Simulation Model of Bacterial Resistance to Antibiotics

Abstract

Antibiotic resistance or antimicrobial resistance(AMR) refers to infections caused by bacteria, fungi, parasites and viruses resistant to antibiotics. Antibiotic resistance has become a major public health threat as it breaks out regardless of geographical conditions or socioeconomic status. Infectious diseases such as HIV/AIDS, Tuberculosis, Malaria, etc., are known to be more widely spread in developing countries compared to other developed countries.Experts from various fields are putting their effort to tackle this problem however is yet to be solved.Various computational approaches including bioinformatics have been conducted such as genome sequencing, constructing databases of resistance genes and antibiotics information, providing tools for analysis and designing simulation models. The purpose of this study is to design and implement simulation models of bacterial growth and antibiotic resistance to find the proper antibiotics against antibiotic resistant bacteria. Simulation models were designed based on Individual based Modeling (IbM). A simulation tool named ARSim was developed in order to conduct experiments using simulation models. We designed models on top ofARSim to observe the growth of bacteria and predict the consequences of adding antibiotics into the bacterial population.Simulations of bacterial growth were conducted by growing K.pneumoniae bacteria on a virtual plate with predefined parameters. By assessing the change in bacterial population as time goes by, the result was nearly identical to the four phases of a bacterial growth curve.The next experiment was predicting the effects of antibiotics when added to two different groups, one group of non-resistant bacteria and another group of both resistant and non-resistant bacteria. We assumed carbapenem class Imipenem and Meropenem as antibiotics and carbapenem resistant bacteria as the bacterial strain. In the first experiment, we predicted that the non-resistant bacterial population steadily grows when 0.05μg/ml of Imipenem is added to the population. On the contrary the population instantly died out when 0.1μg/mlwas added which is greater than the minimum inhibitory concentration of the strain.In the second experiment, we added Imipenem and Meropenem with concentrations of 16μg/ml, 32μg/ml and 64μg/ml each. The results for adding Imipenems were akinto previous lab experiments in literature and results for Meropenemswerevery much alike to Imipenems.We used Individual based Modeling methods to design and implement models of bacteria, antibiotics, enzymes and the environment and conducted simulations of these entities through the ARSim program. Results were shown that properties and interactions among these entities were properly defined,and the models to a certain degree follow the biological principles of bacteria and their mechanisms of antibiotic resistance.Using the computational approaches made in this study, we hope to provide researchers with a better option on finding new ways of fighting antibiotic resistance.