With recent growth in Systems Biology research there has been a significant increase in complex systems modeling research relating to biological systems. Multi-drug resistant (AMR) organisms are a threat not only as hospital-acquired infections, but also now as community-acquired infections. Multilocus sequence typing (MLST) can genetically characterize clones of several bacterial pathogens, allowing the tracking of hypervirulent/ antibiotic resistant lineages and the extent of acquisition and horizontal movement of the resistance genes [23][24][25]. This paper describes the initial research using an agent based cellular automata approach to model the complex subcellular processes in bacteria growth. Rules derived from a biological background simulate the growth of bacteria under a number of conditions including the presence of antibiotic. Altering the level of antibiotic in the bacteria environment and effects on the growth curves was explored and verified. Bacterial survival under a number of conditions (pH, temperature, nutrient concentration) emergent growth patterns and collective behaviour were also studied. A case study using the parameters reflecting the bacterium Escherichia coli was simulated and the results were validated The software provides an in silico laboratory where bacteria can be grown under a variety of rules and conditions thereby learning the underlying mechanisms of behaviour at a local level, which collectively generate the global behaviour of interest.