Title
Modeling Bacteria Growth and Antibiotic Interaction with Cellular Automata
Department, Center, or Institute
Mathematics
Presentation Format
Department Organized Oral Session
Presentation Type
On-campus research
Description
We constructed a cellular automata model to show the growth of bacteria and its interaction with antibiotics. The model uses stochastic rules to model the probability of a bacteria being resistant to a given antibiotic (out of 10 total antibiotics present in the model). The model incorporates the Disk Diffusion Method to compare the resistance of bacteria to many different types of antibiotics. Differences in bacteria types for the given antibiotics are also compared in the model. The goal of this cellular automaton model is to not only serve as a visual representation of the interaction between bacteria and antibiotics, but also be much more efficient than performing tests in the lab in terms of both time and money.
Department Organized Oral Session Title
Mathematics Summer Research Experiences
Moderator/Professor
Liz Bouzarth, Mathematics
Session Number
1
Start Date and Time
4-9-2019 9:45 AM
Location
Riley Hall 107
Recommended Citation
Kuntz, Emma, "Modeling Bacteria Growth and Antibiotic Interaction with Cellular Automata" (2019). Furman Engaged!. 433.
https://scholarexchange.furman.edu/furmanengaged/2019/all/433
Modeling Bacteria Growth and Antibiotic Interaction with Cellular Automata
Riley Hall 107
We constructed a cellular automata model to show the growth of bacteria and its interaction with antibiotics. The model uses stochastic rules to model the probability of a bacteria being resistant to a given antibiotic (out of 10 total antibiotics present in the model). The model incorporates the Disk Diffusion Method to compare the resistance of bacteria to many different types of antibiotics. Differences in bacteria types for the given antibiotics are also compared in the model. The goal of this cellular automaton model is to not only serve as a visual representation of the interaction between bacteria and antibiotics, but also be much more efficient than performing tests in the lab in terms of both time and money.