Stochastic Simulations of the Tetracycline Operon

The tetracycline (tet) operon is a robust self-regulated system. It is found naturally in bacteria where it confers resistance to antibiotic tetracycline. Because of the performance of the molecular elements (promoters, operators, repressor) of the tetracycline operon, these elements are widely used as parts of synthetic gene networks. More specifically, in many synthetic systems the expression of desired proteins is under the transcriptional control of the tet components, where protein production can be efficiently turned on and off in response to the presence or the absence of tetracycline. The present work examines the mechanisms that govern the dynamics of the tetracycline operon. We have formulated a mathematical model of the naturally occuring tetracycline operon guided by experimental findings. This model incorporates the biomolecular interactions of this system, including those involved in transcription, translation, degradation, protein dimerization, repression and induction. The model represents each interaction with biochemical reactions. A hybrid, stochastic-discrete and stochastic-continuous algorithm was used to simulate the tetracycline operon behavior. A sensitivity analysis with respect to important parameters underlying this system was also performed, providing mechanistic insight into the working of this interesting system. The results of the simulations are in agreement with, and explain well experimental observations such as tight repression, fast gene expression, induction with tetracycline, and small intracellular repressor TetR2 amounts. The simulations determine the exact amounts of intracellular TetR2 and TetA protein. The former is the repressor molecule whereas the latter mediates the removal of tetracycline from the cell. Furthermore, the results demonstrate that one of the two promoters of the tetracycline operon is redundant and not functionally important in Escherichia coli. Sensitivity analysis illustrates that changes in the affinity of tetracycline for the repressor TetR2 and of TetR2 for the operator sites have a significant impact on the behavior of the tetracycline operon, suggesting optimum interaction strengths developed through natural selection. This work affords augmented insight into the interplay between the molecular components of the tetracycline operon. It also provides useful explanations of how the components and their interactions have evolved to best serve the bacteria carrying this operon. Thus, it may assist in designing new antibiotics that circumvent the existing resistance mechanism. It can also aid the design of novel, yet superior synthetic gene networks.