Omics integration in bacterial biofilm modeling: A systems biology approach

Infectious diseases represent a major public health problem with global impact. According to the World Health Organization, several cases of antimicrobial resistance to Klebsiella pneumoniae have been reported in recent years, making it one of the bacteria most in need of new antibiotics. Among the evolutionary adaptations that confer resistance, we can mention the formation of biofilm, present in chronic infections. Biofilm is formed in response to stress in the environment, and its formation and dispersion depend on a broad and coordinated network of intercellular signaling and metabolic changes in cells. Therefore, it is interesting to use an integrative data approach to understand the phenomenon as a whole and develop strategies to tackle it. Through Genome-Scale Metabolic Models (GEMs), it is possible to reconstruct the entire network of chemical reactions in a given organism from its genome, making it possible to computationally simulate its growth in specific culture media by flux balance analysis. To simulate specific conditions, Context-Specific GEMs (CSGEMs), were developed to integrate gene transcription information in conventional GEMs, making the model more reliable and increasing its predictive power. With Agent-Based Modeling, it is possible to simulate the dynamics of cell populations in a three-dimensional space and the interactions between them. Although, this project aims to integrate these three modeling strategies to construct a  biofilm model of Klebsiella pneumoniae and simulate the population growth dynamics of the colony in the presence of antibiotics to understand the impact of metabolic reprogramming from planktonic to biofilm lifeforms on the antimicrobial resistance phenomenon. Our preliminary data comparing the planktonic and biofilm CSGEMs suggests a more active and diverse metabolism in planktonic models with reactions associated with hydrogen transport (EX_h_e, Htex), glucose (GLCtex_copy2), and glycolytic pathways (PGK, GAPD, ENO, PGM) showing differences between them, which could indicate metabolic adaptations specific to each condition.