Spatiotemporal metabolic modeling of microbial metabolism is a step closer to achieving higher dimensionalities in numerical studies (in silico) of biofilm maturation. Dynamic Flux Balance Analysis (DFBA) is an advanced modeling technique because this method incorporates Genome Scale Metabolic Modeling (GSMM) to compute the biomass growth rate and metabolite fluxes. Biofilm thickness is pertinent because this variable of biofilm maturation can be measured in a laboratory (in vitro). Pseudomonas aeruginosa (P. aeruginosa) is the model bacterium used in this computational model based on previous research conducted by Dr. Michael Henson, available GSMMs, and the societal significance of patients suffering from P. aeruginosa airway infections. Spatiotemporal Flux Balance Analysis (SFBA) will be the computational method used in this thesis to simulate biofilm growth. Another level of accuracy will be introduced to SFBA which is a dynamic finite difference grid that will vary relative to the biofilm’s velocity of expansion/contraction. This novel idea is governed by a differential equation that defines the biofilm’s velocity and updates the spatial dependency of the finite difference grid which has never been done while utilizing GSMM. Environmental conditions (bulk concentrations of metabolites) are altered to investigate how varying nutrients (glucose, oxygen, lactate, nitrate) affected biofilm maturation.