The gut microbiome is comprised of diverse microbial species which may be linked to host health and stability. Specific gut microbial species have been identified in health and various diseases. However, current binary categorical classification as ‘beneficial’ or ‘harmful’ fails to capture the nuance and the context-dependent nature of microbial phenotypes. An alternative approach to better characterize health-associated species is by investigating the temporal shifts and interactions among different microbial species, functions and host factors. My project focuses on the application of robust computational methods to understand the longitudinal impacts of specific microbiome composition on gut resilience and recovery following perturbations such as antibiotics intake. To achieve this aim, a cohort of 72 children from a randomised double-blind study who had undergone appendectomy followed by the administration of antibiotics was recruited. After the completion of their antibiotic course, these children were then randomly assigned into either probiotics or placebo groups for two weeks. Stool samples and clinical surveys were collected at three different timepoints (baseline, Day 14 and day 28) and shotgun metagenomics sequencing method was performed for further analyses. Understanding how the microbial species fluctuates at healthy and diseased states allow us to measure degree of influence of specific keystone taxa or core influencer species on microbiome restoration following perturbations. Crucially, we aim to design a more precise and dynamic indicators of gut health by incorporating better metrics to classify potential microbial biomarkers associated to healthy microbiome and resilience after disruptions.