Simulation of Bacterial Population Distribution Using Reaction-Diffusion Equation

The distribution of bacterial populations in an environment is influenced by various factors, including the use of antibiotics and environmental conditions that affect the diffusion process. Diffusion is the movement of particles from a high-concentration environment to a low-concentration environment. The diffusion process is essential in the human body system, such as the diffusion of oxygen and carbon dioxide in the respiratory system and the diffusion of Ca in the synapse. This research uses a research method in the form of a simulation method. This method is carried out by designing a simulation program for spreading bacteria in a container with the Python programming language with the application of Partial Differential Equations. The PDE used in this research and observation is from the diffusion reaction function. This mathematical equation is used to predict the movement of bacteria in the available containers in this simulation method research. The animation of the distribution of bacteria in a container shows how fast the bacteria experience a diffusion reaction. The purple color on the screen shows the empty container and the yellow color is the bacteria starting from the highest to the lowest concentration. then there is the anti-bacterial soap factor, which is one drop denoted by a red line. From that, we can conclude that the larger the space, the faster the spread of bacteria. This study successfully developed a bacterial population distribution model using the reaction-diffusion equation and visualized the results with Matplotlib in Python. The application of this method allows us to solve the reaction-diffusion equation numerically and understand the pattern of bacterial spread under various environmental conditions. This study presents an educational simulation designed to enhance understanding of bacterial population dynamics through reaction-diffusion equations. The simulation effectively demonstrates key concepts in bacterial population distribution, providing valuable insights for educators. Visualization of the simulation results shows that Matplotlib effectively provides a clear and informative graphical representation of complex biological phenomena.