Modeling Pseudomonas aeruginosa and Staphylococcus aureus
This repository contains predictive models for the theoretical growth and decay of two bacterial species:
- Pseudomonas aeruginosa
- Staphylococcus aureus
These models simulate bacterial growth under idealized conditions using the logistic growth equation and bacterial reduction based on radiation dose using a decay model.
- Predicts bacterial population growth over time.
- Models bacterial reduction due to radiation dose.
- Visualizes logistic growth curves and decay trends.
- Initial concentration (
N_0):10^3CFU/mL - Maximum concentration (
N_max):1.67e13CFU/mL - Growth rate (
r):0.45 hour^-1
- Initial concentration (
N_0):10^3CFU/mL - Maximum concentration (
N_max):5.96e12CFU/mL - Growth rate (
r):0.40 hour^-1
- Initial CFU Count (
max_cf): Maximum bacterial count before radiation. - Half-Max Dose (
half_max_dose): The dose at which bacterial CFUs reduce to half. - Steepness (
steepness): Controls the steepness of the decay curve.
The bacterial decay due to radiation is modeled using a logistic decay function. The function calculates the CFU count as a function of the applied radiation dose. The formula used is: $$ CFU = \text{max_cf} \cdot \left(1 - \frac{1}{1 + e^{-\text{steepness} \cdot (\text{dose} - \text{half_max_dose})}}\right)$$
- max_cf: Initial bacterial CFU count (e.g., (300)).
- dose: Radiation dose(s) applied to the bacteria.
- half_max_dose: Dose where CFU count is reduced to half.
- steepness: Steepness of the decay curve (higher value = steeper decay).
The code calculates CFUs across a range of radiation doses and plots the decay trend.
- Python 3.x
- Required libraries:
numpymatplotlib
Install dependencies with:
bash
pip install numpy matplotlib
This project is licensed under the MIT License.
For questions please reach out to knowle.marcus@utexas.edu