We are often able to describe what happens at almost all biological length scales, from the molecular level to the whole organism; however, putting things together in order to obtain real comprehension is difficult and less developed. The challenge is to develop novel computational intelligence frameworks that integrate the different layers of molecular information. The introduction of such methodologies would enable to discover the relation between the environmental (external) conditions and the changes in the metabolic multi omic networks (i.e. the adaptive response of the internal environment). Many molecular levels can contribute to adaptability: pathways structure, codon usage bias, transcriptomics and metabolism. Here, we develop a method that combines Probabilistic Suffix Trees and Clustering models to integrate bacterial molecular information and to map different conditions to an omic multi-dimensional objective space. This methodology allows to identify oscillations in network structure of metabolic pathways. We tested our method by considering two case studies (from Escherichia coli) with antibiotics added to the medium. These oscillations provide insights into novel emerging properties of multi omic networks and to a better understanding of antibiotic effects and their gene targeting constraints.