modeling
• Modeling Genetic Circuit

These presentations cover fundamental concepts, case studies, and practical examples used during the course.

MATLAB Scripts

The repository includes several MATLAB scripts that were used in the course to demonstrate different aspects of mathematical modeling:

1. Exponential_growth.mlx: Simulates exponential growth, a fundamental concept in population dynamics and synthetic biology.
2. Volterra_Lotka_Phase_Port_Example.mlx: Demonstrates phase portraits and dynamics in the Volterra-Lotka predator-prey model.
3. SIR_model_SZR.mlx: A script that simulates a standard Susceptible-Infected-Recovered (SIR) model, demonstrating the spread of disease within a population.
4. Genetic_circuit_model_simple.mlx: A genetic circuit model focused on constitutive and regulated gene expression.
5. Genetic_circuit_model_TF.mlx: This script models a genetic circuit with transcription factors.

Python Notebooks

For those who prefer Python, we have included Jupyter notebooks that replicate some of the MATLAB examples:

1. ODEs_Exp_Growth_pyLab.ipynb: A Python notebook demonstrating the simulation of exponential growth using ordinary differential equations (ODEs).
2. Simulation_Gene_Expression_pyLab.ipynb: This notebook simulates gene expression and its regulation, providing an introduction to Python-based modeling.

Survey

This repository includes the survey used to assess the course's effectiveness. The survey provides insights into students' understanding, engagement, and satisfaction with the course. The feedback gathered from this survey has been instrumental in refining the course content.

How to Use

1. Presentations: Review the PowerPoint slides to understand the theoretical foundations of the course. You can use them in your own teaching with appropriate citation.
2. MATLAB Scripts: Open the .mlx files in MATLAB and run the simulations to visualize the modeling concepts.
3. Python Notebooks: Open the .ipynb files in Jupyter Notebook, Google Colab, or any compatible Python environment to run and modify the simulations.
4. Survey: Use the survey to evaluate your understanding and provide feedback on the course materials.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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We hope these resources enhance your understanding of mathematical modeling in synthetic biology. If you have any questions or suggestions, feel free to open an issue or contact us directly.