Computational biology and in silico studies have experienced significant growth in recent years due to rapid advances in computing power. Of particular importance in interdisciplinary studies of pathogenic microbiological systems is the modeling of bacterial culture growth in nutrient media. All current approaches (including deterministic, stochastic, and agent-based modeling) are computationally intensive. In this study, we propose a parallel computing technique to numerically implement a reaction-diffusion model of bacterial growth in nutrient media. The mathematical problem is formalized as an initial boundary-value problem for a nonlinear reaction-diffusion system. The modified algorithm for the finite-difference scheme optimizes computations by exploiting multithreaded processing. The algorithm was implemented in C++. A series of computational experiments simulated spatiotemporal distributions of nutrient and biomass concentrations. The results demonstrate improved performance with parallel computing compared to single-threaded approaches. The data from these experiments can help address the challenging task of efficiently predicting the evolution of pathogenic bacteria under antibiotic treatment.