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Stochastic simulations of the tetracycline operon.

Biliouris K, Daoutidis P, Kaznessis YN - BMC Syst Biol (2011)

Bottom Line: The results of the simulations agree well with experimental observations such as tight repression, fast gene expression, induction with tetracycline, and small intracellular TetR2 amounts.Computer simulations of the tetracycline operon afford augmented insight into the interplay between its molecular components.Therefore, simulations may assist in designing novel gene network architectures consisting of tetracycline operon components.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave SE, Minneapolis, MN 55455, USA.

ABSTRACT

Background: The tetracycline operon is a self-regulated system. It is found naturally in bacteria where it confers resistance to antibiotic tetracycline. Because of the performance of the molecular elements of the tetracycline operon, these elements are widely used as parts of synthetic gene networks where the protein production can be efficiently turned on and off in response to the presence or the absence of tetracycline. In this paper, we investigate the dynamics of the tetracycline operon. To this end, we develop a mathematical model guided by experimental findings. Our model consists of biochemical reactions that capture the biomolecular interactions of this intriguing system. Having in mind that small biological systems are subjects to stochasticity, we use a stochastic algorithm to simulate the tetracycline operon behavior. A sensitivity analysis of two critical parameters embodied this system is also performed providing a useful understanding of the function of this system.

Results: Simulations generate a timeline of biomolecular events that confer resistance to bacteria against tetracycline. We monitor the amounts of intracellular TetR2 and TetA proteins, the two important regulatory and resistance molecules, as a function of intrecellular tetracycline. We find that lack of one of the promoters of the tetracycline operon has no influence on the total behavior of this system inferring that this promoter is not essential for Escherichia coli. Sensitivity analysis with respect to the binding strength of tetracycline to repressor and of repressor to operators suggests that these two parameters play a predominant role in the behavior of the system. The results of the simulations agree well with experimental observations such as tight repression, fast gene expression, induction with tetracycline, and small intracellular TetR2 amounts.

Conclusions: Computer simulations of the tetracycline operon afford augmented insight into the interplay between its molecular components. They provide useful explanations of how the components and their interactions have evolved to best serve bacteria carrying this operon. Therefore, simulations may assist in designing novel gene network architectures consisting of tetracycline operon components.

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Maximum average value and variation of the number of intracellular Tc and TetA molecules for different number of administered Tc molecules. Maximum average value (black color) and variation (minimal and maximal values among the population) (red color) of the number of TetA (Figure 6a) and intracellular Tc (Figure 6b) molecules for different number of administered Tc molecules. The variation of both the number of TetA and intracellular Tc molecules is high.
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Figure 6: Maximum average value and variation of the number of intracellular Tc and TetA molecules for different number of administered Tc molecules. Maximum average value (black color) and variation (minimal and maximal values among the population) (red color) of the number of TetA (Figure 6a) and intracellular Tc (Figure 6b) molecules for different number of administered Tc molecules. The variation of both the number of TetA and intracellular Tc molecules is high.

Mentions: In Figure 6 we show the maximum value of the mean and the variation (minimal and maximal values among the population) of the number of TetA (Figure 6a,) and intracellular Tc (Figure 6b) molecules for different amounts of administered Tc. As evident in Figure 6a, the maximum TetA amount produced by each cell upon Tc administration varies significantly. It is important to notice that even though the maximum average value of the TetA amount in the cell is non-zero for all the 9 cases, in the first 4 cases (10,20,50,100 administered Tc molecules) there are cells that produce no TetA protein upon Tc administration. These cells would probably not survive from the Tc administration since expression of the resistance protein was not activated and consequently Tc was not removed.


Stochastic simulations of the tetracycline operon.

Biliouris K, Daoutidis P, Kaznessis YN - BMC Syst Biol (2011)

Maximum average value and variation of the number of intracellular Tc and TetA molecules for different number of administered Tc molecules. Maximum average value (black color) and variation (minimal and maximal values among the population) (red color) of the number of TetA (Figure 6a) and intracellular Tc (Figure 6b) molecules for different number of administered Tc molecules. The variation of both the number of TetA and intracellular Tc molecules is high.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3037858&req=5

Figure 6: Maximum average value and variation of the number of intracellular Tc and TetA molecules for different number of administered Tc molecules. Maximum average value (black color) and variation (minimal and maximal values among the population) (red color) of the number of TetA (Figure 6a) and intracellular Tc (Figure 6b) molecules for different number of administered Tc molecules. The variation of both the number of TetA and intracellular Tc molecules is high.
Mentions: In Figure 6 we show the maximum value of the mean and the variation (minimal and maximal values among the population) of the number of TetA (Figure 6a,) and intracellular Tc (Figure 6b) molecules for different amounts of administered Tc. As evident in Figure 6a, the maximum TetA amount produced by each cell upon Tc administration varies significantly. It is important to notice that even though the maximum average value of the TetA amount in the cell is non-zero for all the 9 cases, in the first 4 cases (10,20,50,100 administered Tc molecules) there are cells that produce no TetA protein upon Tc administration. These cells would probably not survive from the Tc administration since expression of the resistance protein was not activated and consequently Tc was not removed.

Bottom Line: The results of the simulations agree well with experimental observations such as tight repression, fast gene expression, induction with tetracycline, and small intracellular TetR2 amounts.Computer simulations of the tetracycline operon afford augmented insight into the interplay between its molecular components.Therefore, simulations may assist in designing novel gene network architectures consisting of tetracycline operon components.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave SE, Minneapolis, MN 55455, USA.

ABSTRACT

Background: The tetracycline operon is a self-regulated system. It is found naturally in bacteria where it confers resistance to antibiotic tetracycline. Because of the performance of the molecular elements of the tetracycline operon, these elements are widely used as parts of synthetic gene networks where the protein production can be efficiently turned on and off in response to the presence or the absence of tetracycline. In this paper, we investigate the dynamics of the tetracycline operon. To this end, we develop a mathematical model guided by experimental findings. Our model consists of biochemical reactions that capture the biomolecular interactions of this intriguing system. Having in mind that small biological systems are subjects to stochasticity, we use a stochastic algorithm to simulate the tetracycline operon behavior. A sensitivity analysis of two critical parameters embodied this system is also performed providing a useful understanding of the function of this system.

Results: Simulations generate a timeline of biomolecular events that confer resistance to bacteria against tetracycline. We monitor the amounts of intracellular TetR2 and TetA proteins, the two important regulatory and resistance molecules, as a function of intrecellular tetracycline. We find that lack of one of the promoters of the tetracycline operon has no influence on the total behavior of this system inferring that this promoter is not essential for Escherichia coli. Sensitivity analysis with respect to the binding strength of tetracycline to repressor and of repressor to operators suggests that these two parameters play a predominant role in the behavior of the system. The results of the simulations agree well with experimental observations such as tight repression, fast gene expression, induction with tetracycline, and small intracellular TetR2 amounts.

Conclusions: Computer simulations of the tetracycline operon afford augmented insight into the interplay between its molecular components. They provide useful explanations of how the components and their interactions have evolved to best serve bacteria carrying this operon. Therefore, simulations may assist in designing novel gene network architectures consisting of tetracycline operon components.

Show MeSH
Related in: MedlinePlus