Systems Biology Approaches to Study Antimicrobial Resistance

The widespread use of antibiotics has led to the gradual selection and spread of antimicrobial resistance (AMR). The increasing global burden of resistant pathogens poses a big threat in clinical settings. Multidrug-resistant (MDR) bacteria can cause some of the most serious and life-threatening infections. In such cases, clinicians usually choose last-resort drugs, high drug doses, and multiple antibiotics that can be toxic for the patient. Therefore, there is an immediate need to study AMR and identify novel targeting strategies that can be effective against a range of resistance evolution mechanisms. Although several mechanisms of resistance have been well studied and elucidated in literature, there is still a gap in the understanding of molecular events that occur during antibiotic treatment and how such events might give rise to adaptations in the pathogen. With the advent of high-throughput technologies, we have been provided with a wealth of molecular-level data that can allow us to look at AMR in a new light. Combining these data with computational models has offered us a view of the system-wide effects induced by antibiotic treatment, and also expanded our understanding of resistance mechanisms. In this chapter, we provide an overview of systems-level bottom-up models and their contribution to the understanding of genetic and non-genetic mechanisms of AMR leading to the identification of alternate targeting strategies. We also include a section that discusses the top-down approach of using mathematical models to study the factors governing bacterial evolutionary dynamics and how such insights can guide better treatment strategies.