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Antibiotic Selection

Antibiotic Selection

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Related Experiment Video

Updated: Jun 28, 2025

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection

Published on: September 27, 2016

A stochastic programming approach to the antibiotics time machine problem.

Oğuz Mesüm¹, Ali Rana Atilgan¹, Burak Kocuk¹

¹Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey.

Mathematical Biosciences

|April 11, 2024

Summary

This summary is machine-generated.

This study introduces the Antibiotics Time Machine problem to reverse antibiotic resistance. Mathematical models and programming approaches were developed to find optimal strategies for reverting genotypes, even with uncertain growth rates and transition probabilities.

Keywords:

Bacterial growth rate Beta-lactamase Drug delivery Dynamic programming Fitness landscape Mixed-integer linear programming

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Related Experiment Videos

Last Updated: Jun 28, 2025

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection

Published on: September 27, 2016

Visual and Microscopic Evaluation of Streptomyces Developmental Mutants

Visual and Microscopic Evaluation of Streptomyces Developmental Mutants

Published on: September 12, 2018

Population and Single-Cell Analysis of Antibiotic Persistence in Escherichia coli

Population and Single-Cell Analysis of Antibiotic Persistence in Escherichia coli

Published on: March 24, 2023

Area of Science:

Computational Biology
Mathematical Biology
Pharmacology

Background:

Antibiotic resistance is a growing global health threat.
Understanding and reversing resistance mechanisms is crucial.
Mathematical modeling can provide insights into complex biological processes like resistance evolution.

Purpose of the Study:

To model and solve the Antibiotics Time Machine problem, aiming to reverse antibiotic resistance.
To develop computational approaches for optimizing genotype reversion strategies.
To account for uncertainties in growth rates and transition probabilities.

Main Methods:

Modeling antibiotic resistance as a Markov chain with genotypes and gene mutations.
Utilizing stochastic mixed-integer linear programming and dynamic programming.
Employing a Sample Average Approximation approach for uncertainty quantification.

Main Results:

Developed methods to calculate transition probabilities based on growth rates.
Successfully solved static and dynamic versions of the Antibiotics Time Machine Problem.
Demonstrated accurate and effective solutions through out-of-sample analysis.

Conclusions:

The proposed mathematical and computational frameworks offer a viable approach to tackling antibiotic resistance.
The methods provide a pathway to identify optimal strategies for reverting genotypes.
This work contributes to the development of novel interventions against antibiotic resistance.