Finding, and Countering, Future Resistance using Bacterial Antibiotic Adversarial Genetic Algorithm (BAAGA)

Cory Kromer-Edwards; Suely Oliveira

doi:10.1109/IJCNN54540.2023.10191950

Back

Conference proceeding

Finding, and Countering, Future Resistance using Bacterial Antibiotic Adversarial Genetic Algorithm (BAAGA)

Cory Kromer-Edwards and Suely Oliveira

2023 International Joint Conference on Neural Networks (IJCNN), pp.1-8

06/18/2023

DOI: 10.1109/IJCNN54540.2023.10191950

View Online

Abstract

Since the beginning of antimicrobial therapy, antimicrobial resistance from bacteria has been a threat. Now, multiple companies track antibiotic resistance annually. Monitoring helps watch for new resistant mechanisms to restrict spread. This monitoring also determines if a new antimicrobial agent must be created. Proper prescription of an antimicrobial agent also plays a key role in slowing resistance. Proper prescription to a patient requires the determination of a Minimum Inhibitory Concentration (MIC). These MICs are slow to determine, which raises morbidity and mortality rates in hospital settings. Physicians cannot predict the creation of new resistance mechanisms based on the prescription of one agent over another. It is also impossible for researchers to know what agents to create in the future. Because of this, there is a race against the clock when new resistant bacteria emerge, to create appropriate antimicrobial agents to counter the new mechanisms of resistance. In this study, we propose a new simulation algorithm named Bacterial Antibiotic Adversarial Genetic Algorithm (BAAGA) that simulates the generation of new mechanisms of resistance and new antimicrobial agents in a stochastic manner. BAAGA shows the ease at which bacteria can gain resistance mechanisms and how slow the process is to find new antimicrobial agents. We also demonstrate how BAAGA can produce possible new antimicrobial agents to counter mechanisms of resistance that currently do not exist.

Antibiotics

Companies

contig

fingerprint

Genetic Algorithm

Hospitals

K-Mer

MIC

Microorganisms

Microwave integrated circuits

Monitoring

Neural networks

random

Recurrent Neural Network

simulation

SMILES

Details

Title: Subtitle: Finding, and Countering, Future Resistance using Bacterial Antibiotic Adversarial Genetic Algorithm (BAAGA)
Creators: Cory Kromer-Edwards - University of Iowa
Suely Oliveira - University of Iowa
Resource Type: Conference proceeding
Publication Details: 2023 International Joint Conference on Neural Networks (IJCNN), pp.1-8
Publisher: IEEE
DOI: 10.1109/IJCNN54540.2023.10191950
eISSN: 2161-4407
Language: English
Date published: 06/18/2023
Academic Unit: Mathematics; Computer Science
Record Identifier: 9984457959602771

Metrics

5 Record Views